PREreview del Clustering of shoulder movement patterns using K-means algorithm based on the shoulder range of motion
- Publicado
- DOI
- 10.5281/zenodo.19198817
- Licencia
- CC BY 4.0
Short Summary of Main Findings In this 2023 medRxiv preprint, researchers applied unsupervised K-means clustering to shoulder range of motion (ROM) data from 541 young Korean adults (mean age ~24 years) drawn from a national anthropometric survey. Six ROM measures (flexion, extension, horizontal adduction/abduction, internal/external rotation) were used. The optimal number of clusters was determined as 8 based on the highest silhouette score. Distinct patterns emerged:
Clusters 1 & 5: markedly limited flexion (~143°).
Clusters 7 & 8: notably reduced internal rotation (lowest in cluster 8 at ~37°) and horizontal adduction.
Clusters 2 & 6: highest overall flexibility (flexion up to ~203°).
Clusters 3 & 4: moderate flexion but restricted horizontal adduction.
These data-driven clusters showed similarities to established clinical movement impairment syndromes (e.g., Sahrmann’s classification) and potential links to subacromial pain/impingement risk.
How This Work Has Moved the Field Forward It demonstrates a purely objective, machine-learning-based approach to classifying shoulder movement patterns in the general population without relying on symptomatic or theoretical categorization. This provides quantitative baseline profiles of shoulder ROM variability and offers a foundation for future studies linking specific clusters to shoulder pathologies, risk stratification, or personalized rehabilitation in physical therapy and sports medicine.
Major Issues
Still an unreviewed preprint with no identified peer-reviewed publication.
Cross-sectional data from healthy young adults (low disease prevalence); no actual shoulder disorder diagnoses or clinical outcomes, so clusters remain descriptive only.
No statistical testing of differences between clusters (e.g., ANOVA); interpretations rely on mean comparisons.
Limited generalizability (Korean population, young age group, data from 2003–2004).
Minor Issues
Elbow curve showed no clear inflection point; reliance on silhouette score alone for choosing 8 clusters.
Age and gender were collected but not incorporated into the clustering model.
Clinical implications discussed speculatively without supporting longitudinal or patient data.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.