Improvement of Motor Imagination and Manual Ability Through Virtual Reality and Selective and Nonselective Functional Electrical Stimulation: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Motor imagery (MI) is a cognitive process that has been shown to be useful in the rehabilitation process after brain injury. Moreover, functional electrical stimulation (FES) and virtual reality (VR) have also been shown to be effective interventions in many parameters, and there is some evidence of their contribution to the improvement of MI capacity. OBJECTIVE: This study aimed to compare the improvements in MI parameters, grip strength, and manual dexterity obtained using VR, FES, and selective FES based on multifield electrodes in healthy people. METHODS: This clinical randomized controlled trial (RCT)with 4 branches will involve 80 healthy university students, with blinded third-party assessment. Participants will be divided into 4 groups: control (no intervention), selective FES (Fesia Grasp), traditional FES (Globus Elite), and Virtual Rehab Hands (Leap Motion sensor). Each group will receive 5 daily sessions, and assessments will be conducted at baseline, postintervention, and follow-up. The Movement Imagery Questionnaire-Revised (MIQ-RS) and chronometry will be used to assess MI, strength will be measured with a digital dynamometer, and manual dexterity will be evaluated with the Nine Hole Peg Test (NHPT) and the Box and Block Test (BBT). Statistical analyses will include 2-way repeated-measures ANOVA with post hoc Bonferroni correction to compare group differences over time, with nonparametric tests (eg, Kruskal-Wallis) being used if normality or variance assumptions are violated. The study will be organized into 3 phases: preparation, data collection, and analysis. The preparation phase will involve finalizing project protocols and obtaining ethical approvals. The data collection phase will consist of recruiting participants, randomizing them into 4 intervention groups, and conducting baseline assessments, followed by intervention sessions. Finally, the analysis phase will focus on evaluating the data collected from all groups and compiling the results for presentation. RESULTS: The study received approval in July 2023, with recruitment and data collection starting in September 2023. The recruitment phase was expected to conclude by July 2024, and the entire study, including the 2-week follow-up, was set to finish in September 2024. As of July 2024, we had enrolled 100% of the sample (N=80 students). We plan to publish the study findings by the end of 2024. CONCLUSIONS: Improvements in MI and upper limb functionality are expected, particularly in the selective FES group. This RCT will identify which intervention is most effective in enhancing these skills, with potential benefits for patients with neurological motor disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT06109025; https://clinicaltrials.gov/study/NCT06109025. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63329.
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 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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
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".