Produção Nacional
Revisado por pares
Physical activity patterns and clusters in 1001 patients with COPD
2017; SAGE Publishing; Volume: 14; Issue: 3 Linguagem: Inglês
10.1177/1479972316687207
ISSN1479-9731
AutoresRafael Mesquita, Gabriele Spina, Fábio Pitta, David Donaire-González, Brenda Deering, Mehul S. Patel, Katy Mitchell, Jennifer Alison, Arnoldus JR van Gestel, Stefanie Zogg, Philippe Gagnon, Beatriz Abascal-Bolado, Barbara Vagaggini, Judith Garcia‐Aymerich, Sue Jenkins, Elisabeth APM Romme, Samantha S.C. Kon, Paul Albert, Benjamin Waschki, Dinesh Shrikrishna, Sally Singh, Nicholas S Hopkinson, David Miedinger, Roberto P. Benzo, François Maltais, Pierluigi Paggiaro, Zoe McKeough, Michael I. Polkey, Kylie Hill, William D‐C Man, Christian F. Clarenbach, Nídia Aparecida Hernandes, Daniela Savi, Sally L. Wootton, Karina Couto Furlanetto, Li Whye 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 P.A. Rutten, Malcolm Kohler, Vanessa S. Probst, Ruth Tal‐Singer, Esther Garcia Gil, A.C. den Brinker, Jörg D. Leuppi, Peter M.A. Calverley, Frank W.J.M. Smeenk, Richard W. Costello, Marco Gramm, Roger Goldstein, Miriam T.J. Groenen, H Magnussen, Emiel F.�M. Wouters, Richard ZuWallack, Oliver Amft, Henrik Watz, Martijn A. Spruit,
Tópico(s)Cardiovascular and exercise physiology
ResumoWe 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 [FEV 1 ], 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 FEV 1 , 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.
Referência(s)