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Physical activity patterns and clusters in 1001 patients with COPD

Publication Type Journal Article
Authors Rafael Mesquita, Gabriele Spina, Fabio Pitta, David Donaire-Gonzalez, Brenda M Deering, Mehul S Patel, Katy E Mitchell, Jennifer Alison, Arnoldus JR van Gestel, Stefanie Zogg, Philippe Gagnon, Beatriz Abascal-Bolado, Barbara Vagaggini, Judith Garcia-Aymerich, Sue C Jenkins, Elisabeth APM Romme, Samantha SC Kon, Paul S Albert, Benjamin Waschki, Dinesh Shrikrishna, Sally J Singh, Nicholas S Hopkinson, David Miedinger, Roberto P Benzo, François Maltais, Pierluigi Paggiaro, Zoe J McKeough, Michael I Polkey, Kylie Hill, William D-C Man, Christian F Clarenbach, Nidia A Hernandes, Daniela Savi, Sally Wootton, Karina C Furlanetto, Li W Cindy Ng, Anouk W Vaes, Christine Jenkins, Peter R Eastwood, Diana Jarreta, Anne Kirsten, Dina Brooks, David R Hillman, Thaís Sant’Anna, Kenneth Meijer, Selina Dürr, Erica PA Rutten, Malcolm Kohler, Vanessa S Probst, Ruth Tal-Singer, Esther Garcia Gil, Albertus C den Brinker, Jörg D Leuppi, Peter MA Calverley, Frank WJM Smeenk, Richard W Costello, Marco Gramm, Roger Goldstein, Miriam TJ Groenen, Helgo Magnussen, Emiel FM Wouters, Richard L ZuWallack, Oliver Amft, Henrik Watz, Martijn A Spruit
Title Physical activity patterns and clusters in 1001 patients with COPD
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.
Publication Chronic Respiratory Disease
Volume 14
Issue 3
Pages 256-269
Date February 24, 2017
Journal Abbr Chron Respir Dis
Language en
DOI 10.1177/1479972316687207
ISSN 1479-9723
URL Publisher's website
Accessed 2017-02-27T19:47:18Z
Library Catalog SAGE Journals
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