Identifying cluster profiles based on barriers and facilitators to physical activity during COVID-19 confinement: A cross-sectional study using machine learning analysis.

Authors

Fernanda Castro Monteiro, Maria Luiza C Wuillaume, Carlos Linhares Veloso Filho, Karla Figueiredo, Felipe Barreto Schuch, Andrea Nunes de Carvalho, Thiago Sousa Matias, Henrique Nunes Pereira Oliva, Renato Sobral Monteiro-Junior, Lara Carneiro, Andrea Camaz Deslandes

Published in

PloS one. Volume 21. Issue 7. Pages e0354036. Epub Jul 27, 2026.

Abstract

Social restrictions, such as confinement periods, tend to reduce physical activity (PA) levels. However, sociodemographic factors may influence specific barriers and facilitators to PA during such periods. This study aimed to identify cluster profiles of individuals based on barriers and facilitators to physical activity (PA) during COVID-19 confinement. Brazilian adults participated in a cross-sectional online survey. The questionnaire collected demographic data, PA levels, sedentary behavior (SB), and perceived barriers and facilitators for PA. During data preprocessing, correlated barriers and facilitators related to a similar topic were aggregated. Using machine learning analysis, the K-modes evaluated by the Silhouette Score were used for barriers and the ROCK evaluated by the Silhouette Score was used for facilitators. The barriers model produced well-defined profiles, whereas the facilitators model did not. The facilitator model generated clusters with multiple negative silhouette coefficients and exhibited a significantly less cohesive cluster structure. Therefore, only the barriers-based model was used for further analysis. The best model generated eight clusters, each named according to the most frequent barriers in the group, such as "Inactive depressive women", "Active depressive women" and "Super active". The depressive clusters presented more barriers to PA, three barriers each one. Significant differences in PA and SB were observed across clusters. This work highlights the novelty of using unsupervised machine learning to uncover latent subgroups based on multiple concurrent barriers. In conclusion, tailored home-based and outdoor strategies should be developed, particularly targeting individuals with depressive symptoms and those facing significant time constraints.

PMID:
42507738
Bibliographic data and abstract were imported from PubMed on 28 Jul 2026.

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