Home > Research > Publications & Outputs > Physical Activity and Sedentary Behavior Cluste...

Electronic data

  • Zwolinsky et al 2016

    Rights statement: This article will be published in a forthcoming issue of the Journal of Physical Activity & Health. This article appears here in its accepted, peer-reviewed form, as it was provided by the submitting author. It has not been copy edited, proofed, or formatted by the publisher. © 2016 Human Kinetics, Inc.

    Accepted author manuscript, 612 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles

Research output: Contribution to journalJournal article

Published

Standard

Physical Activity and Sedentary Behavior Clustering : Segmentation to Optimize Active Lifestyles. / Zwolinsky, Stephen; McKenna, James; Pringle, Andy; Widdop, Paul; Griffiths, Claire; Mellis, Michelle; Rutherford, Zoe; Collins, Peter.

In: Journal of Physical Activity and Health, Vol. 13, No. 9, 09.2016, p. 921-928.

Research output: Contribution to journalJournal article

Harvard

Zwolinsky, S, McKenna, J, Pringle, A, Widdop, P, Griffiths, C, Mellis, M, Rutherford, Z & Collins, P 2016, 'Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles', Journal of Physical Activity and Health, vol. 13, no. 9, pp. 921-928. https://doi.org/10.1123/jpah.2015-0307

APA

Zwolinsky, S., McKenna, J., Pringle, A., Widdop, P., Griffiths, C., Mellis, M., Rutherford, Z., & Collins, P. (2016). Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles. Journal of Physical Activity and Health, 13(9), 921-928. https://doi.org/10.1123/jpah.2015-0307

Vancouver

Zwolinsky S, McKenna J, Pringle A, Widdop P, Griffiths C, Mellis M et al. Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles. Journal of Physical Activity and Health. 2016 Sep;13(9):921-928. https://doi.org/10.1123/jpah.2015-0307

Author

Zwolinsky, Stephen ; McKenna, James ; Pringle, Andy ; Widdop, Paul ; Griffiths, Claire ; Mellis, Michelle ; Rutherford, Zoe ; Collins, Peter. / Physical Activity and Sedentary Behavior Clustering : Segmentation to Optimize Active Lifestyles. In: Journal of Physical Activity and Health. 2016 ; Vol. 13, No. 9. pp. 921-928.

Bibtex

@article{1b537f18ba6942b59f15ac1226ed6cba,
title = "Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles",
abstract = "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.",
keywords = "Adolescent, Adult, Aged, Cluster Analysis, England, Exercise, Female, Health Policy, Health Promotion, Humans, Life Style, Male, Middle Aged, Models, Statistical, Multivariate Analysis, Posture, Probability, Prognosis, Research Design, Sedentary Lifestyle, Social Class, Young Adult, Journal Article, Observational Study",
author = "Stephen Zwolinsky and James McKenna and Andy Pringle and Paul Widdop and Claire Griffiths and Michelle Mellis and Zoe Rutherford and Peter Collins",
note = "This article will be published in a forthcoming issue of the Journal of Physical Activity & Health. This article appears here in its accepted, peer-reviewed form, as it was provided by the submitting author. It has not been copy edited, proofed, or formatted by the publisher. {\textcopyright} 2016 Human Kinetics, Inc.",
year = "2016",
month = sep
doi = "10.1123/jpah.2015-0307",
language = "English",
volume = "13",
pages = "921--928",
journal = "Journal of Physical Activity and Health",
issn = "1543-3080",
publisher = "HUMAN KINETICS PUBL INC",
number = "9",

}

RIS

TY - JOUR

T1 - Physical Activity and Sedentary Behavior Clustering

T2 - Segmentation to Optimize Active Lifestyles

AU - Zwolinsky, Stephen

AU - McKenna, James

AU - Pringle, Andy

AU - Widdop, Paul

AU - Griffiths, Claire

AU - Mellis, Michelle

AU - Rutherford, Zoe

AU - Collins, Peter

N1 - This article will be published in a forthcoming issue of the Journal of Physical Activity & Health. This article appears here in its accepted, peer-reviewed form, as it was provided by the submitting author. It has not been copy edited, proofed, or formatted by the publisher. © 2016 Human Kinetics, Inc.

PY - 2016/9

Y1 - 2016/9

N2 - 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.

AB - 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.

KW - Adolescent

KW - Adult

KW - Aged

KW - Cluster Analysis

KW - England

KW - Exercise

KW - Female

KW - Health Policy

KW - Health Promotion

KW - Humans

KW - Life Style

KW - Male

KW - Middle Aged

KW - Models, Statistical

KW - Multivariate Analysis

KW - Posture

KW - Probability

KW - Prognosis

KW - Research Design

KW - Sedentary Lifestyle

KW - Social Class

KW - Young Adult

KW - Journal Article

KW - Observational Study

U2 - 10.1123/jpah.2015-0307

DO - 10.1123/jpah.2015-0307

M3 - Journal article

C2 - 27171277

VL - 13

SP - 921

EP - 928

JO - Journal of Physical Activity and Health

JF - Journal of Physical Activity and Health

SN - 1543-3080

IS - 9

ER -