Abstract

To test if the gross motor function measure (GMFM) could be used to improve game balancing allowing youth with cerebral palsy (CP) with different physical abilities to play a cycling-based exercise videogame together. Our secondary objective determined if exergaming with the GMFM Ability-Based algorithm was enjoyable. Eight youth with CP, 8-14 years of age, GMFM scores between 25.2% and 87.4% (evenly distributed between Gross Motor Function Classification System levels II and III), competed against each other in head-to-head races, totaling 28 unique race dyads. Dyads raced three times, each with a different method of minimizing the distance between participants (three balancing algorithms). This was a prospective repeated measures intervention trial with randomized and blinded algorithm assignment. The GMFM Ability-Based algorithm was developed using a least squares linear regression between the players' GMFM score and cycling cadence. Our primary outcome was dyad spread or average distance between players. The GMFM Ability-based algorithm was compared with a control algorithm (No-Balancing), and an idealized algorithm (one-speed-for-all [OSFA]). After each race, participants were asked "Was that game fun?" and "Was that game fair?" using a five-point Likert scale. Participants pedaled quickly enough to elevate their heart rate to an average of 120 ± 8 beats per minute while playing. Dyad spread was lower when using GMFM Ability-Based balancing (4.6 ± 4.2) compared with No-Balancing (11.9 ± 6.8) (P < 0.001). When using OSFA balancing, dyad spread was (1.6 ± 0.9), lower than both GMFM Ability-Based (P = 0.006) and No-Balancing (P < 0.001). Cycling cadence positively correlated to GMFM, equal to 0.58 (GMFM) +33.29 (R2adj= 0.662, P = 0.004). Participants rated the games a median score 4/5 for both questions: "was that game fun?" and "was that game fair?." The GMFM Ability-Based balancing decreased dyad spread while requiring participants to pedal quickly, facilitating interaction and physical activity.

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