26dkou400 10.3205/26dkou400 urn:nbn:de:0183-26dkou4001 Meeting Abstract Arnegger Arnegger Andreas A

OSORA medical GmbH, Ulm, Deutschland

author Niemeyer Niemeyer Frank F

OSORA medical GmbH, Ulm, Deutschland

author Popov Popov Ilya I

ComeBack Mobility Inc., San Antonio, USA

author Protsenko Protsenko Vsevolod V

ComeBack Mobility Inc., San Antonio, USA

author Bilyi Bilyi Vladyslav V

ComeBack Mobility Inc., San Antonio, USA

author German Medical Science GMS Publishing House

Düsseldorf

610 20261020 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0659 400 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2026) Grundlagenforschung | Trauma und Inflammation 1 Berlin 20261020 20261023 DKOU26_Abs_2289 TextObjectives and questions: Bone fracture healing is strongly influenced by mechanical loading, yet clinicians rarely know what patients actually do during rehabilitation [1]. We present a case study that combines mechanobiological fracture-healing simulation (OSORA) with continuous weight-bearing monitoring using smart crutches (Comeback Mobility). Using a data-driven patient-specific digital twin of bone healing can be useful for educating patients on the expected healing progress and ultimately help reducing the rate of complications [2].Material and methods: A surgically treated tibia fracture was reconstructed from clinical imaging (post-OP CT), metadata and observed healing outcome, which was subsequently simulated using OSORA’s mechanobiological model [3]. For outcome evaluation tissue differentiation, interfragmentary movement (IFM) and consolidation over time were computed. First, the attending surgeon’s recommended ideal weight-bearing build-up was used to simulate the base scenario with 100% patient compliance presumed. Second, the clinically observed case with loadings recorded by sensor-equipped Smart Crutch Tips™ during daily activities was set up.Results: Simulation results show that the patient frequently exceeded the recommended peak loads of the planned weight-bearing corridor, in particular during the first four to five weeks. Particularly on day 7, the patient applied up to 80% of full weight-bearing load, resulting in high implant stress beyond the fatigue strength in the screws as well as the nail in proximity to the fracture location. Continued overloading can lead to failure of the bone-implant-construct and thus requires monitoring. In silico comparison suggests that strict adherence to the prescribed loading corridor would have led to slightly faster bony consolidation than the actual loading pattern. Episodes of elevated peak load were associated with correspondingly increased IFM and thus locally increased mechanical stimulus. However, the effect on the overall outcome is limited, with a slight delay in bone healing of two days.Discussion and conclusions: A limitation of the methodical approach is, that real weight-bearing is imposed without regard of the healing progress, and thus individual pain thresholds might hinder an actual patient from applying this level of load bearing. This case highlights how combining wearable-derived loading data with predictive simulation can make hidden aspects of rehabilitation visible and clinically actionable. For clinicians, the workflow supports holistic planning from osteosynthesis choice to weight-bearing progression and potential revision. For patients, quantified feedback on both average and peak load can improve understanding and compliance. Next steps include streamlined data integration, enabling near-real-time computation and validation in larger prospective cohorts. 0 0 0 0