Article Text
Abstract
The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.
- physical activity
- epidemiology
- statistics
- accelerometer
- sedentary
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Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Twitter @Jairohm8, @epiAgeing
Correction notice This article has been corrected since it published Online First. The co-corresponding author has been added.
Collaborators Alexander Burchartz (Institute for Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany), Cain Clark (Centre for Sport, Exercise and Life Sciences, Coventry University, Coventry, UK), Paddy Dempsey (MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK; Physical Activity & Behavioural Epidemiology Laboratories, Baker Heart and Diabetes Institute, Melbourne, Australia), Aiden Doherty (Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK), Ulf Ekelund (Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway), Timothy Olds (Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, Adelaide, Australia), Eric J Shiroma (Laboratory of Epidemiology and Population Science, National Institute on Aging, Baltimore, Maryland), Emmanuel Stamatakis (Charles Perkins Centre, Prevention Research Collaboration, Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia), Richard P Troiano (Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, HHS, Rockville, Maryland, USA), Stewart Trost (Institute of Health and Biomedical Innovation at Queensland Centre for Children's Health Research, Queensland University of Technology, South Brisbane, Australia; Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia) and Vadim Zipunnikov (Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University).
Contributors JHM and FBO conceived the idea, organised and led the workshop, the meeting, and the GRANADA consensus design and writing. All authors contributed to the idea, reviewed the manuscript and provided detailed suggestions for revisions.
Funding This study was conducted under the umbrella of the ActiveBrains and the SmarterMove projects supported by the MINECO/FEDER (DEP2013‐47540, DEP2016‐79512‐R, RYC‐2011‐09011) and the CoCA project supported by the European Union’s 2020 research and innovation programme (667302). JHM is supported by a grant from the Spanish Ministry of Education, Culture and Sport (FPU15/02645). AR is supported by the NIHR Leicester Biomedical Research Centre, and the Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands. SS is supported by the French National Research Agency (ANR-19-CE36-0004-01). RW is supported by a Medical Research Council Industrial Strategy Studentship (MR/S502509/1). Additional funding was obtained from the Andalusian Operational Programme supported with European Regional Development Funds (ERDF in English, FEDER in Spanish, project ref: B-CTS-355-UGR18), the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Scientific Excellence Unit on Exercise and Health (UCEES), Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and European Regional Development Funds (ref. SOMM17/6107/UGR). In addition, funding was provided by the SAMID III network, RETICS, funded by the PN I+D+I 2017‐2021 (Spain), ISCIII‐Sub‐Directorate General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/0022), the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005‐00046/ACTI).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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