Physical activity and sedentary behavior clustering: Segmentation to optimize active lifestyles

Zwolinsky, S, McKenna, J, Pringle, A, Widdop, P ORCID: https://orcid.org/0000-0003-0334-7053, 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. ISSN 1543-3080

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Abstract

© 2016 Human Kinetics, Inc. Background: Increasingly 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. Methods: 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. Results: 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. Conclusions: 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.