Development and evaluation of an interactive home therapy technology for children with neuromotor disorders: exemplification of a design thinking approach
Bibliographic record
Abstract
PURPOSE: To describe the process of developing an interactive home therapy technology and evaluate its usability with children. MATERIALS AND METHODS: Design thinking guided our technology development with knowledge holders. User- and theory-informed design needs were defined by empathizing with users through observation, interviews and literature review. Solutions were ideated through sketches that led to prototypes. Informal testing with knowledge holders was conducted before formal usability testing with 7 school-aged children (5 neurotypical, 2 with cerebral palsy). Children practiced exercises using the technology before completing a study-specific survey (5-point Likert scales and open-ended questions) that was analyzed using descriptive statistics and content analysis. RESULTS: Bootle Boot Camp, an interactive therapy game that enables clinicians to prescribe customized home exercise programs, was created. Through exercise videos, motion tracking, multimodal feedback that fades to summary, self-controlled form (i.e., star ratings) based on a child's performance, rewards and training resources, the game aims to promote safe and high-quality exercise according to design needs. Children found feedback helpful (mean 3.7/5) and audio cues easy to understand (mean 4.6/5). Users' recommendations to improve audio feedback delivery guided game refinements. CONCLUSIONS: Application of the design thinking methodology supported robust end-user involvement that facilitated development of a user-friendly technology.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".