A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance

Alex V Rowlands; Lauren B Sherar; Stuart J Fairclough; Tom Yates; Charlotte L Edwardson; Deirdre M Harrington; Melanie J Davies; Fehmidah Munir; Kamlesh Khunti; Victoria H Stiles

doi:10.1016/j.jsams.2019.06.016

A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance

J Sci Med Sport. 2019 Oct;22(10):1132-1138. doi: 10.1016/j.jsams.2019.06.016. Epub 2019 Jul 1.

Authors

Affiliations

¹ Diabetes Research Centre, University of Leicester, Leicester General Hospital, UK; NIHR Leicester Biomedical Research Centre, UK; Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Australia. Electronic address: alex.rowlands@leicester.ac.uk.
² School of Sport, Exercise and Health Sciences, Loughborough University, UK.
³ Movement Behaviours, Health, and Wellbeing Research Group, Department of Sport and Physical Activity, Edge Hill University, UK.
⁴ Diabetes Research Centre, University of Leicester, Leicester General Hospital, UK; NIHR Leicester Biomedical Research Centre, UK.
⁵ Diabetes Research Centre, University of Leicester, Leicester General Hospital, UK; NIHR Collaboration for Leadership in Applied Health Research and Care East Midlands, Leicester General Hospital, UK.
⁶ Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, UK.

PMID: 31288983
DOI: 10.1016/j.jsams.2019.06.016

Abstract

Objectives: Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person's most active minutes are accumulated, can (a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and (b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and potentially comparable across datasets.

Design: Cross-sectional, secondary data analysis.

Methods: Analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a person's most active 60, 30 and 2min are accumulated: M60_ACC; M30_ACC and M2_ACC, respectively.

Results: The proportion of participants with M60_ACC (children) and M30_ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17 to 68% in children and 15 to 81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2_ACC values meeting thresholds for bone health ranged from 6 to 13%.

Conclusions: These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex- specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the potential advantages of these metrics lie.

Keywords: Acceleration; Measurement; Physical activity; Population; Research-grade accelerometer; Wrist-worn.

MeSH terms

Accelerometry / standards*
Adolescent
Adult
Child
Cross-Sectional Studies
Diabetes Mellitus, Type 2
Exercise*
Female
Humans
Male
Middle Aged
Postmenopause
Wrist

Grants and funding

PHR/13/90/30/DH_/Department of Health/United Kingdom