Protocol for evaluation of a virtual wheelchair simulator in assessing mobility skills and cognitive abilities in diverse populations: A multicentric mixed-methods pilot study
Bibliographic record
Abstract
BACKGROUND: Current wheelchair acquisition, prescription, and training programs often require comprehensive assessments integrating both power mobility skills and cognitive abilities. While wheelchair simulators offer promise for these assessments, but they have not been fully validated. OBJECTIVE: This study aims to develop and refine a protocol for evaluating the feasibility, reliability and preliminary validity of virtual wheelchair simulator metrics in assessing users' current power mobility skills and cognitive abilities, following STARD guidelines. Reference standards include the self-report Wheelchair Skill Test (WST), Power Mobility Road Test (PMRT) and the Montreal Cognitive Assessment (MoCA). METHODS: This multicentric, mixed-methods pilot study will recruit participants with mobility disabilities, a control group of individuals without disabilities, and healthcare professionals to use a virtual wheelchair simulator. Healthcare professionals will evaluate the simulator's assessments and provide expert feedback on the protocol. Quantitative data will include simulator-derived performance metrics compared to reference standards, and physiological data (e.g., heart rate, skin conductance, temperature, inter-beat-intervals, accelerometer and eye-gaze tracking). Qualitative data (semi-structured interviews) will capture user experiences and insights for protocol refinement. The Quality of Experience (QoE) evaluation framework will assess cognitive workload (NASA-TLX and PAAS), usability (System Usability Scale), immersion (IGroup Presence Questionnaire), and emotion (Self-Assessment Manikin). Data analysis will include correlation analysis, regression models, thematic analysis, and statistical tests (e.g., independent t-tests, Mann-Whitney U tests) to compare simulator-based performance across groups. DISCUSSION: This pilot study seeks to fill critical gaps in current wheelchair training and prescription methods by exploring the use of a virtual simulator to objectively assess both cognitive abilities and power mobility skills. Integrating the QoE assessment framework will provide insights into user interactions, ensuring that the simulator supports tailored training and improve user outcomes in mobility, and safety. Future research may extend this protocol to clinical settings to further evaluate its applicability and effectiveness.
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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 | 0.104 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".