Tibial acceleration alone is not a valid surrogate measure of tibial load in response to stride length manipulation.

PURPOSE: This study aimed to evaluate the relationship between peak tibial acceleration and peak ankle joint contact forces in response to stride length manipulation during level-ground running. METHODS: Twenty-seven physically active participants ran 10 trials at preferred speed in each of 5 stride length conditions: preferred, ±5 %, and ±10 % of preferred stride length. Motion capture, force platform, and tibial acceleration data were directly measured, and ankle joint contact forces were estimated using an inverse-dynamics-based static optimization routine. RESULTS: In general, peak axial tibial accelerations (p < 0.001) as well as axial (p < 0.001) and resultant (p < 0.001) ankle joint contact forces increased with stride length. When averaged within the 10 strides of each stride condition, moderate positive correlations were observed between peak axial acceleration and joint contact force (r = 0.49) as well as peak resultant acceleration and joint contact force (r = 0.51). However, 37% of participants illustrated either no relationship or negative correlations. Only weak correlations across participants existed between peak axial acceleration and joint contact force (r = 0.12) as well as peak resultant acceleration and ankle joint contact force (r = 0.18) when examined on a step-by-step basis. CONCLUSION: These results suggest that tibial acceleration should not be used as a surrogate for ankle joint contact force on a step-by-step basis in response to stride length manipulations during level-ground running. A 10-step averaged tibial acceleration metric may be useful for some runners, but an initial laboratory assessment would be required to identify these individuals.

Predicting Tibia-Fibula Geometry and Density From Anatomical Landmarks Via Statistical Appearance Model: Influence of Errors on Finite Element-Calculated Bone Strain.

State-of-the-art participant-specific finite element models require advanced medical imaging to quantify bone geometry and density distribution; access to and cost of imaging is prohibitive to the use of this approach. Statistical appearance models may enable estimation of participants' geometry and density in the absence of medical imaging. The purpose of this study was to: (1) quantify errors associated with predicting tibia-fibula geometry and density distribution from skin-mounted landmarks using a statistical appearance model and (2) quantify how those errors propagate to finite element-calculated bone strain. Participant-informed models of the tibia and fibula were generated for thirty participants from height and sex and from twelve skin-mounted landmarks using a statistical appearance model. Participant-specific running loads, calculated using gait data and a musculoskeletal model, were applied to participant-informed and CT-based models to predict bone strain using the finite element method. Participant-informed meshes illustrated median geometry and density distribution errors of 4.39-5.17 mm and 0.116-0.142 g/cm3, respectively, resulting in large errors in strain distribution (median RMSE = 476-492 µÎµ), peak strain (limits of agreement =±27-34%), and strained volume (limits of agreement =±104-202%). These findings indicate that neither skin-mounted landmark nor height and sex-based predictions could adequately approximate CT-derived participant-specific geometry, density distribution, or finite element-predicted bone strain and therefore should not be used for analyses comparing between groups or individuals.