Trends and hotspots in running shoe research: a bibliometric study from 2005 to 2024
Bibliographic record
Abstract
Background Running shoes can protect the feet, enhance performance and lower the injury risk during running. While extensive research has been investigated on footwear design and innovation in running, the scientific guideline underlying running shoe research remain inadequately explored and established. Purpose The aims of this study was to conduct a bibliometric analysis of publications in running shoes for identifying research hotspots and future trends. The results from this study can provide valuable references for future studies and contribute to the scientific advancement of running shoe design. Method Articles on running shoes were collected and screened from the Web of Science Core Collection database covering the years 2005–2024. After duplicate and irrelevant articles removed, CiteSpace, VOSviewer, and R-biblioshiny were used to perform visualized analyses of authors, titles, journals, countries, institutions, keywords, research directions, and cited references. Co-citation maps were created to provide a clear representation of research hotspots and knowledge structures. Result A total of 1,576 articles on running shoes were identified across 394 journals spanned 69 countries and 3,599 institutions, with peak publication volume found in 2022. The United States generated the highest number of publications, followed by China and the United Kingdom. The University of Calgary produced the highest publication output. Gu YD was the top author to produce the most publications, while Lieberman DE was identified as the most influential scholar in the field. The Medicine & Science in Sports & Exercise have been the most prominent journals in this field. Trend keywords had centered on running injuries (e.g., “barefoot,” “ground reaction force,” and “injuries”) and performance (e.g., “running economy,” “performance,” and “metabolic cost”), which have been clustered into eight distinct labels. Conclusion This is the first study to present bibliometric analysis on running shoes literature over the past 20 years, highlighting the key hotspots and future trends. Overall, the annual publications on running shoes has steadily increased. Current research have focused on the biomechanics and physiological indicators of runners whilst wearing running shoes to explore the associated injury risks and running performance, with particular emphasis on the impact of minimalist shoes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.100 | 0.150 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".