Balancing for Gross Motor Ability in Exergaming Between Youth with Cerebral Palsy at Gross Motor Function...

To test how three custom-built balancing algorithms minimize differences in game success, time above 40% heart rate reserve (HRR), and enjoyment between youth with cerebral palsy (CP) who have different gross motor function capabilities. Youth at Gross Motor Function Classification System (GMFCS) level II (unassisted walking) and level III (mobility aids needed for walking) competed in a cycling-based exercise video game that tested three balancing algorithms.

Three algorithms: a control (generic-balancing [GB]), a constant non-person specific (One-Speed-For-All [OSFA]), and a person-specific (Target-Cadence [TC]) algorithms were built. In this prospective repeated measures intervention trial with randomized and blinded algorithm assignment, 10 youth with CP aged 10-16 years (X ± standard deviation = 12.4 ± 1.8 years; GMFCS level II n = 4, III n = 6) played six exergaming sessions using each of the three algorithms. Outcomes included game success as measured by a normalized game score, time above 40% HRR, and enjoyment.

The TC algorithm balanced game success between GMFCS levels similarly to GB (P = 0.11) and OSFA (P = 0.41). TC showed poorer balancing in time above 40% HRR compared to GB (P = 0.02) and OSFA (P = 0.02). Enjoyment ratings were high (6.4 ± 0.7/7) and consistent between all algorithms (TC vs. GB: P = 0.80 and TC vs. OSFA: P = 0.19).

TC shows promise in balancing game success and enjoyment but improvements are needed to balance between GMFCS levels for cardiovascular exercise.