Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease

Rosemary Walmsley; Shing Chan; Karl Smith-Byrne; Rema Ramakrishnan; Mark Woodward; Kazem Rahimi; Terence Dwyer; Derrick Bennett; Aiden Doherty

doi:10.1136/bjsports-2021-104050

Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease

Br J Sports Med. 2021 Sep 6;56(18):1008-1017. doi: 10.1136/bjsports-2021-104050. Online ahead of print.

Authors

Rosemary Walmsley^{1

2}, Shing Chan^{1

2}, Karl Smith-Byrne³, Rema Ramakrishnan⁴, Mark Woodward^{5

6

7}, Kazem Rahimi^{4

8

9

10}, Terence Dwyer^{4

11}, Derrick Bennett^#^{12

8}, Aiden Doherty^#^{12

2

8}

Affiliations

¹ Nuffield Department of Population Health, University of Oxford, Oxford, UK.
² Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
³ Genomic Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
⁴ Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
⁵ Professorial Unit, The George Institute for Global Health, University of New South Wales, Camperdown, New South Wales, Australia.
⁶ Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.
⁷ The George Institute for Global Health, School of Public Health, Imperial College London, London, UK.
⁸ National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁹ Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.
¹⁰ Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
¹¹ Heart Group, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
¹² Nuffield Department of Population Health, University of Oxford, Oxford, UK derrick.bennett@ndph.ox.ac.uk aiden.doherty@ndph.ox.ac.uk.

^# Contributed equally.

Abstract

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.

Keywords: cardiovascular diseases; methods; physical activity; sedentary behavior; sleep.

Abstract

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