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Physical Activity and Psychosocial Characteristics of the Peer Supporters in the PLAN-A Study—A Latent Class Analysis

Author

Listed:
  • Ruth Salway

    (Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol BS8 1TZ, UK)

  • Simon J. Sebire

    (Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol BS8 1TZ, UK)

  • Byron Tibbitts

    (Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol BS8 1TZ, UK)

  • Emily Sanderson

    (Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TZ, UK
    Bristol Randomised Trials Collaboration, Bristol Trials Centre, University of Bristol, Bristol BS8 1TZ, UK)

  • Rebecca Kandiyali

    (Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TZ, UK
    Bristol Randomised Trials Collaboration, Bristol Trials Centre, University of Bristol, Bristol BS8 1TZ, UK)

  • Kate Willis

    (Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol BS8 1TZ, UK)

  • Stephanie J. MacNeill

    (Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TZ, UK
    Bristol Randomised Trials Collaboration, Bristol Trials Centre, University of Bristol, Bristol BS8 1TZ, UK)

  • Russell Jago

    (Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol BS8 1TZ, UK)

Abstract

PLAN-A is a cluster randomised controlled trial of a peer-led physical activity intervention which uses peer supporters to increase the physical activity of 13–14-year-old girls in the UK. This paper uses latent class analysis to identify classes in the whole study population and investigate how those selected as peer supporters in PLAN-A were drawn from different social groups. We identified five classes of girls, based on psychosocial variables (self-esteem, physical activity self-efficacy, motivation, physical activity values among friends and peer support for physical activity (PA) and physical activity behaviour variables (average minutes of weekday MVPA, sedentary time and screen viewing). Peer supporters were similar to the whole study population in terms of overall demographics, but were drawn unequally from the five classes. In addition, there was considerable variation in the distribution of peer supporters between schools. The selection of peer supporters is an integral component of peer-led interventions and should be explored and linked to underlying theory to understand the characteristics of those recruited. However, demographic representativeness is not necessarily the aim, and simple reporting of overall demographic comparisons may mask important differences within subgroups.

Suggested Citation

  • Ruth Salway & Simon J. Sebire & Byron Tibbitts & Emily Sanderson & Rebecca Kandiyali & Kate Willis & Stephanie J. MacNeill & Russell Jago, 2020. "Physical Activity and Psychosocial Characteristics of the Peer Supporters in the PLAN-A Study—A Latent Class Analysis," IJERPH, MDPI, vol. 17(21), pages 1-15, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7980-:d:437344
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    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    3. Ruth Salway & Lydia Emm-Collison & Simon J. Sebire & Janice L. Thompson & Deborah A. Lawlor & Russell Jago, 2019. "A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity," IJERPH, MDPI, vol. 16(24), pages 1-16, December.
    4. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    5. Russell Jago & Byron Tibbitts & Emily Sanderson & Emma L. Bird & Alice Porter & Chris Metcalfe & Jane E. Powell & Darren Gillett & Simon J. Sebire, 2019. "Action 3:30R: Results of a Cluster Randomised Feasibility Study of a Revised Teaching Assistant-Led Extracurricular Physical Activity Intervention for 8 to 10 Year Olds," IJERPH, MDPI, vol. 16(1), pages 1-15, January.
    6. Audrey, Suzanne & Holliday, Jo & Campbell, Rona, 2006. "It's good to talk: Adolescent perspectives of an informal, peer-led intervention to reduce smoking," Social Science & Medicine, Elsevier, vol. 63(2), pages 320-334, July.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. William Boyce & Torbjorn Torsheim & Candace Currie & Alessio Zambon, 2006. "The Family Affluence Scale as a Measure of National Wealth: Validation of an Adolescent Self-Report Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 78(3), pages 473-487, September.
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