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Methodology for using long-term accelerometry monitoring to describe daily activity patterns in COPD.

ABSTRACT: We sought to develop procedures for computerized analysis of long-term, high-resolution activity monitoring data that allow accurate assessment of the time course of activity levels suitable for use in chronic obstructive pulmonary disease (COPD) patients. Twenty-two COPD patients utilizing long-term oxygen recruited from 5 sites of the COPD Clinical Research Network wore a triaxial accelerometer (RT3, Stayhealthy, Monrovia, CA) during waking hours over a 14-day period. Computerized algorithms were composed allowing minute-by-minute activity data to be analyzed to determine, for each minute, whether the monitor was being worn. Temporal alignment allowed determination of average time course of activity level, expressed as average vector magnitude units (VMU, the vectorial sum of activity counts in three orthogonal directions) per minute, for each hour of the day. Mid-day activity was quantified as average VMU/minute between 10AM and 4PM for minutes the monitor was worn. Over the 14 day monitoring period, subjects wore the monitor an average of 11.4 +/- 3.0 hours x day(-1). During mid-day hours, subjects wore the monitor 76.3% of the time and generated an average activity level of 112 +/- 55 VMU x min(-1). Increase in precision of activity estimates with longer monitoring periods was demonstrated. This analysis scheme allows a detailed temporal pattern of activity to be defined from triaxial accelerometer recordings and has the potential to facilitate comparisons among subjects and between subject groups. This trial is registered at ClinicalTrials.gov (NCT00325754).

SUBMITTER: Hecht A

PROVIDER: S-EPMC2862250 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe 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 methodsVigorous 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.ResultsFor 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.ConclusionDistinct 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.

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Methodology for using long-term accelerometry monitoring to describe daily activity patterns in COPD.

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