The Effects of VR-based Wii Fit Yoga on Physical Function in Middle-aged Female LBP Patients
Bibliographic record
Abstract
[Purpose] The purpose of this research was to determine the effects of a virtual reality-based yoga program on middle-aged female low back pain patients. [Subjects and Methods] Thirty middle-aged female patients who suffered from low back pain were assigned to either a physical therapy program or a virtual reality-based yoga program for a period of four weeks. Participants could check their posture and weight bearing on a monitor as they shifted their weight or changed their postures on a Wii balance board. There were a total of seven exercise programs. A 30-minute, three times per week, virtual reality-based Wii Fit yoga program or trunk stabilizing exercise was performed, respectively. [Results] Repeated-measures analysis of covariance revealed significant differences in between pre- and post-training VAS, algometer, Oswestry low-back pain disability index (ODI), Roland Morris disability questionnaire (RMDQ), and fear avoidance beliefs questionnaire (FBQ) scores. The VAS, algometer, ODI, RMDQ, and FBQ scores showed significant differences in groups. Regarding the effect of time-by-group interaction, there were significant differences in VAS, ODI, ODI, and FBQ scores. [Conclusion] In conclusion, for middle-aged female patients who have low back pain, a virtual reality-based yoga program was shown to have positive effects on physical improvements, and this program can be employed as a therapeutic medium for prevention and cure of low back pain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".