{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:04:39Z","timestamp":1785175479493,"version":"3.55.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNPQ","award":["CNPq-301483\/2016-7"],"award-info":[{"award-number":["CNPq-301483\/2016-7"]}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["FAPERJ- E26\/202.523\/2019"],"award-info":[{"award-number":["FAPERJ- E26\/202.523\/2019"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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 \u201cInactive depressive women\u201d, \u201cActive depressive women\u201d and \u201cSuper active\u201d. 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0354036","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T17:37:09Z","timestamp":1785173829000},"page":"e0354036","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying cluster profiles based on barriers and facilitators to physical activity during COVID-19 confinement: A cross-sectional study using machine learning analysis"],"prefix":"10.1371","volume":"21","author":[{"given":"Fernanda","family":"Castro Monteiro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Luiza C. 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