dc.contributor.author | Rowlands, A | |
dc.contributor.author | Fairclough, S | |
dc.contributor.author | Yates, T | |
dc.contributor.author | Edwardson, C | |
dc.contributor.author | Davies, M | |
dc.contributor.author | Munir, F | |
dc.contributor.author | Khunti, K | |
dc.contributor.author | Stiles, VH | |
dc.date.accessioned | 2019-05-22T14:39:37Z | |
dc.date.issued | 2019-11-01 | |
dc.description.abstract | 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 paper aims to: 1) 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 datasets; and 2) illustrate the future potential of the metrics for generation of age and sexspecific percentile norms. Methods: Secondary data analyses were carried out on five diverse
datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): 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 (T2D) (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 post-menopausal women); and d) physical function (adults with T2D).
Results: Multiple regression analyses showed the 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 IG accelerometer metrics facilitate investigation of whether
volume and intensity of physical activity have independent, additive or interactive effects on health
markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sexspecific norms that would be beneficial to researchers. | en_GB |
dc.description.sponsorship | National Institute for Health Research (NIHR) | en_GB |
dc.description.sponsorship | Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands | en_GB |
dc.identifier.citation | Vol. 51 (11), pp. 2410–2422 | en_GB |
dc.identifier.doi | 10.1249/MSS.0000000000002047 | |
dc.identifier.grantnumber | NIHR Leicester Biomedical Research Centre | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/37183 | |
dc.language.iso | en | en_GB |
dc.publisher | Lippincott, Williams & Wilkins / American College of Sports Medicine (ACSM) | en_GB |
dc.rights.embargoreason | Under embargo until 1 November 2020 in compliance with publisher policy | en_GB |
dc.rights | © 2019 American College of Sports Medicine | |
dc.subject | GENEActiv | en_GB |
dc.subject | ActiGraph | en_GB |
dc.subject | Axivity | en_GB |
dc.subject | wrist-worn | en_GB |
dc.subject | GGIR | en_GB |
dc.subject | intensity gradient | en_GB |
dc.title | Activity Intensity, Volume, and Norms:
Utility and Interpretation of Accelerometer Metrics | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-05-22T14:39:37Z | |
dc.identifier.issn | 0195-9131 | |
dc.description | This is the author accepted manuscript. The final version is available from Lippincott, Williams & Wilkins via the DOI in this record | en_GB |
dc.identifier.journal | Medicine and Science in Sports and Exercise | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-05-16 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-05-16 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-05-22T10:38:15Z | |
refterms.versionFCD | AM | |
refterms.panel | C | en_GB |