Trends and Scientific Production on Isometric Training: A Bibliometric Analysis
Bibliographic record
Abstract
Isometric training is a method focused on muscle strengthening without joint movement and has gained attention in recent years due to its applicability in rehabilitation and sports medicine. However, no comprehensive bibliometric analysis focused exclusively on adult populations has been performed. This study aimed to analyze the scientific production related to isometric training in adults; identify prominent authors, journals, and thematic trends; and evaluate the evolution of interest in this field over time. A bibliometric review was performed using the Web of Science Core Collection (SCI-E, SSCI, and ESCI). A specific search strategy was applied to identify articles and reviews focused on isometric training in adult populations. A total of 238 records met the inclusion criteria. Data were analyzed using Excel 2016 and VOSviewer software1.6.20. Bibliometric indicators such as Price’s Law, Bradford’s Law, Lotka’s Law, h-index, and co-occurrence and co-authorship network analysis were applied. The results showed a steady increase in publications in the last decade, highlighting the categories of Sports Science, Physiology, and Cardiovascular. The Journal of Applied Physiology was the most frequent source, and Springer Nature was the most prolific publisher. The h-index identified 21 highly cited papers, and Lotka’s Law confirmed the existence of a small group of prolific authors. VOSviewer analysis revealed clear thematic clusters, mainly around blood pressure regulation, rehabilitation, and aging. International collaboration was evident, with the United States, Canada, and the United Kingdom leading the co-authorship networks. Scientific interest in isometric training for adult populations is growing, particularly in relation to cardiovascular health and rehabilitation. Despite this, gaps remain in terms of methodological consistency and standardized protocols. Addressing these issues could improve the applicability and scientific impact of this training modality.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.246 | 0.276 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".