Ability-Based Balancing Using the Gross Motor Function Measure in Exergaming for Youth with Cerebral Palsy
Bibliographic record
Abstract
Objective: 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. Materials and Methods: 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. Results: 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?.” Conclusion: The GMFM Ability-Based balancing decreased dyad spread while requiring participants to pedal quickly, facilitating interaction and physical activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".