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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
Garcia-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:
It is part of: Chronic Respiratory Disease, 2017, vol. 14, num. 3, p. 256-269
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ISSN: 1479-9723
Appears in Collections:Articles publicats en revistes (ISGlobal)

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