2025

Frontiers in Bioengineering and Biotechnology

Gait analysis with smart insoles can identify patients at risk of tibial shaft fracture nonunion as early as six weeks after surgery: longitudinal and cross-sectional study

Elke Warmerdam, Marcel Orth, Max Müller, Tim Pohlemann, Bergita Ganse

Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg, Germany, Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg Germany

Keywords

bone regeneration, diaphyseal fracture, fracture monitoring, orthopaedic trauma, patient-reported outcome measures, plantar pressure, postoperative monitoring, wearables

Abstract

Background: Nonunion, a common and detrimental complication of tibial shaft fractures, is usually diagnosed via X-ray-based imaging. Unfortunately, it often takes months of observation until the indication for revision surgery or other interventions is given, which is why additional methods are desirable to enhance the ability to predict and prevent nonunion earlier. Objective: As gait patterns and subjective outcomes obtained by questionnaires improved during regular fracture healing, the aim of this study was to determine whether gait analyses with instrumented insoles and patient-reported outcome measurement information system (PROMIS) questionnaires could be used to detect patients at risk of developing nonunion during the healing phase after tibial shaft fracture. Methods: Data were collected from a longitudinal and a cross-sectional tibial fracture cohort via gait analysis with instrumented insoles (OpenGO, Moticon GmbH, Munich, Germany) and in addition via PROMIS questionnaires. The gait parameters included pressure, temporal, angular velocity and acceleration-related parameters. The PROMIS covered the global health, physical function and pain questionnaires. Comparisons between patients with and without union were performed at 1 week, 6 weeks, 3 months and 6 months after surgery. Results: Six-month postoperative radiographs revealed nonunion in seven of 18 longitudinally assessed patients and in four patients who underwent a single assessment after nonunion diagnosis. Overall gait patterns, primarily reflected by temporal and pressure distribution parameters, differed significantly between patients with and without union from 6 weeks onward. These differences between union and nonunion patients were detected via gait patterns significantly earlier than by radiographs or PROMIS questionnaires. In detail, 6 weeks after surgery, 16 out of the 33 gait parameters were significantly different between the longitudinal union and longitudinal nonunion groups. Three months after surgery, the center of pressure width (p=0.022, stride time (p=0.035) stride frequency (p=0.008), maximal angular velocity (p=0.014), and asymmetry of the maximal angular velocity (p=0.035) differed significantly between the longitudinal union and longitudinal nonunion groups. Conclusion: Gait analysis with instrumented insoles can be used to detect patients at high risk of developing nonunion of a tibial shaft fracture already 6 weeks postoperative.

Moticon's Summary

This study investigated whether Moticon OpenGO sensor insoles could identify patients at risk of tibial shaft fracture nonunion early in the healing process. The OpenGO insoles, equipped with 16 pressure sensors, a triaxial accelerometer, and a triaxial gyroscope, were used to collect gait data, including pressure, temporal, angular velocity, and acceleration-related parameters. The research found that gait patterns, especially temporal and pressure distribution, significantly differed between patients with and without nonunion as early as six weeks post-surgery, which was earlier than detection by X-rays or PROMIS questionnaires. This suggests that Moticon's OpenGO insoles can be a valuable tool for early detection of nonunion, allowing for timely intervention and potentially improving patient outcomes and reducing healthcare costs.

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