Bibliometric analysis of articles on high intensity training (HIIT) and sleep in the Web of Science database
Bibliographic record
Abstract
The relationship between high-intensity interval training (HIIT) and sleep has received increasing attention in sports science and health. This study aimed to analyze HIIT and sleep literature using a bibliometric approach to examine publication trends, thematic fields, interdisciplinary structures, and collaboration networks. We analyzed 68 articles published between 2000 and 2025 from the Web of Science database. The findings showed that publication volume peaked in 2019, whereas citation impact intensified in 2020-2023. Keyword co-occurrence analysis revealed that the literature is centered on exercise physiology, recovery processes, and circadian rhythm effects. Sports Science (23.5%) was the largest contributing discipline, with Clinical Neurology (14.7%) and Public Health (11.8%) as other important fields. The USA, Canada, and Australia were the leading countries, with the University of Milan and Italian researchers, such as Matteo Bonato, making the most prolific contributions. The International Journal of Environmental Research and Public Health hosted the largest number of publications. The discussion highlights knowledge gaps in literature (e.g., the effects of HIIT timing on sleep) and the need for interdisciplinary research. Recommendations include diversifying specific research fields, increasing global collaboration, and developing sleep-focused HIIT programs for sports practitioners. This study provides a comprehensive framework for a better understanding of the relationship between HIIT and sleep quality.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.186 | 0.245 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".