Logo image
Physical activity patterns and clusters in 1001 patients with COPD
Journal article   Open access   Peer reviewed

Physical activity patterns and clusters in 1001 patients with COPD

Rafael Mesquita, Gabriele Spina, Fabio Pitta, David Donaire-Gonzalez, Brenda M. Deering, Mehul S. Patel, Katy E. Mitchell, Jennifer Alison, Arnoldus J. R. van Gestel, Stefanie Zogg, …
Chronic respiratory disease, Vol.14(3), pp.256-269
2017
PMCID: PMC5720232
PMID: 28774199
pdf
Published765.57 kBDownloadView
Published (Version of Record)CC BY-NC V4.0 Open Access

Abstract

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

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Metrics

2 File views/ downloads
45 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Industry collaboration
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.65 Allergy
1.65.192 COPD
Web Of Science research areas
Respiratory System
ESI research areas
Clinical Medicine
Logo image