Retrospective Analysis of Movement Data Before and After an Ankle Fracture: A Descriptive Study Using Apple Health
Artikel i vetenskaplig tidskrift, 2026

Objective: To evaluate movement patterns in patients with ankle fractures before and after injury. Patients and Methods: This descriptive study analyzed movement data from patients treated for ankle fractures at Sahlgrenska University Hospital, Sweden (January 1 to December 31, 2022). Patients were identified using ICD-10 codes and medical records. Inclusion criteria: surgical and nonsurgically treated patients with >6 months of preinjury iPhone use. Step count, length, and speed were collected through a mobile application integrated with Apple Health. Double support and gait asymmetry were excluded due to limited external validity. Data spanned 6-12 months preinjury to 1 year post-injury. The primary aim was to evaluate whether patients reach their preinjury movement patterns. Results: Of 1131 patients, 90 were analyzed. Preinjury means: 5435.0 steps (SD 4215.3), step length 0.70 m (SD 0.07), and step speed 1.28 m/s (SD 0.2). At 1 year: 5420.3 steps (SD 3887.0), step length 0.68 m (SD 0.08), and step speed 1.22 m/s (SD 0.19). A post-injury plateau was reached in step parameters at 84.8 days, with no further recovery thereafter. Step count largely recovered, but deficits in step length and speed persisted at 12 months. Conclusion: Smartphone-derived movement data provide a cost-effective alternative to laboratory gait analysis, enabling long-term monitoring. Preinjury data allow individualized baseline comparisons and may support earlier identification of patients needing adjusted rehabilitation.

Författare

Erik Börjesson

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Emilia Möller Rydberg

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Carl Bergdahl

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Sebastian Andreasson

Göteborgs universitet

Torbjörn Lundh

Okinawa Institute of Science and Technology Graduate University

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Michael Möller

Göteborgs universitet

Sahlgrenska universitetssjukhuset

David Wennergren

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Mayo Clinic Proceedings Digital Health

29497612 (eISSN)

Vol. 4 2 100362

Ämneskategorier (SSIF 2025)

Idrottsvetenskap och fitness

DOI

10.1016/j.mcpdig.2026.100362

Mer information

Senast uppdaterat

2026-05-29