Bibliographic record
Abstract
INTRODUCTION: Recent reviews have documented the beneficial effects of seated meditation on executive function (EF). However, there has yet to be a comprehensive review on the effects of Hatha yoga, a moving meditation, on EF. OBJECTIVE: To examine the empirical literature on the effects of Hatha yoga on EF. METHODS: MEDLINE, Scopus, and PsycINFO databases were searched for experimental studies (between- or within-subject designs) testing the effects of Hatha yoga (acute bouts, short-term interventions, longer-term interventions) on EF. RESULTS: A total of 11 published studies met eligibility criteria: Three studies involved healthy adults, 2 studies involved healthy older adults (n = 2), 1 study involved children and adolescents, and 5 studies involved medical (n = 4) or forensic (n = 1) populations. In healthy adults, 2 of 3 studies suggested that acute bouts of Hatha yoga improved EF; however, 1 study using a short-term intervention found no improvements in EF. Among healthy older adults, 1 study provided evidence that Hatha yoga improves EF. In child/adolescent samples, 1 study supported the contention that Hatha yoga improves EF after short-term interventions. Among medical populations, EF improved in patients with type 2 diabetes mellitus and in 1 of 3 studies involving patients with multiple sclerosis. The sole study involving impulsive prisoners showed positive effects on EF with a short-term intervention. CONCLUSION: Hatha yoga shows promise of benefit for EF in healthy adults, children, adolescents, healthy older adults, impulsive prisoners, and medical populations (with the exception of multiple sclerosis). However, more good-quality studies that evaluate the efficacy of Hatha yoga's effects on EF are essential to build on this evidence base.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".