Objective Chronic mechanical low back pain (CMLBP) is characterized by heterogeneous neuromotor responses that limit the effectiveness of standardized therapies. The study aimed to characterize neuromotor adaptation trajectories in response to core stability versus aerobic training and to develop a predictive tool for individualized rehabilitation.
Methods Forty-five women with CMLBP (aged 18–45 years) completed 8-week intervention programs: core stability plus conventional therapy (n = 15), aerobic exercise plus conventional therapy (n = 15), or conventional therapy alone (n = 15). Pain, disability, flexibility, and trunk range of motion were assessed. Latent class growth analysis identified response trajectories, and a machine learning model predicted optimal treatment assignment based on baseline profiles.
Results Four distinct trajectories emerged: fast responders (31.1%), moderate responders (33.3%), gradual adapters (22.2%), and minimal responders (13.3%). Core stability training resulted in the greatest pain (62.3% reduction) and disability reduction (63.6% reduction), with synchronized gain across movement and function. Aerobic exercise benefited patients with high movement variability. The predictive algorithm classified trajectory membership with an accuracy of 83.7%.
Conclusion Distinct neuromotor trajectories underpin variability in CMLBP rehabilitation. Early identification of these patterns enables precision rehabilitation, where core stability exercise is optimal for patients with preserved motor control and proprioception, while aerobic exercise is more suitable for those with elevated movement variability. This trajectory-based framework provides a clinically relevant model for mechanism-based, personalized care.
Authors and Reviewers
DownLoad: