Can Activity Monitors Predict Outcomes in Patients with Heart Failure? A Systematic Review Authors: Matthew K.H. Tan 1 , Joanna K.L. Wong 1 , Kishan Bakrania 2,3 , Yusuf Abdullahi 1 , Leanne Harling 2,3,4 , Roberto Casula 2,3,4 , Alex V. Rowlands 2,3,4 , Thanos Athanasiou 1 , Omar A. Jarral 1 1 Department of Surgery and Cancer, Imperial College London, London, United Kingdom. 2 Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK 3 NIHR Leicester Biomedical Research Centre, UK 4 Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide, Australia Corresponding Author: Omar A. Jarral, Department of Surgery and Cancer, Imperial College London, London, W2 1NY. Email: [email protected]; Tel: +447855773118
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Can Activity Monitors Predict Outcomes in Patients with Heart
Failure? A Systematic Review
Authors:
Matthew K.H. Tan1, Joanna K.L. Wong1, Kishan Bakrania2,3, Yusuf Abdullahi1, Leanne Harling2,3,4,
Roberto Casula2,3,4, Alex V. Rowlands2,3,4, Thanos Athanasiou1, Omar A. Jarral1
1Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
2Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
3NIHR Leicester Biomedical Research Centre, UK
4Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health
Research, Division of Health Sciences, University of South Australia, Adelaide, Australia
CVD contexts. Current morbidity and mortality risk scores are static and rely on fixed equations.
Though extensively validated, these scores are decades old and may no longer represent the population
today. “Smart” algorithms on the other hand can evolve constantly with each patient added to the
database, allowing dynamic characterisations of patients. AQPA shows longitudinal predictive value
for cognitive function and mortality and might be incorporated into these algorithms to identify at-risk
patients prior to decline. Earlier identification would allow prompt intervention and prevention of
impairment.
6. CONCLUSION
AQPA is increasingly feasible with advancements and integration of technology into clinical
practice. Early studies show clear predictive ability of these objectively measured PA parameters on
mortality, morbidity and HRQoL, with poorer prognosis associated with lower free-living PA. The
results are consistent with current established knowledge and mirror findings from self-reported PA
measures but has an advantage in providing quantifiable values. This objective data may therefore
play a role in predicting morbidity and mortality outcomes in CHF patients and may also provide
more accurate risk stratification through complementing pre-existing risk scores. There needs to be
increased technology adoption in CVDs besides HF. Increased use of actigraphy, potentially with
inclusion of commercial devices, will result in greater data generation, making big data strategies
relevant to realise its full prognostic potential.
7. ACKNOWLEDGEMENTS AND FUNDING
This project was jointly funded by the National Institute for Health Research (NIHR)
Biomedical Research Centre (based at Imperial College London and Imperial College Healthcare
NHS Trust) and the NIHR Cardiovascular Biomedical Research Unit (based at the Royal Brompton
and Harefield NHS Foundation Trust). A.R. and K.B. are with the NIHR Biomedical Research Centre
(based at University Hospitals of Leicester and Loughborough University), the NIHR Collaboration
for Leadership in Applied Health Research and Care – East Midlands and the Leicester Clinical Trials
Unit. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or
the Department of Health.
8. AUTHOR CONTRIBUTION
M.T. and J.W. contributed to study selection, data extraction, data analysis and preparation of the
manuscript.
O.J. contributed to study selection, and preparation of the manuscript.
9. CONFLICT OF INTERESTS
None declared.
10. ETHICS APPROVAL
No formal ethics approval was required.
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Figure Legend
Figure 1: Study selection process.
Figure 2: Outcomes of cardiovascular disease predicted by activity monitors.
Figure 3: Recommendations for future research.
Figure 2
Table 1: Summary of devices used.
Device Manufacturer Accelerometer Type No. of Axes
Sensor Placement (*: wear-sites in included studies)
Data Transmission
Weight (g)
Dimensions (mm)
Included Studies
Kenz Lifecorder uniaxial accelerometer
Suzuken Co. Ltd., Nagoya, Japan
Piezoelectric 1 Waist* USB 60 72.5 x 41.5 x 27.5
(43, 44)
ActiGraph 7164 accelerometer
Actigraph, Pensacola, FL, USA
Bimorph piezoelectric cantilever beam
1 Wrist, waist*, thigh, ankle USB 43 51 x 14 x 15 (48, 49)
ActiGraph GT1M Actigraph, Pensacola, FL, USA
Capacitive: ADXL320 (Analog Devices, Norwood, MA)
2 Wrist, waist*, thigh, ankle USB 27 38 x 37 x 18 (36-40)
ActiGraph GT3X Actigraph, Pensacola, FL, USA
Capacitive: ADXL335 (Analog Devices, Norwood MA)
3 Wrist, waist*, thigh, ankle USB 27 38 x 37 x 18 (46)
ActiGraph GT3X+ Actigraph, Pensacola, FL, USA
Capacitive 3 Wrist*, waist*, thigh, ankle USB 19 46 x 33 x 15 (47)
ActiGraph wGT3X-BT
Actigraph, Pensacola, FL, USA
Capacitive 3 Wrist*, waist*, thigh, ankle USB 19 46 x 33 x 15 (47)
Actical actigraph device
Minimitter Inc., Respironics, Bend, OR, USA
Bimorph lead zirconate titanate piezoelectric: muRata Piezotite® (Kyoto, Japan) PKGS-LD-R series
Omni Ankle, waist, wrist* 9-pin RS-232 serial port
17 28 x 27 x 10 (42)
ICD/CRT-D with the OptiVol feature
Medtronic Inc, Minneapolis, MN, USA
NA 1 Implanted Wireless NA NA (41)
Kinetic activity monitor (specific model not named)
Kersh Health, Plano, TX, USA
Capacitive (KXUD9-2050, Kionix, Ithaca, NY, USA)
3 Waist* USB NA NA (51)
HJA-350IT Omron Healthcare Co. Ltd., Kyoto, Japan
NA 3 Waist* NA 60 74 x 46 x 34 (50)
Activity monitor (specific device not
Temec Instruments, the Netherlands
Capacitive 1 Waist* (note: placement of accelerometers in study was on
NA 500 NA (52)
named) sternum and thigh)Accelerometer (specific device not named)
Aipermon® GmbH, Germany
NA 3 NA NA NA NA (45)
Supplemental Material
S1: Terms used in the search strategy.
(angina OR “coronary artery disease” OR CAD OR “heart failure” OR HF OR “ischaemic heart
disease” OR IHD OR “pulmonary hypertension” OR arrhythmia OR “atrial fibrillation” OR
“coronary artery bypass graft” OR CABG OR “valve repair” OR “valve replacement” OR “valve
stenosis” OR “valve regurgitation” OR “aortic aneurysm” OR “aortic dissection” OR “aortic
rupture” OR “carotid artery stenosis” OR “peripheral artery disease” OR “intermittent claudication”)
AND (“single axis” OR triaxial OR accelerometry OR accelerometer OR “activity monitor” OR
geneactiv OR actigraph OR actiwatch OR activinsights OR hexoskin OR “lumo back” OR “shine
misfit” OR “stayhealthy RT3” OR RT3)
Table S1: Summary of findings of included studies.
Author, year of publication, study period and design,
study quality
Research question and patient no. and characteristics
Device used and wear-site
Duration of measurement Primary outcomesMeasurement time points
Main findings related to activity monitorsActivity analysis method
Alosco et al. 2015 (36)
Study period not reported
Prospective cohort study
Good quality
Examine the benefits of physical activity on the brain in CHF patients and related cognitive implications
92 patients recruited, 50 patients included in analysis 68.2±9.32 years old 62.0% male
GT1M accelerometer (Actigraph, Pensacola, FL)
Right waist
7 days Subcortical brain volume: whole-brain standard 3D T1-weighted images
Cognitive function: attention/executive function, episodic memory, language function
Daily step count was positively correlated with cognitive function in all domains:- Attention/executive function (β=0.31, p=0.03)- Episodic memory (β=0.27, p=0.049)- Language (β=0.35, p=0.01)
Baseline and 12-months Incomplete actigraphy data due to mechanical issues and/or invalid wear resulted in 48 patients excluded from analysis
Reduced baseline daily step count (p=0.04) and less time spent in Matthews' moderate activity at baseline (p=0.049) was a significant predictor of 12-month attention/executive function
Less time spent in Matthew's moderate activity at baseline (p=0.05) predicted worse cerebral blood flow volume
Examine if changes in physical activity predicts cognitive changes over 12 weeks in older adults with CHF
145 patients recruited, 57 patients included in analysis 69.7±10.3 years old 59.6% male
GT1M accelerometer (Actigraph, Pensacola, FL)
Right waist
7 days Cognitive function: attention/executive function, episodic memory, language function
Baseline and 12-weeks Excluded patients due to attrition, missing data and invalid accelerometer data due to invalid wear or mechanical issues
High rates of physical inactivity at baseline with an average of:- 597.8±75.9 minutes/day being sedentary (<100 counts/min)- 46.0±34.6 minutes/day in moderate-vigorous activity (>760 counts/min)
High rates of physical inactivity at 12 weeks:- 583.3±62.9 minutes/day being sedentary- Significant decline in daily step count
Physical activity decline predicts changes in attention/executive function
See Alosco et al. 2014 (37)
Alosco et al. 2012 (39)
Study period not reported
Prospective cohort study
Good quality
Examine the association of low physical activity with adverse outcome measures in CHF patients
123 patients recruited, 96 patients included in analysis 69.8±8.79 years old 63.5% male
GT1M accelerometer (Actigraph, Pensacola, FL)
Right waist
7 days Quality of life: SF-12
Cognitive function: MMSE and Trail Making Test A and B
Baseline Generally low levels of physical activity:- Average 3,677±2,121.16 steps/day (32.3% inactive, 45.8% limited physical activity, 21.9% physically active)- Minimal time in Freedson's moderate (1,952-5,724 counts/min) (7.50±11.5min/day), Matthew's moderate (760 -5724 counts/min) (48.2±37.2min/day) or vigorous (>5,724 counts/min) (0.31±1.55min/day) intensity
Sedentary group significantly different from:- Both limited physical activity and physically active group on the SF-12 physical composite score- Limited physical activity group on the MMSE
See Alosco et al. 2014 (37)
Fulcher et al. 2014 (40)
Study period not reported
Examine the effects of different aspects of physical activity on cognitive function in older
GT1M accelerometer (Actigraph, Pensacola, FL)
7 days Cognitive function: global function, attention/executive function, episodic memory
Baseline 65 patients excluded due to incomplete actigraphy data due to mechanical issuesNot reported
Prospective cohort study
Good quality
adults with CHF
159 patients recruited, 93 patients included in analysis 60.7±15.4 years old 52% male
Right waist Generally low levels of physical activity (46.3% inactive, 27.5% limited physical activity, 26.3% physically active) with large proportion of time being sedentary (587±75 minutes/day)
Average daily step count independently predicted global cognitive function (p<0.026), attention/executive function (p<0.001), processing speed (p<0.032)
Lower physical activity predicts poorer global cognitive function (p<0.022) and attention/executive function (p<0.001) in CHF but not memory
Conraads et al. 2014 (41)
2005 to 2009
Prospective cohort study
Good quality
Determine the extent that early daily physical activity measured by implanted devices is related to CHF patient outcomes
836 patients recruited, 731 patients included in analysis 65±10 years old 85% male
ICD/CRT-D (InSync Sentry, Concerto and Virtuoso) with the OptiVol feature (Medtronic Inc, Minneapolis, MN)
Implanted device
15-18 months Death or HF hospitalisationContinuous measurement 5% relative risk reduction for death or CHF
hospitalisation for each 10 minutes/day additional activity- HR=0.92 for death- HR=0.97 for CHF hospitalisation
Higher activity levels were associated with lower incidence of primary outcome (death or CHF hospitalisation):- High activity (>235 minutes/day): 12.5% primary outcome, 2.5% mortality- Medium activity (146-235 minutes/day): 17.5% primary outcome, 9.9% mortality- Low activity (<145 minutes/day): 30% primary outcome, 22.0% mortality
Adding physical activity to a validated risk score (CHARM) for all-cause death in CHF patients improved its predictive ability
Minute is considered active if threshold of number and magnitude of deflections in accelerometer signal is reached
Number of minutes a patient is active per day is recorded
Early physical activity is defined as the average daily activity over earliest 30-day period in study
Howell et al. 2010 (42)
Study period not reported
Prospective cohort study
Good quality
Determine the feasibility of continuous monitoring using actigraphy and associations between peak daily 6 minutes of activity with functional capacity measurements, intercurrent morbid events and its prognostic utility in CHF
9 months Occurrence of nonelective intercurrent events, including: Deaths Hospitalisations Emergency department visits Intercurrent illness Outpatient procedures
Continuous measurement Most active 6-minute MET value of 22 was found to be the most robust actigraphy parameter
Most active 6-minute value was a significant predictor
Activity was recorded in 1-minute epochs generating activity count for each minute of the day
60.7±15.4 years old 52% male Average activity counts generated
for most active epochs (6 minutes, 15 minutes, 1 hour, 10 hours) and least active epochs (5 hours)
Activity counts were converted to METs using Actical software
of subsequent intercurrent events (HR=2.73; 95% CI=1.10-6.77; p=0.03) on multivariate analysis
Izawa et al. 2013 (43)
November 2002 to October 2010
Prospective cohort study
Good quality
Determine the relationship between peak VO2, VE/VCO2 slope, physical activity and mortality and cut-off values associated with a reduction in mortality in CHF patients
477 patients recruited, 201 patients met inclusion criteria, and 174 patients included in analysis 65.2±8.5 years old 77% male
7 days Mortality from cardiac-related deathNot reported Multivariate analysis revealed only step count
≤4,889.4/day to be a significant strong and independent predictor of survival (HR=2.28)
Patients with ≤4,889.4 steps/day had a significantly higher mortality rate than those with >4,889.4 steps/day (88% v.s. 63%; p=0.0005)
Average daily number of steps taken over 7 days
Izawa et al. 2014 (44)
November 2006 to October 2011
Cross-sectional study
Poor quality
Determine self-reported mental health-related differences associated with PA and target values of PA for improved mental health in CHF outpatients
261 patients recruited, 243 patients included in analysis and divided into high mental health (n=148, 57.3±11.1 years old, 76.7% male) and poor mental health (n=95, 56.8±11.3 years old, 88.6% male) groups
7 days Mental health as measured by SF-36Not reported PA was strongly positively correlated to mental health
in all patients for both steps (r=0.46, p<0.001) and energy expenditure (r=0.43, p<0.001)
Average daily number of steps taken over 7 days
Energy expenditure is calculated every 4 seconds using body weight and exercise index
Jehn et al. 2009 (45)
Study period not reported
Assess habitual walking performance in CHF patients and investigate if this information can be used to distinguish NYHA
Accelerometer (Aipermon® GmbH, Germany)
6 days NYHA functional class based on clinical data and self-reported exercise tolerance
Not reported Difference in mean total walking, walking and fast walking times was statistically significant between:- NYHA class II and III (p=0.001)
Total times per day spent: Passively (not defined)
Prospective cohort study
Poor quality
functional class
50 patients 60.9±14.0 years old 78% male
Left waist Actively (not defined) Walking (0 to 80m/minute) Fast walking (83 to
115m/minute)
- NYHA class I and III (p=0.001)
Fast walking time was also a strong determinant for discriminating moderate heart failure (NYHA class III)
Only 4 days of monitoring required for fast walking time to have a sensitivity and specificity of 74%
Melin et al. 2016 (46)
May 2009 to June 2013
Prospective cohort study
Good quality
Assess the additive value of variability characterised by accelerometer data to other risk factors in a prognostic model for CHF patients
60 patients recruited, 56 patients included in analysis 70.3 years old 76.8% male
GT3X accelerometer (Actigraph, Pensacola, FL)
Waist (side not specified)
7 days All-cause mortalityBaseline 1, 3 and 12-hour skewness showed the most significant
contribution to mortality amongst the accelerometer variables
Addition of peak 3-hour skewness to the HFSS model significantly improved predictive ability (c-index improved from 0.71 to 0.74)
Activity was recorded in 1-minute epochs generating activity count for each minute of the day
Used to estimate: 1, 3- and 12-hour skewness,
kurtosis and IQR Total no. of minutes monitor
was worn Sedentary time: vertical axis
counts per minute (cpm) <100 Light activity time: vertical
axis cpm between 100-1,951 Moderate vigorous physical
activity time: vertical axis cpm >1,952
Waring et al. 2017 (47)
October 2014 to March 2015
Prospective cohort study
Good quality
Test the hypothesis that physical inactivity is a predictor of rehospitalisation in HF
61 patients recruited, 50 patients included in analysis 71±15 years old 46.0% male
ActiGraph wGT3X-BT or GT3X+ (Actigraph, Pensacola, FL)
Non-dominant wrist & ipsilat. waist
Non-dominant hand: 30 daysIpsilateral waist: 7 days
All-cause readmissions within 30 days
After discharge from intial hospitalisation to 30 days or readmission
13 patients (26%) had all-cause readmission within 30 days: - CHF readmission: 9 patients- Non-HF readmission: 4 patients (due to dehydration, COPD exacerbation, cellulitis, gastrointestinal bleed)
Outpatients v.s. patients with eventual readmission:- No significant difference in minutes of higher-intensity activity over week 1- Significant differences in minutes of higher-intensity activity over weeks 2, 3 and 4
Lower level of activity in specific weeks showed significantly higher odds ratio of readmission:- All-cause: weeks 1 (OR=5.0, p=0.02), 2 (OR=16.9, p=0.001), 3 (OR=4.8, p=0.047) and 4 (OR=16.1, p=0.003)
≥3,000 vector magnitude units (sum of movements in 3 planes per minute of device use) was defined as higher-intensity activity for each minute of valid wrist activity output
Patients with ≥60 minutes of higher-intensity activity were considered more active while those with <60 minutes were considered less active
- CHF-related: weeks 1 (OR=5.5, p=0.03) and 2 (OR=8.6, p=0.01)
Edwards and Loprinzi 2016 (48)
2003 to 2006
Retrospective cohort study
Fair quality
Examine the association of sedentary behaviour on HRQoL in CHF patients
Sedentary behaviour was associated with worse HRQoL (β=0.004; 95% CI=0.0004-0.007; p=0.03) and was still significant when MVPA was added as a covariate
Trivariate model including all three activity levels resulted in no association between sedentary behaviour and HRQoL
At least 7 days CHF rehospitalisation during 6-months post-dischargeContinuous measurement 20 patients classified as low activity, while 21 patients
were considered to have high activity
Rate of CHF rehospitalisation was significantly higher in patients with low activity than in patients with high activity (30% v.s. 5%)
Total physical activity was the only significant predictor of rehospitalisation on multivariable regression analysis
Output signal from accelerometer was processed using commercial software (Omron, Kyoto, Japan) to calculate METs: LIPA: 1.5-2.9 METs
MVPA: ≥3 METs
METs were expressed as MET-hours/day
(OR = 0.65, p = 0.03)
Light-intensity physical activity was the strongest predictor of CHF rehospitalisation (OR = 0.60, p = 0.03)
Snipelisky et al. 2017 (51)
Study period not reported
Ancillary study from a randomised controlled trial (NEAT-HFpEF)
Good quality
Understand the determinants of baseline physical activity in CHF and relationships to functional assessments, and observing the impact of changes in daily activity with isosorbide mononitrate to the same assessments
110 patients recruited, 99 patients included in analysis 69 years old 40% male
Kinetic Activity Monitor (Kersh Health, Plano, TX)
Waist
2 weeks NYHA class, HRQoL (Kansas City Cardiomyopathy Questionnaire, Minnesota Living with Heart Failure Questionnaire), 6 minute walk distance, NT-proBNP
Continuous measurement of baseline and after isosorbide mononitrate
Lower average daily accelerometer units and hours active per day: higher proportion of patients in NYHA class III/IV, lower HRQoL scores, higher NT-proBNP
No significant relationships between changes in average daily accelerometer units and hours active per day to changes in standard CHF functional assessments
Accelerometer units stored as 15-minute epochs for a total of 96 data points per day and averaged to give average daily accelerometer units
Hours active per day were calculated using the number of epochs with accelerometer units >50
van den Berg-Emons et al. 2005 (52)
Study period not reported
Prospective cohort study
Good quality
Investigate factors related to daily activity in CHF patients and if level of activity is associated with quality of life
36 patients recruited 59 years old 75% male
ADXL202 accelerometer (Temec Instruments, the Netherlands)
Waist (accelerometers attached to sternum and thigh)
2 days HRQoL: Minnesota Living with Heart Failure Questionnaire
Continuous measurement No relationship between movement related physical activity and HRQoLDuration of movement-related
activity expressed as a percentage of a 24-hour period
Abbreviations: CHARM, Candesartan in Heart Failure Assessment of Reduction in Mortality and Morbidity; CHF, congestive heart failure; CI, confidence