Dataset Information

Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

ABSTRACT:

Background

Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.

Methods

Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.

Results

103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.

Conclusions

It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

SUBMITTER: Doherty A

PROVIDER: S-EPMC5287488 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

Doherty Aiden A Jackson Dan D Hammerla Nils N Plötz Thomas T Olivier Patrick P Granat Malcolm H MH White Tom T van Hees Vincent T VT Trenell Michael I MI Owen Christoper G CG Preece Stephen J SJ Gillions Rob R Sheard Simon S Peakman Tim T Brage Soren S Wareham Nicholas J NJ

PloS one 20170201 2

<h4>Background</h4>Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.<h4>Methods</h4>Participants were approached by email ...[more]

PMID: 28146576

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Project description:BACKGROUND:Physical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem. OBJECTIVE:This study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers. METHODS:Wrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion. RESULTS:In total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ?2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ?14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA. CONCLUSIONS:This study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies.

Project description:BackgroundUK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes.MethodsWe calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEESR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEEDLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEESR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers.ResultsPAEESR explained 27% variance in gold-standard PAEEDLW estimates, with no mean bias. However, error was strongly correlated with PAEEDLW (r = -.98; p < 0.001), and PAEESR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEESR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11-17%) lower mortality for individuals meeting lower physical activity guidelines.ConclusionsThe PAEESR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEESR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

Dataset Information

Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

Background

Methods

Results

Conclusions

Publications

Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

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