Bibliographic record
Abstract
BACKGROUND: Proprioception is one of the most significant factors in balance, stability, fine movements, coordination, and injury prevention. Proprioception research helps clarify how the nervous system integrates sensory inputs to plan and execute movements. Bibliometric analyses offer a systematic and comprehensive understanding of a field's structure, evolution, trends, research clusters, and gaps, laying a scientific foundation for future research. This study employs bibliometric analysis to provide a panoramic view of proprioception research and to identify its thematic structure, evolution, production, and impact. METHODS: A total of 4506 original studies from 1979 to 2024 were extracted from the WoS. Using the Bibliometrix application in RStudio, a bibliometric analysis examined scientific performance, production, citation impact, research trends, developments, and the conceptual framework related to proprioception research. The Biblioshiny application performed the scientific mapping. RESULTS: Proprioception research has increased linearly. The most influential article was Sensorimotor System Measurement Techniques, published in the Journal of Athletic Training, with 455 citations. Uwe Proske was the most influential author, with an h-index of 20 in proprioception. The literature utilized 6797 keywords. Of these, 29% was proprioception, 4% joint position sense, and 4% rehabilitation. Keyword trends showed a shift toward rehabilitation and neurophysiology, with terms such as "rehabilitation," "balance," and "stroke" becoming more prevalent. However, an emerging interest in psychophysics, which investigates the interaction between proprioception and sensory perception, is also evident. This theme offers significant opportunities for future research. The USA leads in productivity, contributing 57.70% of the total publications, followed by Canada with 19.32%, and the UK with 18.28%. CONCLUSIONS: The results indicate a significant upward trend in research output, highlighting the increasing importance of proprioception in clinical and research settings. The findings emphasize several gaps in current proprioception research, including the need for greater interdisciplinary collaboration, particularly with neuroprosthetics and AI-driven proprioceptive modeling. Furthermore, geographical diversity in research, particularly from underrepresented regions, is critical for comprehensively understanding proprioception across diverse populations. This study provides actionable information for researchers, clinicians, and policymakers. It urges future investigations to address these gaps and explore innovative approaches to enhance proprioception-based therapies and technologies.
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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 | 0.057 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.267 | 0.290 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".