Effects of online yoga and tai chi on physical health outcome measures of adult informal caregivers
Bibliographic record
Abstract
<br><b>Aims:</b> This study aimed to investigate the effects of online Vinyasa Yoga (VY) and Taijifit™ (12 weeks) in informal caregivers (≥18 years of age). <b>Methods:</b> Twenty-nine participants were randomized to two groups: VY (<i>n</i> = 16, 55.87 ± 12.31 years) or Taijifit™ (<i>n</i> = 13, 55.07 ± 12.65 years). <b>Main Outcome Measures:</b> 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). <b>Results:</b> There was a significant increase over time for muscle strength, muscle endurance, tasks of functionality, and flexibility (<i>P</i> = 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; <i>P</i> = 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; <i>P</i> = 0.034) compared to the Taijifit™ group. <b>Conclusions:</b> 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.<br>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".