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Forefoot strike is increasingly being adopted by runners because it can better attenuate impact than rearfoot strike. However, forefoot strike may overload the plantar fascia and alter the plantar fascia elasticity. This study aimed to use ultrasound elastography to investigate and compare shear wave elasticity of the plantar fascia between rearfoot strikers and forefoot strikers. A total of 35 participants (21 rearfoot strikers and 14 forefoot strikers), who were free of lower limb injuries and diseases, were recruited from a local running club. Individual foot strike patterns were identified through the measured plantar pressure during treadmill running. The B-Mode ultrasound images and shear wave elastographic images of the plantar fascia were collected from each runner. Two independent investigators reviewed the images and examined the plantar fascia qualitatively and quantitatively. The results demonstrated an overall good agreement between the investigators in the image review outcomes (ICC:0.96–0.98, κ: 0.89). There were no significant differences in the fascial thickness (p = 0.50) and hypoechogenicity on the gray-scale images (p = 0.54) between the two groups. Shear wave elastography showed that forefoot strikers exhibited reduced plantar fascia elasticity compared to rearfoot strikers (p = 0.01, Cohen’s d = 0.91). A less elastic fascial tissue was more easily strained under loading. Tissue overstrain is frequently related to the incidence of plantar fasciitis. While further study is needed for firm conclusions, runners using forefoot strike were encouraged to enhance their foot strength for better protection of the plantar fascia.

Complementary to experimental studies, computational biomechanics has become useful tool for the understanding of human foot biomechanics and pathomechanics. Its findings have been widely used for the evaluation of the effectiveness of surgical and conservative interventions. These models, however, were developed with a wide range of variations in terms of simplifications and assumptions on the representation of geometrical structures and material properties, as well as boundary and loading conditions. These variations may create differences in prediction accuracy, and restrict practical and clinical applications. This paper reviews the state-of-the-art technologies and challenges in computational model development, focusing on foot problem-specific models for the assessment of the effectiveness and accessibility of clinical treatments. The computational models have provided valuable biomechanical information for clinical applications but further investigations come with many challenges in terms of detailed and patient-specific models, accurate representations of tissue properties, and boundary and loading conditions. Multi-scale computational models are expected to be an efficient platform to fully address the biomechanical and biological concerns.

Forefoot strike becomes popular among runners because it facilitates better impact attenuation. However, forefoot strike may overload the plantar fascia and impose risk of plantar fasciitis. This study aimed to examine and compare the foot arch deformation and plantar fascia tension between different foot strike techniques in running using a computational modelling approach. A three-dimensional finite element foot model was reconstructed from the MRI of a healthy runner. The foot model included twenty bones, bulk soft tissue, ligaments, tendons, and plantar fascia. The time-series data of segmental kinematics, foot muscle force, and ankle joint reaction force were derived from a musculoskeletal model of the same participant based on the motion capture analysis and input as the boundary conditions for the finite element analysis. Rearfoot strike and forefoot strike running were simulated using a dynamic explicit solver. The results showed that, compared to rearfoot strike, forefoot strike reduced the foot arch height by 9.12% and increased the medial longitudinal arch angle by 2.06%. Forefoot strike also increased the plantar connective tissues stress by 18.28–200.11% and increased the plantar fascia tensile force by 18.71–109.10%. Although it is currently difficult to estimate the threshold value of stress or force that results in injury, forefoot strike runners appeared to be more vulnerable to plantar fasciitis.

Introduction Frailty in chronic kidney disease (CKD) patients is associated with increased risk of adverse health outcomes. Understanding the contributing factors to frailty in this population is crucial for developing targeted interventions and improving patient care. The objective of this study is to identify and quantify potential risk factors associated with frailty in chronic kidney disease patients. Methods We conducted a cross‐sectional study using data from the China Health and Retirement Longitudinal Study (CHARLS) cohorts of 2011 and 2015. A Bayesian mixed‐effects logistic regression model was utilized to analyze the relationship between selected features and frailty in chronic kidney disease. Results Of 1,924 participants, about one‐third (n = 634) were classified as frail. Depression emerged as the strongest predictor of frailty (adjusted OR 289.49, 95%CrI 47.24–2284.82). Other significant factors included stroke (adjusted OR 18.04, 95%CrI 1.94–183.99), chronic lung disease (adjusted OR 7.96, 95%CrI 1.84–39.44), and rheumatism (adjusted OR 3.84, 95%CrI 1.12–15.14). Poor vision (adjusted OR 5.84, 95% CrI 1.68–25.25) and poor sleep quality (adjusted OR 5.12, 95% CrI 1.53–21.13), though the latter showed some instability after data imputation, were also associated with higher frailty risk. Elevated cystatin C levels showed a strong positive association (adjusted OR 52.46, 95%CrI 9.51–356.12), while higher creatinine levels were associated with lower frailty risk (adjusted OR 0.24, 95%CrI 0.07–0.81). Conclusion This study identified several potential contributing factors to frailty in CKD patients, with depression emerging as the strongest predictor. The counterintuitive relationship between creatinine levels and frailty underscores the complex interplay between muscle mass/quality and kidney function in frailty development, warranting further investigation.

Foot and ankle impact injury is common in geriatric trauma and often leads to fracture of rearfoot, including calcaneus and talus. The objective of this study was to assess the influence of foot impact on the risk of calcaneus and talus fracture via finite element analysis. A three-dimensional finite element model of foot and ankle was constructed based on magnetic resonance images of a female aged 28. The foot sustained a 7-kg passive impact through a foot plate. The simulated impact velocities were from 2.0 to 7.0 m/s with 1.0 m/s interval. At 5.0 m/s impact velocity, the maximum von Mises stress of the trabecular calcaneus and talus were 3.21MPa and 2.41MPa respectively, while that of the Tresca stress were 3.46MPa and 2.55MPa. About 94% and 84% of the trabecular calcaneus and talus exceeded the shear yielding stress, while 21.7% and 18.3% yielded the compressive stress. The peak stresses were distributed around the talocalcaneal articulation and the calcaneal tuberosity inferiorly, which corresponded to the common fracture sites. The prediction in this study showed that axial compressive impact at 5.0 m/s could produce considerable yielding of trabecular bone in both calcaneus and talus, dominantly by shear and compounded with compression that predispose the rearfoot in the risk of fracture. This study suggested the injury pattern and fracture mode of high energy trauma that provides insights in injury prevention and fracture management.

Ankle arthrodesis is one popular surgical treatment for ankle arthritis, chronic instability, and degenerative deformity. However, complications such as foot pain, joint arthritis, and bone fracture may cause patients to suffer other problems. Understanding the internal biomechanics of the foot is critical for assessing the effectiveness of ankle arthrodesis and provides a baseline for the surgical plan. This study aimed to understand the biomechanical effects of ankle arthrodesis on the entire foot and ankle using finite element analyses. A three-dimensional finite element model of the foot and ankle, involving 28 bones, 103 ligaments, the plantar fascia, major muscle groups, and encapsulated soft tissue, was developed and validated. The biomechanical performances of a normal foot and a foot with ankle arthrodesis were compared at three gait instants, first-peak, mid-stance, and second-peak. Changes in plantar pressure distribution, joint contact pressure and forces, von Mises stress on bone and foot deformation were predicted. Compared with those in the normal foot, the peak plantar pressure was increased and the center of pressure moved anteriorly in the foot with ankle arthrodesis. The talonavicular joint and joints of the first to third rays in the hind- and mid-foot bore the majority of the loading and sustained substantially increased loading after ankle arthrodesis. An average contact pressure of 2.14 MPa was predicted at the talonavicular joint after surgery and the maximum variation was shown to be 80% in joints of the first ray. The contact force and pressure of the subtalar joint decreased after surgery, indicating that arthritis at this joint was not necessarily a consequence of ankle arthrodesis but rather a progression of pre-existing degenerative changes. Von Mises stress in the second and third metatarsal bones at the second-peak instant increased to 52 MPa and 34 MPa, respectively, after surgery. These variations can provide indications for outcome assessment of ankle arthrodesis surgery.

Ankle arthrodesis and total ankle arthroplasty are the two primary surgeries for treatment of end-stage degenerative ankle arthritis. The biomechanical effects of them on the inner foot are insufficient to identify which is superior. This study compared biomechanical parameters among a foot treated by ankle arthrodesis, a foot treated by total ankle arthroplasty, and an intact foot using computational analysis. Validated finite element models of the three feet were developed and used to simulate the stance phase of gait. The results showed total ankle arthroplasty provides a more stable plantar pressure distribution than ankle arthrodesis. The highest contact pressure, 3.17 MPa, occurred in the medial cuneonavicular joint in the total ankle arthroplasty foot. Neither of the surgeries resulted in contact pressure increase in the subtalar joint. The peak stress in the metatarsal bones was increased in both surgical models, especially the second and third metatarsals. This study enables us to get visual to the biomechanics inside of an intact foot, and feet treated by total ankle arthroplasty and ankle arthrodesis during walking.

The aim of our scoping review was to identify and summarize current evidence on the biomechanics of fencing to inform athlete development and injury prevention. Scoping review. Peer-reviewed research was identified from electronic databases using a structured keyword search. Details regarding experimental design, study group characteristics and measured outcomes were extracted from retrieved studies, summarized and information regrouped under themes for analysis. The methodological quality of the evidence was evaluated. Thirty-seven peer-reviewed studies were retrieved, the majority being observational studies conducted with experienced and elite athletes. The methodological quality of the evidence was \"fair\" due to the limited scope of research. Male fencers were the prevalent group studied, with the lunge and use of a foil weapon being the principal movement evaluated. Motion capture and pedabarography were the most frequently used data collection techniques. Elite fencers exhibited sequential coordination of upper and lower limb movements with coherent patterns of muscle activation, compared to novice fencers. These elite features of neuromuscular coordination resulted in higher magnitudes of forward linear velocity of the body center of mass and weapon. Training should focus on explosive power. Sex- and equipment-specific effects could not be evaluated based on available research.

Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse- and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants’ data (n = 6) for model validation, and the remaining six participants’ data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.