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Physical activity patterns and clusters in 1001 patients with COPD

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dc.contributor.author Mesquita, Rafael
dc.contributor.author Donaire-Gonzalez, David
dc.contributor.author García Aymerich, Judith
dc.contributor.author Spruit, Martijn A.
dc.date.accessioned 2024-03-26T07:08:39Z
dc.date.available 2024-03-26T07:08:39Z
dc.date.issued 2017
dc.identifier.citation Mesquita R, Spina G, Pitta F, Donaire-Gonzalez D, Deering BM, Patel MS, et al. Physical activity patterns and clusters in 1001 patients with COPD. Chron Respir Dis. 2017 Aug;14(3):256-69. DOI: 10.1177/1479972316687207
dc.identifier.issn 1479-9731
dc.identifier.uri http://hdl.handle.net/10230/59558
dc.description Includes supplementary materials for the online appendix.
dc.description.abstract We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV1], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV1, worse dyspnoea and higher ADO index compared to other clusters (p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher SAGE Publications
dc.relation.ispartof Chronic Respiratory Disease. 2017 Aug;14(3):256-69
dc.rights This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
dc.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
dc.title Physical activity patterns and clusters in 1001 patients with COPD
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1177/1479972316687207
dc.subject.keyword Chronic obstructive pulmonary disease
dc.subject.keyword Physical activity
dc.subject.keyword Outcome assessment (healthcare)
dc.subject.keyword Principal component analysis
dc.subject.keyword Cluster analysis
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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