Mapping the Scientific Landscape: A Bibliometric Analysis of Exercise and Skin Health Research (2005–2025)
Bibliographic record
Abstract
Introduction: Physical activity has long been recognized as a key factor in promoting overall health, including cardiovascular, metabolic, and immune function. However, its role in skin health particularly in dermatological and aesthetic contexts remains underexplored in scientific literature. The bibliometric approach identifies research trends and gaps, guiding future studies on exercise and skin health. This study aims to map the global research landscape related to the impact of exercise on skin health using a bibliometric approach. Methods: Data were retrieved from the Scopus database using a combination of keywords such as “exercise,” “training,” “sports medicine,” “skin health,” and “aesthetics.” A total of 43 relevant articles published between 2005 and 2025 were identified. The analysis utilized Microsoft Excel for initial data processing, VOSviewer for keyword network visualization, and R Studio with Biblioshiny for advanced bibliometric mapping. Results: The findings show a notable increase in publication volume starting in 2018, peaking in 2023–2024. The United States and the United Kingdom led in research output, followed by Canada, India, and Turkey. Most studies were published in journals focused on aesthetic surgery and rehabilitative medicine. Keyword analysis revealed dominant themes such as “aesthetic surgery,” “training,” and “skin regeneration.” Conclusion: Research on exercise and skin health is gaining momentum, supported by interdisciplinary interest from the fields of sports science, dermatology, and aesthetic medicine. Conclusion: The study highlights current trends, identifies research gaps, and provides a foundation for future integrative research aimed at enhancing both health and appearance through physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.036 | 0.141 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".