Home-based virtual reality training after discharge from hospital-based stroke rehabilitation: a parallel randomized feasibility trial
Bibliographic record
Abstract
BACKGROUND: Virtual reality training (VRT) uses computer software to track a user's movements and allow him or her to interact with a game presented on a television screen. VRT is increasingly being used for the rehabilitation of arm function, balance and walking after stroke. Patients often require ongoing therapy post discharge from inpatient rehabilitation. Outpatient therapy may be limited or inaccessible due to waiting lists, transportation issues, distance etc.; therefore, home-based VRT could provide the required therapy in a more convenient and accessible setting. The objectives of this parallel randomized feasibility trial are to determine (1) the feasibility of using VRT in the home post stroke and (2) the feasibility of a battery of quantitative and qualitative outcome measures of stroke recovery. METHODS: Forty patients who can stand for at least 2 min and are soon to be discharged from inpatient or outpatient rehabilitation post stroke are being recruited in Ottawa, Canada and being randomized to control and experimental groups. Participants in the experimental group use home-based VRT to do rehabilitative exercises for standing balance, stepping, reaching, strengthening and gentle aerobic fitness. Control group participants use an iPad with apps selected to rehabilitate cognition, hand fine motor skills and visual tracking/scanning. Both groups are instructed to perform 30 min of exercise 5 days a week for 6 weeks. VRT intensity and difficulty are monitored and adjusted remotely. Weekly telephone contact is made with all participants. Ability to recruit participants, ability to handle the technology and learn the activities, compliance, safety, enjoyment, perceived efficacy and cost of program delivery will be assessed. A battery of assessments of standing balance, gait and community integration will be assessed for feasibility of completion within this population and potential for improvement following the intervention. Effect sizes will be calculated. DISCUSSION: The results of this study will be used to support the creation of a definitive randomized controlled trial on the efficacy of home-based VRT for rehabilitation post stroke. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03261713 . Registered on 21 August 2017. Registration amended on 1 June 2018 to decrease enrollment from 40 to 20 due to a cut in study funding and difficulty recruiting participants.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".