The Effect of Leisure-Time Exercise on Mental Health Among Adults: A Bibliometric Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Adequate levels of leisure-time exercise (LTE) are associated with mental health benefits. Despite increased research in recent years through randomized controlled trials (RCTs), a systematic literature review summarizing these findings is lacking. Here, we examined publication trends, impact, and research gaps regarding LTE’s effects on mental health in the form of a bibliometric analysis. Methods: Five electronic databases (PubMed, EMBASE, Web of Science, Ovid Medline, and the Cumulative Index for Nursing and Allied Health Literature) were searched from their inception until 20 November 2024. Citations were independently screened by two authors and included based on pre-determined eligibility criteria. Bibliometric analysis was conducted using SciVal and VOSviewer under five themes: (1) descriptive analysis, (2) network analysis, (3) thematic mapping, (4) co-citation and co-occurrence analysis, and (5) bibliometric coupling. Results: The systematic search identified 5792 citations, of which 78 RCTs met the inclusion criteria. Only one study was conducted in a low- or middle-income country. Sixty-four percent of studies were published in quartile-one journals. Most studies were conducted in the United States, followed by Australia, Canada, and the United Kingdom. National collaborations yielded the highest citation rates, reflecting the influence of cultural and social norms on exercise and mental health. Research gaps were identified with regards to the validity of mental health measures, the paucity of data from low- and middle-income countries, and emerging research sources. Conclusions: This bibliometric analysis highlights the existing evidence on LTE’s impact on mental health and identifies areas for future research and policy. Trials exploring valid mental health outcomes, biomarkers such as mood and oxidative stress, and collaborative research are needed, particularly in underrepresented regions of the world.
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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 | 0.133 | 0.465 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.164 | 0.168 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".