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http://hdl.handle.net/2445/115592
Title: | Physical activity patterns and clusters in 1001 patients with COPD |
Author: | Mesquita, Rafael Spina, Gabriele Pitta, Fabio Donaire González, David Deering, Brenda M. Patel, Mehul S. Mitchell, Katy E. Alison, Jennifer van Gestel, Aarnoldus J. R. Zogg, Stefanie Gagnon, Philippe Abascal-Bolado, Beatriz Vagaggini, Barbara García Aymerich, Judith Jenkins, Sue C. Romme, Elisabeth A. Kon, Samantha S. Albert, Paul S. Waschki, Benjamin Shrikrishna, Dinesh Singh, Sally J. Hopkinson, Nicholas S. Miedinger, David Benzo, Roberto P. Maltais, François Paggiaro, Pierluigi McKeough, Zoe J. Polkey, Michael I. Hill, Kylie Man, William D.-C. Clarenbach, Christian F. Hernandes, Nidia A. Savi, Daniela S. Wootton, Sally Furlanetto, Karina C. Cindy Ng, Li W. Vaes, Anouk W. Jenkins, Christine Eastwood, Peter R. Jarreta, Diana Kirsten, Anne-Marie Brooks, Dina Hillman, David R. Sant'Anna, Thaıs Meijer, Kenneth Durr, Selina Rutten, Erika P. Kohler, Malcolm Probst, Vanessa S. Tal-Singer, Ruth Garcia Gil, Esther den Brinker, Albertus C. Leuppi, Jorg D. Calverley, Peter M. Smeenk, Frank W. Costello, Richard W. Gramm, Marco Goldstein, Roger Groenen, Miriam T. Magnussen, Helgo Wouters, Emiel ZuWallack, Richard L. Amft, Oliver Watz, Henrik Spruit, Martijn A. |
Keywords: | Malalties pulmonars obstructives cròniques Estudi de casos Condició física Chronic obstructive pulmonary diseases Case studies Physical fitness |
Issue Date: | 24-Feb-2017 |
Publisher: | Hodder Arnold |
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. |
Note: | Reproducció del document publicat a: http://dx.doi.org/10.1177/1479972316687207 |
It is part of: | Chronic Respiratory Disease, 2017, vol. 14, num. 3, p. 256-269 |
URI: | http://hdl.handle.net/2445/115592 |
Related resource: | http://dx.doi.org/10.1177/1479972316687207 |
ISSN: | 1479-9723 |
Appears in Collections: | Articles publicats en revistes (ISGlobal) |
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