Purpose <p>Low back pain (LBP) is a debilitating condition with highly variable treatment responses due to its complex biopsychosocial nature. This study utilized standardized in-clinic sensor-based spinal motion assessments to identify distinct motion profile subgroups in LBP patients and examine their association with biopsychosocial measures and longitudinal outcomes after usual care.</p> Methods <p>In this prospective observational study, 607 LBP patients underwent standardized sensor-based spinal motion assessments to quantify extent of low back impairment. Machine learning clustering of patient’s spinal motion signatures identified patient subgroups, which were compared across demographic, clinical, and biopsychosocial measures. Finally, we used patient’s global impression of change (PGIC) as our primary outcome to compare variation in 3-month responses between clusters after usual care treatments.</p> Results <p>We identified three baseline motion-based clusters: Poor function (<i>n</i> = 179), Moderate function (<i>n</i> = 281), and High function (<i>n</i> = 147), reflecting low, moderate, and high spinal mobility. Demographic and clinical characteristics including age, body mass index, employment and comorbidity differed significantly across clusters. Notably, although clusters were derived using motion features only, we found significant variation in pain outcomes including pain intensity and interference. Moreover, cluster membership was independently associated with key biopsychosocial measures, including pain intensity, pain interference, physical function, social role, and self-efficacy, indicating that these phenotypes capture clinically relevant differences in pain experience and related biopsychosocial domains. At 3 months, PGIC responses differed significantly by cluster, with the Poor function group showing a higher proportion of negative outcomes.</p> Conclusions <p>Three distinct motion-based clusters were identified that demonstrated variability across demographic, clinical, and biopsychosocial measures. Although derived solely from spinal motion, these clusters were associated with key patient-reported outcomes, suggesting that motion-based clustering may offer complementary insight into patient heterogeneity and identifying subgroups at risk of suboptimal outcomes.</p> Clinical Trials Registration ID: <p>NCT05776771.</p> Key findings <p><b>Question</b> Can sensor-enabled motion assessments be utilized for patient phenotyping to identify unique low back pain subgroups and predict treatment responders?</p> Findings <p>Three distinct functional clusters were identified using wearable sensor-enabled motion assessments and cluster membership was significantly associated with 3-month responses.</p> Meaning <p>The use of sensor-enabled motion assessments can inform patient heterogeneity and identify clusters associated with poor outcomes.</p>