Feasibility and acceptability of a home-based virtual group exercise program in global Asian adult population: Baseline characteristics of a cohort study
Bibliographic record
Abstract
BACKGROUND: To determine the potential influence of a home-based virtual group exercise on people's long-term overall health consequences in global Asian population. METHODS: We recruited 1021 participants from more than 7 regions across the globe including Taiwan, Malaysia, Singapore, Hong Kong, United States, Canada, Europe, and other regions. All the participants attended the virtual group Qigong exercise 60-minute bi-weekly with instructors for 6 months from June 2022 to December 2022. The physical, mental, and social well-being and other variables were measured via online questionnaires. RESULTS: The majority were 51 to 65 (50.6%) years old, female (90.2%), married (68.5%), and came from Taiwan (48.9%). Older adults had higher scores on measures of overall health and exercise adherence, and lower scores on measures of sleep quality and depressive symptoms compared with younger counterparts (P < .05). Most of them (95.3%) acknowledged that the improvement of health status was their motivating factor for exercise. Eighty nine percent of the participants believed that social media played an important role in this exercise program. CONCLUSION: This study will suggest such approach has great potential to reduce health disparities and can be implemented to underserved population who has limited recourses to join in-person exercise program.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".