The correlation between spatiotemporal gait and pain in patients with musculoskeletal pain using smartphone-based gait technology. A retrospective cross-sectional study

Background This study aimed to investigate the relationship between spatiotemporal gait parameters and pain severity in patients with musculoskeletal (MSK) conditions, specifically knee and back pain. Additionally, it sought to compare how gait compensation strategies differ based on pain location and their association with pain intensity. Methods A retrospective analysis was conducted on 3,595 patients attending clinics in the US between December 2023 and April 2025. Participants performed barefoot gait assessments using an AI-driven smartphone-based app, which collected data to extract gait metrics such as velocity, step length, cadence, and limb support times. Pain severity was assessed via the Numeric Pain Rating Scale (NPRS). Gait parameters were stratified into quintiles, and logistic regression analyses examined associations between gait deviations and severe pain (NPRS > 7), adjusting for age and gender. Results Patients with gait parameters indicating greater impairment exhibited higher odds ratio of reporting severe pain. Gait velocity emerged as the most influential predictor, with walking speeds below 80 cm/s associated with over a 2.7-fold increased likelihood of severe pain. Subgroup analyses revealed that knee pain was more strongly linked to reduced cadence, while back pain correlated primarily with shorter step length. Conclusion Spatiotemporal gait parameters are significantly associated with pain severity in MSK conditions and can be effectively measured using accessible mobile technology. Recognizing distinct gait patterns based on pain location supports the development of tailored clinical interventions and targeted objectives for monitoring treatment outcomes.