Journal article
Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
- Abstract:
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Objective To improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults.
Methods Using free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machine-learning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.
Results In leave-one-participant-out analysis, our machine-learning method classified free-living movement behaviours with mean accuracy 88% (95% CI 87% to 89%) and Cohen’s kappa 0.80 (95% CI 0.79 to 0.82). Among 87 498 UK Biobank participants, there were 4105 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with lower CVD risk. For an average individual, reallocating 20 min/day to MVPA from all other behaviours proportionally was associated with 9% (95% CI 7% to 10%) lower risk, while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with 5% (95% CI 3% to 7%) higher risk.
Conclusion Machine-learning methods classified movement behaviours accurately in free-living accelerometer data. Reallocating time from other behaviours to MVPA, and from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD, and should be promoted by interventions and guidelines.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.1MB, Terms of use)
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- Publisher copy:
- 10.1136/bjsports-2021-104050
Authors
- Publisher:
- BMJ Publishing Group
- Journal:
- British Journal of Sports Medicine More from this journal
- Volume:
- 56
- Issue:
- 18
- Pages:
- 1008–1017
- Publication date:
- 2021-09-06
- Acceptance date:
- 2021-08-14
- DOI:
- EISSN:
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1473-0480
- ISSN:
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0306-3674
- Language:
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English
- Keywords:
- Pubs id:
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1192716
- Local pid:
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pubs:1192716
- Deposit date:
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2021-08-26
- ARK identifier:
Terms of use
- Copyright holder:
- Walmsley et al.
- Copyright date:
- 2022
- Rights statement:
- © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made.
- Licence:
- CC Attribution (CC BY)
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