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

Abstract

Introduction:Actimetry 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 algorithm's accuracy in differentiating between self-propulsion and non-propulsion.Methods:Experimental 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.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.