Functional Balance and Dual-Task Reaction Times in Older Adults Are Improved by Virtual Reality and Biofeedback Training
Bibliographic record
Abstract
Virtual reality (VR) training has been used successfully to rehabilitate functional balance and mobility in both traumatic brain injury (TBI) survivors and elderly subjects. Similarly, computer-based biofeedback (BF) training has resulted in decreased sway during quiet stance and decreased reaction times during a dual-task reaction time paradigm in elderly subjects. The objective of this study was to determine the effect of VR and BF training on balance and reaction time in older adults. Two groups of twelve healthy older adults completed 10-week training programs consisting of two 30-min sessions per week. VR training required that participants lean sideways to juggle a virtual ball. Participants in the BF group viewed a red dot representing their center of gravity on a screen and were required to move the dot to the four corners of the monitor. Measures of functional balance and mobility (Community Balance and Mobility Scale [CB&M]), sway during quiet stance, and reaction time during a dual task paradigm were recorded before training, as well as 1 week and 1 month after the end of the program. Both groups showed significant improvements on the CB&M, as well as decreased reaction times with training. Postural sway during quiet stance did not change significantly.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".