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Gamified mHealth System for Evaluating Upper Limb Motor Performance in Children: Cross-Sectional Feasibility Study
Md Raihan Mia1, Sheikh Iqbal Ahamed1, Samuel Nemanich2
1Department of Computer Science, Marquette University, Milwaukee, WI, United States.
JMIR Serious Games
|March 7, 2025
Summary
Mobile health gamification effectively quantifies upper limb (UL) motor skills in children. MoEvGame provides a fast, flexible, and objective assessment tool, moving beyond traditional methods for developmental and neurological disorders.
Area of Science:
- Pediatric Neurology
- Rehabilitation Technology
- Human Motor Control
Background:
- Many children face developmental or neurological disorders impacting upper limb (UL) function for daily activities.
- Traditional UL assessments are precise but often lack portability or are time-intensive.
- Mobile health (mHealth) gamification offers a novel approach to assess UL motor functions.
Purpose of the Study:
- To assess the feasibility of MoEvGame, an mHealth gamified system, for evaluating children's whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination.
- To develop and validate novel mHealth tools for quantifying UL movement features.
- To analyze spatiotemporal game data using advanced algorithms to measure speed, accuracy, and precision.
Main Methods:
- Feasibility study involving 31 elementary school children (median age 9.0 years).
- Participants played 5 gamified tasks targeting whole limb reaching, fine motor control, manual dexterity, and bilateral coordination.
- Spatiotemporal data were analyzed using change point detection, signal processing, and statistical methods to quantify motor performance and age-related differences.
Main Results:
- A negative correlation between speed and accuracy was observed in whole limb movements (r=-0.30 to -0.42).
- Older children demonstrated improved UL performance (faster completion, fewer errors) compared to younger children.
- Significant differences in bimanual coordination were identified, with in-phase modes showing higher speed and travel distance than antiphase modes.
Conclusions:
- Spatiotemporal data from the mHealth app effectively quantify pediatric motor performance.
- MoEvGame offers a fast, flexible, and objective tool for UL motor assessment, integrating gamification with accessible technology.
- This approach represents an advancement over traditional assessment methods for children with motor impairments.
