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Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study

BACKGROUND: Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-...

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Autores principales: Chen, Mathilde, Landré, Benjamin, Marques-Vidal, Pedro, van Hees, Vincent T., van Gennip, April C.E., Bloomberg, Mikaela, Yerramalla, Manasa S., Benadjaoud, Mohamed Amine, Sabia, Séverine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772789/
https://www.ncbi.nlm.nih.gov/pubmed/36568684
http://dx.doi.org/10.1016/j.eclinm.2022.101773
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author Chen, Mathilde
Landré, Benjamin
Marques-Vidal, Pedro
van Hees, Vincent T.
van Gennip, April C.E.
Bloomberg, Mikaela
Yerramalla, Manasa S.
Benadjaoud, Mohamed Amine
Sabia, Séverine
author_facet Chen, Mathilde
Landré, Benjamin
Marques-Vidal, Pedro
van Hees, Vincent T.
van Gennip, April C.E.
Bloomberg, Mikaela
Yerramalla, Manasa S.
Benadjaoud, Mohamed Amine
Sabia, Séverine
author_sort Chen, Mathilde
collection PubMed
description BACKGROUND: Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-derived dimensions of movement behaviours that predict mortality risk in older populations. METHODS: We used data on 21 accelerometer-derived features of daily movement behaviours in 3991 participants of the UK-based Whitehall II accelerometer sub-study (25.8% women, 60–83 years, follow-up: 2012–2013 to 2021, mean = 8.3 years). A machine-learning procedure was used to identify core PA and SB features predicting mortality risk and derive a composite score. We estimated the added predictive value of the score compared to traditional sociodemographic, behavioural, and health-related risk factors. External validation in the Switzerland-based CoLaus study (N = 1329, 56.7% women, 60–86 years, follow-up: 2014–2017 to 2021, mean = 3.8 years) was conducted. FINDINGS: In total, 11 features related to overall activity level, intensity distribution, bouts duration, frequency, and total duration of PA and SB, were identified as predictors of mortality in older adults and included in a composite score. Both in the derivation and validation cohorts, the score was associated with mortality (hazard ratio = 1.10 (95% confidence interval = 1.05–1.15) and 1.18 (1.10–1.26), respectively) and improved the predictive value of a model including traditional risk factors (increase in C-index = 0.007 (0.002–0.014) and 0.029 (0.002–0.055), respectively). INTERPRETATION: The identified accelerometer-derived PA and SB features, beyond the currently recommended total duration, might be useful for screening of older adults at higher mortality risk and for diversifying PA and SB targets in older populations whose adherence to current guidelines is low. FUNDING: National Institute on Aging; UK Medical Research Council; British Heart Foundation; Wellcome Trust; French National Research Agency; GlaxoSmithKline; Lausanne Faculty of Biology and Medicine; Swiss National Science Foundation.
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spelling pubmed-97727892022-12-23 Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study Chen, Mathilde Landré, Benjamin Marques-Vidal, Pedro van Hees, Vincent T. van Gennip, April C.E. Bloomberg, Mikaela Yerramalla, Manasa S. Benadjaoud, Mohamed Amine Sabia, Séverine eClinicalMedicine Articles BACKGROUND: Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-derived dimensions of movement behaviours that predict mortality risk in older populations. METHODS: We used data on 21 accelerometer-derived features of daily movement behaviours in 3991 participants of the UK-based Whitehall II accelerometer sub-study (25.8% women, 60–83 years, follow-up: 2012–2013 to 2021, mean = 8.3 years). A machine-learning procedure was used to identify core PA and SB features predicting mortality risk and derive a composite score. We estimated the added predictive value of the score compared to traditional sociodemographic, behavioural, and health-related risk factors. External validation in the Switzerland-based CoLaus study (N = 1329, 56.7% women, 60–86 years, follow-up: 2014–2017 to 2021, mean = 3.8 years) was conducted. FINDINGS: In total, 11 features related to overall activity level, intensity distribution, bouts duration, frequency, and total duration of PA and SB, were identified as predictors of mortality in older adults and included in a composite score. Both in the derivation and validation cohorts, the score was associated with mortality (hazard ratio = 1.10 (95% confidence interval = 1.05–1.15) and 1.18 (1.10–1.26), respectively) and improved the predictive value of a model including traditional risk factors (increase in C-index = 0.007 (0.002–0.014) and 0.029 (0.002–0.055), respectively). INTERPRETATION: The identified accelerometer-derived PA and SB features, beyond the currently recommended total duration, might be useful for screening of older adults at higher mortality risk and for diversifying PA and SB targets in older populations whose adherence to current guidelines is low. FUNDING: National Institute on Aging; UK Medical Research Council; British Heart Foundation; Wellcome Trust; French National Research Agency; GlaxoSmithKline; Lausanne Faculty of Biology and Medicine; Swiss National Science Foundation. Elsevier 2022-12-13 /pmc/articles/PMC9772789/ /pubmed/36568684 http://dx.doi.org/10.1016/j.eclinm.2022.101773 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Chen, Mathilde
Landré, Benjamin
Marques-Vidal, Pedro
van Hees, Vincent T.
van Gennip, April C.E.
Bloomberg, Mikaela
Yerramalla, Manasa S.
Benadjaoud, Mohamed Amine
Sabia, Séverine
Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title_full Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title_fullStr Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title_full_unstemmed Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title_short Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study
title_sort identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: development of a machine learning model in the whitehall ii accelerometer sub-study and external validation in the colaus study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772789/
https://www.ncbi.nlm.nih.gov/pubmed/36568684
http://dx.doi.org/10.1016/j.eclinm.2022.101773
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