Effect of Teleyoga Before COVID-19 and During Pandemic: ANarrative Review
Bibliographic record
Abstract
Background: Yoga plays a beneficial adjunctive role in various disorders due to its physiological and psychological benefits. COVID-19 pandemic led to a paradigm shift in delivery of health interventions from on-site to online/ tele-intervention mode. Focus shifted to tele-yoga as a reasonable and feasible alternative to in-person yoga. Studies have evaluated its effect among patients suffering from various disorders, their care givers, healthcare workers, and the general public. We have assessed the effect of tele- Yoga, including its appropriateness, acceptability, and benefits, via this narrative review. Methods: We searched PubMed data base using predefined keywords. Inclusion criteria included controlled trials and Randomized Controlled Trials (RCTs) which are completed and published in English language up to February 2022 with tele-yoga/online yoga as part of intervention. Exclusion criteria included articles in other language or articles whose full text is unavailable. Results: After removing duplications and reviewing articles based on title, abstracts, and available full texts, seven studies with 391 participants were included. Majority of the trials took place in United States, with United Kingdom, Canada, and India following closely behind. Yoga can be safely administered via various online /tele interventions in both diseased and healthy individuls. Tele yoga or modules incorporating tele-yoga has been shown to improve symptoms like dyspnea, psychiatric/psychological burden including stress,anxiety and depression levels and may promote positive effects like spirituality. Conclusion: Tele-yoga is feasible and beneficial in healthy and diseased individuals. Larger well-designed RCTs comparing in-person yoga with tele-yoga are needed to ascertain their full benefits.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".