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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns
Sarah Kozey Keadle1, Joshua N Sampson, Haocheng Li
11Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; 2Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD; 3Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; 4Departments of Oncology and Community Health Sciences, University of Calgary, Alberta, CANADA; 5Misfit Inc., San Francisco, CA; 6Department of Statistics, Texas A&M University, College Station, TX; and 7School of Mathematical and Physical Sciences, University of Technology Sydney, AUSTRALIA.
Introduction:
The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured.
Methods:
We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d.
Results:
In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65).
Conclusions:
Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.
