Contributors: Epidemiology of Ageing and Neurodegenerative diseases (EpiAgeing CRESS - U1153 / UMR_A 1125 ); Centre for Research in Epidemiology and Statistics; Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE); Karolinska Institutet Stockholm; Plant Health Institute of Montpellier (UMR PHIM); Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM); Amsterdam University Medical Centers (Amsterdam UMC); University College of London London (UCL); National Institute on Aging, NIH (R01AG056477, RF1AG062553); UK Medical Research Council (R024227, S011676, K013351); the British Heart Foundation (RG/16/11/32334); the Wellcome Trust (221854/Z/20/Z).; the European Union (ERC, RHYTHM IN DEMENTIA, 101043884); ANR-19-CE36-0004,ActivHealth,Rôle de l'intensité, de la durée et des profils d'activité physique mesurés par accéléromètres pour la santé cardiométabolique(2019)
Abstract: International audience ; Objectives We identified profiles of wake-time movement behaviours (sedentary behaviours, light intensity physical activity and moderate-to-vigorous physical activity) based on accelerometer-derived features among older adults and then examined their association with all-cause mortality. Methods Data were drawn from a prospective cohort of 3991 Whitehall II accelerometer substudy participants aged 60-83 years in 2012-2013. Daily movement behaviour profiles were identified using k-means cluster analysis based on 13 accelerometer-assessed features characterising total duration, frequency, bout duration, timing and activity intensity distribution of movement behaviour. Cox regression models were used to assess the association between derived profiles and mortality risk. Results Over a mean follow-up of 8.1 (SD 1.3) years, a total of 410 deaths were recorded. Five distinct profiles were identified and labelled as 'active' (healthiest), 'active sitters', 'light movers', 'prolonged sitters', and 'most sedentary' (most deleterious). In model adjusted for sociodemographic, lifestyle, and health-related factors, compared with the 'active' profile, 'active sitters' (HR 1.57, 95% CI 1.01 to 2.44), 'light movers' (HR 1.75, 95% CI 1.17 to 2.63), 'prolonged sitters' (HR 1.67, 95% CI 1.11 to 2.51), 'most sedentary' (HR 3.25, 95% CI 2.10 to 5.02) profiles were all associated with a higher risk of mortality. Conclusion Given the threefold higher mortality risk among those with a 'most sedentary' profile, public health interventions may target this group wherein any improvement in physical activity and sedentary behaviour might be beneficial. ⇒ Public health interventions may focus on older adults with the 'most sedentary' profile by designing programmes wherein any improvement in physical activity and sedentary behaviour might be beneficial. Around 20% of the study population had an active profile, this shows that this profile can be achieved and be used as the ultimate target.
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