Developing a Prediction Model for Identification of Distinct Perioperative Clinical Stages in Spine Surgery With Smartphone-Based Mobility Data

Abstract

BACKGROUND: Spine surgery outcomes assessment currently relies on patient-reported outcome measures, which satisfy established reliability and validity criteria, but are limited by the inherently subjective and discrete nature of data collection. Physical activity measured from smartphones offers a new data source to assess postoperative functional outcomes in a more objective and continuous manner. OBJECTIVE: To present a methodology to characterize preoperative mobility and gauge the impact of surgical intervention using objective activity data garnered from smartphone-based accelerometers. METHODS: Smartphone mobility data from 14 patients who underwent elective lumbar decompressive surgery were obtained. A time series analysis was conducted on the number of steps per day across a 2-year perioperative period. Five distinct clinical stages were identified using a data-driven approach and were validated with clinical documentation. RESULTS: Preoperative presentation was correctly classified as either a chronic or acute mobility decline in 92% of patients, with a mean onset of acute decline of 11.8 ± 2.9 weeks before surgery. Postoperative recovery duration demonstrated wide variability, ranging from 5.6 to 29.4 weeks (mean: 20.6 ± 4.9 weeks). Seventy-nine percentage of patients ultimately achieved a full recovery, associated with an 80% ± 33% improvement in daily steps compared with each patient’s preoperative baseline (P = .002). Two patients subsequently experienced a secondary decline in mobility, which was consistent with clinical history. CONCLUSION: The perioperative clinical course of patients undergoing spine surgery was systematically classified using smartphone-based mobility data. Our findings highlight the potential utility of such data in a novel quantitative and longitudinal surgical outcome measure.