Device-measured physical activity and cardiometabolic health: the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium.

Cardiometabolic outcomes Cohort consortium Compositional data analysis Physical activity Sedentary behaviour Sleep Standing

Journal

European heart journal
ISSN: 1522-9645
Titre abrégé: Eur Heart J
Pays: England
ID NLM: 8006263

Informations de publication

Date de publication:
10 Nov 2023
Historique:
received: 11 04 2023
revised: 06 09 2023
accepted: 10 10 2023
medline: 11 11 2023
pubmed: 11 11 2023
entrez: 11 11 2023
Statut: aheadofprint

Résumé

Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers. Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours. The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with -0.63 (95% confidence interval -0.48, -0.79), -0.43 (-0.25, -0.59), -0.40 (-0.25, -0.56), and -0.15 (0.05, -0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day. Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.

Sections du résumé

BACKGROUND AND AIMS OBJECTIVE
Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers.
METHODS METHODS
Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours.
RESULTS RESULTS
The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with -0.63 (95% confidence interval -0.48, -0.79), -0.43 (-0.25, -0.59), -0.40 (-0.25, -0.56), and -0.15 (0.05, -0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day.
CONCLUSIONS CONCLUSIONS
Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.

Identifiants

DOI: 10.1093/eurheartj/ehad717 PMID: 37950859
pubmed: 37950859
pii: 7343176
doi: 10.1093/eurheartj/ehad717
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : British Heart Foundation
ID : SP/F/20/150002
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom

Investigateurs

Nidhi Gupta (N)
Coen Stehouwer (C)
Hans Savelberg (H)
Bastiaan de Galan (B)
Carla van de Kallen (C)
Dick H J Thijssen (DHJ)

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

Auteurs

Joanna M Blodgett (JM)

Institute of Sport Exercise and Health, Division of Surgery and Interventional Sciences, University College London, London, UK.
ORCID: 0000-0001-7684-3571

Matthew N Ahmadi (MN)

Mackenzie Wearables Research Hub, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.
School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
ORCID: 0000-0002-3115-338X

Andrew J Atkin (AJ)

School of Health Sciences and Norwich Epidemiology Centre, University of East Anglia, Norwich, UK.

Sebastien Chastin (S)

School of Health and Life Science Glasgow Caledonian University, Glasgow, UK.
Department of Movement and Sport Sciences, Ghent University, Ghent, Belgium.

Hsiu-Wen Chan (HW)

School of Public Health, The University of Queensland, Brisbane, Queensland, Australia.

Kristin Suorsa (K)

Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland.
Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.
ORCID: 0000-0002-3520-8069

Esmee A Bakker (EA)

Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain.
Department of Medical BioSciences, Exercise Physiology Research Group, Radboud University Medical Center, Nijmegen, The Netherlands.

Pasan Hettiarcachchi (P)

Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Sweden.

Peter J Johansson (PJ)

Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Sweden.
Occupational and Environmental Medicine, Uppsala University Hospital, Uppsala, Sweden.

Lauren B Sherar (LB)

School of Sport, Exercise and Health Sciences, Loughborough University, UK.

Vegar Rangul (V)

HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway.

Richard M Pulsford (RM)

Faculty of Health and Life Sciences, University of Exeter, UK.

Gita Mishra (G)

School of Public Health, The University of Queensland, Brisbane, Queensland, Australia.
ORCID: 0000-0001-9610-5904

Thijs M H Eijsvogels (TMH)

Department of Medical BioSciences, Exercise Physiology Research Group, Radboud University Medical Center, Nijmegen, The Netherlands.

Sari Stenholm (S)

School of Public Health, The University of Queensland, Brisbane, Queensland, Australia.
Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland.
Research Services, Turku University Hospital and University of Turku, Finland.

Alun D Hughes (AD)

MRC Unit for Lifelong Health and Ageing, UCL Institute of Cardiovascular Science, UCL, UK.
UCL BHF Research Accelerator, University College London, London, UK.
University College London Hospitals NIHR Biomedical Research Centre, London, UK.
ORCID: 0000-0001-5432-5271

Armando M Teixeira-Pinto (AM)

School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia.

Ulf Ekelund (U)

Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway.
Departmentof Chronic Diseases, Norwegian Public Health Institute, Oslo, Norway.

I Min Lee (IM)

Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Andreas Holtermann (A)

National Research Centre for the Working Environment, Copenhagen, Denmark.

Annemarie Koster (A)

Department of Social Medicine, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.

Emmanuel Stamatakis (E)

Mackenzie Wearables Research Hub, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.
School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
ORCID: 0000-0001-7323-3225

Mark Hamer (M)

Institute of Sport Exercise and Health, Division of Surgery and Interventional Sciences, University College London, London, UK.
University College London Hospitals NIHR Biomedical Research Centre, London, UK.
ORCID: 0000-0002-8726-7992

Classifications MeSH