Multi-domain Postoperative Recovery Trajectories After Lumbar and Thoracolumbar Spine Surgery

Introduction: Understanding early postoperative recovery is crucial for improving perioperative care and long-term outcomes. Traditional recovery assessments relying primarily on cross-sectional patient-reported measures may not fully capture the complexity of the recovery process. Our study evaluates early postoperative recovery using mobile health (mHealth) assessment and explores the relationship between early recovery patterns, 30-day complications and long-term outcomes.

Methods: A prospective cohort study recruited patients aged 21-85 undergoing lumbar/thoracolumbar surgery for degenerative diseases. During the first month postoperatively, patients completed daily EMA surveys assessing pain, depression, and physical function and were passively monitored with Fitbit. EMA and Fitbit data were clustered using functional principal component analysis. Outcomes including pain, physical function, and disability were collected at 6 and 12 months postoperatively.

Results: 129 patients were included (median age: 62; 56% female). Two dominant recovery patterns were identified across all domains. At 12 months, patients with more favorable recovery across all domains except for activity bout number and depression, showed better outcomes. Favorable recovery in pain intensity and steps per minute was associated with greater improvements in all outcomes, while favorable maximum 30-minute cadence recovery was associated with greater disability and physical function improvement. Patients with less favorable pain recovery had a significantly higher complication rate (23 vs 7%). Additionally, shorter length of stay was associated with favorable recovery in pain intensity (2.9 vs. 5.2 days), pain interference (3.3 vs. 4.8 days), maximum 30-minute cadence (3 vs. 6 days), and steps per minute (3.2 vs. 4.7 days). A greater number of favorable recovery domains was associated with better 12-month outcomes.

Conclusion : Early postoperative recovery patterns, captured using mHealth tools, provide valuable insights into long-term outcomes. With further validation, these findings highlight the potential of integrating mHealth into clinical practice to personalize rehabilitation strategies, improve resource allocation, and enhance patient care.