A Step in the Right Direction: Evaluating the Effectiveness of Customized Stepping Game Software and Balance Boards for Balance Rehabilitation Therapy and Measurement
Bibliographic record
Abstract
The incidence of falls significantly increases with age in older populations. Traditionally, exercise interventions have been effective in improving balance and strength, thereby reducing the number and risk of falls. To minimize costs, to make exercise more engaging, and to reduce the burden on therapists, the use of serious gaming using virtual reality technologies has become more common. These gaming systems allow the evaluation of compensatory strategies to reduce falls. One of the easiest compensatory strategies to reduce falls is to step forward. To evaluate stepping, the time it takes to step, response times, and reaction times, a stepping game was developed using two Nintendo Wii Balance Boards™.The objective of this study was to investigate the stepping game system for its efficacy as a supplemental balance therapy by evaluating functional outcomes and participant satisfaction. The game was able to identify changes in performance measures (response time, reaction time and step time) for some participants over the span of the exercise program; however, a larger sample size and a stricter protocol is necessary to evaluate the clinical significance.Clinical Relevance- The stepping game appears to be more sensitive to detecting change in balance related measures including step time, reaction time, response time, than the Berg Balance Scale.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".