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E-41 Free Communication/Poster - Soccer Friday, June 3, 2016, 7: 30 AM - 12: 30 PM Room: Exhibit Hall A/B

Sedentary Behaviour And Physical Activity

A 2-step Hierarchical Cluster Analysis

2771 Board #294 June 3, 9

30 AM - 11

00 AM

Zwolinsky, Stephen; McKenna, Jim; Pringle, Andy; Widdop, Paul; Griffiths, Claire; Mellis, Michelle; Rutherford, Zoe; Collins, Peter

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Medicine & Science in Sports & Exercise: May 2016 - Volume 48 - Issue 5S - p 779
doi: 10.1249/01.mss.0000487340.47625.cb
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The negative health consequences of physical inactivity are independent from those of sedentary behaviour. However,injurious health prognoses occur when these behaviours coalesce. Whilst physical inactivity and sedentary behaviour are irreducible components of modern lifestyles, the evidence base connecting the two behaviours is limited.Aggregating our knowledge of how these behaviours cluster and who they cluster with may facilitate the development of more effective policy and intervention.

PURPOSE: This study investigates how physical activity and sedentary behaviour cluster. It further examines how individuals cluster through shared behaviours and characteristics.

METHODS: A non-probability sample of 22,836 participant’s self-reported demographics and completed the International Physical Activity Questionnaire (IPAQ). Using an observational between-subjects design, a 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences.

RESULTS: A three cluster solution was identified. There were 27.7% (n=6,254) of participants assigned to cluster 1 (Ambulatory& Active), 44.4% (n=10,028) of participants within cluster 2 (Moderation) and 27.9% (6,286) of participants allocated to cluster 3 (Sedentary& Low Active). The ‘Ambulatory& Active’ (n=6,254) cluster sat for 2.5 to 5 hours daily and were highly active. In comparison, the ‘Sedentary& Low Active’ cluster (n=6,286) achieved ≤60 MET.min.wk-1 of physical activity and sat for ≥8 hours daily.

CONCLUSIONS:This study adopted an original approach to understanding how people can be classified according to similarities in physical activity and sedentary behaviour. Data indicated that high levels of sedentary behaviour, determined by sitting time, clustered with low levels of physical activity. Importantly, the clusters can be distinguished conceptually and are likely to respond differently to varying approaches and/or interventions; therefore they are amenable to Public Health campaigns. Given the associated health implications, policy or intervention that is responsive to ‘Sedentary & Low Active’ group’s needs is not only a major Public Health challenge, but a best buy.

© 2016 American College of Sports Medicine