Validation of an innovative algorithm for detecting self-propulsion in manual wheelchair users.

Introduction: Actimetry is increasingly used to measure physical activity (PA) for manual wheelchair (MWC) users. However, conversion of raw data into interpretable PA outcomes remains imprecise, and the differentiation between propulsion and non-propulsion is challenging. Using a previously developed algorithm, the objectives of this study were to: (1) measure the accuracy of total distance collected, and (2) validate the algorithm's accuracy in differentiating between self-propulsion and non-propulsion. Methods: Experimental study consisting of two data collection sessions. Actimetry data (Actigraph GT3X+) were collected indoors (controlled conditions) during 100 repetitions (n = 40 MWC propulsion, n = 60 pushing the MWC) over three distances (10, 50 and 100 m). Actimetry data were also collected outdoors (uncontrolled condition) during self-propulsion over 1000 m (10 repetitions). Descriptive statistics (mean and standard deviation) with confidence intervals and accuracy measures (percentage of true value) were conducted for each trial. Results: The algorithm measured total distance covered indoors with an excellent accuracy (98.9% to 99.8%). It differentiated between self-propulsion and non-propulsion with an accuracy between 96.2% and 99.2% under controlled condition, and between 91.3% and 100.0% under uncontrolled condition. Conclusions: The algorithm tested allowed precise measurement of total distance covered, as well as an excellent discrimination between self-propulsion and non-propulsion. Preprint: Gagnon R, Best KL and Routhier F. Validation of an innovative two-part algorithm for detecting self-propulsion in manual wheelchair users.