Effects of online yoga and tai chi on physical health outcome measures of adult informal caregivers
Bibliographic record
Abstract
Aims: This study aimed to investigate the effects of online Vinyasa Yoga (VY) and Taijifit™ (12 weeks) in informal caregivers (≥18 years of age). Methods: Twenty-nine participants were randomized to two groups: VY (n = 16, 55.87 ± 12.31 years) or Taijifit™ (n = 13, 55.07 ± 12.65 years). Main Outcome Measures: Prior to and following the study, assessments were made for muscle strength (1-RM leg press, chest press, and handgrip), muscle endurance (leg press and chest press; maximal number of repetitions performed to fatigue at 80% and 70% baseline 1-RM, respectively), abdominal endurance (maximum number of consecutive curl-ups to fatigue), tasks of functionality (dynamic balance and walking speed), and flexibility (sit and reach). Results: There was a significant increase over time for muscle strength, muscle endurance, tasks of functionality, and flexibility (P = 0.001). The VY group experienced a greater improvement in chest press endurance (VY: pre 19.25 ± 5.90, post 28.06 ± 7.60 reps; Taijifit™ pre 15.69 ± 4.49, post 21.07 ± 5.85 reps; P = 0.019) and abdominal endurance (VY: pre 37.12 ± 31.26, post 68.43 ± 55.07 reps; Taijifit™ pre 19.23 ± 19.00, post 32.07 ± 20.87 reps; P = 0.034) compared to the Taijifit™ group. Conclusions: VY and Taijifit™ are effective for improving muscle strength and endurance, tasks of functionality, and flexibility in informal caregivers. VY led to greater gains in chest press endurance and abdominal curl-ups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".