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. 2017 Aug;14(3):256-269.
doi: 10.1177/1479972316687207. Epub 2017 Feb 24.

Physical activity patterns and clusters in 1001 patients with COPD

Rafael Mesquita  1   2 Gabriele Spina  3   4 Fabio Pitta  5 David Donaire-Gonzalez  6   7 Brenda M Deering  8 Mehul S Patel  9 Katy E Mitchell  10 Jennifer Alison  11   12 Arnoldus Jr van Gestel  13   14 Stefanie Zogg  15 Philippe Gagnon  16 Beatriz Abascal-Bolado  17   18 Barbara Vagaggini  19 Judith Garcia-Aymerich  6   7   20 Sue C Jenkins  21 Elisabeth Apm Romme  22 Samantha Sc Kon  9 Paul S Albert  23 Benjamin Waschki  24 Dinesh Shrikrishna  9   25 Sally J Singh  10 Nicholas S Hopkinson  9 David Miedinger  15 Roberto P Benzo  18 François Maltais  16 Pierluigi Paggiaro  19 Zoe J McKeough  11 Michael I Polkey  9 Kylie Hill  21 William D-C Man  9 Christian F Clarenbach  13 Nidia A Hernandes  5 Daniela Savi  26 Sally Wootton  11 Karina C Furlanetto  5 Li W Cindy Ng  21 Anouk W Vaes  1   27 Christine Jenkins  28 Peter R Eastwood  29 Diana Jarreta  30 Anne Kirsten  24 Dina Brooks  31 David R Hillman  29 Thaís Sant'Anna  5 Kenneth Meijer  32 Selina Dürr  15 Erica Pa Rutten  1 Malcolm Kohler  13 Vanessa S Probst  5   33 Ruth Tal-Singer  34 Esther Garcia Gil  30 Albertus C den Brinker  4 Jörg D Leuppi  15 Peter Ma Calverley  23 Frank Wjm Smeenk  22 Richard W Costello  8 Marco Gramm  24 Roger Goldstein  31 Miriam Tj Groenen  1 Helgo Magnussen  24 Emiel Fm Wouters  1   2 Richard L ZuWallack  35 Oliver Amft  3   36 Henrik Watz  24 Martijn A Spruit  1   2   37
Affiliations

Physical activity patterns and clusters in 1001 patients with COPD

Rafael Mesquita et al. Chron Respir Dis. 2017 Aug.

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.

Keywords: Chronic obstructive pulmonary disease; cluster analysis; outcome assessment (healthcare); physical activity; principal component analysis.

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Conflict of interest statement

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Daily physical activity hourly patterns of the patients with chronic obstructive pulmonary disease after stratification for (a) and (b) – modified Medical Research Council (mMRC) grades, data available for 868 subjects only; (c) and (d) – body mass index (BMI) classification; (e) and (f) – Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades (1 to 4); and (g) and (h) – GOLD groups (a to d). (a), (c), (e) and (g) represent weekdays, while (b), (d), (f) and (h) represent weekend days. Data pooled per hour as mean (95% confidence intervals).
Figure 2.
Figure 2.
The five clusters identified. (a) Graph in three dimensions presenting the three principal component analysis (PCA) components; (b) graph in two dimensions presenting the first and second components; (c) graph in two dimensions presenting the first and third components; and (d) graph in two dimensions presenting the second and third components. Details about the relationship between components and clusters can be found in the supplementary material.
Figure 3.
Figure 3.
Daily time in activities of very light intensity (a), light intensity (b) and moderate-to-vigorous intensity (c) by clusters of patients with chronic obstructive pulmonary disease. Data are presented as median (interquartile range).
Figure 4.
Figure 4.
Daily physical activity hourly pattern of clusters of patients with chronic obstructive pulmonary disease during weekdays (a) and weekend days (b). Data pooled per hour as mean (95% confidence intervals).

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