Mixed Reality–Based Physical Therapy in Older Adults With Sarcopenia: Preliminary Randomized Controlled Trial
Bibliographic record
Abstract
Background: Sarcopenia in older adults is associated with reduced muscle mass and function, leading to frailty, increased fall risk, and decreased quality of life (QOL). Mixed reality (MR)-based interventions have emerged as promising tools to enhance physical therapy engagement and effectiveness through immersive, interactive environments. Objective: This study aimed to investigate the effects of a Mixed Reality-Based Physical Therapy (Mr.PT) platform on quadricep muscle thickness, balance confidence, activities of daily living, and QOL in older adults with sarcopenia. Methods: A preliminary randomized controlled trial was conducted involving 30 older women (mean age 75.3, SD 9.9 y) diagnosed with sarcopenia based on the Asian Working Group for Sarcopenia criteria. Participants were randomly assigned to either the Mr.PT group or a conventional physical activity (CPA) group. Both groups participated in 30-minute exercise sessions, 3 times per week, over 4 weeks. The Mr.PT program used head-mounted MR devices with gamified, interactive training, while the CPA group received standard therapist-led exercises using resistance bands. Outcome measures included ultrasound imaging of quadricep muscle thickness, the Korean version of the Activities of Daily Living scale (Katz Index of Independence in Activities of Daily Living), the Activities-Specific Balance Confidence (ABC) scale, and the 12-Item Short-Form Health Survey. Statistical analysis was performed using repeated-measures ANOVA and Tukey post hoc tests. Results: The Mr.PT group showed significantly greater improvement in quadricep muscle thickness than the CPA group (P=.001). Within-group improvements in balance confidence (ABC scale) and daily functioning (Katz Index of Independence in Activities of Daily Living) were observed in both groups (P<.05), though between-group differences were not statistically significant. However, the Mr.PT group demonstrated significantly greater gains in QOL as measured by 12-Item Short-Form Health Survey (P=.02). All participants completed the intervention without dropouts or adverse events. Conclusions: MR-based exercise using the Mr.PT platform appears effective in increasing muscle mass and enhancing QOL among older adults with sarcopenia. Its interactive and adaptive features may improve engagement and motivation, suggesting potential advantages over traditional programs. Further research with larger cohorts and longer follow-up is recommended to confirm these preliminary findings and explore long-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".