Performance Evaluation of Device-Based Algorithms to Estimate Step Counts in Free-Living Adults Compared with Direct...

Step counts are a widely used indicator of activity in studies of physical activity and health, but direct evidence of the accuracy and precision of these measures in free-living adults is limited. We evaluated the performance of six methods to estimate step counts using research-oriented devices compared with video-recorded direct observation.

Twenty adults (mean ± SD age, 36.1 ± 14.7 yr; 50% female) were affixed with the thigh-worn activPAL and wrist-worn ActiGraph GT3X+ devices for 7 days. The activPAL was processed using standard software, and five algorithms (ActiLife, Oak, Step Detection Threshold, Verisense, and stepcount) were applied to the ActiGraph data. Participants completed two 3-h sessions during which they were recorded using a GoPro camera. Camera data were annotated where step counts, activity type, posture, and whole-body movements were labeled. Linear mixed-effects regression and equivalence testing were used to compare each algorithm's step counts to direct observation.

Among the wrist algorithms, the mean absolute percent error was lowest for stepcount (17.1%) and highest for Step Detection Threshold (231.5%) compared with direct observation. The variance explained was moderate-to-high (R2 = 0.64-0.90). The stepcount algorithm was similar to activPAL estimates; both were statistically equivalent to direct observation at a 15% level and provided similar estimates over a 7-day period (R2 = 0.87, mean absolute percentage error = 12.8%). Accuracy was highest during walking/running for all algorithms, and was lower and highly variable for biking, modified walking, and mixed movements, such as pushing a stroller, working at a screen, and food preparation.

Step count estimates differed among algorithms, however, those measured from the thigh-worn activPAL and the wrist algorithm stepcount were the most accurate.