BACKGROUND: Knee problems are the most common complaints of the lower extremity in the Netherlands. Osteoarthritis has the highest prevalence of all knee complaints. Knee osteoarthritis is likely to start at the patellofemoral joint and is associated with the aggravation of pain. Mechanical overloading is hypothesized to contribute to the development and progression of patellofemoral osteoarthritis (PFOA). Several studies have explored the mechanical loading pattern of the patellofemoral joint. Because different biomechanical models were used, vastly different estimations of the patellofemoral joint contact force (PFJCF) were concluded. This diversity prevents a clear understanding of the role of mechanical overloading in PFOA, since it is unknown how different biomechanical models affect the PFJCF estimation. OBJECTIVES: This study will explore how different biomechanical models of the patellofemoral joint affect the estimated PFJCF for common weight-bearing activities. METHODS: Ten healthy participants were included in this study. Common weight-bearing activities (walking, stair ascending, stair descending, sit-to-stand and stand-to-sit) were performed in the motion lab. Marker trajectory data and force plate data were collected. The data were input to biomechanical models used to estimate the quadriceps muscle force and subsequently the PFJCF. The quadriceps muscle force was estimated using the inverse dynamics and static optimization method. From there on, the PFJCF was estimated using three PFJCF to quadriceps muscle force ratios (P2QFRs), each based on a different patellofemoral joint model (i.e. van Eijden’s model, Yamaguchi’s model and Gill’s model). For each weight-bearing activity, the peak PFJCF was obtained and the magnitude of the difference among the biomechanical models was explored. RESULTS: The static optimization method resulted in a significantly higher peak PFJCF compared to the inverse dynamics method in walking (largest effect size was 0.10 BW). However, for stair descending, sit-to-stand and stand-to-sit, the inverse dynamics method resulted in a significantly higher peak PFJCF compared to the static optimization method (largest effect size was 0.45 BW, 1.17 BW, 1.25 BW, respectively). No significantly difference was found for stair ascending. For walking, Yamaguchi’s model resulted in a significantly higher peak PFJCF compared to van Eijden’s model and Gill’s model, and van Eijden’s model resulted in a significantly higher peak PFJCF compared to Gill’s model (largest effect size was 0.06 BW). For stair ascending, stair descending, sit-to-stand and stand-to-sit, Gill’s model resulted in a significantly higher peak PFJCF compared to van Eijden’s model and Yamaguchi’s model. For stair descending the van Eijden’s model resulted in a significantly higher peak PFJCF compared to Yamaguchi’s model. The largest effect size was 0.15 BW, 0.32 BW, 0.72 BW and 0.72 BW, for respectively stair ascending, stair descending, sit-to-stand and stand-to-sit. CONCLUSION: The choice of a biomechanical model has a critical effect on the estimation of the magnitude of the PFJCF. Its differences might reach half the clinical size effects when comparing control to symptomatic PF pain patients.

BACKGROUND: Research on the role of patellar biomechanics in patellofemoral (PF) joint pathologies requires accurate and reliable measurement of the patella relative to the femur (patellar tracking) but current methods have limitations. In vivo measurement of PF kinematics is done using medical imaging techniques but these are expensive, involve radiation exposure or only allow slow, supine motions. A widely used alternative is optical motion capture combined with musculoskeletal modeling. However, due to the large motion of the skin relative to the patella, the typical approach using anatomical calibrated skin-markers is unsuitable. As the shape of the patella is grossly visible underneath the skin, placement of a grid of skin-mounted markers on the skin covering the patella might offer an alternative for patellar tracking. OBJECTIVE: The objective of this study was to develop a patellar tracking framework designed to measure in vivo PF translations using a grid of skin-mounted markers and to compare it to in vivo measures derived from dynamic CT images and musculoskeletal model estimates. MATERIALS AND METHODS: Two participants performed a supine knee flexion-extension motion with a grid of 42 retroreflective markers placed on the right knee during four-dimensional computed tomography (4D-CT) measurement and optical motion capture. A patellar identification algorithm (PIA) was developed to estimate the PF translations in the medial – lateral (ML), superior- inferior (SI) and anterior – posterior (AP) directions. PF translations were determined by four different methods: CT bone geometry-based (CT_BONES), CT marker grid-based (CT_GRID), optical motion capture marker grid-based (OMC_GRID) and by musculoskeletal modeling (OMC_MODEL). To evaluate the feasibility of our method we first compared CT_BONES to CT_GRID. Next, results obtained by different measurement types (CT_GRID and OMC_GRID) and results obtained by two different marker-based methods (OMC_GRID and OMC_MODEL) were compared. To compare ranges of motions, patellar excursions relative to the femur were evaluated and compared for all knee angles (KA_ALL) and for knee angles > 20° (KA_ENGAGED). RESULTS: Differences in patellar excursions given by CT_BONES and CT_GRID during KA_ENGAGED were less than 4 mm and RMSEs less than 3 mm. These differences were less than 5 mm between CT_GRID and OMC_GRID (RMSE: 5 mm) and less than 16 mm between OMC_GRID and OMC_MODEL (RMSE: 10 mm) for knee angles lower than 20°. Larger differences in patellar excursions and RMSEs were seen during KA_ALL. CONCLUSION: PF translations could be measured using a marker grid with an accuracy within 3 mm for knee angles larger than 20° (i.e. during patellar engagement). However, our method performed worse for knee angles smaller than 20° flexion (an accuracy within 12 mm). The findings of this study provide valuable baseline knowledge for non-invasive, marker-based patellar tracking. Future studies should include larger sample sizes to allow for proper validation.