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Lookup NU author(s): Professor Emily OliverORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Physical activity prescription, commonly through exercise referral schemes, is an estab-lished disease prevention and management pathway. There is considerable heterogeneity in terms of uptake, adherence, and outcomes, but because within-scheme analyses dominate previous research, there is limited contextual understanding of this variance. Both the impact of schemes on health inequalities and best practices for inclusion of at-risk groups are unclear. To address this, we modelled secondary data from the multi-scheme National Referral Database, comprising 23,782 individuals across 14 referral schemes, using a multilevel Bayesian inference approach. Scheme-level local demographics identified over-sampling in uptake; on the basis of uptake and completion data, more inclusive schemes (n = 4) were identified. Scheme coordinators were interviewed, and data were analyzed using a grounded theory approach. Inequalities presented in a nuanced way. Schemes showed promise for engaging populations at greater risk of poor health (e.g., those from more deprived areas or of an ethnic minority background). However, the completion odds were lower for those with a range of complex circumstances (e.g., a mental health-related referral). We identified creative best practices for widening access (e.g., partnership building), maintaining engagement (e.g., workforce diversity), and tailoring support, but recommend changes to wider operational contexts to ensure such approaches are viable.
Author(s): Oliver EJ, Dodd-Reynolds C, Kasim A, Vallis D
Publication type: Article
Publication status: Published
Journal: International Journal of Environmental Research and Public Health
Year: 2021
Volume: 18
Issue: 6
Pages: 1-10
Online publication date: 16/03/2021
Acceptance date: 12/03/2021
Date deposited: 19/08/2022
ISSN (print): 1661-7827
ISSN (electronic): 1660-4601
Publisher: MDPI AG
URL: https://doi.org/10.3390/ijerph18063033
DOI: 10.3390/ijerph18063033
PubMed id: 33809451
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