Trends in exercise therapy research for neurological diseases: a bibliometric and visualization approach from 2000 to 2024
Bibliographic record
Abstract
Background: Neurological disorders are one of the major global health burdens, and exercise therapy has been widely recognized as a beneficial intervention. However, the existing literature has primarily focused on summarizing the interventions, complications, and influencing factors, with relatively limited systematic comparisons and summaries. Methods: This study employed a bibliometric analysis approach, using VOSviewer and CiteSpace software to analyze the literature on the application of exercise therapy in neurological disorders from 2000 to 2024, including annual publication volumes, countries/regions, institutions, authors, journals, keyword co-occurrences, keyword clustering, keyword timelines, and keyword bursts. Results: The study found that the United States is the leading contributor in this field, and the University of Toronto and the University of Illinois are the most active research institutions. Keyword analysis revealed that the research hotspots in this field are concentrated on the role of exercise therapy in the prevention, treatment, rehabilitation, and improvement of quality of life for neurological disorders, and are gradually delving into its potential physiological mechanisms. Conclusion: This study provides valuable references for subsequent research in this field, helping to track the research frontiers and predict future research directions. Future research could further explore the specific mechanisms and clinical efficacy for different neurological diseases, providing more targeted evidence for clinical practice.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.180 | 0.198 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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, 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".