Association between Physical Activity and Exercise with Cognitive Function: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Visualization is a practical method to determine a scientific field’s underlying intellectual framework. This study aims to conduct a scientometric analysis of selected scientific literature to assess research trends regarding the association between physical activity exercise, and cognitive function domains. The objective is to present a summary of the findings and identify the trending topics between 1970 and 2023 for this field of study.Methods: In the current bibliometric analysis, relevant documents based on a reliable search strategy taken from the Web of Science (WOS) database were checked and evaluated using Excel, VOSviewer, and the bibliometrix R-package.ssResults: The hot topics included Physical Activity, Exercise, Cognition, Aging, Dementia, Depression, Alzheimer’s disease, and Rehabilitation. “Frontiers in Psychology” and “International Journal of Environmental Research and Public Health” were the most active journals in this research area. Also, developed countries such as the United States, the United Kingdom, Canada, Australia, and Germany were the most productive countries. In addition, the top organizations which produced the most scientific documents were from Europe, Oceania, and North America. In the same vein, Arthur F Kramer was identified as the most active author. The study results will greatly contribute to future interdisciplinary articles by showing common trends in this research area.Conclusion: The combination of PA and cognitive function is still a hot zone of future research. According to this study, the majority of literature on PA and cognitive function is from developed countries, and other cognition topics such as executive function, memory, and anxiety have obtained less attention from researchers.
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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.183 | 0.189 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".