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

IntroductionActimetry is increasingly used to measure physical activity (PA) for 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 two-part algorithm, the objectives of this study were to 1) measure the accuracy of total distance collected, and 2) validate the algorithms accuracy in differentiating between self-propulsion and non-propulsion. MethodsExperimental study consisting of two data collection sessions. Actimetry data (Actigraph) were collected indoors (controlled conditions) during 100 repetitions (n=40 MWC propulsion, n=60 pushing the MWC) over three distances (10, 50 and 100 meters). Actimetry data (Actigraph) were also collected outdoors (uncontrolled condition) during self-propulsion over 1,000 meters (10 repetitions). Descriptive statistics (e.g., mean, standard deviation) with confidence intervals and accuracy measures (percentage of true value) were conducted for each trial. ResultsThe 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. ConclusionsThe algorithm tested allowed precise measurement of total distance covered, as well as an excellent discrimination between self-propulsion and non-propulsion.