24-hour accelerometry in COPD: Exploring physical activity, sedentary behavior, sleep and clinical characteristics

• Main Authors: Orme MW, Steiner MC, Morgan MD, Kingsnorth AP, Esliger DW, Singh SJ, Sherar LB
• Format: Article
• Language: English
• Published: Dove Medical Press 2019-02-01
• Series: International Journal of COPD
• Subjects: Accelerometry; COPD; physical activity; principal components analysis; sedentary behavior
• Online Access: https://www.dovepress.com/24-hour-accelerometry-in-copd-exploring-physical-activity-sedentary-be-peer-reviewed-article-COPD

Mark W Orme,1–3 Michael C Steiner,1–3 Mike D Morgan,1 Andrew P Kingsnorth,2,3 Dale W Esliger,2–4 Sally J Singh,1–3,* Lauren B Sherar2–4,* 1Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre – Respiratory, Leicester, UK; 2School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK; 3National Centre for Sport and Exercise Medicine, Loughborough, UK; 4NIHR Leicester Biomedical Research Centre, Leicester, UK *These authors contributed equally to this work Background: The constructs and interdependency of physical behaviors are not well described and the complexity of physical activity (PA) data analysis remains unexplored in COPD. This study examined the interrelationships of 24-hour physical behaviors and investigated their associations with participant characteristics for individuals with mild–moderate airflow obstruction and healthy control subjects. Patients and methods: Vigorous PA (VPA), moderate-to-vigorous PA (MVPA), light PA (LPA), stationary time (ST), average movement intensity (vector magnitude counts per minute), and sleep duration for 109 individuals with COPD and 135 healthy controls were obtained by wrist-worn accelerometry. Principal components analysis (PCA) examined interrelationships of physical behaviors to identify distinct behavioral constructs. Using the PCA component loadings, linear regressions examined associations with participant (+, positive correlation; -, negative correlation), and were compared between COPD and healthy control groups. Results: For both groups PCA revealed ST, LPA, and average movement intensity as distinct behavioral constructs to MVPA and VPA, labeled “low-intensity movement” and “high-intensity movement,” respectively. Sleep was also found to be its own distinct behavioral construct. Results from linear regressions supported the identification of distinct behavioral constructs from PCA. In COPD, low-intensity movement was associated with limitations with mobility (-), daily activities (-), health status (+), and body mass index (BMI) (-) independent of high-intensity movement and sleep. High-intensity movement was associated with age (-) and self-care limitations (-) independent of low-intensity movement and sleep. Sleep was associated with gender (0= female, 1= male; [-]), lung function (-), and percentage body fat (+) independent of low-intensity and high-intensity movement. Conclusion: Distinct behavioral constructs comprising the 24-hour day were identified as “low-intensity movement,” “high-intensity movement,” and “sleep” with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD. Keywords: accelerometry, COPD, physical activity, principal components analysis, sedentary behavior