The use of digital avatars to improve virtual rehabilitation health outcomes amongst adults with health conditions: a scoping review
Bibliographic record
Abstract
PURPOSE: Virtual rehabilitation (VRehab) offers alternatives to traditional therapy, overcoming barriers like distance, cost, and clinician access. This scoping review examines how digital avatars are used in adult rehabilitation and their impact on health outcomes. MATERIALS AND METHODS: We systematically searched Medline, Embase, CINAHL, Web of Science, Engineering Village, IEEE Xplore, and ACM Digital Library. After screening 3,877 unique titles and abstracts and 190 full-text studies, 45 studies were included. Data were charted on study characteristics, health outcomes, avatar features, ethics, AI, and co-design. RESULTS: Avatars were most used in stroke (48.8%) and amputee rehabilitation (15.5%), primarily for motor function, gait, and pain. Most employed humanoid avatars on screens or mobile devices, providing real-time feedback. Many reported improved function, adherence, or engagement, though few used advanced AI, privacy measures, or co-design. Standardized assessments were rarely applied. CONCLUSIONS: Avatar-based rehabilitation shows promise to enhance health outcomes and engagement. Further work should explore long-term effects, ethics, privacy, AI-driven personalization, and integration with health systems.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".