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Physical activity patterns and clusters in 1001 patients with COPD

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journal contribution
posted on 21.05.2020, 14:57 by Rafael Mesquita, Gabriele Spina, Fabio Pitta, David Donaire-Gonzalez, Brenda M Deering, Mehul S Patel, Katy E Mitchell, Jennifer Alison, Arnoldus Jr van Gestel, Stefanie Zogg, Philippe Gagnon, Beatriz Abascal-Bolado, Barbara Vagaggini, Judith Garcia-Aymerich, Sue C Jenkins, Elisabeth Apm Romme, Samantha Sc Kon, Paul S Albert, Benjamin Waschki, Dinesh Shrikrishna, Sally J Singh, Nicholas S Hopkinson, David Miedinger, Roberto P Benzo, François Maltais, Pierluigi Paggiaro, Zoe J McKeough, Michael I Polkey, Kylie Hill, William D-C Man, Christian F Clarenbach, Nidia A Hernandes, Daniela Savi, Sally Wootton, Karina C Furlanetto, Li W 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 Pa Rutten, Malcolm Kohler, Vanessa S Probst, Ruth Tal-Singer, Esther Garcia Gil, Albertus C den Brinker, Jörg D Leuppi, Peter Ma Calverley, Frank Wjm Smeenk, Richard W Costello, Marco Gramm, Roger Goldstein, Miriam Tj Groenen, Helgo Magnussen, Emiel Fm Wouters, Richard L ZuWallack, Oliver Amft, Henrik Watz, Martijn A Spruit
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.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: RM is supported by the National Council of Scientific and Technological Development (CNPq), Brazil (246704/2012-8). DB holds a Canada Research Chair, Canada. SSCK was funded by the Medical Research Council, UK. WD-CM was funded by the Medical Research Council, UK, and the National Institute for Health Research, UK. MSP was supported by an unrestricted research grant from Astra Zeneca. KCF is supported by the Coordination for the Improvement of Higher Education Personnel (CAPES), Brazil. SZ, DM, SD and JDL were supported by the following foundations: ‘Gottfried und Julia Bangerter-Rhyner-Stiftung’, ‘Freiwillige Akademische Gesellschaft Basel’ and ‘Forschungsfonds der Universität Basel’, Switzerland. DS was supported by GSK and by the Medical Research Council, UK (G0701628). FP is supported by CNPq, Brazil. PRE was supported by an NHMRC Research Fellowship, Australia (1042341). MIP’s contribution to this manuscript was funded by the NIHR Respiratory Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College, UK. EFMW was supported by Point-One funding from AgentschapNL, Dutch Ministry of Economic affairs, the Netherlands. AWV was supported by ‘Stichting de Weijerhorst’ and Point-One funding from AgentschapNL, Dutch Ministry of Economic affairs, Netherlands.

MAS was supported by Point-One funding from AgentschapNL, Dutch Ministry of Economic affairs, the Netherlands. Part of the data was sponsored by GlaxoSmithKline (data from the ECLIPSE cohort sub-study). Data from Ireland was supported by Beaumont Foundation, Ireland and SwordMedical Ltd, Ireland. The Australian sites were supported by a National Health and Medical Research Grant, Australia (grant no.: 570814). Part of the data collection in the UK (data from Leicester) was supported by the National Institute for Health Research (NIHR) Leicestershire, Northamptonshire and Rutland Collaboration for Leadership in Applied Health Research and Care and took place at University Hospitals of Leicester NHS Trust, UK, and by the NIHR Leicester Respiratory Biomedical Research Unit, UK. Data from the PAC-COPD study was funded by grants from the following Spanish institutions: Fondo de Investigación Sanitaria, Ministry of Health (FIS PI020541); Agència d’Avaluació de Tecnologia i Recerca Mèdiques, Catalonia Government (AATRM 035/20/02); Spanish Society of Pneumology and Thoracic Surgery (SEPAR 2002/137); Catalan Foundation of Pneumology (FUCAP 2003 Beca Marià Ravà); Red RESPIRA (RTIC C03/11); Red RCESP (RTIC C03/09), Fondo de Investigación Sanitaria (PI052486); Fondo de Investigación Sanitaria (PI052302); Fundació La Marató de TV3 (No. 041110); and DURSI (2005SGR00392); and by unrestricted educational grants from Novartis Farmacèutica and AstraZeneca Farmacéutica.

History

Citation

Chronic Respiratory Disease 2017, Vol. 14(3) 256–269

Author affiliation

Centre for Exercise and Rehabilitation Science

Version

VoR (Version of Record)

Published in

Chronic Respiratory Disease

Volume

14

Issue

3

Pagination

256 - 269

Publisher

SAGE Publications

issn

1479-9723

eissn

1479-9731

Acceptance date

15/10/2016

Copyright date

2017

Language

eng