Comparison of Occupational Performance in Immersive Virtual and Real Environments Among Patients With Stroke: Observational Randomized Crossover Pilot Study
Bibliographic record
Abstract
Background: Conventional rehabilitation approaches involve therapists simulating various occupational tasks in health care settings or recreating real-life situations to assess and train patients in instrumental activities of daily living (IADLs). As an alternative, immersive virtual reality (IVR) has been widely used in stroke rehabilitation for years, but research comparing occupational performance between virtual and real environments is limited. Objective: This study aims to introduce a novel IVR shopping system designed for patients with stroke and to investigate the correlation of occupational performance in virtual and real environments among patients with stroke. Methods: Ten patients with stroke were recruited from the Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, who met the inclusion and exclusion criteria for this observational, randomized crossover study; the patients were predominantly male (n=7), had experienced ischemic stroke (n=9), were aged 14 to 73 years, and had a time since stroke of 1 to 42 months. All patients attempted shopping tasks in virtual and real environments. The Mini-Mental State Examination (MMSE), Timed Up and Go Test (TUGT), modified Barthel index (MBI), and Lawton index (LI) were used to assess cognition, ambulation, and activities of daily living. Memory capacity and duration in the virtual and real environments were recorded as the primary parameters of occupational performance. The Wilcoxon test and Spearman correlation coefficients were used to analyze the differences and correlations between the 2 environments. Results: The Wilcoxon test showed no significant differences between the virtual and real environments in memory capacity and duration of task completion (P>.99 and P=.99), and memory capacity in both environments correlated with the LI (ρ=0.81; P=.005). Memory duration had a relationship with the TUGT in the virtual environment (ρ=0.68; P=.03) and a borderline negative correlation with MMSE in the real environment (ρ=-0.58; P=.08). Conclusions: Considering the small sample size used in this study and the study's limitations, despite the significant correlation between shopping performance in IVR and the real world, it is still too early to conclude that IVR is a noninferior approach, but it presents the potential to be an alternative for assessment and training in IADLs when resources are limited. However, further research is needed to investigate the psychometric properties, clinical effects, and impact of virtual training on real-world performance. The implications for practice might include the following: (1) occupational performance in virtual shopping might be the same as real-world shopping, and more virtual IADLs could thus be developed; (2) virtual IADL assessment and training systems could be used in remote locations or locations with limited resources; and (3) more objective parameters of IADLs could be extracted from virtual environments.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".