• Subgrouping non-specific low b...

*Result*: Subgrouping non-specific low back pain based on spinal marker trajectory data: An unsupervised machine learning approach.

Title:
Subgrouping non-specific low back pain based on spinal marker trajectory data: An unsupervised machine learning approach.
Authors:
Yoo HI; Department of Physical Therapy, Kyungdong University, Wonju 26495, Republic of Korea. Electronic address: yoohi@kduniv.ac.kr., Hwang UJ; Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Hong Kong, China. Electronic address: uj.hwang@polyu.edu.hk., Choi JG; Department of Disaster and Safety, Kyung Hee Cyber University, Seoul 02447, Republic of Korea. Electronic address: hallaman@naver.com., Kwon OY; Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea. Electronic address: kwonoy@yonsei.ac.kr.
Source:
Gait & posture [Gait Posture] 2026 Feb; Vol. 124, pp. 110026. Date of Electronic Publication: 2025 Nov 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Sciencem Country of Publication: England NLM ID: 9416830 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2219 (Electronic) Linking ISSN: 09666362 NLM ISO Abbreviation: Gait Posture Subsets: MEDLINE
Imprint Name(s):
Publication: <2007->: Oxford, UK : Elsevier Sciencem
Original Publication: Oxford, UK : Butterworth-Heinemann, c1993-
MeSH Terms:
Low Back Pain*/physiopathology , Low Back Pain*/classification , Low Back Pain*/diagnosis , Unsupervised Machine Learning*, Movement/physiology ; Thoracic Vertebrae/physiopathology ; Lumbar Vertebrae/physiopathology ; Humans ; Male ; Cross-Sectional Studies ; Female ; Adult ; Biomechanical Phenomena ; Case-Control Studies ; Middle Aged ; Video Recording ; Cluster Analysis
Contributed Indexing:
Keywords: Clustering; Low back pain; Movement phenotyping; Occupational health; Spinal kinematics
Entry Date(s):
Date Created: 20251105 Date Completed: 20251211 Latest Revision: 20251211
Update Code:
20260130
DOI:
10.1016/j.gaitpost.2025.110026
PMID:
41192006
Database:
MEDLINE

*Further Information*

*Background: Non-specific low back pain (LBP) is a heterogeneous condition. Therefore, it is important to investigate whether clinically feasible assessments can identify diverse movement patterns in individuals with LBP.
Purpose: To identify distinct movement-based subgroups among individuals with non-specific LBP using thoraco-lumbo-pelvic marker trajectories during forward bending and to compare the resulting clusters with healthy controls.
Study Design: Cross-sectional study.
Methods: Kinematic data were collected from 127 individuals with non-specific LBP and 58 healthy controls during a forward bending task using a smartphone-based video recording system. Three markers were placed over T12, L2, and S2, and their x- and y-axis displacements were extracted using an open-source software. Unsupervised machine learning (K-means clustering) was applied to classify movement patterns within the LBP group based on six kinematic features (the horizontal and vertical displacements of the T12, L2, and S2 markers).
Results: Two clusters were identified within the LBP group: cluster 1 (large-excursion, 54 %) and cluster 2 (small-excursion, 46 %). Both clusters showed significant differences from healthy controls in marker displacement (p < 0.001). Cluster 2 reported a slightly higher pain intensity (p = 0.036), with no difference in disability scores.
Conclusions: Unsupervised clustering revealed distinct spinal movement subgroups in individuals with non-specific LBP. These findings indicate that both excessive and limited movement may relate to pain-related adaptation, supporting the need for movement-based subgrouping to guide individualized management.
(Copyright © 2025 Elsevier B.V. All rights reserved.)*

*Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*