Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study.

Area of Science:

• Orthopedics
• Biomedical Engineering
• Digital Health

Background:

• Regaining function after lower-extremity fractures is a priority.
• Measuring patient function has historically been challenging.
• Smartphones offer high-fidelity, continuous mobility data, providing pre- and post-injury gait history.

Purpose of the Study:

• To assess if pre-injury mobility data, combined with demographic and injury information, can reliably predict post-fracture mobility.
• To explore the utility of smartphone-derived mobility metrics in orthopedic recovery.

Main Methods:

• 107 adult patients with surgically treated lower-extremity fractures were enrolled.
• Patients' smartphone mobility metrics (step count, walking speed, etc.) were collected.
• Nonlinear modeling integrated mobility, demographic, and injury data to predict recovery.

Main Results:

• Pre-injury function strongly predicted post-injury mobility across all models.
• Average daily step count increased weekly post-injury, with faster gains early in recovery.
• Pre-injury step count positively correlated with post-injury mobility; walking speed initially declined then improved.

Conclusions:

• High-fidelity pre-injury mobility data is valuable for predicting recovery after lower-extremity fractures.
• These findings support the use of smartphone data for personalized patient counseling.
• Predictive models offer clinical insights for surgeons managing fracture recovery.