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Activity Intensity, Volume, and Norms

Utility and Interpretation of Accelerometer Metrics


Medicine & Science in Sports & Exercise: November 2019 - Volume 51 - Issue 11 - p 2410–2422
doi: 10.1249/MSS.0000000000002047
SPECIAL COMMUNICATIONS: Methodological Advances

Purpose The physical activity profile can be described from accelerometer data using two population-independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This article aims 1) to demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across data sets and 2) to illustrate the future potential of the metrics for generation of age and sex-specific percentile norms.

Methods Secondary data analyses were conducted on five diverse data sets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (n = 145), adolescent girls (n = 1669), office workers (n = 114), premenopausal (n = 1218) and postmenopausal (n = 1316) women, and adults with type 2 diabetes (n = 475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were (a) zBMI (children), (b) %fat (adolescent girls and adults), (c) bone health (pre- and postmenopausal women), and (d) physical function (adults with type 2 diabetes).

Results Multiple regression analyses showed that IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile “norms” showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC.

Conclusion The ACC and the IG accelerometer metrics facilitate the investigation of whether volume and intensity of physical activity have independent, additive, or interactive effects on health markers. In future studies, the adoption of data-driven metrics would facilitate the generation of age- and sex-specific norms that would be beneficial to researchers.

1Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UNITED KINGDOM

2NIHR Leicester Biomedical Research Centre, Leicester, UNITED KINGDOM

3Division of Health Sciences, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, University of South Australia, Adelaide, AUSTRALIA

4Movement Behaviours, Health, and Wellbeing Research Group, Department of Sport and Physical Activity, Edge Hill University, Ormskirk, UNITED KINGDOM

5School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UNITED KINGDOM

6NIHR Collaboration for Leadership in Applied Health Research and Care East Midlands, Leicester General Hospital, UNITED KINGDOM

7Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UNITED KINGDOM

Address for correspondence: Alex Rowlands, Ph.D., Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, LE5 4PW, United Kingdom; E-mail:

Submitted for publication December 2018.

Accepted for publication May 2019.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (

Online date: July 17, 2019

© 2019 American College of Sports Medicine