The impact of high-intensity interval training on women’s health: A bibliometric and visualization analysis
Bibliographic record
Abstract
BACKGROUND: High-intensity interval training (HIIT) can significantly improve health indicators such as cardiopulmonary function, metabolic efficiency, and muscle strength in a short period. However, due to significant physiological and metabolic differences between males and females, the effects of HIIT vary between genders. Therefore, exploring the specific impacts of HIIT on women's health is crucial. Although there is a considerable amount of individual research on the impact of HIIT on women's health, a systematic bibliometric analysis is still lacking. METHODS: Publications related to HIIT in women's health were retrieved from the Web of Science Core Collection database, and tools like Microsoft Office Excel 2021, VOSviewer, and Citespace were used to create visualized tables and views. RESULTS: The study included 808 publications distributed across 1234 institutions in 61 countries, authored by 3789 researchers. The United States, Australia, and Canada lead in this domain. Researchers like Astorino TA and Gibala MJ are notably influential in this field. The research has been prominently published in specific academic journals and widely cited by high-impact journals. Highly cited and bursting documents primarily discuss the effects of HIIT on metabolic adaptation, muscle adaptation, cardiovascular health, insulin sensitivity, and exercise performance. Frequent keywords include "aerobic exercise," "sprint interval training," "resistance training," "obesity," "body composition," "aging," and "insulin resistance." Keyword burst analysis reveals that early studies focused primarily on basic concepts and training models, which then expanded to specific physiological responses, applications in particular populations, and impacts on specific diseases. CONCLUSION: This field has emerged as a research hotspot with international characteristics and extensive academic productivity. Journals and cited journals hold high academic influence, with highly cited and bursty references laying a solid theoretical and practical foundation for the field. In the rapid development of the past decade, research hotspots and frontier directions such as metabolic adaptation, muscle adaptation, cardiovascular health, exercise performance, and personalized training plans have been formed.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.129 | 0.168 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".