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dc.contributor.authorZwolinsky, Stephen
dc.contributor.authorMcKenna, James
dc.contributor.authorPringle, Andy
dc.contributor.authorWiddop, Paul
dc.contributor.authorGriffiths, Claire
dc.contributor.authorMellis, Michelle
dc.contributor.authorRutherford, Zoe
dc.contributor.authorCollins, Peter
dc.date.accessioned2020-07-24T14:54:41Z
dc.date.available2020-07-24T14:54:41Z
dc.date.issued2016-09
dc.identifier.citationZwolinsky, S., McKenna, J., Pringle, A., Widdop, P., Griffiths, C., Mellis, M., Rutherford, Z. and Collins, P., (2016). 'Physical activity and sedentary behavior clustering: segmentation to optimize active lifestyles'. Journal of Physical Activity and Health, 13(9), pp. 921-928.en_US
dc.identifier.issn1543-3080
dc.identifier.doi10.1123/jpah.2015-0307
dc.identifier.urihttp://hdl.handle.net/10545/625050
dc.description.abstractIncreasingly the health impacts of physical inactivity are being distinguished from those of sedentary behavior. Nevertheless, deleterious health prognoses occur when these behaviors combine, making it a Public Health priority to establish the numbers and salient identifying factors of people who live with this injurious combination. Using an observational between-subjects design, a nonprobability sample of 22,836 participants provided data on total daily activity. A 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences. High levels of sitting clustered with low physical activity. The Ambulatory & Active cluster (n = 6254) sat for 2.5 to 5 h·d−1 and were highly active. They were significantly younger, included a greater proportion of males and reported low Indices of Multiple Deprivation compared with other clusters. Conversely, the Sedentary & Low Active cluster (n = 6286) achieved ≤60 MET·min·wk−1 of physical activity and sat for ≥8 h·d−1. They were the oldest cluster, housed the largest proportion of females and reported moderate Indices of Multiple Deprivation. Public Health systems may benefit from developing policy and interventions that do more to limit sedentary behavior and encourage light intensity activity in its place.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherHuman Kineticsen_US
dc.relation.urlhttps://journals.humankinetics.com/view/journals/jpah/13/9/article-p921.xmlen_US
dc.relation.urlhttp://eprints.leedsbeckett.ac.uk/2415/en_US
dc.subjectOrthopedics and Sports Medicineen_US
dc.titlePhysical activity and sedentary behavior clustering: segmentation to optimize active lifestylesen_US
dc.typeArticleen_US
dc.identifier.eissn1543-5474
dc.contributor.departmentLeeds Beckett Universityen_US
dc.identifier.journalJournal of Physical Activity and Healthen_US
dc.source.journaltitleJournal of Physical Activity and Health
dc.source.volume13
dc.source.issue9
dc.source.beginpage921
dc.source.endpage928
dcterms.dateAccepted2016
dc.author.detail787106en_US


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