Transhumeral loading during advanced upper extremity activities of daily living

Percutaneous osseointegrated (OI) implants for direct skeletal attachment of upper extremity prosthetics represent an alternative to traditional socket suspension that may yield improved patient function and satisfaction. This is especially true in high-level, transhumeral amputees where prosthetic fitting is challenging and abandonment rates remain high. However, maintaining mechanical integrity of the bone-implant interface is crucial for safe clinical introduction of this technology. The collection of population data on the transhumeral loading environment will aid in the design of compliance and overload protection devices that mitigate the risk of periprosthetic fracture. We collected marker-based upper extremity kinematic data from non-amputee volunteers during advanced activities of daily living (AADLs) that applied dynamic loading to the humerus. Inverse dynamic analysis was applied to calculate the axial force, bending and torsional moments at three virtual amputation levels representing 25, 50, and 75% residual humeral length. The influences of amputation level, elbow flexion constraint, gender and anthropometric scaling were assessed. Results indicate that the proximal (25%) amputation level experienced significantly higher axial forces and bending moments across all subjects when compared to distal amputation levels (p <= 0.030). Constraining elbow flexion had a limited influence on peak transhumeral loads. Male subjects experienced higher axial forces during all evaluated activities (p <= 0.023). Peak axial force for all activities occurred during jumping jacks (174.5N). Peak bending (57.6Nm) and torsional (57.2Nm) moments occurred during jumping jacks and rapid internal humeral rotation, respectively. Calculated loads fall within the range of implant fixation failure loads reported in cadaveric investigations of humeral stem fixation; indicating that periprosthetic fracture may occur during non-contact AADLs. These kinematic data, collected over a range of AADLs, will aid in the development of overload protection devices and appropriate post-operative rehabilitation protocols that balance return to an active lifestyle with patient safety.