Global Trends and Research Hotspots of Exercise for Intervening Diabetes: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Diabetes is a chronic metabolic disease characterized by hyperglycemia that often occurs in adults. Many studies have indicated that exercise is beneficial to the medical management of diabetes. Bibliometric analysis can help investigators to identify the current research concerns to guide future research directions. Nevertheless, the overview bibliometric analysis of this global research topic related to exercise and diabetes is lacking. The present bibliometric study aimed to investigate development trends and research hotspots of exercise and diabetes research and provide researchers with new perspectives in further studies. Materials and Methods: The articles and reviews regarding exercise and diabetes between 2000 and 2020 were retrieved from the Web of Science Core Collection. The scientometrics analytical tool CiteSpace software was used to analyze the cooperation among countries/institutions/journals/authors, analysis of co-occurrence keywords, keywords bursts, and references. Results: In all, 3,029 peer-reviewed papers were found with a persistently increased tendency over time. The most prolific country and institution were the USA (965) and Univ Alberta (76), respectively. Diabetes Care published most papers (178) and was the most co-cited journal (2,630). Riddell MC had the most publications (53), and Sigal RJ was the most influential author (503 cited times). Colberg et al.'s paper (co-citation counts: 183) showed the strongest citation bursts by the end of 2020, which was the most representative reference. The four research focuses were mellitus, exercise, physical activity, and glycemic control. The two frontiers trends were sedentary behavior and stress. The combination of aerobic and resistance training can effectively improve glycemic control, decrease HbA1c levels, enhance cardiorespiratory fitness, improve lipid levels, and decrease the demand for non-insulin antihyperglycemic agents. Conclusions: This study offers a scientific perspective on exercise and diabetes research and provides investigators with valuable information to detect the current research condition, hotspots, and emerging trends for further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.189 | 0.306 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".