Challenges and Benefits of Virtual Reality in Home-Based Poststroke Rehabilitation: Co-Design Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Stroke often leads to persistent impairments that limit daily functioning and psychosocial well-being. Virtual reality (VR) has emerged as a promising adjunct in stroke rehabilitation, although research has largely focused on clinical outcomes, with limited attention to user involvement and the experiences of multiple stakeholders in the design process. OBJECTIVE: This study aimed to explore the challenges and benefits of co-designing and using VR to support home-based poststroke rehabilitation. METHODS: A qualitative co-design case study was applied to gain an in-depth understanding of the challenges and benefits. Rapid co-design principles were used in developing VR prototypes delivered via head-mounted displays through 3 workshops with participants with stroke, health care professionals, and VR developers from November 2023 to May 2024. A design prototype revision was done based on feedback from the workshops. Data were collected via audio-taped co-design workshops with all participants and 10 successive semistructured interviews with health care professionals and VR developers conducted in a rehabilitation hospital. A thematic analysis was performed on transcribed recordings. RESULTS: In total, five main themes emerged: (1) adaptability for stroke-related impairments in home rehabilitation, (2) safety and ease of use, (3) goal orientation, (4) motivation, and (5) VR as a complementary tool. One of the primary challenges identified lies in the adaptability of VR systems for individuals with hemiparesis. Additionally, customization and safety concerns remain a complex barrier, as VR solutions must be capable of addressing a wide range of stroke-related impairments and aligning specific rehabilitation goals. VR demonstrated potential to enhance rehabilitation by simulating real-life tasks that encourage goal-oriented and motivating therapy. As a complementary tool, VR can enhance traditional rehabilitation by increasing the intensity and volume of therapy. CONCLUSIONS: This study offers insight into how VR can be effectively integrated into rehabilitation practices. Its integration into rehabilitation requires alignment with established therapeutic principles within VR applications, such as adjustable task-specific training and meaningful outcomes tailored to individual needs, to ensure clinical relevance and user engagement. VR should complement, rather than replace, conventional therapy by increasing training intensity, reducing therapist workload, and extending rehabilitation into the home. Thoughtful co-design with stakeholders is key to creating VR tools that bridge the gap between structured clinical care and independent recovery, offering continuous support throughout the rehabilitation process.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".