1. Physical activity patterns and clusters in 1001 patients with COPD.
- Author
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Mesquita R, Spina G, Pitta F, Donaire-Gonzalez D, Deering BM, Patel MS, Mitchell KE, Alison J, van Gestel AJ, Zogg S, Gagnon P, Abascal-Bolado B, Vagaggini B, Garcia-Aymerich J, Jenkins SC, Romme EA, Kon SS, Albert PS, Waschki B, Shrikrishna D, Singh SJ, Hopkinson NS, Miedinger D, Benzo RP, Maltais F, Paggiaro P, McKeough ZJ, Polkey MI, Hill K, Man WD, Clarenbach CF, Hernandes NA, Savi D, Wootton S, Furlanetto KC, Cindy Ng LW, Vaes AW, Jenkins C, Eastwood PR, Jarreta D, Kirsten A, Brooks D, Hillman DR, Sant'Anna T, Meijer K, Dürr S, Rutten EP, Kohler M, Probst VS, Tal-Singer R, Gil EG, den Brinker AC, Leuppi JD, Calverley PM, Smeenk FW, Costello RW, Gramm M, Goldstein R, Groenen MT, Magnussen H, Wouters EF, ZuWallack RL, Amft O, Watz H, and Spruit MA
- Subjects
- Actigraphy, Age Factors, Aged, Agnosia, Body Mass Index, Cluster Analysis, Cross-Sectional Studies, Dyspnea etiology, Female, Forced Expiratory Volume, Humans, Male, Middle Aged, Principal Component Analysis, Pulmonary Disease, Chronic Obstructive complications, Sedentary Behavior, Severity of Illness Index, Exercise, Pulmonary Disease, Chronic Obstructive physiopathology
- 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 [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 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.- Published
- 2017
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