BACKGROUND:Gait analysis provides objective metrics to evaluate mobility in populations such as individuals with knee osteoarthritis. However, in-lab assessments may not reflect real-world gait. Wearable inertial sensors offer a promising alternative, but few studies have directly compared concurrent gait measures from motion capture and wearable sensor-based gait analysis with free-living data collections. METHODS:This study collected gait data preoperatively from 45 older adults with end-stage knee osteoarthritis using a concurrent collection of in-lab markerless motion capture and wearable sensors placed on the proximal shank, followed by up to seven days of continuous free-living wearable sensor recordings. Agreement between measurement types and effect size were calculated for concurrently available gait measurements, such as stride, stance, and swing times, as well as peak mediolateral angular velocity of the proximal shank segment. RESULTS:In-lab sensor and motion capture demonstrated excellent agreement, particularly for stride time (r = 0.96), while free-living sensor data captured slower, more variable gait with lower peak angular velocity (288 deg/s vs 254 deg/s). In-lab gait variables correlated more strongly with function (r = -0.45) and depressive symptoms (r = 0.43), whereas free-living peak angular velocity was weakly associated with pain (r = -0.31). Agreement between in-lab and free-living measures was lower (ICC > 0.67), though peak angular velocity retained moderate-to-good agreement (ICC = 0.75). SIGNIFICANCE:These findings suggest that certain spatiotemporal gait metrics, particularly peak angular velocity, may be viable for use in extended free-living assessments. Across settings, peak angular velocity showed correlations with function comparable to gait speed, supporting its potential as a clinically relevant metric. The study supports the integration of wearable sensors into long-term gait monitoring for clinical populations.

BACKGROUND:While knee osteoarthritis (OA) is incurable, end-stage OA can be managed surgically with partial knee arthroplasty (PKA) or total knee arthroplasty (TKA). Most studies that compare PKA and TKA cohorts rely on patient-reported outcome measures (PROMs) and lack objective joint-level biomechanics. The purpose of this study was to examine preoperative joint-level kinematics during multiple functional tasks including preferred-pace walking, fast-paced walking, and sit-to-stand alongside self-reported outcomes in patients that received partial versus total knee arthroplasty. METHODS:Participants with end-stage knee osteoarthritis were recruited from St. Joseph's Healthcare Hamilton. Self-reported measures included the Oxford knee score, pain ratings, quality of life, and depression. Functional tasks were recorded using markerless motion capture, and joint-level kinematics were analyzed with linear mixed models to test main and interaction effects of surgery type and task condition. FINDINGS:The study included 15 patients that received partial knee arthroplasty and 56 patients that received total knee arthroplasty. No significant differences were observed in self-reported outcomes, nor in single-speed gait or sit-to-stand performance. However, differences emerged when examining walking patterns across speeds. Compared to the total knee arthroplasty group, patients that received partial knee arthroplasty demonstrated greater changes in stride length, peak stance and swing knee flexion, knee excursion, peak stance hip flexion, and overall hip range of motion when going from preferred- to fast-paced walking. INTERPRETATIONS:These findings suggest that multi-speed gait assessments may provide a more sensitive approach to detecting kinematic differences in osteoarthritis patients and may be valuable in other clinical contexts.

Anterior cruciate ligament (ACL) injuries represent one of the most common and disruptive conditions in sport. Effective return-to-play (RTP) monitoring requires multidimensional approaches capturing physical, psychological, and sport-specific components rather than reliance on isolated benchmarks. This study aimed to longitudinally examine the RTP process of a female varsity basketball athlete following ACL reconstruction, using a framework integrating physical performance (capacity), biomechanical sport-specific (capability), and psychological (confidence) components relative to pre-injury benchmarks. Data collection included countermovement jump testing with force plates, on-court inertial measurement unit (IMU) monitoring of limb-loading, and psychological questionnaires, analyzed relative to the athlete’s pre-injury baseline, with post-surgical change interpreted against that individualized reference using minimal detectable change thresholds. Pre-injury monitoring indicated stable movement profiles with minor fluctuations. Following ACL reconstruction, jump height recovered within seven weeks of RTP initiation, but notable inter-limb asymmetries persisted in force plate and IMU measures despite high confidence scores. Symmetry improved over time, yet variability in on-court loading remained after clinical clearance. These findings highlight the value of integrated, multidimensional monitoring to detect residual deficits that may be overlooked by traditional outcome-based assessments. This study demonstrates that integrating athlete-specific biomechanical, psychological, and sport-specific assessments relative to pre-injury baselines can support RTP decision-making to enhance individualized recovery trajectories in female athletes.

Gait analysis is a valuable approach for understanding human movement, but the space and setup requirements of traditional marker-based systems can limit their use outside specialized laboratories. Markerless motion capture may provide a more flexible option, though its agreement in constrained environments compared with traditional spaces is not well established. This study compared a 10-camera markerless system deployed in a hallway with a traditional 8-camera laboratory setup. Twenty-five healthy adults (15 females, 10 males; age 34 [16] y) completed quiet standing, 60 seconds of self-selected walking, and 5-repetition sit-to-stand tasks at both sites on the same day. Three-dimensional pose estimates were processed to calculate alignment during standing, lower-limb joint kinematics during walking, and trunk flexion during sit-to-stand. Agreement within and between sites was assessed using Pearson correlations, root mean square error, Bland Altman limits of agreement, and intraclass correlation coefficients. Standing and walking outcomes showed excellent agreement (intraclass correlation coefficients >= .97; root mean square error < 2.3 degrees; mean differences < 1.1 degrees). Sit-to-stand was more variable (limits of agreement 12 degrees-20 degrees) but remained highly reliable (intraclass correlation coefficients > .96). These findings indicate that a constrained markerless setup can yield kinematic data comparable to a laboratory arrangement, suggesting potential for broader use of markerless approaches in diverse environments.

BACKGROUND:Mobility assessment in knee osteoarthritis spans perception (patient-reported outcomes), capacity (in-clinic performance), and performance (free-living behaviour). By integrating all three domains, this study explored emerging pre-operative mobility phenotypes. METHODS:Fifty-six patients awaiting knee arthroplasty completed patient-reported measures (Oxford Knee Score (OKS), quality of life (EQ-5D)), in-clinic testing with markerless motion capture (60-s preferred-pace and, in a subset, 30-s fast-paced walk and five-repetition sit-to-stand), and seven days of free-living monitoring with shank-mounted inertial sensors (steps, sedentary time, stride time). Hierarchical clustering was applied to standardized features in two models: Model 1 (n = 56; in-clinic preferred-pace walk) and Model 2 (n = 37; included in-clinic fast-pace walk and sit-to-stand time). Principal component scores (PCs) described knee kinematics. An exploratory tertile transition analysis tracked cross-domain consistency. FINDINGS:Both models yielded two phenotypes: a smaller low-functioning group and a larger higher-functioning group. The low-functioning group had higher body mass index and more females, worse self-reported function and quality of life (OKS, EQ-5D), slower in-clinic gait, and lower free-living activity (fewer daily steps, greater sedentary time) with slower gait (longer stride time). The PCs revealed reduced knee flexion magnitude and range of motion, as well as a varus-thrust-like pattern in the low-functioning group. Within the higher-functioning group, only 11-15% of patients remained in the same tertile across domains. INTERPRETATION:The unified mobility assessment identified two distinct pre-operative mobility phenotypes with substantial cross-domain heterogeneity among higher-functioning patients. The implications of this assessment approach may support domain-specific interventions to personalize care and potentially improve recovery after knee arthroplasty.

ABSTRACT:Palanisamy, AC, Ahluwalia, J, Bahrami, B, Randhawa, A, and Kobsar, D. Beyond jump height: The value of phase-specific metrics for monitoring fatigue in basketball. J Strength Cond Res 40(5): 591-596, 2026-Basketball athletes must balance intense training demands with recovery to maintain peak performance, while minimizing fatigue-related injuries. Assessing the acute effects of basketball practice on countermovement jump (CMJ) metrics offers a valuable approach to evaluating the impact of practice volume on neuromuscular performance. Fourteen male athletes from the McMaster University basketball team participated in this study, with data collected pre- and postpractice for a 10-week period. Results revealed significant decreases in performance output metrics, such as jump height and modified reactive strength index, after practice. In addition, phase-specific temporal metrics, including braking phase duration, increased, whereas driver metrics, such as eccentric mean braking force and eccentric rate of force development, decreased, indicating altered neuromuscular strategies due to fatigue. However, these changes had limited associations to practice volume measured by inertial sensors, suggesting substantial individual variability in fatigue responses. These findings demonstrate the sensitivity of CMJ metrics to acute fatigue, particularly phase-specific force-time components, providing deeper insights into neuromuscular adaptations beyond performance output alone. Although CMJ metrics effectively capture fatigue-related changes, the magnitude of these changes does not exhibit a clear relationship with practice load, highlighting the complexity of monitoring fatigue responses in team sports. This study enhances the understanding of player fatigue and underscores the practical application of force plate technology in sports science to inform individualized training and recovery strategies.

OBJECTIVES:Toddler movement patterns challenge current accelerometer-based detection of physical activity (PA) and sedentary time (SED). The objectives of this study were to: (1) develop a novel machine learning (ML) model to detect toddlers' PA and SED; and (2) compare this ML model to existing cut-point methods to analyze toddlers' PA (independent sample cross-validation of existing methods). DESIGN AND METHODS:We recruited 111 toddlers (21 ± 7 months; 51% female) to two 1-hour semi-structured visits wearing a waist-worn ActiGraph wGT3X-BT accelerometer. Video recordings were manually annotated using a modified Children's Activity Rating Scale to determine a ground truth. We extracted 40 time and frequency domain features from raw accelerations and trained 4 gradient boosted tree ML models (distinguishing SED, total PA (TPA), light PA (LPA), moderate-to-vigorous PA (MVPA), and non-volitional movement (NVM)). Models were assessed using accuracy, F1 scores, and confusion matrices. For the validation of 11 existing methods, we calculated accuracy, F1, and mean absolute differences in TPA and MVPA estimation. RESULTS:ML models classifying NVM/SED/TPA and NVM/SED/LPA/MVPA reached 82% and 74% accuracy with mean absolute differences of 3.0 and 3.2 min/h, respectively. Independent sample cross-validation found accuracies from 33 to 74% and mean absolute differences from 7.6 to 18.6 min/h in TPA and 10.7 to 25.8 min/h in MVPA. CONCLUSIONS:We recommend the NVM/SED/TPA or NVM/SED/LPA/MVPA models' given alignment with toddler TPA guidelines and MVPA link to health outcomes, respectively. We additionally present an open-access interface for using these ML models that does not require coding knowledge. This presents a substantial step forward in the measurement of toddlers' physical activity.

IntroductionMotion capture provides objective biomechanical data that can inform clinical decisions surrounding joint arthroplasty. However, results are often communicated through technical figures and terminology that patients find difficult to interpret. Clear, accessible reporting is essential to support shared decision-making and engagement in perioperative treatment and rehabilitation. This study examined older adults’ preferences for communicating gait analysis results to develop a patient-centred gait report and software pipeline that translates complex biomechanical data into accessible formats.MethodsFifteen adults undergoing knee or hip arthroplasty participated in semi-structured interviews. Initial interviews explored preferences for visual, textual, and structural presentation of gait analysis results, informing development of a prototype gait report that was subsequently evaluated through cognitive interviews. A Python‑based system was developed to integrate clinical data, motion capture outputs, and patient‑reported outcomes into individualized reports and deployed within a longitudinal joint arthroplasty cohort (n > 200).Findings Participants preferred results directly tied to daily activities, including walking speed, step length, joint angles, and stair performance. Comparative metrics (pre-/post-operative changes, bilateral symmetry, and age-matched benchmarks) were viewed as highly valuable for contextualizing progress. Tables were preferred for clarity, while graphical elements required clear labeling and simplified presentation. Participant feedback informed refinements to report content, language, and visual design. The system has generated 121 reports, with feedback indicating strong usability.Interpretations Older adults value personalized, visually intuitive gait reports that prioritize interpretability and meaningful comparisons. Integrating patient‑centred design with automated biomechanical reporting may improve the accessibility and clinical utility of gait analysis in joint arthroplasty care.

Objectives To examine (i) the association of adiposity with pain intensity and/or effusion-synovitis in people with knee osteoarthritis (OA), adjusting for body mass index (BMI), and (ii) whether indicators of systemic immune inflammation (i.e., the systemic immune-inflammation index (SII) and the systemic immune response index (SIRI)) moderate the above associations. Methods Individuals with knee OA were sampled from the Western Ontario Registry for Early Osteoarthritis Knee Study. Total body and visceral fat percentages were measured using bioimpedance analysis, and effusion-synovitis was graded using knee ultrasonography. Multiple regression models with interaction terms were used to examine the association between fat percentages and pain intensity (linear)/effusion-synovitis (logistic), and the interaction effect of fat and SII/SIRI on pain intensity/effusion-synovitis. The analyses were adjusted for confounders (age, sex, BMI, radiographic severity of the opposite knee, and anxio-depressive symptoms). Results Data from 225 participants (mean age: 61.1 (10.9), 68% female, mean BMI: 31.7 (7.7)) were analyzed. The associations for adjusted fat and pain intensity models were as follows: total body fat: β): - 0.03 (-0.54 to 0.46) and visceral fat: β (: -0.25 (-1.03 to 0.51)., The odds ratios for adjusted fat and effusion-synovitis models were total body fat: OR): 0.98 (0.92 to 1.05) and visceral fat: OR (): 1.01 (0.91 to 1.11)). Neither the main nor the interaction effects were significant. Conclusion Our preliminary results do not support an association of adiposity and its interaction with generalized inflammation with pain/effusion-synovitis adjusted for BMI. Further studies are needed.

Wearable sensors have become valuable tools for assessing gait in both laboratory and free-living environments. However, detection of walking in free-living environments remains challenging, especially in clinical populations. Machine learning models may offer more robust gait identification, but most are trained on healthy participants, limiting their generalizability to other populations. To extend a previously validated machine learning model, an updated model was trained using an open dataset (PAMAP2), before progressively including training datasets with additional healthy participants and a clinical osteoarthritis population. The performance of the model in identifying walking was also evaluated using a frequency-based gait detection algorithm. The results showed that the model trained with all three datasets performed best in terms of activity classification, ultimately achieving a high accuracy of 96% on held-out test data. The model generally performed on par with the heuristic, frequency-based method for walking bout identification. However, for patients with slower gait speeds (<0.8 m/s), the machine learning model maintained high recall (>0.89), while the heuristic method performed poorly, with recall as low as 0.38. This study demonstrates the enhancement of existing model architectures by training with diverse datasets, highlighting the importance of dataset diversity when developing more robust models for clinical applications.

OBJECTIVE:Pain phenotypes (PP) have been identified across different stages of knee osteoarthritis (KOA) and understanding the stability of PPs prior to the development of symptomatic KOA can help to inform preventative strategies. We aimed to identify PPs and their transitions in people without radiographic KOA and profile participant characteristics. DESIGN:Data from 5 - (T1), 7- (T2) and 12-year (T3) visits from the Multicenter Osteoarthritis Study (MOST) were used. Individuals with Kellgren-Lawrence grade 0 and knee pain ≤30/100 at T1 were sampled. PP variables included pressure pain thresholds, temporal summation (with method changed at T3), depressive symptoms, pain catastrophizing, sleep quality, and widespread pain. Latent Transition Analysis using Bayesian Information Criteria informed class numbers and transitions. Unconstrained, constrained, and modified constrained models (conditional response probabilities fixed for indicator variables except for TS) were compared for fit. Participant characteristics were used to profile class membership. RESULTS:348 individuals (59% females), mean age (SD): 59.3 (6.7) were included. The optimal model fit for data across T1-T3 was a "modified" constrained model with 3 classes (class 1: low pain burden, class 2: high pain sensitization, class 3: high psychological burden). Classes were similar over time except for the increased probability of TS at T3. Most (86%) participants remained in the same class; only 14% transitioned overtime. CONCLUSION:Distinct PPs were identified in those at risk of KOA that remained stable over time, suggesting these trait-like features may require consideration for comprehensive management of the symptom experience.

Objective:This scoping review investigated the definitions, assessment methods, and current applications of varus thrust (VT) in knee osteoarthritis (OA). Methods:Five databases (MEDLINE, EMBASE, CINAHL, SPORTDiscus, and Web of Science Core Collection) were searched in this scoping review for studies assessing VT during walking in adults with knee OA using the terms "varus" and "lateral" in proximity to thrust. Data were extracted and categorized by study characteristics (OA sample, publication year, design, and aim) and VT assessment protocol (method and definition). Results:A total of 63 studies were included, examining 12,569 individuals with knee OA using visual (n ​= ​24), optical motion capture (n ​= ​27), or inertial/wearable sensor (n ​= ​19) methods. Designs included prospective, experimental, cross-sectional, and case series. VT was most often assessed to examine disease severity, progression, surgical outcomes, and symptom associations. Visual VT was commonly defined as dynamic worsening or abrupt onset of varus alignment during weight acceptance. Optical motion capture commonly measured VT as frontal plane knee excursion from foot contact to mid-stance, while inertial methods typically used peak lateral tibial acceleration or angular velocity. Conclusion:Despite growing research interest in VT, inconsistent definitions and measurement protocols limit comparability across studies and hinder broader adoption. Greater standardization and validation are needed to clarify its potential clinical utility in knee OA.

Markerless motion capture addresses key barriers limiting the clinical uptake of biomechanical assessments by enabling efficient data collection and standardized modeling, making it well-suited for multicentre research. This study assessed whether gait deviations associated with knee osteoarthritis (OA) could be consistently detected using markerless motion capture across three clinical centres in Canada. Gait data from 486 participants (351 with knee OA; 135 controls) were analyzed, with body segment kinematics estimated from video using Theia3D. Principal component analysis and linear models were used to evaluate joint kinematics and temporal-distance parameters across groups and sites. After pooling data across centres, individuals with knee OA exhibited characteristic gait deviations, including slower walking speed, reduced hip, knee, and ankle range of motion, and increased knee adduction, compared to controls. These deviations were observed consistently across all three centres. Inter-site differences in joint kinematics were minor (RMS < 3°), remained within reported inter-site error thresholds from marker-based systems, and did not obscure group-level effects. These findings demonstrate that clinically meaningful gait deviations can be reliably detected using markerless motion capture in varied clinical environments without extensive standardization. This work supports its use in multicentre studies and highlights its potential to enable large-scale biomechanical research, an essential step toward broader clinical integration of movement analysis.

Introduction: Anterior cruciate ligament (ACL) injuries represent one of the most common and disruptive conditions in sport, with fewer than two-thirds of athletes return to competitive play. Effective return-to-play (RTP) monitoring requires multidimensional approaches that capture physical, psychological, and sport-specific components rather than reliance of isolated benchmarks. Purpose: This study aimed to longitudinally examine the RTP process of a female varsity basketball athlete following ACL reconstruction, using a framework that integrates physical performance (capacity), biomechanical sport-specific (capability), and psychological (confidence) components relative to pre-injury benchmarks. Methods: Data collection included countermovement jump testing with dual force plates, on-court inertial measurement unit (IMU) monitoring of limb-loading, and psychological questionnaires, analyzed relative to pre-injury and post-surgery baselines using minimal detectable change thresholds. Results: Pre-injury monitoring indicated stable movement profiles with only minor fluctuations. Following ACL reconstruction, jump height recovered within seven weeks of RTP initiation, but notable inter-limb asymmetries persisted in force plate and IMU measures despite high confidence scores. Discussion: Symmetry improved with continued training, yet variability in on-court loading remained even after clinical clearance. These findings highlight the value of integrated, multidimensional monitoring to detect residual deficits that may be overlooked by traditional outcome-based assessments. Conclusion: This study demonstrates that integrating athlete-specific biomechanical, psychological, and sport-specific assessments relative to pre-injury baselines can support RTP decision-making to enhance individualized recovery trajectories in female athletes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of McMaster University gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.

Purpose (the aim of the study): There are many contributors to the pain experience in knee OA and understanding pain phenotypes (PPs) can help tailor treatments. Studies of PPs have been conducted in those with existing radiographically defined knee osteoarthritis (KOA). However, little is known regarding PPs among people with minimal symptoms and no KOA, nor the stability or transition of PPs over time. Such knowledge may inform whether some cluster of features in some people are static or change over time.

Background. The growth in participation in collegiate athletics has been accompanied by increased sport-related injuries. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and integrated biopsychosocial approach, as opposed to contemporary practices that silo these facets of health. Methods. Data collected over two competitive basketball seasons were used in a principal component analysis (PCA) model with the following objectives: (i) investigate whether biomechanical PCs (i.e., on-court and countermovement jump (CMJ) metrics) were correlated with psychological state across a season and (ii) explore whether subject-specific significant fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data, were collected on the female collegiate basketball team for two seasons. Results. While some relationships (n = 2) were identified between biomechanical PCs and psychological state metrics, the magnitude of these associations was weak (r = |0.18-0.19|, p<0.05), and no other overarching associations were identified at the group level. However, post-hoc case study analysis showed subject-specific relationships that highlight the potential utility of red-flagging meaningful fluctuations from normative biomechanical and psychological patterns. Conclusion. Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of and detect statistically and clinically relevant fluctuations in student-athlete performance, health, and well-being and the need for more tailored and athlete-centered monitoring practices.