A Bibliometric Analysis of High-Intensity Interval Training in Cardiac Rehabilitation
Bibliographic record
Abstract
As global quality of life has improved, the risk factors for cardiovascular diseases have gradually increased in prevalence. People have consequently sought to improve their health through physical exercise. High-intensity interval training (HIIT) is a cardiac rehabilitation (CR) tool that has been of great interest for several years. However, its feasibility and safety remain controversial. This study aimed to explore hot research topics and new directions regarding the role of HIIT in CR and to describe the dynamic development of the field. We used the Web of Science Core Collection database to develop visualizations using CiteSpace software (v.6.1.R2). The number of articles published, institutional collaboration networks, author partnerships, and keyword co-occurrence and clustering were used to analyze the impact of HIIT on CR. Our results showed that Norway, Canada, and the United States were the most prominent contributors to this field. Articles by Nigam, A and Juneau, M had the highest number of citations. The Norwegian University of Science and Technology had performed the most in-depth research in this area. The European Journal of Preventive Cardiology had published the most articles. The United States had the highest number of publishing journals. Relevant issues focused on coronary artery disease, exercise capacity, heart failure, cardiorespiratory fitness, and physical activity. HIIT in heart transplantation may be at the forefront of research in this field and future studies should focus on this topic. HIIT-based CR can therefore improve the exercise capacity and quality of life of cardiovascular patients and improve patient compliance in a safe manner.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.121 | 0.193 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".