Performance Evaluation of Algorithms to Estimate Daily Sedentary Time Using Wrist-Worn Sensors in Free-Living Adults

Purpose: Given the limited real-world testing of algorithms for wrist-worn sensors to estimate sedentary time, we examined the performance of 21 algorithms in free-living adults. Methods: Seventy-one adults (35–65 years) wore a GENEActiv (wrist) and an activPAL (thigh) sensor for up to 10 days. activPAL was our reference measure. We estimated sedentary time (hours/day) using 21 classification algorithms, including cut-point and machine-learning methods. Valid days from each monitor were matched by date and mean values were calculated. Equivalence testing (±10%) and linear regression were used to compare each algorithm’s estimate to the reference, over all participants and by sex and age. Results: activPAL recorded a mean of 9.4 hr/day sedentary. Five of 21 algorithms (24%) estimated sedentary time within 10% (±0.94 hr) of the reference. Two of these methods employed machine-learning algorithms (Trost Extended, OxWearables) and three employed cut-points (GGIR Euclidean norm minus one [ENMO] 40 mg; Bakrania ENMO 32.6 mg; Fraysse ENMOa 62.5 mg). Variance explained in linear regression was relatively high for the machine-learning (R2 = .44–.63) and cut-point algorithms developed for younger (R2 = .30–.64) and older (R2 = .45–.66) adults. More accurate performance was noted for algorithms developed in studies using posture-based ground truth measures and conducted in free-living settings. Conclusion: Fifteen of 21 (71%) algorithms produced estimates of sedentary time that were moderate-strongly correlated with the reference measure, but only five (24%) were within 10% of the reference. Free-living benchmarking studies like this can identify more accurate and precise algorithms to estimate sedentary time and identify characteristics of algorithm development studies that yield better results.