Background: Lumbar Joint Mobilization (LJM) applies rhythmic forces to lumbar joints to improve mechanics and pain. Recent evidence suggests potential benefits for somatosensory input and postural control, though direction-specific effects remain unclear. Objectives: To investigate short-term effects of LJM on standing stability and trunk control through pre-post intervention comparison. Design: Quasi-experimental study. Methods: Twenty-five participants were randomized to experimental (n=13) or control (n=12) groups. Single-leg balance was assessed using force platform measurement with eyes open/closed conditions. The experimental group received Maitland-technique LJM including spinal compression, lateral compression, transverse process mobilization, rotational oscillation, and traction. Balance parameters included medio-lateral standard deviation (ML-SD), anteroposterior standard deviation (AP-SD), sway area, and path length. Results: The experimental group demonstrated significant medio-lateral stability enhancement (64% ML-SD reduction, P =0.003) with significant group×time interaction (F=4.20, P=0.043, η²p=0.044). Sway area decreased by 67% and path length showed large effect size (d=1.19, P<0.001). Improvements occurred in both visual conditions without increased visual dependence (stable Romberg Quotient), indicating somatosensory-driven enhancement. Machine learning classification achieved near-perfect accuracy (AUC=1.000). Conclusion: LJM produces immediate, direction-specific improvements in lateral trunk control through enhanced somatosensory feedback. Larger trials with long-term follow-up are needed to confirm sustained benefits.

INTRODUCTION
SUBJECTS AND METHODS
    Subjects
    Measurements
    Intervention
    Data and Statistical Analysis
RESULTS
    Descriptive Postural Stability Changes
    Mixed-Model ANOVA
    Post-hoc and Effect Size Analysis
    Stabilogram and COP Trajectory Visualization
    Sway Ellipse and Direction-Specific BalanceImprovement
    Individual Response Pattern
    Directional Analysis
    Balance Asymmetry and Visual Dependence
    Traditional Postural Stability Metrics
    Effect Size Heatmap
    Machine-Learning Classification
    Summary of Findings
DISCUSSION
CONCLUSIONS
FUNDING
CONFLICTS OF INTEREST
AUTHOR CONTRIBUTIONS
REFERENCES

• Jiaqi Shan(Department of Rehabilitation Therapy, Langfang Vocational College of Health, Hebei, China)
• Kunyu Suo(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Xinyu Cao(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Jingwen Cheng(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Xiaoke Shi(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Yuhan Gao(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Weihan Yi(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Heejoon Shin(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Hyungsoo Shin(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea)
• Wansuk Choi(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea) Corresponding author