The Practical Work of Ensuring Effective Use of Serious Games in a Rehabilitation Clinic: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Many rehabilitation clinics adopted serious games to support their physiotherapy sessions. Serious games can monitor and provide feedback on exercises and are expected to improve therapy and help professionals deal with more patients. However, there is little understanding of the impacts of serious games on the actual work of physiotherapists. OBJECTIVE: This study aimed to understand the impact of an electromyography-based serious game on the practical work of physiotherapists. METHODS: This study used observation sessions in an outpatient rehabilitation clinic that recently started using a serious game based on electromyography sensors. In total, 44 observation sessions were performed, involving 3 physiotherapists and 22 patients. Observation sessions were documented by audio recordings or fieldnotes and were analyzed for themes with thematic analysis. RESULTS: The findings of this study showed that physiotherapists played an important role in enabling the serious game to work. Physiotherapists briefed patients, calibrated the system, prescribed exercises, and supported patients while they played the serious game, all of which amounted to relevant labor. CONCLUSIONS: The results of this work challenge the idea that serious games reduce the work of physiotherapists and call for an overall analysis of the different impacts a serious game can have. Adopting a serious game that creates more work can be entirely acceptable, provided the clinical outcomes or other advantages enabled by the serious game are strong; however, those impacts will have to be assessed on a case-by-case basis. Moreover, this work motivates the technology development community to better investigate physiotherapists and their context, offering implications for technology design.
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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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".