Global Trends and Hotspots of Exercise Psychology
Bibliographic record
Abstract
Aim : This chapter provides a bibliometric analysis of global research trends in exercise psychology from its inception through 2025. The goal is to map the intellectual structure and thematic evolution of the field. Methods : Data were retrieved from the Web of Science Core Collection, focusing on the five journals with the highest impact factor that include “exercise” and “psychology” in the title. Bibliometric techniques were used to analyze publication trends, author productivity, international collaboration, citation patterns, and keyword dynamics. Results : A total of 2,071 publications, 1,885 empirical and 186 review articles, authored by 5,313 researchers across 68 countries, were analyzed. The United States, Canada, the United Kingdom, and Australia accounted for 90% of the total output and citations based on researcher affiliations. Keyword clustering and burst analysis revealed four core themes: physical activity, psychological constructs, methodological approaches, and population-specific topics. Thematic evolution followed three phases: measurement and affective responses, theoretical expansion, and a recent focus on mental health and executive functions. Conclusion : The findings highlight the increasing interdisciplinarity and applied relevance of exercise psychology. The field has evolved into a dynamic, multidisciplinary research domain. Future progress would benefit from enhanced inclusivity, methodological rigor, and global collaboration to address pressing societal and health-related challenges.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.067 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".