How many segments are enough to biomechanically model the feet? A comparison of inverse kinematics and dynamics in multisegmented foot models.

Multisegmented foot models (MSFMs) capture kinematic and kinetic data of specific regions of the foot instead of representing the foot as a single, rigid segment. Models differ by the number of segments and segment definitions, so there is no consensus for best practice. It is unknown whether MSFMs yield the same joint kinematic and kinetic data and what level of detail is necessary to accurately measure such values. We compared the angle, moment, and power measurements at the tibiotalar, midtarsal, and metatarsophalangeal joints of four MSFMs using motion capture data of young adult runners during stance phase of barefoot walking and jogging. Of these models, three were validated: Oxford Foot Model, Milwaukee Foot Model, and Ghent Foot Model. One model was developed based upon literature review of existing models: the "Vogel" model. We performed statistical parametric mapping comparing joint measurements from each model to the corresponding results from the Oxford Model, the most heavily studied MSFM. We found that the Oxford Foot Model, Milwaukee Foot Model, Vogel Foot Model, and Ghent Foot Model do not provide the same results. The changes in model segment definitions impact the degrees of freedom in ways that alter the measured kinematic function of the foot, which in turn impacts the kinetic results. We also found that dynamic function of the midfoot/arch may be better captured by MSFMs with a separate midfoot segment. The results of this study capture the variability in performance of MSFMs and indicate a need to standardize the design of MSFMs.

Factors Associated With High-Risk and Low-Risk Bone Stress Injury in Female Runners: Implications for Risk Factor Stratification and Management.

Background: Bone stress injury (BSI) is a common overuse injury in active women. BSIs can be classified as high-risk (pelvis, sacrum, and femoral neck) or low-risk (tibia, fibula, and metatarsals). Risk factors for BSI include low energy availability, menstrual dysfunction, and poor bone health. Higher vertical load rates during running have been observed in women with a history of BSI. Purpose/Hypothesis: The purpose of this study was to characterize factors associated with BSI in a population of premenopausal women, comparing those with a history of high-risk or low-risk BSI with those with no history of BSI. It was hypothesized that women with a history of high-risk BSI would be more likely to exhibit lower bone mineral density (BMD) and related factors and less favorable bone microarchitecture compared with women with a history of low-risk BSI. In contrast, women with a history of low-risk BSI would have higher load rates. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Enrolled were 15 women with a history of high-risk BSI, 15 with a history of low-risk BSI, and 15 with no history of BSI. BMD for the whole body, hip, and spine was standardized using z scores on dual-energy x-ray absorptiometry. High-resolution peripheral quantitative computed tomography was used to quantify bone microarchitecture at the radius and distal tibia. Participants completed surveys characterizing factors that influence bone health-including sleep, menstrual history, and eating behaviors-utilizing the Eating Disorder Examination Questionnaire (EDE-Q). Each participant completed a biomechanical assessment using an instrumented treadmill to measure load rates before and after a run to exertion. Results: Women with a history of high-risk BSI had lower spine z scores than those with low-risk BSI (-1.04 ± 0.76 vs -0.01 ± 1.15; P < .05). Women with a history of high-risk BSI, compared with low-risk BSI and no BSI, had the highest EDE-Q subscores for Shape Concern (1.46 ± 1.28 vs 0.76 ± 0.78 and 0.43 ± 0.43) and Eating Concern (0.55 ± 0.75 vs 0.16 ± 0.38 and 0.11 ± 0.21), as well as the greatest difference between minimum and maximum weight at current height (11.3 ± 5.4 vs 7.7 ± 2.9 and 7.6 ± 3.3 kg) (P < .05 for all). Women with a history of high-risk BSI were more likely than those with no history of BSI to sleep <7 hours on average per night during the week (80% vs 33.3%; P < .05). The mean and instantaneous vertical load rates were not different between groups. Conclusion: Women with a history of high-risk BSI were more likely to exhibit risk factors for poor bone health, including lower BMD, while load rates did not distinguish women with a history of BSI.

Cues to land softly and quietly result in acute reductions in ground reaction force loading rates in runners.

BACKGROUND: A common gait retraining goal for runners is reducing vertical ground reaction force (GRF) loading rates (LRs), which have been associated with injury. Many gait retraining programs prioritize an internal focus of attention, despite evidence supporting an external focus of attention when a specific outcome is desired (e.g., LR reduction). RESEARCH QUESTION: Does an external focus of attention (using cues for quiet, soft landings) result in comparable reductions in LRs to those achieved using a common internal focus (forefoot striking while barefoot)? METHODS: This observational study included 37 injured runners (18 male; mean age 36 (14) years) at the OMITTED Running Center. Runners wore inertial measurement units over the distal-medial tibia while running on an instrumented treadmill at a self-selected speed. Data were collected for three conditions: 1) Shod-Control (wearing shoes, without cues); 2) Shod-Quiet (wearing shoes, cues for quiet, soft landings); and 3) Barefoot-FFS (barefoot, cues for forefoot strike (FFS)). Within-subject variables were compared across conditions: vertical instantaneous loading rate (LR, primary outcome); vertical stiffness during initial loading; peak vertical GRF; peak vertical tibial acceleration (TA); and cadence. RESULTS: Vertical LR, stiffness, and TA were lower in the Shod-Quiet compared to Shod-Control p < 0.001). Peak vertical GRF and cadence were not different between Shod-Quiet and Shod-Control. Reductions in stiffness and LR were similar between Shod-Quiet and Barefoot-FFS, and GRF in Barefoot-FFS remained similar to both shod conditions. However, runners demonstrated additional reductions in TA and increased cadence when transitioning from Shod-Quiet to the Barefoot-FFS condition (p < 0.05). SIGNIFICANCE: These results suggests that a focus on quiet, soft landings may be an effective gait retraining method for future research.

Interaction of Biomechanical, Anthropometric, and Demographic Factors Associated with Patellofemoral Pain in Rearfoot Strike Runners: A Classification and Regression Tree Approach.

BACKGROUND: Patellofemoral pain (PFP) is among the most common injuries in runners. While multiple risk factors for patellofemoral pain have been investigated, the interactions of variables contributing to this condition have not been explored. This study aimed to classify runners with patellofemoral pain using a combination of factors including biomechanical, anthropometric, and demographic factors through a Classification and Regression Tree analysis. RESULTS: Thirty-eight runners with PFP and 38 healthy controls (CON) were selected with mean (standard deviation) age 33 (16) years old and body mass index 22.3 (2.6) kg/m2. Each ran at self-selected speed, but no between-group difference was identified (PFP = 2.54 (0.2) m/s x CON = 2.55 (0.1) m/s, P = .660). Runners with patellofemoral pain had different patterns of interactions involving braking ground reaction force impulse, contact time, vertical average loading rate, and age. The classification and regression tree model classified 84.2% of runners with patellofemoral pain, and 78.9% of healthy controls. The prevalence ratios ranged from 0.06 (95% confidence interval: 0.02-0.23) to 9.86 (95% confidence interval: 1.16-83.34). The strongest model identified runners with patellofemoral pain as having higher braking ground reaction force impulse, lower contact times, higher vertical average loading rate, and older age. The receiver operating characteristic curve demonstrated high accuracy at 0.83 (95% confidence interval: 0.74-0.93; standard error: 0.04; P < .001). CONCLUSIONS: The classification and regression tree model identified an influence of multiple factors associated with patellofemoral pain in runners. Future studies may clarify whether addressing modifiable biomechanical factors may address this form of injury.

The Effects of a Simple Sensor Reorientation Procedure on Peak Tibial Accelerations during Running and Correlations with Ground Reaction Forces.

While some studies have found strong correlations between peak tibial accelerations (TAs) and early stance ground reaction forces (GRFs) during running, others have reported inconsistent results. One potential explanation for this is the lack of a standard orientation for the sensors used to collect TAs. Therefore, our aim was to test the effects of an established sensor reorientation method on peak Tas and their correlations with GRFs. Twenty-eight runners had TA and GRF data collected while they ran at a self-selected speed on an instrumented treadmill. Tibial accelerations were reoriented to a body-fixed frame using a simple calibration trial involving quiet standing and kicking. The results showed significant differences between raw and reoriented peak TAs (p < 0.01) for all directions except for the posterior (p = 0.48). The medial and lateral peaks were higher (+0.9-1.3 g), while the vertical and anterior were lower (-0.5-1.6 g) for reoriented vs. raw accelerations. Correlations with GRF measures were generally higher for reoriented TAs, although these differences were fairly small (Δr2 = 0.04-0.07) except for lateral peaks (Δr2 = 0.18). While contingent on the position of the IMU on the tibia used in our study, our results first showed systematic differences between reoriented and raw peak accelerations. However, we did not find major improvements in correlations with GRF measures for the reorientation method. This method may still hold promise for further investigation and development, given that consistent increases in correlations were found.

Clinical Application of Gait Retraining in the Injured Runner.

Despite its positive influence on physical and mental wellbeing, running is associated with a high incidence of musculoskeletal injury. Potential modifiable risk factors for running-related injury have been identified, including running biomechanics. Gait retraining is used to address these biomechanical risk factors in injured runners. While recent systematic reviews of biomechanical risk factors for running-related injury and gait retraining have been conducted, there is a lack of information surrounding the translation of gait retraining for injured runners into clinical settings. Gait retraining studies in patients with patellofemoral pain syndrome have shown a decrease in pain and increase in functionality through increasing cadence, decreasing hip adduction, transitioning to a non-rearfoot strike pattern, increasing forward trunk lean, or a combination of some of these techniques. This literature suggests that gait retraining could be applied to the treatment of other injuries in runners, although there is limited evidence to support this specific to other running-related injuries. Components of successful gait retraining to treat injured runners with running-related injuries are presented.