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Record W4286532397 · doi:10.2196/37506

Virtual Reality–Based Rehabilitation as a Feasible and Engaging Tool for the Management of Chronic Poststroke Upper-Extremity Function Recovery: Randomized Controlled Trial

2022· article· en· W4286532397 on OpenAlexaffvenue
Alejandro Hernández, Liudmila Bubyr, Philippe S. Archambault, Johanne Higgins, Mindy F. Levin, Dahlia Kairy

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill UniversityUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsRehabilitationTelerehabilitationPhysical therapyRandomized controlled trialStroke (engine)Physical medicine and rehabilitationMedicineIntervention (counseling)Chronic strokeOccupational therapyTelemedicineHealth careNursing

Abstract

fetched live from OpenAlex

BACKGROUND: A growing number of stroke survivors are left with little to no rehabilitation services upon discharge from stroke rehabilitation, although arm deficits may persist or develop from disuse once rehabilitation services have ceased. Virtual reality (VR)-based rehabilitation, combined with new technologies such as telerehabilitation, including serious games using VR environments that encourage users to practice functional movements from home with minimal supervision, may have an important role to play in optimizing and maintaining upper extremity (UE) function. OBJECTIVE: The primary objective of this study is to determine the extent to which a 1-month intervention using a VR-based serious game is effective in improving UE function compared with an evidence-based home exercise program. A secondary objective is to assess the feasibility of implementing the intervention for chronic stroke rehabilitation in participants' homes. METHODS: A total of 51 chronic stroke participants were randomized to treatment (n=26, 51%; Jintronix system) or standard care (n=25, 49%; standardized Graded Repetitive Arm Supplementary Program kit home program) groups. The participants were evaluated at baseline (before), immediately after the intervention (after), and at follow-up (4 weeks). The primary outcome measure was the Fugl-Meyer Assessment for UE (FMA-UE). Secondary outcome measures included the Stroke Impact Scale and an abridged version of the Motor Activity Log-14. Self-reported number of sessions was logged for the standard care group. RESULTS: No statistically significant differences between groups were found across measures. Overall time effects were found for the FMA-UE (P=.045), specifically between preintervention and postintervention time points for both groups (P=.03). A total of 9 participants in the treatment group reached or surpassed the minimal clinically important difference in scores for the FMA-UE, with 7 (78%) of them having baseline low or moderate arm function, compared with 3 (33%) participants in the standard care group. Furthermore, 56% (9/16) of the participants in the treatment group who actively engaged with the system reached the minimal clinically important difference for the FMA-UE, compared with none for the 0% (0/10) less-active participants. CONCLUSIONS: These findings suggest that UE training for chronic stroke survivors using virtual rehabilitation in their home may be as effective as a gold standard home exercise program and that those who used the system the most achieved the greatest improvement in UE function, indicating its relevance to being included as part of ongoing rehabilitation services. TRIAL REGISTRATION: ClinicalTrials.gov NCT02491203; https://clinicaltrials.gov/ct2/show/NCT02491203. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2015.12.006.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.281
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2022
Admission routes2
Has abstractyes

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