Tools and methods for assessing the usability and related aspects of usability of extended reality and telerehabilitation technologies in stroke rehabilitation: a scoping review
Bibliographic record
Abstract
PURPOSE: Extended reality (XR) and telerehabilitation (TR) technologies are increasingly being used into stroke rehabilitation. These technologies have the potential to enhance therapy intensity, motivate users through engaging and interactive environments, and improve access to rehabilitation services in both clinical and home settings. Usability assessment is essential to ensure effective, engaging, and accessible interventions. This scoping review aims to identify tools used to evaluate XR and TR technologies in stroke rehabilitation. MATERIALS AND METHODS: This scoping review was conducted following the methodological framework of Arksey and O'Malley, further refined by Levac et al. and the Joanna Briggs Institute. A literature search was performed across five databases (MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science) using keywords and their variations related to stroke, virtual reality, augmented reality, mixed reality, telerehabilitation, and usability evaluation. Peer-reviewed articles and conference abstracts published up to December 2024 were included if they reported on the usability evaluation of XR or TR technologies in neurological rehabilitation. Two reviewers independently screened studies for eligibility. Relevant data were extracted using a standardized data charting framework. RESULTS: The search yielded 2,290 articles, of which 111 were included in the review. Twenty-eight tools were identified, encompassing both direct usability assessments and complementary tools addressing related aspects. These tools were grouped into six categories: (1) standardized questionnaires, (2) custom questionnaires, (3) semi-structured interviews, (4) task-based usability testing, (5) modified standardized questionnaires, and (6) think-aloud protocols. The most frequently used tool was the System Usability Scale (SUS), followed by custom questionnaires. Among studies explicitly evaluating usability, 55.9% combined two to six tools to capture multiple facets of usability. Usability was assessed in 67.6% of publications using quantitative methods (e.g., questionnaires), in 4.5% using qualitative methods (e.g., interviews, focus groups), and in 27.9% using mixed methods approaches. CONCLUSION: Usability of XR and TR technologies is assessed with a wide variety of tools. Combining tools helps capture different aspects of usability, highlighting the importance of addressing its multifaceted nature in stroke rehabilitation. Future research could develop and validate a framework integrating multiple aspects of usability to ensure both relevance and comparability across studies.
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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 | 0.090 | 0.195 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.068 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".