Comparison of Nonimmersive Virtual Reality and Task-Oriented Circuit Training on Gait, Balance, and Cognition Among Elderly Population: A Single-Blind Randomized Control Trial
Bibliographic record
Abstract
Purpose:The purpose of this study was to determine the effects of nonimmersive virtual reality (VR) and task-oriented circuit training on gait, balance, cognitive domains, and quality of life among the elderly population. Methods:This is a single-blind randomized control trial in which 28 elderly individuals between 60 and 75 years were selected. The participants were divided into two groups, task-oriented circuit training and the other receiving nonimmersive VR treatment, with 14 participants in each group. To analyze the effects of both training on balance and gait time up and go test (TUGT), dynamic gait index (DGI), measures were used and for cognitive domains Montreal cognitive assessment (MoCA) scale was used. Short form 12 (SF-12) (along with two domains, Physical Component Summary and Mental Component Summary) was used for the measurement of quality of life. The treatment duration was 8 weeks for both groups. Both the pre-test and post-test readings were analyzed and compared. Results:The mean age of participants was 66.91 ± 3.79 years. Within-group comparison between the pre-test and post-test indicated that significant differences (P < 0.05) existed in all the outcome measures (TUGT, DGI, MoCA, and SF-12) in both groups. However, between-group comparison, significant differences were only found in TUGT and DGI in nonimmersive VR group. Conclusion:This study concluded that both interventions nonimmersive VR and task-oriented circuit training were beneficial for improving balance, gait, quality of life, and cognitive domains like memory, attention spans, executive functions, and so on in elderly population. However nonimmersive virtual reality training is a more effective, feasible, and safe alternative and can be proposed as a form of fall prevention exercise for the older adults.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".