International PhD Thesis Asier Mañas Bote

ROLE OF SEDENTARY AND

PHYSICAL ACTIVITY PATTERNS

ON FRAILTY SYNDROME

PhD in Public Health Research and Physical Activity

International PhD Thesis

Asier Mañas Bote

Toledo, 2019


ROLE OF SEDENTARY AND

PHYSICAL ACTIVITY PATTERNS

ON FRAILTY SYNDROME

Asier Mañas Bote

Supervisors:

Ignacio Ara Royo

Borja del Pozo Cruz

Amelia Guadalupe Grau

University of Castilla-La Mancha

PhD in Public Health Research and Physical Activity

Department of Physical Activity and Sport Sciences

Faculty of Sport Sciences

Toledo, Spain (2019)

I have noticed even people who claim everything is predestined,

and that we can do nothing to change it,

look before they cross the road.

Stephen Hawking, English theoretical physicist

Any man could,

if he were so inclined,

be the sculptor of his own brain.

Santiago Ramón y Cajal, Spanish neuroscientist.

ROLE OF SEDENTARY

AND PHYSICAL ACTIVITY PATTERNS

ON FRAILTY SYNDROME

PHD SUPERVISORY BOARD

PhD. Ignacio Ara

Royo

PhD. Borja del Pozo

Cruz

PhD. Amelia

Guadalupe Grau

Faculty of Sport

Sciences.

University of Castilla-

La Mancha.

Institute for Positive

Psychology and

Education.

Australian Catholic

University.

Faculty of Physical

Activity and Sport.

Technical University

of Madrid.

PHD EXAMINATION BOARD

President Secretary Vocal

PhD. Francisco B.

Ortega

PhD. Eric J. Shiroma PhD. Ulf Ekelund

Faculty of Sport

Sciences.

University of

Granada.

Laboratory of

Epidemiology and

Population Science.

National Institute on

Aging.

Department of Sport

Medicine.

Norwegian School of

Sport Sciences.

Dr. Ignacio Ara Royo

Associate Professor


Faculty of Sport Sciences, Toledo

University of Castilla-La Mancha

Dr. Ignacio Ara Royo, Associate Professor of the University of Castilla-La

Mancha in the Faculty of Sport Sciences of Toledo,

Certify:

That the International PhD thesis entitled "Role of sedentary and

physical activity patterns on frailty syndrome" presented by Mr. Asier Mañas

Bote to the committee designated by the University of Castilla-La Mancha,

has been carried out under my direction, being an expression of the

technical and interpretative capacity of its author in conditions that he

deserves the title of Doctor with International Mention, provided by the

committee if it considers so.

Signed by Ignacio Ara Royo

In Toledo, 25 October 2019

Dr. Borja del Pozo Cruz

Senior Research Fellow

Institute for Positive Psychology and Education

Faculty of Health Sciences, Sydney

Australian Catholic University

Dr. Borja del Pozo Cruz, Senior Research Fellow of the Australian

Catholic University in the Faculty of Health Sciences of Sydney,

Certify:








Signed by Borja del Pozo Cruz


Dra. Amelia Guadalupe Grau

Assistant Professor

Department of Health and Human Performance

Faculty of Sport Sciences, Madrid

Technical University of Madrid

Dra. Amelia Guadalupe Grau, Assistant Professor of the Technical

University of Madrid in the Faculty of Sport Sciences,

Certify:








Signed by Amelia Guadalupe Grau


TABLE OF CONTENTS

TABLE OF CONTENTS

List of tables ............................................................................................ 23

List of figures .......................................................................................... 25

List of abbreviations ............................................................................... 27

Publications ............................................................................................. 29

Contributions in congresses .................................................................. 31

Awards and recognitions ...................................................................... 35

Research projects .................................................................................... 37

Grants and scholarships ........................................................................ 39

Research stays ......................................................................................... 41

Abstract .................................................................................................... 43

1. INTRODUCTION ............................................................................. 49

1.1. Population aging: where are we going? .................................... 51

1.1.1. An aging population: demographics and population

projections ...................................................................................... 51

1.1.2. Spanish population aging ................................................... 53

1.1.3. Health status and burden disease ..................................... 55

1.2. Frailty ............................................................................................. 59

1.2.1. Frailty definition .................................................................. 59

1.2.2. Frailty constructs ................................................................. 65

1.2.2.1. Fried's Frailty Phenotype - the CHS index ........... 65

1.2.2.2. Frailty Index of Accumulative Deficits ................. 67

1.2.2.3. Frailty Trait Scale ..................................................... 69

TABLE OF CONTENTS

1.2.3. Prevalence and incidence of frailty ................................... 72

1.2.4. Frailty pathogenesis ............................................................ 74

1.2.5. Frailty interventions ............................................................ 77

1.3. Physical activity and sedentary behaviour ............................... 81

1.3.1. Definitions and concepts .................................................... 81

1.3.1.1. Physical activity........................................................ 82

1.3.1.2. Exercise ...................................................................... 83

1.3.1.3. Sedentary behaviour ................................................ 84

1.3.1.4. Breaks in sedentary time ......................................... 84

1.3.1.5. Physical inactivity .................................................... 84

1.3.1.6. Physical fitness ......................................................... 85

1.3.2. Physical activity and sedentary behaviour

assessment ...................................................................................... 86

1.3.2.1. Reference methods ................................................... 87

1.3.2.2. Objective methods ................................................... 88

1.3.2.3. Subjective methods .................................................. 91

1.3.3. Physical activity recommendations for health ................ 93

1.4. Physical activity, sedentary behaviour and frailty .................. 97

1.4.1. Importance of physical activity for health ....................... 97

1.4.2. Relationship between physical activity, sedentary

behaviour and frailty .................................................................... 102

2. JUSTIFICATION ............................................................................... 109

3. OBJECTIVES AND HYPOTHESES ............................................... 115

TABLE OF CONTENTS

4. MATERIAL AND METHODS ........................................................ 125

4.1. Systematic Review ....................................................................... 127

4.1.1. Literature search .................................................................. 127

4.1.2. Eligibility criteria ................................................................. 129

4.2. Participants of the studies ........................................................... 131

4.3. Data collection .............................................................................. 133

4.3.1. Cross-sectional studies........................................................ 134

4.3.2. Longitudinal studies ........................................................... 136

4.4. Assessments .................................................................................. 137

4.4.1. Anthropometrics and confounding variables ................. 137

4.4.2. Physical activity and sedentary behaviour ...................... 139

4.4.2.1. Time ........................................................................... 139

4.4.2.2. Patterns ...................................................................... 140

4.4.2.3. Categories .................................................................. 141

4.4.3. Frailty .................................................................................... 144

4.4.4. Physical Function ................................................................ 146

4.5. Data analyses ................................................................................ 147

5. RESULTS ............................................................................................ 155

5.1. Study 1 ........................................................................................... 157

5.2. Study 2 ........................................................................................... 173

5.3. Study 3 ........................................................................................... 185

TABLE OF CONTENTS

5.4. Study 4 ........................................................................................... 193

5.5. Study 5 ........................................................................................... 205

5.6. Study 6 ........................................................................................... 213

5.7. Study 7 ........................................................................................... 249

6. DISCUSSION .................................................................................... 291

6.1. Study 1 ........................................................................................... 293

6.2. Study 2 ........................................................................................... 303

6.3. Study 3 ........................................................................................... 307

6.4. Study 4 ........................................................................................... 311

6.5. Study 5 ........................................................................................... 317

6.6. Study 6 ........................................................................................... 321

6.7. Study 7 ........................................................................................... 325

7. CONCLUSIONS ................................................................................ 331

8. FUTURE PERSPECTIVES ............................................................... 341

9. ACKNOWLEDGMENTS ................................................................. 347

10. REFERENCES ................................................................................... 357

11. APPENDIX........................................................................................ 407

11.1. Appendix 1 .................................................................................. 409

11.2. Appendix 2 .................................................................................. 411

11.3. Appendix 3 .................................................................................. 413

LIST OF TABLES

23

LIST OF TABLES

INTRODUCTION

Table 1 Projection of the Spanish population residing in

Spain by age groups, 2018-2033 ............................ 54

MATERIAL AND METHODS

Table 2 Main characteristics of the subjects included in

studies 2 and 3 ........................................................ 134


the study 4 ............................................................... 135


the study 5 ............................................................... 135


studies 6 and 7 ........................................................ 136

RESULTS

(Study 1)

Table 1 Main characteristics of the selected studies ........ 163

(Study 2)

Table 1 Characteristics of the participants in the study .. 179

Table 2 Linear regression analysis for the association of

various sedentary behaviour patterns with

frailty in the TSHA study ...................................... 180

(Study 3)

Table 1 Comparison of Characteristics in Those

Included and Excluded by Accelerometry From

the Study .................................................................. 189

LIST OF TABLES

24

Table 2 Single Behavior, Partition, and Isotemporal

Substitution Models for Frailty (FTS) in Elderly

People ....................................................................... 189

Table 3 Single Behavior, Partition, and Isotemporal

Substitution Models for Frailty (FTS)

Subdivided by Comorbidity (Charlson Index)

in Elderly People .................................................... 190

(Study 4)

Table 1 Participant characteristics ..................................... 198

Table 2 Categorical associations with physical function

and frailty ................................................................ 200

Table 3 Continuous associations with physical function

and frailty ................................................................ 200

(Study 5)

Table 1 Participant Characteristics .................................... 208

(Study 6)

Table 1 Sociodemographic and descriptive data ............. 244

Supplementary

File 1

Comparison of characteristics at baseline of

participants retained with those of participants

not retained from wave1-wave2 ........................... 248

(Study 7)

Table 1 Sociodemographic and descriptive data ............. 283

Supplementary

File 1

Comparison of characteristics at baseline of

participants retained with those of individuals

not retained from T1-T2 ......................................... 288

LIST OF FIGURES

25

LIST OF FIGURES

INTRODUCTION

Figure 1 Global population by broad age group, in 1980, 2017,

2030 and 2050 ..................................................................... 52

Figure 2 Vulnerability of frail older adults to external stressors 60

Figure 3 Venn diagram displaying extent of overlap of frailty

with activities of daily living disability and

comorbidity (≥2 diseases) ................................................. 64

Figure 4 Cycle of frailty .................................................................... 66

Figure 5 Pathogenesis of the frailty syndrome: potential

underlying mechanisms and hypothetical modal

pathways leading to frailty .............................................. 75

Figure 6 Conceptual model of movement-based terminology

arranged around a 24-h period ........................................ 81

Figure 7 Components of total daily energy expenditure and

measurement approaches ................................................. 86

Figure 8 Proportion of time-awake at different categories of

accelerometer counts for U.S. adults, by sex and age

group, 2003-2004 ................................................................ 98

Figure 9 Low cardiorespiratory fitness as a major health

problem ............................................................................... 99

Figure 10 Physical activity positively affects function of multiple

components of the syndrome of frailty .......................... 103


Figure 11 Mutually exclusive behavioural categories ................... 142

RESULTS

(Study 1)

LIST OF FIGURES

26

Figure 1 Flow diagram on identification, screening, eligibility

and inclusion of full-text articles ..................................... 162

(Study 2)

Figure 1 Frailty trait scale score in different groups .................... 180

(Study 4)

Figure 1 Ternary plots of the mutually exclusive behavioural

categories of time spent in sedentary behavior (SB),

light physical activity (LPA) and moderate-to-

vigorous physical activity (MVPA) ................................. 199

(Study 5)

Figure 1 Conditional effect of sedentary time on frailty as

function of moderate-to-vigorous physical activity ..... 209

Appendix

1

Relationship between sedentary time and moderate-

to-vigorous physical activity with frailty ....................... 212

(Study 6)

Figure 1 Flow diagram of the process for obtaining the final

sample of the study ........................................................... 245

Figure 2 Cross-lagged panel model 1: Moderate-to-vigorous

physical activity ................................................................. 246

Figure 3 Cross-lagged panel model 2: Sedentary behavior ......... 247

(Study 7)

Figure 1 Cross-lagged panel model 1: Total sample .................... 285

Figure 2 Cross-lagged panel model 2: Physically active

individuals .......................................................................... 286

Figure 3 Cross-lagged panel model 3: Physically inactive

individuals .......................................................................... 287

LIST OF ABBREVIATIONS

27


ATC Anatomical Therapeutic Chemical

BMI Body Mass Index

BST Break in Sedentary Time

CFI Confirmatory Fit Index

CHS Cardiovascular Health Study

CSHA Canadian Study of Health and Aging

FI Frailty Index

FRADEA Frailty and Dependence in Albacete

FTS Frailty Trait Scale

IFN Interferon

IL Interleukin

LIFE Lifestyle Interventions and Independence for Elders

LPA Light Physical Activity

MeSH Medical Subject Headings

MET Metabolic Equivalent of Task

MMSE Mini-Mental State Examination

MPA Moderate Physical Activity

MVPA Moderate-to-Vigorous Physical Activity

RCT Randomized Control Trial

RMSEA Root Mean Square Error of Approximation

SB Sedentary Behaviour

SD Standard Deviation

SHARE Survey of Health, Ageing and Retirement

SPPB Short Physical Performance Battery

SRMR Standardized Root Mean Square Residual


28

ST Sedentary Time

ST-10 ≥10-min Bout of Sedentary Time

TLI Tucker-Lewis Index

TNF Tumour Necrosis Factor

TSHA Toledo Study for Healthy Aging

VPA Vigorous Physical Activity

WHO World Health Organization

WOS Web of Science

PUBLICATIONS

29

PUBLICATIONS

This PhD Thesis is a compendium of previously published scientific,

accepted for publication or submitted for review papers (form more

information see Appendix 1). The references of each of the articles that

make up this document are detailed below:

1. Mañas A, Del Pozo-Cruz B, Garcia-Garcia FJ, Guadalupe-Grau A,

Ara I. Role of objectively measured sedentary behaviour in

physical performance, frailty and mortality among older adults: A

short systematic review. European Journal of Sport Science.

2017;17(7):940-53. DOI: 10.1080/17461391.2017.1327983.

2. Del Pozo-Cruz B*, Mañas A*, Martin-Garcia M, Marin-Puyalto J,

Garcia-Garcia FJ, Rodriguez-Mañas L, Guadalupe-Grau A, Ara I.

Frailty is associated with objectively assessed sedentary behaviour

patterns in older adults: Evidence from the Toledo Study for

Healthy Aging (TSHA). PLoS One. 2017;12(9):e0183911. DOI:

10.1371/journal.pone.0183911.

*These authors contributed equally to this work.

3. Mañas A, Del Pozo-Cruz B, Guadalupe-Grau A, Marin-Puyalto J,

Alfaro-Acha A, Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I.

Reallocating Accelerometer-Assessed Sedentary Time to Light or

Moderate- to Vigorous-Intensity Physical Activity Reduces Frailty

Levels in Older Adults: An Isotemporal Substitution Approach in

the TSHA Study. Journal of the American Medical Directors

PUBLICATIONS

30

Association. 2018;19(2):185 e1- e6. DOI:

10.1016/j.jamda.2017.11.003.

4. Mañas A, Del Pozo-Cruz B, Rodriguez-Gomez I, Leal-Martin J,

Losa-Reyna J, Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. Dose-

response association between physical activity and sedentary time

categories on ageing biomarkers. BMC Geriatrics. 2019;19(1):270.

DOI: 10.1186/s12877-019-1284-y.

5. Mañas A, Del Pozo-Cruz B, Rodriguez-Gomez I, Losa-Reyna J,

Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. Can Physical Activity

Offset the Detrimental Consequences of Sedentary Time on

Frailty? A Moderation Analysis in 749 Older Adults Measured

With Accelerometers. Journal of the American Medical Directors

Association. 2019;20(5):634-8.e1. DOI: 10.1016/j.jamda.2018.12.012.


Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. Which came first: the

movement behavior or the frailty? A cross-lagged panel model in

the THSA study. Journal of Cachexia, Sarcopenia and Muscle.

2019. DOI: 10.1002/jcsm.12511.


Judice PB, Sardinha LB, Rodriguez-Mañas L, Garcia-Garcia FJ, Ara

I. Temporal and bidirectional associations between breaks in

sedentary time and frailty in older adults: A cross-lagged panel

model in the TSHA study. JAMA Internal Medicine. Submitted.

CONTRIBUTIONS IN CONGRESSES

31


Of the aforementioned scientific works, some previous data have been

presented in different congresses and symposia. Next, all of them are

detailed:

Study 1.

Mañas A. [Importance of sedentary behaviour over physical

function, frailty and mortality]. Importancia del sedentarismo sobre

la funcionalidad física, la fragilidad y la mortalidad. I Simposio de

Avances en Fisiología del Ejercicio, Madrid, Spain (2016). Invited

presentation.

Study 2.

Mañas A, Del Pozo-Cruz B, Martin-Garcia M, Marin-Puyalto J,

Garcia-Garcia FJ, Guadalupe-Grau A, Ara I. The way you spend your

sedentary time also matters: an analysis of the association between

sedentary time, sedentary patterns and frailty in the elderly. 21st

Annual Congress of the European College of Sport Science (ECSS),

Vienna, Austria (2016). Mini-oral presentation.

Mañas A, Del Pozo-Cruz B, Martin-Garcia M, Marin-Puyalto J,

Garcia-Garcia FJ, Guadalupe-Grau A, Ara I. [The way you spend

your sedentary time also matters: an analysis of the association

between sedentary time, sedentary patterns and frailty in the

elderly]. La forma en la que acumulas tu tiempo sedentario también

importa: asociación entre el tiempo sedentario, el patrón sedentario


32

y la fragilidad en personas mayores. VI Jornadas Doctorales

Universidad de Castilla-La Mancha, Toledo, Spain (2016). Poster.

Del Pozo-Cruz B, Mañas A, Martin-Garcia M, Marin-Puyalto J,

Garcia-Garcia FJ, Guadalupe-Grau A, Ara I. Interrupting 10 minutes

blocks of sedentary time is associated with frailty in older adults:

evidence from the Toledo Study for Healthy Aging. 6th

International Society of Physical Activity & Health (ISPAH)

Congress, Bangkok, Thailand (2016). Poster.

Mañas A, Leal-Martín J, Guadalupe-Grau A, Rodriguez-Mañas L,

Garcia-Garcia FJ, Ara I. Breaks in sedentary time could have similar

effects as moderate-to-vigorous physical activity on the reduction of

frailty in older people with comorbidities. I Jornadas

Internacionales de Investigación en Actividad Física y Salud,

Cuenca, Spain (2017). Oral presentation.

Study 3.

Mañas A, Guadalupe-Grau A, Del Pozo-Cruz B, Rodriguez-Gomez

I, Garcia-Garcia FJ, Ara I. [Isotemporal substitution models for the

prevention of frailty: replacing sedentary time with physical

activity]. Modelos de sustitución isotemporal para la prevención de

la fragilidad: reemplazando sedentarismo por actividad física. IX

Congreso Internacional de la Asociación Española de Ciencias del

Deporte, Toledo, Spain (2016). Poster.


33

Leal-Martin J, Mañas A, Alegre L, Guadalupe-Grau A, Garcia-Garcia

FJ, Ara I. [Association between moderate-to-vigorous physical

activity, sedentary time and sedentary breaks with frailty in people

over 65]. Asociación entre actividad física moderada-vigorosa,

tiempo sedentario y rupturas del sedentarismo con la fragilidad en

personas mayores de 65 años. X International Symposium in

Strength Training, Madrid, Spain (2017). Poster.

Study 4.

Mañas A, Del Pozo-Cruz B, Rodriguez-Gomez I, Leal-Martin J, Losa-

Reyna J, Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. [Moving more

or sitting less? Analysis over physical function in 771 older adults].

¿Moverte más o sentarte menos? Análisis sobre la función física en

771 personas mayores. VIII Jornadas Doctorales Universidad de

Castilla-La Mancha, Cuenca, Spain (2018). Poster.

Mañas A, Del Pozo-Cruz B, Rodriguez-Gomez I, Leal-Martin J, Losa-

Reyna J, Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. Associations of

mutually exclusive categories of physical activity and sedentary time

on physical function. Moving more or sitting less?. Annual Meeting

International Society of Behavioral Nutrition and Physical

Activity (ISBNPA), Prague, Czech Republic (2019). Poster.


34

Study 5.

Mañas A, Del Pozo-Cruz B, Rodriguez-Gomez I, Losa-Reyna J,

Rodriguez-Mañas L, Garcia-Garcia FJ, Ara I. [Can moderate-to-

vigorous physical activity offset the association between sedentary

time and frailty in older people? A moderation analysis]. ¿Puede la

actividad física moderada-vigorosa compensar la asociación entre

el tiempo sedentario y la fragilidad en personas mayores? Un

análisis de moderación. VI Simposio EXERNET. Investigación en

Ejercicio, Salud y Bienestar. "Exercise is Medicine", Pamplona,

Spain (2018). Poster.

AWARDS AND RECOGNITIONS

35


Below are the awards and recognitions resulting from the works presented

in this PhD thesis:

Outstanding student.

University of Castilla-La Mancha. 18/06/2019.

2nd Poster Presentation Award.

Leal-Martin J, Mañas A, Alegre L, Guadalupe-Grau A, Garcia-Garcia FJ,

Ara I.

[Association between moderate-to-vigorous physical activity, sedentary

time and sedentary breaks with frailty in people over 65]. Asociación entre

actividad física moderada-vigorosa, tiempo sedentario y rupturas del

sedentarismo con la fragilidad en personas mayores de 65 años.

X International Symposium in Strength Training. Technical University of

Madrid and National Strength and Conditioning Association (NCSA

Spain). 16/12/2017.

Young Investigators Award (YIA). 5th equal Short Oral Communication.

Mañas A, Del Pozo-Cruz B, Martin-Garcia M, Marin-Puyalto J, Garcia-

Garcia FJ, Guadalupe-Grau A, Ara I.

The way you spend your sedentary time also matters: an analysis of the

association between sedentary time, sedentary patterns and frailty in the

elderly.

21th Annual Congress of the European College of Sport Science. European

College of Sport Science (ECSS). 09/07/2016.


36

RESEARCH PROJECTS

37

RESEARCH PROJECTS

The PhD thesis presented below, as well as the articles that comprise it, are

framed within the following research project:

Toledo Study for Healthy Aging (TSHA), Instituto de Salud Carlos

III, 2012-2018.

The studies that are part of this PhD thesis have been partially funded by

the following organizations:

Biomedical Research Networking Center on Frailty and Healthy

Aging (CIBERFES) and FEDER funds from the European Union

(CB16/10/00477, CB16/10/00456, and CB16/10/00464).

European Grant. “FRAILOMIC INITIATIVE” (FP7-305483-2 from

FP7-Health-2012-Innovation, European Union).

Spanish Government (Spanish Ministry of Economy, “Ministerio

de Economía y Competitividad,” Instituto de Salud Carlos III,

PI031558 and RD12/0043, PI10/01532, PI11/01068, PI15/01305).

Government of Castilla-La Mancha (FISCAM – “Junta de

Comunidades de Castilla-La Mancha” PI2010/020; and Institute of

Health Sciences, “Consejería de Sanidad de Castilla-La Mancha”

03031-00).

RESEARCH PROJECTS

38

GRANTS AND SCHOLARSHIPS

39


The following funding organizations have directly contributed to the

realization of the present PhD Thesis:

Pre-doctoral grant: Universidad de Castilla-La Mancha.

“Contratos predoctorales para la formación de personal investigador en el

marco del Plan Propio de I+D+i, cofinanciados por el Fondo Social

Europeo (2015/4062)”. Duration: 48 months.

Pre-doctoral stay scholarship: CYTEMA – Universidad de

Castilla-La Mancha. “Becas Pre-doc CYTEMA-Net II Convocatoria de

ayudas pre‐doctorales para estancias cortas en Universidades agregadas

al CEI CYTEMA”. Duration: 3 months.

Pre-doctoral stay scholarship: Universidad de Castilla-La

Mancha. “Ayudas para estancias en universidades y centros de

investigación en el extranjero para el año 2019 en el ámbito del Plan

Propio de Investigación, cofinanciadas por el Fondo Europeo de Desarrollo

Regional (Feder). [2018/14343]”. Duration: 2 months.


40

RESEARCH STAYS

41

RESEARCH STAYS

The PhD candidate has completed two research stays during the time of his

PhD in the following destinations:

Institute for Positive Psychology and Education, Motivation and

Behaviour Research Program. Australian Catholic University.

Sydney (Australia).

Supervisor: Borja del Pozo Cruz, PhD.

Date: from 03rd July 2019 to 03rd October 2019.

Duration: 3 months.

Certificate in Appendix 2.

Exercise and Health Laboratory. University of Lisbon.

Lisbon (Portugal).

Supervisor: Luís B. Sardinha.

Date: from 01st July 2019 to 31st August 2019.

Duration: 2 months.

Certificate in Appendix 3.

RESEARCH STAYS

42

ABSTRACT

43

ABSTRACT

Background

Frailty syndrome is a condition of increased vulnerability related to aging

that leads to a number of adverse health outcomes, including disability,

falls, hospitalization, and death. Due to the high pre-frailty and frailty

prevalence and the world’s population aging, the prevention and reduction

of this syndrome is one of the most crucial challenges facing public health

authorities. Reducing the levels of sedentary behaviour and increasing

physical activity have been recognized to be key strategies for achieving

healthy aging. However, there is a lack of evidence regarding the

relationship between sedentary behaviour and physical activity with

frailty. Therefore, the general aim of this PhD thesis was to study the role

that objectively measured sedentary and physical activity patterns

specifically plays in frailty.

Methods

The present PhD thesis is composed by 7 studies. The systematic review

(study 1) was conducted and reported in accordance with the PRISMA

statement using PubMed and Web of Science online databases. The

participants of the observational studies were taken from the Toledo Study

for Healthy Aging (TSHA). For studies 2 and 3, data were collected from

wave 2 with a total subsample of 519 participants finally included. For

studies 4 and 5, data were collected from wave 2 and 3 with a total

subsample of 771 participants with a single assessment point between both

waves. Longitudinal studies (studies 6 and 7) were based on both two data

ABSTRACT

44

collection waves separated by 4-years with 186 subjects with complete data

on all exposures and outcomes. Briefly, sedentary patterns and physical

activity were measured by accelerometry. Frailty Trait Scale (FTS) was used

to determine frailty levels. Age, sex, educational status, income, and marital

status were self-reported by the participants and used as confounding

variables in the statistical analyses. Moreover, other health-related

outcomes such as body mass index, waist-to-hip ratio, number of drugs,

functional fitness, comorbidity status, and cognitive function were assessed

with standard procedures and also used as confounding variables.

Significance levels were set at P<0.05 in all the analyses performed.

Results

The systematic review (study 1) evidenced that objectively measured

sedentary behaviour was adversely related to physical performance.

However, the association between sedentary behaviour and frailty levels

or mortality rates remained unclear and warranted further research. In the

cross-sectional study 2, we found that sedentary time per day and the

proportion of the day spent in sedentary bouts of 10 minutes or more were

positively linked to frailty. In contrast, breaks in sedentary time were

negatively associated with frailty levels. In the cross-sectional study 3, we

observed that replacing sedentary time with moderate-to-vigorous

physical activity is associated with positive theoretical effects on the frailty

status. In addition, people with comorbidities may also benefit from

substituting sedentary time with light physical activity. When participants

were classified into four movement patterns derived from the physically

active/inactive and low/high sedentary behaviour categories (study 4), it

was found that those physically active older adults had better physical

ABSTRACT

45

function and frailty profiles than those considered physically inactive, even

in the presence of high sedentary time. Higher levels of light physical

activity relative to sedentary time seemed to confer additional

improvements in the frailty profile between those who meet the physical

activity recommendations and also for those who do not. In the study 5, we

found that moderate-to-vigorous physical activity was a moderator in the

relationship between sedentary time and frailty in older adults, offsetting

the detrimental effects of sedentary behaviour with 27 min/d of moderate-

to-vigorous activity. In longitudinal studies 6 and 7, cross-lagged panel

models revealed that the relationship between moderate-to-vigorous

physical activity and sedentary behaviour with frailty was unidirectional:

initial moderate-to-vigorous physical activity predicted future frailty and

baseline frailty was a predictor of sedentary behaviour at follow-up.

Conversely, a reciprocal inverse relationship between breaks in sedentary

time and frailty was displayed in physically inactive participants, while in

active individuals no associations were found.

Conclusions

The relationship between objectively measured sedentary patterns and

frailty in older adults was identified as a gap in the scientific literature

through the systematic review (study 1). In cross-sectional studies, we

found that both reducing sedentary time and introducing frequent short

periods of activity within sedentary periods were associated with reduced

frailty levels (study 2). In addition, we observed that replacing sedentary

time with moderate-to-vigorous physical activity could have positive

theoretical effects on frailty (study 3). Light physical activity could be

ABSTRACT

46

proposed as a middle step strategy for those individuals with comorbidities

(study 3), besides to confer additional improvements on frailty status in

both those who meet and those who do not meet the recommendations of

physical activity (study 4). We also observed that engaging in 27 min/day

of moderate-to-vigorous physical activity could eliminate the potential

negative effects of sedentary behaviour on frailty (study 5). Finally, based

on longitudinal data we found that moderate-to-vigorous physical activity

predicts future frailty (study 6), and therefore, whenever possible, efforts

should be directed towards the promotion of moderate-to-vigorous

physical activity in early stages. For those individuals who do not meet

with physical activity recommendations, the relationship between breaks

in sedentary time with frailty was negative in both directions (study 7), and

consequently, breaking-up sedentary time more frequently could be a good

strategy to attenuate the burden associated with frailty. Future evidence

should move towards experimental studies in order to address the

hypotheses derived from this PhD thesis.

ABSTRACT

47

ABSTRACT

48

49

CHAPTER 1

INTRODUCTION

INTRODUCTION

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51

1.1. POPULATION AGING:

WHERE ARE WE GOING?

1.1.1. An aging population: demographics and population

projections

The increase in life expectancy and, in some countries, the low birth rate

are causing an accelerated growth in the percentage of older adults, which

has as a consequence an increase in the aging population [1, 2].

The increase in life expectancy is due to the improvement in the quality of

life and fundamentally to the advances in medical science that have

occurred in recent decades [3]. Individuals are reaching ages that were

unthinkable in earlier times, and have significantly increased the number

of octogenarian people [4, 5]. The growing weight of the older adult

population is therefore one of the most significant changes that has

occurred in developed societies in the second half of the 20th century [6, 7].

The overall population aged 60 or over consisted of 962 million in 2017,

which has more than double since 1980, when there were 382 million older

persons worldwide [8]. In addition, the older persons rate is expected to

double again by 2050, when it is estimated to reach nearly 2.1 billion [8].

The number of older persons is a growing phenomenon worldwide: in

reality, it is considered that between 2017 and 2050, every country around

the world will suffer a significant increase in the size of the population aged

60 or over [9]. Without exception, the number of older persons is growing

faster than the number of people in all younger age groups. As a matter of

fact, in 1980, children aged 0-9 considerably exceeded the number of

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persons aged 60 or over (1.1 billion versus 0.4 billion), however by 2030 the

global population of older persons is expected to have outnumbered the

rate of under 10 aged children (1.41 billion versus 1.35 billion) (Figure 1)

[8]. The projections also estimate that in 2050 the number of persons aged

60 or over will be higher than adolescents and youth at ages 10-24 years (2.1

billion versus 2.0 billion) [8]. Lastly, the rate of people at very advanced

ages is increasing as well: the global population aged 80 or over is expected

to triple between 2017 and 2050, expanding from 137 million to 425 million

[8].

Data Source: United Nations (2017). World Population Prospects: the 2017 Revision.

Figure 1. Global population by broad age group, in 1980, 2017, 2030 and

2050.

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1.1.2. Spanish population aging

According to the estimates carried out by the United Nations, the

population aging phenomenon is even more alarming in Spain, which

place it as the second oldest country in the world in 2050, whose population

41.9% would be above 60 years old [8].

If an analysis of the data focused on Spain was carried out in comparison

with the rest of the countries, the phenomenon of aging would particularly

mark an obvious growth in our country, as a consequence of a greater

longevity, since in less than 30 years the number of people over 65 has

considerably increased [10]. This process is accentuated by the low birth

rate that has been recorded for some decades. This reduction is registered

in Spain since the mid-70s. In 1975, the average number of children was

almost 3 per woman of childbearing age, while currently it is only 1.31 [11].

Current data in Spain show that the population over 65 years old is around

19% of the total population, with almost 9 million people, of which

approximately 32% are octogenarian [10]. In this sense and according to the

projections made by the Spanish National Institute (Table 1), in 2033 people

over 65 will be over 25% of the population and the octogenarians will be

close to 4 million, which would represent more than 30% of the total older

population [10].

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Table 1. Projection of the Spanish population residing in Spain by age

groups, 2018-2033.

Age Groups 2018* 2033 2018-2033

Growth

TOTAL 46,659,302 49,016,091 2,356,790

0 to 4 yrs 2,104,793 1,984,806 -119,987

5 to 9 yrs 2,423,912 1,995,913 -427,998

10 to 14 yrs 2,448,415 2,080,238 -368,177

15 to 19 yrs 2,263,927 2,347,271 83,344

20 to 24 yrs 2,261,685 2,796,807 535,121

25 to 29 yrs 2,512,596 2,968,547 455,952

30 to 34 yrs 2,853,574 2,803,431 -50,143

35 to 39 yrs 3,577,880 2,707,978 -869,901

40 to 44 yrs 3,972,611 2,828,356 -1,144,255

45 to 49 yrs 3,767,952 3,041,851 -726,102

50 to 54 yrs 3,592,122 3,613,980 21,858

55 to 59 yrs 3,205,235 3,898,538 693,303

60 to 64 yrs 2,713,921 3,618,870 904,949

65 to 69 yrs 2,406,215 3,369,080 962,864

70 to 74 yrs 2,126,891 2,907,752 780,861

75 to 79 yrs 1,538,815 2,300,769 761,954

80 to 84 yrs 1,422,838 1,766,105 343,267

85 to 89 yrs 953,500 1,194,524 241,024

90 to 94 yrs 401,328 531,257 129,929

95 to 99 yrs 99,845 213,652 113,807

≥100 yrs 11,248 46,366 35,118

*Provisional Data

Data Source: Spanish National Institute (2018). Population Projections 2018.

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1.1.3. Health status and burden disease

This last phenomenon is known as "aging of the aging" or "over-aging". Far

from establishing a pessimistic perspective, these demographic

phenomena, is a clear example of the progress and social, economic and

health development of the peoples, as a result of which mortality rates have

been reduced, especially infant mortality, whereas life expectancy has

increased [12]. Undoubtedly it deserves to be considered a triumph, but at

the same time it constitutes a challenge, since aging produces considerable

social, political and economic changes in health systems, pensions, etc. [13].

It is currently estimated that public health expenditure in Spain for 2050-

2060 will lead to an increase of 1.6 points of gross domestic product in total

health expenditure and 0.9 points of gross domestic product in long-term

care. Older people consume 40% of the pharmaceutical benefit and 70% of

the pharmaceutical expenditure [14]. We also know that the greatest health

expense of any person is concentrated in the last phase of his life, especially

in the last year [15, 16].

In parallel with the change in the demographic pattern, this increasingly

aging population shows a change in the epidemiological pattern of both

morbidity and mortality. The disease pattern has evolved from acute

diseases, of a transmissible nature (infectious and contagious) that were

generally isolated, to chronic, degenerative, non-communicable diseases,

the result of habits and lifestyles, and which are generally associated

(pluripathology), interacting negatively in a synergistic way with each

other (comorbidity) [17, 18]. These diseases, in the elderly, have a well

differentiated and unique behaviour characterized by its tendency to

chronicity, not presenting a restitution or complete cure, by the association

of more than one disease per person (multipathology or comorbidity), with

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the negative interactions that it entails and in addition, by printing different

degrees of clinical frailty, deterioration in the functional domain, loss of

autonomy, disability and dependency for the activities of the daily life [19-

21]. Diseases are responsible for disability and dependence, that is, it is

disease, and not exclusively age, that causes dependence, although

dependence obviously increases in those over 65 and, above all, in the

elderly 80 years old. In Spain, between 15 and 25% of people over 65 are

dependent for basic activities of daily life, and when we consider those over

75 years, these figures reach 25-35% [22].

The aging population is one of the four main trends (along with increases

in non-communicable disease, shifts towards disabling causes and away

from fatal causes, and changes in risk factors, particularly the influence of

obesity) driving a change in the leading causes of disease burden. As a

consequence of the change in the pattern of morbidity, the elderly, in

general, need multiple medications to alleviate, improve or cure their

health problems, finding the phenomenon of “polymedication” or

“pluripharmacy” [23]: continuous consumption during the last 6 months of

four or more active principles, specifically related to chronic and multi-

pathological elderly patients. Thus, polypharmacy is also defined as the

continued consumption of six or more active substances. Today, we know

that 88.9% of people over 65 take any medication, rising to 93.4% when

considering those over 75, and that 94% of people with chronic diseases are

polymedicated [24]. The average medication taken by the elderly varies

between two and four medications per day, but when self-medication is

considered, it rises between four and six, and in institutionalised people

between 4.2 and eight [24].

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That is to say, we find older and very old people, multi-pathological,

polymedicated, dependent, with a compromised functional reserve, which

makes them frail and vulnerable. Therefore, they require continued or

prolonged professionalized care, which in the community-domiciliary

field, in some occasions, can hardly be guaranteed. First of all, we must

consider as a challenge not only to live many years, but to live with the best

possible state of health, in order to prevent disability and dependency.

Moreover, it is known that medium-term estimates show that the increase

in disability and dependence expected in the coming years is not

attributable exclusively to population demographic change, but to other

factors, some of them modifiable through appropriate intervention, such as

tobacco use, obesity, alcohol use, high blood pressure and physical

inactivity leading lifestyle risk factors [25-28]. Good health is one of the

essential pillars of an economically and socially prosperous society that

also contributes to the sustainability of the system. In this sense, the actions

to be implemented are based on three fundamental pillars: the promotion

of autonomy and self-care through "active aging", the integral prevention

of dependence, and the active participation of health services in these

activities [29, 30].

This increase in population aging is one of the main challenges that we will

have to face in the medium and long term, and it can be an important

challenge for governments that must implement policies to address the

needs and interests of the older population, especially those related to with

health care.

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1.2. FRAILTY

1.2.1. Frailty definition

The health of the elderly must be measured in terms of function and not of

disease, since it is that which determines the life expectancy, the quality of

life and the resources or care that each population will need [31]. The

functional situation prior to the development of disability (restriction or

loss of ability to perform an activity), and dependence (help from a person

to carry out the activity) is one of the best indicators of health status and it

is also a better predictor of disability incident than morbidity [32, 33]. In

this regard, the importance of including this term is to focus on

functionality and not in the diagnosis of disease.

Currently, frailty can be considered as a pre-disability or risk of developing

a new disability from a situation of incipient functional limitation. In other

words, it can be said that frailty is a syndrome characterised by a decrease

in strength and endurance, with an increase in vulnerability to low

intensity stressors, produced by an alteration in multiple interrelated

systems, which decreases the homeostatic reserve and the capacity of

adaptation of the organism, predisposing it to adverse health events, higher

probabilities of dependence and even death (Figure 2).

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Data Source: British Columbia Guidelines (2017). Frailty in Older Adults - Early Identification

and Management.

Figure 2. Vulnerability of frail older adults to external stressors.

The concept of frailty is a key factor in the context of geriatric care.

However, it has been used with different meanings, as a result of its

historical evolution, from the frailty phenotype model proposed by Fried,

to accumulated deficits model by Rockwood, and Frailty Trait Scale (FTS)

by García-García, up to Morley’s definition. This has caused that different

international organisms have urged to agree a definition.

Campbell and Buchner defined the term frailty in 1997 as a “biological

syndrome of decreased functional reserve and resistance to stressors, due

to the cumulative decline of multiple physiological systems that cause loss

of homeostatic capacity and vulnerability to adverse events”. Since then,

many definitions and constructs have been proposed, based almost always

on alteration in several domains, although sharing common bases, such as

the reduction of functional reserve and the vulnerability to presenting

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adverse events. The two main theoretical frameworks on which the frailty

construct has been developed are the one defended by Fried and colleagues

in 2001 based on data from the Cardiovascular Health Study (CHS), in

which they established a phenotype as a risk situation to develop disability,

and the one promoted by Rockwood et al. based on data from the Canadian

Study of Health and Aging (CSHA), which determined that frailty consists

of the addition of several health conditions that include, among others,

comorbidity and disability. These approaches diverge both in their

conceptual framework and in the operational approach to frailty, and each

could have a clinical utility in differentiated areas. Despite these

differences, there is a growing consensus that frailty is a state or condition

that precedes disability and that it is intrinsically related to the biological

phenomenon of aging. Recently, a consensus conference sought, using the

Delphi methodology, an operative definition of frailty.

Experts agreed that no single biomarker by itself was adequate for the

assessment of frailty, suggesting a need for a combination of multiple

biomarkers. However, none of the proposed combinations of biomarkers

was able to reach the 80% threshold of agreement required by the Delphi

process. Remarkably important it is the fact that among all the laboratory

biomarkers suggested for the assessment of frailty, none was accepted.

Although an absolute consensus was not reached on the constituent

elements that would be included in an operational definition of frailty, a

high percentage of agreement was detected in the aspects of frailty that are

detailed below:

Frailty is a syndrome of decreased functional reserve and

resistance to stressors, which causes vulnerability.

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It identifies subjects at high risk of disability and/or other adverse

outcomes.

It is multidimensional, dynamic and nonlinear.

It is different from disability and comorbidity, although the

diseases modulate its appearance.

Diagnosis is useful in primary and community care.

Gait speed, mobility and physical activity can be useful for

diagnosis, as well as the assessment of mental and nutritional

status.

It can be reversible. Physical activity is a treatment.

Subsequently, in 2013, a consensus group led by J.E. Morley defined frailty

as “a medical syndrome of multiple causes characterised by loss of strength

and endurance, and decreased physiological function, which increases

individual vulnerability to develop dependence or death” [34]. This is the

last definition of consensus that we have to identify frailty. In this same

consensus there were also four major points of agreement [34]:

1. Physical frailty is an important medical syndrome. The group

defined physical frailty as “a medical syndrome with multiple

causes and contributors that is characterised by diminished

strength, endurance, and reduced physiologic function that

increases an individual's vulnerability to develop increased

dependency and/or death”.

2. Physical frailty can potentially be prevented or treated with

specific modalities, such as exercise, protein-calorie

supplementation, vitamin D, and reduction of polypharmacy.

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3. Simple, rapid screening tests have been developed and validated,

such as the simple FRAIL scale, to allow physicians to objectively

recognize frail persons.

4. For the purposes of optimally managing individuals with physical

frailty, all persons older than 70 and all individuals with significant

weight loss (>5%) due to chronic disease should be screened for

frailty.

It is important to highlight that frailty is an autonomous concept, different

from comorbidity and disability. Despite the possibility of coexistence, it

can be present in the absence of both in 26.6% of the elderly (Figure 3). In

the CHS on 2,762 people with disabilities, comorbidity or frailty, only 3%

had all three entities at the same time and 14% had two of them. In the

Toledo Study for Healthy Aging (TSHA) it was shown that 87% of the

dependent elderly for instrumental activities and 67% of dependents for

basic activities were not frail. Therefore, by definition we should not

confuse comorbidity-pluripathology-multimorbidity with frailty, or

disability or dependence with frailty.

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Data Source: Fried et al. (2001). Frailty in Older Adults: Evidence for a Phenotype. Journal of

Gerontology: Medical sciences.

Figure 3. Venn diagram displaying extent of overlap of frailty with

activities of daily living disability and comorbidity (≥2 diseases).

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1.2.2. Frailty constructs

Regardless of what definition of frailty is used, to be applied practically,

frailty first needs to be operationally defined. A breakthrough in frailty

measurement came in the mid-1990s, when it was verified that when frailty

manifestations, such as slow walking speed and weight loss, were grouped

together to form combination scores, prediction of adverse clinical

outcomes was better than when components were considered alone [35,

36]. Frailty combination scores have been used to operationally define

frailty ever since. In 2001, Fried and colleagues proposed their landmark

frailty phenotype measurement, which assessed frailty by measuring five

of its physical components [37]. Following this, and also in 2001, Rockwood

and Mitnitski released their accumulated deficits model of frailty, which

considered not only the physical components of frailty, but also the

psychosocial aspects of frailty [38]. Both of these frailty models are highly

regarded and in common use today.

1.2.2.1. Fried's Frailty Phenotype - the CHS index

Fried's Frailty Phenotype is a popular measurement of frailty, also known

as the CHS Index from the study it was originally applied to [37]. The frailty

phenotype considers frailty by its physical characteristics, creating a

construct whose bases are sarcopenia and energy imbalance, and

establishes a feedback relationship between them, called “cycle of frailty”

(Figure 4). They identify five dimensions in the construct: shrinking

(unintentional weight loss of 4.5 kg or more in the last year), weakness (low

grip strength), exhaustion (self-reported), slowness (slow walking speed)

and low physical activity [37]. This scale divides the population into frail

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(those who meet three or more criteria), pre-frail (if they meet one or two

criteria) and non-frail or robust (those who meet none). It has a solid

foundation of biological causative theory [39] and has been applied to

multiple epidemiological studies where it is predictive of adverse clinical

outcomes such as hospitalization, falls, disability, decreased mobility and

short- and medium-term mortality [37, 40-42]. The Fried construct meets

the criteria of the frailty definition: it is a biological phenomenon, which

affects multiple systems and confers vulnerability to the individual. It is

also a defined situation, precursor of geriatric syndromes (disability, falls,

etc.), with standardised criteria, which has enabled the development of

numerous epidemiological studies and the comparison between them.

Data Source: Fried et al. (2001). Frailty in Older Adults: Evidence for a Phenotype. Journal of

Gerontology: Medical sciences.

Figure 4. Cycle of frailty.

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1.2.2.2. Frailty Index of Accumulative Deficits

The Frailty Index (FI) of Accumulative Deficits was first proposed by

Rockwood and Mitnitski as a way to incorporate the multidimensional

nature of frailty into an operational definition [43]. Just as cells,

physiological processes and systems accumulate deficits with aging,

human present an increase in the number of diseases and situations that

condition their relationship with the environment and their response to

internal and external stressors. The load of more or less deficit will

determine the time remaining until the individual death, thus acting as an

estimator of biological age.

The FI is supported by biological causative theory [38, 44] and involves the

accumulation of up to 80 (with 30–70 items being typically counted)

comorbidities, symptoms, diseases, disabilities or any deficiency in health

with the idea that a greater number of health deficits indicates a higher

frailty [45]. The spectrum of deficits is broad and includes different

dimensions: cognitive, emotional, nutritional and functional state, ability

to communicate, motivation and perception of health status, strength,

balance and mobility, sleep and social aspects. The FI is expressed as a ratio.

For instance, if a list of possible health deficits obtainable from a study

cohort is 60, a person with six of these deficits has a FI of 0.1. The exact list

of health deficits for inclusion in the FI does not specifically matter, other

than they should: increase in incidence but not have a ceiling effect with

age; be reflective of a range of physiological systems; and be associated

with health and not age per se [45]. Comprehensive guidelines for creating

a FI have been provided by Searle et al. [45].

The FI is well validated, and has been applied to multiple datasets,

including the Survey of Health, Ageing and Retirement (SHARE) study in

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Europe [46, 47]. Ideally, the FI should be used as a continuous variable,

however for comparison studies, various cut-off points have been

considered to identify frailty [48, 49].

Several studies have found that the FI has a higher predictive ability of

adverse clinical events than other frailty measurements in both hospital

and community settings [48, 50, 51]. Additionally, it has been reported that

it is the total FI score, rather than type of health deficits included in the FI,

that is most predictive of adverse outcomes [38]. An upper limit to the FI is

believed to exist at around 0.67, beyond which survival is unlikely [52].

Both frailty phenotype and frailty index have some weaknesses [53].

The FI of Rockwood et al. [38] has been criticised due to different possible

limitations: the inclusion of too many elements, the inclusion of conditions

that do not share a biological construct make it little useful to identify the

pathophysiological determinants of frailty, and the inclusion of functional

deficits away from the concept of frailty as a “precursor” state of disability

on which action can be taken to prevent it.

Similarly, the frailty phenotype of Fried et al. [37] also has some restrictions

for clinical practice and research purposes. Firstly, it should be borne in

mind that several biological mechanisms of the clinical syndrome (such as

obesity, inflammation, hormonal changes, low muscular strength,

sarcopenia, insulin resistance, physical inactivity, deficient cardiovascular

balance, etc.) seem to be interdependent, creating a constant gradient of

multisystemic biological dysfunction from vigorous to the most vulnerable

individual. The previously mentioned is the one known as “Frailty Trait”.

Fried et al. [37] definition does not include the continuous change from

robustness to frailty, since the worst population quintile is used as the base

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for some of the criteria to assess the different domains, being complicated

to establish any contrast among older people over/under the thresholds.

Secondly, as a result, Fried et al. [37] scale shows some difficulties with

regards to the assessment of small changes in the patient’s status and to the

supervised evolution in elderly individuals. Thirdly, the concept of frailty

most accepted in its broadest sense [54] includes the multisystem

involvement as the central element of the construct. However, contrary to

the nature of frailty, the frailty phenotype definition is mainly

oligodimensional. Therefore, there is an increasing movement to extend the

frailty phenotype to other dimensions as in nutrition and cognitive

functioning [55]. In this line, there is a very close relationship between

vascular disease and frailty [56]. Both of them share some biological

elements (inflammation, coagulation disorder, insulin resistance, hormonal

changes) as well as risk factors (e.g. inactivity, obesity), a bidirectional

pathogenic path, and outcomes [57]. The relation is then clear from the

early stages of the disease [58]. These conclusions are linked to vascular

disease within the spectrum of frailty.

1.2.2.3. Frailty Trait Scale

Based on the characteristics of the biological trait of the frailty syndrome

and overcoming some of the problems mentioned above, a new operational

scale called Frailty Trait Scale (FTS) appears [59]. This construct

incorporates new relevant domains according to the most recent findings

on the pathophysiology of the frailty syndrome (vascular and nervous

system and nutrition), makes a more extensive assessment of the domains

and classifies the elderly in a more proper way according to their frailty

status. This biological trait is a continuous phenomenon: the more it

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decreases in the biological reserve, the closer it is to the threshold of

presenting adverse effects derived from it (functional deterioration,

hospitalization, mortality, etc.). This threshold is not a qualitative jump

(presence/absence of risk of adverse events) but a continuous

intensification in the risk.

The FTS includes 7 aspects such as the following: energy balance and

nutrition, physical activity, nervous system, vascular system, weakness,

endurance, and slowness. These domains become operational through 12

items [59]:

1. Energy balance and nutrition were estimated by using the body

mass index (BMI), central obesity (waist circumference),

unintentional weight loss and serum albumin level.

2. Activity was assessed by using the total score of the Physical

Activity Scale for the Elderly [60].

3. The nervous system was calculated by considering verbal fluency

and balance. Verbal fluency was estimated by asking the

participants to give names of animals during one minute [61].

Balance was measured by Romberg test [62].

4. The vascular system was measured by the brachial-ankle index

done with Doppler ultrasound [63].

5. Weakness was estimated by assessing grip strength in the

dominant arm and the knee extension strength [64].

6. Endurance was assessed by the chair stand test, which measures

the number of times that a person stands up in 30 seconds [65].

7. Slowness was estimated by calculating the time to walk 3 m at a

“normal pace” according to a standard protocol [62].

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Each item score represents a biological trait. When appropriate, items were

analysed according to the item’s quintile distribution in the population.

Participants had to complete at least 75% of the items included in the FTS

in order to be included in the study analytical sample. The total score was

determined according to the formula [59]: Total score = (Σ items score/total

score possible by individual)*100. Therefore, the total FTS score ranged

from 0 (best score) to 100 (worst score).

The FTS was built following the methodology proposed by Searle et al. [45],

as in the original article by using 40 items, which basically kept the

dimensions and scaling.

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1.2.3. Prevalence and incidence of frailty

Depending on the diagnostic criteria used, cut-off points, areas of study

and inclusion criteria of the populations studied, different studies of

international cohorts have found prevalences that fluctuate between 4 and

59.1%.

The prevalence of this syndrome increases exponentially as it ages [66],

from 3.2% on average to 65 years old, going through 16.3% in those over 80

years old, until reaching 23.1% at 90 years old.

It is also estimated that this syndrome is more prevalent in women than in

men, with an approximate ratio of 2:1 [66].

The prevalence differs among countries, in countries with higher incomes,

the prevalence of frailty is lower [67]. This is explained by the expenditure

that each country devotes to health, those with less investment in health,

have a higher rate of frailty. These differences can be explained by

socioeconomic factors, especially due to low individual income and high

social vulnerability.

Collard et al. [66] conducted a joint analysis of the main international

epidemiological studies, detecting a prevalence of frailty in 61,500

community elders of 10.7% (9.6% applying the Fried phenotype and 13.6%

applying broad-spectrum criteria such as those of Rockwood). The

prevalence was higher in women than in men, and it increased along with

the age. In this review, an estimated 44.2% of the studied population were

at risk of becoming frail in the two following years [66]. However, data

from none of the Spanish epidemiological studies were not included in the

analysis, as they were not yet published at the time of preparing the article.

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The data from the Spanish studies confirm the severity of frailty among

elderly individuals living in the community. The TSHA of 8.4% (95% CI:

7.1-9.8%) in 3,214 older than 64 years of the community [64]; the FRADEA

study (Frailty and Dependence in Albacete) detected a prevalence of frailty

of 16.3% (95% CI: 14.0-18.6%) in 993 older than 70 years of Albacete

(included older than the community and institutionalised) [68]; and the

Peñagrande study (Madrid) of 10.5% (95% CI: 8.9-12.3%) in 1,250

community-dwelling older than 64 years [69]. Other cohorts, such as those

of Leganes [70], Lleida (FRALLE) [71], and Barcelona (Octabaix) [72], have

found prevalences between 9.6 and 20.4% according to population strata

and criteria used.

Recently, frailty prevalence data have also been published in nursing

homes. The studies of Cuenca and Albacete (FINAL study) have shown

very high figures, between 53.7 and 68.8%, respectively [73, 74].

There are not many studies that estimate the incidence of frailty. A recent

systematic review found only 6 studies where the incidence of frailty was

estimated [75]. Frailty incidence ranged from 5% (follow-up 22.2 years; age

≥ 30) to 13% (follow-up 1 year, age ≥ 55) [75]. Therefore, due to the

heterogeneity of the data, more well-designed prospective frailty studies

are needed to overcome the general scarcity of data on the onset and

progression of this dynamic condition over time.

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1.2.4. Frailty pathogenesis

Frailty is characterised by multisystem dysregulations, leading to a loss of

dynamic homeostasis, decreased physiologic reserve, and increased

vulnerability for subsequent morbidity and mortality [76]. This is often

manifested by maladaptive response to stressors, leading to a vicious cycle

toward functional decline and other serious adverse health outcomes [77-

80].

In recent years, great efforts have been made to know the origins and causes

of this syndrome. This evidence suggests several important multisystem

pathophysiological processes in the pathogenesis of frailty syndrome,

especially those related to the musculoskeletal and endocrine systems, and

chronic inflammation and immune activation [76].

In this complex process sarcopenia has been proposed as one of the

cornerstones. Sarcopenia is defined as the loss of muscle mass and strength,

which can occur rapidly after the age of 50 years [81]. It can be further

accelerated by chronic diseases, and is a major contributor to disability [82].

Its causes include age-related changes in α-motor neurons, type I muscle

fibres, muscular atrophy, poor nutrition, growth hormone (GH)

production, sex-steroid levels, and physical activity [83]. The

musculoskeletal system is the body system that consumes more energy at

rest, so that its reduction involves a decrease in the amount of energy

consumed at rest. Likewise, sarcopenia decreases the energy consumed

with exercise, since subjects move less, walk slower, have greater fatigue

and avoid exercise. Both processes entail a decrease in the total energy

expenditure that produces a down regulation of the appetite, with the

consequent decrease in the intake of nutrients (especially proteins), which

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causes lower protein synthesis. This approach was initially postulated by

Fried [37], denominating cycle of the frailty, although later it has been

completed with other elements.

Chronic inflammation is likely a key underlying mechanism that

contributes to frailty both directly and indirectly through other

intermediate pathophysiologic processes (Figure 5).

Data Source: Chen et al. (2014). Frailty syndrome: An overview. Clin Interv Aging.

Figure 5. Pathogenesis of the frailty syndrome: potential underlying

mechanisms and hypothetical modal pathways leading to frailty.

Added to these phenomena, there is a state of chronic low-grade

inflammation characteristic of old age, triggered by oxidative stress and by

the production of cytokines from different body systems, including visceral

fat, and that becomes more evident in frail people [84-86]. The so-called

term “inflammaging” is known as the upregulation of certain pro-

inflammatory cytokines that occurs in adulthood and during chronic

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diseases associated with aging, highlighting interleukin 6 (IL-6) [87, 88], IL-

1a, tumour necrosis factor α (TNF-α) and interferon α (IFN-α) [89, 90]. This

activation of cytokines produces, as deleterious effects, chronic

inflammation, release of acute phase hepatic reactants, insulin resistance

and osteoclastic activity [91, 92].

To counteract this inflammatory state, the body acts through the anti-

inflammatory cytokines IL-4, IL-10 and IL-13 producing activation of the

hypothalamic-pituitary-adrenal axis, and causing an elevation of the

cortisol, which will cause secondarily, and as unwanted effects, bone

resorption, lipolysis, protein catabolism, gluconeogenesis and immune

dysfunction, depending on the system on which it acts, ultimately

producing frailty and chronic disease [93]. The coexistence of inflammatory

and anti-inflammatory phenomena in the elderly will have a negative effect

on metabolism, bone density, strength, exercise tolerance, vascular system,

cognition and affect, ultimately helping to trigger the phenotype of frailty

[94]. Chronic inflammation is also an important contributor to sarcopenia,

thus interrelating the two key pathophysiological factors in frailty.

Since frailty is a multifactorial process, it is not surprising that many other

elements have been implicated in its pathogenesis. It is noteworthy the

neuroendocrine dysregulation (relationship with testosterone levels,

growth hormone-insulin-like growth factor axis, cortisol, estradiol, leptin,

ghrelin, obestatin or vitamin D), endothelial dysfunction and the presence

of a procoagulant state, favoured by stress oxidative and chronic

inflammation [95-98]. All this can prompt atherosclerosis, with the

consequent visceral damage, cognitive deterioration, depression, obesity,

osteoporosis, insulin resistance, alterations of the circadian rhythm and

alterations of balance and gait, among others.

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1.2.5. Frailty interventions

Frailty is the new paradigm on which health care for the elderly should be

based. The relevance of frailty as a public health problem lies in the number

of elderly people who suffer from it. It is a dynamic process with evidence

of reversibility [99], therefore, early identification of the state of pre-frailty

is currently a vitally important issue in primary prevention, because pre-

frail individuals have more than doubled the risk of becoming frail during

the next three years [100].

Frailty has become the most problematic expression of aging, as it is the

prelude to disability. It is known that once the disability is established, the

probability of reversing it is scarce, and although its treatment is highly

expensive, it has been shown to be ineffective. In this regard, the European

economic projections for the years 2016-2070 refer to an early intervention

in frail people, which will improve the quality of life and reduce the

economic cost [9].

A consensus group in 2013 supported four possible treatments that

appeared to be effective in the treatment of frailty. These are: caloric and

protein support, vitamin D deficit correction, reduction of polypharmacy,

and exercise (resistance and aerobic).

Weight loss is an important component of the frailty syndrome [101-103].

Nutritional interventions with calorie supplementation increased weight

gain and reduced mortality in malnourished elderly people [104].

Nutritional supplementation is effective in the treatment of weight loss

[105, 106]. Protein supplementation increases muscle mass [107, 108],

reduces complications [109], improves grip strength [109], produces weight

gain [109], and can act synergistically with resistance exercises in older

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people [110, 111]. However, other studies of protein supplementation have

not shown benefits in the functional improvement [104].

Regarding vitamin D, it has been proved that vitamin D supplementation

will reduce falls [112], hip fractures [113], and mortality [114] in older

people who are vitamin D deficient. It may also help the muscle to have a

better functioning [115]. Despite the fact that the absence of large-scale

clinical trials show that frailty can be prevented or treated by vitamin D,

there is sufficient evidence of efficacy in frailty-appearing populations to

assume that vitamin D in frail persons who are vitamin D deficient would

be useful [34].

Polypharmacy is likely to be recognized as the main contributor to the

pathogenesis of frailty [116, 117]. Therefore, reduction in inappropriate

medicines can clearly decrease costs [118] and medication side effects in

frail populations [119-121]. The STOPP and START criteria [122, 123] can

be helpful guidelines to reduce inappropriate medicine use in this

population.

The therapy that has proven most effective so far to prevent and treat frailty

is physical exercise, which has been shown to reduce mortality and

disability in the elderly, maintaining muscle mass, increasing strength and

functionality, stabilizing bone mineral density and favouring

cardiovascular system [124-128]. In this connection, Singh et al. [129]

proved that a year of resistance exercise in frail persons following hip

fracture implied a decrease in hospitalisations and nursing home

placement. Other study carried out in a community-based exercise

program involving 610 frail persons, found that exercise was cost effective

in preventing frailty progression and disability [130]. Theou et al. [127] in a

systematic review, found that 45 to 60 minutes of exercise 3 times a week

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seemed to have positive effects on frail older adults and may be used for

the management of frailty. In the Lifestyle Interventions and Independence

for Elders study (LIFE) according to which the patients received two

different kinds of intervention, that is to say, physical activity versus

education in successful aging, it has been shown that the prevalence of

frailty at 12 months was considerably low regarding the physical activity

group (10%), when compared to the control group that received education

in successful aging (19.1%) [131]. Thus, it can be stated that exercise in frail

subjects increases some functional performances, such as walking speed,

chair stand, stair climbing, and balance, and decreased depression and fear

of falling [132, 133]. Likewise, it has also been found that exercise can

improve cognitive performance in frail individuals, mainly executive

functions, processing speed and working memory [134, 135].

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1.3. PHYSICAL ACTIVITY AND

SEDENTARY BEHAVIOUR

1.3.1. Definitions and concepts

Physical behaviour during the 24 hours of the day must be correctly

defined to properly understand the relationships between one and the

other behaviours with health outcomes. Figure 6 organises the movements

that take place throughout the day into two components [136]:

Data Source: Sedentary Behaviour Research Network (2012). Letter to the editor:

standardized use of the terms "sedentary" and "sedentary behaviours". Appl Physiol Nutr

Metab.

Figure 6. Conceptual model of movement-based terminology arranged

around a 24-h period.

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The inner ring represents the main behaviour categories using energy

expenditure. The outer ring provides general categories using posture. The

proportion of space occupied by each behaviour is not prescriptive of the

time that should be spent in these behaviours each day.

1.3.1.1. Physical activity

Since 1996 approximately and as a result of a broad international consensus

[137], physical activity has been defined as any movement produced by

skeletal muscles that requires a substantial increase in energy expenditure

above the resting level. What basically characterises physical activity is the

energy expenditure it produces.

The quantification and prescription of physical activity, as indicated below,

is the product of four variables: intensity, duration, frequency, and type of

physical activity. Thus, the dose of physical activity for health could be

described as a combination of these four variables.

Intensity is an essential prerequisite for physical activity to induce

improvements in physical fitness and other components of physical health.

The intensity of physical activity is usually measured in metabolic

equivalents, or METs. One MET is defined as the amount of oxygen

consumed while sitting at rest and is equal to 3.5 ml O2 per kg body weight

x minute [138]. The MET concept represents a simple, practical, and easily

understood procedure to express the energy cost of physical activities as a

multiple of the resting metabolic rate. Light physical activity (LPA) is

defined between 1.6 and less than 3.0 METs. Moderate physical activity

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(MPA) is between 3.0 and less than 6.0 METs. Finally, vigorous physical

activity (VPA) requires 6.0 or greater METs.

The duration is the amount of time in hours or minutes per day dedicated

specifically to physical activity. Although years ago it was established that

the minimum duration of time to account for a physical activity bout was

10 minutes [139], currently, the new physical activity guidelines are

eliminating it within their recommendations [140].

The frequency is the physical activity regularity and is usually expressed

by the number of times or days per week of physical activity.

The type of physical activity refers to the specific modality performed and

the context in which the physical activity is developed (e.g. walking,

running).

To obtain health benefits through physical activity and exercise, a specific

dose is necessary [141]. The principles of overload, progression and

specificity are the main determinants of how the body responds to the dose

of physical activity [142].

1.3.1.2. Exercise

Exercise is a subset of physical activity that is planned, structured, and

repetitive and has as a final or an intermediate objective the improvement

or maintenance of physical fitness [143].

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1.3.1.3. Sedentary behaviour

Sedentary behaviour (SB) is any waking behaviour characterized by an

energy expenditure ≤1.5 METs, while in a sitting, reclining or lying posture

[144]. In general, this means that any time a person is sitting or lying down,

they are engaging in SB. Common sedentary time (ST) include TV viewing,

video game playing, computer use (collective termed “screen time”),

driving automobiles, and reading.

1.3.1.4. Breaks in sedentary time

A break in ST (BST) is generally defined as a period of non-sedentary

activity (e.g. standing or walking) in between two sedentary conditions

(e.g. sitting or reclining posture) [145].

1.3.1.5. Physical inactivity

In a letter to the editor in 2012, physical inactivity is defined as people who

are performing insufficient amounts of moderate-to-vigorous physical

activity (MVPA) (i.e., not meeting specified physical activity guidelines)

[136].

This recommendation was made after a high confusion was generated

because the sedentary term was also used to define those individuals with

lack of physical activity.

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1.3.1.6. Physical fitness

Physical fitness is a set of attributes that are either health- or skill-related

[143]. The most common components of fitness are: cardiorespiratory

endurance, muscle endurance, muscle strength, muscle power, flexibility,

balance, and speed.

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1.3.2. Physical activity and sedentary behaviour assessment

The accurate assessment of physical activity and SB (Figure 7) is important

for monitoring the prevalence and trends in different population groups

including compliance with guidelines, determining dose-response

associations with health outcomes, and informing intervention strategy

design and effectiveness.

Data Source: Hills et al. (2014). Assessment of Physical Activity and Energy Expenditure: An

Overview of Objective Measures. Front Nutr.

Figure 7. Components of total daily energy expenditure and measurement

approaches.

Depending on the methodology used, they are usually divided into three

groups:

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1.3.2.1. Reference methods

Reference methods are based on obtaining energy expenditure, and the

main ones are: direct calorimetry, indirect calorimetry, and doubly labelled

water.

Human energy metabolism leads to the production of energy from the

combustion of fuel via carbohydrate, protein, fat, or alcohol. This way,

oxygen is consumed and carbon dioxide is produced. The measurement of

energy expenditure involves the assessment of heat generation or heat loss

directly, evaluated through direct calorimetry. The evaluation of heat

production or loss by determining oxygen consumption and/or carbon

dioxide production is termed indirect calorimetry [146]. First calorimeters

for the quantification of human energy expenditure were direct

calorimeters; nevertheless, currently most measurement of energy

expenditure is carried out by indirect calorimetry. This method is based on

the relationship between O2 consumption and energy produced, i.e., for

each litre of O2 consumed by the body, the equivalent of ~5 kcal is utilized.

Simply, by determining O2 consumption during defined tasks such as

resting, standing, walking, and running, the energy cost or energy

expenditure can be calculated.

Doubly labelled water, developed in the early 1950s by Nathan Lifson and

colleagues, is the gold standard method to assess total energy expenditure

due to its high degree of accuracy [147, 148]. In the doubly labelled water

technique, daily urine samples are collected over a 7- to 14-day period and

successively analysed through isotope ratio mass spectrometry [149]. This

approach has been previously explained by Hills et al. [150]. The stable

isotopes, deuterium (2H) and oxygen-18 (18O) are administered orally via a

drink of water, and elimination of the isotopes from the body is tracked

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[151, 152]. The difference between the elimination rates of 2H and 18O is

equivalent to the rate of carbon dioxide generation that can then be

converted to average total daily energy expenditure [153]. Generally, the

method is appropriate in a wide range of populations including the most

vulnerable, is used in a free-living context, is non-invasive and demands

minimal participant burden. Another main advantage is the accuracy and

precision of the technique. Despite being the gold standard method for the

measurement of total energy expenditure, the doubly labelled water

technique does not bring specific information concerning daily physical

activity [154]. Briefly, the technique provides an accurate estimate of total

energy expenditure from which average daily of energy expenditure can

be determined, but does not quantify activity type, intensity, or duration

from the energy expenditure [155]. Likewise, as the study of biological

samples (commonly urine) for the technique needs the use of refined

laboratory-based material, the combined cost of isotopes, and the

examination of samples is a potential barrier for large-scale studies.

1.3.2.2. Objective methods

Objective methods are tools recognised for their quality and high precision

when collecting data corresponding to the performance of physical activity

(intensity, frequency and duration) and SB (duration, breaks) in

epidemiological studies. In this category are pedometers, accelerometers,

heart rate monitoring, and the combination of devices.

The most common and controversial form to use motion sensor is by

employing a pedometer. Pedometers count the steps taken by an individual

while the person concerned goes walking and running and they have been

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popularised as a motivational tool to stimulate sedentary or inactive

individuals to turn into more physically active people. Physical activity

targets in “steps per day” take into account the commonly used adult

objective of “10,000 steps”, which is both well-known and understood by

the lay public [150]. The objective review of pedometers has shown

extensive and significant flaws in accuracy, for instance the inaccuracy at

slow speeds, the variation according to the place where it is worn or the

possible manipulation of data. However, some important advantages

related to pedometers should be mentioned as well. They are relatively

inexpensive, easy to use, and output data can be used to raise awareness

regarding the level of physical activity, which causes a motivation to

increase physical activity.

Accelerometers are motion sensors that detect accelerations of the body.

Acceleration is therefore defined as the variation rate of velocity in a

specific time frame; thus, the frequency, intensity, in addition to the length

of the physical activity, and the length and interruption of sedentary

periods can be assessed as a function of body movement [156].

Accelerometers comprise piezoelectric transmitters that are stressed by

acceleration forces. This is associated with the production of an electrical

signal that will be subsequently converted by processing units to produce

an indication of movement [157]. Accelerometers have become quite

popular in recent years as a tool to achieve an objective approach to

measure daily physical activity and SB, and they have also achieved a

significant improvement over self-report methodologies [158]. Numerous

papers [159, 160] have informed of the objective, practical, non-invasive,

accurate, and reliable traits of accelerometers, which are used as a tool to

quantify physical activity volume and intensity with minimal discomfort

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[161]. In addition, accelerometers provide information (outputs) linked to

the body movement in counts per unit time (also known as an epoch).

Piezoelectric sensors catch the rate or intensity of movements to detect

acceleration in one plane (uni-axial), two planes (bi-axial), or three

orthogonal planes (tri-axial) which is equivalent to vertical,

anteroposterior, and mediolateral directions [157]. Accelerometers are

more complex and therefore greater motion sensors than pedometers.

Additionally, the most considerable advantages of accelerometers are their

relatively small size and the regular capacity to record data over a

prolonged period (days or weeks) [154] and the absence of visual feedback

to the subject who wear the device. This way, the lack of immediate

feedback involves the likelihood of a reduce overestimation of physical

activity or an underestimation of ST (such as the manipulation of a

pedometer). Accelerometry has then facilitated the assessment of both

physical activity and SB with a greater accuracy and precision than in

previous studies by using subjective methodologies to quantify physical

activity [162]. However, accelerometers also have certain limitations such

as the possibility of reactivity based on the individual awareness to be

monitored. Another significant limitation is related to the epoch chosen,

since the final result depends on the fact and as well as on the algorithms

introduced for the non-wear time, or the cut-off points used, among others.

The use of heart rate monitoring as part of the estimation of energy

expenditure and physical activity is well-known, convenient, relatively

inexpensive, non-invasive, and versatile. Along with pedometers and

accelerometers, heart rate monitors are the greatest samples of objective

measurement. Monitoring heart rate minute-by-minute provides precise

information on frequency, intensity, and duration of free-living physical

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activity [163]. In addition, heart rate monitoring is also used to estimate

energy expenditure based on the assumption of a linear relationship

between heart rate and oxygen consumption. However, the method has

important limitations. Due to the differences in the relationship between

heart rate–oxygen consumption between upper-body and lower-body

activities [164], the use of a single regression line derived from an activity

such as walking or running will not be precise for other activities. As a

matter of fact, it is safe to say that there is a very close relationship between

heart rate and energy expenditure during exercise, however, this is not the

case during rest and light activity [154, 165].

As it is not possible to quantify all the aspects of physical activity under

free-living conditions through any technique, it is suggested the use of

multiple complementary methods [163]. For instance, a potentially

powerful approach to quantify energy expenditure is the simultaneous use

of accelerometry and heart rate monitoring [154, 166]. The reason to

combine these techniques is that accelerometer counts verify that elevations

in heart rate are caused by physical activity.

1.3.2.3. Subjective methods

Subjective methods are indirect approaches, generally linked to the

individual’s record of his or her own activity [156]. These include direct

observation, activity diaries, physical activity and SB questionnaires, and

interviews [165]. They are widely used for their practicality, both for the

evaluator and for those who are evaluated, which facilitates their use in

population studies. These methods are based on prediction equations in

which a count is made of the activities performed in one or more days.

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Energy expenditure is sometimes the result of multiplying the time spent

on an activity by an estimated rate of energy expenditure for that activity.

Considering these subjective methods, questioners are the most used

instrument to assess physical activity and sedentary lifestyle, due to the

absence of inexpensive, readily available, non-invasive, valid, and reliable

objective technology employed for the evaluation of activity energy

expenditure. Nevertheless, most of the physical activity and SB

questionnaires have shown a reduced reliability and validity [167] and it is

uncertain whether any of them are valid to estimate the activity energy

expenditure at the individual and group levels (176).

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1.3.3. Physical activity recommendations for health

The physical activity recommendations from a health perspective have

evolved over time, focusing several aspects of it, either towards its intensity

(moderate and vigorous), the type of physical activity (aerobic, resistance,

flexibility), frequency and, duration.

In 2010, the World Health Organization (WHO) publishes the global

recommendations on physical activity for health (currently under review)

[139]. Specifically, for older adults (65 years old and above), these are the

recommendations [139]:

o Older adults should do at least 150 minutes of aerobic MVPA

throughout the week or do at least 75 minutes of aerobic VPA

throughout the week or an equivalent combination of MVPA.

o Aerobic activity should be performed in bouts of at least 10

minutes duration.

o For additional health benefits, older adults should increase their

aerobic MVPA to 300 minutes per week, or engage in 150 minutes

of aerobic VPA per week, or an equivalent combination of MVPA.

o Older adults, with poor mobility, should perform physical activity

to enhance balance and prevent falls on 3 or more days per week.

o Muscle-strengthening activities, involving major muscle groups,

should be done on 2 or more days a week.

o When older adults cannot do the recommended amounts of

physical activity due to health conditions, they should be as

physically active as their abilities and conditions allow.

In November 2018, the U.S. Department of Health and Human Services

issued the second edition of Physical Activity Guidelines for Americans

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[140], thus updating those of 2008. These guidelines provide the most

current physical activity recommendations, prepared by a Physical Activity

Guidelines Advisory Committee formed by experts in the sciences of

physical activity and health who conducted an extensive literature review.

The update in physical activity recommendations published by the WHO

is expected to be similar to these ones.

In 2018, the new committee accepted most of the recommendations made

by the 2008 committee, but not all of them. The major change in the activity

recommendation in 2018 was to no longer require bouts of aerobic activity

which consisted of at least 10 minutes in length; activity bouts of any length

now contribute to accomplish the weekly goal. Although there is still

limited published research testing for health benefits of exercise

accumulated throughout the day in bouts lasting less than 5 minutes each,

data are beginning to emerge that show the potential efficacy [168, 169].

However, understanding the specific health benefits of very short bouts

remains in need of further research.

The physical activity recommendations for American older adults are as

follows [140]:

o Older adults should move more and sit less throughout the day.

Some physical activity is better than none. Older adults who sit less

and do any amount of MVPA gain some health benefits.

o For substantial health benefits, older adults should do at least 150

minutes (2 hours and 30 minutes) to 300 minutes (5 hours) a week

of moderate-intensity, or 75 minutes (1 hour and 15 minutes) to 150

minutes (2 hours and 30 minutes) a week of VPA, or an equivalent

combination of MVPA. Preferably, aerobic activity should be

spread throughout the week.

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o Additional health benefits are gained by engaging in physical

activity beyond the equivalent of 300 minutes (5 hours) of aerobic

MPA a week.

o Older adults should also do muscle-strengthening activities of

moderate or greater intensity and that involve all major muscle

groups on 2 or more days a week, as these activities provide

additional health benefits.

o As a part of their weekly physical activity, older adults should do

multicomponent physical activity that includes balance training as

well as aerobic and muscle strengthening activities.

o Older adults should determine their level of effort for physical

activity relative to their level of fitness.

o Older adults with chronic conditions should understand whether

and how their conditions affect their ability to do regular physical

activity safely.

o When older adults cannot do 150 minutes of aerobic MPA a week

because of chronic conditions, they should be as physically active

as their abilities and conditions allow.

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1.4. PHYSICAL ACTIVITY,

SEDENTARY BEHAVIOUR AND FRAILTY

1.4.1. Importance of physical activity for health

The first texts that talk about the value of physical activity to improve

health were found in China more than 4600 years ago. Two thousand years

later, Hippocrates (460-370 BC) highlighted that food alone would not

maintain the health of a person, it was necessary that exercise was also

performed, improving the health of people such combination.

For most of the history of the human species, physical activity has been an

essential part of daily life. Physical activity was not a choice, but it was

necessary. Nowadays, since the industrial revolution, the development of

new technologies, jobs with less physical load, rapid changes in transport

and communications, and the new leisure time modes, have caused the

reduction in the amount of physical activity of the population (Figure 8).

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Data Source: 2018 Physical Activity Guidelines Advisory Committee (2018). 2018 Physical

Activity Guidelines Advisory Committee Scientific Report. Adapted from data found in

Matthew, 2005 [170], and Troiano et al., 2008 [171].

Figure 8. Proportion of time-awake at different categories of accelerometer

counts for U.S. adults, by sex and age group, 2003-2004.

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Physical inactivity has been recognised as the fourth leading risk factor for

global mortality (6% of deaths globally) [139]. In 2009, Blair [172]

considered physical inactivity as the biggest public health problem of the

21st century (Figure 9).

Data Source: Blair (2009). Physical inactivity: the biggest public health problem of the 21st

century. Br J Sports Med.

Figure 9. Low cardiorespiratory fitness as a major health problem.

Attributable fractions (%) for all-cause deaths in 40,842 (3,333 deaths) men and 12,943 (491

deaths) women in the Aerobics Center Longitudinal Study. The attributable fractions are

adjusted for age and each other item in the figure. *Cardiorespiratory fitness determined by a

maximal exercise test on a treadmill.

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According to the WHO, chronic noncommunicable diseases, especially

those derived from the cardio-metabolic system (high blood pressure,

obesity, type 2 diabetes, ischemic heart disease, etc.), represent a serious

public health problem with crucial social, psychological and physical

consequences, and are associated with a higher risk of morbidity and

mortality [173].

Today it is well known that physical activity is an effective factor in

reducing the risk of mortality and preventing the loss of health, and

therefore should be part of the activities of the daily life of all people (2).

There is a multitude of evidence that shows that the enhancement in fitness

improves health [174] and reduces the risk of a number of chronic diseases,

such as cardiovascular diseases [175, 176], type 2 diabetes mellitus [177],

osteoporosis [178], obesity [179], depression [180] and breast and colon

cancer [181] among others. In addition, habitual physical activity improves

the quality and duration of sleep [182] and also improves a wide range of

other cardiovascular, hemodynamic, metabolic, neural, and arterial and

cardiac risk factors, with the overall result of reduced clinical events [183].

The reduction of these diseases would entail the consequent decrease of

direct and indirect medical costs. In addition, people would have a better

quality of life, which would their energy and vitality. Despite this

knowledge, a large proportion of the world’s population remains

physically inactive.

While greater attention has been placed on promoting physical activity for

general health, the negative effects of SB have been shown to be highly

important. Thus, there is now rapidly emerging evidence on the adverse

associations for SB with health outcomes, showing a crucial role on diseases

such as cardiovascular diseases, type 2 diabetes, cancer, obesity, and

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metabolic syndrome [184-187], mental disorders, and musculoskeletal

disorders [188]. Numerous studies have shown that some people can meet

the physical activity recommendations and yet display high levels of SB.

The reverse could also be true. In fact, some of these studies indicate that

the detrimental consequences of SB may be independent of the time spent

in physical activities [189].

Therefore, because both physical activity and SB contribute to the burden

of chronic disease, it is necessary to mutually address the problem for

global health population.

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1.4.2. Relationship between physical activity, sedentary behaviour

and frailty

As described above, physical activity plays an important role in

maintaining health, well-being, and quality of life. Physical inactivity is

considered to be a major contributing factor for sarcopenia, physiological

systems dysregulation, decreased physical and cognitive performance, and

disability [190, 191].

Fried [192] establishes three systems by which physical activity can

prevent, delay or treat physical frailty (Figure 10): the frailty phenotype

itself, physiologic dysregulation and cellular and molecular dysfunction.

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Data Source: Fried (2016). Interventions for Human Frailty: Physical Activity as a Model.

Cold Spring Harb Perspect Med.

Figure 10. Physical activity positively affects function of multiple

components of the syndrome of frailty.

DHEA-S, didehydroepiandrosterone sulfate; GH, growth hormone; HPA, hypothalamic–

pituitary–adrenal; SNS, sympathetic nervous system.

If physical activity is considered to be part of the frailty cycle, the decreases

in physical activity predicts the development of the rest of the phenotype

and probably exacerbate both the frailty-related outcomes and the

underlying dysregulation [192]. For example, Xue et al. [193] found that

those who promptly declined in physical activity over 12 years or who were

permanently sedentary (adjusting for chronic diseases, disability, obesity,

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and other confounders) had hazard ratios for mortality of 2.34 and 3.34,

respectively, compared with those who were permanently active.

Physical activity is an effective protective strategy for avoiding the loss of

muscle mass and functionality (i.e., sarcopenia), which in turn seems to be

one of the cornerstones of frailty [194, 195]. Although sarcopenia and frailty

are not the same construct they share some pathogenic mechanisms.

Regular physical activity is a valuable strategy to attenuate age-related

general decline of muscle structure and function [196, 197]. Different

studies have shown that physical activity decreased the risk of low

functionality and had the ability to delay the onset of disability [198-200].

Similarly, in a study conducted in older Finnish men and women it was

found that those involved in a high level of daily physical activity

(household chores, gardening, etc.) showed a significantly lower decrease

in physical performance after 5 years, compared to those who were

sedentary [201]. Ultimately, sarcopenia is highly predictive of disability,

poor quality of life and all-cause mortality [202].

Physical activity can also maintain or improve protein synthesis, glucose

metabolism, inflammation, anaemia and exercise tolerance, thus eluding

other triggers in the cycle of physical frailty [192].

In addition, physical activity is able to regulate cellular and molecular

functions [192]. Although physical activity does not mitigate the aging

process, it attenuates many of the systemic and cellular detrimental effects,

also improving the function of most of the mechanisms involved in aging

[203].

Nevertheless, frailty is a multidimensional entity that includes other factors

such as cognitive function. The growing evidence suggests that physical

INTRODUCTION

105

activity decreases the risk of early cognitive impairment and poor

cognition. A review of meta-analyses concluded that physical activity has

a neuroprotective effect for older adults [204]. Furthermore, a recent study

has shown that physical activity can reduce by 36% the risk of death among

cognitively frail people [205].

All of these might be reasons why physical activity can be a good strategy

in the frailty process. However, the role that physical activity and SB

specifically plays in frailty is poorly understood.

Epidemiological studies suggest an association between lower risk of

frailty and physical activity [206], especially that of MVPA. A cross-

sectional study showed that low MVPA was independently associated with

higher levels of frailty, inferior self-reported health, increased disability

and higher healthcare utilisation [207]. Similarly, Rogers et al. [208] found

that higher intensities of physical activity are needed for continued

improvement in frailty trajectories. Studies also show that there is a dose-

response relationship between greater amounts of physical activity and

lower frailty levels [206, 209-212]. For example, the likelihood of incident

frailty was decreased by 6% for each additional 1 MET-h/week increase in

physical activity [209]. However, most of these studies have been based on

self-reported measures of physical activity levels, which according to

Prince et al. [213], tend to be less accurate and overestimate physical

activity compared to objective methods such as accelerometry.

Although the relationship between physical behaviours and frailty has

been mainly focused on physical activity, there is currently a growing

interest in the association between frailty and SB. To our knowledge, there

are no clinical trials that have studied the effect of reducing ST on frailty.

Thus, the impact of SB on frailty is based on observational evidence. da

INTRODUCTION

106

Silva Coqueiro et al. [214] showed a positive association between self-

reported SB and frailty in a community-dwelling older adults. Blodgett et

al. [207] cross-sectional study performed in a sample of >50 years-old adults

found that ST was related with various adverse health outcomes including

frailty, regardless of the time spent in MVPA. The scarce longitudinal

evidence also indicates a harmful relationship between SB and frailty. For

example, a study in two Spanish cohorts of older adults demonstrated that

baseline self-reported television viewing time was linked to frailty at

follow-up [215]. Another longitudinal study carried out in 1333 middle to

older aged adults reported that every additional hour per day of ST

increased the odds of becoming frail at follow-up by 36%, independently

of MVPA [216]. Nonetheless, all these studies have important limitations

due to the wide heterogeneity in the age of the sample, the use of self-

reported measures, or poor approaches in the frailty construct.

Other SB accumulation patterns such as the frequency of BST have been

related to cardiometabolic health risk factors [217], physical function [218],

impairment in activities of daily living [219], and disability [220]. However,

the evidence is highly limited in relation to the way ST is spent on frailty.

INTRODUCTION

107

108

109

CHAPTER 2

JUSTIFICATION

JUSTIFICATION

110

JUSTIFICATION

111

Given that the older population has increased dramatically in recent

decades, successful aging is a major concern in developed societies. This

situation represents a challenge for health and social care resources, in

order to reduce the risk of non-communicable diseases, frailty and

disability associated with aging.

There is a large amount of evidence indicating that maintaining an active

lifestyle is crucial for healthy aging. In addition, SB has recently emerged

as an independent risk factor for different health outcomes. However, the

role that objectively measured ST and physical activity plays specifically in

frailty is insufficiently understood.

A review of how objectively measured ST is associated to frailty, disability,

and mortality in older adults can help clarify its importance as a risk factor

in these health outcomes (Study 1). Beyond the total time spent on SB, the

way in which SB is accumulated may impact on the wider health of

individuals. Specifically, the relationship between sedentary patterns and

frailty has not been yet investigated (Study 2).

Furthermore, a deeper understanding on how ST and physical activity

jointly interact with frailty in older adults could be relevant to identify

evidence-based strategies for the prevention and management of frailty in

this population group (Studies 3, 4 and 5).

Finally, a major limitation of the existing evidence to date is that mainly

relies on cross-sectional data, precluding us from making any causal

inferences due to the inability of establishing the temporal sequence of the

effects of ST or physical activity on frailty outcomes. Moreover, the absence

of longitudinal data assessing relationships between ST, physical activity,

and frailty are further complicated by the possibility of reverse causality.

JUSTIFICATION

112

Therefore, understanding the temporal order and the potential

bidirectionality underpinning the relationship between patterns of SB and

physical activity with frailty has the potential to inform about future public

health interventions aimed at reducing the frailty burden among older

adults (Studies 6 and 7).

JUSTIFICATION

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114

115

CHAPTER 3

OBJECTIVES AND

HYPOTHESES

OBJECTIVES AND HYPOTHESES

116


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Study 1

Objectives:

- To systematically review the published literature related to the

potential role of objectively assessed ST with regards to some

of the most relevant outcomes of aging such as frailty,

disability, and mortality among older adults.

- To identify gaps in the current knowledge and future

directions for research.

Hypotheses:

- The available evidence will be limited, particularly in the

relationship between ST with frailty and mortality.

- The association between objectively measured ST and physical

performance in older adults will be strong.

- A wide variety of methodologies for data extraction and

analysis in the measurement of ST will be observed in the

included articles.


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Study 2

Objectives:

- To examine the associations of various SB patterns with frailty

in older people.

- To observe the association of the number of BST with frailty in

both active and inactive groups.

Hypotheses:

- Total ST per day and the proportion of the day spent in 10-

minutes bouts of ST will be positively linked to frailty.

- Breaks in sedentary time will be negatively associated with

frailty.

- Inactive subjects with less BST will have higher frailty values

compared to those with greater number of BST. In active

individuals, there will be no such differences between both

groups.


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Study 3

Objective:

- To use the isotemporal substitution technique in order to

investigate the displacement effect of replacing ST with LPA

and MVPA on frailty status among older adults (both in the

whole group and also separately with/without comorbidities).

Hypotheses:

- Replacing ST with MVPA will produce reductions in frailty in

both older adults with and without comorbidities.

- Light physical activity will only be beneficial in older adults

with comorbidities.


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Study 4

Objective:

- To examine the combination of mutually exclusive categories

of accelerometer-measured physical activity and ST on

physical function and frailty in a community-dwelling sample

of older adults.

Hypotheses:

- Meeting the physical activity guidelines will be associated with

the most beneficial physical function and frailty profiles in our

sample, regardless of sedentary status.

- Engaging in more LPA relative to ST will be associated with

better frailty and functional status only among inactive

individuals.


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Study 5

Objective:

- To determine whether the detrimental effects of SB on frailty

were moderated by objectively measured MVPA and the

magnitude of this moderation in a sample of older adults.

Hypotheses:

- Moderate-to-vigorous physical activity will be a moderator in

the relationship between ST and frailty in older adults.

- A MVPA amount close to the guidelines will be necessary to

eliminate the harmful effects of ST on frailty.


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Study 6

Objective:

- To examine the longitudinal and temporal order of the

association of accelerometer-assessed MVPA and ST with

frailty through a cross-lagged panel model during a follow-up

period of 4-years in a population sample of older adults.

Hypotheses:

- The relationship between MVPA and ST with frailty will be

unidirectional. Individuals who spent less time on MVPA at

baseline will be more likely to increase their future frailty score,

and individuals who are more frail will be more likely to spent

more time on SB at follow-up.


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Study 7

Objective:

- To investigate the longitudinal and temporal order of the

association between the number of daily BST and frailty using

a cross-lagged panel model in a total sample of community-

dwelling older adults as well as separately depending of their

level of physical activity (inactive vs. active participants) over

a 4-year period.

Hypotheses:

- There will be no relationship between daily BST and frailty

neither in the whole sample or in physically active individuals,

while the association will be inversely reciprocal in those

physically inactive individuals.

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CHAPTER 4

MATERIAL AND

METHODS


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127

4.1. Systematic Review

(Study 1)

The current short systematic review follows the PRISMA recommendations

for reporting systematic reviews [221].

4.1.1. Literature search

(Study 1)

The Literature search was conducted (October 2016) in PubMed and Web

of Science (WoS) online databases. The following Medical Subject Headings

(MeSH) of the United States National Library of Medicine and search terms

with the correspondent operators were included in this Boolean search

syntax: (elderly OR “older adults” OR “old people” OR “elders”) AND

(sedentarism OR sedentary OR sitting) AND (accelerometer OR

accelerometry OR “objectively measured sedentary” OR “objectively

measured physical”) AND:

(“physical function” OR “physical performance” OR “walking

performance” OR “walking velocity” OR “gait speed” OR “activity

of daily living”) to identify the section of articles related with

physical performance;

(frail OR frailty) to distinguish the section of articles related with

frailty status;

(mortality OR death OR “life expectancy”) to detect the section of

articles related with mortality.


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The search was limited to English language and full text availability of

eligible articles. Additional suitable studies were included by screening the

reference lists of each included study and other relevant reviews recently

published.


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4.1.2. Eligibility criteria

(Study 1)

For the review, studies were included if (i) they were journal articles in full,

(ii) participants were humans aged ≥60 years old, (iii) SB was assessed

using objective techniques and (iv) measurement of physical performance

was carried out by filed or laboratory objective tests. Performance was

defined as aspects of physical function (such as strength, endurance,

flexibility, speed and agility) that are associated with daily life activities

that are important for maintaining independence in older adults [222]. In

addition, frailty should be evaluated with a validated scale.

Studies were excluded from the analysis if (i) they were not available in

English and (ii) the association of sedentary lifestyle evaluated with

accelerometers was not examined with physical performance, frailty or

mortality. In the studies with participants younger than 60 years old we

only included the subsamples older than 60 years old when reported. The

retrieved studies were imported into the EndNote Web® reference

management software to remove any duplicates.

Firstly, titles and abstracts were screened by two independent reviewers

(AM and IA). Relevant articles were then selected for a full read of the

article. If no consensus was achieved between the two reviewers, a third

reviewer was consulted (AGG).


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4.2. Participants of the studies

(Studies 2, 3, 4, 5, 6, and 7)

Data were taken from the TSHA, whose complete methodology has been

reported elsewhere [59, 64]. Briefly, the TSHA is a population prospective

cohort study aimed at studying the determinants and consequences of

frailty in institutionalized and community-dwelling individuals older than

65 years living in the province of Toledo, Spain.

Signed informed consent was obtained from all volunteers prior

participation in the study. The complete study was approved by the

Clinical Research Ethics Committee of the Toledo Hospital, which was

conducted according to the ethical standards defined in the 1964

Declaration of Helsinki.


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4.3. Data collection

(Studies 2, 3, 4, 5, 6, and 7)

Data were collected in 3 stages for each wave. In the first stage, 6

psychologists conducted computer-assisted interviews face-to-face with

potential subjects. In the second stage, 3 nurses performed a physical

examination followed by clinical and performance tests at the subject’s

home. In the third stage, the participants went to their health centre to

provide a blood sample while fasting. At this stage, participants were

invited to wear an accelerometer for a week. Only participants who wore

the accelerometer for a week were included in these studies.


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4.3.1. Cross-sectional studies

(Studies 2, 3, 4, and 5)

For studies 2 and 3, data were collected from wave 2 from July 2012 to June

2014 with a total subsample of 626 volunteers. From these, 107 were

excluded due to incomplete or invalid accelerometer data. A total of 519

participants were finally included (Table 2).

Table 2. Main characteristics of the subjects included in studies 2 and 3.

Variables Sample

N = 519

Age (years)a 78.8 ± 4.6

Sexb

Male 234 (45.1)

Female 285 (54.9)

Body mass index (kg/m2)a 30.5 ± 4.7

Frailty trait scale (points)a 37.8 ± 14.2

aContinuous variable; mean ± standard deviation.

bCategorical variable; n (%).

For studies 4 and 5, data were collected from wave 2 (July 2012 to June 2014)

and 3 (May 2015 to July 2017), with a total sample of 871 volunteers with a

single accelerometer assessment between both waves. Of all eligible

subjects, 771 participants were finally included for study 4 (100 participants

had insufficient accelerometer wear time) (Table 3), and 749 participants

for study 5 (100 participants with insufficient accelerometer wear time and

22 participants with missing data related to frailty) (Table 4).


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Table 3. Main characteristics of the subjects included in the study 4.

Variables Sample

N = 771

Age (years)a 76.7 ± 4.9

Sexb

Male 355 (46.0)

Female 416 (54.0)





Table 4. Main characteristics of the subjects included in the study 5.

Variables Sample

N = 749

Age (years)a 76.7 ± 4.9

Sexb

Male 346 (46.2)

Female 403 (53.8)






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4.3.2. Longitudinal studies

(Studies 6 and 7)

Longitudinal studies were based on two data collection waves separated

by 4-years (3.8 ± 0.8 years).

The first time point of assessment for the longitudinal studies started in July

2012 and lasted until June 2014. A total of 628 participants over 65 years of

age at baseline were included, although 494 participants concluded the

three stages of assessment and provide with valid data for the analyses.

Participants were contacted again in 2015 and invited to participate in a

follow-up study conducted between May 2015 and July 2017. After the

follow-up, 200 participants (59.5% missing) completed the second

evaluation. However, 186 subjects with complete data on all exposures,

outcomes and ≥80% covariates were included in the final analyses of these

studies (Table 5).

Table 5. Main characteristics of the subjects included in studies 6 and 7.

Variables Baseline

N = 186

Follow-up

N = 186

Age (years)a 76.68 ± 3.90 80.44 ± 4.24

Sexb

Male 88 (47.3) 88 (47.3)

Female 98 (52.7) 98 (52.7)

Body mass index (kg/m2)a 30.82 ± 4.62 30.33 ± 4.40

Frailty trait scale (points)a 35.35 ± 13.94 43.79 ± 13.86




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4.4. Assessments

(Studies 2, 3, 4, 5, 6, and 7)

4.4.1. Anthropometrics and confounding variables

(Studies 2, 3, 4, 5, 6, and 7)

Participants self-reported their age, sex and ethnicity (used in studies 2, 3,

4, 5, 6, and 7). Other socio-demographic variables such as education,

income, and marital status were also self-reported in face-to-face

interviews.

Educational status was assessed using seven categories ranging from 0 (no

schooling) to 6 (university studies). We standardized this item into none

(no schooling), low (primary school) and high (completed secondary school

or more) categories of education (used in studies 3, 4, 5, 6, and 7).

Marital status was coded into 4 categories: single, married/living together,

widowed, divorced/separated (used in studies 4, 5, 6 and 7).

Income was coded into 3 categories ranging from any income to

€3000/month (used in studies 4, 5, and 6).

Height and body mass were obtained on each subject using calibrated

balance and stadiometer (Seca 711 scales, Hamburg, Germany). Both

measurements were performed in the upright position, in underwear and

barefoot. Height was recorded in the Frankfort plane with a precision of 1

mm, and the body mass was determined with a 100 g precision. Body mass

index (BMI) was calculated as body mass (kg) divided by height (m)

squared (kg/m2) (used in studies 5 and 7).


138

Waist and hip circumference were measured using standard procedures

[223]. Waist-to-hip ratio was then calculated by dividing waist

circumference by hip circumference (used in the study 3).

We also assessed objective cognitive function using the Mini-Mental State

Examination (MMSE) [224] (used in studies 2, 3, 6, and 7).

The Charlson Comorbidity Index was used to account for comorbidity

status of participants [225] (used in studies 2 and 3). Diseases included in

this Index and their weighting are myocardial infarction, congestive heart

failure, peripheral vascular disease, dementia, cerebrovascular disease,

chronic lung disease, connective tissue disease, ulcer, chronic liver disease,

diabetes (weight 1); hemiplegia, moderate or severe kidney disease,

diabetes with complication, tumour, leukaemia, lymphoma (weight 2);

moderate or severe liver disease (weight 3); metastatic solid tumour, and

AIDS (weight 6).

Finally, the number of prescription and non-prescription drugs within the

Anatomical Therapeutic Chemical (ATC) Classification System taken by

the participant was calculated [226] (used in studies 2 and 3).


139

4.4.2. Physical activity and sedentary behaviour

(Studies 2, 3, 4, 5, 6, and 7)

Physical activity and SB were objectively assessed by accelerometry

(ActiTrainer and wGT3X-BT; ActiGraph, LLC, Pensacola, FL). All

participants were instructed how to wear an accelerometer on the left hip

during waking hours. Participants wore the accelerometer for 7 consecutive

days and removed it during any bathing or swimming activities.

Accelerometer output is an activity count, which is the weighted sum of the

number of accelerations measured over a time period or epoch. The devices

were initialized to collect data using 1-minute epochs and all data were

collected using the vertical axis collection mode. Non-wear time was

defined as periods of at least 60 consecutive minutes of zero counts, with

allowance for 2 minutes of counts between zero and 100 [227]. Inclusion

criteria comprised at least 4 days with at least 8 hours recorded per day

without excessive counts (i.e., >20,000 counts).

4.4.2.1. Time

(Studies 2, 3, 4, 5, 6, and 7)

Accelerometer counts were used to derive the time spent in each intensity

band: SB (<100 counts/min), LPA (100-1951 counts/min), MPA (1952-5724

counts/min), VPA (≥5725 counts/min), and MVPA (≥1952 counts/min)

[228]. Although there is a lack of consensus on the use of cut-off points to

classify the intensity of the activity, the cut-off points used in these studies

are the most commonly reported in this population group [229]; making

our results comparable to other studies. Time spent in ST, LPA, MPA, VPA,


140

and MVPA was then registered. All these outcomes were weighted by daily

averaged wear time on valid days (i.e. outcome summed over all wear time

divided by the number of successfully monitored days for each

participant). Minutes per day spent in each intensity band were then

calculated (used in studies 2, 3, 4, 5, 6, and 7). In addition, SB, MPA, VPA,

and MVPA time accumulated in bouts of ≥10 min, allowing for a two-

minute exception in the intensity threshold, were also derived (used in

studies 2 and 4).

4.4.2.2. Patterns

(Studies 2 and 7)

Each minute with less than 100 counts was considered ST [228, 229].

Therefore, a BST was defined as at least 1 min where the accelerometer

registers ≥100 counts following a sedentary period. The number per day of

BST was recorded (used in studies 2 and 7). The duration (min) per day of

BST was also recorded (used in the study 2).

A ≥10-min bout of sedentary time (ST-10) was defined as a period of at least

10 consecutive minutes where the accelerometer registered <100

counts/min. The number, duration (min) and proportion over total ST-10

per day were recorded (used in the study 2). The corresponding variables

were weighted by daily average wear time on valid days.

Different sedentary pattern variables were standardized (Z-score =

[observed - sample mean]/sample standard deviation (SD)).

In an effort to account for the combined effect of both duration and number

of the different patterns, two composite Z-scores representing patterns of


141

ST [ST COMP = Z-score ST-10 (number/day) + Z-score ST-10 (minutes/day)]

and patterns of BST [BST-COMP = Z-score BST (number/day) + Z-score BST

(minutes/day)] were then computed (used in the study 2).

4.4.2.3. Categories

(Studies 4 and 7)

We followed the methods outlined in Bakrania et al. [230] to classify

participants into 4 mutually exclusive behavioural categories according to

their levels of physical activity and SB (used in the study 4). Based on

Bakrania et al. [230], and other studies [231, 232], the LPA-to-ST ratio was

used to classify participants in this study as low sedentary if they resided

in the first quartile. Given that most of our sample was expected to be

sedentary [233, 234], the remaining participants (i.e. those in quartiles 2, 3,

and 4 of LPA-to-ST ratio) were classified as high sedentary. MVPA status

was classified as ‘physically active’ or ‘physically inactive’ on the basis of

whether or not participants met the WHO physical activity

recommendations for older adults [139]. For this, at least one of these three

premises had to be met: accumulate 150 minutes of MPA per week over

periods of at least 10 minutes; accumulate 75 minutes of VPA per week over

periods of at least 10 minutes, or accumulate 150 minutes per week of an

equivalent combination of MVPA over periods of at least 10 minutes.


142

Based on previous studies [230, 235], four groups of mutually exclusive

movement patterns were created (Figure 11): (1) ‘physically active and low

sedentary’, (2) ‘physically active and high sedentary’, (3) ‘physically

inactive and low sedentary’, and (4) ‘physically inactive and high

sedentary’.

Data Source: Bakrania et al (2016). Associations of mutually exclusive categories of physical

activity and sedentary time with markers of cardiometabolic health in English adults: a

cross-sectional analysis of the Health Survey for England. BMC Public Health.

Figure 11. Mutually exclusive behavioural categories.

‘Busy Bees’: Physically Active and Low Sedentary, ‘Sedentary Exercisers’: Physically Active

and High Sedentary, ‘Light Movers’: Physically Inactive and Low Sedentary, ‘Couch

Potatoes’: Physically Inactive and High Sedentary. a Low Sedentary: Quartile 1 of the ratio

between the average ST and the average LPA. b High Sedentary: Quartiles 2, 3 or 4 of the ratio

between the average ST and the average LPA. c Physically Active: ≥150 min of MVPA per

week. d Physically Inactive: <150 min of MVPA per week.


143

Compliance with physical activity recommendations was calculated in

order to classify individuals as active or inactive (used in the study 7). To

be active, at least one of these three premises had to be met [140]:

accumulate 150 minutes of MPA per week; accumulate 75 minutes of VPA

per week; or accumulate 150 minutes per week of an equivalent

combination of MVPA.


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4.4.3. Frailty

(Studies 2, 3, 4, 5, 6, and 7)

The FTS [59] was used to assess frailty in this study. The FTS includes 7

aspects: energy balance and nutrition, activity, nervous system, vascular

system, weakness, endurance, and slowness. These domains become

operational through 12 items:

Energy balance and nutrition were assessed by BMI, central obesity (waist

circumference), unintentional weight loss, and serum albumin level.

Activity was assessed by using the total score of the Physical Activity Scale

for the Elderly [60].

The nervous system was calculated by considering verbal fluency and

balance. Verbal fluency was estimated by asking the participants to give

names of animals during one minute [61]. Balance was measured by

Romberg test [62].

The vascular system was measured by the brachial-ankle index done with

Doppler ultrasound [63].

Weakness was estimated by assessing grip strength in the dominant arm

and the knee extension strength [64].

Endurance was assessed by the chair stand test, which measures the

number of times that a person stands up in 30 seconds [65].

Slowness was estimated by calculating the time to walk 3 m at a “normal

pace” according to a standard protocol [62].

Each item score represents a biological trait and ranges from 0 (the best) to

4 (the worst), except in the “chair test” where the range is from 0 to 5 points


145

because of the necessity of scoring those unable to stand a single time.

When appropriate, items are analysed according to the item’s quintile

distribution in the population.

To be included in the study, the participants had to overcome at least 75%

(9 of the 12) of the items included in the FTS [59]. The total score was

calculated by adding all the scores in each item divided by total score for

each individual and multiplying by 100, standardizing the measure to a

range from 0 (best score) to 100 (worst score), according to the formula

Total score = (Σ items score/total score possible by individual)*100.


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4.4.4. Physical Function

(Study 4)

The Short Physical Performance Battery (SPPB) was used to assess the

physical function in this study [62]. The SPPB consists of the following tests:

1) balance test: time standing in 3 different positions (feet together, semi-

tandem and full tandem) a maximum time of 10 s each; 2) usual gait speed:

tested in a distance of three meters; and 3) chair stand test: time necessary

to get up and sit on the chair five times with the arms crossed on the chest

as fast as possible. The tests were scored as described in the original

protocol [62].


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4.5. Data analyses

(Study 2)

Data were analysed using PASW Statistic, version 23.0.0, with statistical

significance set at p<0.05 (two-tailed). Descriptive statistics (mean ± SD)

were calculated for all outcome measurements of the study.

Multiple linear regressions were used to examine the associations between

frailty and the different SB patterns assessed. A model adjusted by age,

gender, comorbidity status, mental health, and polypharmacy status was

fitted for each of the SB pattern outcomes.

Participants were clustered into 2 different groups according to the

adherence to WHO physical activity guidelines status (i.e. meeting or not

the guidelines) and were compared using t-test for independent

measurements. Participants within each of the former groups were re-

allocated to either less BST group (i.e. group falling below the 50th

percentile of BST) or more BST group (i.e. group falling over the 50th

percentile of BST) and then compared using t-test for independent

measurements.

(Study 3)

All analyses were performed using the statistical software SPSS, version

24.0 (IBM Corp, Armonk, NY). Mean (SD) and frequency (percentage) were

provided for continuous and categorical variables, respectively.

Descriptive variables were compared between included and excluded

participants with an independent t test or chi-square test for continuous

and categorical variables, respectively.


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Linear regression models were used to examine associations between time

spent (minutes/d) in SB, LPA, and MVPA with the score in the FTS. Models

were adjusted for prespecified covariates hypothesized to be

independently associated with both exposure and outcome variables,

including sex, age, educational status, polypharmacy status, functional

fitness (SPPB), waist-to-hip ratio, comorbidity status (Charlson Index), and

cognitive function (MMSE).

Variance inflation factor was calculated to quantify the severity of

multicollinearity in the regression analyses. All variance inflation factors

were below 10. The Condition Number and Durbin-Watson statistic were

also analysed.

When performing subgroup analysis by comorbidity status, the Charlson

Index was removed from the covariates. Subjects without comorbidity

were those who scored 0 and subjects with comorbidity scored 1 or higher

on the Charlson Index.

Three different linear regression models were used [236]. The first set of

models are single-factor, examining the association of each intensity

category (ST, LPA, and MVPA) with frailty status without mutual

adjustment for other activity categories. The second are partition models

examining the association of each intensity category while controlling for

each of the other categories of activity. The third are isotemporal

substitution models that represent the estimated effects of substituting ST

with an equal amount of time spent in LPA or in MVPA. In this model, ST

was excluded whereas total wear time was kept constant in the equation

[237]. For ease of interpretation, 30-minute units were chosen as time units

for each behaviour. These models assume linear relationships between


149

dependent and independent variables, which were determined prior to

performing these analyses.

(Study 4)

Analyses were performed using the statistical software SPSS version 24.0

(IBM Corp., Armonk, NY). Participant characteristics of the full sample,

stratified by each category, were tabulated. Mean (SD) and frequency

(percentage) were provided for continuous and categorical variables,

respectively.

Ternary plots with the three behaviours were generated to show the

distribution of the sample compositions using R statistical system version

3.1.1.

To test our hypothesis, a multiple linear regression analysis with the

behavioural category as independent variable and frailty or physical

function as dependent variable was fitted. Covariates in the model

included: age, sex, education, marital status, and income. The ‘physically

inactive and high sedentary’ category was selected as the reference

category.

Also, the continuous association between time spent in sedentary activities

as well as MVPA with the outcomes of interest in the study were explored

via regression. The same set of covariates in addition to accelerometer wear

time as well as both continuous MVPA time and sedentary status was used.

All analyses were two-sided where p≤0.05 was considered to be statistically

significant.


150

(Study 5)

Statistical analyses were conducted using R software (R project version

3.5.1). Summary statistics were used to describe variables of interest.

Significance levels were set at P<0.05.

A multiple linear regression was conducted to determine the associations

between time per day spent in SB and frailty in the sample population.

An interaction term was then included in the equation to test the

moderation effects of MVPA in the SB-frailty relationship.

Lastly, the Johnson-Neyman technique [238, 239] was used to elucidate the

MVPA values in which a significant and nonsignificant relationship exists

between SB and frailty in the sample population. To confirm the robustness

of our estimates, rank-based regression analyses were performed. All

models above were adjusted for age, sex, education, income, marital status,

BMI, MVPA, and accelerometer wear time.

(Study 6)

Preliminary analyses examined variable distributions, sample

characteristics and attrition using R software (R project version 3.5.1).

Descriptive variables were compared between participants retained with

those of participants not retained from wave1-wave2 with an independent

t test or chi-square test for continuous and categorical variables,

respectively.


151

Descriptive statistics (mean and SD for continuous variables and as

frequencies and percentages for categorical variables) were calculated for

all outcome measurements. Comparison between baseline and follow-up

time continuous variables was performed using a paired sample t-test.

We tested our hypotheses using structural equation modelling with

maximum likelihood estimation using functions from the R package

Lavaan [240]. Full information maximum likelihood was used to provide

unbiased and efficient estimates of the parameters of interest missingness

at random [241]. Two cross-lagged panel models were used to test the

hypothesis of the study. A cross-lagged panel model was implemented to

test the relationships between SB and frailty status across the two time

points for the present study (i.e., initial assessment and 4-year follow-up).

The second cross-lagged panel model was used to test the relationships

between MVPA and frailty status. The null hypotheses would be supported

if neither of the coefficients associated with the cross paths were

significantly different from zero. If the cross path towards frailty in time 2,

but not towards SB/MVPA in time 2, was significant, then hypotheses 1 (H1)

would be supported. If it were the reverse of the latter, then H2 would be

supported. Finally, if both paths were significant, then H3 would be

supported. Analyses included sex as time-invariant variable; in addition,

age, education, marital status, income, BMI, MMSE, and accelerometer

wear time were allowed to be time-varying covariates (i.e., allowing for

possible changes in these measures from initial assessment to follow-up).

Among the strengths of using a cross-lagged panel approach is that it

allows simultaneous analysis of the two dependent outcomes, thereby

permitting the identification of possible bidirectional associations over

time.


152

Model fit was assessed using a selection of fit indices and criteria: root mean

square error of approximation (RMSEA) (≤0.06), standardized root mean

square residual (SRMR) (≤0.08), confirmatory fit index (CFI) (≥0.95), and

Tucker-Lewis index (TLI) (≥0.95) [242].

(Study 7)

All analyses were conducted with the R software (R project version 3.5.1)

and significance level was set at usual P<0.05.

Baseline participants that did and did not complete the follow-up

assessment were compared on their demographics and other

characteristics with an independent t-test or chi-square test for continuous

and categorical variables, respectively. The Mean (SD) and frequencies

(percentages) were used to describe continuous variables and categorical

variables respectively. A paired t-test was used to compare participants

retained in the study across time points of assessment. An independent t-

test was conducted to compare active and inactive participants.

We addressed our main objective using a structural equation modelling

framework using functions from the R package Lavaan [240]. The full

information maximum likelihood was used to provide unbiased and

efficient estimates of the parameters of interest using the complete available

information [241]. A cross-lagged panel model was designed to test the

relationships between number of daily BST and frailty status between the

initial assessment and the 4-year follow-up. Among the strengths of using

a cross-lagged panel approach is that it tolerates simultaneous analysis of

the two dependent outcomes, thereby allowing the identification of

possible bidirectional associations over time. Covariates included sex as


153

time-invariant variable, in addition to age, education, marital status, BMI,

MMSE, MVPA, and accelerometer wear time as time-variant.

Subsequently, two more cross-lagged panel models were made by

stratifying the sample in those active and inactive individuals.

Model fit was considered using a selection of fit indices and criteria as

previously published [242]: RMSEA (≤0.06), SRMR (≤0.08), CFI (≥0.95), and

TLI (≥0.95).

154

155

CHAPTER 5

RESULTS

RESULTS

156

RESULTS

157

5.1. STUDY 1

“Role of objectively measured

sedentary behaviour in physical

performance, frailty and mortality

among older adults: A short

systematic review”

RESULTS

158

Role of objectively measured sedentary behaviour in physicalperformance, frailty and mortality among older adults: A shortsystematic review

ASIER MAÑAS 1,2, BORJA DEL POZO-CRUZ3, FRANCISCO JOSÉ GARCÍA-GARCÍA2,4,AMELIA GUADALUPE-GRAU2,5, & IGNACIO ARA 1,2

1Genud Toledo Research Group, Universidad de Castilla-La Mancha, Toledo, Spain; 2CIBER of Frailty and Healthy Aging(CIBER FES), Madrid, Spain; 3Department of Exercise Sciences, University of Auckland, Auckland, New Zealand;4Geriatric Department, Complejo Hospitalario de Toledo, Toledo, Spain & 5ImFINE Research Group, Department of Healthand Human Performance, Technical University of Madrid, Madrid, Spain

AbstractSedentary behaviour (SB) has recently emerged as an independent risk factor for different health outcomes. Older adultsaccumulate long time in SB. Understanding the role that SB plays on health is crucial for a successful aging. This shortsystematic review summarizes the current evidence related to the effects of objectively measured SB on frailty, physicalperformance and mortality in adults ≥60 years old. The literature search produced 271 records for physical performance(n= 119), frailty (n= 31), and mortality (n= 121). Finally, only 13 articles fulfilled the inclusion criteria and were includedin this review. All articles but one included in the physical performance section (n= 9) showed a negative associationbetween longer time spent in SB and physical performance. A significant association of SB with higher odds of frailty wasfound, however this association disappeared after adjusting for cognitive status. Lastly, two of the three included studiesshowed positive associations between SB and mortality, but this effect decreased or even disappeared in the more adjustedmodels. In conclusion, there is consistency that SB is negatively associated with physical performance. However, therelationship between objectively measured SB and frailty incidence and mortality rates remains unclear and deservesfurther research. The use of homogenous criteria to assess SB and the inclusion of more robust research designs will helpclarifying the independent effects that SB could have on physical performance, frailty, and mortality. This will ultimatelyhelp designing more efficient and comprehensive physical activity guidelines for older adults.

Keywords: Sedentary lifestyle, accelerometer, physical function, frailty, mortality rates, elderly

Highlights. Older adults spend the great majority of their daily time in sedentary behaviours, linked to an increased risk of suffering

numerous diseases. Physical performance and frailty, as well as mortality, are outcomes closely related to successfulaging. Understanding the role of objectively measured sedentary lifestylein these outcomes is important for healthy aging.

. The association between objectively measured sedentary behaviour and physical performance in the elderly is strong. Allstudies except one included in the review showed a negative association between increased sedentary time and some of thecomponents of the physical performance.

. Most of the articles found an association among sedentary lifestyle and frailty incidence or mortality, but the effect of theseassociations diminished or even disappeared in the more adjusted models.

. A wide variety of methodologies was observed in the included articles. A more homogeneous methodology to assesssedentary behaviour and more consistent research designs are needed to confirm the findings observed in this review.

Introduction

Successful aging is a big concern in western societies.Globally, the older adult population has dramaticallyincreased worldwide in the last two decades, and it is

estimated that by 2015 the older population willapproximately represent 22% of the world’s popu-lation (Scully, 2012). This situation provides a chal-lenge for health and social care resources, in order

© 2017 European College of Sport Science

Correspondence: Ignacio Ara, GENUD Toledo Research Group, Universidad de Castilla-La Mancha, Avda. Carlos III s/n, 45071 Toledo,Spain. Email: [email protected]

European Journal of Sport Science, 2017https://doi.org/10.1080/17461391.2017.1327983

to reduce the risk of non-communicable diseases anddisability. In that regard, sarcopenia (i.e. loss ofmuscle mass and strength) plays a central role, indu-cing to a reduced physical performance and impairedability to perform activities of daily living thereforeincreasing the risk of being frail (Roubenoff, 2000).Frailty, a common condition among the older

population (Landi et al., 2010), has been describedas a biological status in which resistance to stressorsis reduced mainly due to cumulative declines in thefunction of different biological systems (Fried et al.,2001), including the immune, endocrine, musculos-keletal and nervous system (Walston et al., 2006).Frailty leads to a state of high vulnerability toadverse health outcomes in the individuals and it isassociated with worsening of physical functioning,falls, higher rates of admissions to hospital, co-mor-bidity and mortality (Landi et al., 2010).There is substantial evidence indicating that main-

tenance of an active lifestyle is central to successfulaging. Consequently, the relationship between phys-ical activity (PA), especially moderate-to-vigorousphysical activity (MVPA), and frailty is now wellestablished. In a recent study, Blodgett, Theou, Kirk-land, Andreou, and Rockwood (2015) demonstrateda relationship between MVPA and frailty among agroup of people over 50 years old. Peterson et al.(2009) concluded that PA is a preventive factor forfrailty among the older population. A recent meta-analysis conducted by Chang and Lin (2015)suggested that older adults with frailty have thehighest risks of mortality when compared withrobust elderly, followed by individuals in the pre-frail phase.While greater attention has been placed on pro-

moting MVPA for general health, the negativeeffects of sedentary behaviour (SB), including thosebehaviours characterized by very low energy expendi-ture while in a sitting or reclining posture, have beenshown to be highly important. In a study published byStamatakis and Hamer (2011), SB emerged as anindependent risk factor for different health outcomessuch as cardiovascular and chronic diseases. Inaddition, large epidemiological studies have indi-cated that self-reported SB is associated with allcauses of mortality in a dose–response manner (Katz-marzyk, Church, Craig, & Bouchard, 2009) and withthe incidence of cardiovascular diseases among thegeneral population (Manson et al., 2002). SB ishighly prevalent among the older population (Daviset al., 2011; Matthews et al., 2008). Hallal et al.(2012) conducted a global assessment in more than60 countries and found that the elderly had thehighest prevalence of self-reported sitting time ascompare with younger adults. The scarce numberof studies conducted among older adults indicate

that SB is an independent risk factor for importantaging outcomes including declining physical function(Santos et al., 2012; Seguin et al., 2012), greater dis-ability in activities of daily living (Dunlop et al.,2015), and increased mortality (Leon-Munoz et al.,2013). Finally, some reviews have systematically ana-lysed the detrimental effects of sedentary lifestyle on avariety of health outcomes in older people (deRezende, Rey-Lopez, Matsudo, & do Carmo Luiz,2014; Wirth et al., 2017), but none have done so con-sidering only objectively measured data of SB andrelating it to physical performance, frailty and mor-tality. Identifying the health outcomes of objectivelyassessed SB in the older population seems to becrucial for a successful aging.Therefore, the aim of this short systematic review is

to provide a brief summary of the published literaturerelated to the potential role of objectively assessed SBwith regards to some of the important outcomes ofaging. Thus, this review is divided into three differentsections, summarizing separately the existing evi-dence in regards to the potential role of objectivelyassessed SB for frailty, disability and mortalityamong older adults. For each section, the limitedavailable evidence is critically reviewed, while gapsin the current knowledge and future directions forresearch are identified.

Methods

The current short systematic review follows thePRISMA recommendations for reporting systematicreviews (Hutton et al., 2015).

Literature search

Literature search was conducted (October 2016) inPubMed and Web of Science (WoS) online data-bases. The following Medical Subject Headings(MeSH) of the United States National Library ofMedicine (NLM) and search terms with the corre-spondent operators were included in this Booleansearch syntax: (elderly OR “older adults” OR “oldpeople” OR “elders”) AND (sedentarism OR seden-tary OR sitting) AND (accelerometer OR accelero-metry OR “objectively measured sedentary” OR“objectively measured physical”) AND:

. (“physical function”OR “physical performance”OR “walking performance” OR “walking vel-ocity” OR “gait speed” OR “activity of dailyliving”) to identify the section of articles relatedwith physical performance;

. (frail OR frailty) to distinguish the section ofarticles related with frailty status;

2 A. Mañas et al.

. (mortality OR death OR “life expectancy”) todetect the section of articles related withmortality.

The search was limited to English language and fulltext availability of eligible articles. Additional suitablestudies were included by screening the reference listsof each included study and other relevant reviewsrecently published.

Eligibility criteria

For the review, studies were included if (i) they werejournal articles in full, (ii) participants were humansaged ≥60 years old, (iii) SB was assessed using objec-tive techniques and (iv) measurement of physical per-formance was carried out by filed or laboratoryobjective tests. Performance was defined as aspectsof physical function (such as strength, endurance,flexibility, speed and agility) that are associated withdaily life activities that are important for maintainingindependence in older adults (Guralnik, Ferrucci,Simonsick, Salive, & Wallace, 1995). In addition,frailty should be evaluated with a validated scale.Studies were excluded from the analysis if (i) theywere not available in English and (ii) the associationof sedentary lifestyle evaluated with accelerometerswas not examined with physical performance, frailtyor mortality. In the studies with participants youngerthan 60 years old we only included the subsamplesolder than 60 years old when reported. The retrievedstudies were imported into the EndNote Web® refer-ence management software to remove any duplicates.Firstly, titles and abstracts were screened by two inde-pendent reviewers (AM and IA). Relevant articleswere then selected for a full read of the article. If noconsensus was achieved between the two reviewers, athird reviewer was consulted (AGG).

Results

The literature search produced 271 records, 119 inthe physical performance section, 31 in the frailtystatus section and 121 in the mortality section.After the removal of duplicates, 177 articles wereexcluded based on title and abstract screening (59in the physical performance section, 17 in the frailtystatus section and 101 in the mortality section) and31 were excluded based on eligibility criteria (19 inthe physical performance section, 6 in the frailtystatus section and 6 in the mortality section) (seedetails in Figure 1).After all this process, nine full-text article remained

in the in the physical performance section, one in thefrailty status section, and three in the mortality

section. Thus, in total 13 full-text articles werefinally included in the review (a summary of themost relevant study details of these studies areincluded in Table I).

SB and physical performance

Seven cross-sectional studies, one interventionalstudy and one randomized clinical trial (RCT) inves-tigated the relationship between SB and physical per-formance (Table I). Fleig et al. (2016) showed anegative association between time spent in sedentaryactivities and gait speed (Beta (β): −90.13; standarderror (SE): 42.03) in 53 older adults with hip frac-ture. Cooper et al. (2015) conducted a study in alarge cohort of 1727 participants from the MRCNational Survey of Health and Development inEngland, Scotland and Wales. They showed thatone standard deviation score greater time spentsedentary was associated with lower grip strength(−0.588 kg; 95% CI: −1.062, −0.115), chair risespeed (−0.550 stands/min; 95% CI: −0.898,−0.201), standing balance time (−0.050 s; 95% CI:−0.076, −0.024) and Timed Up-&-Go speed(−0.021 m/s; 95% CI: −0.028, −0.013). Theseeffect estimates remained similar after additionaladjustment for other potential confounders, exceptfor the association with chair rise speed (−0.084stands/min; 95% CI: −0.426, 0.257) and standingbalance time (−0.024 s; 95% CI: −0.050, 0.002)which were substantially attenuated, largely due toadjustment for long-term limiting illness or disability.A total of 117 males and 195 females, aged 65–

103 years, were assessed in the article of Santoset al. (2012). They found a negative associationbetween the composite Z-score for functional fitnessand the sedentary time (β: −0.002; 95% CI:−0.003, −0.001), even adjusting for MVPA andother confounders (β: −0.002; 95% CI: −0.002,−0.001). Likewise, Rosenberg et al. (2016) con-firmed these findings showing that higher sedentaryactivity was statistically significant associated withworse physical function (Short Physical PerformanceBattery (SPPB), balance task scores, 400-m walktime, chair stand time and gait speed), regardless ofparticipation in MVPA.Rosenberg et al. (2015) examined the effects of an

eight-week behavioural intervention to reduce seden-tary time among older overweight and obese olderadults. An improvement in gait speed (p= .01; d:0.52) but not in chair stands (p= .46; d: 0.11) andSPPB total score (p= .37; d: 0.14) was found as aresult of the intervention.Barone Gibbs et al. (2016), in a RCT, divided par-

ticipants into one of two arms: Sit Less or Get Active.

Role of objectively measured sedentary behaviour 3

The Sit Less group had the aim to reduce SB by 1hour each day. The Get Active group had a goal toreach 150 min of MVPA each week. The Sit Lessgroup improved SPPB significantly from 11.1 ± 0.3to 11.6 ± 0.1 points (p< .05) over 12 weeks but nochanges were detected in the Get Active group. Ifthe components of the SPPB were separated, a sig-nificant improvement in the Sit Less Group in thechair stands was shown but not in gait speed andbalance test.In contrast, Gennuso, Thraen-Borowski,

Gangnon, and Colbert (2016) found no significantassociations between sedentary time and physicalperformance (SPPB, chair stands, gait speed).However, statistical significant associations werefound between breaks in sedentary time (BST) andphysical performance, independently of MVPA.The former was found in men but not in women.Similarly, Davis et al. (2014) showed that both

sedentary time (β: −0.111; 95% CI: −0.163,

−0.060) and BST (β: −0.721; 95% CI: −0.463,−0.978) were negative associated with lower extre-mity function (p< .001). But in fully adjustedmodels, only BST and not overall sedentary timeremained significant.Finally, the study of Sardinha, Santos, Silva, Bap-

tista, and Owen (2015) found a significant associ-ation between BST and physical performance (β:0.154; 95% CI: 0.027, 0.280), even after fully adjust-ment of the models (β: 0.180; 95% CI: 0.052, 0.310).Additionally, SB was a significant predictor of phys-ical performance, independently of BST andMVPA (p< .05).

SB and frailty status

The only article that met the inclusion criteria forfrailty showed a significant association of SB withhigher odds of frailty (Odd ratio (OR): 1.010916;

Figure 1. Flow diagram on identification, screening, eligibility and inclusion of full-text articles.

4 A. Mañas et al.

Tab

leI.

Mainch

aracteristicsof

theselected

stud

ies.

Study

Design

No.

ofpa

rticipan

ts;sex;

age[years

(mean±

SD

orrang

e)]

Main

characteristic

ofthesubjects

Deviceused

toassess

SB

Param

eter

(and

value)

inwhich

SBassessmen

tis

basedon

Valid

days;

Hou

rsfor

valid

day∗

Primary

Outco

me

Secon

dary

Outco

mes

a Magnitude

oftheassociation

bMagnitude

oftheassociation

Baron

eGibbs

etal.

(201

6)

RCT

38;F

(17),M

(11);

68±7

Inactive;

Com

mun

ity-

dwellin

g

Sen

seW

earP

roarmba

nd≤1.5M

ETs

≥4;

≥10

Phy

sical

Fun

ction

MVPA

SPPB:0.5¥

400-W

:0.07

¥

GS:−0.04

¥

Daviset

al.

(201

4)Cross-

sectiona

l21

7;F(109

),M

(108

);78

±6

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

ActiG

raph

GT1M

<10

0CPM

≥5;

≥10

Phy

sical

Perform

ance

BST,

MVPA

SPPB:β=

−0.11

1BT:β=

−0.03

0CR:β=

−0.04

2GS:β=

−0.03

9

SPPB:β=

−0.05

0BT:β=

−0.01

4CR:β=

−0.10

0GS:β=

−0.02

7

Fleig

etal.

(201

6)Cross-

sectiona

l49

;F(32),M

(17);

80±8

After

hipfracture;

Com

mun

ity-

dwellin

g

ActiG

raph

GTX3+

<10

0CPM

≥3;

≥8

Phy

sical

Perform

ance

LIP

A,

MVPA,

Steps,

Qua

lityof

life,

Fallsself-

effic

acy

GS:β=

−90

.13

Coo

peret

al.

(201

5)Cross-

sectiona

l17

27;F(837

),M

(890

);60

–64

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

Actiheart

≤1.5M

ETs

N/R;≥48

Phy

sical

Perform

ance

MVPA,

PAEE

HS:β=

−0.58

8θ

CR:β=

−0.55

0θ

BT:β=

−0.05

0θ

TUG:β=

−0.02

1θ

HS:β=

−0.54

0θ

CR:β=

−0.08

4θ

BT:β=

−0.02

4θ

TUG:β=

−0.01

1θ

Gen

nuso

etal.

(201

6)

Cross-

sectiona

l44

;F(28),M

(16);

70±8

Abilityto

walk

unaide

d;Com

mun

ity-

dwellin

g

activP

AL

Posture

(sitting

/lying)

N/R;N/R

Phy

sical

Perform

ance

BST,

MVPA,

Qua

lityof

life,

Postural

stab

ility,

Fallrisk

SPPB:RC=

−0.09

CR:RC=

−0.21

400-W

:RC=

−0.01

(Contin

ued)


Tab

leI.

Con

tinued

.

Study

Design

No.

ofpa

rticipan

ts;sex;

age[years

(mean±

SD

orrang

e)]

Main

characteristic

ofthesubjects

Deviceused

toassess

SB

Param

eter

(and

value)

inwhich

SBassessmen

tis

basedon

Valid

days;

Hou

rsfor

valid

day∗

Primary

Outco

me

Secon

dary

Outco

mes

a Magnitude

oftheassociation

bMagnitude

oftheassociation

Rosen

berg

etal.

(201

6)

Cross-

sectiona

l30

7;F(222

),M

(85);84

±6

Gen

eral

popu

lation

;Retirem

ent

commun

ities

ActiG

raph

GT3X

+<10

0CPM

≥1;

≥10

Phy

sical

Perform

ance

Men

tala

ndCog

nitive

Health,

Phy

sical

Health

SPPB:β=

−0.55

400-W

:β=

20.72

BT:β=−0.15

CR:β=1.02

GS:β=0.23

Rosen

berg

etal.

(201

5)

Exp

erim

ental

24;F(17),M

(7);

71±6

Overw

eigh

tan

dob

ese;

Com

mun

ity-

dwellin

g

activP

AL

Posture

(sitting

/lying)

N/R;N/R

Phy

sical

Perform

ance

BST,

MVPA,

Steps,

Qua

lityof

life,

Dep

ressive

symptom

s

SPPB:d=

0.14

GS:d=0.52

CR:d=0.11

San

toset

al.

(201

2)Cross-

sectiona

l31

2;F(195

),M

(117

);74

±7

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

ActiG

raph

GT1M

<10

0CPM

≥3(≥

1w);

≥10

Phy

sical

Perform

ance

MVPA

SFT:β=

−0.00

2CS:β=

−0.01

3AC:β=

−0.01

08F

UG:β=

0.01

56M

WT:β=

−0.30

1CSR:β=

−0.03

1BS:β=

−0.01

5

SFT:β=

−0.00

2CS:β=

−0.01

1AC:β=

−0.01

08F

UG:β=

0.01

66M

WT:β=

−0.10

0CSR:β=

−0.02

4BS:β=

−0.00

2

6 A. Mañas et al.

Sardinh

aet

al.

(201

5)

Cross-

sectiona

l21

5;F(128

),M

(87);73

±6

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

ActiG

raph

GT1M

<10

0CPM

≥3(≥

1w);

≥10

Phy

sical

Perform

ance

BST,

MVPA

SFT:β=

−0.19

8SFT:β=

−0.16

5

Bastone

etal.

(201

5)Cross-

sectiona

l26

;F(24),M

(12);

66–86

Frailan

dno

nfrail;

Com

mun

ity-

dwellin

g

ActiG

raph

GT3X

<10

0CPM

N/R;≥10

Frailty

Aerob

icfitne

ss,

LIP

A,

MVPA,Steps

FS:OR=

1.00

87FS:OR=

1.02

52

Ensrudet

al.

(201

4)Prospective

2918

;F(30),M

(291

8);79

±5

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

Sen

seW

earP

roarmba

nd≤1.5M

ETs

≥5;

≥90

%Mortality

LIPA,

MVPA

ACM:HR=

1.78

ᶾ

ACM:HR=

1.79

ᶾ

Fox

etal.

(201

5)Prospective

213;

F(104

),M

(109

);70

-+85

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

ActiG

raph

GT1M

s<10

0CPM

≥5;

≥10

Mortality

MVPA,

Steps,

Trips,

Phy

sical

Fun

ction

ACM

:HR=

0.51

μACM

:HR=

1.01

μ

Klenk

etal.

(201

6)Prospective

1271

;F(554

),M

(717

);76

±7

Gen

eral

popu

lation

;Com

mun

ity-

dwellin

g

activP

AL

Posture

(sitting

/lying)

N/R;24

Mortality

Walking

duration

ACM:HR=

2.05

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ACM

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Notes:S

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95% CI: 1.00127, 1.020655), but this associationdisappeared when the statistical model was adjustedfor cognitive function (OR: 1.025228; 95% CI:0.999848, 1.051252) (Table I).

SB and mortality

Three prospective cohort studies investigated therelationship between SB and mortality (all-cause,cardiovascular, cancer, other causes) (Table I).Ensrud et al. (2014) showed that more time spent

in sedentary activities was associated with greaterrisk of death. Individuals in the highest SB quartilehad a higher all-cause mortality (Hazard ratio (HR):1.56, 95% CI: 1.15, 2.14) than those in the lowestSB quartile (reference group) after adjusting ofmodels for multiple confounders. Further adjust-ment did not attenuate this association (HR: 1.79,95% CI: 1.19, 2.70).Similarly, Klenk et al. (2016) found a higher mor-

tality risk in those subjects with the longer SB timescompared with their physically active counterparts(HR: 2.05, 95% CI: 1.13, 3.73). However, afteradjusting for various health outcomes and biomarkersthis association disappeared (HR: 1.63, 95% CI:0.88, 3.02).In Fox et al. (2015) individuals were classified as

low, medium and high sedentary time per day.They showed no associations between sedentarytime and all-cause mortality in any case, with unad-justed (low group, HR= 0.51, 95% CI: 0.21, 1.26)and after more completely adjusted models (lowgroup, HR = 1.01, 95% CI: 0.35, 2.98).

Discussion

To the best of our knowledge, this is the first reviewthat examines the association between objectivelymeasured SB and its effects on physical performance,frailty and mortality in older people. Although thenumber of studies in which accelerometers wereused in order to ascertain SB is very limited in thispopulation, a relationship between SB and a wor-sened physical performance is observed. However,the association between SB and frailty incidenceand mortality rates remains unclear due to thereduced number of studies available in the literature.

Effects of SB on physical performance

Earlier studies where sedentary lifestyle has beenmeasured by auto-reported questionnaires show thatthe longer time older adults spend on SB, the higheradverse health outcomes (i.e. diabetes, cardiovascular

diseases) present, independently of MVPA (Wilmotet al., 2012). Disability is a major adverse healthoutcome resulting in limitations in the activities ofdaily living. This is of special interest, since PA hasbeen proposed for the prevention of impaired physicalfunctioning in older ages (Lang, Guralnik, & Melzer,2007). However, these studies do not consider seden-tary time as an independent domain of behaviour.In the current review, we have found a negative

association between SB and physical performance,regardless of MVPA in two of the cross-sectionalstudies reviewed (Rosenberg et al., 2016; Santoset al., 2012). Likewise, Fleig et al. (2016) andCooper et al. (2015) found a negative associationbetween time spent on sedentary activities andvarious physical performance tests in older adults.Accordingly, Dunlop et al. (2015) found a strongrelationship between greater time spent in SB andthe presence of activities of daily living disability,and Ikezoe, Asakawa, Shima, Kishibuchi, and Ichiha-shi (2013) with a slower time in the Timed Up-&-Gotest and lower muscle strength. The independentrelationship of SPPB extends recent findings demon-strating that objectively measured sedentary time,controlled for MVPA, is related to metabolic syn-drome (Bankoski et al., 2011), cancer (Lynch et al.,2011) and mortality (Koster et al., 2012). In contrast,investigations performed in adults failed to relatesitting time with impaired muscle strength or gait/mobility (Reid et al., 2016). These discrepanciesmay be attributable, at least in part, to the heterogen-eity in the participant study samples examined.SB and physical performance have also been

related longitudinally. Seguin et al. (2012) studied62,000 woman aged 50–79 years from the Women’sHealth Initiative, and observed that those with thehigher auto-reported sitting time and total sedentarytime at the beginning of the study, had the higherreduction in self-reported physical performanceafter 12.3 years’ follow-up. Unfortunately, self-report is susceptible to socially desirable responding(Adams et al., 2005), and older adults have a lessaccurate recall (Bonnefoy et al., 2001).Thus, objectively assessed SB as well as home-

based physical performance tests may provide moreaccurate and reliable results. According to our litera-ture review, the RCT study performed by BaroneGibbs et al. (2016) demonstrated that a 12 weekintervention aimed to reduce SB has a higher effecton physical performance rather than on time spenton MVPA in older sedentary but highly physicallyfunctional adults. In agreement, Rosenberg et al.(2015) showed that an eight-week behavioural inter-vention to reduce SB is feasible and effective amongolder overweight and obese adults in order to increasephysical performance.

8 A. Mañas et al.

The present findings highlight the need to separateSB from insufficient MVPA patterns. This is impor-tant because it enables SB as a modifiable additionalrisk factor for impaired physical performance, disabil-ity and loss of independence. Beyond this, thereseems to be a negative relationship between spendingmore time on sedentary activities and physicalperformance.Moreover, it is important to discuss that the way

sedentary time is spent also matters. For example,Sardinha et al. (2015) as well as Davis et al. (2014)found that breaking-up time in SB was positivelyassociated with physical performance in olderadults, even after controlling for overall time inMVPA and SB. Davis et al. (2014) also reportedthat breaking-up time in SB predicted overall physicalperformance and was associated to higher scores inselected fitness parameters like upper and lowerbody muscle strength. This is not the case of highfunctioning older adults who spend over an hour aday walking, where higher SB and lower breakswere associated with an improved muscle quality(Chastin, Ferriolli, Stephens, Fearon, & Greig,2012). Given the surprising results, authors explainit by a higher body fat that might provide a trainingstimulus to maintain muscle power.Gennuso et al. (2016) reported that longer bouts

and fewer breaks in SB is negatively associated withphysical function in older adults, regardless of partici-pation inMVPA. This adds to previous research werethe odds for abdominal obesity decreased 7% foreach additional hourly break in sedentary time inolder women (Judice, Silva, Santos, Baptista, & Sar-dinha, 2015), as well as triglycerides and plasmaglucose (Healy, Dunstan, et al., 2008).These findings represent a new challenge for public

health recommendations regarding how to break upsedentary patterns complementary to those for PAin order to improve physical functionality.

Effects of SB on frailty status

Current scientific evidence consistently shows thatchanges in body composition, especially loss ofmuscle mass, together with low PA and high SB,could be an important contributor for developingfrailty in older adults (Fried et al., 2001). Interest-ingly, regular exercise is probably the only non-drugderived therapy effective to improve physical func-tion, cognitive performance and mood (Landi et al.,2010), besides sarcopenia (Gianoudis, Bailey, &Daly, 2015), which is the central problem in thefrailty syndrome.Despite all the potential benefits of PA in relation to

frailty, frail older adults spend 84.9% (about 10 h), oftheir daily time in SBs (Jansen et al., 2015). Previous

evidence indicates that physically inactive individualswho have higher levels of functional disability (Trem-blay, Kho, Tricco, & Duggan, 2010), and those indi-viduals who have high levels of SB are more likely tobe frail (Peterson et al., 2009).DA Silva Coqueiro et al. (2016) found a positive

association between self-reported sedentary timeand frailty in 316 community-dwelling older adults.The authors calculated that 7 h per day of SB wasthe best cut-off point to discriminate frail individuals.However, this cut-off point is quite low in compari-son with other studies reporting objectively measuredSB (Jansen et al., 2015).The only study that met the inclusion criteria in the

present literature review for the frailty section was theone recently published by Bastone Ade, Ferriolli,Teixeira, Dias, and Dias (2015). This investigationfound that the frail group spent more time in SBthat their robust peers. SB was significantly associ-ated to frailty, even after adjusting by the number ofchronic health conditions, but this association disap-peared when the statistical model was adjusted bycognitive status. Bastone and coworkers did notreport an association between SB and frailty statusindependently of the PA levels. This was the case inBlodgett et al. (2015) study, where a positive associ-ation was observed between SB and various adversehealth outcomes (frailty, self-reported health, activi-ties of daily living disability and healthcare utiliz-ation), independent of MVPA in a community-dwelling older adults (>50 years) sample. As a limit-ation, these cross-sectional studies do not take intoconsideration causality. Therefore, it is not possibleto certainly know if SB causes the appearance offrailty or if frailty can cause that individuals chooseto have a more sedentary lifestyle.Longitudinal studies like the one by Song et al.

(2015) support the existing idea of a relationshipamong daily sedentary time and the development ofa frailty status, regardless of MVPA. But the scarceavailable data prevent to robustly demonstrate thisassociation, and more studies using similar method-ologies both to measure SB and frailty are needed.

Effects of SB on mortality

As early as in the 1950s, we can found the first indi-cation that SB could markedly increase adversehealth outcomes. Morris, Heady, Raffle, Roberts,and Parks (1953) demonstrated a double age-adjusted rate of fatal coronary heart disease in busdrivers (sedentary) when compared with conductors(active) workers.Since then, much research efforts have been

focused on the relationship of an active lifestyle andvarious health outcomes, even with all-cause


mortality rates (Bembom, van der Laan, Haight, &Tager, 2009). However, much less attention hasbeen devoted on the effects of SB on mortality.Again, scientific literature relies on self-reported ques-tionnaires to demonstrate an association between SBand mortality (all-cause, cardiovascular, colorectalcancer and other causes) in adults and older adults,independently of PA levels (Dunstan et al., 2010).This implies the limitation that questionnaires maynot correctly differentiate sedentary time from lightPA (Pate, O’Neill, & Lobelo, 2008), but the existingscientific literature using objective PA measurementsis very scarce at the moment (Pate et al., 2008).While Ensrud et al. (2014) observed that individ-

uals in the higher SB quartile had a higher all-causemortality than those in the lower SB quartile, Foxet al. (2015) found that despite spending a meantime of 11 SB hours, the study participants did notshow an association among mortality rates andsedentary time volume.Klenk et al. (2016) found a higher mortality risk in

those subjects who spent more time in sedentaryactivities. However, when biomarkers were includedas a confounding variable the association disappeared.Interestingly, a large recent review combining data

from over one million participants found that 60–75 min of PA a day eliminated the harms of sittingwhen it came to measuring death from cardiovasculardisease or death by all causes (Ekelund et al., 2016).Despite the large number of people included in thereview, the results should be taken with caution asthey are based on self-report PA and SB data. Whenwe take into consideration populations younger thanthose included in this review, studies mainly report asignificant effect of SB onmortality (Healy,Wijndaele,et al., 2008; Koster et al., 2012). Among those, Kosteret al. (2012) concluded that SB is a risk factor for mor-tality independent of moderate-to-vigorous PA.Unfortunately, drawing conclusions in this section iscomplicated because of the small number of studiesand the confusing results of each of them.

Methodological issues

To date, the use of accelerometers is considered themost valid and reliable method to assess SB, despitenot all devices are able to discriminate betweensitting and standing changes in the posture (An,Kim, & Lee, 2017). In order tomake stronger the con-clusions of this review, only studies using acceler-ometers to assess SB were included. However, thevariety of devices utilized and the diversity in tech-niques regarding data extraction and analysis acrossstudies makes difficult drawing definitive conclusions.Reactivity is an important point to take into

account when measuring PA and SB with

accelerometry because it may introduce a relevantbias. Although the Hawthorne effect has been recog-nized as a potential limitation of the accelerometrymethod, evidence remains limited (Dossegger et al.,2014). It seems clear that in children and adolescentsthere may be some reactivity (Kremers & Brug,2008). However, tampering with devices seems tobe less likely among older adults (Pedisic &Bauman, 2015). None of the studies included inthis review use strategies to avoid reactivity, thereforethe results and conclusions must be interpreted withcaution since the evidence in this area is still scarce.The number of valid days and the minimum hours

per day included in the analysis from the acceler-ometer data is another important methodologicalissue when working with these type of devices. Theaverage number of valid days to include acceler-ometer data in the analysis is 3.6 ± 1.4 days amongthe studies reporting this value included in thereview. However, according to the study of Hart,Swartz, Cashin, and Strath (2011) conducted inolder people, at least five days are necessary to ade-quately capture the SB. Thus, studies which takeless than five valid days for the accelerometry analysismight be unrepresentative. Moreover, cut-offs pointsfor sedentary strip establishment is also important,knowing that they are dependent on the analysesunit (i.e. epoch length and axes) (Aguilar-Farias,Brown, & Peeters, 2014).The third important methodological variable to

consider is the criteria for non-wear time of the accel-erometers. In that regard, published studies aredivided between the algorithm proposed by Troiano(2007) or the algorithm recommended by Choi,Liu, Matthews, and Buchowski (2011). The latterincorporates improvements for the misclassificationof time intervals spent in SB that do not pass thewear/non-wear classification criteria for the lowactivity counts. Thus, studies in populations with alow PA and high SB patterns, such as older adults,could likely benefit from these improvements (Choiet al., 2011).Although according to the definition of SB (Seden-

tary Behaviour Research Network, 2012) only SBshould be accounted during waking hours, onestudy in this review included the time that individualsspend sleeping as SB (Klenk et al., 2016). This canlead to an overestimation of sedentary time andshould be taken into account when comparingresults from studies using different approaches.Finally, another important aspect that should be

considered when studying SB in relation to healthoutcomes is MVPA. This factor should be takeninto account within the covariates included in thestatistical models so that the independent effect ofSB can be ascertain. The same applies with health

10 A. Mañas et al.

status, especially in older adults studies in order toavoid confounding interactions (Andrade & Fer-nandes, 2012).Although Pedisic and Bauman (2015) concluded

that accelerometer-based studies had limitationsregarding generalisability, validity, comprehensive-ness, simplicity, affordability, adaptability, between-study comparability and sustainability, many ofthese methodological aspects have not yet been hom-ogenized. Overall, the discrepancy in the methodo-logical aspects across the analysed studies in thisreview may preclude us from drawing definitive con-clusions, although a recent review that could helpresearchers to make better decisions before andafter data collection using accelerometers, in orderto obtain more valid and comparable data has beenpublished (Migueles et al., 2017). Consistency inthe methodological aspects when assessing SB andstronger research designs are crucial points toconfirm the observed findings in this review.

Summary and conclusion

There is consistent evidence of the relationshipbetween objectively measured SB and physical per-formance in the elderly. The association amongsedentary lifestyle, frailty incidence and mortalityrates warrant further investigation. The lack ofstudies assessing these outcomes and the widevariety of methodological issues reported among thereviewed studies make difficult to draw definitiveconclusions. Another important aspect that deservesfurther investigation is the manner that SB is accu-mulated. BST seem to minimize the decline of phys-ical performance with aging. Future research shouldtest this hypothesis also regarding frailty and mor-tality outcomes.While sedentary lifestyle can have an independent

relationship on the outcomes of interest for thisreview, future studies should consider how PA andSB could simultaneously influence these outcomes.The latter has already been studied in relation tocardio-metabolic health variables (Bakrania et al.,2015) but, to our knowledge, no studies have ana-lysed this combined effect on physical performance,frailty and mortality. Nonetheless, homogeneitywith regards to the assessment of SB and other meth-odological issues commented in this review will helpclarifying the potential role of SB (and patterns) onphysical performance, frailty and mortality amongolder adults.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Biomedical Research NetworkingCenter on Frailty and Healthy Aging (CIBERFES) and FEDERfunds from the European Union [CB16/10/00477]. AM hasreceived a PhD Grant from the Universidad de Castilla LaMancha [2015/4062].

ORCID

Asier Mañas http://orcid.org/0000-0002-1683-1365Ignacio Ara http://orcid.org/0000-0002-2854-6684

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RESULTS

173

5.2. STUDY 2

“Frailty is associated with objectively

assessed sedentary behaviour

patterns in older adults: Evidence

from the Toledo Study for Healthy

Aging (TSHA)”

RESULTS

174

RESEARCH ARTICLE

Frailty is associated with objectively assessedsedentary behaviour patterns in older adults:Evidence from the Toledo Study for HealthyAging (TSHA)

Borja del Pozo-Cruz1 , Asier Ma as2,3 , Marı́a Martı́n-Garcı́a2,3, Jorge Marı́n-Puyalto4,Francisco J. Garcı́a-Garcı́a3,5, Leocadio Rodriguez-Ma as3,6, Amelia Guadalupe-Grau5,7,Ignacio Ara2,3

1 Department of Exercise Sciences, University of Auckland, Auckland, New Zealand, 2 GENUD ToledoResearch Group, University of Castilla-La Mancha, Toledo, Spain, 3 CIBER of Frailty and Healthy Aging(CIBERFES), Madrid, Spain, 4 GENUDResearch Group, Faculty of Health and Sport Sciences, University ofZaragoza, Huesca, Spain, 5 Geriatric Department, Hospital Virgen del Valle, Toledo, Spain, 6 GeriatricDepartment, Hospital Universitario de Getafe, Getafe, Spain, 7 ImFINE Research Group, Department ofHealth and Human Performance, Technical University of Madrid, Madrid, Spain

These authors contributed equally to this work.* [email protected]

Abstract

ObjectiveThe aim of this study was to examine the association of sedentary behaviour patterns with

frailty in older people.

SettingClinical setting.

DesignCross-sectional, observational study.

Participants andmeasurementsA triaxial accelerometer was used in a subsample from the Toledo Study for Healthy Aging

(519 participants, 67–97 years) to assess several sedentary behaviour patterns including

sedentary time per day, the number and duration (min) of breaks in sedentary time per day,

and the proportion of the day spent in sedentary bouts of 10 minutes or more. Frailty was

assessed using the Frailty Trait Scale (FTS). Regression analysis was used to ascertain the

associations between sedentary behaviour patterns and frailty.

PLOSONE | https://doi.org/10.1371/journal.pone.0183911 September 11, 2017 1 / 9

a1111111111a1111111111a1111111111a1111111111a1111111111

Citation: del Pozo-Cruz B, Ma as A, Martı́n-Garcı́aM, Marı́n-Puyalto J, Garcı́a-Garcı́a FJ, Rodriguez-Ma as L, et al. (2017) Frailty is associated withobjectively assessed sedentary behaviour patternsin older adults: Evidence from the Toledo Study forHealthy Aging (TSHA). PLoS ONE 12(9):e0183911. https://doi.org/10.1371/journal.pone.0183911

Editor: Stephen D Ginsberg, Nathan S KlineInstitute, UNITED STATES

Received:May 11, 2017

Accepted: August 14, 2017

Published: September 11, 2017

Copyright: 2017 del Pozo-Cruz et al. This is anopen access article distributed under the terms ofthe Creative Commons Attribution License, whichpermits unrestricted use, distribution, andreproduction in any medium, provided the originalauthor and source are credited.

Data Availability Statement:We are unable toprovide the minimal dataset because of legalrestrictions, i.e. the Spanish Data Protection Policy.However, there is an established infrastructure,including a website (http://www.reticef.es/) and areview committee, through which data requests arehandled. The hospital reviews and determines thepurposes for the data requests and what data canbe released. Data requests can be sent to:Research and teaching unit, Virgen del Valle

ResultsSedentary time per day and the proportion of the day spent in sedentary bouts of 10 minutes

or more, were positively associated with frailty in the study sample. Conversely, the time

spent in breaks in sedentary time was negatively associated with frailty.

ConclusionIn summary, breaking up sedentary time and time spent in sedentary behaviour are associ-

ated with frailty in older people.

IntroductionFrailty in older adults is considered a biological condition where poor resolution of several

physiological systems to maintain homoeostasis occurs after a low-power stressor event [1, 2].

Frailty is associated with alterations in the musculoskeletal, vascular and central nervous sys-

tems [3]. Prevalence of frailty in Spain is 8.4%, with an additional prevalence of pre-frailty of

41.8%, therefore approximately 50% of people over 65 years are categorized as frail or pre-frail

[4]. Socio-economic costs associated with frailty are also of relevance [5]. Frailty has been asso-

ciated with an increase in hospitalization rates, falls, incident disability, decreased mobility,

and higher mortality rates [2, 6, 7]. In the last decade, frailty has been recognized as one of the

most promising indicators to help prevent disability [8]. Finding mechanisms to prevent frailty

are therefore of interest.

Sedentary behaviours, including those characterized by low energy expenditure while in a

sitting or reclining posture, have been shown to contribute to adverse outcomes. Even in the

absence of other risk factors, sedentary behaviour has recently emerged as an independent car-

diovascular risk factor [9–11] and is related to all causes of mortality in a dose-response man-

ner [12], possibly because sedentary behaviour influences homeostasis and function of many if

not all body systems [13]. Lack of exercise and a sedentary lifestyle is one of the most signifi-

cant public health problems of the 21st century [14], the effect that a sedentary lifestyle exerts

on frailty is poorly assessed [1].

Da Silva Coqueiro et al. [15] recently reported an association between sedentary behaviour

as assessed by a questionnaire and frailty status among adults over 60 years. Similarly, objec-

tively assessed sedentary behaviour was associated with frailty among adults over 50 years old

in the National Health and Nutrition Examination Survey [16] independent of moderate-to-

vigorous physical activity (MVPA). Sedentary behaviour patterns (i.e. how sedentary behav-

iour is accumulated) can make an impact on the wider health of individuals [17]. For example,

an increased number of bouts of sedentary behaviour per day is associated with worse health

outcomes including reduced cardiovascular health or physical function [17]. Inserting bouts of

activity into otherwise sedentary time (ST) is associated with better physical function in older

adults [18]. Breaking up sedentary time has also been positively associated with a lower risk of

disability in the activities of daily life and inversely associated with impairments and physical

dependence in older age, independent of MVPA [19]. Hence, reducing or breaking up long

sedentary periods seems to be a feasible and promising approach expected to attenuate the

consequences of frailty among older adults. Nonetheless, there is a lack of data regarding the

relationship between sedentary behaviour patterns and frailty in this population group [1].

Sedentary behaviour and frailty in the elderly


Hospital Ctra. Cobisa S/N45071 Toledo - [email protected].

Funding: This work was supported by grantsRD12/0043/0026, RD12/0040/0020, RD12/0040/0001 CB16/10/00456, CB16/10/00477, CB16/10/00464, PI15/01305 and PI10/01532, from theInstituto de Salud Carlos III (Ministerio deEconomia, Industria y Competitividad), Spain, byGrant FP7-305483-2 from the FP7-Health-2012Innovation program, European Union andBiomedical Research Networking Center on Frailtyand Healthy Aging (CIBERFES) and FEDER fundsfrom the European Union.

Competing interests: The authors have declaredthat no competing interests exist.

Therefore, the aim of this study was to examine the associations of various sedentary behaviour

patterns with frailty in older people.

Methods

Research design and participantsData were taken from the Toledo Study for Healthy Aging (TSHA), whose complete method-

ology has been reported elsewhere [4, 20]. Briefly, the TSHA is a population prospective cohort

study aimed at studying the determinants and consequences of frailty in institutionalised and

community-dwelling individuals older than 65 years living in the province of Toledo, Spain. A

subsample of 626 volunteers was assessed on sedentary behaviour patterns. From these, 107

were excluded due to incomplete or invalid accelerometer data. A total of 519 participants

were finally included. All the subjects gave their informed consent in written and the study

was performed in accordance with the Helsinki Declaration of 1975, as last modified in 2000,

regarding the conduct of clinical research, and was approved by the Ethical Committee of the

Toledo Hospital (CEIC).

OutcomemeasuresFrailty. The Frailty Trait Scale (FTS) was used to assess frailty [21]. Briefly, the FTS

includes 7 dimensions (i.e. energy balance and nutrition, activity, nervous system, vascular sys-

tem, weakness, endurance, and slowness) operationalised through 12 items. Each item repre-

sents a biological trait. All items but one (“chair test”, which scored 0 [worst status] to -5 [best

status]) are scored 0 (best status) to 4 (worst status). The total score was calculated by adding

all the scores in each item divided by total score possible for each individual, multiplied by

100. The total score ranged from 0 (best score) to 100 (worst score).

Sedentary behaviour patterns. Sedentary behaviour patterns were assessed by accelero-

metry (ActiGraph, ActiTrainer 3X, Fort Walton Beach, FL). Accelerometer output is an activ-

ity count, which is the weighted sum of the number of accelerations measured over a time

period or epoch (a 1 minute epoch was used in this study). The intensity of activity is assessed

from the weighted sums, which are proportional to the magnitude of measured acceleration.

Participants were asked to wear the accelerometer on their left hip for 7 consecutive days.

Sleeping periods were removed from the analyses and considered non-wear time. Moreover,

periods of at least 60 consecutive minutes of zero counts were also considered as non-wear

time and removed as well [22]. Only data from participants with 4 or more valid days of accel-

erometer data (i.e. a day with 480 minutes or more of wear time) were included in the analyses

(n = 519).

Each minute with less than 100 counts was considered sedentary time [23, 24]. Time per

day (min) spent in sedentary time was then registered. A break in sedentary time (BST) was

defined as at least 1 min where the accelerometer registers�100 counts following a sedentary

period. The number and duration (min) per day of BST were recorded. A 10-min bout of sed-

entary time (ST-10) was defined as a period of at least 10 consecutive minutes where the accel-

erometer registered<100 counts/min. The number, duration (min) and proportion over total

10-min bouts per day of ST-10 were recorded. All outcomes were weighted by daily averaged

wear time on valid days (i.e. outcome summed over all wear time divided by the number of

successfully monitored days for each participant). The different sedentary pattern variables

were standardized (Z-score = [observed—sample mean]/sample SD). In an effort to account

for the combined effect of both duration and number of the different patterns, two composite

Z-scores representing patterns of ST [ST-COMP = Z-score ST-10 (number/day) + Z-score ST-



10 (minutes/day)] and patterns of BST [BST-COMP = Z-score BST (number/day) + Z-score

BST (minutes/day)] were then computed.

Adherence to WHO physical activity guidelines. In order to analyse the adherence of

physical activity (PA) in the study with recommendations for public health proposed by the

World Health Organization, the accumulation of at least 150 min/week of moderate physical

activity or 75 min/week of vigorous physical activity or as an equivalent combination of mod-

erate and vigorous physical activity was assessed with an accelerometer [25].

Anthropometrics and confounding variables. Height was measured to the nearest milli-

metre using a portable stadiometer (Medizintechnik seit 1890, KaWe, Germany), and weight

was measured with a SECA precision scale (SECA 884 floor scale, Germany). Individuals

removed their shoes, socks and heavy clothes prior to weighing. Body mass index (BMI) was

calculated as weight (kg) divided by height squared (m2). A number of confounders were

assessed. Mental status was assessed using the Minimental Scale questionnaire [26]. Comor-

bidity history was assessed using the Charlson Comorbidity index [27]. Physical performance

was assessed by means of the Short Physical Performance Battery [28]. MVPA was assessed by

accelerometry (i.e. each minute of 1952 or more counts was considered MVPA) [23]. Current

medication (i.e. number of drugs), age, and gender were also recorded.

Statistical analysisData were analysed using PASW Statistic, version 23.0.0, with statistical significance set at

p<0.05 (two-tailed). Descriptive statistics (mean ± SD) were calculated for all outcome mea-

surements of the study.

Multiple linear regressions were used to examine the associations between frailty and the

different sedentary behaviour patterns assessed. A model adjusted by age, gender, comorbidity

status, mental health, and polypharmacy status was fitted for each of the sedentary behaviour

pattern outcomes.

Participants were clustered into 2 different groups according to the adherence to WHO PA

guidelines status (i.e. meeting or not the guidelines) and were compared using t-test for inde-

pendent measurements. Participants within each of the former groups were re-allocated to

either less BST group (i.e. group falling below the 50th percentile of BST) or more BST group

(i.e. group falling over the 50th percentile of BST) and then compared using t-test for indepen-

dent measurements.

ResultsCharacteristics of the participants are shown in Table 1. A total of 519 older adults aged 67–97

including 234 males and 285 females, were included in the study.

Table 2 shows the results of different multiple regressions analyses testing the associations

between various sedentary behaviour patterns and frailty in the TSHA study. After adjusting

for several covariates (i.e. age, gender, comorbidity status, mental health, and polypharmacy

status) ST ( , 95CI% = 0.015, 0.004 to 0.027; p = 0.03), ST-10 (proportion) ( , 95CI% = 0.079,

0.234 to 0.195; p = 0.04), BST (minutes/day) ( , 95CI% = -0.031, -0.048 to -0.014; p = 0.03),

and BST-COMP ( , 95CI% = -0.805, -1.339to -0.210; p = 0.01), were significantly associated

with frailty in the study sample. However, ST-10 (number/day and minutes/day), BST (num-

ber/day), and ST-COMP were not significantly associated with frailty.

Participants who met the WHO PA guidelines scored statistically significant less in the

Frailty Trait Scale than those who did not meet the PA guidelines (Fig 1). In both groups,

those who fell into the bottom half of interruptions of sedentary time had a statistically signifi-

cant higher frailty level than those depicting more BST (p<0.05) (Fig 1).



DiscussionTo our knowledge, this is the first study that comprehensively analyses the impact of objec-

tively assessed sedentary patterns beyond total time spent in sedentary behaviour on frailty in

older adults. The main findings were that in adjusted models (i.e. in models adjusted by rele-

vant demographic and medical confounders) frailty was associated with sedentary time per

day, the proportion of the day spent in sedentary bouts of 10 minutes or more, and time spent

in breaks of sedentary time. These results may shed some light on the ongoing discussion

regarding the health consequences of sedentary lifestyles and could generate novel hypotheses

that could help in informing future public health interventions in order to prevent frailty

among older adults.

Available evidence suggests that, regardless of MVPA, spending time in sedentary activities

increases the odds of being frail among older adults [15, 16]. Our results confirm and extend

Table 1. Characteristics of the participants in the study (n = 519).

Variables

Age (yr) 78.84 (4.55)

Gender, male (%) 45.10

Body Mass Index (kg/m2) 30.54 (4.74)

Charlson Comorbidity Index (0–31) 1.12 (1.56)

Mini Mental State Examination (0–30) 23.01 (4.40)

Polypharmacy status (%)

3 19.10

3–4 26.60

5 53.90

Educational Status (%)

Never attended to school 41.10

Less than primary school 41.10

At least primary school 17.40

Short Physical Performance Battery (0–12) 8.44 (2.26)

Accelerometer data

Average wear time per valid day (min) 780.70 (84.68)

MVPA (min/day) 17.62 (21.87)

LPA (min/day) 223.03 (91.69)

ST (min/day) 540.04 (93.87)

10-min bouts of ST (number/day) 16.09 (3.46)

10-min bouts of ST (min/day) 494.29 (114.98)

10-min bouts of ST (percentage) (%) 68.37 (14.48)

BST (number/day) 69.17 (19.28)

BST (min/day) 240.66 (99.39)

BST (number/hour) 5.29 (1.29)

BST (min/hour) 18.30 (6.91)

Meeting WHO PA guidelines, yes (%) 29.60

Frailty trait scale (0–100) 39.84 (15.12)

All values are mean (SD) unless otherwise stated. All accelerometer variables are adjusted for wear time;

ST: Sedentary time; LPA: Light physical activity; MVPA: Moderate-to-vigorous physical activity; BST: Breaks

in sedentary time; PA: physical activity; MeetingWHO PA guidelines: accumulating 150 min/week of

moderate physical activity or 75 min/week of vigorous physical activity or equivalent combination of

moderate and vigorous physical activity.

https://doi.org/10.1371/journal.pone.0183911.t001



those of Da Silva Coqueiro et al. [15] and Blodgett et al. [16] (i.e. frailty was associated with ST

in our sample), but also verified thatthe proportion of the day spent in sedentary bouts of 10

minutes or more is a more powerful predictor of frailty than raw sedentary time or sedentary

time spent in 10 min blocks. The former opens the hypothesis that health consequences of sed-

entary behaviour may be connected to the display of other behaviours. Compositional analysis

of time spent in different behaviours is therefore required to fully understand the impact sed-

entary behaviour may have on health, including frailty among older adults.

Table 2. Linear regression analysis for the association of various sedentary behaviour patterns with frailty in the TSHA study.

Predictors of interest

Models,(95%CI)

ST (minutes/day)

ST-10(number/day)

ST-10(minutes/day)

ST-10(proportion)

BST (number/day)

BST (minutes/day)

ST-COMPa BST-COMPb

Model† 0.015 (0.004to 0.027)*

0.044 (-0.371to 0.459)

0.007 (-0.006to 0.021)

0.157 (0.079 to0.234)*

-0.065 (-0.148to 0.018)

-0.031 (-0.048to -0.014)*

0.220 (-0.386to 0.829)

-0.805 (-1.339 to-0.210)*

R2 0.224 0.223 0.225 0.238 0.227 -0.232 0.224 0.234

†Model, (95%CI): Adjusted for age, gender, comorbidity status (Charlson index), mental health (Mini Mental Scale), and polypharmacy status

ST: Sedentary time; ST-10: 10-min bouts of ST; BST: Breaks in sedentary time; ST-COMP: ST Composite score; BST-COMP: BST Composite score

All predictors of interest were adjusted for wear timeaSum of ST-10 (minutes/day) and ST-10 (number/day) z-scoresbSum of BST (number/day) and BST (minutes/day) z-scores

*Significant at p 0.05

https://doi.org/10.1371/journal.pone.0183911.t002

Fig 1. Frailty trait scale score in different groups. BST: breaks in sedentary time (number/hour); WHO;World Health Organization; PA: physicalactivity; MeetingWHOPA guidelines: the accumulation of at least 150 min/week of moderate physical activity or 75 min/week of vigorous physicalactivity or equivalent combination of moderate and vigorous physical activity; More BST: BST�P50 (P50 = 4.47); Less BST: BST P50; #: p 0.05.

https://doi.org/10.1371/journal.pone.0183911.g001



In an attempt to understand more precisely how the pattern of accumulation of sedentary

time may have an impact on frailty scores in older adults, we combined the number and

duration of bouts of 10 minutes or more spent in sedentary time into a novel sedentary time

compositional score. Previous work has shown an association between sedentary behaviour

and frailty status among adults and older adults [15, 16]. Similarly, in order to reflect the

potential combined implication of both duration and number of BST on frailty, a composi-

tional score from the number of and minutes spent in breaks of sedentary time was created

(BST-COMP). Previous work has demonstrated that BST is associated with physical func-

tion and disability in older adults [18, 19]. Our empirical work extends and confirms the

hypothesis that interrupting ST has the potential of enhancing the wider health of individu-

als by demonstrating that not only the number of BST but also duration of those BST may

have an impact on frailty status among older individuals. Collectively, the results from

our study support and extend to frailty the inactivity physiology hypothesis. Future experi-

mental research is warranted to clarify the potential mechanisms underpinning these

associations.

From a public health perspective, reducing sedentary behaviour and engaging in light

physical activity, for instance, by inserting short bouts of activity into otherwise sedentary

periods, may be a more feasible and less challenging approach for older adults than taking

part in more strenuous activities in order to promote health [29]. Our findings reveal that

having fewer breaks in sedentary periods was associated with higher frailty level among the

study sample. The reverse is also true. This is of interest, as only a minor proportion of older

adults meet the WHO PA recommendations (30% in our sample). Therefore, while efforts

on MVPA promotion should be sustained, guidelines for older adults should also reinforce

the idea of breaking up ST more often in order to prevent frailty among this population

group.

Key strengths of the study include the relatively large sample, the objective measures of sed-

entary behaviour patterns, and the inclusion of a novel analytical approach by deriving new

variables that reflects more accurately sedentary behaviour patterns and therefore provide

unique knowledge in the field with potential clinical relevance. The cross-sectional nature of

the research design used does not allow definitive conclusions to be drawn around the causal

relationship between the variables of the study. There are some inherent issues with sedentary

behaviour patterns being derived from accelerometers, such as the use of<100 counts/minute

as a threshold to determine sedentary activities [30] or the use of 1-min epoch length that may

impact the generalization of the results [31]. Despite these limitations, our findings contribute

to the current literature and ongoing discussion on the impact of sedentary behaviour on

frailty among older adults. More research is warranted around the potential effects of activity

insertion of different intensities to prevent frailty in this population group. Moreover, longitu-

dinal experimental designs are necessary to overcome some of the research-design inherent

limitations of this study and confirm the results showed here.

In conclusion, sedentary time, time spent in more active behaviour, and daily proportion of

time spent in sedentary behaviour bouts of 10 minutes or more are associated with frailty in

older people in the TSHA study. Altogether, these results may point to the pathways through

which engaging in frequent, short bouts of activity insertions into otherwise sedentary periods

can attenuate frailty among older adults. Our results suggest that interventions should there-

fore not only be focused on reducing the time spent in sedentary activities but also on how

that time is accrued in order to prevent frailty in older people. However, longitudinal, experi-

mental research, preferably in form of RCT, is required to confirm the causality of the relation-

ships observed in the current study.



Author ContributionsConceptualization: Borja del Pozo-Cruz, Asier Mañas, Ignacio Ara.

Formal analysis: Borja del Pozo-Cruz, Asier Mañas, Leocadio Rodriguez-Mañas, Ignacio Ara.

Funding acquisition: Francisco J. Garcı́a-Garcı́a, Leocadio Rodriguez-Mañas.

Methodology:Marı́a Martı́n-Garcı́a, Leocadio Rodriguez-Mañas.

Resources:Marı́a Martı́n-Garcı́a, Jorge Marı́n-Puyalto, Francisco J. Garcı́a-Garcı́a.

Software: Jorge Marı́n-Puyalto.

Supervision: Amelia Guadalupe-Grau, Ignacio Ara.

Writing – original draft: Borja del Pozo-Cruz, Jorge Marı́n-Puyalto, Francisco J. Garcı́a-

Garcı́a.

Writing – review & editing: Borja del Pozo-Cruz, Asier Mañas, Marı́a Martı́n-Garcı́a, Leoca-

dio Rodriguez-Mañas, Amelia Guadalupe-Grau, Ignacio Ara.

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sedentary behaviour in physical performance, frailty and mortality among older adults: A short system-atic review. Eur J Sport Sci. 2017; 17(7):940–53. https://doi.org/10.1080/17461391.2017.1327983PMID: 28532299

2. Fried LP, Tangen CM,Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evi-dence for a phenotype. The journals of gerontology Series A, Biological sciences andmedical sciences.2001; 56(3):M146–56. PMID: 11253156

3. Walston J, Hadley EC, Ferrucci L, Guralnik JM, Newman AB, Studenski SA, et al. Research agenda forfrailty in older adults: toward a better understanding of physiology and etiology: summary from theAmerican Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults.Journal of the American Geriatrics Society. 2006; 54(6):991–1001. https://doi.org/10.1111/j.1532-5415.2006.00745.x PMID: 16776798

4. Garcia-Garcia FJ, Gutierrez Avila G, Alfaro-Acha A, Amor Andres MS, De Los Angeles De La TorreLanza M, Escribano Aparicio MV, et al. The prevalence of frailty syndrome in an older population fromSpain. The Toledo Study for Healthy Aging. The journal of nutrition, health & aging. 2011; 15(10):852–6.

5. Sirven N, Rapp T. The cost of frailty in France. The European journal of health economics: HEPAC:health economics in prevention and care. 2017; 18(2):243–53. https://doi.org/10.1007/s10198-016-0772-7 PMID: 26914932

6. Cawthon PM, Marshall LM, Michael Y, Dam TT, Ensrud KE, Barrett-Connor E, et al. Frailty in oldermen: prevalence, progression, and relationship with mortality. Journal of the American Geriatrics Soci-ety. 2007; 55(8):1216–23. https://doi.org/10.1111/j.1532-5415.2007.01259.x PMID: 17661960

7. Ensrud KE, Ewing SK, Cawthon PM, Fink HA, Taylor BC, Cauley JA, et al. A comparison of frailtyindexes for the prediction of falls, disability, fractures, and mortality in older men. Journal of the Ameri-can Geriatrics Society. 2009; 57(3):492–8. https://doi.org/10.1111/j.1532-5415.2009.02137.x PMID:19245414

8. Daniels R, van Rossum E, deWitte L, Kempen GI, van den Heuvel W. Interventions to prevent disabilityin frail community-dwelling elderly: a systematic review. BMC health services research. 2008; 8:278.https://doi.org/10.1186/1472-6963-8-278 PMID: 19115992

9. Ford ES, Caspersen CJ. Sedentary behaviour and cardiovascular disease: a review of prospectivestudies. International journal of epidemiology. 2012; 41(5):1338–53. https://doi.org/10.1093/ije/dys078PMID: 22634869

10. Stamatakis E, Hamer M. Sedentary behaviour: redefining its meaning and links to chronic disease. Br JHosp Med (Lond). 2011; 72(4):192–5.

11. Gomez-Cabello A, Pedrero-Chamizo R, Olivares PR, Hernandez-Perera R, Rodriguez-Marroyo JA,Mata E, et al. Sitting time increases the overweight and obesity risk independently of walking time inelderly people from Spain. Maturitas. 2012; 73(4):337–43. https://doi.org/10.1016/j.maturitas.2012.09.001 PMID: 23021800



12. Katzmarzyk PT, Church TS, Craig CL, Bouchard C. Sitting time and mortality from all causes, cardio-vascular disease, and cancer. Medicine and science in sports and exercise. 2009; 41(5):998–1005.https://doi.org/10.1249/MSS.0b013e3181930355 PMID: 19346988

13. Tremblay MS, Colley RC, Saunders TJ, Healy GN, Owen N. Physiological and health implications of asedentary lifestyle. Applied Physiology Nutrition and Metabolism-Physiologie Appliquee Nutrition EtMetabolisme. 2010; 35(6):725–40.

14. Blair SN, Sallis RE, Hutber A, Archer E. Exercise therapy—the public health message. ScandinavianJournal of Medicine & Science in Sports. 2012; 22(4):E24–E8.

15. DASCR, DEQ BM, Oliveira DS, DASMMC, Carneiro JA, Pereira R, et al. Cross-sectional relationshipsbetween sedentary behavior and frailty in older adults. The Journal of sports medicine and physical fit-ness. 2016.

16. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. The association between sedentary behav-iour, moderate-vigorous physical activity and frailty in NHANES cohorts. Maturitas. 2015; 80(2):187–91.https://doi.org/10.1016/j.maturitas.2014.11.010 PMID: 25542406

17. Jefferis BJ, Sartini C, Shiroma E, Whincup PH, Wannamethee SG, Lee IM. Duration and breaks in sed-entary behaviour: accelerometer data from 1566 community-dwelling older men (British Regional HeartStudy). British journal of sports medicine. 2014.

18. Sardinha LB, Santos DA, Silva AM, Baptista F, Owen N. Breaking-up sedentary time is associated withphysical function in older adults. The journals of gerontology Series A, Biological sciences and medicalsciences. 2015; 70(1):119–24. https://doi.org/10.1093/gerona/glu193 PMID: 25324221

19. Sardinha LB, Ekelund U, dos Santos L, Cyrino ES, Silva AM, Santos DA. Breaking-up sedentary time isassociated with impairment in activities of daily living. Exp Gerontol. 2015; 72:278-. https://doi.org/10.1016/j.exger.2015.10.010 PMID: 26514290

20. Guadalupe-Grau A, Carnicero JA, Gomez-Cabello A, Gutierrez Avila G, Humanes S, Alegre LM, et al.Association of regional muscle strength with mortality and hospitalisation in older people. Age and age-ing. 2015; 44(5):790–5. https://doi.org/10.1093/ageing/afv080 PMID: 26163682

21. Garcia-Garcia FJ, Carcaillon L, Fernandez-Tresguerres J, Alfaro A, Larrion JL, Castillo C, et al. A NewOperational Definition of Frailty: The Frailty Trait Scale. J AmMed Dir Assoc. 2014; 15(5).

22. Colley R, Connor Gorber S, Tremblay MS. Quality control and data reduction procedures for accelero-metry-derived measures of physical activity. Health reports. 2010; 21(1):63–9. PMID: 20426228

23. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. acceler-ometer. Med Sci Sports Exerc. 1998; 30(5):777–81. PMID: 9588623

24. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle NystromC, Mora-Gonzalez J, Lof M, et al. Accel-erometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: ASystematic Review and Practical Considerations. Sports Med. 2017.

25. In: Global Recommendations on Physical Activity for Health. WHOGuidelines Approved by the Guide-lines Review Committee. Geneva2010.

26. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitivestate of patients for the clinician. Journal of psychiatric research. 1975; 12(3):189–98. PMID: 1202204

27. Katz JN, Chang LC, Sangha O, Fossel AH, Bates DW. Can comorbidity be measured by questionnairerather than medical record review?Medical care. 1996; 34(1):73–84. PMID: 8551813

28. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical per-formance battery assessing lower extremity function: association with self-reported disability and pre-diction of mortality and nursing home admission. J Gerontol. 1994; 49(2):M85–94. PMID: 8126356

29. BumanMP, Hekler EB, Haskell WL, Pruitt L, Conway TL, Cain KL, et al. Objective light-intensity physi-cal activity associations with rated health in older adults. Am J Epidemiol. 2010; 172(10):1155–65.https://doi.org/10.1093/aje/kwq249 PMID: 20843864

30. Chastin SF, Egerton T, Leask C, Stamatakis E. Meta-analysis of the relationship between breaks insedentary behavior and cardiometabolic health. Obesity. 2015; 23(9):1800–10. https://doi.org/10.1002/oby.21180 PMID: 26308477

31. Aguilar-Farias N, BrownWJ, Peeters GM. ActiGraph GT3X+ cut-points for identifying sedentary behav-iour in older adults in free-living environments. Journal of science and medicine in sport / Sports Medi-cine Australia. 2014; 17(3):293–9.



RESULTS

184

RESULTS

185

5.3. STUDY 3

“Reallocating Accelerometer-

Assessed Sedentary Time to Light or

Moderate- to Vigorous-Intensity

Physical Activity Reduces Frailty

Levels in Older Adults: An

Isotemporal Substitution Approach

in the TSHA Study”

RESULTS

186

Original Study

Reallocating Accelerometer-Assessed Sedentary Time to Light orModerate- to Vigorous-Intensity Physical Activity Reduces FrailtyLevels in Older Adults: An Isotemporal Substitution Approach in theTSHA Study

Asier Mañas MSc a,b, Borja del Pozo-Cruz PhD c, Amelia Guadalupe-Grau PhD b,d,Jorge Marín-Puyalto MSc e, Ana Alfaro-Acha PhD, MDb,f,Leocadio Rodríguez-Mañas PhD, MDb,g, Francisco J. García-García PhD, MDb,f,Ignacio Ara PhD a,b,*aGENUD Toledo Research Group, University of Castilla-La Mancha, Toledo, Castilla-La Mancha, SpainbCIBER of Frailty and Healthy Aging (CIBERFES), Madrid, Comunidad de Madrid, SpaincDepartment of Exercise Sciences, University of Auckland, Auckland, New Zealandd ImFINE Research Group, Department of Health and Human Performance, Technical University of Madrid, Madrid, SpaineGENUD Research Group, Faculty of Health and Sport Sciences, University of Zaragoza, Huesca, Aragón, SpainfGeriatric Department, Hospital Virgen del Valle, Toledo, Castilla-La Mancha, SpaingGeriatric Department, Hospital Universitario de Getafe, Getafe, Comunidad de Madrid, Spain

Keywords:Accelerometrysedentary behaviorelderlyagingexercisecomorbidity

a b s t r a c t

Introduction: The effects of replacing sedentary time with light or moderate- to vigorous-intensityphysical activity on frailty are not well known.Aim: To examine the mutually independent associations of sedentary time (ST), light-intensity physicalactivity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with frailty status in olderadults.Methods: A total of 628 people aged �65 years from the Toledo Study of Healthy Aging (TSHA) partici-pated in this cross-sectional study. Frailty was measured using the Frailty Trait Scale. Hip-worn accel-erometers were used to capture objective measurements of ST, LPA, and MVPA. Linear regression andisotemporal substitution analyses were used to examine associations of ST, LPA, and MVPA with frailtystatus. Analyses were also stratified by comorbidity.Results: In single and partition models, LPA and MVPA were negatively associated with frailty. Time insedentary behavior was not associated with frailty in these models. In the isotemporal substitutionmodels, replacing 30 minutes/d of ST with MVPA was associated with a decrease in frailty [b �2.460;95% confidence interval (CI): �3.782, �1.139]. In contrast, replacing ST with LPA was not associatedwith favorable effects on this outcome. However, when the models were stratified by comorbidity,replacing ST with MVPA had the greatest effect on frailty in both the comorbidity (b �2.556; 95% CI:�4.451, �0.661) and the no comorbidity group (b �2.535; 95% CI: �4.343, �0.726). Moreover, thefavorable effects of LPA in people with comorbidities was found when replacing 30 minutes/d of STwith LPA (b �0.568; 95% CI: �1.050, �0.086).Conclusions: Substituting ST with MVPA is associated with theoretical positive effects on frailty. Peoplewith comorbidity may also benefit from replacing ST with LPA, which may have important clinical im-plications in order to decrease the levels of physical frailty.

� 2017 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

The authors declare no conflicts of interest.This work was supported by the Biomedical Research Networking Center on

Frailty and Healthy Aging (CIBERFES) and FEDER funds from the European Union(CB16/10/00477). It was further funded by grants from the Government of Castilla-La Mancha (PI2010/020; Institute of Health Sciences, Ministry of Health of Castilla-La Mancha, 03031-00), Spanish Government (Spanish Ministry of Economy,“Ministerio de Economía y Competitividad,” Instituto de Salud Carlos III, PI10/

01532, PI031558, PI11/01068), and by European Grants (Seventh FrameworkProgramme: FRAILOMIC). A.M. has received a PhD Grant from the Universidad deCastilla-La Mancha (2015/4062).* Address correspondence to Ignacio Ara, PhD, GENUD Toledo Research Group,

University of Castilla-La Mancha, Avda. Carlos III s/n, Toledo 45071, Spain.E-mail address: [email protected] (I. Ara).

JAMDA

journal homepage: www.jamda.com

https://doi.org/10.1016/j.jamda.2017.11.0031525-8610/� 2017 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

JAMDA 19 (2018) 185.e1e185.e6

According to Clegg et al,1 one of the most problematic manifesta-tions of population ageing is the clinical condition of frailty. In Spain, 1in 2 people over 65 years are prefrail, and there is a frailty prevalenceof 27.3%, the highest of the 10 European countries tested in the studyof Santos-Eggimann et al.2 Frailty is a multifaceted condition thatcoincides with a decreased functional reserve capacity in differentorgan systems. Frailty leads to a number of adverse health outcomes,including disability, falls, hospitalization, and death.1 Frailty syndromeincreases the need of medical and social care of patients and, there-fore, increases health careederived costs. Consequently, the preven-tion and reduction of frailty is one of the most important challengesthat public health authorities face in ageing societies.3

Lifestyle is considered one of the keystones in the development offrailty,4 and increasing physical activity (PA) has been suggested as afundamental strategy to prevent the onset, perpetuation, and pro-gression of this syndrome.5 According to the World Health Organi-zation,6 all older adults over 65 years should accumulate at least150 minutes of moderate-intensity aerobic PA, or at least 75 minutesof vigorous-intensity aerobic PA, or an equivalent combination of bothintensities in 10-minute bouts in order to achieve the health-enhancing benefits of physical activity. However, relatively fewadults meet the physical activity guidelines, and the odds of doing sodrop as a person ages. Older adults are the most inactive age group,spending 8.7 minutes/d for males and 5.4 minutes/d for females inobjectively measured moderate- to vigorous-intensity physical activ-ity (MVPA).7

The negative consequences of sedentary behavior, characterizedby very low energy expenditure (eg, sitting or reclining posture),8 onhealth have been recently acknowledged.9,10 Studies in the UnitedStates and Europe report that older adults spend between 60% and80% of their awake time in sedentary behavior, which represents 8to 12 hours per day.11,12 It has been previously suggested thatsedentary time (ST) is associated with frailty in the elderly13e15 in-dependent of MVPA,16,17 but in recent international research con-cerning sedentary time it is necessary to include health resultsrelevant to the geriatric population with the quantification of thedose-response relationship.18

Recently, light-intensity PA (LPA) has been suggested to improvevarious health outcomes.19 LPA may be more appealing and feasiblefor currently inactive populations. This is relevant among older adults,where MVPA guidelines are generally not met. Previous research hasdemonstrated that in inactive older adults and older adults withcomorbidities (which tend to be less active and more frail), LPA isassociated with better cardiometabolic19,20 and mortality outcomes.21

Whether LPA may reduce the frailty level among older adults withcomorbidities is still unknown.

In addition, the isotemporal substitution approach,22 which hasbeen recently developed, assumes that activity time in a day is finiteand that performing one activity involves substitution for another.Depending on the kind of activity that is replaced, the effects on healthmay be varied. Despite the recently acknowledged impact of seden-tary behaviors,13,15 the potential benefits of replacing sedentary be-haviors with LPA or MVPA on frailty among older adults are largelyunknown. A deeper understanding on how ST and PA interact withfrailty in older adults is highly relevant to public health professionalsfor identifying research-informed strategies for prevention andmanagement of frailty in this population group.

Therefore, the aim of this study was to use the isotemporal sub-stitution technique to investigate the displacement effect of replacingST with LPA and MVPA on frailty status among older adults in theToledo Study for Healthy Aging (TSHA). We hypothesized thatreplacing ST with MVPA will produce reductions in frailty in botholder adults with and without comorbidities and that LPA will only bebeneficial in older adults with comorbidities.

Methods

Study Design and Participants

This cross-sectional investigation used baseline data from theTSHA. The complete methodology of the TSHA has been reportedelsewhere.23,24 Briefly, the TSHA is a population prospective cohortstudy aimed at studying the determinants and consequences of frailtyin institutionalized and community-dwelling individuals older than65 years living in the province of Toledo, Spain. Data were collected in3 stages. In the first stage, 6 psychologists conducted computer-assisted interviews face-to-face with potential subjects. In the sec-ond stage, 3 nurses performed a physical examination followed byclinical and performance tests at the subject’s home. In the third stage,the participants went to their health center to provide a blood samplewhile fasting. At this stage, participants were invited to wear anaccelerometer for a week. Only those who agreed to and wore theaccelerometer were included in the study (n ¼ 628). Data werecollected from July 2012 until June 2014. Signed informed consent wasobtained from all participants. The study was approved by the ClinicalResearch Ethics Committee of the Toledo Hospital.

Measurements

Frailty statusThe Frailty Trait Scale (FTS)25 was used to assess frailty in this

study. The FTS includes 7 aspects: energy balance and nutrition, ac-tivity, nervous system, vascular system, weakness, endurance, andslowness. These domains become operational through 12 items. Eachitem score represents a biological trait. Each item ranges from 0 (thebest) to 4 (theworst) except in the “chair test”where the range is from0 to 5 points because of the necessity of scoring those unable to standa single time. When appropriate, items are analyzed according to theitem’s quintile distribution in the population.

To be included in the study, the participants had to overcome atleast 75% (9 of the 12) of the items included in the FTS. The total scorewas calculated by adding all the scores in each item divided by totalscore for each individual and multiplying by 100, standardizing themeasure to a range from 0 (best score) to 100 (worst score), accordingto the formula Total score ¼ (S items score/total score possible byindividual) � 100.

Physical activity and sedentary behavior assessmentPhysical activity and sedentary behavior were objectively assessed

by accelerometry (ActiTrainer; ActiGraph, LLC, Fort Walton Beach, FL).All participants were instructed how to wear an accelerometer on theleft hip during waking hours. Participants wore the accelerometer for7 consecutive days and removed it during any bathing or swimmingactivities. The ActiTrainer device was initialized to collect data using1-minute epochs. Nonwear time was defined as periods of at least 60consecutive minutes of zero counts, with allowance for 2 minutes ofcounts between zero and 100.26 The study included the results fromparticipants with at least 4 valid days with at least 480 minutes(8 hours) of wear time without excessive counts (ie, >20,000 counts).Accelerometer counts were used to derive the time spent in each in-tensity band: sedentary behavior (<100 counts/min), light-intensityphysical activity (100-1951 counts/min), and moderate- to vigorous-intensity physical activity (�1952 counts/min).27 Although there is alack of consensus on the use of cut-off points to classify the intensity ofthe activity, the cut-off points used in this study are the mostcommonly reported in this population group28; this makes our resultscomparable to other studies. Minutes spent in each of these 3 be-haviors were tallied per day and averaged over all available valid days,expressed as proportions of 24 hours.

A. Mañas et al. / JAMDA 19 (2018) 185.e1e185.e6185.e2

Anthropometrics and confounding variablesParticipants self-reported their age, sex, ethnicity, and educational

status. Height and weight were measured using standard procedures.Body mass index was then calculated as weight (in kilograms)divided by height-squared (in meters). Waist and hip circumferencewere measured using standard procedures.29 Waist-to-hip ratio wasthen calculated by dividing waist circumference (cm) by hipcircumference (cm).

The Charlson Comorbidity Index was used to account for comor-bidity status of participants in the study.30 Diseases included in thisIndex and their weighting are myocardial infarction, congestive heartfailure, peripheral vascular disease, dementia, cerebrovascular dis-ease, chronic lung disease, connective tissue disease, ulcer, chronic

liver disease, diabetes (weight 1); hemiplegia, moderate or severekidney disease, diabetes with complication, tumor, leukemia, lym-phoma (weight 2); moderate or severe liver disease (weight 3);metastatic solid tumor, and AIDS (weight 6). We also assessedobjective cognitive function using the Mini-Mental State Examina-tion.31 Finally, the number of prescription and nonprescription drugswithin the Anatomical Therapeutic Chemical (ATC) ClassificationSystem taken by the participant was calculated.32

The Short Physical Performance Battery (SPPB) was used to assessphysical function in this study.33 The SPPBmeasures gait speed (8-footwalk), standing balance, and lower extremity strength and endurance(chair rise task). The tests were performed and scored as described inthe original protocol.33

Statistical Analysis

All analyses were performed using the statistical software SPSS,version 24.0 (IBM Corp, Armonk, NY). Mean (standard deviation) andfrequency (percentage) were provided for continuous and categoricalvariables, respectively. Descriptive variables were compared betweenincluded and excluded participants with an independent t test or chi-square test for continuous and categorical variables, respectively.Linear regression models were used to examine associations betweentime spent (minutes/d) in sedentary behavior, LPA, andMVPAwith thescore in the FTS. Models were adjusted for prespecified covariateshypothesized to be independently associated with both exposure andoutcome variables, including sex, age, educational status, poly-pharmacy status, functional fitness (SPPB), waist-to-hip ratio,comorbidity status (Charlson Index), and cognitive function (Mini-Mental State Examination). Variance inflation factor was calculated toquantify the severity of multicollinearity in the regression analyses. Allvariance inflation factors were below 10. The Condition Number andDurbin-Watson statistic were also analyzed. When performing sub-group analysis by comorbidity status, the Charlson Indexwas removedfrom the covariates. Subjects without comorbidity were those whoscored 0 and subjects with comorbidity scored 1 or higher on theCharlson Index. Three different linear regression models were used.34

The first set of models are single-factor, examining the association ofeach intensity category (ST, LPA, andMVPA) with frailty status withoutmutual adjustment for other activity categories. The second arepartition models examining the association of each intensity categorywhile controlling for each of the other categories of activity. The thirdare isotemporal substitution models that represent the estimated ef-fects of substituting ST with an equal amount of time spent in LPA or inMVPA. In this model, ST was excluded whereas total wear time waskept constant in the equation.22 For ease of interpretation, 30-minute

Table 1Comparison of Characteristics in Those Included and Excluded by AccelerometryFrom the Study

Included Sample(n ¼ 519)

Excluded Sample(n ¼ 65)

P Value

Age, y 78.8 (4.6) 79.0 (4.7) .847Sex, n (%) .311Male 234 (45.1) 27 (41.5)Female 285 (54.9) 38 (58.5)

BMI 30.5 (4.7) 31.1 (5.1) .419WHR 0.90 (0.11) 0.90 (0.08) .991Whole body fat mass (%) 36.7 (7.5) 38.0 (6.6) .216Whole body lean mass (kg) 43.79 (8.23) 43.17 (7.68) .560Education, n (%) .379None 213 (41.0) 31 (47.7)Lower than primary 215 (41.4) 26 (40.0)Completed primary or more 90 (17.3) 8 (12.3)

Number of drugs 5.1 (2.9) 4.8 (2.6) .481FTS (0-100) 37.81 (14.17) 41.45 (14.10) .438SPPB score 8.44 (2.26) 8.40 (2.47) .640MMSE score 23.02 (4.40) 22.83 (3.98) .756Charlson Index 1.13 (1.56) 0.80 (1.25) .118Comorbidity status, n (%) .217With comorbidity 272 (52.6) 36 (55.4)Without comorbidity 245 (47.4) 29 (44.6)

AccelerometryValid minutes per valid dayof wear time, min

780.71 (84.68) d

SB, % 69.48 (11.52) d

LPA, % 28.30 (10.66) d

MVPA, % 2.21 (2.68) d

Meet WHO Guidelines, n (%)No 362 (70.4) d

Yes 152 (29.6) d

BMI, body mass index; MMSE, Mini-Mental State Examination; WHO, World HealthOrganization; WHR, waist-to-hip ratio.Values are means (SD), unless otherwise indicated.

Table 2Single Behavior, Partition, and Isotemporal Substitution Models for Frailty (FTS) in Elderly People (n ¼ 519)

Model Regression Coefficient (95% CI), R2

SB LPA MVPA

Single behaviors (models 1-3) 0.006 (�0.004, 0.016), 0.461 L0.014 (L0.024, �0.004)**, 0.468 L0.094 (�0.136, �0.052)**, 0.481Partition behaviors (models 4-6) �0.004 (�0.017, 0.08), 0.486 �0.012 (�0.025, 0.000)*, 0.486 L0.086 (�0.129, �0.043)**, 0.486Isotemporal substitutiona

Replace SB (models 7 and 8) Dropped �0.237 (�0.573, 0.099), 0.486 L2.460 (�3.782, �1.139)**, 0.486

Bold indicates statistical significance (*P < .05, **P < .01). Italics indicates R2 of the model.Covariates for models included sex, age, educational status, number of drugs, functional fitness (SPPB), waist-to-hip ratio, comorbidity status (Charlson Index), and cognitivefunction (Mini-Mental State Examination).For model 1, SB and covariates were entered in the model.For model 2, LPA and covariates were entered in the model.For model 3, MVPA and covariates were entered in the model.For models 4, 5, and 6, SB, LPA, MVPA, and covariates were entered in the model.For models 7 and 8, LPA, MVPA, and covariates (plus total behavior time) were entered in the model (sedentary behavior dropped).

aPrior to the regression models, all behavior variables were divided by a constant of 30 so that unit increase in the behavior represented an increase of 30 minutes/d withinthe given behavior.

A. Mañas et al. / JAMDA 19 (2018) 185.e1e185.e6 185.e3

units were chosen as time units for each behavior. These models as-sume linear relationships between dependent and independent var-iables, which were determined prior to performing these analyses.

Results

From the initial sample (n¼ 628), data from 519 participants (male,45.1%; mean age 78.8 � 4.6 years) were included in the analysis.Exclusion criteria were missing frailty data (n ¼ 27) or covariates(n ¼ 17), and insufficient accelerometer wear time data (n ¼ 65).Descriptive variables between those who were included in the ana-lyses versus those who were excluded because of insufficient accel-erometer wear time are presented in Table 1. Compared with theincluded sample, excluded participants were not different in anyoutcome. For the included sample, participants spent 17.6 minutes perday of the wear time in MVPA and 224 minutes in LPA on average.Sedentary time accounted for 69.5% of the wear time (ie,540.0 � 93.9 minutes).

Table 2 presents single, partition, and isotemporal substitutionmodels for the associations between specific activity categories andfrailty status after the adjustment for potential confounders.

In single and partition models, LPA and MVPA showed an inverserelationship with the frailty score, but no association was found be-tween ST and frailty. In isotemporal substitution models, replacing30 minutes/d of ST with 30 minutes of MVPA was associated with adecrease in the frailty score [b �2.460; 95% confidence interval(CI): �3.782, �1.139]. However, replacing ST with LPA was not asso-ciated with changes in the frailty score.

Table 3 displays the results of the single, partition, and isotemporalsubstitution models for ST, LPA, and MVPA on frailty status divided bycomorbidity status. In people without comorbidity, only MVPA wasassociated with a decrease in the frailty score, as shown by the singleand partition models. However, for people with comorbidity LPAshowed an inverse relationship with the frailty score, in addition toMVPA. Associations were also observed between increased sedentarytime and increased frailty status in the single model in people withcomorbidity.

Reallocating 30 minutes/d of ST to 30 minutes of LPA or MVPA inpeople with comorbidity resulted in an estimated reduction in the FTS(adjusted b �0.568; 95% CI: �1.050, �0.086; adjusted b �2.556; 95%CI: �4.451, �0.661, respectively). Similarly, replacing 30 minutes/d ofST with 30 minutes of MVPA showed a decrease in the frailty score(adjusted b �2.535; 95% CI: �4.343, �0.726) in people without co-morbidity. However, in people categorized as being without comor-bidities, the reallocation of 30 minutes/d of ST to 30 minutes of LPAwas not associated with significant changes in the frailty score.

Discussion

This study aimed to investigate the relationship between physicalactivity, sedentary behavior, and frailty. We first assessed the asso-ciation between these factors using a classical approach, then using atheoretical model, to examine how the displacement of activity ofdifferent intensities is associated with changes in the frailty scoreusing isotemporal substitution modeling. Our results estimate thatreplacing 30 minutes of ST with an equivalent amount of MVPA isassociated with a more theoretically favorable frailty status in olderadults, regardless of comorbidity or physical function status. Equaltime-exchange of ST with LPA is predicted to reduce frailty but onlyin older adults with comorbidity (52.6% in our subsample). In addi-tion, the modeled relationships also suggest potential benefits of LPAin those with comorbid conditions, which may be a more feasibleand less challenging approach than more strenuous activity.

In our study, we found that replacing sitting time with MVPAresulted in reductions in the Frailty Trait Scale. This is consistent with Ta

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previous findings where MVPA was associated with frailty even aftercontrolling for ST.16 MVPA is well known to affect cognitive andphysical outcomes, all known to impact frailty. Supporting our resultsare the findings of Song et al17 and Peterson et al,35 which show evi-dence that ST negatively impacts frailty in older adults. Likewise,Fanning et al36 only found an improvement in self-regulatory behaviorand executive functioning when 30 minutes of ST was replaced by30 minutes of MVPA time.

We have found in single and partitionmodels that LPA has a role onfrailty. Some authors have found a positive relationship between LPAand different outcomes regarding frailty, whereas others have not.Elkins37 showed that daily time spent in LPA is associated with lowerrisk of onset and progression of disability whereas Lee et al38 found apositive effect of LPA in cognitive status. Jantunen et al39 also showedthat LPA was positively associated with better physical performance.However, Pau et al40 found that LPA is not the most adequate intensityto improve daily static and dynamic motor tasks.

As a novelty, our grouping analysis shows that only in peoplereporting comorbidities did LPA bring benefits in terms of frailty.Other studies have shown the beneficial effects of LPA in the iso-temporal substitution analyses. Ekblom-Bak et al41 showed signifi-cant lower metabolic syndrome prevalence by replacing 10 minutesof ST with the same amount of LPA. Similarly, Fishman et al42 andSchmid et al43 found that replacing 30 minutes of ST with LPA wasassociated with significant reduction in mortality risk. Our findingsshow that in frail individuals, with low fitness even minimal move-ment can positively impact health.44 But when a certain fitness levelis reached, more strenuous activity is needed to elicit more beneficialresults.

Despite requiring between 4 to 5 times more LPA to elicit the samechanges in frailty compared with MVPA, according to our estimates,the benefits of LPA for improving frailty status in those with comor-bidities is of relevance from a public health perspective as might be apopulation, of those with comorbidities, that cannot or do not findopportunities to (frail older adults spend 84.9% of their daily time insedentary behaviors45) become engaged in MVPA successfully. Thus,replacing ST (eg, television viewing, sitting) with LPA (eg, leisurewalking, active transport) may be a more feasible strategy to reducethe risk of frailty in older adults with additional disease. Future lon-gitudinal experimental studies should confirm these results.

This study has several strengths and limitations. The sample in-cludes a relatively large number of community-dwelling older adultswith objectively assessed frailty and physical activity. However, whencomparing the full cohort with the included sample, there were dif-ferences in most outcomes used in this study, so caution should beexercised when interpreting the results. Although there is no estab-lished gold standard to identify frailty, the Frailty Trait Scale, derivedfrom the classical model proposed by Fried et al46 in combinationwiththe positive aspects of the Frailty Index of Rockwood et al,47 has beensuggested as a more sensitive scale for detecting changes in the in-dividual’s biological status.25 The validity of this scale was evaluatedby assessing its associationwith comorbidities, biomarkers associatedwith frailty status, and by comparing its predictive value for adverseevents with the 2 most frequently used frailty scales: Frailty Pheno-type46 and the Frailty Index.47 Although accelerometry has some ad-vantages over questionnaires and other self-report methods,48 it is notexempt from error. Waist-worn accelerometers are not able to detectdifferences between sitting and standing positions, and therefore themeasurement of ST can be overestimated. In addition, the cut-offpoints used in this study and the algorithm chosen to discard zero-value periods can affect the amount of different physical activity in-tensity ranges and sedentary behavior.

Causal inferences are limited towing to the cross-sectional natureof the study. Moreover, isotemporal substitution models represent amathematical way of replacing one behavior with another, so the

results should be interpreted with caution. There is therefore a needfor more experimental research in this area, especially in the clinicalsetting, in order to better understand the impact of replacing ST withactivity of different intensities on frailty status among older adults.Another limitation of the study may be the nonmeasurement of sleep,an important behavior that may affect the associations found in thisstudy.

Conclusions

In conclusion, this study demonstrates that replacing 30 minutes/d of sedentary behavior with the same amount of MVPA could bringbenefits in terms of frailty status among older adults. Participants withcomorbidities may also benefit from the substitution of ST by LPA.From a public health perspective, this is an important message inorder to improve frailty status through increasing LPA, which a prioriseems to be a more feasible approach as opposed to increasing MVPAin this population group. Future research should move beyond thishypothetical, observational evidence and identify more-robust indi-cation of the frailty outcomes of experimentally reallocating timespent in sedentary behaviors with physical activities of differentintensities.49

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RESULTS

193

5.4. STUDY 4

“Dose-response association between

physical activity and sedentary time

categories on ageing biomarkers”

RESULTS

194

RESEARCH ARTICLE Open Access

Dose-response association betweenphysical activity and sedentary timecategories on ageing biomarkersAsier Mañas1,2, Borja del Pozo-Cruz3, Irene Rodríguez-Gómez1,2, Javier Leal-Martín1,2, José Losa-Reyna1,2,4,Leocadio Rodríguez-Mañas2,5, Francisco J. García-García2,4 and Ignacio Ara1,2*

Abstract

Background: Physical activity and sedentary behaviour have been suggested to independently affect a number ofhealth outcomes. To what extent different combinations of physical activity and sedentary behaviour may influencephysical function and frailty outcomes in older adults is unknown. The aim of this study was to examine the combinationof mutually exclusive categories of accelerometer-measured physical activity and sedentary time on physical function andfrailty in older adults.

Methods: 771 older adults (54% women; 76.8 ± 4.9 years) from the Toledo Study for Healthy Aging participated in thiscross-sectional study. Physical activity and sedentary time were measured by accelerometry. Physically active was definedas meeting current aerobic guidelines for older adults proposed by the World Health Organization. Low sedentary wasdefined as residing in the lowest quartile of the light physical activity-to-sedentary time ratio. Participants were thenclassified into one of four mutually exclusive movement patterns: (1) ‘physically active & low sedentary’, (2) ‘physicallyactive & high sedentary’, (3) ‘physically inactive & low sedentary’, and (4) ‘physically inactive & high sedentary’. The ShortPhysical Performance Battery was used to measure physical function and frailty was assessed using the Frailty Trait Scale.

Results: ‘Physically active & low sedentary’ and ‘physically active & high sedentary’ individuals had significantly higherlevels of physical function (β = 1.73 and β = 1.30 respectively; all p < 0.001) and lower frailty (β = − 13.96 and β = − 8.71respectively; all p < 0.001) compared to ‘physically inactive & high sedentary’ participants. Likewise, ‘physically inactive &low sedentary’ group had significantly lower frailty (β = − 2.50; p = 0.05), but significance was not reached for physicalfunction.

Conclusions: We found a dose-response association of the different movement patterns analysed in this study withphysical function and frailty. Meeting the physical activity guidelines was associated with the most beneficial physicalfunction and frailty profiles in our sample. Among inactive people, more light intensity relative to sedentary time wasassociated with better frailty status. These results point out to the possibility of stepwise interventions (i.e. targeting lessstrenuous activities) to promote successful aging, particularly in inactive older adults.

Keywords: Exercise, Ageing, Functioning and disability, Health behaviour, Lifestyle

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence: [email protected] Toledo Research Group, Universidad de Castilla-La Mancha, Avda.Carlos III s/n, 45071 Toledo, Spain2CIBER of Frailty and Healthy Aging (CIBERFES), Madrid, SpainFull list of author information is available at the end of the article

Mañas et al. BMC Geriatrics (2019) 19:270 https://doi.org/10.1186/s12877-019-1284-y

BackgroundThere is compiling evidence showing the benefits of regu-lar physical activity to improve physical functioning andreduce frailty among the elderly [1, 2]. Consequently,physical activity, particularly more strenuous activity isnow routinely recommended in the clinical managementof frailty [2, 3]. Blodgett et al. [4] and Manas et al. [5] haveshown that moderate-to-vigorous physical activity(MVPA) is inversely associated with frailty and adversehealth outcomes in middle-age (≥50 years) and olderadults (≥65 years), respectively. However, few older adultsmeet the physical activity recommendations (i.e., 150minof moderate intensity aerobic activity, 75min of vigorousintensity aerobic activity, or an equivalent combination, in10-min bouts [6]). In fact, previous research has foundthat older adults spend between 8 and 12 h of their wakingday sedentary [7]. Sedentary behaviours, such as TV view-ing, motorized transport, or leisure-time sitting, have beenshown to contribute to adverse health outcomes in olderpeople, including lower levels of physical functioning andhigher levels of frailty [4, 8–10].Nevertheless, we are far from a complete understand-

ing of the inter-relationships between MVPA and seden-tary behaviours and the role they may play on preservingphysical function and reducing frailty levels among olderadults. Several studies have shown that some people canmeet the physical activity recommendations and yet dis-play high levels of sedentary behaviours. The reversecould also be true. Thus, different combinations of be-haviours (i.e. ‘physically active and low sedentary’, ‘phys-ically active and high sedentary’, ‘physically inactive andlow sedentary’, and ‘physically inactive and high seden-tary’) are plausible during waking times. Potentially,these distinct combinations of behaviours may lead to agradient of health consequences [11, 12]. For example,Bakrania et al. [11] found that physically active adults,even those who spent much of their time on sedentarybehaviours, had better cardiometabolic health than thosewho were inactive. It was also suggested that those indi-viduals with lower sedentary status in the absence ofmeeting the physical activity recommendations had bet-ter cardiometabolic health profile compared to thosewith higher sedentary status and that were physically in-active (i.e. did not meet the physical activity guidelines).This dose-response pattern has also been confirmedfor biological markers and mortality in previousstudies [12, 13].Appreciation of potential physical function and frailty

consequences that different combinations of mutuallyexclusive waking behaviours may have among olderadults will be advantageous to target successful publichealth interventions. For instance, increasing light inten-sity physical activity could be a feasible approach to im-prove the physical functioning and reduce the level of

frailty of older adults categorize as inactive and high sed-entary. Further, if a dose-response exists between thedifferent movement behaviour patterns and physicalfunctioning/frailty in older individuals (i.e., if more activepatterns of behaviour are associated with better healthprofiles), a gradual range of stepwise interventions canbe proposed. For example, if someone is sitting in thelowest movement category (i.e., inactive, high sedentary),we could focus on an intervention that targets sedentarybehaviour first to move that particular person from in-active, high sedentary to inactive, low sedentary. Thereare, however, no existing studies analysing the associa-tions between mutually exclusive categories of physicalactivity and sedentary time with physical function andfrailty in older adults. Therefore, the purpose of thisstudy was to examine the combination of mutually ex-clusive categories of accelerometer-measured physicalactivity and sedentary time on physical function andfrailty in a community-dwelling sample of older adults.

MethodsStudy design and participantsThe current study included a sample of 871 community-dwelling older adults (416 women) from wave 2 (2012 to2014) and wave 3 (2015 to 2017) of the Toledo Study forHealthy Aging (TSHA) [14]. The methodology of theTSHA study has been described in detail elsewhere [5,15]. Briefly, the TSHA is a population-based prospectivecohort study originally conceived to explore the determi-nants and consequences of ageing and frailty in olderadults from Toledo, Spain. All participants gave theirwritten informed consent prior enrolment. All proce-dures were approved by the Clinical Research EthicsCommittee of the Toledo Hospital and were conductedin accordance with the Declaration of Helsinki for hu-man studies.

MeasurementsFrailty statusFrailty was assessed by means of the Frailty Trait Scale(FTS) [16]. The FTS includes 7 domains calculated from12 items including energy balance and nutrition,assessed using the body mass index, central obesity(waist circumference), unintentional weight loss andserum albumin levels; activity levels, assessed using thetotal score of the Physical Activity Scale for the Elderly[17]; the nervous system performance, evaluated basedon was verbal fluency (estimated by asking the partici-pants to give names of animals during one minute [18])and balance (Romberg test [19]); the vascular system,measured by the brachial-ankle index done with Dopplerultrasound [20]; weakness, estimated with the gripstrength in the dominant arm and the knee extensionstrength [14]; endurance, assessed by the chair stand

Mañas et al. BMC Geriatrics (2019) 19:270 Page 2 of 9

test, which measures the number of times that a personstands up in 30 s [21]; and slowness, estimated by calcu-lating the time to walk 3m at a “normal pace” accordingto a standard protocol [19]. Scoring is detailed elsewhere[16]. The Total FTS score ranged from 0 (less frailty) to100 (more frailty).

Physical FunctionThe Short Physical Performance Battery (SPPB) wasused to assess physical function in this study [19]. Previ-ous studies have shown that low scores on the SPPBhave a high predictive value for a wide range of healthconsequences comprising disability [22], hospitalization[23], and death [24].The SPPB measures gait speed (8-ft walk), standing

balance, and lower extremity strength and endurance(chair rise task). A maximum of 4 points each for thebalance, chair stand, and gait speed tests may beawarded, for a score between 0 and 12 (best), in whichonly integers are allowed [19].

Physical activity and sedentary time assessmentPhysical activity and sedentary time were assessed viaaccelerometry (ActiTrainer and ActiGraph wGT3X-BT;ActiGraph, LLC, Pensacola, FL). Participants wereinstructed to wear an accelerometer on the left hip dur-ing waking hours for 7 consecutive days and to removethe accelerometer only before going to bed or for wateractivities [25]. A valid day was defined as having ≥480min (≥8 h) of monitor wear, and the study included theresults from participants with at least four valid days[26, 27]. Accelerometer cut-points for sedentary timewere 0–99 cpm, 100–1951 cpm for light physical activ-ity, 1952–5724 cpm for moderate physical activity,and ≥ 5725 cpm for vigorous physical activity based onpreviously established cut-points [28]. These cut-offvalues have been used in previous analyses from theTSHA [5, 15]. In addition, moderate physical activity,vigorous physical activity and MVPA time accumulatedin bouts of ≥10 min, allowing for a two-minute excep-tion in the intensity threshold, were also derived. Thetotal minutes in each intensity band were averaged overthe number of valid days to estimate the mean timespent in each activity band.

Physical activity and sedentary time categoriesdeterminationWe followed the methods outlined in Bakrania et al. [11]to classify participants in this study into 4 mutually ex-clusive behavioural categories according to their levels ofphysical activity and sedentary behaviour. Based on Bak-rania et al. [11], and other studies [12, 29], the lightphysical activity-to-sedentary time ratio was used toclassify participants in this study as low sedentary if they

resided in the first quartile. Given that most of oursample was expected to be sedentary [7, 15], theremaining participants (i.e. those in quartiles 2, 3, and 4of light physical activity-to-sedentary time ratio) wereclassified as high sedentary. MVPA status was classifiedas ‘physically active’ or ‘physically inactive’ on the basisof whether or not participants met the WHO (WorldHealth Organization) physical activity recommendationsfor older adults [30]. For this, at least one of these threepremises had to be met: accumulate 150 min of moder-ate physical activity per week over periods of at least 10min; accumulate 75 min of vigorous physical activity perweek over periods of at least 10 min, or accumulate 150min per week of an equivalent combination of MVPAover periods of at least 10 min.Based on previous studies [31], four groups of mu-

tually exclusive movement patterns were created: [1]‘physically active and low sedentary’, [2] ‘physicallyactive and high sedentary’, [3] ‘physically inactive andlow sedentary’, and [4] ‘physically inactive and highsedentary’.

Confounding variablesParticipants were asked about their age, sex and ethni-city. Other socio-demographic variables such as educa-tion, income, and marital status were also self-reportedin face-to-face interviews as described elsewhere [15].

Statistical analysisAnalyses were performed using the statistical softwareSPSS version 24.0 (IBM Corp., Armonk, NY). Participantcharacteristics of the full sample, stratified by each cat-egory, were tabulated. Mean (standard deviation) andfrequency (percentage) were provided for continuousand categorical variables, respectively. Ternary plots withthe three behaviours were generated to show the distri-bution of the sample compositions using R statisticalsystem version 3.1.1. To test our hypothesis, a multiplelinear regression analysis with the behavioural categoryas independent variable and frailty or physical functionas dependent variable was fitted. Covariates in the modelincluded: age, sex, education, marital status, and income.The ‘physically inactive and high sedentary’ category wasselected as the reference category.Also, the continuous association between time spent in

sedentary activities as well as MVPA with the outcomes ofinterest in the study were explored via regression. Thesame set of covariates in addition to accelerometer weartime as well as both continuous MVPA time and seden-tary status was used.All analyses were two-sided where p ≤ 0.05 was con-

sidered to be statistically significant.


ResultsDescriptiveOf the 871 eligible subjects, 100 participants had insuffi-cient accelerometer wear time so 771 participants werefinally included (Table 1).

The sample splits across the four different categoriesof movement as follows: [1] ‘physically active and lowsedentary’: n = 38; 4.9%, [2] ‘physically active and highsedentary’: n = 89; 11.5%, [3] ‘physically inactive and lowsedentary’: n = 154; 20.0%, and [4] ‘physically inactive

Table 1 Participant characteristics

Characteristics Sample ‘Physically active& low sedentary’

‘Physically active& high sedentary’

‘Physically inactive& low sedentary’

‘Physically inactive& high sedentary’

N = 771 n = 38; 4.9% n = 89; 11.5% n = 154; 20.0% n = 490; 63.6%

Age (years) a 76.8 (4.9) 74.4 (4.0) 74.8 (3.7) 75.9 (4.5) 77.7 (5.1)

Sex b

Male 355 (46.0) 23 (60.5) 62 (69.7) 50 (32.5) 220 (44.9)

Female 416 (54.0) 15 (39.5) 27 (30.3) 104 (67.5) 270 (55.1)

Education b

None 487 (63.2) 19 (50.0) 46 (51.7) 97 (63.0) 325 (66.3)

Primary school 169 (21.9) 11 (28.9) 26 (29.2) 39 (25.3) 93 (19.0)

Secundary or more 109 (14.1) 8 (21.1) 17 (19.1) 16 (10.4) 68 (13.9)

Missing c 6 (0.8) 0 (0.0) 0 (0.0) 2 (1.3) 4 (0.8)

Income b

Low 369 (47.8) 23 (60.5) 45 (50.6) 66 (42.8) 235 (48.0)

Medium 299 (38.8) 11 (29.0) 33 (37.1) 75 (48.7) 180 (36.7)

High 56 (7.3) 3 (7.9) 6 (6.7) 7 (4.6) 40 (8.1)

Missing c 47 (6.1) 1 (2.6) 5 (5.6) 6 (3.9) 35 (7.1)

Marital status b

Single 42 (5.4) 2 (5.3) 1 (1.1) 9 (5.8) 30 (6.1)

Married 541 (70.2) 28 (73.7) 73 (82.0) 112 (72.7) 328 (66.9)

Widowed 171 (22.2) 7 (18.4) 13 (14.6) 30 (19.5) 121 (24.7)

Divorced/Separated 12 (1.6) 1 (2.6) 2 (2.2) 0 (0.0) 9 (1.8)

Missing c 5 (0.6) 0 (0.0) 0 (0.0) 3 (1.9) 2 (2.4)

Body mass index (kg/m2) a 30.3 (4.8) 26.9 (3.8) 28.8 (3.6) 30.2 (4.4) 30.8 (5.0)

Short physical performance battery (points) a 8.4 (3.2) 10.7 (1.6) 10.2 (2.1) 8.4 (2.9) 7.9 (3.4)

Missing c 6 (0.8) 0 (0.0) 0 (0.0) 1 (0.6) 5 (1.0)

Frailty trait scale (points) a 38 (14.5) 23.6 (11.7) 28.9 (11.8) 37.7 (13.9) 40.9 (13.9)

Missing c 22 (2.9) 0 (0.0) 0 (0.0) 4 (2.6) 18 (3.7)

Accelerometer wear time (min/valid day) a 786.0 (82.6) 810.0 (84.3) 828.9 (80.1) 799.9 (81.5) 772.0 (79.6)

Sedentary time (min/valid day) a 539.9 (90.6) 433.2 (46.7) 557.9 (67.4) 447.0 (65.1) 574.0 (76.1)

Light physical activity (min/valid day) a 226.8 (86.2) 311.6 (50.1) 211.9 (44.0) 337.1 (58.4) 188.2 (64.5)

Moderate-to-vigorous physical activity(min/valid day) a

19.4 (23.8) 65.2 (22.0) 59.1 (23.8) 15.8 (13.8) 9.8 (12.1)

≥ 10-min bouts of moderate-to-vigorousphysical activity (min/day) b

9.6 (17.7) 42.6 (18.2) 42.6 (22.3) 4.3 (6.1) 2.7 (5.1)

Meet WHO guidelines b

Yes 127 (16.5) 38 (100.0) 89 (100.0) 0 (0.0) 0 (0.0)

No 644 (83.5) 0 (0.0) 0 (0.0) 154 (100.0) 490 (100.0)

Light physical activity-to-sedentary time ratio a 0.45 (0.24) 0.72 (0.11) 0.39 (0.09) 0.78 (0.26) 0.34 (0.13)aContinuous variable; Mean (Standard Deviation)bCategorical variable; n (Proportion (%))cMissing data; n (%)


and high sedentary’: n = 490; 63.6%. Ternary plots repre-sent the time spent in each movement behaviour at atime for the different categories (Fig. 1).Compared to ‘physically inactive and high seden-

tary’ participants, ‘physically active and low sedentary’and ‘physically active and high sedentary’ individualshad significantly higher levels of physical functioning(β = 1.73; confidence interval [CI] = 0.77, 2.68; andβ = 1.30; CI = 0.63, 1.98; respectively; p < 0.001) andlower frailty trait (β = − 13.96; CI = − 18.31, − 9.62;and β = − 8.71; CI = − 11.77, − 5.65; respectively; p <0.001). Furthermore, ‘physically inactive and low

sedentary’ group had significantly lower frailty score(β = − 2.50; CI = − 4.98, − 0.03; p < 0.05). However, dif-ferences on physical function between this twogroups were not significant (β = 0.31; CI = − 0.23,0.84; p = 0.26) (Table 2).Increased time spent in MVPA was significantly as-

sociated with higher levels of physical functioning(p < 0.001) and lower frailty trait (p < 0.001). Likewise,a higher light physical activity-to-sedentary time ratiowas significantly associated with higher physical func-tioning score (p = 0.03) and lower frailty trait (p =0.008) (Table 3).

Fig. 1 Ternary plots of the mutually exclusive behavioral categories of time spent in sedentary behavior (SB), light physical activity (LPA) andmoderate-to-vigorous physical activity (MVPA). Low Sedentary: Quartile 1 of the ratio between the average light-intensity physical activity timeand the average sedentary time. High Sedentary: Quartiles 2, 3 or 4 of the ratio between the average light-intensity physical activity time and theaverage sedentary time. Physically Active: ≥150min of moderate-to-vigorous physical activity per week. Physically Inactive: < 150min ofmoderate-to-vigorous physical activity per week. The overlapped heat map represents the distribution of the data points (the more intense thecolor the higher the concentration of data points)


DiscussionThe way in which time packed in a given day remainsrelevant for a wide range of health outcomes [32]. Previ-ous research has identified the cardiometabolic [11] andmortality outcomes [13] of different movement patternsin adults and older adults, respectively. This is the firststudy assessing the associations of mutually exclusivecategories of accelerometer-derived physical activity andsedentary time with physical function and frailty in olderadults. The main findings were that participants who en-gaged in ≥150 min/week of MVPA had more favourablephysical function and frailty profiles than those classifiedin the other movement pattern groups, regardless of sed-entary status. Our results also suggest that engaging inmore light intensity relative to sedentary time may havea positive impact on physical function and frailty statuson the studied population, even in those individualsalready meeting the physical activity guidelines. Thismight provide alternative intervention strategies to im-prove physical function and prevent frailty, as light activ-ities are more feasible than more strenuous activity,particularly among previously inactive individuals.Previous research have demonstrated that MVPA is ef-

fective to prevent, delay or even reverse functional limi-tations and frailty among older adults [33]. The presentstudy provides novel data indicating that older adultswho meet recommended physical activity levels, regard-less of time spent in light-intensity activities relative tosedentary activities, have better physical function levelsand frailty status compared to older adults who do notmeet the required physical activity levels. These results

emphasize the importance of engaging in sufficientMVPA, which could buffer some of the negative conse-quences of sedentary behaviour in preserving the phys-ical functionality and reduce frailty in this populationgroup [34, 35]. A recent meta-analysis involving morethan 1 million adults has shown that engaging in higheramounts of strenuous activity can eliminate the mortal-ity risk associated with too much sitting reported else-where [36]. The association of more intense activity withfitness levels partially explains why meeting the physicalactivity recommendations may overcome the harmful ef-fects of sedentary behaviours. Thus, cardiovascularfitness has been proposed as a plausible mechanism me-diating the relationship between sedentary behaviourand cardiometabolic health in older adults [37]. Morestudies are required to elucidate the role of fitness in therelationship between MVPA, sedentary behaviour, phys-ical functioning and frailty in older adults.Contemporary experimental [38, 39] and observational

[40, 41] evidence emphasizes the health-enhancing roleof light-intensity activities. In a recent meta-analysis byChastin et al. [42], light-intensity physical activityemerged as potentially relevant for cardiometabolichealth and mortality in adults and older adults, inparticular among impaired individuals. Our estimatessuggest that increasing the time in light physical activityrelative to sedentary time has a positive impact on frailtylevels in those considered physically inactive. Otherstudies have suggested the potential benefits of replacingsedentary behaviour with light-intensity physical activityto reduce frailty in older adults with multiple diseases [5].

Table 2 Categorical associations with physical function and frailty (beta coefficients (95% CIs) and corresponding p-values)

Outcome ‘Physically active& low sedentary’

‘Physically active& high sedentary’

‘Physically inactive& low sedentary’

‘Physically inactive& high sedentary’

Beta (95% CI) p-value Beta (95% CI) p-value Beta (95% CI) p-value

Short Physical PerformanceBattery (n = 765)

1.73 (0.77, 2.68) < 0.001 1.30 (0.63, 1.98) < 0.001 0.31 (−0.31, 0.84) 0.263 Reference

Frailty Trait Scale (n = 749) −13.96 (−18.31, −9.62) < 0.001 −8.71 (−11.77, −5.65) < 0.001 −2.50 (−4.98, −0.03) 0.047 Reference

Adjusted linear regression models were fitted for physical function and frailty outcomes. The models were controlled for: age, sex, education, income andmarital statusBold indicates statistical significance at α = 0.05

Table 3 Continuous associations with physical function and frailty (beta coefficients (95% CIs) and corresponding p-values)

Outcome Moderate-to-vigorous physical activity time Light physical activity-to-sedentary behavior ratio

Beta (95% CI) a p-value Beta (95% CI) b p-value

Short Physical PerformanceBattery (n = 765)

0.03 (0.02, 0.04) < 0.001 0.96 (0.09, 1.82) 0.030

Frailty Trait Scale (n = 749) −0.18 (− 0.22, − 0.14) < 0.001 −5.39 (− 9.34, − 1.44) 0.008

Adjusted linear regression models were fitted for physical function and frailty outcomes. The models were controlled for: age, sex, education, income, maritalstatus, moderate-to-vigorous physical activity time, light physical activity-to-sedentary time ratio, and accelerometer wear-time.Bold indicates statistical significance at α = 0.05aBeta coefficients represent a one minute increase in moderate-to-vigorous physical activity time per daybBeta coefficients represent a one unit increase in the light physical activity-to-sedentary behavior ratio


It might be the case that in the more frail and functionallycompromised individuals even small stimulus from lightintensities can benefit their wider health [5]. Collectively,these findings are policy-relevant. Light-intensity physicalactivity is normally naturally embedded into the dailyliving of individuals (e.g. walking a dog, doing homechores or standing up while talking on the phone), there-fore requiring no mental or physical effort or starting levelto perform such activities, and thereby making light-intensity activities a pragmatic target for future public in-terventions to reduce frailty and improve physical functionof older adults, particularly among those inactive (i.e.83.5% in our sample) and that also depict very high levelsof sedentary time (i.e. 63.6% in our sample) which mightalso be the most impaired individuals.Interestingly, we identified the group meeting the

physical activity guidelines (i.e. active) and showinghigher levels of light intensity relative to sedentary timeas the group with better frailty and physical functionprofile in our sample. Others have found similar resultsfor cardiometabolic health [11] and mortality [13]. Re-cent epidemiologic evidence suggests that sitting timehas deleterious cardiovascular and metabolic effects thatare independent of whether or not adults meet the phys-ical activity guidelines [31]. Our results suggest that en-gaging in more light-intensity activity relative tosedentary time beyond meeting the physical activity rec-ommendations can provide with extra benefits in im-proving physical function and reducing frailty in olderadults. Those individuals in our sample meeting thephysical activity guidelines and engaging in more light-intensity activities extend their total volume of physicalactivity as supposed to those that meet the recom-mended amount of physical activity yet are sedentary,which could partially explain the extra benefit associatedto that movement pattern [43]. Thus, promoting light-intensity activities could be a good approach to increasethe total volume of physical activity and reduce seden-tary time in those already meeting the physical activityguidelines, thereby enhancing their health, including in-creasing physical function and improving their frailtyprofile.

Strengths and limitationsThe present study has several strengths. First, the studyincludes a relatively-large sample of community-dwellingolder adults with advanced age. Although there is nocurrent established gold standard to determine physicalfunction and frailty in older adults, the short physicalfunction battery has positioned as one of the most usedtools to objectively evaluate functional performanceamong older adults [44]. Similarly, the Frailty Trait Scalehas been suggested as a more sensitive scale for detect-ing changes in the individual’s biological status than

previously validated frailty instruments [16]. We alsoused accelerometer-measured procedures to assess phys-ical activity and sedentary time.Our study has also limitations. Firstly, the cut-off

points used in the study to categorize the activity inten-sity of participants in the study can lead to a misclassifi-cation of both physical activity and sedentary time.However, the cut-off points used in this study are themost commonly reported in the literature for this agegroup [45], which make the results found here compar-able with other investigations. Furthermore, ActiGraphdevices are not able to discriminate between sitting andstanding changes in the posture [46]. In order to obtainthe activity status, bouts of at least 10 min were used,which may underestimate the time spent in MVPA.Nevertheless, further research is needed to consider theimpact of the bout duration on frailty syndrome. Similarto what Bakrania et al. [11] reported, data could be over-estimating the sedentary time [47], we therefore decidedto use a more conservative approach for the extractionof sedentary status based on the behaviour of our popu-lation, an approach used in previous studies [11].Loprinzi et al. defined low sedentary status as a positivelight physical activity-to-sedentary time ratio [12]. If wehad used the Loprinzi et al. [12] method, only 2.1% ofour population would have been categorized as low sed-entary status. This procedure used may have limitationsand strengths. On the one hand, it is not influenced bythe measurement of the accelerometer, but on the otherhand, because is data-driven, may not be applicable toother populations. The use of this novel approach allowscombining in mutually exclusive categories that bestrepresent the different plausible combinations of phys-ical activity and sedentary time within waking hours.Nonetheless, the cross-sectional nature of the researchdesign used does not allow definitive conclusions to bedrawn around the causal relationship between the out-comes of the study.

ConclusionsWe observed that physically active older adults hadbetter physical function and frailty profiles than thoseconsidered physically inactive, even in the presence ofhigh sedentary time. Higher levels of light-intensityphysical activity relative to sedentary time seems toprovide additional benefits in both physical function andfrailty outcomes among those meeting the physical activ-ity guidelines. Lower sedentary levels were associatedwith decreased frailty in physically inactive participants.Altogether, our findings reinforce the idea of the health-enhancing benefits of meeting the current physical activ-ity guidelines. Also, our results highlight the relevance oflight-intensity physical activity for inactive older adults.If our results remain experimentally true, light intensity


physical activity can be promoted as a middle stepintervention among inactive individuals to achieve therecommended levels of physical activity and improvetheir health. We should move beyond observationalstudies and confirm our results in well-design longitu-dinal, experimental studies.

AbbreviationsFTS: Frailty Trait Scale; MVPA: Moderate-to-Vigorous Physical Activity;SPPB: Short Physical Performance Battery; TSHA: Toledo Study for HealthyAging; WHO: World Health Organization

AcknowledgementsThe authors would like to thank the cohort members, investigators, researchassociates, and team members.

Authors’ contributionsAM: conceptualization, investigation, data curation, formal analysis, writingoriginal draft preparation; BdPC, IRG, JLM & JLR: investigation, data curation,writing/review & editing; LRM, FJGG & IA: funding acquisition, writing/review& editing; AM, BdPC and IA performed the statistical analyses and areguarantors. All the authors read the draft, made contributions and approvedthe final manuscript.

FundingThis work was supported by the Biomedical Research Networking Center onFrailty and Healthy Aging (CIBERFES) and FEDER funds from the EuropeanUnion (CB16/10/00477) and (CB16/10/00456). It was further funded by grantsfrom the Government of Castilla-La Mancha (PI2010/020; Institute of HealthSciences, Ministry of Health of Castilla-La Mancha, 03031–00), Spanish Gov-ernment (Spanish Ministry of Economy, “Ministerio de Economía y Competiti-vidad,” Instituto de Salud Carlos III, PI10/01532, PI031558, PI11/01068), and byEuropean Grants (Seventh Framework Programme: FRAILOMIC). Asier MañasBote and Irene Rodríguez Gómez received a PhD grant from the Universidadde Castilla-La Mancha “Contratos predoctorales para la formación de per-sonal investigador en el marco del Plan Propio de I+D+i, cofinanciados porel Fondo Social Europeo” (2015/4062 and 2014/10340, respectively). Thesefunding bodies played no role in the design of the study, collection, analysis,and interpretation of data or in writing the manuscript.

Availability of data and materialsThere is an established infrastructure, including a website (http://http://www.ciberfes.es/) and a review committee, through which data requests arehandled. The hospital reviews and determines the purposes for the datarequests and what data can be released. Data requests can be sent to:Research and teaching unit, Virgen del Valle Hospital Ctra. Cobisa S/N, 45071Toledo – Spain, [email protected].

Ethics approval and consent to participateAll participants volunteered and signed an informed consent. All theobjectives, physical tests, and aspects related to the methodology wereexplained orally and in writing. Data confidentiality and anonymity wereguaranteed in all phases of the study following the 2009 law regulatingpersonal data protection. The study was approved by the appropriate EthicsCommittee (The Clinical Research Ethics Committee of the Toledo Hospital –June 2012).

Consent for publicationNot applicable.

Competing interestsThe authors declare that they have no competing interests.

Author details1GENUD Toledo Research Group, Universidad de Castilla-La Mancha, Avda.Carlos III s/n, 45071 Toledo, Spain. 2CIBER of Frailty and Healthy Aging(CIBERFES), Madrid, Spain. 3Motivation and Behaviour Research Program,Institute for Positive Psychology and Education, Faculty of Health Sciences,Australian Catholic University, Sydney, Australia. 4Geriatric Department,

Hospital Virgen del Valle, Toledo, Spain. 5Geriatric Department, HospitalUniversitario de Getafe, Getafe, Spain.

Received: 22 January 2019 Accepted: 16 September 2019

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13. Bayán-Bravo A, Pérez-Tasigchana RF, López-García E, Martínez-Gómez D,Rodríguez-Artalejo F, Guallar-Castillón P. The association of major patterns ofphysical activity, sedentary behavior and sleeping with mortality in olderadults. J Sports Sci. 2018:1–10.

14. Garcia-Garcia FJ, Gutierrez Avila G, Alfaro-Acha A, Amor Andres MS, Lanza MDLADLT, Escribano Aparicio MV, et al. The prevalence of frailty syndrome inan older population from Spain. The Toledo study for healthy aging. J NutrHealth Aging. 2011;15(10):852–6.

15. Manas A, Pozo-Cruz BD, Rodriguez-Gomez I, Losa-Reyna J, Rodriguez-Manas L, Garcia-Garcia FJ, et al. Can physical activity offset thedetrimental consequences of sedentary time on frailty? A moderationanalysis in 749 older adults measured with accelerometers. J Am MedDir Assoc. 2019.

16. Garcia-Garcia FJ, Carcaillon L, Fernandez-Tresguerres J, Alfaro A, Larrion JL,Castillo C, et al. A new operational definition of frailty: the Frailty Trait Scale.J Am Med Dir Assoc. 2014;15(5):371.e7–e13.

17. Schuit AJ, Schouten EG, Westerterp KR, Saris WH. Validity of thephysical activity scale for the elderly (PASE): according to energyexpenditure assessed by the doubly labeled water method. J ClinEpidemiol. 1997;50(5):541–6.


18. del Ser Quijano T, Sanchez Sanchez F, Garcia de Yebenes MJ, Otero PuimeA, Zunzunegui MV, Munoz DG. [Spanish version of the 7 Minute screeningneurocognitive battery. Normative data of an elderly population sampleover 70]. Neurologia (Barcelona, Spain). 2004;19(7):344–358.

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20. Fowkes FG, Low LP, Tuta S, Kozak J. Ankle-brachial index and extent ofatherothrombosis in 8891 patients with or at risk of vascular disease: resultsof the international AGATHA study. Eur Heart J. 2006;27(15):1861–7.

21. Jones CJ, Rikli RE, Beam WC. A 30-s chair-stand test as a measure oflower body strength in community-residing older adults. Res Q ExercSport. 1999;70(2):113–9.

22. Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, Ostir GV,et al. Lower extremity function and subsequent disability: consistencyacross studies, predictive models, and value of gait speed alonecompared with the short physical performance battery. J Gerontol ABiol Sci Med Sci. 2000;55(4):M221–31.

23. Penninx BW, Ferrucci L, Leveille SG, Rantanen T, Pahor M, Guralnik JM.Lower extremity performance in nondisabled older persons as apredictor of subsequent hospitalization. J Gerontol A Biol Sci Med Sci.2000;55(11):M691–7.

24. Cesari M, Onder G, Zamboni V, Manini T, Shorr RI, Russo A, et al. Physicalfunction and self-rated health status as predictors of mortality: results fromlongitudinal analysis in the ilSIRENTE study. BMC Geriatr. 2008;8:34.

25. Rodríguez-Gómez I, Mañas A, Losa-Reyna J, Rodríguez-Mañas L, Chastin S,Alegre L, et al. Associations between sedentary time, physical activity andbone health among older people using compositional data analysis. PloSone. 2018;13(10):e0206013-e.

26. Chudyk AM, McAllister MM, Cheung HK, McKay HA, Ashe MC. Are wemissing the sitting? Agreement between accelerometer non-wear timevalidation methods used with older adults' data. Cogent Med. 2017;4:1313505.

27. Rodríguez-Gómez I, Mañas A, Losa-Reyna J, Rodríguez-Mañas L, Chastin SF,Alegre LM, et al. The impact of movement behaviors on bone health inelderly with adequate nutritional status: compositional data analysisdepending on the frailty status. Nutrients. 2019;11(3):582.

28. Freedson PS, Melanson E, Sirard J. Calibration of the computer science andapplications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5):777–81.

29. Kozakova M, Palombo C, Morizzo C, Nolan JJ, Konrad T, Balkau B, et al. Effectof sedentary behaviour and vigorous physical activity on segment-specificcarotid wall thickness and its progression in a healthy population. Eur HeartJ. 2010;31(12):1511–9.

30. World Health Organization. Global Recommendations on Physical Activityfor Health. WHO Press. Geneva: World Health Organization; 2010.

31. Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N. Too littleexercise and too much sitting: inactivity physiology and the need for newrecommendations on sedentary behavior. Current cardiovascular riskreports. 2008;2(4):292–8.

32. Chastin SFM, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effectsof time spent in physical activity, sedentary behaviors and sleep on obesityand cardio-metabolic health markers: a novel compositional data analysisapproach. PLoS One. 2015;10(10):e0139984.

33. Fried LP. Interventions for Human Frailty: Physical Activity as a Model. ColdSpring Harb Perspect Med. 2016;6(6).

34. Santos DA, Silva AM, Baptista F, Santos R, Vale S, Mota J, et al. Sedentarybehavior and physical activity are independently related to functionalfitness in older adults. Exp Gerontol. 2012;47(12):908–12.

35. Trudelle-Jackson E, Jackson AW. Do Older Adults Who Meet 2008 PhysicalActivity Guidelines Have Better Physical Performance Than Those Who DoNot Meet? J Geriatr Phys Ther (2001). 2018;41(3):180–185.

36. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N,Powell KE, et al. Does physical activity attenuate, or even eliminate, thedetrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet(London, England). 2016;388(10051):1302–1310.

37. Pollock RD, Duggal NA, Lazarus NR, Lord JM, Harridge SDR. Cardiorespiratoryfitness not sedentary time or physical activity is associated withcardiometabolic risk in active older adults. Scand J Med Sci Sports. 2018;28(6):1653–60.

38. Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, et al.Breaking up prolonged sitting reduces postprandial glucose and insulinresponses. Diabetes Care. 2012;35(5):976–83.

39. McCarthy M, Edwardson CL, Davies MJ, Henson J, Bodicoat DH, Khunti K,et al. Fitness moderates glycemic responses to sitting and light activitybreaks. Med Sci Sports Exerc. 2017;49(11):2216–22.

40. Buman MP, Hekler EB, Haskell WL, Pruitt L, Conway TL, Cain KL, et al.Objective light-intensity physical activity associations with rated health inolder adults. Am J Epidemiol. 2010;172(10):1155–65.

41. Hamer M, de Oliveira C, Demakakos P. Non-exercise physical activityand survival: English longitudinal study of ageing. Am J Prev Med.2014;47(4):452–60.

42. Chastin SFM, De Craemer M, De Cocker K, Powell L, Van Cauwenberg J, DallP, et al. How does light-intensity physical activity associate with adultcardiometabolic health and mortality? Systematic review with meta-analysisof experimental and observational studies. Br J Sports Med. 2018.

43. Wolff-Hughes DL, Fitzhugh EC, Bassett DR, Churilla JR. Total activity countsand Bouted minutes of moderate-to-vigorous physical activity: relationshipswith Cardiometabolic biomarkers using 2003-2006 NHANES. J Phys ActHealth. 2015;12(5):694–700.

44. Pavasini R, Guralnik J, Brown JC, di Bari M, Cesari M, Landi F, et al. Shortphysical performance battery and all-cause mortality: systematic review andmeta-analysis. BMC Med. 2016;14:215.

45. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nystrom C, Mora-Gonzalez J, Lof M, et al. Accelerometer Data Collection and ProcessingCriteria to Assess Physical Activity and Other Outcomes: A SystematicReview and Practical Considerations. Sports medicine (Auckland, NZ). 2017.

46. An HS, Kim Y, Lee JM. Accuracy of inclinometer functions of theactivPAL and ActiGraph GT3X+: a focus on physical activity. Gait &posture. 2017;51:174–80.

47. Judice PB, Santos DA, Hamilton MT, Sardinha LB, Silva AM. Validity of GT3Xand Actiheart to estimate sedentary time and breaks using ActivPAL as thereference in free-living conditions. Gait & posture. 2015;41(4):917–22.

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RESULTS

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RESULTS

205

5.5. STUDY 5

“Can Physical Activity Offset the

Detrimental Consequences of

Sedentary Time on Frailty? A

Moderation Analysis in 749 Older

Adults Measured With

Accelerometers”

RESULTS

206

Brief Report

Can Physical Activity Offset the Detrimental Consequences ofSedentary Time on Frailty? A Moderation Analysis in 749 OlderAdults Measured With Accelerometers

Asier Mañas MSc a,b, Borja del Pozo-Cruz PhD c, Irene Rodríguez-Gómez MSc a,b,José Losa-Reyna PhD a,b,d, Leocadio Rodríguez-Mañas PhD, MDb,e,Francisco J. García-García PhD, MDb,d, Ignacio Ara PhD a,b,*aGENUD Toledo Research Group, University of Castilla-La Mancha, Toledo, SpainbCIBER of Frailty and Healthy Aging (CIBERFES), Madrid, SpaincMotivation and Behaviour Research Program, Institute for Positive Psychology and Education, Faculty of Health Sciences, Australian CatholicUniversity, Sydney, AustraliadGeriatric Department, Hospital Virgen del Valle, Toledo, SpaineGeriatric Department, Hospital Universitario de Getafe, Getafe, Spain

Keywords:Frailtyexercisesedentary lifestyleagingpublic healthepidemiology

a b s t r a c t

Objectives: To determine whether or not and to what extent the association between sedentary time andfrailty was moderated by moderate-to-vigorous physical activity in older adults.Design: Cross-sectional.Setting: Community-dwelling individuals.Participants: 749 (403 females and 346 males) white older adults.Measurements: Sedentary time and moderate-to-vigorous physical activity were measured with accel-erometers. Frailty was objectively measured using the Frailty Trait Scale. All models were adjusted forage, sex, education, income, marital status, body mass index, moderate-to-vigorous physical activity, andaccelerometer wear time.Results: The regression model reported a significant effect of sedentary time on frailty (P < .05).Nevertheless, the results indicated that moderate-to-vigorous physical activity moderates the relation-ship between frailty status and sedentary time. The Johnson-Neyman technique determined that theestimated moderate-to-vigorous physical activity point was 27.25 minutes/d, fromwhich sedentary timehas no significant effect on frailty.Conclusions:Moderate-to-vigorous physical activity is a moderator in the relationship between sedentarytime and frailty in older adults, offsetting the harmful effects of sedentary behavior with 27 minutes/d ofmoderate-to-vigorous activity. Engaging in moderate-to-vigorous physical activities should be encour-aged. Reducing sedentary behavior may also be beneficial, particularly among inactive older adults.

� 2018 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

Frailty is considered a condition of increased vulnerability as aconsequence of aging-associated decline in reserve and function acrossmultiple physiologic systems1 and is associated with increased disability,

hospitalization, and death.2 Therefore, in an aging society, prevention offrailty and reduction of its consequences is one of the most importantchallenges that public health authorities face in the 21st century.

The authors declare no conflicts of interest.This work was supported by the Biomedical Research Networking Center on

Frailty and Healthy Aging (CIBERFES) and FEDER funds from the European Union(CB16/10/00477) and (CB16/10/00456). It was further funded by grants from theGovernment of Castilla-La Mancha (PI2010/020; Institute of Health Sciences, Min-istry of Health of Castilla-La Mancha, 03031-00), Spanish Government (SpanishMinistry of Economy, “Ministerio de Economía y Competitividad,” Instituto de SaludCarlos III, PI10/01532, PI031558, PI11/01068), and by European Grants (Seventh

Framework Programme: FRAILOMIC). A. Mañas and I. Rodríguez-Gómez received aPhD grant from the Universidad de Castilla-La Mancha “Contratos predoctoralespara la formación de personal investigador en el marco del Plan Propio deI þ D þ i, cofinanciados por el Fondo Social Europeo” (2015/4062 and 2014/10340,respectively).* Address correspondence to Ignacio Ara, GENUD Toledo Research Group, Uni-

versity of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain.E-mail address: [email protected] (I. Ara).

https://doi.org/10.1016/j.jamda.2018.12.0121525-8610/� 2018 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

JAMDA

journal homepage: www.jamda.com

JAMDA xxx (2019) 1e5

Promoting physical activity, particularly of moderate-to-vigorousintensity, is central to prevent the onset, perpetuation, and progres-sion of frailty.3 On the other hand, some evidence has identifiedsedentary behavior as an important predictor of healthy aging.4 Forexample, Dogra and Stathokostas5 found a dose-dependent relation-ship between sedentary behavior and different components of suc-cessful aging among middle-aged and older adults. A number ofexisting studies, but not all, have claimed that the detrimental asso-ciations between sedentary behavior and health outcomes are inde-pendent of physical activity levels.6 Authors such as Song et al7 andBlodgett et al8 have confirmed these associations also for frailty out-comes. However, the relationships between physical activity, seden-tary behavior, and health outcomes are far more complex. In fact,more recent evidence has suggested that the negative consequencesof sedentary behavior on older adults can be mitigated with adequatephysical activity levels. For example, Garcia-Hermoso et al9 demon-strated that moderate-to-vigorous physical activity attenuated therelationship between self-reported sitting time and cognitive functionin a nationally representative sample of 954 community-dwellingolder adults. Others have shown that the effects of sedentarybehavior even disappear after consideration of time spent inmoderate-to-vigorous physical activity. Beyond these associations, arecent meta-analysis with 1,005,791 individuals concluded that highlevels of moderate-to-vigorous physical activity eliminate theincreased risk of death associated with sitting time.10 Whetherphysical activity can ameliorate or even offset the negative conse-quences of sedentary behavior on frailty in older adults remains un-known. This study aimed to determine whether or not and to whatextent the detrimental effects of sedentary behavior on frailty weremoderated by moderate-to-vigorous physical activity in a sample of749 community-dwelling older adults measured with accelerometers.

Methods

Study Design and Participants

This cross-sectional investigation considered data from 871 olderadults (395 men) interviewed between 2012 and 2017 in the ToledoStudy for Healthy Aging (TSHA), a Spanish population-based pro-spective cohort study involving men and women older than 65 yearsand for which the primary aim was the study of the ageing process.The full methodology of the TSHA has been previously described.11 Allparticipants provided written consent for partaking in the currentstudy before enrolment. The Clinical Research Ethics Committee of theToledo Hospital approved the research protocol.

Measurements

Frailty statusThe Frailty Trait Scale (FTS)12 was used to measure frailty in this

study. The FTS assessed 7 aspects including energy balance and nutri-tion, physical activity, nervous system, vascular system, weakness,endurance, and slowness. These domains become operational through12 items. Each itemscore represents a biological trait. Only participantswith at least data on 75% (9 of the 12) of the items included in the FTSwere included in the study. The total scorewasdeterminedaccording tothe formula: Total score ¼ (S items score/total score possible by indi-vidual)*100. Therefore, the total FTS score ranged from0 (best score) to100 (worst score).

Physical activity and sedentary behavior assessmentPhysical activity and sedentary behavior were assessed during 7

consecutive days by accelerometry (ActiTrainer and ActiGraphwGT3X-BT; ActiGraph, LLC, Pensacola, FL). All participants wore theaccelerometer in an elastic waistband on the left hip during waking

hours, exceptwhile bathing or swimming. Monitors were set to recordaccelerometer counts in 1-minute epochs. Nonwear time was definedas 60 consecutive minutes or longer of zero-intensity counts, with nomore than 2 minutes of counts between zero and 100.13 The studyincluded data only from participants with at least 4 valid days with 8or more hours per day of wear time. The intensity threshold forsedentary behaviors was <100 cpm, whereas moderate-to-vigorousphysical activity was defined as �1952 cpm.14 The values were aver-aged over the number of valid days to derive an estimate of the meantime (minutes) spent in sedentary behavior andmoderate-to-vigorousphysical activity per day.

Anthropometrics and confounding variablesHeight was measured to the nearest centimeter using a stadi-

ometer (Seca 711 Scales, Hamburg, Germany), and weight wasmeasured with a SECA precision scale (Seca 711 Scales, Hamburg,Germany). Individuals removed their shoes, socks, and heavy clothesprior to weighing. Body mass index was calculated as weight (in ki-lograms) divided by height (in meters) squared.

Participants self-reported their age, sex, and ethnicity. Education(no studies, primary school completed, secondary school completedor more), marital status (single, married/living together, widowed,divorced/separated), and income (it was coded into 3 categoriesranging from any income to V3000/mo) were also self-reported inface-to-face interviews.

Analytical approachStatistical analyses were conducted using R software (R project

version 3.5.1). Summary statistics were used to describe variables ofinterest. Significance levels were set at P < .05. A multiple linearregressionwas conducted to determine the associations between timeper day spent in sedentary behavior and frailty in the sample popu-lation. An interaction term was then included in the equation to testthe moderation effects of moderate-to-vigorous physical activity inthe sedentary behaviorefrailty relationship. Lastly, the Johnson-Neyman technique15,16 was used to elucidate the moderate-to-vigorous physical activity values in which a significant and nonsig-nificant relationship exists between sedentary behavior and frailty inthe sample population. To confirm the robustness of our estimates,

Table 1Participant Characteristics

Characteristics Sample (N ¼ 749)

Age, y* 76.7 (4.9)Sexy

Male 346 (46.2)Female 403 (53.8)

Educationy

None 471 (62.9)Primary school 169 (22.6)Secondary or more 109 (14.5)

Incomey

Low 381 (50.9)Medium 310 (41.4)High 58 (7.7)

Marital statusy

Single 41 (5.5)Married 530 (70.8)Widowed 166 (22.1)Divorced/separated 12 (1.6)

Body mass index* 30.3 (4.8)Frailty Trait Scale, points* 38.0 (14.5)Accelerometer wear time, min/valid day* 786.6 (83.0)Sedentary time, min/valid day* 538.7 (90.3)Moderate-to-vigorous physical activity, min/valid day* 19.7 (23.9)

*Continuous variable; mean (standard deviation).yCategorical variable; n (%).

A. Mañas et al. / JAMDA xxx (2019) 1e52

rank-based regression analyses were performed. All models abovewere adjusted for age, sex, education, income, marital status, bodymass index, moderate-to-vigorous physical activity, and accelerom-eter wear time.

Results

Characteristics of the participants are shown in Table 1. Of the 871eligible subjects with all the data collected, 749 participants in totalwere included in all analyses. Reasons to exclude participants wereinsufficient accelerometer wear time data (n ¼ 100) and missing datarelated to frailty (n¼ 22). Appendix 1 shows the relationship betweensedentary time and moderate-to-vigorous physical activity withfrailty in our sample.

The mean age of participants was 76.7 (standard deviation 4.9)years and of those, 54.0% were women. The FTS ranged from 4.4 to86.7 points, with a mean of 38.0 (standard deviation 14.5) points. Onaverage, participants spent 538.7 minutes/d (68.5%) in sedentary be-haviors and 19.7 minutes/d (2.5%) in moderate-to-vigorous activities.

In the crudemodels, therewas a statistically significant associationbetween sedentary behavior and frailty in our study sample (b¼ 0.207[95% confidence interval {CI} 0.115, 0.299], P < .001). The sedentary

behavior � moderate-to-vigorous physical activity interaction termcontributed uniquely to the model (b ¼ �0.422 [�0.830, �0.014],P ¼ .042). These associations remained significant after adjusting forthe potential covariates (b ¼ 0.152 [0.066, 0.238], P ¼ .006), forsedentary behavior and frailty and b ¼ �0.391 (�0.762, �0.021),P ¼ .039 for the interaction. The Johnson-Neyman technique revealeda significant relationship between sedentary behavior and frailtywhen moderate-to-vigorous physical activity levels were below27.25 minutes/d (P < .05; 72% of sample) but not at higher levels(P � .05; 28% of sample). The strength of this inverse relationshipdecreased as moderate-to-vigorous physical activity levels increased(see Figure 1).

Similar results were obtained for the robustness analyses per-formed (data available on request).

Discussion

The main finding of the current study was that 27 minutes/d ofmoderate-to-vigorous physical activity eliminated the increased riskof frailty associated with sedentary time.

The detrimental health consequences of sedentary behavior havebeen demonstrated for a range of health outcomes such as metabolic

Fig. 1. Conditional effect of sedentary time on frailty as function of moderate-to-vigorous physical activity. The dashed blue vertical line (MVPA ¼ 27.25) represents the point wherethe relationship between frailty status and sedentary time transitions from statistically significant to nonsignificant and is determined using the Johnson-Neyman technique. Thedashed red vertical line represents the amount of MVPA required to meet the WHO physical activity recommendations. MVPA, moderate-to-vigorous physical activity.

A. Mañas et al. / JAMDA xxx (2019) 1e5 3

syndrome, waist circumference, and overweight/obesity.4 Forexample, Gao et al17 found that greater time spent viewing televisionwas associatedwith highwaist-to-hip ratio, and Gennuso et al18 foundthat more time spent in objectively measured sedentary behavior wasassociated with a high waist circumference and body mass index. Thisdamaging effect of time spent in sedentary activities seems to beconfirmed for frailty in older adults7,8,19,20 and remains true also in oursample. Several factors such as loss of maximal aerobic capacity andmuscle strength, decreased cognitive function, dysfunction of theimmune system, and damaged metabolic function or weight gain maypartially explain these associations.21

The main contribution of our study is the confirmation of themoderating role of physical activity on the sedentary behaviorefrailtyassociation. Our results suggest that engagement in 27 minutes/d ofmoderate-to-vigorous physical activity could eliminate the negativeconsequences of spending too much time in otherwise sedentary ac-tivities. Recent evidence in Chilean older adults also suggests thatmore intense physical activity attenuates the negative effects of sittingtime on cognition.9 This estimated protective role of physical activityhas also been confirmed in a large meta-analysis with more than 1million adults and older adults.10 The former study suggests that inhighly physically active people (ie, participants accumulating 60-75 minutes/d of moderate-to-vigorous physical activity), sitting timewas no longer a predictive factor of all-cause mortality. Also, Theouet al22 reported that in active individuals over 50 years old, sedentarytime does not affect the risk of mortality, regardless the level of frailty.Altogether, these findings seem to reinforce the idea of increasingmoderate-to-vigorous activity to offset the harmfulness that seden-tary time may have on frailty in older adults. Future studies need toconfirm our results in longitudinal, experimental studies.

Despite the well-established health benefits,23,24 engaging in moremoderate-to-vigorous physical activity may not be feasible for allolder adults. In fact, only 28% in our sample met the suggestedthreshold of 27 minutes/d of moderate-to-vigorous activity (which is6 minutes/d above the current physical activity guidelines) necessaryto theoretically offset the impact of sedentary behavior on frailty. Arecent systematic review and meta-analysis25 concluded that replac-ing accelerometer-assessed sedentary time with light-intensity ac-tivities is a feasible health-enhancing strategy, particularly amongpreviously sedentary individuals. A previous study also demonstratedthat replacing sedentary behavior with light-intensity activities canreduce the frailty levels among vulnerable older adults.26 Our esti-mations confirm the benefits of reducing sedentary behavior to reducefrailty levels among inactive older adults (ie, 68% of the sample in thecurrent study). Some studies have also demonstrated that reductionsin sedentary behavior (ie, short breaks of light activity into otherwiseinactive periods) are possible27 and have the potential of improvingthe physical function of older individuals.28e31 Collectively, thesefindings indicate that both increasing moderate-to-vigorous physicalactivity and reducing sedentary time should be encouraged to reducefrailty among older adults. The inclusion of strategies to reduce timespent sitting and increase time spent in light-intensity activities toreduce frailty may be of particular interest among the most inactiveolder adults and could be taken as a first, feasible step toward accu-mulation of more moderate-to-vigorous physical activity throughoutthe day.

Strengths and Limitations

An important strength of our study is that it includes a relativelylarge sample of community-dwelling older adults with accelerometer-derived sedentary behavior and physical activity estimations. Also,although there is no established gold standard to identify frailty, theFrailty Trait Scale has been suggested as a more sensitive scale fordetecting comorbidities, biomarkers, and adverse events associated

with frailty than previously validated frailty scales such as FrailtyPhenotype32 and the Frailty Index.33 Some limitations have to beacknowledged. Despite the validity and widespread use of acceler-ometers to assess physical activity in free living conditions, thesedevices are not able to discriminate between sitting and standing34 oractivity type, thus potentially biasing the estimations in our study. Thecross-sectional nature of the observations does not allow definitiveconclusions to be drawn around the causal relationship between thevariables of interest. Additional evidence is required to extend thesefindings using longitudinal and experimental data.

Conclusions

The present study shows that moderate-to-vigorous physical ac-tivity is a moderator in the relationship between sedentary time andfrailty in older adults, offsetting the harmful effects of sedentarybehavior with 27 minutes/d of moderate-to-vigorous physical activity.Whenever possible, efforts should be directed toward the promotionof moderate-to-vigorous physical activity in older adults. Also,reducing sedentary behavior may be beneficial, particularly in thoseengaging in less-intense activity throughout the day.

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21. Thyfault JP, Du M, Kraus WE, et al. Physiology of sedentary behavior and itsrelationship to health outcomes. Med Sci Sports Exerc 2015;47:1301e1305.

22. Theou O, Blodgett JM, Godin J, Rockwood K. Association between sedentarytime and mortality across levels of frailty. Can Med Assoc J 2017;189:E1056eE1064.

23. Lee IM, Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and lifeexpectancy. Lancet 2012;380:219e229.

24. Wen CP, Wu X. Stressing harms of physical inactivity to promote exercise.Lancet 2012;380:192e193.

25. Del Pozo-Cruz J, Garcia-Hermoso A, Alfonso-Rosa RM, et al. Replacing seden-tary time: Meta-analysis of objective-assessment studies. Am J Prev Med 2018;55:395e402.

26. Manas A, Del Pozo-Cruz B, Guadalupe-Grau A, et al. Reallocatingaccelerometer-assessed sedentary time to light or moderate- to vigorous-intensity physical activity reduces frailty levels in older adults: An

isotemporal substitution approach in the TSHA Study. J Am Med Dir Assoc2018;19:185.e1e185.e6.

27. Gardiner PA, Eakin EG, Healy GN, Owen N. Feasibility of reducing older adults’sedentary time. Am J Prev Med 2011;41:174e177.

28. Sardinha LB, Santos DA, Silva AM, et al. Breaking-up sedentary time is associ-ated with physical function in older adults. J Gerontol A Biol Sci Med Sci 2015;70:119e124.

29. Buman MP, Hekler EB, Haskell WL, et al. Objective light-intensity physicalactivity associations with rated health in older adults. Am J Epidemiol 2010;172:1155e1165.

30. Tse ACY, Wong TWL, Lee PH. Effect of low-intensity exercise on physical andcognitive health in older adults: A systematic review. Sports Medicine Open2015;1:37.

31. Brach JS, FitzGerald S, Newman AB, et al. Physical activity and functional statusin community-dwelling older women: A 14-year prospective study. ArchIntern Med 2003;163:2565e2571.

32. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for aphenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146eM156.

33. Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness andfrailty in elderly people. Can Med Assoc J 2005;173:489e495.

34. An HS, Kim Y, Lee JM. Accuracy of inclinometer functions of the activPAL andActiGraph GT3Xþ: A focus on physical activity. Gait Posture 2017;51:174e180.

A. Mañas et al. / JAMDA xxx (2019) 1e5 5

Appendix 1. Relationship between sedentary time and moderate-to-vigorous physical activity with frailty.

A. Mañas et al. / JAMDA xxx (2019) 1e55.e1

RESULTS

213

5.6. STUDY 6

“Which came first: the movement

behavior or the frailty? A cross-

lagged panel model in the THSA

study”

RESULTS

214

1

Which came first: the movement behavior or the frailty? A cross-lagged panel 1

model in the THSA study. 2

3

Asier Mañasa,b* MSc, Borja del Pozo-Cruzc* PhD, Irene Rodríguez-Gómeza,b PhD, José 4

Losa-Reynaa,b,d PhD, Leocadio Rodríguez-Mañasb,e PhD, MD, Francisco J. García-5

Garcíab,d PhD, MD, Ignacio Araa,b† PhD 6

7

aGENUD Toledo Research Group, University of Castilla-La Mancha (Toledo, Spain). 8

bCIBER of Frailty and Healthy Aging (CIBERFES). 9

cMotivation and Behaviour Research Program, Institute for Positive Psychology and 10

Education, Faculty of Health Sciences, Australian Catholic University, Sydney, 11

Australia. 12

dGeriatric Department. Hospital Virgen del Valle (Toledo, Spain). 13

eGeriatric Department. Hospital Universitario de Getafe (Getafe, Spain). 14

*These authors contributed equally to this work. 15

†Address correspondence to Ignacio Ara, GENUD Toledo Research Group, University 16

of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain. 17

E-mail address: [email protected] (Ignacio Ara) 18

19

20

Word count: 3625 21

2

Abstract 22

Background: There has been limited longitudinal assessment of the relationship 23

between moderate-to-vigorous physical activity (MVPA) or sedentary behavior (SB) 24

with frailty and no studies have explored the possibility of reverse causality. This study 25

aimed to determine the potential bidirectionality of the relationship between 26

accelerometer-assessed MVPA, SB and frailty over time in older adults. 27

Methods: Participants were from the Toledo Study for Healthy Aging. We analyzed 186 28

older people aged 67 to 90 (76.7 ± 3.9; 52.7% females) over a 4-year period. Time spent 29

in SB and MVPA was assessed by accelerometry. Frailty Trait Scale (FTS) was used to 30

determine frailty levels. A cross-lagged panel model design was used to test the 31

reciprocal relationships between MVPA/SB and frailty. 32

Results: FTS score changed from 35.4 to 43.8 points between the two times (p < 0.05). 33

We also found a reduction of 7 min/day in the time spent on MVPA (p < 0.05), and 34

participants tended to spend more time on SB (p = 0.076). Our analyses revealed that 35

lower levels of initial MVPA predicted higher levels of later frailty (std. β = -0.126; CI 36

= -0.231, -0.021; p < 0.05); whereas initial spent time on SB did not predict later frailty 37

(std. β = -0.049; CI = -0.185, 0.087; p = 0.48). Conversely, an initial increased frailty 38

status predicted higher levels of later SB (std. β = 0.167; CI = 0.026, 0.307; p < 0.05), 39

but not those of MVPA (std. β = 0.071; CI = -0.033, 0.175; p = 0.18). 40

Conclusions: Our observations suggest that the relationship between MVPA/SB and 41

frailty is unidirectional: individuals who spent less time on MVPA at baseline are more 42

likely to increase their frailty score, and individuals who are more frail are more likely 43

to spent more time on SB at follow-up. Interventions and policies should aim to increase 44

MVPA levels from earlier stages to promote successful aging. 45

3

Keywords: Structural equation modeling; Longitudinal; Exercise; Sedentary time; 46

Ageing; Functioning and disability 47

48

Abbreviations 49

MVPA: Moderate-to-Vigorous Physical Activity 50

SB: Sedentary Behavior 51

TSHA: Toledo Study of Health Aging 52

FTS: Frailty Trait Scale 53

BMI: Body Mass Index 54

MMSE: Mini-Mental State Examination 55

RMSEA: Root Mean Square Error of Approximation 56

SRMR: Standardized Root Mean Square Residual 57

CFI: Confirmatory Fit Index 58

TLI: Tucker-Lewis Index 59

CI: Confidence Interval 60

61

62

63

64

65

4

Introduction 66

Globally, the population aged 65 and over is growing faster than all other age groups [1, 67

2]. One of the most remarkable changes in body composition related to aging is the loss 68

of skeletal muscle (i.e., sarcopenia) [3]. Frailty, defined as a condition of increased 69

vulnerability associated with aging, and sarcopenia have been linked because both can 70

lead to disability, hospitalization and premature death [4-6]. Sarcopenia has been 71

considered both as the biological substrate for the development of physical frailty and 72

the pathway through which adverse health-related outcomes of physical frailty occur 73

[7]. Consequently, interventions to reduce the burden associated with frailty should be 74

focused, among others, on skeletal muscle and its functionality [8]. 75

Increasing physical activity and reducing the levels of sedentary behavior has been 76

suggested to be a key strategy to attenuate the declines in muscle mass and physical 77

function associated with aging, and may also delay the clinical symptoms of frailty in 78

older adults [9]. Several cross-sectional studies suggest that objectively-assessed 79

moderate-to-vigorous physical activity (MVPA) and sedentary behavior (SB) are related 80

with frailty in middle- to older-aged adults [10-13]. Nonetheless, a major limitation of 81

the existing evidence is that it mainly relies on cross-sectional designs, thus precluding 82

us from making any causal inferences due to the inability of establishing the temporal 83

sequence of the effects of MVPA or SB on frailty outcomes. 84

Therefore, longitudinal studies are essential because they provide an opportunity to 85

explore in more detail the causal direction of the associations between MVPA or SB 86

and frailty, knowledge which could then contribute to the development of intervention 87

strategies to favor successful aging outcomes, including frailty and associated 88

symptoms. To date, most of the existing longitudinal studies found a positive 89

association between SB and frailty [14, 15] and an inverse association for MVPA and 90

5

frailty [16]. For example, a longitudinal study in two Spanish cohorts of community-91

dwelling older adults reported baseline television viewing time was also associated with 92

frailty at 4-years follow-up [17]. Nevertheless, all of these studies used self-report 93

methods to assess the movement behavior of interest. Song et al. [18] showed a 94

relationship between objectively-assessed sedentary time and development of physical 95

frailty. However, an important caveat with this study is that used gait speed as a proxy 96

measure of frailty, thus could not capture the multidimensional nature of frailty [4]. To 97

our knowledge, there is no longitudinal study that has objectively measured both frailty 98

and MVPA/SB. 99

The relationships between physical activity, sedentary time and frailty are further 100

complicated by the possibility of reverse causality [16, 19]. In all previous longitudinal 101

studies, whether using objective or subjective measures of the variables of interest, the 102

authors investigated the prospective associations of MVPA or SB with frailty, not 103

taking into account the potential reverse or temporal order in the causality chain. We 104

cannot rule out the possibility that a high level of frailty can be associated with lower 105

levels of physical activity and a greater amount of sedentary time at a future time. The 106

reverse may also be true. Estimating the temporal ordering, and potential 107

bidirectionality of the association of SB and MVPA with frailty would be advantageous 108

to inform subsequent interventions aimed at reducing the burden of frailty among the 109

older population. However, no studies exist investigating this issue. 110

With fill in this gap by examining the longitudinal association of accelerometer-111

assessed MVPA and SB with frailty over a period of 4-years in a population sample of 112

older adults from the Toledo Study of Health Aging (TSHA). In doing so, we applied a 113

cross-lagged panel model, a statistical technique appropriate for the context and aims of 114

the current study [20, 21]. Specifically, this study examined whether MVPA or SB 115

6

predicted frailty in the future and whether frailty predicted subsequent movement 116

behaviors (or both). As far as we know, this is the first study that has examined the 117

potentially reciprocal relationships between movement behaviors and frailty in older 118

adults. In the current study, four hypotheses were tested: 119

H0: Frailty does not predict changes in SB/MVPA, and SB/MVPA does not predict 120

changes in frailty. 121

H1: Frailty predicts changes in SB/MVPA, but SB/MVPA does not predict changes in 122

frailty. 123

H2: SB/MVPA predicts changes in frailty, but frailty does not predict changes in 124

SB/MVPA. 125

H3: SB/MVPA and frailty have a reciprocal relationship - frailty predicts changes in SB/ 126

MVPA, and SB/ MVPA predicts changes in frailty. 127

128

Methods 129

Study Design and Participants 130

This is a longitudinal study consisting of two data collection waves separated by 4-years 131

(3.8 ± 0.8 years). This investigation used data from the second and third wave of the 132

TSHA. Details of the protocol of the TSHA is described elsewhere [22, 23]. Briefly, the 133

TSHA is a population prospective cohort study aimed at studying the determinants and 134

consequences of frailty in institutionalized and community-dwelling individuals older 135

than 65 years living in the province of Toledo, Spain. In the current study a subsample 136

of the TSHA with accelerometer data was included. A total of 277 men and 351 women 137

over 65 years of age at baseline, although 494 participants concluded the three stages of 138

7

assessment and provide with valid data for the analyses [224 men (45.3%)]. The first 139

time point of assessment for this study started in July 2012 and lasted until June 2014. 140

In the first stage, six psychologists conducted computer-assisted interviews face to face 141

with potential subjects. In the second stage, three nurses performed a physical 142

examination followed by clinical and performance tests at the subject’s home. In the 143

third stage, the participants were invited to wear an accelerometer for a week. 144

Participants were contacted again in 2015 and invited to participate in a follow-up study 145

conducted between May 2015 and July 2017 [24]. After the follow up, 200 participants 146

(59.5% missing) completed the second evaluation. However, 186 subjects (88 men 147

(47.3%) with complete data on all exposures, outcomes and ≥80% covariates were 148

included in the final analyses of this study (see Figure 1 for the study participant flow 149

diagram). Signed informed consent was obtained from all participants prior 150

participation in the study. The study was approved by the Clinical Research Ethics 151

Committee of the Toledo Hospital, which was conducted according to the ethical 152

standards defined in the 1964 Declaration of Helsinki. 153

Measurements 154

Frailty status 155

The Frailty Trait Scale (FTS) [25] was used to assess frailty in this study. The FTS 156

includes 7 aspects: energy balance and nutrition, activity, nervous system, vascular 157

system, weakness, endurance, and slowness. These domains become operational 158

through 12 items: 159

Body mass index, central obesity (waist circumference), unintentional weight loss and 160

serum albumin level were used to assess energy balance and nutrition. 161

8

Activity was assessed using the total score of the Physical Activity Scale for the Elderly 162

[26]. 163

The nervous system was calculated by considering verbal fluency and balance. Verbal 164

fluency was estimated by asking the participants to give names of animals during one 165

minute [27]. Balance was measured by Romberg test [28]. 166

The vascular system was measured by the brachial-ankle index done with Doppler 167

ultrasound [29]. 168

Weakness was estimated assessing grip strength in the dominant arm and the knee 169

extension strength [23]. 170

Endurance was assessed by the chair stand test, which measures the number of times 171

that a person stands up in 30 seconds [30]. 172

Slowness was estimated by calculating the time to walk 3 m at a “normal pace” 173

according to a standard protocol [28]. 174

Each item score represents a biological trait and ranges from 0 (the best) to 4 (the 175

worst), except in the “chair test” where the range is from 0 to 5 points because of the 176

necessity of scoring those unable to stand a single time. When appropriate, items are 177

analyzed according to the item’s quintile distribution in the population. 178

To be included in the study, the participants had to overcome at least 75% (9 of the 12) 179

of the items included in the FTS [25]. The total score was calculated by adding all the 180

scores in each item divided by total score for each individual and multiplying by 100, 181

standardizing the measure to a range from 0 (best score) to 100 (worst score), according 182

to the formula Total score = (Σ items score/total score possible by individual)*100. 183

184

9

Physical activity and sedentary behavior assessment 185

The ActiGraph accelerometer ActiTrainer and wGT3X-BT (ActiGraph, LLC, 186

Pensacola, FL) were used to assess the participants’ physical activity and SB levels 187

during a week as previously described [11]. In brief, participants were instructed to 188

wear an accelerometer on the left hip during waking hours, with exception for water 189

activities. The devices were initialized to collect data using one-minute epochs and all 190

data were collected using the vertical axis collection mode. Inclusion criteria comprised 191

at least 4 days with at least 8 hours recorded per day without excessive counts (i.e., 192

>20,000 counts) [31]. Non-wear time was defined as a minimum of 60 minutes with 193

allowance of 1‐2 minutes of counts below 100 counts [32]. Daily average times spent in 194

SB (<100 counts/min) and MVPA (≥1952 counts/min) were derived according to 195

previous work [33]. Although there is a lack of consensus on the use of cut-off points to 196

classify the intensity of the activity, the cut-off points used in this study are the most 197

commonly reported in this population group [34], this makes our results comparable to 198

other studies. Minutes spent in each of these three behaviors were tallied per day and 199

averaged over all available valid days. 200

Anthropometrics and confounding variables 201

Height was measured to the nearest centimeter using a stadiometer (Seca 711 scales, 202

Hamburg, Germany), and weight was measured with a SECA precision scale (Seca 711 203

scales, Hamburg, Germany). Individuals removed their shoes, socks and heavy clothes 204

prior to weighing. Body mass index (BMI) was calculated as weight (kg) divided by 205

height squared (m2). 206

Participants self-reported their age, sex and ethnicity. Education (no studies, primary 207

school completed, secondary school completed or more), marital status (single, 208

10

married/living together, widowed, divorced/separated), and income (it was coded into 3 209

categories ranging from any income to 3000€/month) were also self-reported in face-to-210

face interviews. We also evaluated objective cognitive function using the Mini-Mental 211

State Examination (MMSE) [35]. 212

Statistical analysis 213

Preliminary analyses examined variable distributions, sample characteristics and 214

attrition using R software (R project version 3.5.1). Descriptive variables were 215

compared between participants retained with those of participants not retained from 216

wave1-wave2 with an independent t test or chi-square test for continuous and 217

categorical variables, respectively. Descriptive statistics (mean and standard deviation 218

(SD) for continuous variables and as frequencies and percentages for categorical 219

variables) were calculated for all outcome measurements. Comparison between baseline 220

and follow-up time continuous variables was performed using a paired sample t-test. 221

We tested our hypotheses using structural equation modeling with maximum likelihood 222

estimation using functions from the R package Lavaan [36]. Full information maximum 223

likelihood was used to provide unbiased and efficient estimates of the parameters of 224

interest missingness at random [37]. Two cross-lagged panel models were used to test 225

the hypothesis of the study. A cross-lagged panel model was implemented to test the 226

relationships between SB and frailty status across the two time points for the present 227

study (i.e., initial assessment and 4-year follow-up). The second cross-lagged panel 228

model was used to test the relationships between MVPA and frailty status. The null 229

hypotheses would be supported if neither of the coefficients associated with the cross 230

paths were significantly different from zero. If the cross path towards frailty in time 2, 231

but not towards SB/MVPA in time 2, was significant, then H1 would be supported. If it 232

were the reverse of the latter, then H2 would be supported. Finally, if both paths were 233

11

significant, then H3 would be supported. Analyses included sex as time-invariant 234

variable; in addition, age, education, marital status, income, BMI, MMSE, and 235

accelerometer wear time were allowed to be time-varying covariates (i.e., allowing for 236

possible changes in these measures from initial assessment to follow-up). Among the 237

strengths of using a cross-lagged panel approach is that it allows simultaneous analysis 238

of the two dependent outcomes, thereby permitting the identification of possible 239

bidirectional associations over time. Model fit was assessed using a selection of fit 240

indices and criteria: root mean square error of approximation (RMSEA) (≤0.06), 241

standardized root mean square residual (SRMR) (≤0.08), confirmatory fit index (CFI) 242

(≥0.95), and Tucker-Lewis index (TLI) (≥0.95) [38]. 243

244

Results 245

Attrition/missing data across time points 246

Participants decreased from 494 with complete data at baseline to 186 with complete 247

data at follow-up assessment (see Figure 1). The causes and numbers who were lost to 248

the follow-up assessment were death (n = 42), refusal (n = 225), and could not be 249

located (n = 27). Additional missing data were lost by insufficient accelerometer wear 250

time data (n = 9), missing frailty data (n = 2) or losing more than 80% of the covariates 251

(n = 3). Compared with the retained sample, participants who dropped the study at 4-252

year follow-up were significantly older, less educated and spent more time on SB 253

(Supplementary File 1). Also, MVPA had a trend toward significance reductions in 254

those participants. Missing data were addressed using a full information maximum 255

likelihood algorithm, as recommended elsewhere [39]. 256

257

12

Descriptive statistics 258

Means and standard deviations for MVPA, SB and frailty as well as confounders at each 259

of the two time-points of assessment (namely T1 and T2) for the present study are 260

shown in Table 1. At baseline, participants had a mean age of 76.68 (SD = 3.90), a 261

mean FTS of 35.35 (SD = 13.94), and a mean time (min/day) spent on SB of 530.18 262

(SD = 84.86), and MVPA of 20.12 (SD = 23.30). FTS score declined significantly 263

between T1 and T2 (p<0.05). It was found that participants tended to spend more time 264

on SB (p=0.076), and there was a significant reduction in the time spent on MVPA 265

between the two times (p<0.05). BMI and MSSE also decreased significantly at both 266

time points (p<0.05). 267

Cross-lagged panel model 1: Moderate-to-vigorous physical activity 268

Figure 2 shows the final cross-lagged model. The data fit the model well (RMSEA = 269

0.000; SRMR = 0.013; CFI = 1.000; TLI = 1.023). The largest effects on T2 MVPA and 270

T2 frailty were those determined by the autoregressive pathways. That is, past MVPA 271

and frailty scores predicted future MVPA and frailty scores respectively. The cross-272

lagged effect from MVPA at T1 to frailty status at T2 was statistically significant 273

(standardized regression coefficient of -0.126; 95% Confidence Interval [CI] = -0.231, -274

0.021; p<0.05), indicating that higher levels of MVPA at baseline predicted lower 275

frailty score 4-years later, adjusting for baseline frailty status. In contrast, the cross-276

lagged effect from frailty status to MVPA was not statistically significant (standardized 277

regression coefficient of -0.049; 95% CI = -0.185, 0.087; p=0.48), suggesting that 278

frailty did not predict future levels of MVPA. 279

280

281

13

Cross-lagged panel model 2: Sedentary behavior 282

Figure 3 shows the final cross-lagged model. The data fit the model well (RMSEA = 283

0.012; SRMR = 0.018; CFI = 0.997; TLI = 0.992). Similar to the previous model, the 284

autoregressive SB and frailty pathways were statistically significant. The cross-lagged 285

effect from frailty to SB levels was statistically significant (standardized regression 286

coefficient of 0.167; 95% CI = 0.026, 0.307; p<0.05), indicating that higher levels of 287

frailty at baseline predicted higher SB 4-years later, adjusting for baseline SB. In 288

contrast, the cross-lagged effect from SB levels to frailty was not statistically significant 289

(standardized regression coefficient of 0.071; 95% CI = -0.033, 0.175; p=0.18), 290

suggesting that SB levels did not predict future frailty status. 291

292

Discussion 293

The present study investigated the longitudinal relationships between MVPA and SB 294

with frailty status in a community-based sample of older adults. As a novelty, we 295

applied a cross-lagged panel model to test for potential reciprocal relationships between 296

MVPA/SB and frailty over a 4-years period. The main finding in our study was that 297

accelerometer-assessed initial MVPA predicted frailty score at follow-up. However, 298

baseline sedentary time was not significantly related to frailty after the follow-up. We 299

further found that initial frailty status predicted subsequent sedentary time (i.e., more 300

frailty status was related to posterior higher levels of SB), but not of MVPA. These 301

results have the potential to inform future interventions that aim at reducing the burden 302

associated with frailty among older adults. 303

Different cross-sectional [10, 11, 40] and longitudinal [16] studies have linked 304

moderate-to-vigorous physical activity with frailty. Blodgett et al. [10] found that 305

14

MVPA was associated with frailty in a group of community dwelling adults aged over 306

50 from the National Health and Nutrition Examination Survey. Other longitudinal 307

studies such as Rogers et al. [16] have also confirmed these results in 8649 adults aged 308

50 and over an average of 10 years of follow-up. We also found that MVPA 309

prospectively predicted frailty levels in our sample. There are numerous arguments 310

supporting these findings [41-44]. It has been demonstrated that physical activity, 311

particularly of moderate intensity plays an important role on multiple components of the 312

frailty syndrome including the frailty phenotype, physiologic dysregulation, and cellular 313

function [9]. Increases in MVPA seem to also preserve or even improve muscle 314

function and structure, protein synthesis, glucose metabolism or inflammation [43]. 315

Furthermore, regular physical activity can maintain a set of bioenergetically functional 316

mitochondria that, by improving systemic mitochondrial function, contribute to 317

reducing the risk of morbidity and mortality throughout life [41]. Not surprisingly, 318

MVPA is considered a cornerstone for the prevention, delay or treatment of frailty 319

among older adults. 320

On the other hand, our results did not support the hypothesis that initial frailty levels 321

predict future MVPA levels. Several studies support the predictive ability of physical 322

functioning on subsequent MVPA levels [45-47]. However, it may also be plausible that 323

other, non-biological mechanisms (e.g., behavioral) accounted for the observations of 324

the current study. Different intervention studies have shown the possibility of increasing 325

physical activity also in frail participants [48]. For example, Yamada et al. [49] found 326

that is possible to promote exercise of moderate-to-vigorous intensity among very frail 327

older adults. Future studies are warranted to clarify the role of frailty in subsequent 328

MVPA levels in older adults. 329

15

Sedentary behavior has recently been considered as an important factor for numerous 330

health outcomes [50, 51]. A recent systematic review has shown that SB may be 331

associated with increased levels of frailty, particularly among the most vulnerable 332

population [52]. Interestingly, our results indicate that SB was not a determinant of 333

frailty, but rather a consequence of an altered state of increased frailty. A smaller study 334

by Edholm et al. [53] with 60 older woman found that only activities of at least 335

moderate intensity were associated with physical function in a subsequent follow-up 336

time but not activities of lighter intensity or sedentary activities. In addition, Marques et 337

al. [54], in a study conducted with 131 males and 240 females aged 65-103 years, 338

suggested that sedentary time was not a significant predictor of loss of physical 339

independence in later life. In a previous cross-sectional study, we showed that engaging 340

in high levels of MVPA (i.e., 27 minutes/day) could cancel out the detrimental effects 341

of sedentary behavior on frailty, which may partly explain our current observations [55]. 342

Given that the relationship between SB and frailty may go beyond total accumulated 343

time [12], future studies should enquire whether or not the results of this, and other 344

studies are confirmed for different patterns of accumulation of SB. 345

According to our findings, the promotion of MVPA at earlier stages will translate into 346

more MVPA and less frailty markers in the future. Also, the observations of the current 347

study point out to the possibility that the detrimental effects on frailty are primarily 348

defined by insufficient amounts of MVPA rather than an excessive amount of sedentary 349

time. Public health organizations should target MVPA to reduce the burden associated 350

with frailty in older adults. 351

Strengths and limitations 352

An important strength of our study is that includes a relatively large sample of 353

community-dwelling older adults with longitudinal data separated by 4-years. It also 354

16

includes accelerometer-derived sedentary and physical activity behavior estimations. 355

Also, although there is no established gold standard to identify frailty, the FTS has been 356

suggested as a measurement of frailty with superior predictive validity than previously 357

validated scales such as the Frailty Phenotype [56] and the Frailty Index [57]. 358

Additionally, a key strength of this study was that the statistical analysis deployed has 359

allowed to explore the auto-regressive and cross-lagged pathways in exploring how 360

frailty relates over time with both MVPA and SB. Despite the methodological rigor of 361

this study, some limitations have to be acknowledged. First, we cannot rule out the 362

possibility that our estimations could be influenced by the characteristics of the 363

participants who did not provide valid data at follow-up (i.e., older, less active, more 364

sedentary, less educated) and therefore our results should be interpreted with caution. A 365

further limitation of our work was that despite validity and widely used of 366

accelerometers to assess physical activity in free living conditions, these devices are not 367

able to discriminate between sitting and standing [58] or activity type (e.g., running vs. 368

muscle strength), which could potentially bias the estimations in our study. Finally, 369

physical activity and sedentary behavior have been examined separately from other 370

lifestyle behaviors (e.g., diet, smoking, alcohol consumption). However, lifestyle 371

behaviors tend to cluster together. Therefore, it could be that our results rather reflect 372

the synergistic consequences of different observed (i.e., physical activity and sedentary 373

behavior) and unobserved (e.g., diet quality) lifestyle behaviors [59, 60]. Future studies 374

may want to test this hypothesis. 375

376

Conclusion 377

In summary, our findings suggest that moderate-to-vigorous physical activity, but not 378

sedentary behavior, predicts frailty in older adults. In contrast, frailty seems to be a 379

17

predictor of sedentary behavior but not of moderate-to-vigorous physical activity. 380

Efforts should be directed at increasing moderate-to-vigorous physical activity from 381

earlier stages. Future experimental studies should examine the best strategies to include 382

moderate-to-vigorous physical activity in the daily lives of older people [61]. 383

384

Acknowledgements 385

We are deeply thankful to Linda Fried, Professor (University of Columbia, USA) and 386

Jack Guralnik, PhD (University of Maryland School of Medicine, USA), for their 387

assistance in reviewing the final version of the manuscript. The authors would like to 388

thank the cohort members, investigators, research associates, and team members. 389

The authors certify that they comply with the ethical guidelines for authorship and 390

publishing of the Journal of Cachexia, Sarcopenia and Muscle [62]. 391

392

Conflict of interest 393

None to declare. 394

395

Funding: 396

This work was supported by the Biomedical Research Networking Center on Frailty and 397

Healthy Aging (CIBERFES) and FEDER funds from the European Union 398

(CB16/10/00477), (CB16/10/00456) and (CB16/10/00464). It was further funded by 399

grants from the Government of Castilla-La Mancha (PI2010/020; Institute of Health 400

18

Sciences, Ministry of Health of Castilla-La Mancha, 03031-00), Spanish Government 401

(Spanish Ministry of Economy, “Ministerio de Economía y Competitividad,” Instituto 402

de Salud Carlos III, PI10/01532, PI031558, PI11/01068), and by European Grants 403

(Seventh Framework Programme: FRAILOMIC). Asier Mañas Bote received a PhD 404

grant from the Universidad de Castilla-La Mancha “Contratos predoctorales para la 405

formación de personal investigador en el marco del Plan Propio de I+D+i, cofinanciados 406

por el Fondo Social Europeo” (2015/4062). 407

408

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Table Legends 621

622

Table 1. Sociodemographic and descriptive data. 623

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; MVPA, 624

moderate-to-vigorous physical activity. 625 aContinuous variable; mean (standard deviation). 626 bCategorical variable; n (%). 627 cMissing data; n (%). 628

*Significant differences between baseline vs. follow-up (p<0.05). 629 ŦTrend toward significance between baseline vs. follow-up (p<0.08>0.05) 630

631

632

633

Figure Legends 634

635

Figure 1. Flow diagram of the process for obtaining the final sample of the study. 636

637

Figure 2. Cross-lagged panel model 1: Moderate-to-vigorous physical activity 638

Abbreviations: MVPA, moderate-to-vigorous physical activity Model adjusted for age, sex, BMI, education, income, marital status and MMSE Bold indicates statistical significance (p<0.05) 639

Figure 3. Cross-lagged panel model 2: Sedentary behavior 640

Model adjusted for age, sex, BMI, education, income, marital status and MMSE Bold indicates statistical significance (p<0.05) 641

28

Supplementary Files 642

643

Supplementary File 1. Comparison of characteristics at baseline of participants 644

retained with those of participants not retained from wave1-wave2. 645

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; MVPA, 646

moderate-to-vigorous physical activity. 647 aContinuous variable; mean (standard deviation). 648 bCategorical variable; n (%). 649 cMissing data; n (%). 650

Bold indicates statistical significance (p<0.05) and Italics a trend toward significance 651

(p<0.08>0.05). 652

Table 1. Sociodemographic and descriptive data.

Variables Baseline Follow-up (n=186) (n=186)

Age (years)a 76.68 ± 3.90 80.44 ± 4.24* Sexb Men 88 (47.3) 88 (47.3) Women 98 (52.7) 98 (52.7) BMI (kg/m2)a 30.82 ± 4.62 30.33 ± 4.40* Educationb None 139 (74.7) 116 (62.4) Primary school 30 (16.1) 51 (27.4) Secundary or more 14 (7.5) 19 (10.2) Missingc 3 (1.6) - Incomeb Low 87 (46.8) 75 (40.4) Medium 87 (46.8) 70 (37.6) High 9 (4.8) 6 (3.2) Missingc 3 (1.6) 35 (18.8) Marital statusb Single 7 (3.8) 7 (3.8) Married 136 (73.1) 125 (67.2) Widower 40 (21.5) 51 (27.4) Separated/Divorced 1 (0.5) 2 (1.1) Missingc 2 (1.1) 1 (0.5) MSSEa 24.02 ± 3.73 23.32 ± 3.54* Missingc 15 (8.1) 15 (8.1) Frailty Trait Scale, pointsa 35.35 ± 13.94 43.79 ± 13.86* Accelerometer wear time, min/valid daya 781.36 ± 83.14 777.61 ± 74.45 Sedentary time, min/valid daya 530.18 ± 84.86 542.61 ± 75.91Ŧ MVPA, min/valid daya 20.12 ± 23.30 13.21 ± 18.73*

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; MVPA, moderate-

to-vigorous physical activity. aContinuous variable; mean standard ± deviation. bCategorical variable; n (%). cMissing data; n (%).

*Significant differences between baseline vs. follow-up (p<0.05). ŦTrend toward significance between baseline vs. follow-up (p<0.08>0.05)

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Supplementary File 1. Comparison of characteristics at baseline of participants retained with those of participants not retained from wave1-wave2.

Variables Retained Not retained P value

(n=186) (n=308)

Age (years)a 76.68 ± 3.90 78.17 ± 4.69 0.003 Sexb 0.495 Men 88 (47.3) 137 (44.2) Women 98 (52.7) 173 (55.8) BMI (kg/m2)a 30.82 ± 4.62 30.48 ± 4.82 0.497 Educationb 0.014 None 139 (74.7) 262 (84.5)

Primary school 30 (16.1) 25 (8.1) Secundary or more 14 (7.5) 19 (6.1) Missingc 3 (1.6) 4 (1.3) Incomeb 0.888 Low 87 (46.8) 136 (43.9) Medium 87 (46.8) 166 (44.2) High 9 (4.8) 21 (6.8) Missingc 3 (1.6) 16 (5.2) Marital statusb 0.579 Single 7 (3.8) 18 (5.8) Married 136 (73.1) 211 (68.1) Widower 40 (21.5) 76 (24.5) Separated/Divorced 1 (0.5) 1 (0.3) Missingc 2 (1.1) 4 (1.3) MSSEa 24.02 ± 3.73 22.90 ± 4.82 0.207 Missingc 15 (8.1) 36 (11.7) Frailty Trait Scale, pointsa 35.35 ± 13.94 40.53 ± 14.32 0.093 Accelerometer wear time, min/valid daya 781.36 ± 83.14 781.76 ± 86.86 0.693

Sedentary time, min/valid daya 530.18 ± 84.86 542.79 ± 98.05 0.043 MVPA, min/valid daya 20.12 ± 23.30 16.71 ± 21.19 0.055

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; MVPA,

moderate-to-vigorous physical activity. aContinuous variable; mean (standard deviation). bCategorical variable; n (%). cMissing data; n (%).

Bold indicates statistical significance (p<0.05) and Italics a trend toward significance

(p<0.08>0.05).

RESULTS

249

5.7. STUDY 7

“Temporal and bidirectional

associations between breaks in

sedentary time and frailty in older

adults: A cross-lagged panel model in

the TSHA study”

RESULTS

250

JJournal of Cachexia, Sarcopenia and Muscle

Temporal and bidirectional associations between breaks in sedentary time and frailty:A cross-lagged panel model

--Manuscript Draft--

Manuscript Number: JCSM-D-19-00483

Full Title: Temporal and bidirectional associations between breaks in sedentary time and frailty:A cross-lagged panel model

Article Type: Original Article

Corresponding Author: Ignacio AraUniversidad de Castilla-La ManchaToledo, Toledo SPAIN

Corresponding Author SecondaryInformation:

Corresponding Author's Institution: Universidad de Castilla-La Mancha

Corresponding Author's SecondaryInstitution:

Corresponding Author E-Mail: [email protected]

First Author: Asier Mañas, MSc

First Author Secondary Information:

Order of Authors: Asier Mañas, MSc

Borja del Pozo-Cruz, PhD

Irene Rodríguez-Gómez, PhD

José Losa-Reyna, PhD

Pedro B. Júdice, PhD

Luís B. Sardinha, PhD

Leocadio Rodríguez-Mañas, PhD, MD

Francisco José García-García, PhD, MD

Ignacio Ara

Order of Authors Secondary Information:

Abstract: BackgroundCross-sectional evidence exist on the beneficial effects of breaks in sedentary time(BST) on frailty in older adults. Nonetheless, the longitudinal nature of theseassociations is unknown. This study aimed to investigate the direction and temporalorder of the association between accelerometer-derived BST and frailty over time inolder adults.MethodsThis longitudinal cohort study analyzed a total of 186 older adults aged 67 to 90 (76.7 ±3.9 y.; 52.7% females) from the Toledo Study for Healthy Aging over a 4-year period.Number of daily BST was measured by accelerometry. Frailty was objectivelyassessed with the Frailty Trait Scale. A cross-lagged panel model was used to test thetemporal and reciprocal relationship between BST and frailty. In a subsequentanalysis, participants were divided into physically active and inactive depending onwhether or not meet the physical activity recommendations.ResultsNumber of daily BST changed from 71.7 to 65.9 between two times ( p <0.05). Wealso found an increase of 8 points (35.4 to 43.8 points) in the FTS score ( p <0.05).We did not find a significant association between BST and frailty for the whole sample.For those physically inactive (n=126), our analyses revealed a reciprocal inverserelationship between BST and frailty, such as higher initial BST predicted lower levels

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of later frailty (std. β= -0.150, 95% CI= -0.281, -0.018; p <0.05); as well as initial lowerfrailty levels predicted higher future BST (std. β= -0.161, 95% CI= -0.310, -0.011; p<0.05). Conversely, no significant pathway was found in the active participants (n=60).ConclusionsIn inactive older adults, the relationship between BST and frailty is bidirectional, whilein active individuals no associations were found. This investigation provides robustlongitudinal evidence that breaking-up sedentary time more often may reduce frailty inthose older adults who do not meet physical activity recommendations. Targetingfrequent BST may bring a feasible approach to decrease the burden of frailty amongmore at risk inactive older adults.

Suggested Reviewers: Matteo CesariInstitut national de la santé et de la recherche médicale (UMR1027), Université deToulouse III Paul [email protected]

Jeremy David WalstonDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School [email protected]

Marco PahorOsloMet - storbyuniversitetet Norsk institutt for forskning om oppvekst velferd [email protected]

Author Comments: Ignacio AraGENUD Toledo Research Group,Universidad de Castilla-La ManchaAvda. Carlos III s/n, 45071, Toledo, [email protected]

October 12th 2019

Stefan Anker, MD, PhDEditor-in-Chief of the Journal of Cachexia, Sarcopenia and Muscle

Dear Prof. Stefan Anker,Please find attached the manuscript entitled “Temporal and bidirectional associationsbetween breaks in sedentary time and frailty: A cross-lagged panel model” forconsideration in the Journal of Cachexia, Sarcopenia and Muscle.

For the first time, we have observed the association between breaks in sedentary time(BST) and frailty status over time in older adults. In addition, we have applied a cross-lagged panel model approach to test the potential bidirectionality of this relationship.We used data from 186 older people assessed with accelerometers over a 4-yearperiod.

Recently, the work entitled “Which came first: the movement behavior or the frailty? Across-lagged panel model in the THSA study” [1] has been accepted in yourprestigious journal, which is a first part of the piece of work being submitted. Webelieve the current submission nicely develop on the previous paper by covering thedifferent movement patterns together with the breaks in sedentary time, which haveacquired in recent years an important relevance in public health research and mayeasily be translated into practice [2-4].

Our results suggest that the daily number of sedentary breaks and the frailty levels isbidirectional over time in physically inactive older individuals, that is, higher levels ofbaseline BST predicted lower levels of frailty score at follow-up after adjusting forpotential confounders and baseline frailty status as well as initial frailty predictedsubsequent frailty. Conversely, we found no evidence of a longitudinal associationbetween breaks of sedentary time and frailty in older adults considered physicallyactive.

Our findings are important and future-policy relevant: these results suggest thatbreaking-up sedentary time more often may reduce frailty in older adults who do not


meet physical activity recommendations and therefore, targeting frequent reductions insedentary time may provide with a feasible approach to reduce the burden of frailtyamong more at risk inactive older adults.

We will also take this opportunity to state that this is an original piece of research thathas never been submitted elsewhere.

We hope that you consider this submission positively and look forward to hearing fromyou.Yours sincerely,Ignacio Ara (on behalf of all authors)

References

1.Manas A, Pozo-Cruz BD, Rodriguez-Gomez I, Losa-Reyna J, Rodriguez-Manas L,Garcia-Garcia FJ, et al. Which came first: the movement behavior or the frailty? Across-lagged panel model in the TSHA study. J Cachexia Sarcopenia Muscle. 2019.2.Chastin SF, Egerton T, Leask C, Stamatakis E. Meta-analysis of the relationshipbetween breaks in sedentary behavior and cardiometabolic health. Obesity (SilverSpring, Md). 2015;23(9):1800-10.3.Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Breaks insedentary time: beneficial associations with metabolic risk. Diabetes care.2008;31(4):661-6.4.Loh R, Stamatakis E, Folkerts D, Allgrove JE, Moir HJ. Effects of InterruptingProlonged Sitting with Physical Activity Breaks on Blood Glucose, Insulin andTriacylglycerol Measures: A Systematic Review and Meta-analysis. Sports medicine(Auckland, NZ). 2019.


Temporal and bidirectional associations between breaks in sedentary time and 1

frailty: A cross-lagged panel model 2

3

Asier Mañasa,b* MSc, [email protected] 4

Borja del Pozo-Cruzc* PhD, [email protected] 5

Irene Rodríguez-Gómeza,b PhD, [email protected] 6

José Losa-Reynaa,b,d PhD, [email protected] 7

Pedro B. Júdicee PhD, [email protected] 8

Luís B. Sardinhae PhD, [email protected] 9

Leocadio Rodríguez-Mañasb,f PhD, MD, [email protected] 10

Francisco J. García-Garcíab,dᶿ PhD, MD, [email protected] 11

Ignacio Araa,bᶿ† PhD, [email protected] 12

13

aGENUD Toledo Research Group, University of Castilla-La Mancha (Toledo, Spain). 14

bCIBER of Frailty and Healthy Aging (CIBERFES). 15

cMotivation and Behaviour Research Program, Institute for Positive Psychology and 16

Education, Faculty of Health Sciences, Australian Catholic University, Sydney, 17

Australia. 18

dGeriatric Department. Hospital Virgen del Valle (Toledo, Spain). 19

eExercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, 20

Universidade de Lisboa (Lisbon, Portugal). 21

Manuscript Click here to access/download;Manuscript;Manas et alManuscript.docx

Click here to view linked References

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fGeriatric Department. Hospital Universitario de Getafe (Getafe, Spain). 22

*These authors contributed equally to this work. 23

ᶿThese authors contributed equally to this work. 24

†Address correspondence to Ignacio Ara, GENUD Toledo Research Group, University 25

of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain. 26

E-mail address: [email protected] (Ignacio Ara) 27

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Abstract 42

Background: Cross-sectional evidence exist on the beneficial effects of breaks in 43

sedentary time (BST) on frailty in older adults. Nonetheless, the longitudinal nature of 44

these associations is unknown. This study aimed to investigate the direction and 45

temporal order of the association between accelerometer-derived BST and frailty over 46

time in older adults. 47

Methods: This longitudinal cohort study analyzed a total of 186 older adults aged 67 to 48

90 (76.7 ± 3.9 y.; 52.7% females) from the Toledo Study for Healthy Aging over a 4-49

year period. Number of daily BST was measured by accelerometry. Frailty was 50

objectively assessed with the Frailty Trait Scale. A cross-lagged panel model was used 51

to test the temporal and reciprocal relationship between BST and frailty. In a subsequent 52

analysis, participants were divided into physically active and inactive depending on 53

whether or not meet the physical activity recommendations. 54

Results: Number of daily BST changed from 71.7 to 65.9 between two times (p<0.05). 55

We also found an increase of 8 points (35.4 to 43.8 points) in the FTS score (p<0.05). 56

We did not find a significant association between BST and frailty for the whole sample. 57

For those physically inactive (n=126), our analyses revealed a reciprocal inverse 58

relationship between BST and frailty, such as higher initial BST predicted lower levels 59

of later frailty (std. β= -0.150, 95% CI= -0.281, -0.018; p<0.05); as well as initial lower 60

frailty levels predicted higher future BST (std. β= -0.161, 95% CI= -0.310, -0.011; 61

p<0.05). Conversely, no significant pathway was found in the active participants 62

(n=60). 63

Conclusions: In inactive older adults, the relationship between BST and frailty is 64

bidirectional, while in active individuals no associations were found. This investigation 65

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provides robust longitudinal evidence that breaking-up sedentary time more often may 66

reduce frailty in those older adults who do not meet physical activity recommendations. 67

Targeting frequent BST may bring a feasible approach to decrease the burden of frailty 68

among more at risk inactive older adults. 69

70

Keywords: Structural equation modeling; Longitudinal; Patterns; Sedentary time; 71

Ageing; Functioning and disability 72

73

Abbreviations 74

MVPA: Moderate-to-Vigorous Physical Activity 75

ST: Sedentary Time 76

BST: Breaks in Sedentary Time 77

TSHA: Toledo Study of Health Aging 78

FTS: Frailty Trait Scale 79

RMSEA: Root Mean Square Error of Approximation 80

SRMR: Standardized Root Mean Square Residual 81

CFI: Confirmatory Fit Index 82

TLI: Tucker-Lewis Index 83

CI: Confidence Interval 84

85

86

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Introduction 87

Frailty is a condition of increased vulnerability associated with aging that leads to a 88

number of adverse health outcomes, including disability, falls, hospitalization, and 89

death [1]. During the past decade, robust evidence has continued to accumulate on the 90

health benefits of optimal levels of moderate-to-vigorous physical activity (MVPA) [2-91

4], including frailty [5-8]. Consequently, physical activity, particularly of moderate-to-92

vigorous intensity is considered as one of the keystones to prevent, delay, or even 93

reverse the frailty syndrome in older adults [9]. Unfortunately, MVPA occupies 2-3% of 94

the waking day in older adults [5, 10-12]. The majority of waking time is spent in 95

sedentary behaviors (i.e., sitting or reclining). Sedentary time (ST) is increasingly 96

recognized as a novel risk factor for the wider health of individuals [13, 14], also 97

comprising frailty [15, 16]. For instance, a cross-sectional study with 3146 older people 98

in the USA showed that total time spent sitting was associated with frailty [7]. Other 99

studies seem to confirm this ST throughout the day (i.e., frequency of breaks in ST 100

(BST)) have been demonstrated to be relevant for a variety of health-related outcomes 101

including physical function, performance of activities of daily living, and disability [17-102

20]. Two studies in the field found that breaking-up ST was associated with better 103

physical function in older adults [19, 20]. However, in a sample of high functioning and 104

high active older adults, more time spent in sedentary behavior and lower BST were 105

associated with improved lower limb extensor muscle quality [21]. These findings 106

suggest that the relationship between BST and health outcomes may vary depending on 107

whether the sample is active or inactive. 108

The number of studies assessing the role of BST on frailty is scarce [15]. A recent 109

cross-sectional study with 519 participants ≥65 years old assessed with accelerometers 110

concluded that increasing daily BST was associated with less frailty in the population 111

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studied, regardless of meeting physical activity guidelines [22]. In contrast, in another 112

cross-sectional study with 2317 people from the National Health and Nutrition 113

Examination Survey (NHANES), it was found no association between the frequency of 114

BST and frailty [8]. Nonetheless, the longitudinal association of BST and frailty 115

outcomes remains unknown. In addition, there is the possibility of reverse causality, 116

such as a high level of frailty being associated with lower number of BST in the future. 117

Understanding the temporal order underpinning the relationship between BST and 118

frailty has the potential to inform future public health interventions aimed to reduce the 119

frailty burden among older adults. 120

In this study, we used a cross-lagged panel model to investigate the longitudinal and 121

temporal order of the association between the number of daily BST and frailty in a 122

sample of community-dwelling older adults assessed with accelerometers over a 4-year 123

period. Based on some literature findings [21], we conducted the analyses in the whole 124

sample as well as separately for physically inactive and active participants, to determine 125

the potential impact of BST on these two groups. We hypothesized that there will be no 126

relationship between daily BST and frailty in the whole sample or in physically active 127

individuals, while the association will be inversely reciprocal between those physically 128

inactive individuals. 129

130

Methods 131

Study Design and Participants 132

This is a longitudinal study involving two data collection time points separated by 4 133

years (3.8 ± 0.8 years). This investigation used data from the second and third waves of 134

the Toledo Study of Health Aging (TSHA). Details of the protocol of the TSHA have 135

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been described elsewhere [23, 24]. In brief, the TSHA is a population prospective 136

cohort study designed to examine the determinants and consequences of frailty in 137

community-dwelling individuals ≥65 years residing in the province of Toledo, Spain. In 138

the present study, a subsample of the TSHA with accelerometer data was included. The 139

baseline assessment for this study started in July 2012 and lasted until June 2014. A 140

total of 277 men and 351 women over 65 years of age were assessed at baseline. 141

However, 494 participants finally provided valid data for the analyses [270 females 142

(54.7%)]. Those 494 participants were contacted again in 2015 and invited to participate 143

in a follow-up study, conducted between May 2015 and July 2017 [25]. Of the 494 144

participants invited to partake in the follow-up study, 200 participants (59.5% missing) 145

completed the second evaluation. Of these, 186 participants (98 women (52.7%) with 146

complete data on all exposures, outcomes and ≥80% covariates were included in the 147

final analyses of this study. The flow of participants in the study have been described 148

elsewhere [26]. Signed informed consent was obtained from all participants prior to 149

their involvement in the study. The study was approved by the Clinical Research Ethics 150

Committee of the Toledo Hospital (approval code: 2010/93), which was conducted 151

according to the ethical standards defined in the 1964 Declaration of Helsinki. 152

Measurements 153

Frailty status 154

Frailty was assessed using the Frailty Trait Scale (FTS) [27]. The FTS includes 7 155

domains to complete a continuous frailty scale in which the more decrease in the 156

biological reserve, the closer it is to the threshold of presenting adverse effects derived 157

from it (functional deterioration, hospitalization, mortality, etc.). These domains 158

become operational through 12 items [27]: 159

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1. Body mass index, waist circumference, unintentional weight loss and serum 160

albumin level were used to assess energy balance and nutrition. 161

2. Activity was assessed using the total score of the Physical Activity Scale for the 162

Elderly [28]. 163

3. The nervous system was calculated by considering verbal fluency and balance. 164

Verbal fluency was estimated by asking the participants to give names of 165

animals during one minute [29]. Balance was measured by Romberg test[30]. 166

4. The vascular system was measured by the brachial-ankle index done with 167

Doppler ultrasound [31]. 168

5. Weakness was estimated assessing grip strength in the dominant arm and the 169

knee extension strength [24]. 170

6. Endurance was assessed by the chair stand test, which measures the number of 171

times that a person stands up in 30 seconds [32]. 172

7. Slowness was estimated by calculating the time to walk 3 m at a “normal pace” 173

according to a standard protocol [30]. 174

Each item score was obtained as recommended elsewhere [27]. The total score was 175

calculated according to the formula Total score = (Σ items score/total score possible by 176

individual)*100, standardizing the measure to a range from 0 (less frailty) to 100 (more 177

frailty). 178

To be included in the study, the participants had to overcome at least 75% (9 of the 12) 179

of the items included in the FTS [27]. 180

Physical activity and sedentary behavior assessment 181

Physical activity and ST were measured by accelerometry (ActiTrainer and wGT3X-182

BT, ActiGraph, LLC, Pensacola, FL) as previously described [5]. Briefly, all 183

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participants were asked to wear the accelerometer on the left hip during waking hours of 184

a whole week, with exception for water activities. A valid day was defined as having 185

≥480 min (≥8 h) of monitor wear, and the study included the results from participants 186

with at least four valid days [33, 34]. 187

Each minute during which the accelerometer counts were below 100 cpm was defined 188

as ST [35, 36]. BST was defined as at least 1 min where the accelerometer registers 189

≥100 cpm following a sedentary period. Number of daily BST was then calculated to be 190

used in the analyses. 191

Accelerometer counts more than or equal to 100 cpm were classified as physical activity 192

with additional separation into light-intensity (LPA: 100–1951 cpm), moderate-intensity 193

(MPA: 1952-5724 cpm), and vigorous-intensity (VPA: ≥5725 cpm) physical activity. 194

In addition, compliance with physical activity recommendations was calculated in order 195

to classify individuals as active or inactive at the baseline moment. To be active, at least 196

one of these three premises had to be met [37]: accumulate 150 minutes of moderate 197

physical activity per week; accumulate 75 minutes of vigorous physical activity per 198

week; or accumulate 150 minutes per week of an equivalent combination of MVPA. 199

Anthropometrics and confounding variables 200

Height and weight were measured using a balance-stadiometer with a precision of 100 g 201

and 1 mm, respectively (Seca 711 scales, Hamburg, Germany). Individuals removed 202

their shoes, socks and heavy clothes prior to weighing. Body mass index was 203

determined as weight (kg) divided by height squared (m2). 204

Participants were asked about their age, sex, and ethnicity. Other sociodemographic 205

variables such as education (no studies, primary school completed, secondary school 206

completed or more) and marital status (single, married/living together, widowed, 207

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divorced/separated) were also self-reported in face-to-face interviews. Mini-Mental 208

State Examination was also collected in order to evaluate objective cognitive function 209

[38]. 210

Statistical analysis 211

All analyses were conducted with the R software (R project version 3.5.1) and 212

significance level was set at usual P<0.05. Baseline participants that did and did not 213

complete the follow-up assessment were compared on their demographics and other 214

characteristics with an independent t-test or chi-square test for continuous and 215

categorical variables, respectively. The Mean ± SD and frequencies (percentages) were 216

used to describe continuous variables and categorical variables respectively. A paired t-217

test was used to compare participants retained in the study across time points of 218

assessment. An independent t-test was conducted to compare active and inactive 219

participants. 220

We addressed our main objective using a structural equation modeling framework using 221

functions from the R package Lavaan [39]. The full information maximum likelihood 222

was used to provide unbiased and efficient estimates of the parameters of interest using 223

the complete available information [40]. A cross-lagged panel model was designed to 224

test the relationships between number of daily BST and frailty status between the initial 225

assessment and the 4-year follow-up. Among the strengths of using a cross-lagged panel 226

approach is that it tolerates simultaneous analysis of the two dependent outcomes, 227

thereby allowing the identification of possible bidirectional associations over time. 228

Covariates included sex as time-invariant variable, in addition to age, education, marital 229

status, body mass index, Mini-Mental State Examination, MVPA, and accelerometer 230

wear time as time-variant. Subsequently, two more cross-lagged panel models were 231

made by stratifying the sample in those active and inactive individuals. Model fit was 232

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considered using a selection of fit indices and criteria as previously published [41]: root 233

mean square error of approximation (RMSEA) (≤0.06), standardized root mean square 234

residual (SRMR) (≤0.08), confirmatory fit index (CFI) (≥0.95), and Tucker-Lewis index 235

(TLI) (≥0.95). 236

237

Results 238

Attrition data across time points 239

Participants decreased from 494 with complete data at baseline to 186 with complete 240

data at follow-up assessment [26]. The causes and numbers who were lost to the follow-241

up assessment were death (n=42), withdrawing (n=225), and could not be located 242

(n=27). Additional missing data were lost by insufficient accelerometer wear time data 243

(n=9), missing frailty data (n=2) or losing ≥80% of the covariates (n=3). Compared with 244

the retained sample, individuals who dropped the study at 4-year follow-up were 245

significantly older, less educated and spent more time on ST (Supplementary File 1). 246

Missing data were addressed using a full information maximum likelihood algorithm, as 247

recommended elsewhere [42]. 248

Descriptive statistics 249

Table 1 displays the descriptive statistics for frailty, accelerometry variables as well as 250

confounders at each of the two measurement time-points divided into the whole sample, 251

active individuals, and inactive individuals. At baseline, the whole sample had a mean 252

age of 76.68 (SD=3.90), a mean FTS of 35.35 (SD=13.94), and a mean daily BST 253

(n/day) of 71.66 (SD=18.54). FTS score and daily BST declined significantly from 254

baseline to follow-up (P<0.05). Body mass index, MSSE, and MVPA also decreased 255

significantly between both time points (all P<0.05). According to the current physical 256

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activity guidelines [37], one third of the sample (n=60) was categorized as physically 257

active and the remaining as physically inactive (n=126). There were significant 258

differences between baseline and follow-up in body mass index, FTS, and MVPA for 259

physically active individuals (all P<0.05). For those physically inactive, significant 260

differences between baseline and follow-up were found in MSSE, FTS, and daily BST. 261

Compared to physically active participants, those physically inactive were older, had a 262

higher body mass index, greater FTS, lower accelerometer wear time, and spent more 263

time in ST and lower time in LPA and MVPA. There were not significant differences in 264

daily BST between the two groups. 265

Cross-lagged panel model 1: Total sample 266

Figure 1 shows the final cross-lagged model for the whole sample. The model fit the 267

data well (RMSEA=0.000; SRMR=0.012; CFI=1.000; TLI=1.010). Significant 268

associations were only detected in the autoregressive pathways. That is, initial daily 269

BST predicted future daily BST (standardized regression coefficient [β]=0.366, 95% 270

Confidence Interval [CI]=0.253, 0.480; P<0.01) and baseline frailty scores predicted 271

future frailty scores, respectively (β=0.320, CI=0.204, 0.436; P<0.01). The cross-lagged 272

effect from baseline daily BST to follow-up frailty status was not statistically significant 273

(β=-0.081, 95% CI=-0.182, 0.020; P=0.12). Similarly, the cross-lagged effect from 274

initial frailty status to later daily BST was also not statistically significant (β=-0.130, 275

95% CI=-0.268, 0.008; P=0.07). 276

Cross-lagged panel model 2: Physically active individuals 277

Figure 2 shows the final cross-lagged model for the physically active individuals. The 278

model fit the data well (RMSEA=0.025; SRMR=0.025; CFI=0.992; TLI=0.980). As in 279

the previous model, the autoregressive pathways were the only significant ones. 280

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Baseline daily BST predicted daily BST in the follow-up (β=0.376, 95% CI=0.176, 281

0.577; P<0.01) as well as baseline frailty status predicted later frailty status (β=0.226, 282

95% CI=0.010, 0.442; P<0.05). The cross-lagged effects from initial daily BST to 283

subsequent frailty status and initial frailty status to later daily BST were not statistically 284

significant (β=-0.056, 95% CI=-0.267, 0.156; P=0.61; and β=0.043, 95% CI=-0.171, 285

0.256; P=0.70 respectively). 286

Cross-lagged panel model 3: Physically inactive individuals 287

Figure 3 shows the final cross-lagged model for the inactive individuals. The model fit 288

the data well (RMSEA=0.021; SRMR=0.020; CFI=0.994; TLI=0.986). The 289

autoregressive pathways were again significant for both BTS and FTS (β=0.371, 95% 290

CI=0.235, 0.507; and β=0.339, 95% CI=0.203, 0.475 respectively; P<0.01). The cross-291

lagged pathways were also significant. Specifically, initial daily BST predicted future 292

frailty (β=-0.150, 95% CI=-0.281, -0.018; P<0.05), indicating that lower daily BST at 293

baseline predicted higher frailty score 4 years later, adjusting for baseline frailty status. 294

Likewise, initial FTS predicted future daily BST (β=-0.161, 95% CI=-0.310, -0.011; 295

P<0.05), adjusting for baseline daily BST, pointing out that higher frailty at baseline 296

predicted lower daily BST 4 years later. 297

298

Discussion 299

To our knowledge, the present study tested for the first time the temporal and 300

bidirectional associations between the number of daily BST and frailty status in a 301

sample of community-dwelling older adults. The main findings were that, in physically 302

inactive individuals there was an inverse relationship between BST and frailty status 4 303

years later. The reverse was also true (i.e., frailty status at baseline was inversely 304

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associated with BST 4 years later). There was no evidence of longitudinal association 305

between BST and frailty in older adults who were considered physically active. 306

Consequently, increasing daily BST could be a promising strategy to reduce the burden 307

of frailty syndrome in physically inactive older adults. 308

Different cross-sectional studies have explored the associations of BST with physical 309

function [19], disability [17], and frailty [22] in older adults. A previous study found a 310

positive association between BST and physical function, after adjusting for total ST and 311

MVPA [19]. In a study comprising 1634 Japanese older adults, greater BST was 312

associated with lower likelihood of instrumental activities of daily living disability [17]. 313

In another study conducted in older adults assessed with accelerometers, daily BST was 314

associated with lower frailty in older adults [22]. Our study extends these previous 315

findings and investigated the longitudinal and bidirectional associations between daily 316

BST and frailty in a sample of community-dwelling participants 65 years and over. In 317

physically inactive individuals, a higher daily number of BST predicted a lower frailty 318

status after 4 years. Further, a lower baseline frailty status predicted greater daily 319

number of BST 4 years later. Mechanisms underlying these findings are likely to be 320

complex. Experimental studies have provided evidence related to the physiologic and 321

cardiometabolic benefits of breaking-up and reducing sitting time [43]. The evidence 322

suggests that beyond the increase in energy expenditure that requires a transition from 323

sitting to standing position [44], the benefits of frequent BST can be explained by the 324

muscular contractions derived from such transitions [45]. These muscular contractions, 325

which will mostly be provided in light physical intensity activities, can lead to 326

important functional adaptations through different physiological and molecular 327

pathways [45-49] that ultimately affect human health [50]. Therefore, it seems evident 328

that physically inactive individuals who break their ST more often may reduce their 329

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frailty status. Likewise, less frail individuals, will find it easier to break ST more often 330

compared to other more structured physical task, thus becoming a positive circle 331

(negative if the question is raised backwards). This is in accordance with a previous 332

study of our group that demonstrated that older adults with comorbidities may benefit 333

more from replacing ST with light-intensity physical activity compared to healthier 334

counterparts [5]. A recent meta-analysis also reported the benefits of replacing sitting 335

time with light-intensity physical activity activities for cardiometabolic health and 336

mortality [51]. Together, these findings suggest the potential of targeting reductions in 337

ST to improve the health of older adults [43, 52]. 338

In contrast, we did not detect a statistical association between baseline daily BST and 339

follow-up frailty status among physically active older adults. In a previous study, it was 340

suggested that lower BST were linked with better muscle quality in a sample of highly 341

functioning and highly active older adults [21]. It is plausible that active participants are 342

also fit [53] and that a stimulus stronger than muscle contractions resulting from 343

breaking-up ST is necessary to evoke reductions in the frailty status among these 344

individuals. This suggest that the effects of BST on frailty may be moderated by fitness. 345

Similarly, we speculate a higher physiological reserve (i.e., less frailty, as described in 346

Table 1) present in active participants may have accounted for the lack of association 347

between initial frailty status and daily BST at follow-up. Further experimental studies 348

are required to investigate our observations. The lack of significant results for the total 349

sample is likely to reflect the heterogeneity in the estimations for active and inactive 350

individuals in this study (i.e., the positive findings for inactive individuals are canceled 351

out by the null findings among active individuals). These findings are likely to be 352

policy-relevant: current physical activity guidelines for older adults focused mainly on 353

MVPA [37, 54]. Further to increase MVPA, our results also stress the relevance of 354

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breaking-up ST, particularly in those physically inactive individuals. From a health 355

promotion perspective, encouraging small bouts of activity into otherwise sedentary 356

periods may be a more feasible and less challenging approach for older adults than 357

taking part in more intense activities. Breaking-up ST can occur trivially in a variety of 358

daily living activities (at home, during transport, or leisure time) because: i) it does not 359

require a high degree of commitment or planning, ii) it can be achieved with a 360

physically lower load, and iii) it does not require a high level of fitness or complex 361

motor skills. Altogether, our observations indicate the possibility of targeting BST to 362

reduce the frailty levels in physically inactive older adults [43]. 363

Strengths and limitations 364

A key strength of our work is that it comprises a relatively large sample of community-365

dwelling older adults with data follow-up of 4 years. The use of accelerometer-derived 366

sedentary and physical activity behavior in this study is also a strength. Another 367

strength is the robustness of the FTS to assess the frailty level of participants. The FTS 368

has demonstrated superior predictive validity and responsiveness than previously 369

validated constructs such as the Frailty Phenotype [55] and the Frailty Index [56]. 370

Furthermore, another key strength of this study is the use of a statistical method (i.e., 371

cross-lagged panel model) that allowed us to investigate the temporal order and 372

bidirectional, longitudinal associations of BST with frailty over time in the study 373

participants. 374

Despite the methodological rigor of this study, some limitations must to be 375

acknowledged. First, we cannot rule out the possibility that our estimations could be 376

biased by the characteristics of the participants who did not provided valid data at 377

follow-up (i.e., they were older, less active, more sedentary, and less educated than 378

those included in the final analysis). Consequently, our results should be taken with 379

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465

caution. An additional limitation is the limited ability of accelerometers to discriminate 380

between sitting and standing compared with posture-based devices [57-59]. Importantly, 381

in our study, a break in ST reflects a modification in acceleration instead of a change in 382

posture, corresponding to a transition from none or slight movement (<100 cpm) to 383

some movement (≥100 cpm). Future studies should replicate our analysis with posture-384

based devices. Lastly, experimental studies are required to confirm our observations 385

[60]. Future studies should investigate optimal strategies to increase the number of BST 386

throughout the day in older adults, particularly among those not achieving the 387

recommended level of physical activity. 388

389

Conclusion 390

According to our hypothesis, the longitudinal relationship between the daily number of 391

sedentary breaks and the frailty levels is bidirectional in physically inactive older 392

individuals. We found no evidence of a longitudinal association between BST and 393

frailty in older adults considered physically active. Our study provides robust 394

longitudinal evidence that breaking-up ST more often may reduce frailty in older adults 395

that do not meet physical activity recommendations. Pending on experimental 396

confirmation, targeting frequent reductions in ST may provide with a feasible approach 397

to reduce the burden of frailty among more at risk inactive older adults. 398

399

Acknowledgements 400

The authors would like to thank the cohort members, investigators, research associates, 401

and team members. 402

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The authors certify that they comply with the ethical guidelines for authorship and 403

publishing of the Journal of Cachexia, Sarcopenia and Muscle [61]. 404

Conflict of interest Disclosures 405

None to declare. 406

Funding 407

This work was supported by the Biomedical Research Networking Center on Frailty and 408

Healthy Aging (CIBERFES) and FEDER funds from the European Union 409

(CB16/10/00477), (CB16/10/00456) and (CB16/10/00464). It was further funded by 410

grants from the Government of Castilla-La Mancha (PI2010/020; Institute of Health 411

Sciences, Ministry of Health of Castilla-La Mancha, 03031-00), Spanish Government 412

(Spanish Ministry of Economy, “Ministerio de Economía y Competitividad,” Instituto 413

de Salud Carlos III, PI10/01532, PI031558, PI11/01068), and by European Grants 414

(Seventh Framework Programme: FRAILOMIC). Asier Mañas received a PhD grant 415

from the Universidad de Castilla-La Mancha “Contratos predoctorales para la formación 416

de personal investigador en el marco del Plan Propio de I+D+i, cofinanciados por el 417

Fondo Social Europeo” (2015/4062). Pedro B. Júdice is supported by the Portuguese 418

Foundation for Science and Technology (SFRH/BPD/115977/2016). 419

420

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614

615

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Table Legends 616

617

Table 1. Sociodemographic and descriptive data. 618

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; LPA, light 619

physical activity; MVPA, moderate-to-vigorous physical activity; BST, number of breaks in 620

sedentary time. 621 aContinuous variable; mean ± standard deviation. 622 bCategorical variable; n (%). 623 cMissing data; n (%). 624

*Significant differences between baseline vs. follow-up (P<0.05). 625

ᶿSignificant differences between active vs. inactive individuals at baseline (P<0.05). 626 ŦTrend toward significance between baseline vs. follow-up (P<0.08>0.05). 627

628

629

630

631

632

633

634

635

636

637

638

639

640

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465

Figure Legends 641

642

Figure 1. Cross-lagged panel model 1: Total sample 643

Abbreviations: BST, number of daily breaks in sedentary time. 644

Model adjusted for age, sex, BMI, education, marital status, Mini-Mental State 645

Examination, MVPA, and accelerometer wear time. 646

Bold indicates statistical significance (P<0.05). 647

648

Figure 2. Cross-lagged panel model 2: Physically active individuals 649

Abbreviations: BST, daily number of breaks in sedentary time. 650




654

Figure 3. Cross-lagged panel model 3: Physically inactive individuals 655

Abbreviations: BST, daily number of breaks in sedentary time. 656




660

661

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465

Supplementary Material 662

663

Supplementary File 1. Comparison of characteristics at baseline of participants 664

retained with those of individuals not retained from T1-T2. 665

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; LPA, 666

light physical activity; MVPA, moderate-to-vigorous physical activity; BST, number of 667

breaks in sedentary time. 668

aContinuous variable; mean ± standard deviation. 669

bCategorical variable; n (%). 670

cMissing data; n (%). 671

Bold indicates statistical significance (p<0.05) and Italics a trend toward significance 672

(p<0.08>0.05). 673

674

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465

Tab

le 1

. Soc

iode

mog

raph

ic a

nd d

escr

iptiv

e da

ta.

Tota

l Sam

ple

(n=1

86)

Act

ive

Indi

vidu

als (

n=60

) In

activ

e In

divi

dual

s (n=

126)

Var

iabl

es

Bas

elin

e Fo

llow

-up

Bas

elin

e Fo

llow

-up

Bas

elin

e Fo

llow

-up

Age

(yea

rs)a

76.6

8 ±

3.90

80

.44

± 4.

24*

75.4

2 ±

3.05

79

.08

± 3.

37*

77.2

9 ±

4.13

ᶿ 81

.08

± 4.

47*

Sexb

M

en

88 (4

7.3)

88

(47.

3)

39 (6

5.0)

39

(65.

0)

49 (3

8.9)

49

(38.

9)

W

omen

98

(52.

7)

98 (5

2.7)

21

(35.

0)

21 (3

5.0)

77

(61.

1)

77 (6

1.1)

B

MI (

kg/m

2 )a 30

.82

± 4.

62

30.3

3 ±

4.40

* 29

.11

± 3.

60

28.3

9 ±

3.62

* 31

.63

± 4.

84ᶿ

31.2

6 ±

4.45

Ŧ

Educ

atio

nb

N

one

139

(74.

7)

116

(62.

4)

50 (8

3.3)

38

(63.

3)

89 (7

0.6)

78

(61.

9)

Prim

ary

scho

ol

30 (1

6.1)

51

(27.

4)

4 (6

.7)

14 (2

3.3)

26

(20.

6)

37 (2

9.4)

S

econ

dary

or m

ore

14 (7

.5)

19 (1

0.2)

6

(10.

0)

8 (1

3.3)

8

(6.3

) 11

(8.7

) M

issi

ngc

3 (1

.6)

- -

- 3

(2.4

) -

Mar

ital s

tatu

sb

S

ingl

e 7

(3.8

) 7

(3.8

) 1

(1.7

) 1

(1.7

) 6

(4.8

) 6

(4.8

) M

arrie

d 13

6 (7

3.1)

12

5 (6

7.2)

51

(85.

0)

47 (7

8.3)

85

(67.

5)

78 (6

1.9)

W

idow

er

40 (2

1.5)

51

(27.

4)

8 (1

3.3)

11

(18.

3)

32 (2

5.4)

40

(31.

7)

Sep

arat

ed/D

ivor

ced

1 (0

.5)

2 (1

.1)

- -

1 (0

.8)

2 (1

.6)

Mis

sing

c 2

(1.1

) 1

(0.5

) -

1 (1

.7)

2 (1

.6)

- M

SSEa

24.0

2 ±

3.73

23

.32

± 3.

54*

24.4

9 ±

3.52

24

.27

± 2.

92

23.7

9 ±

3.82

22

.89

± 3.

74*

Mis

sing

c 15

(8.1

) 15

(8.1

) 5

(8.3

) 5(

8.3)

10

(7.9

) 10

(7.9

) Fr

ailty

Tra

it Sc

ale,

poi

ntsa

35.3

5 ±

13.9

4 43

.79

± 13

.86*

27

.45

± 12

.54

34.5

6 ±

10.1

1*

39.1

1 ±

13.0

0ᶿ

48.1

9 ±

13.2

5*

Acc

eler

omet

er w

ear t

ime,

m

in/v

alid

day

a 78

1.36

± 8

3.14

77

7.61

± 7

4.45

80

7.95

± 7

9.31

80

0.40

± 6

9.19

76

8.69

± 8

2.22

ᶿ 76

6.77

± 7

4.67

Tabl

e 1

Sede

ntar

y tim

e, m

in/v

alid

day

a 53

0.18

± 8

4.86

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tif

Supplementary File 1. Comparison of characteristics at baseline of participants retained with those of individuals not retained from T1-T2.

Variables Retained (n=186)

Not retained (n=308)

P value

Age (years)a 76.68 ± 3.90 78.17 ± 4.69 0.003 Sexb 0.495 Men 88 (47.3) 137 (44.2) Women 98 (52.7) 173 (55.8) BMI (kg/m2)a 30.82 ± 4.62 30.48 ± 4.82 0.497 Educationb 0.014 None 139 (74.7) 262 (84.5)

Primary school 30 (16.1) 25 (8.1) Secundary or more 14 (7.5) 19 (6.1) Missingc 3 (1.6) 4 (1.3) Incomeb 0.888 Low 87 (46.8) 136 (43.9) Medium 87 (46.8) 166 (44.2) High 9 (4.8) 21 (6.8) Missingc 3 (1.6) 16 (5.2) Marital statusb 0.579 Single 7 (3.8) 18 (5.8) Married 136 (73.1) 211 (68.1) Widower 40 (21.5) 76 (24.5) Separated/Divorced 1 (0.5) 1 (0.3) Missingc 2 (1.1) 4 (1.3) MSSEa 24.02 ± 3.73 22.90 ± 4.82 0.207 Missingc 15 (8.1) 36 (11.7) Frailty Trait Scale, pointsa 35.35 ± 13.94 40.53 ± 14.32 0.093 Accelerometer wear time, min/valid daya 781.36 ± 83.14 781.76 ± 86.86 0.693

Sedentary time, min/valid daya 530.18 ± 84.86 542.79 ± 98.05 0.043 LPA, min/valid daya 231.05 ± 86.50 222.26 ± 94.39 0.433 MVPA, min/valid daya 20.12 ± 23.30 16.71 ± 21.19 0.055 Daily BST, n/daya 71.66 ± 18.54 67.97 ± 19.49 0.631

Abbreviations: BMI, body mass index; MSSE, mini-mental scale examination; LPA, light physical activity;

MVPA, moderate-to-vigorous physical activity; BST, number of breaks in sedentary time. aContinuous variable; mean (standard deviation). bCategorical variable; n (%). cMissing data; n (%).

Bold indicates statistical significance (p<0.05) and Italics a trend toward significance

(p<0.08>0.05).

RESULTS

289

290

291

CHAPTER 6

DISCUSSION

DISCUSSION STUDY 1

292

STUDY 1 DISCUSSION

293

6.1. Study 1

To the best of our knowledge, this is the first review that examines the

association between objectively measured SB and its effects on physical

performance, frailty and mortality in older people. Although the number

of studies in which accelerometers were used in order to ascertain SB is

very limited in this population, a relationship between SB and a worsened

physical performance is observed. However, the association between SB

and frailty incidence and mortality rates remains unclear due to the

reduced number of studies available in the literature.

Effects of SB on physical performance

Earlier studies where sedentary lifestyle has been measured by auto-

reported questionnaires show that the longer time older adults spend on

SB, the higher adverse health outcomes (i.e. diabetes, cardiovascular

diseases) present, independently of MVPA [243]. Disability is a major

adverse health outcome resulting in limitations in the activities of daily

living. This is of special interest, since physical activity has been proposed

for the prevention of impaired physical functioning in older ages [244].

However, these studies do not consider ST as an independent domain of

behaviour.

In the current review, we have found a negative association between SB

and physical performance, regardless of MVPA in two of the cross-

sectional studies reviewed [245, 246]. Likewise, Fleig et al. [247] and Cooper

et al. [248] found a negative association between time spent on sedentary

DISCUSSION STUDY 1

294

activities and various physical performance tests in older adults.

Accordingly, Dunlop et al. [249] found a strong relationship between

greater time spent in SB and the presence of activities of daily living

disability, and Ikezoe et al. [250] with a slower time in the Timed Up-&-Go

test and lower muscle strength. The independent relationship of ST and

physical performance extends recent findings demonstrating that

objectively measured ST, controlled for MVPA, is related to metabolic

syndrome [251], cancer [252], and mortality [253]. In contrast,

investigations performed in adults failed to relate sitting time with

impaired muscle strength or gait/mobility [254]. These discrepancies may

be attributable, at least in part, to the heterogeneity in the participant study

samples examined.

Sedentary behaviour and physical performance have also been related

longitudinally. Seguin et al. [255] studied 62,000 woman aged 50 to 79 years

from the Women´s Health Initiative, and observed that those with the

higher auto-reported sitting time and total ST at the beginning of the study,

had the higher reduction in self-reported physical performance after 12.3

years’ follow-up. Unfortunately, self-report is susceptible to socially

desirable responding [256], and older adults have a less accurate recall

[257].

Thus, objectively assessed SB as well as home-based physical performance

tests may provide more accurate and reliable results. According to our

literature review, the randomized control trial (RCT) study performed by

Barone Gibbs et al. [258] demonstrated that a 12 week intervention aimed

to reduce SB has a higher effect on physical performance rather than on

time spent on MVPA in older sedentary but highly physically functional

adults. In agreement, Rosenberg et al. [259] showed that an 8-week

STUDY 1 DISCUSSION

295

behavioural intervention to reduce SB is feasible and effective among older

overweight and obese adults in order to increase physical performance.

The present findings highlight the need to separate SB from insufficient

MVPA patterns. This is important because it enables SB as a modifiable

additional risk factor for impaired physical performance, disability and loss

of independence. Beyond this, there seems to be a negative relationship

between spending more time on sedentary activities and physical

performance.

Moreover, it is important to discuss that the way ST is spent also matters.

For example, Sardinha et al. [218] as well as Davis et al. [260] found that

breaking-up time in SB was positively associated with physical

performance in older adults, even after controlling for overall time in

MVPA and SB. Davis et al. [260] also reported that breaking-up time in SB

predicted overall physical performance and was associated to higher scores

in selected fitness parameters like upper and lower body muscle strength.

This is not the case of high functioning older adults who spend over an

hour a day walking, where higher SB and lower breaks were associated

with an improved muscle quality [261]. Given the surprising results,

authors explain it by a higher body fat that might provide a training

stimulus to maintain muscle power.

Gennuso et al. [262] reported that longer bouts and fewer breaks in SB is

negatively associated with physical function in older adults, regardless of

participation in MVPA. This adds to previous research were the odds for

abdominal obesity decreased 7% for each additional hourly BST in older

women [263], as well as triglycerides and plasma glucose [264].

DISCUSSION STUDY 1

296

These findings represent a new challenge for public health

recommendations regarding how to break-up sedentary patterns

complementary to those for physical activity in order to improve physical

functionality.

Effects of SB on frailty status

Current scientific evidence consistently shows that changes in body

composition, especially loss of muscle mass, together with low physical

activity and high SB, could be an important contributor for developing

frailty in older adults [37]. Interestingly, regular exercise is probably the

only non-drug derived therapy effective to improve physical function,

cognitive performance and mood [191], besides sarcopenia [265], which is

the central problem in the frailty syndrome.

Despite all the potential benefits of physical activity in relation to frailty,

frail older adults spend 84.9% (about 10 hours), of their daily time in SB

[266]. Previous evidence indicates that physically inactive individuals who

have higher levels of functional disability [267], and those individuals who

have high levels of SB are more likely to be frail [268].

da Silva Coqueiro et al. [214] found a positive association between self-

reported ST and frailty in 316 community-dwelling older adults. The

authors calculated that 7 hours per day of SB was the best cut-off point to

discriminate frail individuals. However, this cut-off point is quite low in

comparison with other studies reporting objectively measured SB [266].

The only study that met the inclusion criteria in the present literature

review for the frailty section was the one recently published by Bastone

STUDY 1 DISCUSSION

297

Ade et al. [269]. This investigation found that the frail group spent more

time in SB that their robust peers. Sedentary behaviour was significantly

associated to frailty, even after adjusting by the number of chronic health

conditions, but this association disappeared when the statistical model was

adjusted by cognitive status. Bastone and coworkers did not report an

association between SB and frailty status independently of the physical

activity levels. This was the case in Blodgett et al. [207] study, where a

positive association was observed between SB and various adverse health

outcomes (frailty, self-reported health, activities of daily living disability,

healthcare utilization), independent of MVPA in a community dwelling

older adults (>50 years) sample. As a limitation, these cross-sectional

studies do not take into consideration causality. Therefore, it is not possible

to certainly know if SB causes the appearance of frailty or if frailty can cause

that individuals choose to have a more sedentary lifestyle.

Longitudinal studies like the one by Song et al. [216] support the existing

idea of a relationship among daily ST and the development of a frailty

status, regardless of MVPA. But the scarce available data prevent to

robustly demonstrate this association, and more studies using similar

methodologies both to measure SB and frailty are needed.

Effects of SB on mortality

As early as in the 1950s, we can found the first indication that SB could

markedly increase adverse health outcomes. Morris et al. [270]

demonstrated a double age-adjusted rate of fatal coronary heart disease in

bus drivers (sedentary) when compared with conductors (active) workers.

DISCUSSION STUDY 1

298

Since then, much research efforts have been focused on the relationship of

an active lifestyle and various health outcomes, even with all-cause

mortality rates [271]. However, much less attention has been devoted on

the effects of SB on mortality. Again, scientific literature relies on self-

reported questionnaires to demonstrate an association between SB and

mortality (all-cause, cardiovascular, colorectal cancer, other causes) in

adults and older adults, independently of physical activity levels [272]. This

implies the limitation that questionnaires may not correctly differentiate ST

from LPA [273], but the existing scientific literature using objective physical

activity measurements is very scarce at the moment [273].

While Ensrud et al. [274] observed that individuals in the higher SB quartile

had a higher all-cause mortality than those in the lower SB quartile, Fox et

al. [275] found that despite spending a mean time of 11 SB hours, the study

participants did not show an association among mortality rates and ST

volume.

Klenk et al. [276] found a higher mortality risk in those subjects who spent

more time in sedentary activities. However, when biomarkers were

included as a confounding variable the association disappeared.

Interestingly, a large recent review combining data from over one million

participants found that 60-75 minutes of physical activity a day eliminated

the harms of sitting when it came to measuring death from cardiovascular

disease or death by all causes [277]. Despite the large number of people

included in the review, the results should be taken with caution as they are

based on self-report physical activity and SB data. When we take into

consideration populations younger than those included in this review,

studies mainly report a significant effect of SB on mortality [253, 278].

Among those, Koster et al. [253] concluded that SB is a risk factor for

STUDY 1 DISCUSSION

299

mortality independent of MVPA. Unfortunately, drawing conclusions in

this section is complicated because of the small number of studies and the

confusing results of each of them.

Methodological issues

To date, the use of accelerometers is considered the most valid and reliable

method to assess SB, despite not all devices are able to discriminate

between sitting and standing changes in the posture [279]. In order to make

stronger the conclusions of this review, only studies using accelerometers

to assess SB were included. However, the variety of devices utilized and

the diversity in techniques regarding data extraction and analysis across

studies makes difficult drawing definitive conclusions.

Reactivity is an important point to take into account when measuring

physical activity and SB with accelerometry because it may introduce a

relevant bias. Although the Hawthorne effect has been recognized as a

potential limitation of the accelerometry method, evidence remains limited

[280]. It seems clear that in children and adolescents there may be some

reactivity [281]. However, tampering with devices seems to be less likely

among older adults [282]. None of the studies included in this review use

strategies to avoid reactivity, therefore the results and conclusions must be

interpreted with caution since the evidence in this area is still scarce.

The number of valid days and the minimum hours per day included in the

analysis from the accelerometer data is another important methodological

issue when working with these type of devices. The average number of

valid days to include accelerometer data in the analysis is 3.6 ± 1.4 days

among the studies reporting this value included in the review. However,

DISCUSSION STUDY 1

300

according to the study of Hart et al. [283] conducted in older people, at least

five days are necessary to adequately capture the SB. Thus, studies which

take less than 5 valid days for the accelerometry analysis might be

unrepresentative. Moreover, cut-offs points for sedentary strip

establishment is also important, knowing that they are dependent on the

analyses unit (i.e. epoch length and axes) [284].

The third important methodological variable to consider is the criteria for

non-wear time of the accelerometers. In that regard, published studies are

divided between the algorithm proposed by Troiano [285] or the algorithm

recommended by Choi et al. [286]. The latter incorporates improvements

for the misclassification of time intervals spent in SB that do not pass the

wear/non-wear classification criteria for the low activity counts. Thus,

studies in populations with a low physical activity and high SB patterns,

such as older adults, could likely benefit from these improvements [286].

Although according to the definition of SB [287] only SB should be

accounted during waking hours, one study in this review included the time

that individuals spend sleeping as SB [276]. This can lead to an

overestimation of ST and should be taken into account when comparing

results from studies using different approaches.

Finally, another important aspect that should be considered when studying

SB in relation to health outcomes is MVPA. This factor should be taken into

account within the covariates included in the statistical models so that the

independent effect of SB can be ascertain. The same applies with health

status, especially in older adults studies in order to avoid confounding

interactions [288].

STUDY 1 DISCUSSION

301

Although Pedisic et al. [282] concluded that accelerometer-based studies

had limitations regarding generalisability, validity, comprehensiveness,

simplicity, affordability, adaptability, between-study comparability and

sustainability, many of these methodological aspects have not yet been

homogenized. Overall, the discrepancy in the methodological aspects

across the analysed studies in this review may preclude us from drawing

definitive conclusions, although a recent review that could help researchers

to make better decisions before and after data collection using

accelerometers, in order to obtain more valid and comparable data has been

published [289]. Consistency in the methodological aspects when assessing

SB and stronger research designs are crucial points to confirm the observed

findings in this review.

DISCUSSION STUDY 1

302

STUDY 2 DISCUSSION

303

6.2. Study 2

To our knowledge, this is the first study that comprehensively analyses the

impact of objectively assessed sedentary patterns beyond total time spent

in SB on frailty in older adults. The main findings were that adjusted

models (i.e. in models adjusted by relevant demographic and medical

confounders) frailty was associated with ST per day, the proportion of the

day spent in sedentary bouts of 10 minutes or more, and time spent in BST.

These results may pose some light on the ongoing discussion regarding the

health consequences of sedentary lifestyles and could generate novel

hypotheses that could help in informing future public health interventions

in order to prevent frailty among older adults.

Sedentary behaviour patterns and frailty

Available evidence suggests that, regardless of MVPA, spending time in

sedentary activities increases the odds of being frail among older adults

[207, 214]. Our results confirm and extend those of da Silva Coqueiro et al.

[214] and Blodgett et al. [207] (i.e. frailty was associated with ST in our

sample), but also verified that the proportion of the day spent in sedentary

bouts of 10 minutes or more is a more powerful predictor of frailty than

raw ST or ST spent in 10-min blocks. The former opens the hypothesis that

health consequences of SB may be hooked on the display of other

behaviours. Compositional analysis of time spent in different behaviours is

therefore required to fully understand the impact SB may have on health,

including frailty among older adults.

DISCUSSION STUDY 2

304

In an attempt to understand more precisely how the pattern of

accumulation of ST may have an impact on frailty scores in older adults,

we combined the number and duration of bouts of 10 minutes or more time

spent in ST into a novel ST compositional score. Previous work has showed

an association between SB and frailty status among adults and older adults

[207, 214]. Similarly, in order to reflect the potential combined implication

of both duration and number of BST on frailty, a compositional score from

the number of and minutes spent in breaks of ST was created (BST-COMP).

Previous work has demonstrated that BST is associated with physical

function and disability in older adults [218, 290]. Our empirical work

extends and confirms the hypothesis that interrupting ST has the potential

of enhancing the wider health of individuals by demonstrating that not

only the number of BST but also duration of those BST may have an impact

on frailty status among older individuals. Collectively, the results from our

study support and extend to frailty the inactivity physiology hypothesis.

Future experimental research is warranted to clarify the potential

mechanisms underpinning the showed associations.

Relevance

From a public health perspective, reducing SB and engaging in LPA, for

instance, by inserting short bouts of activity into otherwise sedentary

periods, may be a more feasible and less challenging approach for older

adults than taking part in more strenuous activities in order to promote

health [291]. Our findings reveal that having fewer breaks of sedentary

periods was associated with higher frailty level among the study sample.

The reverse is also true. This is of interest, as only a minor proportion of

older adults meet the WHO physical activity recommendations (30% in our

STUDY 2 DISCUSSION

305

sample). Therefore, while efforts on MVPA promotion should be sustained,

guidelines for older adults should also reinforce the idea of breaking-up ST

more often in order to prevent frailty among this population group.

Strengths and limitations

Key strengths of the study include the relatively large sample, the objective

measures of SB patterns, and the inclusion of a novel analytical approach

by deriving new variables that reflects more accurately SB patterns and

therefore provide unique knowledge in the field with potential clinical

relevance. The cross-sectional nature of the research design used does not

allow definitive conclusions to be drawn around the causal relationship

between the variables of the study. There are some inherent issues with SB

patterns being derived from accelerometers, such as the use of <100

counts/minute as a threshold to determine sedentary activities [292] or the

use of 1-min epoch length that may impact the generalization of the results

[284]. Despite these limitations, our findings contribute to the current

literature and ongoing discussion on the impact of SB on frailty among

older adults. More research is warranted around the potential effects of

activity insertion of different intensities to prevent frailty in this population

group. Moreover, longitudinal experimental designs are necessary to

overcome some of the research-design inherent limitations of this study

and confirm the results showed here.

DISCUSSION STUDY 2

306

STUDY 3 DISCUSSION

307

6.3. Study 3

This study aimed to investigate the relationship between physical activity,

SB and frailty. We first assessed the association between these factors using

a classical approach, then using a theoretical model, to examine how the

displacement of activity of different intensities is associated with changes

in the frailty score using isotemporal substitution modelling. Our results

estimate that replacing 30 minutes of ST with an equivalent amount of

MVPA is associated with a more theoretically favourable frailty status in

older adults, regardless of comorbidity or physical function status. Equal

time-exchange of ST with LPA is predicted to reduce frailty but only in

older adults with comorbidity (52.6% in our subsample). In addition, the

modelled relationships also suggest potential benefits of LPA in those with

comorbid conditions, which may be a more feasible and less challenging

approach than more strenuous activity.

Replacing ST with MVPA

In our study, we found that replacing sitting time with MVPA resulted in

reductions in the FTS. This is consistent with previous findings where

MVPA was associated with frailty even after controlling for ST [207].

Moderate-to-vigorous physical activity is well known to affect cognitive

and physical outcomes, all known to impact frailty. Supporting our results

are the findings of Song et al. [216] and Peterson et al. [268] which show

evidence that ST negatively impacts on frailty in older adults. Likewise,

Fanning et al. [293] only found an improvement in self-regulatory

DISCUSSION STUDY 3

308

behaviour and executive functioning when 30 min of ST was replaced by

30 min of MVPA time.

Replacing ST with LPA

We have found in single and partition models that LPA has a role on frailty.

Some authors have found a positive relationship between LPA and

different outcomes regarding frailty, while others have not. Elkins [294]

showed that daily time spent in LPA is associated with lower risk of onset

and progression of disability whilst Lee et al. [295] found a positive effect

of LPA in cognitive status. Jantunen et al. [296] also showed that LPA was

positively associated with better physical performance. However, Pau et al.

[297] found that LPA is not the most adequate intensity to improve daily

static and dynamic motor tasks.

As a novelty, our grouping analysis shows that only in people reporting

comorbidities did LPA bring benefits in terms of frailty. Other studies have

shown the beneficial effects of LPA in the isotemporal substitution

analyses. Ekblom-Bak et al. [298] showed significant lower metabolic

syndrome prevalence by replacing 10 minutes of ST with the same amount

of LPA. Similarly, Fishman et al. [299] and Schmid et al. [300] found that

replacing 30 min of ST with LPA was associated with significant reduction

in mortality risk. Our findings show that in frail individuals, with low

fitness even minimal movement can positively impact health [301]. But

when a certain fitness level is reached, more strenuous activity is needed to

elicit more beneficial results.

Despite requiring between 4-5 times more LPA to elicit the same changes

in frailty compared with MVPA, according to our estimates, the benefits of

STUDY 3 DISCUSSION

309

LPA for improving frailty status in those with comorbidities is of relevance

from a public health perspective as might be a population, of those with

comorbidities, that cannot or do not find opportunities to (frail older adults

spend 84.9% of their daily time in SB [266]) become engaged in MVPA

successfully. Thus, replacing ST (e.g., television viewing, sitting) with LPA

(e.g., leisure walking, active transport) may be a more feasible strategy to

reduce the risk of frailty in older adults with additional disease. Future

longitudinal experimental studies should confirm these results.


This study has several strengths and limitations. The sample includes a

relatively large number of community-dwelling older adults with

objectively assessed frailty and physical activity. However, when

comparing the full cohort with the included sample there were differences

in most outcomes used in this study so caution should be exercised when

interpreting the results. Although there is no established gold standard to

identify frailty, the FTS, derived from the classical model proposed by Fried

et al. [37] in combination with the positive aspects of the Frailty Index of

Rockwood et al. [302], has been suggested as a more sensitive scale for

detecting changes in the individual's biological status [59]. The validity of

this scale was evaluated by assessing its association with comorbidities,

biomarkers associated with frailty status, and by comparing its predictive

value for adverse events with the two most frequently used frailty scales:

Frailty Phenotype [37] and the Frailty Index [302]. Although accelerometry

has some advantages over questionnaires and other self-report methods

[303], it is not exempt from error. Waist-worn accelerometers are not able

to detect differences between sitting and standing positions, and therefore

DISCUSSION STUDY 3

310

the measurement of ST can be overestimated. In addition, the cut-off points

used in this study and the algorithm chosen to discard zero value periods

can affect the amount of different physical activity intensity ranges and SB.

Causal inferences are limited due to the cross-sectional nature of the study.

Moreover, isotemporal substitution models represent a mathematical way

of replacing one behaviour with another, so the results should be

interpreted with caution. There is therefore a need for more experimental

research in this area, especially in the clinical setting, in order to better

understand the impact of replacing ST with activity of different intensities

on frailty status among older adults. Another limitation of the study may

be the non-measurement of sleep, an important behaviour that may affect

the associations found in this study.

STUDY 4 DISCUSSION

311

6.4. Study 4

The way in which time packed in a given day remains relevant for a wide

range of health outcomes [304]. Previous research has identified the

cardiometabolic [230] and mortality outcomes [305] of different movement

patterns in adults and older adults, respectively. This is the first study

assessing the associations of mutually exclusive categories of

accelerometer-derived physical activity and ST with physical function and

frailty in older adults. The main findings were that participants who

engaged in ≥150 min/week of MVPA had more favourable physical

function and frailty profiles than those classified in the other movement

pattern groups, regardless of sedentary status. Our results also suggest that

engaging in more light intensity relative to ST may have a positive impact

on physical function and frailty status on the studied population, even in

those individuals already meeting the physical activity guidelines. This

might provide alternative intervention strategies to improve physical

function and prevent frailty, as light activities are more feasible than more

strenuous activity, particularly among previously inactive individuals.

Physical activity status, physical function, and frailty

Previous research have demonstrated that MVPA is effective to prevent,

delay or even reverse functional limitations and frailty among older adults

[192]. The present study provides novel data indicating that older adults

who meet recommended physical activity levels, regardless of time spent

in light-intensity activities relative to sedentary activities, have better

DISCUSSION STUDY 4

312

physical function levels and frailty status compared to older adults who do

not meet the required physical activity levels. These results emphasize the

importance of engaging in sufficient MVPA, which could buffer some of

the negative consequences of SB in preserving the physical functionality

and reduce frailty in this population group [246, 306]. A recent meta-

analysis involving more than 1 million adults has shown that engaging in

higher amounts of strenuous activity can eliminate the mortality risk

associated with too much sitting reported elsewhere [277]. The association

of more intense activity with fitness levels partially explains why meeting

the physical activity recommendations may overcome the harmful effects

of SB. Thus, cardiovascular fitness has been proposed as a plausible

mechanism mediating the relationship between SB and cardiometabolic

health in older adults [307]. More studies are required to elucidate the role

of fitness in the relationship between MVPA, SB, physical functioning and

frailty in older adults.

Sedentary status, physical function, and frailty

Contemporary experimental [308, 309] and observational [291, 310]

evidence emphasizes the health-enhancing role of light-intensity activities.

In a recent meta-analysis by Chastin et al. [311], LPA emerged as potentially

relevant for cardiometabolic health and mortality in adults and older

adults, in particular among impaired individuals. Our estimates suggest

that increasing the time in LPA relative to ST has a positive impact on frailty

levels in those considered physically inactive. Other studies have suggested

the potential benefits of replacing SB with LPA to reduce frailty in older

adults with multiple diseases [312]. It might be the case that in the more

frail and functionally compromised individuals even small stimulus from

STUDY 4 DISCUSSION

313

light intensities can benefit their wider health [312]. Collectively, these

findings are policy-relevant. Light-intensity physical activity is normally

naturally embedded into the daily living of individuals (e.g. walking a dog,

doing home chores or standing up while talking on the phone), therefore

requiring no mental or physical effort or starting level to perform such

activities, and thereby making light-intensity activities a pragmatic target

for future public interventions to reduce frailty and improve physical

function of older adults, particularly among those inactive (i.e. 83.5% in our

sample) and that also depict very high levels of ST (i.e. 63.6% in our sample)

which might also be the most impaired individuals.

Interestingly, we identified the group meeting the physical activity

guidelines (i.e. active) and showing higher levels of light intensity relative

to ST as the group with better frailty and physical function profile in our

sample. Others have found similar results for cardiometabolic health [230]

and mortality [305]. Recent epidemiologic evidence suggests that sitting

time has deleterious cardiovascular and metabolic effects that are

independent of whether or not adults meet the physical activity guidelines

[235]. Our results suggest that engaging in more light-intensity activity

relative to ST beyond meeting the physical activity recommendations can

provide with extra benefits in improving physical function and reducing

frailty in older adults. Those individuals in our sample meeting the

physical activity guidelines and engaging in more light-intensity activities

extend their total volume of physical activity as supposed to those that meet

the recommended amount of physical activity yet are sedentary, which

could partially explain the extra benefit associated to that movement

pattern [313]. Thus, promoting light-intensity activities could be a good

approach to increase the total volume of physical activity and reduce ST in

DISCUSSION STUDY 4

314

those already meeting the physical activity guidelines, thereby enhancing

their health, including increasing physical function and improving their

frailty profile.


The present study has several strengths. First, the study includes a

relatively-large sample of community-dwelling older adults with advanced

age. Although there is no current established gold standard to determine

physical function and frailty in older adults, the short physical function

battery has positioned as one of the most used tools to objectively evaluate

functional performance among older adults [314]. Similarly, the FTS has

been suggested as a more sensitive scale for detecting changes in the

individual’s biological status than previously validated frailty instruments

[59]. We also used accelerometer-measured procedures to assess physical

activity and ST.

Our study has also limitations. Firstly, the cut-off points used in the study

to categorize the activity intensity of participants in the study can lead to a

misclassification of both physical activity and ST. However, the cut-off

points used in this study are the most commonly reported in the literature

for this age group [229], which make the results found here comparable

with other investigations. Furthermore, ActiGraph devices are not able to

discriminate between sitting and standing changes in the posture [315]. In

order to obtain the activity status, bouts of at least 10 minutes were used,

which may underestimate the time spent in MVPA. Nevertheless, further

research is needed to consider the impact of the bout duration on frailty

syndrome. Similar to what Bakrania et al. [230] reported, data could be

STUDY 4 DISCUSSION

315

overestimating the ST [316], we therefore decided to use a more

conservative approach for the extraction of sedentary status based on the

behaviour of our population, an approach used in previous studies [230].

Loprinzi et al. defined low sedentary status as a positive LPA-to-ST ratio

[232]. If we had used the Loprinzi et al. [232] method, only 2.1% of our

population would have been categorized as low sedentary status. This

procedure used may have limitations and strengths. On the one hand, it is

not influenced by the measurement of the accelerometer, but on the other

hand, because is data-driven, may not be applicable to other populations.

The use of this novel approach allows combining in mutually exclusive

categories that best represent the different plausible combinations of

physical activity and ST within waking hours. Nonetheless, the cross-

sectional nature of the research design used does not allow definitive

conclusions to be drawn around the causal relationship between the

outcomes of the study.

DISCUSSION STUDY 4

316

STUDY 5 DISCUSSION

317

6.5. Study 5

The main finding of the current study was that 27 minutes/d of MVPA

eliminated the increased risk of frailty associated with ST.

The detrimental health consequences of SB have been demonstrated for a

range of health outcomes such as metabolic syndrome, waist

circumference, and overweightness/obesity [188]. For example, Gao et al.

[317] found that greater time in television viewing was associated with high

waist-to-hip ratio and Gennuso et al. [318] found that more time spent in

objectively measured SB was associated with a high waist circumference

and BMI. This damaging effect of time spent in sedentary activities seems

to be confirmed for frailty in older adults [207, 216, 319, 320] and remains

true also in our sample. Several factors such as the loss of maximal aerobic

capacity and muscle strength, a decreased cognitive function, a dysfunction

of the immune system and a damaged metabolic function or weight gains

may partially explain these associations [321].

Moderating role of MVPA

The main contribution of our study is the confirmation of the moderating

role of physical activity on the SB-frailty association. Our results suggest

that engagement in 27 minutes per day of MVPA could eliminate the

negative consequences of spending too much time in otherwise sedentary

activities. Recent evidence in Chilean older adults also suggests that more

intense physical activity attenuates the negative effects of sitting time on

cognition [322]. This estimated protective role of physical activity has also

DISCUSSION STUDY 5

318

been confirmed in a large meta-analysis with more than 1 million adults

and older adults [277]. The former study suggests that in highly physically

active people (i.e. participants accumulating 60-75 min/day of MVPA),

sitting time was no longer a predictive factor of all-cause mortality. Also,

Theou et al. [323] reported that in active individuals over 50 years old, ST

does not affect the risk of mortality, regardless the level of frailty.

Altogether, these findings seem to reinforce the idea of increasing MVPA

to offset the harmfulness that ST may have on frailty in older adults. Future

studies need to confirm our results in longitudinal, experimental studies.

Sedentary behaviour and frailty

Despite the well-established health benefits [324, 325], engaging in more

MVPA may not be feasible among all older adults. In fact, only 28% in our

sample met the suggested threshold of 27 min/day of MVPA (which is 6

min/day above the current physical activity guidelines) necessary to

theoretically offset the impact of SB on frailty. A recent systematic review

and meta-analysis [326] concluded that replacing accelerometer-assessed

ST with light intensity activities is a feasible health enhancing strategy,

particularly among previously sedentary individuals. A previous study

also demonstrated that replacing SB with light intensity activities can

reduce the frailty levels among vulnerable older adults [312]. Our

estimations confirm the benefits of reducing SB to reduce frailty levels

among inactive older adults (i.e. 68% of the sample in the current study).

Some studies have also demonstrated that reductions in SB (i.e. short

breaks of light activity into otherwise inactive periods) are possible [327]

and that have the potential of improving the physical function of older

STUDY 5 DISCUSSION

319

individuals [218, 328-330]. Collectively, these findings indicate that both

increasing MVPA and reducing ST should be encouraged to reduce frailty

among older adults. The inclusion of strategies to reduce time spent sitting

and increase time spent in light intensity activities to reduce frailty may be

of particular interest among the most inactive older adults and could be

taken as a first, feasible step towards accumulation of more MVPA

throughout the day.


An important strength of our study is that includes a relatively large

sample of community-dwelling older adults with accelerometer-derived

SB and physical activity estimations. Also, although there is no established

gold standard to identify frailty, the FTS has been suggested as a more

sensitive scale for detecting comorbidities, biomarkers and adverse events

associated with frailty than previously validated frailty scales such as

Frailty Phenotype [37] and the Frailty Index [302]. Some limitations have to

be acknowledged. Despite validity and widely used of accelerometers to

assess physical activity in free living conditions, these devices are not able

to discriminate between sitting and standing [315] or activity type,

therefore potentially biasing the estimations in our study. The cross-

sectional nature of the observations does not allow definitive conclusions

to be drawn around the causal relationship between the variables of

interest. Additional evidence is required to extend these findings using

longitudinal and experimental data.

DISCUSSION STUDY 5

320

STUDY 6 DISCUSSION

321

6.6. Study 6

The present study investigated the longitudinal relationships between

MVPA and SB with frailty status in a community-based sample of older

adults. As a novelty, we applied a cross-lagged panel model to test for

potential reciprocal relationships between MVPA/SB and frailty over a 4-

years period. The main finding in our study was that accelerometer-

assessed initial MVPA predicted frailty score at follow-up. However,

baseline ST was not significantly related to frailty after the follow-up. We

further found that initial frailty status predicted subsequent ST (i.e., more

frailty status was related to posterior higher levels of SB), but not of MVPA.

These results have the potential to inform future interventions that aim at

reducing the burden associated with frailty among older adults.

Longitudinal relationship between MVPA and frailty

Different cross-sectional [207, 312, 331] and longitudinal [208] studies have

linked MVPA with frailty. Blodgett et al. [207] found that MVPA was

associated with frailty in a group of community dwelling adults aged over

50 from the National Health and Nutrition Examination Survey. Other

longitudinal studies such as Rogers et al. [208] have also confirmed these

results in 8649 adults aged 50 and over an average of 10 years of follow-up.

We also found that MVPA prospectively predicted frailty levels in our

sample. There are numerous arguments supporting these findings [193,

196, 332, 333]. It has been demonstrated that physical activity, particularly

of moderate intensity plays an important role on multiple components of

DISCUSSION STUDY 6

322

the frailty syndrome including the frailty phenotype, physiologic

dysregulation, and cellular function [192]. Increases in MVPA seem to also

preserve or even improve muscle function and structure, protein synthesis,

glucose metabolism or inflammation [196]. Furthermore, regular physical

activity can maintain a set of bioenergetically functional mitochondria that,

by improving systemic mitochondrial function, contribute to reducing the

risk of morbidity and mortality throughout life [332]. Not surprisingly,

MVPA is considered a cornerstone for the prevention, delay or treatment

of frailty among older adults.

On the other hand, our results did not support the hypothesis that initial

frailty levels predict future MVPA levels. Several studies support the

predictive ability of physical functioning on subsequent MVPA levels [334-

336]. However, it may also be plausible that other, non-biological

mechanisms (e.g., behavioural) accounted for the observations of the

current study. Different intervention studies have shown the possibility of

increasing physical activity also in frail participants [337]. For example,

Yamada et al. [338] found that is possible to promote exercise of MVPA

among very frail older adults. Future studies are warranted to clarify the

role of frailty in subsequent MVPA levels in older adults.

Longitudinal relationship between SB and frailty

Sedentary behaviour has recently been considered as an important factor

for numerous health outcomes [188, 339]. A recent systematic review has

shown that SB may be associated with increased levels of frailty,

particularly among the most vulnerable population [340]. Interestingly, our

results indicate that SB was not a determinant of frailty, but rather a

STUDY 6 DISCUSSION

323

consequence of an altered state of increased frailty. A smaller study by

Edholm et al. [341] with 60 older woman found that only activities of at

least moderate intensity were associated with physical function in a

subsequent follow-up time but not activities of lighter intensity or

sedentary activities. In addition, Marques et al. [342], in a study conducted

with 131 males and 240 females aged 65-103 years, suggested that ST was

not a significant predictor of loss of physical independence in later life. In

a previous cross-sectional study, we showed that engaging in high levels

of MVPA (i.e., 27 minutes/day) could cancel out the detrimental effects of

SB on frailty, which may partly explain our current observations [233].

Given that the relationship between SB and frailty may go beyond total

accumulated time [319], future studies should enquire whether or not the

results of this, and other studies are confirmed for different patterns of

accumulation of SB.

Relevance

According to our findings, the promotion of MVPA at earlier stages will

translate into more MVPA and less frailty markers in the future. Also, the

observations of the current study point out to the possibility that the

detrimental effects on frailty are primarily defined by insufficient amounts

of MVPA rather than an excessive amount of ST. Public health

organizations should target MVPA to reduce the burden associated with

frailty in older adults.

DISCUSSION STUDY 6

324


An important strength of our study is that includes a relatively large

sample of community-dwelling older adults with longitudinal data

separated by 4-years. It also includes accelerometer-derived sedentary and

physical activity behaviour estimations. Also, although there is no

established gold standard to identify frailty, the FTS has been suggested as

a measurement of frailty with superior predictive validity than previously

validated scales such as the Frailty Phenotype [37] and the Frailty Index

[302]. Additionally, a key strength of this study was that the statistical

analysis deployed has allowed to explore the auto-regressive and cross-

lagged pathways in exploring how frailty relates over time with both

MVPA and SB. Despite the methodological rigor of this study, some

limitations have to be acknowledged. First, we cannot rule out the

possibility that our estimations could be influenced by the characteristics

of the participants who did not provide valid data at follow-up (i.e., older,

less active, more sedentary, less educated) and therefore our results should

be interpreted with caution. A further limitation of our work was that

despite validity and widely used of accelerometers to assess physical

activity in free living conditions, these devices are not able to discriminate

between sitting and standing [315] or activity type (e.g., running vs. muscle

strength), which could potentially bias the estimations in our study. Finally,

physical activity and SB have been examined separately from other lifestyle

behaviours (e.g., diet, smoking, alcohol consumption). However, lifestyle

behaviours tend to cluster together. Therefore, it could be that our results

rather reflect the synergistic consequences of different observed (i.e.,

physical activity and SB) and unobserved (e.g., diet quality) lifestyle

behaviours [343, 344]. Future studies may want to test this hypothesis.

STUDY 7 DISCUSSION

325

6.7. Study 7

To our knowledge, the present study tested for the first time the temporal

and bidirectional associations between the number of daily BST and frailty

status in a sample of community-dwelling older adults. The main findings

were that, in physically inactive individuals there was an inverse

relationship between BST and frailty status 4 years later. The reverse was

also true (i.e., frailty status at baseline was inversely associated with BST 4

years later). There was no evidence of longitudinal association between BST

and frailty in older adults who were considered physically active.

Consequently, increasing daily BST could be a promising strategy to reduce

the burden of frailty syndrome in physically inactive older adults.

Longitudinal relationship between BST and frailty

Different cross-sectional studies have explored the associations of BST with

physical function [218], disability [220], and frailty [319] in older adults. A

previous study found a positive association between BST and physical

function, after adjusting for total ST and MVPA [218]. In a study comprising

1634 Japanese older adults, greater BST was associated with lower

likelihood of instrumental activities of daily living disability [220]. In

another study conducted in older adults assessed with accelerometers,

daily BST was associated with lower frailty in older adults [319]. Our study

extends these previous findings and investigated the longitudinal and

bidirectional associations between daily BST and frailty in a sample of

community-dwelling participants 65 years and over. In physically inactive

DISCUSSION STUDY 7

326

individuals, a higher daily number of BST predicted a lower frailty status

after 4 years. Further, a lower baseline frailty status predicted greater daily

number of BST 4 years later. Mechanisms underlying these findings are

likely to be complex. Experimental studies have provided evidence related

to the physiologic and cardiometabolic benefits of breaking-up and

reducing sitting time [345]. The evidence suggests that beyond the increase

in energy expenditure that requires a transition from sitting to standing

position [346], the benefits of frequent BST can be explained by the

muscular contractions derived from such transitions [347]. These muscular

contractions, which will mostly be provided in light physical intensity

activities, can lead to important functional adaptations through different

physiological and molecular pathways [235, 347-350] that ultimately affect

human health [351]. Therefore, it seems evident that physically inactive

individuals who break their ST more often may reduce their frailty status.

Likewise, less frail individuals, will find it easier to break ST more often

compared to other more structured physical task, thus becoming a positive

circle (negative if the question is raised backwards). This is in accordance

with a previous study of our group that demonstrated that older adults

with comorbidities may benefit more from replacing ST with LPA activity

compared to healthier counterparts [312]. A recent meta-analysis also

reported the benefits of replacing sitting time with LPA activities for

cardiometabolic health and mortality [326]. Together, these findings

suggest the potential of targeting reductions in ST to improve the health of

older adults [345, 352].

In contrast, we did not detect a statistical association between baseline daily

BST and follow-up frailty status among physically active older adults. In a

previous study, it was suggested that lower BST were linked with better

STUDY 7 DISCUSSION

327

muscle quality in a sample of highly functioning and highly active older

adults [261]. It is plausible that active participants are also fit [353] and that

a stimulus stronger than muscle contractions resulting from breaking-up

ST is necessary to evoke reductions in the frailty status among these

individuals. This suggest that the effects of BST on frailty may be

moderated by fitness. Similarly, we speculate a higher physiological

reserve (i.e., less frailty) present in active participants may have accounted

for the lack of association between initial frailty status and daily BST at

follow-up. Further experimental studies are required to investigate our

observations. The lack of significant results for the total sample is likely to

reflect the heterogeneity in the estimations for active and inactive

individuals in this study (i.e., the positive findings for inactive individuals

are cancelled out by the null findings among active individuals).

Relevance

These findings are likely to be policy-relevant: current physical activity

guidelines for older adults focused mainly on MVPA [139, 140]. Further to

increase MVPA, our results also stress the relevance of breaking-up ST,

particularly in those physically inactive individuals. From a health

promotion perspective, encouraging small bouts of activity into otherwise

sedentary periods may be a more feasible and less challenging approach

for older adults than taking part in more intense activities. Breaking-up ST

can occur trivially in a variety of daily living activities (at home, during

transport, or leisure time) because: i) it does not require a high degree of

commitment or planning, ii) it can be achieved with a physically lower

load, and iii) it does not require a high level of fitness or complex motor

DISCUSSION STUDY 7

328

skills. Altogether, our observations indicate the possibility of targeting BST

to reduce the frailty levels in physically inactive older adults [345].


A key strength of our work is that it comprises a relatively large sample of

community-dwelling older adults with data follow-up of 4 years. The use

of accelerometer-derived sedentary and physical activity behaviour in this

study is also a strength. Another strength is the robustness of the FTS to

assess the frailty level of participants. The FTS has demonstrated superior

predictive validity and responsiveness than previously validated

constructs such as the Frailty Phenotype [37] and the Frailty Index [302].

Furthermore, another key strength of this study is the use of a statistical

method (i.e., cross-lagged panel model) that allowed us to investigate the

temporal order and bidirectional, longitudinal associations of BST with

frailty over time in the study participants.

Despite the methodological rigor of this study, some limitations must to be

acknowledged. First, we cannot rule out the possibility that our estimations

could be biased by the characteristics of the participants who did not

provided valid data at follow-up (i.e., they were older, less active, more

sedentary, and less educated than those included in the final analysis).

Consequently, our results should be taken with caution. An additional

limitation is the limited ability of accelerometers to discriminate between

sitting and standing compared with posture-based devices [315, 316, 354].

Importantly, in our study, a break in ST reflects a modification in

acceleration instead of a change in posture, corresponding to a transition

from none or slight movement (<100 cpm) to some movement (≥100 cpm).

STUDY 7 DISCUSSION

329

Future studies should replicate our analysis with posture-based devices.

Lastly, experimental studies are required to confirm our observations [355].

Future studies should investigate optimal strategies to increase the number

of BST throughout the day in older adults, particularly among those not

achieving the recommended level of physical activity.

330

331

CHAPTER 7

CONCLUSIONS

CONCLUSIONS

332

CONCLUSIONS

333

Study 1

- There is consistent evidence on the relationship between objectively

measured SB and physical performance in the elderly. The

association among sedentary lifestyle, frailty incidence and mortality

rates warrant further investigation.

- The lack of studies assessing these outcomes and the wide variety of

methodological issues reported among the reviewed studies make

difficult to draw definitive conclusions.

- Another important aspect that deserves further investigation is the

manner that SB is accumulated. Breaks in sedentary time seem to

minimize the decline of physical performance with aging. Future

research should test this hypothesis also regarding frailty and

mortality outcomes.

CONCLUSIONS

334

Study 2

- Sedentary time, breaks in sedentary time, and daily proportion of

time spent in SB bouts of 10 minutes or more are associated with

frailty in older people in the TSHA study. Altogether, these results

may point to the pathways through which engaging in frequent,

short bouts of activity insertions into otherwise continuous

sedentary periods can attenuate frailty among older adults.

- Our results suggest that interventions should therefore not only be

focused on reducing the total time spent in sedentary activities but

also on how that time is accrued in order to prevent frailty in older

people. However, longitudinal, experimental research, preferably in

form of RCT, is required to confirm the causality of the relationships

observed in the current study.

CONCLUSIONS

335

Study 3

- This study demonstrates that replacing 30 minutes/d of SB with the

same amount of MVPA could bring benefits in terms of frailty status

among older adults. Participants with comorbidities may also benefit

from the substitution of ST by LPA.

- From a public health perspective, this is an important message in

order to improve frailty status through increasing LPA, which a

priori seems to be a more feasible approach as opposed to increasing

MVPA in this specific population group. Future research should

move beyond this hypothetical, observational evidence and identify

more-robust indication of the frailty outcomes of experimentally

reallocating time spent in SB with physical activities of different

intensities.

CONCLUSIONS

336

Study 4

- We observed that physically active older adults had better physical

function and frailty profiles than those considered physically

inactive, even in the presence of high ST. Higher levels of LPA

relative to ST seems to provide additional benefits in both physical

function and frailty outcomes among those meeting the physical

activity guidelines. Lower sedentary levels were associated with

decreased frailty in physically inactive participants.

- Altogether, our findings reinforce the idea of the health-enhancing

benefits of meeting the current physical activity guidelines. Also, our

results highlight the relevance of LPA for inactive older adults. If our

results remain experimentally true, light intensity physical activity

can be promoted as a middle step intervention among inactive

individuals to achieve the recommended levels of physical activity

and improve their health. We should move beyond observational

studies and confirm our results in well-design longitudinal,

experimental studies.

CONCLUSIONS

337

Study 5

- The present study shows that MVPA is a moderator in the

relationship between ST and frailty in older adults, offsetting the

harmful effects of SB with 27 minutes per day of MVPA.

- Whenever possible, efforts should be directed towards the

promotion of MVPA in older adults. Also, reducing SB may be

beneficial, particularly in those engaging in less intense activity

throughout the day.

CONCLUSIONS

338

Study 6

- Our findings suggest that based on our longitudinal data MVPA, but

not SB, predicts frailty in older adults. In contrast, frailty seems to be

a predictor of SB but not of MVPA.

- Efforts should be directed at increasing MVPA from earlier stages.

Future experimental studies should examine the best strategies to

include MVPA in the daily lives of older people.

CONCLUSIONS

339

Study 7

- According to our hypothesis, the longitudinal relationship between

the daily number of sedentary breaks and the frailty levels is

bidirectional in physically inactive older individuals. We found no

evidence of a longitudinal association between BST and frailty in

older adults considered physically active.

- Our study provides robust longitudinal evidence that breaking-up

ST more often may reduce frailty in older adults that do not meet

physical activity recommendations. Pending on experimental

confirmation, targeting frequent reductions in ST may provide with

a feasible approach to reduce the burden of frailty among more at

risk inactive older adults.

340

341

CHAPTER 8

FUTURE

PERSPECTIVES

FUTURE PERSPECTIVES

342

FUTURE PERSPECTIVES

343

The worldwide progressive aging of population involves a significant

challenge to the long-term sustainability of public health and social care.

Today, technological, economic and social changes have completely

modified our lifestyle, with important implications for our health and well-

being. Physical activity, and recently SB, are two of the cornerstones which

are more likely to ensure a healthier aging process. From a physical and

public health perspective, the message about the use of time is clear: reduce

total ST, break-up prolonged periods of ST, and move more. In this dissertation,

we have addressed diverse questions in relation to the role played by these

three concepts (reduce, break-up, and move) in a particular health outcome

such as frailty syndrome. Specifically, the association of ST and its different

patterns with frailty has been observed. How physical activity interacts in

the SB and frailty relationship (either replacing time, combining

behaviours, or moderating the association) has also been examined. Finally,

we have tried to establish the temporal order of the associations between

ST, MVPA, and BST with frailty. Previously, the potential strengths and

limitations that the presented investigations may contain have been

discussed. Now, we will identify some of the most promising research

strategies and future directions that could extend our findings and

contribute to the development of research in the field of physical activity,

SB, and health.

A body of evidence on the relationship between SB and different health

variables has grown rapidly. Observational evidence, both transversal and

prospective, has generated and continues creating new hypotheses about

the influence of SB on health. However, experimental evidence must

provide rigorous research that addresses the hypotheses produced by

observational studies. To the best of our knowledge, no specific

FUTURE PERSPECTIVES

344

interventions aiming reductions in SB have been performed to address

frailty. Breaking-up sedentary time more frequently may be a possible first

step for those individuals with a more diminished homeostatic reserve, and

then move on to individualized physical activity programs prescribed by

professionals. Developing this type of staggered interventions will be

paramount from a public health perspective to promote successful aging.

Knowing that both the total ST and the way in which this time is

accumulated is linked to frailty, other reasonable step would be to identify

the dose-response of these relationships. Future research should address

key issues such as how much ST is too much or the adequate frequency,

duration, and intensity with which ST should be broken-up to reduce

adverse health effects.

Studies focusing on how older adults allocate their ST throughout the day,

as well as an understanding of what activities cover the spectrum of ST in

this type of population can be useful in order to design future successful

interventions aimed at reducing ST and increase the time of physical

activity. In this sense, examining the effects of the type of sedentary

activities with cognitive function may be of great interest because probably

not all sitting activities have the same impact on brain activity (e.g., reading

vs. driving), and therefore, some of them could be preventive and others

harmful to cognition.

High quality designs and measurements are necessary for the progress of

the research and its subsequent implementation in the real world. One of

the challenges in this field is to use devices that are capable of not only

capturing the intensity of the movement but also the posture. An example

of this may be inclinometers, devices that have demonstrated a high

capacity to capture postural changes compared to direct observation [356].

FUTURE PERSPECTIVES

345

In addition, other new technologies can be used to provide context for ST

such as wearable cameras and global positioning system.

Future research should study the differences in age and sex in the SB,

physical activity and frailty relationship. Thus, more tailored

recommendations can be provided, making them more attainable and

sustainable in the future.

Finally, identifying personal, social and environmental barriers to reduce

ST and increase physical activity will help create strategies and programs

with high adherence in the medium to long term. Future research should

also address fundamental questions about cost-effective and feasible

interventions. The results found by these investigations should be applied

in institutions, especially public organizations, that combine environment

and policy changes associated with individual and social interventions.

Only in this way, science can reach the real life of individuals, improving

their quality of life through healthy aging.

346

347

CHAPTER 9

ACKNOWLEDGMENTS

ACKNOWLEDGMENTS

348

ACKNOWLEDGMENTS

349

The completion of this PhD thesis has been a journey of 4 years, and it is

fair to show my deepest thanks to all those who have made it possible.

Firstly, I would like to thank my supervisors: ‘Professor’ Ignacio Ara; Dr.

Borja del Pozo Cruz; and, Dr. Amelia Guadalupe Grau. Your expertise,

confidence, and support from each have provided me with valuable

knowledge and research skills that I never could have imagined.

Many thanks to Franjo (head of the “Geriatric Department of the Hospital

Virgen del Valle” in Toledo; Spain) to open the doors of the hospital to the

exercise sciences, giving the importance it deserves in public health. Thank

you to Leocadio (head of the “Geriatric Department of the Hospital

Universitario de Getafe” in Getafe; Spain), co-director of the TSHA together

with Franjo, for allowing me to use data from the TSHA cohort in my PhD.

Additionally, thanks to all the components who are part of the “GENUD

Toledo Research Group”, the “Geriatric Department of the Hospital Virgen

del Valle” and the “Toledo Study for Healthy Aging”.

Thanks also to all the co-authors who have contributed to these studies, to

the University of Castilla-La Mancha, and to the people and research

groups that have allowed me to carry out the research stays.

Lastly, especially thanks to my family and friends, without your love and

support none of this would be possible.

At this point, I would like to show a few words of thanks to each of them

in my native language.

ACKNOWLEDGMENTS

350

Todo empezó hace ya más de 4 años, y una vez esta etapa llega a su fin, es

justo mostrar mi más sincero cariño y agradecimiento a todas aquellas

personas que han hecho posible recorrer este camino. Sirvan estas líneas de

homenaje a cada uno de vosotros.

Si en la vida tengo que agradecer algo, es de tener a mi familia. Mamá y

papá, papá y mamá. Cada día habéis dado y dais lo mejor de vosotros para

mantener esta familia. Siempre habéis sabido estar a la altura de cada

momento. Siempre habéis confiado en mí ciegamente. Me habéis sabido

inculcar valores como el respeto, el sacrificio, la humildad, la justicia, la

responsabilidad o la honestidad. Mi pasión por el ejercicio y la salud es por

vosotros. No se me olvidarán todos los entrenamientos a los que me habéis

tenido que llevar, así como todos los fines de semana malgastados para que

yo pudiera competir. Sé que, tras todo vuestro esfuerzo, nunca habéis

esperado nada a cambio, sólo mi felicidad y bienestar. Por vosotros soy lo

que soy y estoy donde estoy, me siento orgulloso de teneros como padres.

Por todo esto y mucho más que no podría expresar con palabras, gracias,

os quiero mucho. Esta tesis es más vuestra que mía.

A mi hermano pequeño, Aitor. Esta familia no sería lo mismo sin ti. Como

hermano mayor, siempre he intentado ser un ejemplo, pero siento si alguna

vez no ha sido así. En mi recuerdo, aunque espero que todavía queden más,

están todas esas madrugadas simplemente charlando y riendo. Sé que

muchas de ellas has aguantado solo porque sabías que quería desahogarme

sin decirlo. Una de mis mejores terapias para aguantar toda la presión

acumulada de años. Te quiero hermano, muchas gracias por todo.

Mil gracias Cristina. Todos los viajes que hemos hecho han sido meditados

y preparados a conciencia. Sin embargo, tú aceptaste acompañarme

durante todo este viaje, sin saber a dónde nos llevaría, pero sin dudar que

ACKNOWLEDGMENTS

351

saldría bien. Me has apoyado siempre, me has escuchado, me has

entendido, me has aconsejado. Sé que han sido muchos los momentos que

hemos perdido porque yo estaba trabajando o tenía que trabajar, y valoro

enormemente que en esos momentos, lejos de molestarte, tu respuesta

siempre fuera ¿en qué puedo ayudarte? Prometo estar más tiempo a tu

lado, disfrutar de cada momento y hacerte sonreír, al menos, tanto como tú

me has hecho sonreír a mí. Has sido, eres y serás un regalo en mi vida. Te

quiero.

Gracias a Carmen, Jose, Sonia y Miguel por haberme hecho parte de vuestra

familia. Y gracias a la alegría de la casa, Natalia. Ser tu tito Asier para mí es

algo único. Espero poder inculcarte buenos valores, especialmente el amor

por el conocimiento, la pasión por la ciencia y el entusiasmo de una vida

sana. Recuerda que podrás contar conmigo siempre que lo necesites.

No me olvido de vosotros, abuelos. Especialmente de ti, abuelo Pepe. De

pequeño soñábamos juntos que de mayor sería inventor, y aunque no lo

haya conseguido, entiendo que la ciencia es otra forma de invención. Es

verdad que sin “alambrillos”, pero planteamos cuestiones que no existían

antes para intentar responderlas. Gracias por enseñarme el camino del

trabajo duro y la constancia.

Se dice que los amigos son la familia que uno elige. Y yo elegí la mejor de

las familias. Gracias a mi familia de Valdepeñas: Yolanda, Javi, Jesús, Padi,

Ángel, Sandra, Elena, Nuria, Maroto, Raquel, Leire, Álvaro. Gracias a mi

familia de Toledo: Ángel, Marian, Víctor, Laura. La gran escritora Isabel

Allende dijo una vez, «la verdadera amistad resiste el tiempo, la distancia y el

silencio». Gracias Laura F. por resistir esta amistad. A todos vosotros os pido

disculpas si no os he dedicado el tiempo que debiera. Sabéis que sois una

ACKNOWLEDGMENTS

352

parte importante en mi vida, y a partir de ahora, intentaré pasar más tiempo

con cada uno de vosotros. Gracias chic@s, os quiero.

Profesionalmente, «si he visto más lejos es porque estoy sentado sobre los hombros

de gigantes». Nacho, tú eres ese gigante que supiste ver en mí capacidades

que incluso yo no vi. Recordaré para siempre esa revisión de examen en 3º

de carrera, y cómo a partir de ese momento me sentí parte del equipo

GENUD Toledo. Gracias por confiar en mí, por escucharme, por alentarme,

pero gracias especialmente por preocuparte por mi bienestar. Antes de

cualquier reunión en tu despacho, siempre me has preguntado por mi vida

personal, por mi salud. Supiste entender que en ciertos momentos yo

necesitaba un cambio, y nunca te estaré lo suficientemente agradecido por

dejarme hacer lo que actualmente hago.

Gracias a Amelia y Borja, mis co-directores. Por diferentes motivos, con

Amelia pasé más tiempo al principio, y con Borja al final. Sin embargo,

ambos habéis sido, en etapas diferentes, ese apoyo que le gustaría tener a

todo doctorando para construir su tesis.

A mi grupo GENUD Toledo. Gracias a cada uno de los integrantes. A la

cabeza, Nacho y Luis actuando siempre como guías de este equipo. Nunca

se agradece lo suficiente toda la gestión que vosotros hacéis para que los

demás podamos trabajar en las mejores condiciones. Gracias a Sara por

estar atenta a todo y darle el toque más social y divertido a este grupo.

Gracias Jose por mediar la relación con el hospital y que tengamos esta alta

disponibilidad de datos. Qué decir de vosotros chic@s. Recuerdo que

cuando empecé en el laboratorio solo estábamos María, Irene y yo, y ahora

somos cerca de 20 personas. Gracias a María, Irene, Julián, Carlos, Rober,

Javi, Iván y a las nuevas incorporaciones, Miguel, Coral y Héctor. Con

vosotros todo ha sido mucho más sencillo. Es una maravilla compartir sitio

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353

con mentes tan brillantes como las vuestras. Gracias especialmente a ti

Irene, dentro del grupo tú me has entendido mejor que nadie y has sido mi

apoyo dentro y fuera del laboratorio durante todos estos años de risas y

estrés continuo.

Gracias al Servicio de Geriatría del Hospital Virgen del Valle (Complejo

Hospitalario de Toledo). Especialmente, gracias a Franjo por confiar en

nosotros, los educadores físico-deportivos, para que tengamos cabida en el

sistema de salud a través de equipos multidisciplinares. Gracias también a

Leocadio, co-director junto a Franjo del proyecto Estudio Toledo de

Envejecimiento Saludable. Vosotros habéis permitido que esta tesis se haga

realidad. Y finalmente, gracias a vosotros, participantes. Esta tesis es

vuestra, para que podáis no sólo poner más años a la vida sino más vida a

los años. Gracias por colaborar voluntariamente con el único interés de que

la ciencia avance. Espero que ahora los hallazgos de esta tesis reporten en

vuestro beneficio.

Gracias a Borja en Australia y al profesor Sardinha en Portugal por darme

la bienvenida en sus respectivos laboratorios e involucrarme en sus

investigaciones, además de enriquecer las propias. Y muchas gracias

también a todos los co-autores de los estudios por su contribución a esta

tesis.

Gracias a la que considero mi casa, la Universidad de Castilla-La Mancha.

Casi 10 años de experiencias inolvidables, donde he crecido profesional y

personalmente. Quiero destacar haber conocido a Julián Garde, Vicerrector

de Investigación y Política Científica. En mi vida, me he encontrado con

pocas personas más honradas, humildes y trabajadoras. Personas como tú

hacen grande a esta universidad, a la investigación, y a la educación pública

española.

ACKNOWLEDGMENTS

354

Me gustaría hacer una mención especial a Vicky, Pilar y Chema. Aunque

mi trabajo con vosotros no haya sido parte de esta tesis doctoral, estoy

profundamente agradecido de haber estado a vuestro lado en esta etapa.

Mi trabajo en Red Eléctrica de España ha sido fundamental para que haya

afrontado con ilusión todos estos años. Vosotros me habéis hecho sentir

parte de una familia. Muchas gracias por confiar siempre en mí.

Gracias a mi profesión, las ciencias del ejercicio. Desde muy pequeño quise

ser parte de vuestro mundo y hoy soy muy feliz gracias a que me levanto

pensando que toca trabajar en lo que me apasiona.

Gracias a ti también, que estás leyendo esta tesis.

En definitiva, jamás podría decir que esta tesis es solo mía, pues es el

resultado del esfuerzo de todos y cada uno de vosotros. De todos aquellos

que en el camino habéis hecho posible que hoy sea la persona en la que me

he convertido, A TODOS VOSOTROS ¡MUCHAS GRACIAS!

ACKNOWLEDGMENTS

355

356

357

CHAPTER 10

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407

CHAPTER 11

APPENDIX

APPENDIX

408

APPENDIX

409

11.1. Appendix 1

Impact factor and ranking of each journal in the year the articles were

published “ISI Web of Knowledge – Journal Citation Reports”

Paper Journal Impact

factor

Ranking-

Quartile

Paper 1

(P)

European Journal of Sport Science

(Sport Sciences)

2.576

22/81 – Q2

Paper 2

(P)

PLoS One

(Multidisciplinary Sciences)

2.766

15/64 – Q1

Paper 3

(P)

Journal of the American Medical

Directors Association

(Geriatrics & Gerontology)

4.899

6/53 – Q1

Paper 4

(P)

BMC Geriatrics

(Gerontology)

2.818

8/36 – Q1

Paper 5

(P)

Journal of the American Medical

Directors Association


4.899

6/53 – Q1

Paper 6

(A)

Journal of Cachexia, Sarcopenia and

Muscle


10.754

1/53 – Q1

Paper 7

(S)

Journal of Cachexia, Sarcopenia and

Muscle


10.754

1/53 – Q1

(P): Published. (A): Accepted. (S): Submitted.

APPENDIX

410

APPENDIX

411

11.2. Appendix 2

Certificate of research stay in the Institute for Positive Psychology and

Education, Motivation and Behaviour Research Program. Australian

Catholic University. Sydney (Australia).

APPENDIX

412

APPENDIX

413

11.3. Appendix 3

Certificate of research stay in the Exercise and Health Laboratory.

University of Lisbon. Lisbon (Portugal).


Universidad de Castilla-La Mancha


International PhD Thesis Asier Mañas Bote

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