Retrospective Analysis of Movement Data Before and After an Ankle Fracture: A Descriptive Study Using Apple Health

Area of Science:

• Orthopedics
• Biomechanics
• Digital Health

Background:

• Ankle fractures significantly impact patient mobility and quality of life.
• Traditional gait analysis methods are often resource-intensive and limited in scope.
• Long-term, objective movement pattern data is crucial for understanding recovery trajectories.

Purpose of the Study:

• To evaluate changes in movement patterns, including step count, length, and speed, in patients with ankle fractures before and after injury.
• To determine if patients achieve their preinjury movement patterns within one year of treatment.
• To assess the utility of smartphone-derived data for monitoring gait recovery.

Main Methods:

• A descriptive study analyzed movement data from 90 patients treated for ankle fractures.
• Data included step count, length, and speed collected via a mobile application integrated with Apple Health.
• Movement data spanned 6-12 months preinjury to 1 year post-injury, excluding double support and gait asymmetry.

Main Results:

• Preinjury movement patterns included: 5435 steps, 0.70 m step length, and 1.28 m/s step speed.
• One year post-injury: 5420 steps, 0.68 m step length, and 1.22 m/s step speed were recorded.
• Step parameters plateaued at 84.8 days post-injury; step count recovered, but step length and speed deficits persisted.

Conclusions:

• Smartphone-derived movement data offer a cost-effective, long-term alternative to laboratory gait analysis.
• Preinjury data enables individualized baseline comparisons for rehabilitation.
• Persistent deficits in step length and speed highlight the need for targeted interventions post-ankle fracture.