Looking through the Lens: Contextualizing and Operationalizing Design Recommendations for Rehabilitation Games for Young People
Bibliographic record
Abstract
Games for physical therapy can motivate patients, and HCI research has provided various recommendations for their design. However, such recommendations often remain at a high level: They are rarely reviewed with patients or appraised through application to game design and analysis. We address this gap by refining and operationalizing existing lessons for therapeutic games for young people. First, we report on semi-structured interviews with young people (aged 7–16) and parents, reviewing the lessons. Second, we operationalize them using an established collection of game design patterns to provide concrete guidance for game design and analysis. We critically appraise our approach through application to two games for physical therapy, Liberi and Wii Fit. Results show that high-level design implications can be made actionable using existing game design patterns, and we contribute a practical approach for the analysis and design of games for physical therapy.
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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 | 0.001 | 0.001 |
| 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".