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Record W4282929316 · doi:10.2196/29830

Virtual Reality Training Using Nintendo Wii Games for Patients With Stroke: Randomized Controlled Trial

2022· article· en· W4282929316 on OpenAlexvenueno aff
Naveed Anwar, Hossein Karimi, Ashfaq Ahmad, Syed Amir Gilani, Kehkshan Khalid, Ahmed Sohaib Aslam, Asif Hanif

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationPhysical medicine and rehabilitationPhysical therapyStroke (engine)Virtual realityBalance (ability)Randomized controlled trialMedicineBerg Balance ScaleVirtual Reality Exposure TherapyPsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Stroke is a leading cause of disability. It is difficult to devise an optimal rehabilitation plan once stroke survivors are back home. Conventional rehabilitative therapies are extensively used in patients with stroke to recover motor functioning and disability, but these are arduous and expensive. Virtual reality (VR) video games inspire patients to get involved in their therapeutic exercise routine in a fun way. VR in the form of games provides a fruitful, secure, and challenging learning environment for motor control and neural plasticity development in rehabilitation. The effects of upper limb sensorimotor functioning and balance are the main focus of this trial. OBJECTIVE: The aim of this study is to compare the effects of VR training and routine physical therapy on balance and upper extremity sensorimotor function in patients with stroke. METHODS: It was a single assessor-blinded randomized clinical trial. A total of 74 participants with their first chronic stroke were included and rehabilitated in a clinical setting. The lottery method was used to randomly assign patients to either the VR group (n=37) or the routine physical therapy group (n=37). The VR group received a 1-hour session of VR training for 3 weekdays over 6 weeks, and the routine physical therapy group received different stretching and strengthening exercises. The outcome measuring tools were the Berg Balance Scale for balance and the Fugl-Meyer Assessment (upper extremity) scale for sensorimotor, joint pain, and range assessment. The assessment was done at the start of treatment and after the 6 weeks of intervention. Data analysis was done using SPSS 22. RESULTS: The trial was completed by 68 patients. A significant difference between the two groups was found in the Berg Balance Scale score (P<.001), Fugl-Meyer Assessment for motor function (P=.03), and Fugl-Meyer Assessment for joint pain and joint range (P<.001); however, no significant difference (P=.19) in the Fugl-Meyer Assessment for upper extremity sensation was noted. CONCLUSIONS: VR training is helpful for improving balance and function of the upper extremities in the routine life of patients with stroke; although, it was not found to be better than conventional training in improving upper limb sensation. VR training can be a better option in a rehabilitation plan designed to increase functional capability. TRIAL REGISTRATION: Iranian Registry of Clinical Trials RCT20190715044216N1; https://www.irct.ir/user/trial/40898/view.

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)0.001
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 score0.010
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.019
GPT teacher head0.300
Teacher spread0.280 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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