Balancing for Gross Motor Ability in Exergaming Between Youth with Cerebral Palsy at Gross Motor Function Classification System Levels II and III
Bibliographic record
Abstract
OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: TC shows promise in balancing game success and enjoyment but improvements are needed to balance between GMFCS levels for cardiovascular exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".