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

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 fr...

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Autores principales: Walmsley, Rosemary, Chan, Shing, Smith-Byrne, Karl, Ramakrishnan, Rema, Woodward, Mark, Rahimi, Kazem, Dwyer, Terence, Bennett, Derrick, Doherty, Aiden
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484395/
https://www.ncbi.nlm.nih.gov/pubmed/34489241
http://dx.doi.org/10.1136/bjsports-2021-104050
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author Walmsley, Rosemary
Chan, Shing
Smith-Byrne, Karl
Ramakrishnan, Rema
Woodward, Mark
Rahimi, Kazem
Dwyer, Terence
Bennett, Derrick
Doherty, Aiden
author_facet Walmsley, Rosemary
Chan, Shing
Smith-Byrne, Karl
Ramakrishnan, Rema
Woodward, Mark
Rahimi, Kazem
Dwyer, Terence
Bennett, Derrick
Doherty, Aiden
author_sort Walmsley, Rosemary
collection PubMed
description 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.
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spelling pubmed-94843952022-09-20 Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease Walmsley, Rosemary Chan, Shing Smith-Byrne, Karl Ramakrishnan, Rema Woodward, Mark Rahimi, Kazem Dwyer, Terence Bennett, Derrick Doherty, Aiden Br J Sports Med Original Research 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. BMJ Publishing Group 2022-09 2021-09-06 /pmc/articles/PMC9484395/ /pubmed/34489241 http://dx.doi.org/10.1136/bjsports-2021-104050 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/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. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Walmsley, Rosemary
Chan, Shing
Smith-Byrne, Karl
Ramakrishnan, Rema
Woodward, Mark
Rahimi, Kazem
Dwyer, Terence
Bennett, Derrick
Doherty, Aiden
Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title_full Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title_fullStr Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title_full_unstemmed Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title_short Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
title_sort reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484395/
https://www.ncbi.nlm.nih.gov/pubmed/34489241
http://dx.doi.org/10.1136/bjsports-2021-104050
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