Lower Extremity Kinetics and Kinematics in Runners with Patellofemoral Pain: A Retrospective Case–Control Study Using Musculoskeletal Simulation

Patellofemoral pain (PFP) is a common atraumatic knee pathology in runners, with a complex multifactorial aetiology influenced by sex differences. This retrospective case–control study therefore aimed to evaluate lower limb kinetics and kinematics in symptomatic and control male and female runners using musculoskeletal simulation. Lower extremity biomechanics were assessed in 40 runners with PFP (15 females and 25 males) and 40 controls (15 females and 25 males), whilst running at a self-selected velocity. Lower extremity biomechanics were explored using a musculoskeletal simulation approach. Four intergroup comparisons—(1) overall PFP vs. control; (2) male PFP vs. male control; (3) female PFP vs. female control; and (4) male PFP vs. female PFP—were undertaken using linear mixed models. The overall (stress per mile: PFP = 1047.49 and control = 812.93) and female (peak stress: PFP = 13.07 KPa/BW and control = 10.82 KPa/BW) comparisons showed increased patellofemoral joint stress indices in PFP runners. A significantly lower strike index was also shown in PFP runners in the overall (PFP = 17.75% and control = 33.57%) and female analyses (PFP = 15.49% and control = 40.20%), revealing a midfoot strike in control, and a rearfoot pattern in PFP runners. Peak rearfoot eversion and contralateral pelvic drop range of motion (ROM) were shown to be greater in PFP runners in the overall (eversion: PFP = −8.15° and control = −15.09°/pelvic drop ROM: PFP = 3.64° and control = 1.88°), male (eversion: PFP = −8.05° and control = −14.69°/pelvic drop ROM: PFP = 3.16° and control = 1.77°) and female (eversion: PFP = 8.28° and control = −15.75°/pelvic drop ROM: PFP = 3.64° and control = 1.88°) PFP runners, whilst female PFP runners (11.30°) exhibited a significantly larger peak hip adduction compared to PFP males (7.62°). The findings from this investigation highlight biomechanical differences between control and PFP runners, as well as demonstrating distinctions in PFP presentation for many parameters between sexes, highlighting potential risk factors for PFP that may be addressed through focused intervention modalities, and also the need, where appropriate, for sex-specific targeted treatment approaches.

Accounting for Biomechanical Measures from Musculoskeletal Simulation of Upright Posture Does Not Enhance the Prediction of Curve Progression in Adolescent Idiopathic Scoliosis

A major clinical challenge in adolescent idiopathic scoliosis (AIS) is the difficulty of predicting curve progression at initial presentation. The early detection of progressive curves can offer the opportunity to better target effective non-operative treatments, reducing the need for surgery and the risks of related complications. Predictive models for the detection of scoliosis progression in subjects before growth spurt have been developed. These models accounted for geometrical parameters of the global spine and local descriptors of the scoliotic curve, but neglected contributions from biomechanical measurements such as trunk muscle activation and intervertebral loading, which could provide advantageous information. The present study exploits a musculoskeletal model of the thoracolumbar spine, developed in AnyBody software and adapted and validated for the subject-specific characterization of mild scoliosis. A dataset of 100 AIS subjects with mild scoliosis and in pre-pubertal age at first examination, and recognized as stable (60) or progressive (40) after at least 6-months follow-up period was exploited. Anthropometrical data and geometrical parameters of the spine at first examination, as well as biomechanical parameters from musculoskeletal simulation replicating relaxed upright posture were accounted for as predictors of the scoliosis progression. Predicted height and weight were used for model scaling because not available in the original dataset. Robust procedure for obtaining such parameters from radiographic images was developed by exploiting a comparable dataset with real values. Six predictive modelling approaches based on different algorithms for the binary classification of stable and progressive cases were compared. The best fitting approaches were exploited to evaluate the effect of accounting for the biomechanical parameters on the prediction of scoliosis progression. The performance of two sets of predictors was compared: accounting for anthropometrical and geometrical parameters only; considering in addition the biomechanical ones. Median accuracy of the best fitting algorithms ranged from 0.76 to 0.78. No differences were found in the classification performance by including or neglecting the biomechanical parameters. Median sensitivity was 0.75, and that of specificity ranged from 0.75 to 0.83. In conclusion, accounting for biomechanical measures did not enhance the prediction of curve progression, thus not supporting a potential clinical application at this stage.

Regression Equation between Required Force and Lumbar Load of Caregiver in Supporting Standing-up Motion via Computational Musculoskeletal Simulation

Caregivers experience low back pain because of patient handling such as supporting standing-up. The lumbar load of a caregiver depends on the required force for patient handling motions. If the relationship between the required force and the lumbar load is quantitatively clarified, it may be useful for preventing low back pain in caregivers. In this study, we investigated the quantitative relationships between the required force and lumbar loads such as vertebral stress and muscle activity in supporting standing-up by computational musculoskeletal simulation. First, a musculoskeletal model of a caregiver was prepared, and then the model performed simulated supporting standing-up motions. The vertical load used as the required force was placed on the upper limb of the model. The compressive/shear stress of the vertebral (L4–L5) and muscle activities of spinae erector muscle group were recorded as the lumbar load. The results showed that there are highly significant correlations between the required force (r > 0.9, p < 0.01). In addition, regression equations for predicting each lumbar load by the required force with highly determination coefficients (R2 > 0.9) were obtained from these relationships. Furthermore, we found that when the required force was more than 120 N, the compression stresses of the vertebral exceeded injury threshold (3400 N) by the regression equation. These regression equations contribute to quantitatively consider lumbar loads of caregiver during patient handling based on injury thresholds and the required force.