Gamified mHealth System for Evaluating Upper Limb Motor Performance in Children: Cross-Sectional Feasibility Study

Approximately 17% of children in the United States have been diagnosed with a developmental or neurological disorder that affects upper limb (UL) movements needed for completing activities of daily living. Gold-standard laboratory assessments of the UL are objective and precise but may not be portable, while clinical assessments can be time-intensive. We developed MoEvGame, a mobile health (mHealth) gamification software system for the iPad, as a potential advanced technology to assess UL motor functions.

This feasibility study examines whether MoEvGame can assess children's whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination. The specific aims were to (1) design and develop novel mHealth gamified software tools to examine theory-driven features of UL movement, (2) analyze spatiotemporal game data with new algorithms and statistical techniques to quantify movement performance as a parameter of speed, accuracy, and precision, and (3) validate assessment methods with healthy participants from schools.

Elementary school children (N=31, median 9.0, IQR 4.0-14.0 years old) participated by playing 5 games. The game tasks were focused on key features of skilled motor control: (1) whole limb reaching, (2) fine motor control and manual dexterity, and (3) bilateral coordination. Spatiotemporal game data were transferred and stored in a cloud-based data management server for further processing and analysis. We applied change point detection (ie, the pruned exact linear time method), signal processing techniques, and other algorithms to calculate movement speed and accuracy from spatiotemporal parameters. Different statistical methods (ie, Pearson correlation, mean, standard deviation, P value, 95% confidence interval) were used to compare speed-accuracy tradeoffs and evaluate the relationship between age and motor performance.

A negative correlation was identified between speed and accuracy in the whole limb movement (r=-0.30 to -0.42). Significant relationships between age and upper limb performance were found: older participants exhibited lower errors with faster completion times compared to younger participants. Significant differences in bimanual coordination were found related to phase synchronization (in-phase congruent [mean 28.85, SD 18.97] vs antiphase congruent [mean 112.64, SD 25.82] and in-phase mirrored [mean 23.78, SD 16.07] vs antiphase mirrored [mean 121.39, SD 28.19]). Moreover, the average speed (revolutions per second) and travel distance (m) of the in-phase mode were significantly higher than those of the antiphase coordination.

Results of this feasibility study show that spatiotemporal data captured from the mHealth app can quantify motor performance. Moving beyond traditional assessments, MoEvGame incorporates gamification into ubiquitous and accessible technology as a fast, flexible, and objective tool for UL motor assessment.