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Record W4411697079 · doi:10.2196/69750

Poststroke Neurorehabilitation Using a Soft Robotic Glove Combined With a Virtual Environment: Preliminary Study on Feasibility, Safety, Effects, and User Satisfaction

2025· article· en· W4411697079 on OpenAlexafffund
Camille E. Proulx, Johanne Higgins, Thomas Vaughan, Mark Hewko, Dany H. Gagnon

Bibliographic record

VenueJMIR Neurotechnology · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in RehabilitationNational Research Council CanadaUniversité du Québec à Montréal
FundersFonds de Recherche du Québec - SantéNational Research Council CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsNeurorehabilitationPreprintPhysical medicine and rehabilitationIntervention (counseling)Stroke (engine)PsychologyTraining (meteorology)Applied psychologyPhysical therapyMedicineRehabilitationComputer scienceEngineeringNursingWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Optimizing rehabilitation intensity using a robotic-assisted hand rehabilitation exercise (RAHRE) program coupled with a virtual environment is a promising intervention as it aligns with key neuroplasticity principles. Objective: The aim of the study is to assess the feasibility, safety, preliminary effects, and satisfaction of the 2-week RAHRE program offered as an adjunct to conventional rehabilitation. Methods: In total, 11 adults with hand hemiparesis following a recent stroke and undergoing intensive functional rehabilitation were randomized into experimental and control groups. Both groups received conventional rehabilitation therapy over a 2-week period. The experimental group received 10 additional 30-minute sessions of the RAHRE program (5 times per week), incorporating 4 hand opening and closing exercises with personalized glove assistance or resistance levels with virtual reality over the same period. Measures of feasibility (ie, attendance rate, compliance rate, repetitions per session, active training time, therapist verbal cueing, and support required), safety (ie, discomfort and adverse effects), and satisfaction (ie, satisfaction questionnaire) were collected. Functional outcomes (ie, Action Research Arm Test [ARAT], Fugl-Meyer Assessment for the Upper Extremity [FMA-UE], Box and Block Test, ABILHAND) were also assessed before and after the intervention in both groups. Results: Attendance and compliance rates in the experimental group reached 96% (48 completed training sessions of 50 planned sessions) and 95% (1432 completed training minutes of 1500 planned minutes), respectively. Participants performed a median of 2543 (IQR 2368-2951) additional movement repetitions during the RAHRE program (median repetitions per session 260, IQR 173-365; median active training time 24 minutes 39 seconds, IQR 22 minutes 26 seconds-25 minutes 51 seconds). Minimal therapist verbal cueing and support were necessary for technology use (median glove donning time 46, IQR 27-60 seconds; median independence achieved in 6, IQR 4-7 sessions). No abnormal discomfort or adverse effects were reported. Both groups showed functional improvements in ARAT, FMA-UE, Box and Block Test, and ABILHAND. For the primary outcomes (ie, ARAT and FMA-UE), the median score changes were, respectively, 4.50 (IQR 0-9) and 4.00 (IQR 3-4) in the control group, and 4.00 (IQR 1-7.5) and 5.00 (IQR 5-6) in the experimental group. Excellent overall program satisfaction (median 5/5, IQR 5-5) was reported for the RAHRE program. Conclusions: The RAHRE program, as an adjunct to conventional rehabilitation therapy, emerges as being feasible, safe, beneficial, and satisfying for adults with hand hemiparesis following a recent stroke. However, careful interpretation of the results remains recommended given the strength of the evidence. Future studies providing higher-quality evidence are needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.276
Teacher spread0.266 · 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 designObservational
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

Citations2
Published2025
Admission routes2
Has abstractyes

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