Introduction: Physical Function Measurement is divided into subjective measures, such as patient-reported outcomes (e.g., COMI or ODI scores), and objective measures. The latter can be assessed by clinical tests (e.g., 6-minute walk test) or actual performance metrics (e.g., daily step counts). Subjective outcomes are inherently biased and cannot be continuously assessed, only at specific (often arbitrarily chosen) time points. Performance metrics, however, are influenced by motivation. Previous studies using wearable data in spinal surgery patients have identified a pattern of decline due to disease, a postoperative decrease, and a gradual recovery.
Aim: This study introduces a scalable, objective method to analyze preand postoperative activity levels using smartphone sensor data, minimizing motivational biases and the Hawthorne effect.
Method: Data were collected from patients who had undergone single-level fusion or decompression surgery and had used an iPhone for over six months before surgery. The study, approved by an ethical board (202201633), analyzed daily steps to simulate 6-minute and 1-minute walk tests. Data extraction, processing, and analysis were performed with a custom Python script developed in PyCharm Professional 2023.3.5.
Results: Analysis of six patients showed a preoperative activity decline, the lowest postoperative metrics, and varying recovery speeds, with quicker improvements in decompression cases. Baseline or better activity levels were reached within 1 to 16 weeks.
Conclusion: This method effectively uses smartphone data for objective activity tracking post-spinal surgery, offering a scalable tool for enhanced patient care and potential artificial intelligence applications for predictive modeling. It also underscores the significant individual differences in recovery following spinal surgery, supporting the need for an objective and continuous outcome measure over relying solely on patient-reported outcomes when assessing the effectiveness of a treatment modality.
Funding: None.
