Scientific Production on Physical Activity, Physical Education, Global Warming and Climate Change: A Bibliometric Analysis
Bibliographic record
Abstract
Physical activity allows people to obtain multidimensional benefits. Regular practice and following the recommendations can provide the benefits mentioned above. However, it has been evidenced that the variability in the environmental temperature is a determining factor to adhere to the regular practice of physical activity. From this point of view, it has become evident that researchers have joined criteria to explore the effects of climate change or global warming on physical activity or physical education. This study is the first bibliometric analysis of the scientific literature related to physical activity, physical education, global warming, and climate change. The objective of the present bibliometric review was to examine annual publication trends, identifying the categories, journals, and countries with the highest number of publications on this topic. In addition, the secondary objective was to identify the most productive and prominent authors, highlight the most cited articles, and determine the keywords most used by the authors. We analyzed 261 papers published in journals indexed in the Web of Science, examining the trend followed by annual publications, identifying prolific and prominent co-authors, leading countries and journals, most cited papers, and most used author keywords. The annual publications followed an exponential growth trend (R2 = 90%), which means that there is great interest in the scientific community for this object of study. The Journal of Physical Activity & Health was the journal with the most published papers. M.S. Tremblay and E.Y. Lee were the most prominent co-authors, and as reference authors on the subject, M. Nieuwenhuijsen and H. Khreis were the most prominent authors. The three countries with the highest productivity are the USA, the UK, and Canada. Although a total of 29 keywords were identified, only 25 of them were commonly recurrent, with the most used being climate change and physical activity.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.198 | 0.306 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".