Implementation considerations for a telerehabilitation system to improve patients’ adherence to home-based physical exercises: a qualitative study
Bibliographic record
Abstract
Physiotherapists often prescribe home exercise programmes (HEP) to aid in the recovery of their clients with disabilities, which are often poorly adhered to by clients, and can lead to poor clinical outcomes. To improve HEP adherence, our team has proposed a gamification-based telerehabilitation system, which integrates gaming elements (e.g., challenges, rewards, and interactive features) with remote monitoring and guidance from physiotherapists. The purpose of this study was to explore the attitudes, preferences, and experiences of both clients and physiotherapists with gamification-based telerehabilitation solutions as a means to improve clients’ adherence to HEP. A qualitative study using semi-structured interviews was conducted with clients who underwent physical therapy within the past 2 years (n = 8), and physiotherapists (n = 8). The interviews explored attitudes, preferences, and/or experiences related to using technology to support clients’ and physical therapists’ needs. An inductive thematic analysis was used to analyse the data. Four main themes were identified: (1) Virtual and in-person rehabilitation programmes, (2) Adherence to HEP, (3) Users’ preferences towards the technology, and (4) Technology consideration for HEP application. The findings from clients and physiotherapists highlight important design and implementation considerations for a remote gamification system aiming to improve adherence to HEP.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".