A bibliometric analysis of the effects of aerobic exercise on overweight (1978–2025)
Bibliographic record
Abstract
Overweight is a global public health concern associated with multiple chronic conditions. Aerobic exercise, widely recognized for improving weight control, cardiorespiratory fitness, and metabolic function, has become central to intervention strategies. However, the global research landscape addressing aerobic exercise specifically for overweight populations remains insufficiently mapped. This study aimed to analyze scientific development, main research topics, and collaboration patterns using bibliometric methods based on publications indexed in the Web of Science Core Collection (SCIE and SSCI) from 1978 to 2025 (search date: March 23, 2025). A total of 3,983 documents were retrieved and analyzed using the Bibliometrix package (R), with results visualized through publication trends, co-authorship and co-citation networks, and keyword clustering. The field has expanded rapidly since 2000, showing an average annual growth rate of 7.35%. The United States ranks first in publication volume and citation impact, followed by the United Kingdom and Canada. China demonstrates substantial research output but lower citation performance, highlighting the need for stronger international collaboration. High-impact publishing venues are primarily from exercise science and obesity-related research areas. Keyword and network analyses reveal a shift from general intervention studies toward more specific mechanisms, populations, and multidimensional outcomes, including insulin resistance, body composition, and cardiorespiratory fitness. A strong international collaboration structure, especially within North America and Europe, is also evident. Overall, research on aerobic exercise for overweight individuals has evolved into a multidisciplinary and globally collaborative field. Future efforts should emphasize research quality, deeper collaboration, and tailored intervention strategies to support global health.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.102 | 0.173 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".