Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator. METHODS: Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis. RESULTS: Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06). CONCLUSION: Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making. LEVEL OF EVIDENCE: III.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

INTRODUCTION: The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA. MATERIALS AND METHODS: We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA. Two comparison groups, readmissions (n = 79; 3.6%) and non-readmissions (n = 2091; 96.4%) were established. Univariate and multivariate logistic regression analyses were then performed with readmission as the outcome variable to determine whether preoperative PROMIS scores could predict 90-day readmission. RESULTS: The study cohort consisted of 2170 patients overall. Non-white patients (OR = 3.53, 95% CI [1.16, 10.71], p = 0.026) and patients with cardiovascular or cerebrovascular disease (CVD) (OR = 1.66, 95% CI [1.01, 2.71], p = 0.042) were found to have significantly higher odds of 90-day readmission after TKA. Preoperative PROMIS-PF10a (p = 0.25), PROMIS-GPH (p = 0.38), and PROMIS-GMH (p = 0.07) scores were not significantly associated with 90-day readmission. CONCLUSION: This study demonstrates that preoperative PROMIS scores may not be used to predict 90-day readmission following primary TKA. Non-white patients and patients with CVD are 3.53 and 1.66 times more likely to be readmitted, highlighting existing racial disparities and medical comorbidities contributing to readmission in patients undergoing TKA.

Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices.

INTRODUCTION: Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings. The aim of the study is to compare the association of three publicly available national indices of social deprivation with prolonged LOS in revision TJA patients. MATERIALS AND METHODS: 1,047 consecutive patients who underwent a revision TJA were included in this retrospective study. Patient demographics, comorbidities, and behavioral characteristics were extracted. Area deprivation index (ADI), social deprivation index (SDI), and social vulnerability index (SVI) were recorded for each patient, following which univariate and multivariate logistic regression analyses were performed to determine the relationship between deprivation measures and prolonged LOS (greater than five days postoperatively). RESULTS: 193 patients had a prolonged LOS following surgery. Categorical ADI was significantly associated with prolonged LOS following surgery (OR = 2.14; 95% CI = 1.30-3.54; p = 0.003). No association with LOS was found using SDI and SVI. When accounting for other covariates, only ASA scores (ORrange=3.43-3.45; p < 0.001) and age (ORrange=1.00-1.03; prange=0.025-0.049) were independently associated with prolonged LOS. CONCLUSION: The varying relationship observed between the length of stay and socioeconomic markers in this study indicates that the selection of a deprivation index could significantly impact the outcomes when investigating the association between socioeconomic deprivation and clinical outcomes. These results suggest that ADI is a potential metric of social determinants of health that is applicable both clinically and in future policies related to hospital stays including bundled payment plan following revision TJA.

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.

INTRODUCTION: Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository. MATERIALS AND METHODS: We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility. RESULTS: The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age. CONCLUSIONS: Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty.

BACKGROUND: Nonhome discharge disposition following primary total knee arthroplasty (TKA) is associated with a higher rate of complications and constitutes a socioeconomic burden on the health care system. While existing algorithms predicting nonhome discharge disposition varied in degrees of mathematical complexity and prediction power, their capacity to generalize predictions beyond the development dataset remains limited. Therefore, this study aimed to establish the machine learning model generalizability by performing internal and external validations using nation-scale and institutional cohorts, respectively. METHODS: Four machine learning models were trained using the national cohort. Recursive feature elimination and hyper-parameter tuning were applied. Internal validation was achieved through five-fold cross-validation during model training. The trained models' performance was externally validated using the institutional cohort and assessed by discrimination, calibration, and clinical utility. RESULTS: The national (424,354 patients) and institutional (10,196 patients) cohorts had non-home discharge rates of 19.4 and 36.4%, respectively. The areas under the receiver operating curve of the model predictions were 0.83 to 0.84 during internal validation and increased to 0.88 to 0.89 during external validation. Artificial neural network and histogram-based gradient boosting elicited the best performance with a mean area under the receiver operating curve of 0.89, calibration slope of 1.39, and Brier score of 0.14, which indicated that the two models were robust in distinguishing non-home discharge and well-calibrated with accurate predictions of the probabilities. The low inter-dataset similarity indicated reliable external validation. Length of stay, age, body mass index, and sex were the strongest predictors of discharge destination after primary TKA. CONCLUSION: The machine learning models demonstrated excellent predictive performance during both internal and external validations, supporting their generalizability across different patient cohorts and potential applicability in the clinical workflow.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty.

BACKGROUND: The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data. METHODS: Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis. RESULTS: The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts. CONCLUSIONS: This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty.

BACKGROUND: Existing machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA. METHODS: A total of 246,265 THAs were analyzed from a large database. Prolonged LOS was defined as exceeding the 75th percentile of all LOSs in the cohort. Candidate predictors of prolonged LOS were selected by recursive feature elimination and used to construct four machine learning models-artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor. The model performance was assessed by discrimination, calibration, and utility. RESULTS: All models exhibited excellent performance in discrimination (area under the receiver operating characteristic curve [AUC] = 0.72 to 0.74) and calibration (slope: 0.83 to 1.18, intercept: -0.01 to 0.11, Brier score: 0.185 to 0.192) during both training and testing sessions. The artificial neural network was the best performer with an AUC of 0.73, calibration slope of 0.99, calibration intercept of -0.01, and Brier score of 0.185. All models showed great utility by producing higher net benefits than the default treatment strategies in the decision curve analyses. Age, laboratory tests, and surgical variables were the strongest predictors of prolonged LOS. CONCLUSION: The excellent prediction performance of machine learning models demonstrated their capacity to identify patients prone to prolonged LOS. Many factors contributing to prolonged LOS can be optimized to minimize hospital stay for high-risk patients.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

BACKGROUND: A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty. METHODS: A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis. RESULTS: Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis. CONCLUSION: This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

INTRODUCTION: The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort. METHODS: The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility. RESULTS: ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS. CONCLUSION: ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.

Plantar Pressure Variability and Asymmetry in Elderly Performing 60-Minute Treadmill Brisk-Walking: Paving the Way towards Fatigue-Induced Instability Assessment Using Wearable In-Shoe Pressure Sensors.

Evaluation of potential fatigue for the elderly could minimize their risk of injury and thus encourage them to do more physical exercises. Fatigue-related gait instability was often assessed by the changes of joint kinematics, whilst planar pressure variability and asymmetry parameters may complement and provide better estimation. We hypothesized that fatigue condition (induced by the treadmill brisk-walking task) would lead to instability and could be reflected by the variability and asymmetry of plantar pressure. Fifteen elderly adults participated in the 60-min brisk walking trial on a treadmill without a pause, which could ensure that the fatigue-inducing effect is continuous and participants will not recover halfway. The plantar pressure data were extracted at baseline, the 30th minute, and the 60th minute. The median of contact time, peak pressure, and pressure-time integrals in each plantar region was calculated, in addition to their asymmetry and variability. After 60 min of brisk walking, there were significant increases in peak pressure at the medial and lateral arch regions, and central metatarsal regions, in addition to their impulses (p < 0.05). In addition, the variability of plantar pressure at the medial arch was significantly increased (p < 0.05), but their asymmetry was decreased. On the other hand, the contact time was significantly increased at all plantar regions (p < 0.05). The weakened muscle control and shock absorption upon fatigue could be the reason for the increased peak pressure, impulse, and variability, while the improved symmetry and prolonged plantar contact time could be a compensatory mechanism to restore stability. The outcome of this study can facilitate the development of gait instability or fatigue assessment using wearable in-shoe pressure sensors.

Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking.

Real-time detection of fatigue in the elderly during physical exercises can help identify the stability and thus falling risks which are commonly achieved by the investigation of kinematic parameters. In this study, we aimed to identify the change in gait variability parameters from inertial measurement units (IMU) during a course of 60 min brisk walking which could lay the foundation for the development of fatigue-detecting wearable sensors. Eighteen elderly people were invited to participate in the brisk walking trials for 60 min with a single IMU attached to the posterior heel region of the dominant side. Nine sets of signals, including the accelerations, angular velocities, and rotation angles of the heel in three anatomical axes, were measured and extracted at the three walking times (baseline, 30th min, and 60th min) of the trial for analysis. Sixteen of eighteen participants reported fatigue after walking, and there were significant differences in the median acceleration (p = 0.001), variability of angular velocity (p = 0.025), and range of angle rotation (p = 0.0011), in the medial-lateral direction. In addition, there were also significant differences in the heel pronation angle (p = 0.005) and variability and energy consumption of the angles in the anterior-posterior axis (p = 0.028, p = 0.028), medial-lateral axis (p = 0.014, p = 0.014), and vertical axis (p = 0.002, p < 0.001). Our study demonstrated that a single IMU on the posterior heel of the dominant side can address the variability of kinematics parameters for elderly performing prolonged brisk walking and could serve as an indicator for walking instability, and thus fatigue.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Dynamic finite element analyses to compare the influences of customised total talar replacement and total ankle arthroplasty on foot biomechanics during gait.

Objective, Total talar replacement (TTR) using a customised talus prosthesis is an emerging surgical alternative to conventional total ankle arthroplasty (TAA) for treating ankle problems. Upon satisfying clinical reports in the literature, this study explored the advantages of TTR in restoring foot biomechanics during walking compared with TAA through computational simulations.Methods, A dynamic finite element foot model was built from the MRIs of a healthy participant and modified into two implanted counterparts (TTR and TAA) by incorporating the corresponding prosthetic components into the ankle joint. Twenty bony parts, thirty-nine ligament/tendon units, nine muscle contractors, and bulk soft tissue were included in the intact foot model. The TTR prosthesis was reconstructed from the mirror image data of the participant's contralateral talus and the TAA prosthesis was modelled by reproducing the Scandinavian ankle replacement procedure in the model assembly. The model was meshed with explicit deformable elements and validated against existing experimental studies that have assessed specific walking scenarios. Simulations were performed using the boundary conditions (time-variant matrix of muscle forces, segment orientation, and ground reaction forces) derived from motion capture analyses and musculoskeletal modelling of the participant's walking gait. Outcome variables, including foot kinematics, joint loading, and plantar pressure were reported and compared among the three model conditions. Results: Linear regression indicated a better agreement between the TTR model and intact foot model in plots of joint motions and foot segment movements during walking (R2 â€‹= â€‹0.721-0.993) than between the TAA and intact foot (R2 â€‹= â€‹0.623-0.990). TAA reduced talocrural excursion by 21.36%-31.92% and increased (MTP) dorsiflexion by 3.03%. Compared with the intact foot, TTR and TAA increased the midtarsal joint contact force by 17.92% and 10.73% respectively. The proximal-to-distal force transmission within the midfoot was shifted to the lateral column in TTR (94.52% or 210.54 â€‹N higher) while concentrated on the medial column in TAA (41.58% or 27.55 â€‹N higher). The TTR produced a plantar pressure map similar to that of the intact foot. TAA caused the plantar pressure centre to drift medially and increased the peak forefoot pressure by 7.36% in the late stance. Conclusion: The TTR better reproduced the foot joint motions, segment movements, and plantar pressure map of an intact foot during walking. TAA reduced ankle mobility while increasing movement of the adjacent joints and forefoot plantar pressure. Both implant methods changed force transmission within the midfoot during gait progression.The translational potential of this article Our work is one of the few to report foot segment movements and the internal loading status of implanted ankles during a dynamic locomotion task. These outcomes partially support the conjecture that TTR is a prospective surgical alternative for pathological ankles from a biomechanical perspective. This study paves the way for further clinical investigations and systematic statistics to confirm the effects of TTR on functional joint recovery.

Effects of prolonged brisk walking induced lower limb muscle fatigue on the changes of gait parameters in older adults.

BACKGROUND: Lower extremity muscle fatigue affects gait stability and increases the probability of injuries in the elderly. RESEARCH QUESTION: How does prolonged walking-induced fatigue affect lower limb muscle activity, plantar pressure distribution, and tripping risk? METHODS: Eighteen elderly adults walked fast on a treadmill for 60 minutes at a fixed speed. The plantar pressure was measured with an in-shoe monitoring system, eight lower limb muscles were monitored using surface electromyography, and foot movements were tracked by a motion capture analysis system. The above data and participants' subjective fatigue level feedback were collected every 5 minutes. Statistical analysis used the Friedman one-way repeated measures analysis of variance by ranks test followed by Wilcoxon signed-ranks test with Benjamini-Hochberg stepwise correction. RESULTS: The subjective reported fatigue on the Borg scale increased gradually from 1 to 6 (p = 0.001) during the 60 minutes, while the EMG amplitude of vastus medialis significant decreased (p = 0.013). The results of plantar pressure demonstrated that the distribution of load and impulse shifted medially in both the heel and arch regions while shifted laterally in both the toes and metatarsal regions. The significantly increased contact area supports this shift at the medial arch (p = 0.036, increased by 6.94%, the 60th minute vs. the baseline). The symmetry of medial-lateral plantar force increased at the toes, metatarsal, and arch regions. The significantly increased parameters also include the swing time and contact time. The minimum foot clearance was reduced, increasing tripping probability, not significantly, though. SIGNIFICANCE: This study facilitates a better understanding of changes in lower limb muscle activity and gait parameters during prolonged fast walking. Besides, this study has good guiding significance for developing smart devices based on plantar force, inertial measurement units, and EMG sensors to monitor changes in muscle activation in real-time and prevent tripping.

Reversing the Mismatch With Forefoot Striking to Reduce Running Injuries.

Recent studies have suggested that 95% of modern runners land with a rearfoot strike (RFS) pattern. However, we hypothesize that running with an RFS pattern is indicative of an evolutionary mismatch that can lead to musculoskeletal injury. This perspective is predicated on the notion that our ancestors evolved to run barefoot and primarily with a forefoot strike (FFS) pattern. We contend that structures of the foot and ankle are optimized for forefoot striking which likely led to this pattern in our barefoot state. We propose that the evolutionary mismatch today has been driven by modern footwear that has altered our footstrike pattern. In this paper, we review the differences in foot and ankle function during both a RFS and FFS running pattern. This is followed by a discussion of the interaction of footstrike and footwear on running mechanics. We present evidence supporting the benefits of forefoot striking with respect to common running injuries such as anterior compartment syndrome and patellofemoral pain syndrome. We review the importance of a gradual shift to FFS running to reduce transition-related injuries. In sum, we will make an evidence-based argument for the use of minimal footwear with a FFS pattern to optimize foot strength and function, minimize ground reaction force impacts and reduce injury risk.

Joint contact force and movement deceleration among badminton forward lunges: a musculoskeletal modelling study.

Joint contact force is the actual force applied on the articular surface that could predict performance and injuries, but rarely reported for badminton sport. The study sought to calculate lower limb joint contact force and decelerative kinematics for badminton forward lunges. Fifteen badminton players performed backhand and forehand forward lunges in random order. The kinematic and kinetic data were input to scale a musculoskeletal model and solve inverse dynamics in the simulations. Outcome variables were compared between lunge conditions using repeated measures MANOVA. Forehand lunge produced higher compressional ankle contact force (p = 0.040, partial η2 = 0.14), faster touchdown hip abduction (p = 0.031, partial η2 = 0.16), and larger horizontal deceleration of the mass centre (p = 0.016, partial η2 = 0.19) and torso (p = 0.031, partial η2 = 0.16) compared to backhand lunge. Despite the statistical significance, we found that the increments of joint loading in forehand lunge were small (<5%) with limited effect size and could be attributed to the larger movement deceleration during braking. These force changes could possess performance merits. However, its linkage to injury risk is unclear and warrants further investigation.

A half marathon shifts the mediolateral force distribution at the tibiofemoral joint.

Runners' gait patterns vary during a half marathon and influence the knee joint mechanics. Joint contact force is a better estimate of the net joint loadings than external joint moments and closely correlates to injury risks. This study explored the changes of lower limb joint kinematics, muscle activities, and knee joint loading in runners across the running mileages of a half marathon. Fourteen runners completed a half marathon on an instrumented treadmill where motion capture was conducted every 2 km (from 2 to 20 km). A musculoskeletal model incorporating medial/lateral tibiofemoral compartments was used to process the movement data and report outcome variables at the selected distance checkpoints. Statistics showed no changes in joint angles, muscle co-contraction index, ground reaction force variables, and medial tibiofemoral contact force (p > 0.05). Knee adduction moment at 18 km was significantly lower than those at 2 km (p = 0.002, γ = 0.813) and 6 km (p = 0.001, γ = 0.663). Compared to that at 2 km, lateral tibiofemoral contact force was reduced at 18 km (p = 0.030, Hedges' g = 0.690), 16 km (p < 0.001, Hedges' g = 0.782), 14 km (p = 0.045, Hedges' g = 0.859), and 10 km (p < 0.001, Hedges' g = 0.771) respectively. Mechanical realignment of the lower limb may be the cause of the altered knee loadings and possibly led to reduced running economy in response to a prolonged run. The injury potential of the redistributed tibiofemoral forces warranted further studies.

The Influence of Mattress Stiffness on Spinal Curvature and Intervertebral Disc Stress-An Experimental and Computational Study.

Sleeping support systems can influence spinal curvature, and the misalignment of the spinal curvature can lead to musculoskeletal problems. Previous sleep studies on craniocervical support focused on pillow variants, but the mattress supporting the pillow has rarely been considered. This study used a cervical pillow and three mattresses of different stiffnesses, namely soft, medium, and hard, with an indentation load deflection of 20, 42, and 120 lbs, respectively. A novel electronic curvature measurement device was adopted to measure the spinal curvature, whereby the intervertebral disc loading was computed using the finite element method. Compared with the medium mattress, the head distance increased by 30.5 ± 15.9 mm, the cervical lordosis distance increased by 26.7 ± 14.9 mm, and intervertebral disc peak loading increased by 49% in the soft mattress environment. Considering that the pillow support may increase when using a soft mattress, a softer or thinner pillow is recommended. The head distance and cervical lordosis distance in the hard mattress environment were close to the medium mattress, but the lumbar lordosis distance reduced by 10.6 ± 6.8 mm. However, no significant increase in intervertebral disc loading was observed, but contact pressure increased significantly, which could cause discomfort and health problems.

Different Design Feature Combinations of Flatfoot Orthosis on Plantar Fascia Strain and Plantar Pressure: A Muscle-Driven Finite Element Analysis With Taguchi Method.

Customized foot orthosis is commonly used to modify foot posture and relieve foot pain for adult acquired flexible flatfoot. However, systematic investigation of the influence of foot orthotic design parameter combination on the internal foot mechanics remains scarce. This study aimed to investigate the biomechanical effects of different combinations of foot orthoses design features through a muscle-driven flatfoot finite element model. A flatfoot-orthosis finite element model was constructed by considering the three-dimensional geometry of plantar fascia. The plantar fascia model accounted for the interaction with the bulk soft tissue. The Taguchi approach was adopted to analyze the significance of four design factors combination (arch support height, medial posting inclination, heel cup height, and material stiffness). Predicted plantar pressure and plantar fascia strains in different design combinations at the midstance instant were reported. The results indicated that the foot orthosis with higher arch support (45.7%) and medial inclination angle (25.5%) effectively reduced peak plantar pressure. For the proximal plantar fascia strain, arch support (41.8%) and material stiffness (37%) were strong influencing factors. Specifically, higher arch support and softer material decreased the peak plantar fascia strain. The plantar pressure and plantar fascia loading were sensitive to the arch support feature. The proposed statistics-based finite element flatfoot model could assist the insole optimization and evaluation for individuals with flatfoot.