Introduction. Many finite element (FE) studies have been performed in the past to assess the biomechanical performance of TKA and THA components. The boundary conditions have often been simplified to a few peak loads. With the availability of personalized musculoskeletal (MS) models we becomes possible to estimate dynamic muscle and prosthetic forces in a patient specific manner. By combining this knowledge with FE models, truly patient specific failure analyses can be performed. In this study we applied this combined technique to the femoral part of a cementless THR and calculated the cyclic micro-motions of the stem relative to the bone in order to assess the potential for bone ingrowth. Methods. An FE model of a complete femur with a CLS Spotorno stem inserted was generated. An ideal fit between the implant and the bone was modeled proximally, whereas distally an interface gap of 100μm was created to simulate a more realistic interface condition obtained during surgery. Furthermore, a gait analysis was performed on a young subject and fed into the Anybody™ MS modeling system. The anatomical data set (muscle attachment points) used by the Anybody™ system was morphed to the shape of the femoral reconstruction. In this way a set of muscle attachment points was obtained which was consistent with the FE model. The predicted muscle and hip contact forces by the Anybody™ modeling system were dynamic and divided into 37 increments including two stance phases and a swing phase of the right leg. Results. The magnitude and path of interface micromotions was heavily dependent on the location on the implant. In the proximal region, a unidirectional pattern was visible in proximal-distal direction (max. motion was 39μm). Mid stem micromotions were very small (in the order of 4μm), whereas in the distal region, micromotions had a tendency to develop in anterior-posterior and medial-lateral direction (max. motion was 96μm). Hence, in this example, ingrowth is most likely to start in the mid-region. Conclusion. By combining finite element models with musculoskeletal models more realistic, dynamical simulations can be generated to assess the biomechanical behavior of prosthetic components. Both, FE models as well as MS models can be personalized, which offers the possibility to perform truly patient specific predictions. Furthermore, by performing personalized MS and FE calculations, a database is established containing variability of kinematic, force and reconstructive parameters in patients. With this database new implants can be tested in a more robust and reliable manner than before, thereby reducing the chance that innovative ‘defective’ implants are launched on the market