Barriers, Facilitators and Interventions to Support Virtual Reality Implementation in Rehabilitation: A Scoping Review
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Bibliographic record
Abstract
Virtual reality and active video games (VR/AVGs) are promising rehabilitation tools because of their potential to facilitate abundant, motivating, and feedback-rich practice. However, clinical adoption remains low despite a growing evidence base and the recent development of clinically accessible and rehabilitation-specific VR/AVG systems. Given clinicians' eagerness for resources to support VR/AVG use, a critical need exists for knowledge translation (KT) interventions to facilitate VR/AVG integration into clinical practice. KT interventions have the potential to support adoption by targeting known barriers to, and facilitators of, change. This scoping review of the VR/AVG literature uses the Theoretical Domains Framework (TDF) to (1) structure an overview of known barriers and facilitators to clinical uptake of VR/AVGs for rehabilitation; (2) identify KT strategies to target these factors to facilitate adoption; and (3) report the results of these strategies. Barriers/facilitators and evaluated or proposed KT interventions spanned all but 1 and 2 TDF domains, respectively. Most frequently cited barriers/facilitators were found in the TDF domains of Knowledge, Skills, Beliefs About Capabilities, Beliefs About Consequences, Intentions, Goals, Environmental Context and Resources, and Social Influences. Few studies empirically evaluated KT interventions to support adoption; measured change in VR/AVG use did not accompany improvements in self-reported skills, attitudes, and knowledge. Recommendations to target frequently identified barriers include technology development to meet end-user needs more effectively, competency development for end-users, and facilitated VR/AVG implementation in clinical settings. Subsequent research can address knowledge gaps in both clinical and VR/AVG implementation research, including on KT intervention effectiveness and unexamined TDF domain barriers. LEVEL OF EVIDENCE: IV.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it