Association between yoga and related contextual factors with moderate-to-vigorous physical activity among children and youth aged 5 to 17 years across five Indian states
Bibliographic record
Abstract
Physical inactivity is one of the four key preventable risk factors, along with unhealthy diet, tobacco use, and alcohol consumption, underlying most noncommunicable diseases. Promoting physical activity is particularly important among children and youth, whose active living behaviours often track into adulthood. Incorporating yoga, an ancient practice that originated in India, can be a culturally-appropriate strategy to promote physical activity in India. However, there is little evidence on whether yoga practice is associated with moderate-to-vigorous physical activity (MVPA) accumulation. Thus, this study aims to understand how yoga practice is associated with MVPA among children and youth in India. Data for this study were obtained during the coronavirus disease lockdown in 2021. Online surveys capturing MVPA, yoga practice, contextual factors, and sociodemographic characteristics, were completed by 5 to 17-year-old children and youth in partnership with 41 schools across 28 urban and rural locations in five states. Linear regression analyses were conducted to assess the association between yoga practice and MVPA. After controlling for age, gender, and location, yoga practice was significantly associated with MVPA among children and youth (β = 0.634, p < 0.000). These findings highlight the value of culturally-appropriate activities such as yoga, to promote physical activity among children and youth. Yoga practice might have a particularly positive impact on physical activity among children and youth across the world, owing to its growing global prevalence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".