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The purpose of this case study was to determine the effect of patellar taping, patellar bracing, and control condition on (a) patellofemoral congruence angle (PFC), (b) lateral patellar angle (LPA), (c) lateral patellar displacement (LPD), and (d) pain, as determined by the visual analog scale (VAS) during an 8-in. step-down. The subject was a 15-year-old female with a 3-year history of recurrent patellar subluxations and anterior knee pain syndrome. Results revealed the following: control condition—PFC 41.4-1.1°, LPA 19.9-6.9°, LPD 18.6-8.3 mm, VAS 8.8 cm; tape—PFC 46.2-2.3°, LPA 25.1-2.9°, LPD 24.2-7.5 mm, VAS 0.8 cm; brace—PFC 3.4-16.5°, LPA 7.9-0.8°, LPD 9.4-4.7 mm, VAS 0.3 cm. Patellar bracing was effective in centralizing the patella as revealed by the PFC, LPA, and LPD measures; however, patellar taping did not improve patellar position, and in some positions taping actually worsened patellar position. A large reduction in pain as measured by the VAS occurred during an 8-in. step-down for both taping and bracing. More research is necessary to explain the pain reduction without a change in patellar position using tape.

When optimization is used to evaluate a joint contact model's ability to reproduce experimental measurements, the high computational cost of repeated contact analysis can be a limiting factor. This paper presents a computationally-efficient response surface optimization methodology to address this limitation. Quadratic response surfaces were fit to contact quantities (contact force, maximum pressure, average pressure, and contact area) predicted by a discrete element contact model of the tibiofemoral joint for various combinations of material modulus and relative bone pose (i.e., position and orientation). The response surfaces were then used as surrogates for costly contact analyses in optimizations that minimized differences between measured and predicted contact quantities. The methodology was evaluated theoretically using six sets of synthetic (i.e., computer-generated) contact data, and practically using one set of experimental contact data. For the synthetic cases, the response surface optimizations recovered all contact quantities to within 3.4% error. For the experimental case, they matched all contact quantities to within 6.3% error except for maximum contact pressure, which was in error by up to 50%. Response surface optimization provides rapid evaluation of joint contact models within a limited range of relative bone poses and can help identify potential weaknesses in contact model formulation and/or experimental data quality.