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.

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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.
Authors: Chen M; Université Paris Cité, Inserm U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France., Landré B; Université Paris Cité, Inserm U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France., Marques-Vidal P; Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Switzerland., van Hees VT; Accelting, Almere, the Netherlands., van Gennip ACE; Department of Internal Medicine, Maastricht University Medical Centre, the Netherlands.; School for Cardiovascular Diseases CARIM, Maastricht University, the Netherlands., Bloomberg M; Department of Epidemiology and Public Health, University College London, UK., Yerramalla MS; Université Paris Cité, Inserm U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France., Benadjaoud MA; Institute for Radiological Protection and Nuclear Safety (IRSN), Fontenay-Aux-Roses, France., Sabia S; Université Paris Cité, Inserm U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.; Department of Epidemiology and Public Health, University College London, UK.
Source: EClinicalMedicine [EClinicalMedicine] 2022 Dec 13; Vol. 55, pp. 101773. Date of Electronic Publication: 2022 Dec 13 (Print Publication: 2023).
Publication Type: Journal Article
Journal Info: Publisher: The Lancet Country of Publication: England NLM ID: 101733727 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-5370 (Electronic) Linking ISSN: 25895370 NLM ISO Abbreviation: EClinicalMedicine Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2589-5370
DOI:10.1016/j.eclinm.2022.101773