Testing an Innovative Gait Training Program in Immersive Virtual Reality for Healthy Older Adults: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Impaired gait adaptability is one of the major causes of falls among older adults owing to inappropriate gait adjustments in cluttered environments. Training programs designed to improve gait adaptability behavior in a systemic approach may prevent falls in older adults. Recently, virtual reality (VR) technology has been prominent as a relevant gait training tool because of its training implementation potential. OBJECTIVE: This study was designed to compare the effectiveness of a VR-based gait training program (VR group) for improving gait adaptability behavior and, thus, reducing the risk of falls relative to a conventional training program such as Nordic walking (NW; NW group). We hypothesized that the VR-based gait training program will lead to greater gait adaptability improvements. METHODS: We will be conducting a randomized controlled trial with pretests, posttests, retention tests, and follow-up. In total, 40 healthy independent-living community dwellers (aged between 65 and 80 years) will be allocated, after a general medical examination, to the VR or the NW group for a training program of 6 weeks. Primary outcomes related to gait adaptability capacities (ie, analysis of adjustments made in different locomotor tasks) and acceptance of the VR device (ie, analysis of acceptance) will be assessed before and after the intervention and 1 month after the completion of the training program (retention). A follow-up will be done during the 12 months after the completion of the gait training program. RESULTS: Data collection will begin in September 2025, and the first results are expected in December 2025. CONCLUSIONS: The findings of this study may demonstrate the relative relevance of a gait training program in VR versus a conventional one for improving gait adaptability behavior in healthy older adults and, thus, prevent the chances of a fall. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57866.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".