UNIVERSITATIS OULUENSIS MEDICA ACTA D D 1533 ACTA Maisa Niemelä OULU 2019 D 1533 Maisa Niemelä TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFE THE NORTHERN FINLAND BIRTH COHORT 1966 STUDY UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF MEDICINE; INFOTECH OULU; MEDICAL RESEARCH CENTER OULU; OULU UNIVERSITY HOSPITAL; OULU DEACONESS INSTITUTE
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UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND
A C T A U N I V E R S I T A T I S O U L U E N S I S
University Lecturer Tuomo Glumoff
University Lecturer Santeri Palviainen
Senior research fellow Jari Juuti
Professor Olli Vuolteenaho
University Lecturer Veli-Matti Ulvinen
Planning Director Pertti Tikkanen
Professor Jari Juga
University Lecturer Anu Soikkeli
Professor Olli Vuolteenaho
Publications Editor Kirsti Nurkkala
ISBN 978-952-62-2365-0 (Paperback)ISBN 978-952-62-2366-7 (PDF)ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)
U N I V E R S I TAT I S O U L U E N S I S
MEDICA
ACTAD
D 1533
AC
TAM
aisa Niem
elä
OULU 2019
D 1533
Maisa Niemelä
TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFETHE NORTHERN FINLAND BIRTH COHORT 1966 STUDY
UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF MEDICINE;INFOTECH OULU;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL;OULU DEACONESS INSTITUTE
ACTA UNIVERS ITAT I S OULUENS I SD M e d i c a 1 5 3 3
MAISA NIEMELÄ
TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFEThe Northern Finland Birth Cohort 1966 study
Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Health andBiosciences of the University of Oulu for public defence inAuditorium F101 of the Faculty of Biochemistry andMolecular Medicine (Aapistie 7), on 21 November 2019,at 12 noon
Supervised byProfessor Timo JämsäDoctor Maarit KangasProfessor Raija Korpelainen
Reviewed byAssistant Professor Sarah Kozey KeadleAssociate Professor Jari Viik
ISBN 978-952-62-2365-0 (Paperback)ISBN 978-952-62-2366-7 (PDF)
ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)
Cover DesignRaimo Ahonen
JUVENES PRINTTAMPERE 2019
OpponentProfessor Malcolm Granat
Niemelä, Maisa, Temporal patterns of physical activity and sedentary time andtheir association with health at mid-life. The Northern Finland Birth Cohort 1966studyUniversity of Oulu Graduate School; University of Oulu, Faculty of Medicine; University ofOulu, Infotech Oulu; Medical Research Center Oulu; Oulu University Hospital; Oulu DeaconessInstituteActa Univ. Oul. D 1533, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland
Abstract
Physical activity reduces mortality and morbidity and improves physical and psychological health.Lately, the detrimental health associations of excess sedentary time have also been acknowledged.It is still unknown how temporal patterns of physical activity and sedentary time are associatedwith health, as previous studies have mainly focused on summary metrics of these behaviors; forexample, the weekly duration of moderate to vigorous physical activity.
This study aimed to investigate the associations between the amount and temporal patterns ofphysical activity and sedentary time and health at mid-life. Physical activity and sedentary timewere objectively measured for two weeks using an accelerometer-based activity monitor in theNorthern Finland Birth Cohort 1966 46-year follow-up (n=5,621). Participants attended clinicalexaminations and completed health and behaviour questionnaires. A machine learning method (X-means cluster analysis) was used to identify distinct groups of participants with different patternsof physical activity and sedentary behaviour based on the activity data.
A positive, dose-response association was found with perceived health and self-reportedleisure time and objectively measured moderate to vigorous physical activity. Higher prolongedsedentary time was associated with better heart rate variability but not with resting heart rate orpost-exercise heart rate recovery. Four distinct physical activity clusters (inactive, evening active,moderately active and very active) were recognised. The risk of developing cardiovascular diseasewas significantly lower in the very active cluster compared to the inactive, and in women also inthe moderately active cluster compared to the inactive and evening active clusters. On average, thecardiovascular disease risk was low, indicating good cardiovascular health in the study population.
Prolonged sedentary time was associated with cardiac autonomic function, which in this studywas not explained by physical activity or cardiorespiratory fitness level. Higher cardiovasculardisease risk was found in the activity clusters in which the amount of physical activity was lowerand in women took place later in the evening. Results of the study can be used for designingfeasible interventions for risk groups with unhealthy physical activity and sedentary behaviourpatterns.
Niemelä, Maisa, Fyysisen aktiivisuuden ja paikallaanolon ajallisen jakautumisenyhteys terveyteen keski-iässä. Pohjois-Suomen vuoden 1966 syntymä-kohorttitutkimusOulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta; Oulun yliopisto,Infotech Oulu; Medical Research Center Oulu; Oulun yliopistollinen sairaala; OulunDiakonissalaitosActa Univ. Oul. D 1533, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto
Tiivistelmä
Fyysinen aktiivisuus vähentää sairastavuutta, kuolleisuutta sekä parantaa fyysistä ja psyykkistäterveyttä. Viime aikoina on lisäksi tunnistettu liiallisen paikallaanolon terveyshaitat. Vielä ei tie-detä, miten fyysisen aktiivisuuden ja paikallaanolon ajallinen jakautuminen päivän aikana vai-kuttaa terveyteen, koska aiemmat tutkimukset ovat keskittyneet enimmäkseen tiettyihin summa-muuttujiin kuten kohtuullisesti kuormittavan liikkumisen määrään viikossa.
Työn tarkoituksena oli tutkia fyysisen aktiivisuuden ja paikallaanolon määrän ja ajallisenjakautumisen terveysyhteyksiä keski-iässä. Fyysinen aktiivisuus ja paikallaanolo mitattiin kiih-tyvyysanturipohjaisella aktiivisuusmittarilla kahden viikon ajan Pohjois-Suomen vuoden 1966syntymäkohortin 46-vuotistutkimuksessa (n=5621). Tutkittavat osallistuivat kliinisiin tutkimuk-siin ja täyttivät kyselyitä terveydentilastaan ja käyttäytymisestään. Koneoppimismenetelmällä(X-means cluster analysis) tutkittavat luokiteltiin aktiivisuusdatan perusteella ryhmiin, joissafyysisen aktiivisuuden määrä ja ajallinen jakautuminen päivän aikana poikkesi mahdollisimmanpaljon ryhmien välillä.
Positiivinen annos-vasteyhteys löydettiin koetun terveyden ja itseraportoidun vapaa-ajan lii-kunnan sekä mitatun kohtuullisesti kuormittavan liikkumisen väliltä. Suurempi pitkittynyt pai-kallaanoloaika oli yhteydessä parempaan sykevälivaihteluun mutta ei leposykkeeseen tai harjoi-tuksen jälkeiseen sykkeen palautumiseen. Neljä aktiivisuusryhmää tunnistettiin (inaktiiviset,ilta-aktiiviset, kohtuullisen aktiiviset ja erittäin aktiiviset). Sydän- ja verisuonitautien sairastu-misriski oli merkitsevästi pienempi erittäin aktiivisessa ryhmässä verrattuna inaktiiviseen ryh-mään ja lisäksi naisilla kohtuullisen aktiivisessa ryhmässä verrattuna inaktiiviseen ja ilta-aktiivi-seen ryhmään. Sairastumisriski oli keskimäärin matala viitaten hyvään sydänterveyteen tutki-musjoukossa.
Pitkillä paikallaanolojaksoilla oli yhteys sydämen autonomiseen säätelyyn, jota tässä työssäei selittänyt fyysinen aktiivisuus tai aerobinen kunto. Korkeampi sydän- ja verisuonitautien riskilöydettiin aktiivisuusryhmistä, joissa fyysisen aktiivisuuden määrä oli vähäisempää ja naisillapainottunut myöhäisempään iltaan. Tutkimuksen tuloksia voidaan hyödyntää interventioidensuunnittelussa riskiryhmille, joiden fyysisen aktiivisuuden ja paikallaanolon piirteet ovat tervey-delle haitallisia.
All things are so very uncertain, and that’s exactly what makes me feel reassured. – Tove Jansson
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Acknowledgements
This work was carried out at the Research Unit of Medical Imaging, Physics, and
Technology at the University of Oulu during the years 2015–2019.
I express my gratitude to my supervisors, Professor Timo Jämsä, Professor
Raija Korpelainen, and PhD Maarit Kangas for academic guidance and mentoring
throughout the project. You set a bar high, you were supportive and demanding,
and always arranged time for a meeting when I had lost myself somewhere in the
literature or statistics.
I want to thank my co-authors PhD Riikka Ahola, MHSc Anna-Maiju
Leinonen, MSc Vahid Farrahi, Professor Tuija Tammelin, Docent Antti Kiviniemi,
MD, PhD Juha Auvinen, MSc Eeva Vaaramo, Professor Sirkka Keinänen-
Kiukaanniemi, and PhD Katri Puukka for all the help I received. Special thanks go
to Eeva, you always helped and untangled my data issues. I wish to thank the head
of the NFBC project centre, PhD Minna Ruddock, and especially all the
participants in the NFBC1966 46-years follow-up; this research would not have
been possible without your participation.
I want to acknowledge the whole physical activity research group for sharing
ideas and reminding me about the bigger picture. Vahid, thank you for your
inspirational and enthusiastic attitude and all the help with anything related to data
analytics and beyond. I greatly appreciate your effort with this thesis. Petra, you
have shared all the ups and downs with me and your company at conferences and
outside the office has been very important. Thank you for your cheerful comments
and help. Anna-Maiju, your research has been an inspiration and helped me on my
own journey – thank you for the encouragement and support. Antti, thank you for
your strong expertise and generous help with cardiac autonomic function data and
revising the manuscripts.
I am very grateful that I had the opportunity to visit Northeastern University in
Boston, United States in summer 2017. I want to thank my hosts Professor Holly
Jimison and Professor Misha Pavel from the College of Information and Computer
science who supervised my work with a curious and supportive attitude. I am
grateful to the whole research group there for taking me in as a part of your big
family.
Special thanks go to Assistant Professor Sarah Kozey Keadle and Associate
Professor Jari Viik for pre-examination of the thesis. Your valuable comments and
advice greatly improved this thesis. I want to thank my follow-up group members,
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Docent Matti Kinnunen and Adjunct Professor Arto Hautala for their help and ideas
about my research and this thesis.
I owe a debt of gratitude to my colleagues in the MIPT research unit. I want to
thank Anna and Jari for sharing the few square meters we had in our room and the
daily thoughts about research and (mostly) other things. Thank you Maarit for your
support related to research and teaching. Without you, this thesis would not have
been finished I’m sure. You have been a great example on how everything is
possible without forgetting life and exercise outside the confines of work.
Finally, I wish to express loving gratitude to my friends and family. Without
you, this journey would have ended long ago. I appreciate your genuine interest in
my work. Mom, thank you for believing in me and always helping with grammar;
without you, the commas would have been misplaced many times. Hannu, you have
been there for me all the time. Your encouragement and support made this possible.
This study was financed by the Ministry of Education and Culture in Finland,
Oulu University Hospital, Infotech Oulu, and the Tauno Tönning Foundation.
Oulu, September 2019 Maisa Niemelä
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Abbreviations and symbols
ρ Spearman correlation coefficient
χ2 Chi-square test
AC Accelerometer
BMI Body mass index
BP Blood pressure
CI Confidence interval
CO2 Carbon dioxide
CRF Cardiorespiratory fitness
CVD Cardiovascular disease
DLW Doubly-labelled water
DBP Diastolic blood pressure
EE Energy expenditure
fat% Body fat percentage
g Gravitational unit, 1 g=9.81 ms−2
GPAQ Global physical activity questionnaire 2H Deuterium
HbA1c Glycated haemoglobin
HDL High-density lipoprotein
H2O Water
HR Heart rate
HRpeak Peak heart rate during cardiorespiratory fitness test
HRR60 Heart rate recovery during the first 60 seconds in the recovery
phase of the cardiorespiratory fitness test
HRrec Heart rate at 60 seconds in the recovery phase of the
cardiorespiratory fitness test
HRrest Resting heart rate
HRslope Steepness of the largest change in heart rate in the 30 seconds
during the first 60 seconds of the recovery phase of the
cardiorespiratory fitness test
HRV Heart rate variability
IPAQ International physical activity questionnaire
K Number of cluster centres
LCA Latent class analysis
LDL Low-density lipoprotein
LF/HF Ratio of low frequency and high frequency power
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LPA Light physical activity
LTPA Leisure time physical activity
MEMS Micro-electro-mechanical system
MET Metabolic equivalent
ML Machine learning
mPED Mobile phone-based physical activity education
MVPA Moderate to vigorous physical activity
MVPA10 Duration of at least 10-minute bouts of moderate to vigorous
physical activity
NFBC1966 Northern Finland Birth Cohort 1966
NHANES The National Health and Nutrition Examination Survey
O2 Oxygen molecule 18O Oxygen isotope
OR Odds ratio
PA Physical activity
PH Perceived health
r Pearson correlation coefficient
R2 Coefficient of determination
RRi R-R intervals
rMSSD Root mean square of successive differences in R-R intervals
SBP Systolic blood pressure
SD Standard deviation
SED Sedentary time
SED30 Duration of at least 30-minute bouts of sedentary time
SED60 Duration of at least 60-minute bouts of sedentary time
ST Sitting time
TS Traditional statistics
VCO2 Volume of carbon dioxide
VO2 Volume of oxygen
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List of original publications
This thesis is based on the following publications, which are referred to throughout
the text by their Roman numerals:
I Niemelä, M., Kangas, M., Ahola, R., Auvinen, J., Leinonen, A-M., Tammelin, T., . . . Jämsä, T. (2019). Dose-response relation of self-reported and accelerometer-measured physical activity to perceived health in middle age—the Northern Finland Birth Cohort 1966 Study. BMC Public Health 19: 21. doi: 10.1186/s12889-018-6359-8
II Niemelä, M., Kiviniemi, A., Kangas, M., Farrahi, V., Leinonen, A-M., Ahola R., . . . Jämsä, T. (In press). Prolonged bouts of sedentary time and cardiac autonomic function in midlife. Translational Sports Medicine. doi: 10.1002/tsm2.100
III Niemelä, M., Kangas, M., Farrahi, V., Kiviniemi, A., Leinonen, A-M., Ahola, R., . . . Jämsä, T. (2019). Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife. Preventive Medicine 124: 33–41. doi: 10.1016/j.ypmed.2019.04.023
Contributions to research for publications: In all publications I participated in the
planning and design of the studies using previously collected data. I had the main
responsibility for accelerometer-measured physical activity data pre-processing
and data analysis (I–III). I performed the statistical analyses for all the publications,
with statistician advice (I). I was responsible for drafting the manuscripts (I–III).
Total consumption, g/d 8.5 (2.4–21.8) 2.9 (0.6–8.1)
Heavy users, n (%)a 330 (11) 291 (8)
Values are mean (SD) or median (25th–75th percentiles) if not otherwise stated, BMI=body mass index, aAlcohol consumption in men ≥40 g/d, in women ≥20 g/d
5.2 Accelerometer-measured physical activity
Physical activity was objectively measured with an activity monitor from 5,621
participants from which at least four valid days (I and II) were available from 5,481
(98%) and seven consecutive valid days (III) available from 4,582 (82%)
participants. Compared to those with valid PA data from at least four days, there
were less employed (79% vs. 89%, p<0.05) and more participants reporting “other”
as their employment status (10% vs. 4%, p<0.05) among those participants who
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had less than four valid days. Those participants without seven consecutive valid
days were more often men (53% vs. 42%, p<0.001), had 0.41 units higher BMI (95%
CI 0.07–0.74, p=0.018) and 0.68 units higher fat percentage (95% CI 0.04–1.31,
p=0.038), consumed 2.32 g/d (95% CI 1.08–3.56, p<0.001) more alcohol, were
more often heavy users of alcohol (11% vs. 8%, p<0.001), and smokers (23% vs.
18%, p<0.05), and less often non-smokers (48% vs. 55%, p<0.05) compared to
participants with 7-day valid PA data (data not shown).
Accelerometer-measured average daily minutes at different activity intensity
levels from participants with at least four valid days are presented in Table 3. Men
had on average more SED, more moderate and very vigorous PA and less light and
vigorous PA compared to women (p<0.001). Men had also higher total volume of
MVPA compared to women (p<0.001).
Table 3. Accelerometer-measured physical activity by intensity levels.
Variable Men (n=2,413) Women (n=3,068)
SED (1–1.99 MET), min/d 644 (95)* 621 (88)
LPA (2–3.49 MET), min/d 266 (70)* 288 (73)
Moderate intensity PA (3.5–4.99 MET), min/d 48 (25)* 28 (15)
Vigorous intensity PA (5–7.99 MET), min/d 18 (13)* 28 (17)
Very vigorous intensity PA (≥8 MET), min/d 12 (13)* 5 (7)
MVPA (≥3.5 MET), min/d 79 (39)* 61 (29)
vMVPA, MET min/d 422 (234)* 329 (177)
Values are mean (SD), SED=sedentary time, LPA=light physical activity, PA=physical activity,
MVPA=moderate to vigorous physical activity, vMVPA=volume of moderate to vigorous physical activity, *Different from women (p<0.001)
5.3 Physical activity and perceived health (I)
Self-reported STs on a normal weekday in different domains, total ST, and leisure
time PA in brisk and light activities and total volume of LTPA are presented in Table
4. Women reported significantly more light PA per week compared to men (p<0.001)
and men reported 60 min more sitting per day than women (p<0.001). Overall, most
of the daily sitting took place at work and at home watching TV or videos for both
genders.
Over half of the study population (53%) perceived their health as good, 13%
as very good, and 30%, 3%, and 1% as fair, poor and very poor, respectively. When
divided into two groups, those with good PH (answer alternatives good/very good)
had 60 min more MVPA and over 100 min more self-reported LTPA compared to
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those with fair, poor or very poor PH (p<0.001). There was no significant difference
in self-reported ST between the PH groups (455 vs. 459 min/d, in good/very good
and fair/poor/very poor PH groups respectively, p>0.05) (Table 4 in study I).
Table 4. Self-reported leisure time physical activity and sitting time.
Variable Mena Womenb
Self-reported PA
Total PA, min/wk 188 (75–345)* 233 (115–398)
Total PA volume, MET min/wk 713 (248–1350)* 855 (435–1500)
Light PA, min/wk 75 (30–210)* 113 (50–225)
Brisk PA, min/wk 75 (10–188)* 75 (23–188)
Self-reported ST
Total ST, min/d 480 (315–615)* 420 (270–570)
Sitting at work, min/d 210 (60–360)** 210 (60–390)
Sitting at home watching TV or videos, min/d 120 (60–150)* 120 (60–120)
Sitting at home in front of computer, min/d 60 (30–60)* 30 (30–60)
Sitting in a vehicle, min/d 60 (30–90)* 30 (15–60)
Sitting in another place, min/d 0 (0–60)** 0 (0–60)
Values are median (25th–75th percentiles), PA=physical activity, ST=sitting time, aPhysical activity
variables available from 2,878 and sitting time variables from 3,681 men, bPhysical activity variables
available from 3,506 and sitting time variables from 3,054 women, *Different from women (p<0.001), **Different from women (p<0.05)
The association between MVPA and PH (Model 1) and LTPA and PH (Model 2)
was studied through binary logistic regression analysis, Table 5. Both regression
models were statistically significant (p<0.001) in the likelihood ratio test (χ2=704.4
and 882.2 for Models 1 and 2, respectively) and R2 values were 0.212 and 0.262,
respectively. MVPA was positively associated with good PH after adjustment for
gender, BMI, prevalence of diseases, smoking, alcohol consumption, ST and
socioeconomic factors. ORs were greater in the II-IV MVPA quartile (ORs=1.37,
1.49, and 1.66) compared to the lowest quartile. Greater LTPA was also associated
with higher odds of good PH (II-IV quartile ORs=1.72, 2.41, and 4.33). Higher
education and income, lack of diagnosed diseases, and marriage or cohabitation
increased the odds of good PH. In contrast, higher BMI, heavy alcohol consumption
and smoking were associated with lower odds of good PH. There was no
association between ST or gender and PH.
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Table 5. Odds ratios for good perceived health. Model 1 includes accelerometer-
measured MVPA, Model 2 self-reported LTPA.
Variable Model 1. OR (95% CI)a Model 2. OR (95% CI)a
Values are mean (SD) if not otherwise stated, CVD=cardiovascular disease, HDL=high-density
lipoprotein, SBP=systolic blood pressure, *Pairwise comparisons were made if overall p-value was
significant. Only significant (p<0.05) pairwise comparison p-values are reported: ainactive compared to
evening active, binactive compared to moderately active, cinactive compared to very active, devening
active compared to moderately active, eevening active compared to very active, fmoderately active
compared to very active
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6 Discussion
The current study investigated the association between the amount and
accumulation patterns of accelerometer-measured PA and SED and different health
outcomes among middle-aged people. The volume and temporal patterns of PA and
SED showed associations with perceived health, cardiac autonomic function and
cardiometabolic health. The participants with good or very good perceived health
had substantially higher levels of measured MVPA and self-reported LTPA
compared to those with poorer perceived health. Objectively measured prolonged
SED in at least 30-minute bouts was associated with HRV. Four distinct PA clusters
with different temporal patterns and intensity of PA were recognised. These clusters
differed significantly in terms of CVD risk.
We found that self-reported leisure time PA, including light and brisk activities,
was strongly and positively associated with health perception, as well as a dose-
response association, after controlling for lifestyle, sociodemographic and health
factors (study I). This points out the importance of encouraging a range of physical
activities. LTPA often aims for recreation, enjoyment and social interaction and thus
has the potential to improve mood and psychological well-being in addition to the
known physiological health benefits. Also, accelerometer-measured MVPA had a
positive dose-response association with PH, but it was not as strongly associated
with PH compared to self-reported LTPA. This might suggest that the sum of all PA
including leisure time, commuting and occupational PA is beneficial overall for
health, but some aspects of it might attenuate the relationship. For example, work-
related activities can be repetitive and physically demanding, and some previous
studies have reported weaker or a complete lack of association between
occupational PA and health outcomes (Oppert et al., 2006; Sofi et al., 2007). There
have also been previous studies reporting LTPA being more strongly associated
with PH than other PA domains (Abu-Omar & Rütten, 2008; Bogaert et al., 2014;
Lera-López et al., 2017). Self-reported ST in this study was not associated with PH.
This lack of relationship has also been previously reported (Hamer & Stamatakis,
2010; Withall et al., 2014). However, previous studies have reported numerous
other detrimental health associations with self-reported ST such as cancer and CVD
morbidity and mortality, and type 2 diabetes morbidity (Biswas et al., 2015;
Matthews et al., 2012). Thus, the unhealthiness of excessive ST should not be
evaluated only in terms of health perception.
In study II we investigated the association between total and prolonged SED
and cardiac autonomic function which included measures of HRV, resting HR and
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post-exercise HR recovery. Prolonged SED was assessed as the daily average
duration of uninterrupted sedentary bouts lasting at least 30 and 60 minutes. In the
analyses, we controlled for significant confounders including volume of LPA and
MVPA, BP, cholesterol and triglyceride levels, CRF, body fat percentage, smoking,
and consumption of alcohol. Prolonged sedentary bouts but not total SED were
significantly associated with rMSSD after adjustments. Higher SED60 was
associated with higher rMSSD, describing higher parasympathetic (vagal) activity,
in men and women. Additionally, in women SED30 was also positively associated
with rMSSD.
Only a few previous studies have examined the association between
objectively measured SED and HRV. Hallman et al. (2015) reported higher ST at
work being associated with lower nocturnal rMSSD but no relationship was found
between rMSSD and leisure-related ST among blue-collar workers (n=126).
However, in their recent study conducted with a larger sample (n=490, blue-collar
workers), and after controlling for BMI, age, smoking, and leisure time MVPA, no
significant association was found between rMSSD (or other HRV indexes) and
leisure or occupational ST (Hallman et al., 2019). These findings are in line with
the present study’s finding regarding a lack of association between total SED and
HRV indexes after adjusting for potential confounders. The possibly complex
explanation for the positive association between SED bouts and rMSSD obtained
in this study includes both physiological factors and underlying confounders. In
addition, the correlation between SED and HRV variables were small, warranting
cautiousness in interpretation of the obtained results. The physiological effect of
bodily stress (mental, physical) is decreased HRV due to increased sympathetic
activity and the withdrawal of parasympathetic activity (Kim, H., Cheon, Bai, Lee,
& Koo, 2017; Thayer et al., 2010). Presumably, the amount and type of stressors
occurring during leisure time compared to work differ and partly affect this
discrepancy. Measuring HRV in the laboratory might have induced additional stress
in the participants and affected the HRV measurement results. Possible confounders
not accounted for in this study could include socioeconomical factors such
occupation. In addition to sedentary bouts, better CRF, lower SBP, lower levels of
triglycerides, and, in men, lower fat%, LDL cholesterol, and abstinence of smoking,
and, in women, lower HDL cholesterol were associated with higher rMSSD.
Higher SED60 was associated with a lower LF/HF ratio in women but not in
men. LF/HF ratio has been used to describe sympatho-vagal balance; LF power has
been suggested to be generated by sympathetic nervous system and HF power by
parasympathetic nervous system and, therefore, a low LF/HF ratio would reflect
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parasympathetic dominance (Pagani et al., 1986). Previously, no differences in LF
and HF frequencies were reported between SED tertiles in hypertensive patients
(Gerage et al., 2015). A recent study reported a small negative association between
LF and leisure ST, but the association became non-significant after adjustments
(Hallman et al., 2019). The LF/HF ratio has been considered to be a rather
controversial measure, as LF power is suggested to reflect sympathetic outflow
only poorly (Billman, 2013). LF/HF ratio has also been critiqued in short-term HRV
recordings because low frequencies in the signal might be insufficiently sampled
(Heathers, 2014). These limitations should be considered when drawing
conclusions on the negative association in women between SED60 and LF/HF ratio
obtained in this study.
After adjustments, we found no significant association between total SED or
SED bouts and resting HR or HRR after the exercise test. CRF was the most
significant predictor of these HR metrics. A higher volume of MVPA was
associated with better HR recovery and, in men, higher vLPA was associated, albeit
rather weakly, with higher resting HR. In our study, the measurement of HRV was
carried out in a controlled environment in the laboratory at same time of day but
exercising during the previous day was not restricted and might have had some
effect on the results of HRV measurements.
In study III we investigated the association between PA clusters and CVD risk.
Four distinct clusters (inactive, evening active, moderately active and very active)
were identified using X-means cluster analysis for accelerometer data from a one-
week measurement period. The intensity levels of PA were significantly different
between the clusters. In addition, there was temporal variation of the occurrence of
PA, for example the evening active cluster were more active in the evenings, went
to bed later and woke up later compared to the three other clusters. There were also
changes in the temporal PA patterns in all clusters when comparing weekdays and
weekends. During weekends, waking up occurred later in all clusters and more
physical activities took place in the morning and noon, compared to weekdays
when higher MET values occurred throughout the day.
Notably, the amount of MVPA was substantially higher in the very active
cluster, being around 2.5-fold compared to the amount of MVPA obtained in the
inactive cluster. When taking account of bouts of only 10 minutes of MVPA, all
clusters had much lower MVPA duration and the differences between the clusters
were modest. In the very active cluster, the amount of SED was lowest, over
100min/d less compared to other clusters, which had smaller differences in SED
between each other. However, in all clusters on average over half of the wear time
62
was spent being sedentary. The proportion of men was highest in the very active
cluster and lowest in the inactive cluster.
To our knowledge, there are no previous studies using X-means cluster analysis
for PA data collected with an accelerometer, but the K-means approach has been
used previously and rather similar differences in the PA intensity and temporal
patterns between clusters were found compared to this study (Fukuoka et al., 2017;
Lee, P. H. et al., 2013). With a study population of inactive women, Fukuoka et al.
(2017) found three different clusters (afternoon engaged, morning engaged, and
unengaged) using raw minute-level MET data from seven days. Similarly, three
clusters were found, when they applied the K-means algorithm to normalised MET
data (only using ≥3 METs). With the latter approach the clusters differed with the
temporal patterns of MVPA; MVPA evening peak, MVPA afternoon peak and MVPA
morning and evening peak. Lee et al. (2013) found two clusters in Chinese adults
(inactive and active) using mean counts per hour from acceleration data from two
weekdays and two weekend days. The main differences between the clusters were
found in the intensity of PA, maybe because the data was averaged over a longer
time period. For example, differences between the clusters in temporal patterns
such as the times the study subjects woke up and went to bed did not stand out.
In our study, we found significant differences in CVD risk, estimated using the
Framingham risk model, between the PA clusters. Significant differences in the
CVD risk model variables were found in HDL cholesterol levels, which were
higher in the more active clusters compared to the less active. In women, total
cholesterol was also higher in the inactive and evening active clusters compared to
the other two clusters. Overall, the mean HDL level was above the minimum
recommendation and the total cholesterol level was on average slightly above the
recommended maximum level based on the current national guidelines in both
genders in all PA clusters (Working group set up by the Finnish Medical Society
Duodecim and the Finnish Cardiac Society, 2017).
Differences in the CVD risks between clusters were modest, in men 1.2%
percentage units between the inactive and very active clusters and in women only
0.6% percentage units between the evening active and moderately active clusters,
and their clinical relevance can be argued. Overall, the CVD risk in the study
population was low, 97% of women and 74% of men had a low risk (<10% risk)
and only 2% of all had a high risk (>20%) of developing CVD over the next 10
years. In comparison, a recent study reported 63% of the study population having
a low risk and 16% having a high risk of developing CVD (or already had CVD)
among Finnish adults (Vasankari et al., 2017). This discrepancy might be due to the
63
wide age range of the participants (18–85 years) in their study. We also excluded
participants who already had CVD (n=36) from the analyses, which might have
lowered the risk levels in our study. The prevalence of CVD increases in men
around 60–65 years of age and in women even later on (Yazdanyar & Newman,
2009). Thus, at the age of 46 years, the 10-year risk estimate will probably not yet
clearly separate those at high risk later on in life.
This study has some limitations. Causal interactions cannot be concluded due
to the cross-sectional study design. Also, the lack of posture recognition when
measuring SED with a wrist-worn monitor is a limitation, as the physiological
consequences of sitting and standing can be different (Katzmarzyk, 2014), but these
behaviours could not have been reliably separated from each other in this study.
Those participants who provided valid PA data from seven consecutive days (III)
seemed to have more preferable body composition, seemed to smoke less and
appeared to consume less alcohol compared to those without valid PA data, which
might have induced some selection bias in the study sample. However, those
participants providing four valid days of PA data seemed to have only minor
differences compared to those without valid PA data. The study population
consisted of individuals born in 1966 in the two northernmost provinces of Finland,
covering approximately half of the country’s land mass. This limits the
generalizability of the results to other countries or age groups. Based on the
obtained results, some of the associations (especially in study II) were, although
statistically significant, rather weak. Further studies are warranted in order to
confirm the current findings.
The strengths of the study include a wide population-based sample with wide
background information available from the participants. The compliance of
wearing the accelerometer was high in all the studies (98% in I and II, and >80%
in III). In studies I and II we controlled for a variety of variables with known
associations with outcome variables. In study II we controlled for the total volume
of PA including all at least light intensity activity as previous studies have mostly
controlled only for MVPA. To the best of our knowledge, no previous study has
utilized the same machine learning method for objectively measured PA data as was
used in study III. All clinical measurements were carried out in a controlled
environment in a laboratory, which guaranteed uniform and high-quality data.
64
65
7 Conclusions
The present study indicated that the volume and temporal patterns of PA and SED
have associations with perceived health, cardiac autonomic function, and
cardiometabolic health in mid-life. Based on the aims of this study, it can be
concluded that:
1. Both self-reported leisure time PA and objectively measured moderate to
vigorous PA were positively associated with perceived health. The self-
reported ST was not associated with health perception.
2. Objectively measured prolonged SED was positively associated with HRV
after controlling for total PA, CRF, and other potential confounders. This
suggests prolonged SED is positively associated with cardiac parasympathetic
activity in mid-life.
3. Four distinct PA clusters were identified based on the objectively measured PA
data, with clear differences in the intensity and occurrence of PA. A lower CVD
risk was found in more active clusters compared to less active clusters.
66
67
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Appendix 1
Table 9. Multivariate linear regression between total sedentary time, accumulated
prolonged sedentary bouts (30 and 60 min) and cardiac autonomic function
variables in men and women, R2 and standardised beta values for each model.
density lipoprotein, HDL=high-density lipoprotein vLPA=total volume of light physical activity,
vMVPA=total volume of moderate to vigorous physical activity, total SED=total time obtained in
between 1–2 METs intensity, SED30=accumulated daily time in bouts of at least 30 min of consecutive
MET values in between 1–2 METs, SED60=accumulated daily time in bouts of at least 60 min of
consecutive MET values in between 1–2 METs, HRslope=steepness of heart rate recovery after
submaximal exercise test, HRrest=resting heart rate, HRR60=heart rate recovery at 60 sec after
submaximal exercise test, LF=low-frequency power, HF=high-frequency power
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Original publications
I Niemelä, M., Kangas, M., Ahola, R., Auvinen, J., Leinonen, A-M., Tammelin, T., . . . Jämsä, T. (2019). Dose-response relation of self-reported and accelerometer-measured physical activity to perceived health in middle age—the Northern Finland Birth Cohort 1966 Study. BMC Public Health 19: 21. doi: 10.1186/s12889-018-6359-8
II Niemelä, M., Kiviniemi, A., Kangas, M., Farrahi, V., Leinonen, A-M., Ahola R., . . . Jämsä, T. (In press). Prolonged bouts of sedentary time and cardiac autonomic function in midlife. Translational Sports Medicine. doi: 10.1002/tsm2.100
III Niemelä, M., Kangas, M., Farrahi, V., Kiviniemi, A., Leinonen, A-M., Ahola, R., . . . Jämsä, T. (2019). Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife. Preventive Medicine 124: 33–41. doi: 10.1016/j.ypmed.2019.04.023
Published with CC BY licence (I) and CC BY-NC-ND licence (II and III).
Original publications are not included in the electronic version of the thesis.
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TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFETHE NORTHERN FINLAND BIRTH COHORT 1966 STUDY
UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF MEDICINE;INFOTECH OULU;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL;OULU DEACONESS INSTITUTE