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February 22, 2026BMJ Open Sport & Exercise Medicine0 citationsOpen Access

Understanding the factors influencing participant engagement and adherence in exercise referral in the City of Manchester

MSMatthew SharpManchester Metropolitan University CJCraig M. JonesPublic Health England ZMZoë A MarshallManchester Metropolitan University

Key Points

This research aims to identify factors affecting participant engagement and adherence in exercise referral schemes, focusing on demographic and socioeconomic aspects.
Retrospective cohort analysis of 11,909 adults referred for exercise referral in Manchester.
Outcomes included programme adherence, early dropout, and non-participation.
Multinomial logistic regression analyzed predictors of outcomes.
Machine learning methods, such as Random Forest models and k-means clustering, assessed predictive performance.
34.6% of participants completed the exercise programme, while 34.2% declined and 8.0% dropped out early.
Younger age, higher deprivation, and psychosocial factors were linked to lower completion rates.
Outreach referral pathways led to increased likelihood of remaining in an 'intends to participate' state rather than completing the programme.
Machine learning models showed strong predictive accuracy, with deprivation and early engagement being key factors.

Abstract

Objectives To identify demographic, clinical, socioeconomic and referral-pathway determinants of engagement, early dropout and non-participation in a large metropolitan exercise referral scheme (ERS), and to assess whether machine learning (ML) methods provide additional explanatory value beyond conventional regression. Methods This retrospective cohort study analysed data from 11 909 adults (≥18 years) referred for ERS in Manchester, UK, between 27 January 2022 and 28 February 2025. Outcomes were programme adherence (completion of ≥12 exercise sessions), early dropout and non-participation. Multinomial logistic regression was used to examine predictors of outcomes. ML methods, including Random Forest models and k-means clustering, were applied to assess predictive performance, rank feature importance and identify participant subgroups. Results Of 11 909 referrals, 34.6% completed the programme, 34.2% declined to participate and 8.0% left early. Younger age, higher neighbourhood deprivation and psychosocial referral reasons (eg, loneliness, long covid) were associated with lower completion. Participants referred through outreach/social prescribing pathways were substantially more likely to remain in an ‘intends to participate’ state rather than complete, compared with those referred through clinical pathways. Random Forest models demonstrated good predictive accuracy, with early engagement indicators and deprivation emerging as the strongest predictors. Clustering identified a high-risk subgroup characterised by younger age, higher deprivation and low early attendance. Conclusions Engagement and adherence in ERS were strongly shaped by early engagement, deprivation, age and referral pathways. ML methods identified high-risk subgroups and reinforced the importance of early attendance. Targeted early support and ML-informed risk indicators may improve retention, particularly among younger and more deprived participants.

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