Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions.
Purpose: The study evaluates the validity of an integrative, machine-learning method for processing activity monitor data in relation to a portable metabolic analyzer (OM; Oxycon Mobile) and direct observation (DO).
Methods: Forty-nine adults (ages 18-40) each completed 5-minute bouts of 15 activities ranging from sedentary to vigorous intensity in a laboratory setting while wearing the ActiGraph (AG) on the hip, the activPAL (AP) on the thigh and the OM. Estimates of energy expenditure (EE) and categorization of activity intensity were obtained from the AG processed with Lyden’s Sojourns method (SOJ) and from our new Sojourns Including Posture (SIP) method, which integrates output from the AG and AP. Classification accuracy and estimates of EE were then compared to criterion measures (OM, DO) using confusion matrices and comparisons of mean absolute error of the log (MAE ln Q).
Results: The SIP method had a higher overall classification agreement [79%, 95% CI: 75%, 82%] than the SOJ [56%, 95% CI: 52%, 59%] based on DO. Compared to OM, estimates of EE from SIP had lower MAE ln Q than SOJ for light (0.21 vs. 0.27), moderate (0.33 vs. 0.42) and vigorous (0.16 vs. 0.35) intensity activities.
Conclusions: The SIP method was superior to SOJ for distinguishing between sedentary and light activities as well as estimating EE at higher intensities. Thus, SIP is recommended for research in which accuracy of measurement across the full range of activity intensities is of interest.