Transforming Spine Surgery Patient Recovery Assessment with Digital Biomarkers and Smartphone Accelerometer Technology

Introduction: Outcome measurement in spine surgery traditionally relies on patient-reported outcome measures (PROMs), which, while essential, lack the nuance needed to fully capture perioperative functional status. The emergence of digital biomarkers, particularly smartphone accelerometer data, offers an objective, quantitative method for assessing mobility, such as steps-per-day, to better reflect patient recovery.

Methods: A scoping review of the nascent literature was conducted to describe the evolution of spine surgery outcome measurements and outline how digital biomarkers can supplement current clinical-driven measures. The focus was on studies utilizing smartphones to passively collect data related to mobility and emerging technologies in integrating digital biomarkers into spinal surgery.

Results: Objective mobility metrics, including step count, gait speed, and posture analysis, are promising complements to PROMs, enhancing the assessment of recovery trajectories and identifying deviations from expected patterns. Advances in machine learning now allow for predictive models using baseline mobility metrics, social determinants of health, and patient demographics to forecast perioperative outcomes. These models facilitate a more individualized approach, identifying patients who may be at risk for delayed recovery. Furthermore, insights into mobility influenced by factors such as socioeconomic status and lifestyle can deepen understanding of patient variability. As more rigorous datasets are employed, machine learning methods employing prognostic models can potentially predict perioperative course depending on patient’s pre-operative baseline mobility and demographics.

Conclusion : While PROMs remain effective for distinguishing outcomes between surgical and non-surgical interventions, they are less useful for assessing variations within surgical groups. Combining PROMs with objective metrics from smartphones and wearable devices offers a more continuous, precise, and adaptable view of recovery. This approach has the potential to optimize personalized care, refine surgical decision-making, and advance predictive models that support adaptive patient management, representing a significant shift towards data-driven, individualized spine surgery outcomes assessment.