Investigating the relationship between quantitative-based ultrasound and MRI estimations of rotator cuff fatty infiltration.

INTRODUCTION: Fatty infiltration (FI) of the rotator cuff has important clinical implications. Quantitatively estimating FI using ultrasound (US) has considerable benefits for assessing FI in a non-invasive, accessible manner. This research investigated whether FI of the supraspinatus (SS) and infraspinatus (IS), estimated using US was related to intramuscular fat fractions measured from magnetic resonance images (MRI). METHODS: Data from 12 healthy young adult participants were used for analysis. US images of the SS and IS were captured using multiple transducer placement techniques from which echogenicity of the muscle region was quantified. Shoulder MRI were captured from which SS and IS were manually segmented and intramuscular fat fractions calculated. Six upper limb strength exertions were performed, resisted by a hand dynamometer. RESULTS: IS and SS echogenicity explained a significant amount of variance in MRI fat fractions for certain body positions and transducer techniques. Echogenicity agreement was higher for IS than SS. Significant relationships were identified between strength exertions and both echogenicity and MRI muscle volume, but not MRI fat fraction. CONCLUSIONS: This research provides preliminary evidence showing that quantitative-based US methods can be used to estimate MRI calculated fat fractions for the rotator cuff.

Imaging of joint response to exercise with MRI and PET.

Imaging of the joint in response to loading stress may provide additional measures of joint structure and function beyond conventional, static imaging studies. Exercise such as running, stair climbing, and squatting allows evaluation of the joint response to larger loading forces than during weight bearing. Quantitative MRI (qMRI) may assess properties of cartilage and meniscus hydration and organization in vivo that have been investigated to assess the functional response of these tissues to physiological stress. [18F]sodium fluoride ([18F]NaF) interrogates areas of newly mineralizing bone and provides an opportunity to study bone physiology, including perfusion and mineralization rate, as a measure of joint loading stress. In this review article, methods utilizing quantitative MRI, PET, and hybrid PET-MRI systems for assessment of the joint response to loading from exercise in vivo are examined. Both methodology and results of various studies performed are outlined and discussed. Lastly, the technical considerations, challenges, and future opportunities for these approaches are addressed.

Investigating acute changes in osteoarthritic cartilage by integrating biomechanics and statistical shape models of bone: data from the osteoarthritis initiative.

OBJECTIVE: This proof-of-principle study integrates joint reaction forces (JRFs) and bone shape to assess acute cartilage changes from walking and cycling. METHODS: Sixteen women with symptomatic knee osteoarthritis were recruited. Biomechanical assessment estimated JRFs during walking and cycling. Subsamples had magnetic resonance imaging (MRI) performed before and after a 25-min walking (n = 7) and/or cycling (n = 9) activity. MRI scans were obtained to assess cartilage shape and composition (T2 relaxation time). Bone shape was quantified using a statistical shape model built from 13 local participants and 100 MRI scans from the Osteoarthritis Initiative. Statistical parametric mapping quantified cartilage change and correlations between cartilage change with JRFs and statistical shape model features. RESULTS: Cartilage thickness (interior lateral, Δ - 0.10 mm) and T2 (medial, Δ - 4 ms) decreased on the tibial plateau. On the femur, T2 change depended on the activity. Greater tibiofemoral JRF was associated with more cartilage deformation on the lateral femoral trochlea after walking (r - 0.56). Knees more consistent with osteoarthritis showed smaller decreases in tibial cartilage thickness. DISCUSSION: Walking and cycling caused distinct patterns of cartilage deformation, which depended on knee JRFs and bone morphology. For the first time, these results show that cartilage deformation is dependent on bone shapes and JRFs in vivo.

Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.

OBJECTIVES: Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation. MATERIALS AND METHODS: Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using six fold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively. RESULTS: On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11 s per knee. DISCUSSION: The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.

A new technique to evaluate the impact of running on knee cartilage deformation by region.

OBJECTIVES: When measuring changes in knee cartilage thickness in vivo after loading, mean values may not reflect local changes. The objectives of this investigation were: (1) use statistical parametric mapping (SPM) to determine regional deformation patterns of tibiofemoral cartilage in response to running; (2) quantify regional differences in cartilage thickness between males and females; and (3) explore the influence of sex on deformation. MATERIALS AND METHODS: Asymptomatic males (n = 15) and females (n = 15) had MRI imaging of their right knee before and after 15 min of treadmill running. Medial and lateral tibial, and medial and lateral weight-bearing femoral cartilage were segmented. SPM was completed on cartilage thickness maps to test the main effects of Running and Sex, and their interaction. F-statistic maps were thresholded; clusters above this threshold indicated significant differences. RESULTS: Deformation was observed in all four compartments; the lateral tibia had the largest area of deformation (p < 0.0001). Thickness differences between sexes were observed in all four compartments, showing females have thinner cartilage (p ≤ 0.009). The lateral tibia had small clusters indicating an interaction of sex on deformation (p ≤ 0.012). DISCUSSION: SPM identified detailed spatial information on tibiofemoral cartilage thickness differences observed after running, and between sexes and their interaction.

GT3X+ accelerometer placement affects the reliability of step-counts measured during running and pedal-revolution counts measured during bicycling.

UNLABELLED: Accelerometers provide a measure of step-count. Reliability and validity of step-count and pedal-revolution count measurements by the GT3X+ accelerometer, placed at different anatomical locations, is absent in the literature. The purpose of this study was to investigate the reliability and validity of step and pedal-revolution counts produced by the GT3X+ placed at different anatomical locations during running and bicycling. Twenty-two healthy adults (14 men and 8 women) completed running and bicycling activity bouts (5 minutes each) while wearing 6 accelerometers: 2 each at the waist, thigh and shank. Accelerometer and video data were collected during activity. Excellent reliability and validity were found for measurements taken from accelerometers mounted at the waist and shank during running (Reliability: intraclass correlation (ICC) ≥ 0.99; standard error of measurement (SEM) ≤1.0 steps; VALIDITY: Pearson ≥ 0.99) and at the thigh and shank during bicycling (Reliability: ICC ≥ 0.99; SEM ≤1.0 revolutions; VALIDITY: Pearson ≥ 0.99). Excellent reliability was found between measurements taken at the waist and shank during running (ICC ≥ 0.98; SEM ≤1.6 steps) and between measurements taken at the thigh and shank during bicycling (ICC ≥ 0.99; SEM ≤1.0 revolutions). These data suggest that the GT3X+ can be used for measuring step-count during running and pedal-revolution count during bicycling. Only shank placement is recommended for both activities.

Interactions of cumulative load with biomarkers of cartilage turnover predict knee cartilage change over 2 years: data from the osteoarthritis initiative.

The purpose was to investigate relationships of cumulative load and cartilage turnover biomarkers with 2-year changes in cartilage in knee osteoarthritis. From participants with Kellgren-Lawrence (KL) grades of 1 to 3, cartilage thickness and transverse relaxation time (T2) were computed from 24-month (baseline) and 48-month magnetic resonance images. Cumulative load was the interaction term of the Physical Activity Scale for the Elderly (PASE) and body mass index (BMI). Serum cartilage oligomeric matrix protein (COMP) and the nitrated form of type II collagen (Coll2-1 NO2) were collected at baseline. Multiple regressions (adjusted for baseline age, KL grade, cartilage measures, pain, comorbidity) evaluated the relationships of cumulative load and biomarkers with 2-year changes. In 406 participants (63.7 (8.7) years), interactions of biomarkers with cumulative load weakly predicted 2-year cartilage changes: (i) COMP × cumulative load explained medial tibia thickness change (R2 increased 0.062 to 0.087, p < 0.001); (ii) Coll2-1 NO2 × cumulative load explained central medial femoral T2 change (R2 increased 0.177 to 0.210, p < 0.001); and (iii) Coll2-1 NO2 × cumulative load explained lateral tibia T2 change (R2 increased 0.166 to 0.188, p < 0.001). Moderate COMP or Coll2-1 NO2 at baseline appeared protective. High COMP or Coll2-1 NO2, particularly with high BMI and low PASE, associated with worsening cartilage. Moderate serum concentrations of cartilage turnover biomarkers, at high and low physical activity, associated with maintained cartilage outcomes over 2 years. In conclusion, high concentrations of cartilage turnover biomarkers, particularly with high BMI and low physical activity, associated with knee cartilage thinning and increasing T2 over 2 years. Key Points • Higher quality cartilage may be better able to tolerate a larger cumulative load than poor quality cartilage. • Among participants enrolled in the Osteoarthritis Initiative Biomarkers Consortium Project, a representation of cumulative load exposure and its interaction with cartilage turnover biomarkers were weakly related with 2-year change in knee cartilage. • These findings suggest that cartilage turnover is a factor that modifies the relationship between loading exposure and cartilage loss in knee OA.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

Investigating the relationship between physical activity and self-reported outcomes in adults with rotator cuff related shoulder pain.

PURPOSE: Rotator cuff related shoulder pain is common among older adults. While exercise is often recommended for this condition, the relationship between physical activity levels and self-reported outcomes is unclear. This study investigated whether self-reported outcomes (pain, function, depressive symptoms, self-efficacy, general mental and physical health) relate to physical activity, and whether those who participated in shoulder-specific exercise had better self-reported outcomes than those who performed whole-body exercise. METHODS: Forty-six participants with rotator cuff related shoulder pain participated from which 35 had complete datasets. Questionnaires were used to assess physical activity, pain, physical function, general health, and self-efficacy. Physical activity levels were also measured using an accelerometer. RESULTS: Neither pain nor other self-reported outcomes were related to subjective or objective physical activity levels. Participants that completed shoulder-specific exercise had significantly higher exercise self-efficacy than those who completed nonspecific exercise (P = .01; d = 0.91). CONCLUSION: A significant relationship between pain or self-reported outcomes and physical activity was not identified. Those who self-reported regularly exercising their injured shoulder had higher exercise self-efficacy than those who did not. These findings have clinical implications, suggesting that strategies to boost exercise self-efficacy may be important for older adults with rotator cuff related shoulder pain.

Muscle-driven simulations and experimental data of cycling.

Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40-216 W) and cadences (75-99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling_sim for others to reproduce these results and build upon this research.

Muscle Architecture and Subcutaneous Fat Measurements of Rectus Femoris and Vastus Lateralis at Optimal Length Aided by a Novel Ultrasound Transducer Attachment.

Purpose: This cross-sectional study determines the sensitivity of muscle architecture and fat measurements of the rectus femoris (RF) and vastus lateralis (VL) muscles from ultrasound images acquired with varying transducer tilt, using a novel transducer attachment, in healthy adults. Secondary objectives were to estimate intrarater and interrater reliability of image measurement and acquisition, respectively. Methods: Thirty healthy adults participated (15 women and 15 men; 25 [SD 2.5] y). Ultrasound image acquisition was conducted by two raters at different transducer tilts relative to the skin: estimated perpendicular, and five measured angles (80°, 85°, 90°, 95°, 100°) using the transducer attachment. Muscle thickness (MT), subcutaneous fat thickness (FT), pennation angle (PA), and fascicle length (FL) were measured. Sensitivity and reliability were assessed using intra-class correlation coefficients (ICCs) and standard error of measurements (SEMs). Results: MT and FT for RF and VL were not sensitive to transducer tilt. However, PA and FL were sensitive to transducer tilt. MT and FT for both muscles showed high ICCs and low SEMs for intrarater and interrater reliability. For PA of both muscles, standardizing transducer tilt improved interrater ICCs and lowered SEMs. Conclusion: MT and FT measurements of RF and VL acquired at 60° knee flexion are robust to varying transducer tilt angles. PA measurements benefit from standardizing transducer tilt.

Objectif : étude transversale pour déterminer la sensibilité de l'architecture musculaire et des mesures lipidiques du muscle droit antérieur de la cuisse (MDAC) et du muscle vaste externe (MVE) à partir des images échographiques acquises chez des adultes en santé par diverses inclinaisons du transducteur, au moyen d'un nouveau dispositif. Les objectifs secondaires consistaient à évaluer la fiabilité intraévaluateurs et interévaluateurs des mesures et de l'acquisition des images, respectivement. Méthodologie: au total, 30 adultes en santé ont participé (15 femmes et 15 hommes de 25 [ÉT 2,5 ans]). Deux évaluateurs ont acquis des images échographiques à des inclinaisons différentes du transducteur par rapport à la peau : mesure perpendiculaire estimative et mesure à cinq angles (80°, 85°, 90°, 95°, 100°) au moyen du dispositif du transducteur. Ils ont mesuré l'épaisseur des muscles (ÉM), l'épaisseur de la graisse sous-cutanée (ÉG), l'angle de pennation (AP) et la longueur des fascicules (LF). Ils ont aussi évalué la sensibilité et la fiabilité au moyen de coefficients de corrélation intraclasse (CCI) et de l'écart-type des mesures (ÉTM). Résultats: l'ÉM et l'ÉG du MDAC et du MVE n'étaient pas sensibles à l'inclinaison du transducteur, mais l'AP et la LF l'étaient. La fiabilité intraévaluateur et interévaluateur de l'ÉM et de l'ÉG des deux muscles présentait un CCI élevé et un ÉTM faible. Pour ce qui est de l'AP des deux muscles, la standardisation de l'inclinaison du transducteur améliorait la CCI et réduisait l'ÉTM interévaluateurs. Conclusion: les mesures de l'ÉM et de l'ÉG du MDAC et du MVE acquises à une flexion du genou de 60° sont probantes à des angles d'inclinaison variables du transducteur. Les mesures de l'AP tirent profit d'une inclinaison du transducteur standardisée.

Investigating the reliability and validity of subacromial space measurements using ultrasound and MRI.

BACKGROUND: While ultrasound (US) measures of the subacromial space (SAS) have demonstrated excellent reliability, measurements are typically captured by experts with extensive ultrasound experience. Further, the agreement between US measured SAS width and other imaging modalities has not been explored. This research evaluated the agreement between SAS measures captured by novice and expert raters and between US and magnetic resonance imaging (MRI). This study also evaluated the effect of US transducer tilt on measured SAS. METHODS: Nine men and nine women participated in this study. US images were captured by a novice and expert with the participant in both seated and supine positions. An inclinometer was fixed to the US probe to measure transducer tilt. SAS width was measured in real time from freeze framed images. MRI images were captured, and the humerus and acromion manually segmented. The SAS width was measured using a custom algorithm. RESULTS: Intraclass correlation coefficients (ICCs) between novice and expert raters were 0.74 and 0.63 for seated and supine positions, respectively. Intra-rater agreement was high for both novice (ICC = 0.83-0.84) and expert (ICC ≥ 0.94) raters. Agreement between US and MRI was poor (ICC = 0.21-0.49) but linearly related. CONCLUSIONS: Moderate agreement between novice and expert raters was demonstrated, while the agreement between US and MRI was poor. High intra-rater reliability within each rater suggests that US measures of the SAS may be completed by a novice with introductory training.

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation.

Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

Equations to Prescribe Bicycle Saddle Height based on Desired Joint Kinematics and Bicycle Geometry.

Overuse knee injuries are common in bicycling and are often attributed to poor bicycle-fit. Bicycle-fit for knee health focuses on setting saddle height to elicit a minimum knee flexion angle of 25-40°. Equations to predict saddle height include a single input, resulting in a likely suboptimal bicycle-fit. The purpose of this work was to develop an equation to predict saddle height from anthropometrics, bicycle geometry, and user-defined joint kinematics.Methods: Forty healthy adults (17 women, 23 men; mean (SD): 28.6 (7.2) years; 24.2 (2.6) kg/m2) participated. Kinematic analyses were conducted for 18 three-minute bicycling bouts including all combinations of 3 horizontal and 3 vertical saddle positions, and 2 crank arm lengths. For both minimum and maximum knee flexion, predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and final models were fit using linear regression. Secondary analyses determined if saddle height equations were sex dependent.Results: The equation to predict saddle position from minimum knee flexion angle (R2=0.97; root mean squared error (RMSE) = 1.15 cm) was: Saddle height (cm) = 7.41 + 0.82(inseam cm) - 0.1(minimum knee flexion °) + 0.003(inseam cm)(seat tube angle °). The maximum knee flexion equation (R2=0.97; RMSE=1.15 cm) was: Saddle height (cm) = 41.63 + 0.78(inseam cm) - 0.25(maximum knee flexion °) + 0.002(inseam cm)(seat tube angle °). The saddle height equations were not dependent on sex.Conclusions: These equations provide a novel, practical strategy for bicycle-fit that accounts for rider anthropometrics, bicycle geometry and user-defined kinematics.Highlights This work developed simple equations to prescribed bicycle saddle height that elicits desired knee kinematics.Separate equations are presented for prescribing minimum or maximum knee flexion angle.Equations can be generalized to riders of both sexes, and a breadth of anthropometrics and ages.

An Artificial Intelligence Algorithm to Predict Nodal Metastasis in Lung Cancer.

BACKGROUND: Endobronchial ultrasound (EBUS) has features that allow a high accuracy for predicting lymph node (LN) malignancy. However their clinical application remains limited because of high operator dependency. We hypothesized that an artificial intelligence algorithm (NeuralSeg; NeuralSeg Ltd, Hamilton, Ontario, Canada) is capable of accurately identifying and predicting LN malignancy based on EBUS images. METHODS: In the derivation phase EBUS images were segmented twice by an endosonographer and used as controls in 5-fold cross-validation training of NeuralSeg. In the validation phase the algorithm was tested on new images it had not seen before. Logistic regression and receiver operator characteristic curves were used to determine NeuralSeg's capability of discrimination between benign and malignant LNs, using pathologic specimens as the gold standard. RESULTS: Two hundred ninety-eight LNs from 140 patients were used for derivation and 108 LNs from 47 patients for validation. In the derivation cohort NeuralSeg was able to predict malignant LNs with an accuracy of 73.8% (95% confidence interval [CI], 68.4%-78.7%). In the validation cohort NeuralSeg had an accuracy of 72.9% (95% CI, 63.5%-81.0%), specificity of 90.8% (95% CI, 81.9%-96.2%), and negative predictive value of 75.9% (95% CI, 71.5%-79.9%). NeuralSeg showed higher diagnostic discrimination during validation compared with derivation (c-statistic = 0.75 [95% CI, 0.65-0.85] vs 0.63 [95% CI, 0.54-0.72], respectively). CONCLUSIONS: NeuralSeg is able to accurately rule out nodal metastasis and can possibly be used as an adjunct to EBUS when nodal biopsy is not possible or inconclusive. Future work to evaluate the algorithm in a clinical trial is required.

Hip and ankle kinematics are the most important predictors of knee joint loading during bicycling.

OBJECTIVES: To assess the effect of ankle, knee, and hip kinematics on patellofemoral and tibiofemoral joint reaction forces (JRF) during bicycling. Secondarily, to assess if sex, horizontal saddle position, or crank arm length were related to JRFs, after accounting for kinematics. DESIGN: Experimental cross-sectional study. METHODS: Forty healthy adults (mean (SD); 28.6 (7.2) years, 24.2 (2.6)kg/m2, 17 women) bicycled under 18 bicycling positions. One position used commercial guidelines and 17 randomly deviated from commercial. Resultant patellofemoral as well as compressive and shear tibiofemoral JRFs were calculated. Linear mixed-effects models with a random intercept of leg-nested-in-participant were used. RESULTS: Patellofemoral resultant forces were most sensitive to all joint kinematics (i.e., sensitivity was defined as the slope of single predictor models); all JRFs were least sensitive to minimum knee flexion. Tibiofemoral compression was predicted by minimum hip flexion and maximum ankle dorsiflexion (R2=0.90). Tibiofemoral shear (R2=0.86) and the resultant patellofemoral JRF (R2=0.90) were predicted by minimum hip flexion, maximum ankle dorsiflexion, minimum knee flexion, and the interaction between minimum hip flexion and minimum knee flexion. Adding sex as a factor improved fit of all models. This sex-effect was driven by differences in cycling intensity, reflected by the tangential crank arm force. Horizontal saddle position and crank arm length were not related to JRFs. CONCLUSIONS: Optimizing joint kinematics should be the primary goal of bicycle-fit. JRFs were least sensitive to the current gold standard for assessing bicycle-fit, minimum knee flexion. Bicycle-fit is of particular importance for those working at high intensities.

Daily cumulative load and body mass index alter knee cartilage response to running in women.

BACKGROUND: It is unknown whether a greater accumulation of knee load over a typical day is related to how cartilage responds to an acute bout of loading. This information may clarify the role of habitual activity on cartilage function. RESEARCH QUESTION: Is there a relationship between change in tibial and femoral cartilage thickness, volume, and T2 relaxation time following running with daily cumulative knee load in women? Secondarily, is there a relationship between cartilage change following running and the statistical interaction of body mass index (BMI) and daily steps? METHODS: Participants (n = 15) completed gait analyses and wore an accelerometer over a week. Daily cumulative knee load was the statistical interaction between tibial compressive joint reaction force (JRF) impulse with the average number of daily steps measured using accelerometry. Magnetic resonance imaging scans were acquired before and immediately after 15-min of treadmill running. Changes in tibial and femoral cartilage thickness, volume, and T2 relaxation time were calculated. Multiple linear regressions tested the associations of cartilage change outcomes with: baseline (thickness, volume, T2), JRF impulse, steps, and the interaction JRF impulse*steps. Secondarily, BMI was substituted for JRF impulse. RESULTS AND SIGNIFICANCE: Tibial volume change was explained by baseline volume, JRF impulse, steps, and JRF impulse*steps (R2 = 0.50, p = 0.013). Additionally, tibial volume change was explained by baseline volume, BMI, steps, and BMI*steps (R2 = 0.43, p = 0.002). Those who were more physically active with lower JRF impulse (or lower BMI) showed less change in tibial cartilage after a running exposure. This may suggest cartilage conditioning.

Association of Machine Learning-Based Predictions of Medial Knee Contact Force With Cartilage Loss Over 2.5 Years in Knee Osteoarthritis.

OBJECTIVE: The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA). METHODS: In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force-measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined. RESULTS: The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R2 = 0.35, ß = -0.119, P < 0.001). CONCLUSION: Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.

Association of Pain and Steps Per Day in Persons With Mild-to-Moderate, Symptomatic Knee Osteoarthritis: A Mixed-Effects Models Analysis of Multiple Measurements Over Three Years.

OBJECTIVE: Pain is a consistently reported barrier to physical activity by persons with knee osteoarthritis (OA). Nonetheless, few studies of knee OA have investigated the association of pain with daily walking levels. The current study assessed the relationship of 2 distinct measures of knee pain with objectively measured physical activity in adults with knee OA. METHODS: This was a longitudinal, observational investigation of 59 individuals (48 women; mean ± SD age 61.1 ± 6.4 years, mean ± SD body mass index 28.1 ± 5.6 kg/m2 ) with clinical knee OA. Data were collected every 3 months for up to 3 years. Physical activity was characterized as the average steps per day taken over at least 3 days, mea-sured by accelerometry. Pain was measured using 2 patient-administered questionnaires: the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS-pain) and the P4 pain scale (P4-pain). Mixed-effects models determined the association between pain and physical activity levels (over covariates) among adults with knee OA (α = 0.05). RESULTS: All covariates (age [ß = -3.65, P < 0.001], body mass index [ß = -3.06, P < 0.001], season [spring/fall ß = -6.91, P = 0.002; winter ß = -14.92, P < 0.001]) were predictors of physical activity. Neither the inverted KOOS-pain (ß = 0.04, P = 0.717) nor P4-pain (ß = -0.37, P = 0.264) was associated with physical activity. CONCLUSION: Knee pain is not associated with daily walking levels in persons with mild-to-moderate, symptomatic knee OA. While pain management remains an important target of interventions, strategies to increase steps per day in this population should focus on overcoming potentially more crucial barriers to activity participation.

How to Optimize Measurement Protocols: An Example of Assessing Measurement Reliability Using Generalizability Theory.

Purpose: This article identifies how to assess multiple sources of measurement error and identify optimal measurement strategies for obtaining clinical outcomes. Method: Obtaining, interpreting, and using information gained from measurements is instrumental in physiotherapy. To be useful, measurements must have a sufficiently small measurement error. Traditional expressions of reliability include relative reliability in the form of an intra-class correlation coefficient and absolute reliability in the form of the standard error of measurement. Traditional metrics are limited to assessing one source of error; however, real-world measurements consist of many sources of error. The measurement framework generalizability theory (GT) allows researchers to partition measurement errors into multiple sources. GT further allows them to calculate the relative and absolute reliability of any measurement strategy, thereby allowing them to identify the optimal strategy. We provide a brief comparison of classical test theory and GT, followed by an overview of the terminology and methodology used in GT, and then an example showing how GT can be used to minimize error associated with measuring knee extension power. Conclusion: The methodology described provides tools for researchers and clinicians that enable detailed interpretation and understanding of the error associated with their measurements.

Objectif : décrire comment évaluer de multiples sources d'erreur de mesure et les stratégies de mesures optimales pour obtenir des résultats cliniques. Méthodologie : il est important d'obtenir, d'interpréter et d'utiliser l'information tirée des mesures en physiothérapie. Pour que ces mesures soient utiles, leur écart-type doit être suffisamment petit. Les expressions habituelles de fiabilité incluent la fiabilité relative sous forme de coefficient de corrélation intraclasse et la fiabilité absolue sous forme d'écart-type des mesures. Les mesures habituelles sont limitées à l'évaluation d'une source d'erreur. Cependant, les mesures réelles s'associent à plusieurs sources d'erreur. La théorie de généralisabilité (TG) du cadre de mesure permet aux chercheurs de diviser les erreurs de mesure selon de multiples sources. Elle leur permet également de calculer la fiabilité relative et absolue de toute stratégie de mesure, pour parvenir à une stratégie optimale. Le présent article fournit une brève comparaison entre la théorie du test classique et la TG, puis un aperçu de la terminologie et de la méthodologie utilisées en TG. Enfin, les auteurs présentent un exemple démontrant comment utiliser la TG pour limiter l'erreur associée à la mesure de la puissance d'extension du genou. Conclusion : la méthodologie décrite fournit des outils pour les chercheurs et les cliniciens afin de parvenir à une interprétation et une compréhension détaillées des erreurs de mesure.