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Mündermann A, Nüesch C, Ewald H, Jonkers I. Osteoarthritis year in review 2024: biomechanics. Osteoarthritis Cartilage 2024:S1063-4584(24)01405-5. [PMID: 39369839 DOI: 10.1016/j.joca.2024.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Revised: 09/24/2024] [Accepted: 09/27/2024] [Indexed: 10/08/2024]
Abstract
OBJECTIVE We aimed to systematically review and summarize the literature of the past year on OA and biomechanics, to highlight gaps and challenges, and to present some promising approaches and developments. METHODS A systematic literature search was conducted using Pubmed and the Web of Science Core Collection. We included original articles and systematic reviews on OA and biomechanics in human subjects published between April 2023 and April 2024. RESULTS Of the 155 studies that met the inclusion criteria, 9 were systematic reviews and 146 were original (mostly cross-sectional) studies that included a total of 6488 patients and 1921 controls with a mean age of 57.5 and 44.7 years, respectively. Promising advances have been made in medical imaging of affected soft tissue structures, the relationship between soft tissue properties and biomechanical changes in OA, new technologies to facilitate easier assessment of ambulatory biomechanics, and personalized physics-based models that also include complex chemical and mechanobiological mechanisms, all of which are relevant to gaining mechanistic insights into the pathophysiology of OA. CONCLUSIONS There is still an unmet need for larger longitudinal data sets that combine clinical, radiological, and biomechanical outcomes to characterize the biomechanical fingerprint that underlies the trajectory of functional decline and biomechanical phenotypes of OA. In addition, criteria and guidelines for control groups, as well as methods and standards for model verification allowing for comparisons between studies are needed.
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Affiliation(s)
- Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland.
| | - Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland; Department of Spine Surgery, University Hospital Basel, Basel, Switzerland.
| | - Hannah Ewald
- University Medical Library, University of Basel, Spiegelgasse 5, 4051 Basel, Switzerland.
| | - Ilse Jonkers
- Institute for Physics-Based Modeling for In Silico Health, KU Leuven, Leuven, Belgium.
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Koller W, Wallnöfer E, Holder J, Kranzl A, Mindler G, Baca A, Kainz H. ESMAC Best Paper Award 2023: Increased knee flexion in participants with cerebral palsy results in altered stresses at the distal femoral growth plate compared to a typically developing cohort. Gait Posture 2024; 113:158-166. [PMID: 38905850 DOI: 10.1016/j.gaitpost.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 05/21/2024] [Accepted: 06/17/2024] [Indexed: 06/23/2024]
Abstract
INTRODUCTION Femoral deformities are highly prevalent in children with cerebral palsy (CP) and can have a severe impact on patients' gait abilities. While the mechanical stress regime within the distal femoral growth plate remains underexplored, understanding it is crucial given bone's adaptive response to mechanical stimuli. We quantified stresses at the distal femoral growth plate to deepen our understanding of the relationship between healthy and pathological gait patterns, internal loading, and femoral growth patterns. METHODS This study included three-dimensional motion capture data and magnetic resonance images of 13 typically developing children and twelve participants with cerebral palsy. Employing a multi-scale mechanobiological approach, integrating musculoskeletal simulations and subject-specific finite element analysis, we investigated the orientation of the distal femoral growth plate and the stresses within it. Limbs of participants with CP were grouped depending on their knee flexion kinematics during stance phase as this potentially changes the stresses induced by knee and patellofemoral joint contact forces. RESULTS Despite similar growth plate orientation across groups, significant differences were observed in the shape and distribution of growth values. Higher growth rates were noted in the anterior compartment in CP limbs with high knee flexion while CP limbs with normal knee flexion showed high similarity to the group of healthy participants. DISCUSSION Results indicate that the knee flexion angle during the stance phase is of high relevance for typical bone growth at the distal femur. The evaluated growth rates reveal plausible results, as long-term promoted growth in the anterior compartment leads to anterior bending of the femur which was confirmed for the group with high knee flexion through analyses of the femoral geometry. The framework for these multi-scale simulations has been made accessible on GitHub, empowering peers to conduct similar mechanobiological studies. Advancing our understanding of femoral bone development could ultimately support clinical decision-making.
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Affiliation(s)
- Willi Koller
- Department of Sport and Human Movement Science, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria; Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria.
| | - Elias Wallnöfer
- Department of Sport and Human Movement Science, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria; Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Jana Holder
- Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria
| | - Andreas Kranzl
- Laboratory for Gait and Human Movements, Orthopaedic Hospital Speising, Vienna, Austria; Vienna Bone and Growth Center, Vienna, Austria
| | - Gabriel Mindler
- Department of Pediatric Orthopaedics, Orthopaedic Hospital Speising, Vienna, Austria; Vienna Bone and Growth Center, Vienna, Austria
| | - Arnold Baca
- Department of Sport and Human Movement Science, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Hans Kainz
- Department of Sport and Human Movement Science, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria; Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
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Lavikainen J, Stenroth L, Vartiainen P, Alkjær T, Karjalainen PA, Henriksen M, Korhonen RK, Liukkonen M, Mononen ME. Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors. Ann Biomed Eng 2024:10.1007/s10439-024-03594-x. [PMID: 39097542 DOI: 10.1007/s10439-024-03594-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 07/26/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks. METHODS We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics). RESULTS Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data. DISCUSSION The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.
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Affiliation(s)
- Jere Lavikainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland.
| | - Lauri Stenroth
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Paavo Vartiainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Tine Alkjær
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Pasi A Karjalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Marius Henriksen
- The Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Mimmi Liukkonen
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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Kainz H, Koller W, Wallnöfer E, Bader TR, Mindler GT, Kranzl A. A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining. Sci Rep 2024; 14:3567. [PMID: 38347085 PMCID: PMC10861532 DOI: 10.1038/s41598-024-53857-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°-29°, tibial torsion 19°-38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°-52°, tibial torsion 3°-50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.
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Affiliation(s)
- Hans Kainz
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria.
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria.
| | - Willi Koller
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria
| | - Elias Wallnöfer
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria
| | - Till R Bader
- Department of Radiology, Orthopaedic Hospital Speising, Vienna, Austria
| | - Gabriel T Mindler
- Department of Paediatric Orthopaedics and Foot Surgery, Orthopaedic Hospital Speising, Vienna, Austria
- Vienna Bone and Growth Center, Vienna, Austria
| | - Andreas Kranzl
- Vienna Bone and Growth Center, Vienna, Austria
- Laboratory for Gait and Movement Analysis, Orthopaedic Hospital Speising, Vienna, Austria
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Diamond LE, Grant T, Uhlrich SD. Osteoarthritis year in review 2023: Biomechanics. Osteoarthritis Cartilage 2024; 32:138-147. [PMID: 38043858 DOI: 10.1016/j.joca.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modeling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modeling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.
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Affiliation(s)
- Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Tamara Grant
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
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Esrafilian A, Halonen KS, Dzialo CM, Mannisi M, Mononen ME, Tanska P, Woodburn J, Korhonen RK, Andersen MS. Effects of gait modifications on tissue-level knee mechanics in individuals with medial tibiofemoral osteoarthritis: A proof-of-concept study towards personalized interventions. J Orthop Res 2024; 42:326-338. [PMID: 37644668 PMCID: PMC10952410 DOI: 10.1002/jor.25686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/19/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023]
Abstract
Gait modification is a common nonsurgical approach to alter the mediolateral distribution of knee contact forces, intending to decelerate or postpone the progression of mechanically induced knee osteoarthritis (KOA). Nevertheless, the success rate of these approaches is controversial, with no studies conducted to assess alterations in tissue-level knee mechanics governing cartilage degradation response in KOA patients undertaking gait modifications. Thus, here we investigated the effect of different conventional gait conditions and modifications on tissue-level knee mechanics previously suggested as indicators of collagen network damage, cell death, and loss of proteoglycans in knee cartilage. Five participants with medial KOA were recruited and musculoskeletal finite element analyses were conducted to estimate subject-specific tissue mechanics of knee cartilages during two gait conditions (i.e., barefoot and shod) and six gait modifications (i.e., 0°, 5°, and 10° lateral wedge insoles, toe-in, toe-out, and wide stance). Based on our results, the optimal gait modification varied across the participants. Overall, toe-in, toe-out, and wide stance showed the greatest reduction in tissue mechanics within medial tibial and femoral cartilages. Gait modifications could effectually alter maximum principal stress (~20 ± 7%) and shear strain (~9 ± 4%) within the medial tibial cartilage. Nevertheless, lateral wedge insoles did not reduce joint- and tissue-level mechanics considerably. Significance: This proof-of-concept study emphasizes the importance of the personalized design of gait modifications to account for biomechanical risk factors associated with cartilage degradation.
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Affiliation(s)
- Amir Esrafilian
- Department of Technical PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Kimmo S. Halonen
- Central hospital of Päijät‐HämeLahtiFinland
- Department of Materials and ProductionAalborg UniversityAalborgDenmark
| | | | | | - Mika E. Mononen
- Department of Technical PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Petri Tanska
- Department of Technical PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Jim Woodburn
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute QueenslandGriffith UniversityGold CoastQLDAustralia
| | - Rami K. Korhonen
- Department of Technical PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Michael S. Andersen
- Department of Materials and ProductionAalborg UniversityAalborgDenmark
- Center for Mathematical Modeling of Knee Osteoarthritis (MathKOA), Department of Materials and ProductionAalborg UniversityAalborgDenmark
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Moura FA, Pelegrinelli ARM, Catelli DS, Kowalski E, Lamontagne M, da Silva Torres R. On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods. Sci Rep 2024; 14:1379. [PMID: 38228640 PMCID: PMC10791669 DOI: 10.1038/s41598-023-50481-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/20/2023] [Indexed: 01/18/2024] Open
Abstract
Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.
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Affiliation(s)
- Felipe Arruda Moura
- Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil.
- Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands.
| | - Alexandre R M Pelegrinelli
- Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Danilo S Catelli
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
- Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Erik Kowalski
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Mario Lamontagne
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Ricardo da Silva Torres
- Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands.
- Department of ICT and Natural Sciences, NTNU-Norwegian University of Science and Technology, Ålesund, Norway.
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Pelegrinelli ARM, Catelli DS, Kowalski E, Lamontagne M, Moura FA. Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks. Med Eng Phys 2023; 122:104074. [PMID: 38092489 DOI: 10.1016/j.medengphy.2023.104074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/19/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023]
Abstract
The choice of musculoskeletal (MSK) model is crucial for performing MSK estimations to evaluate muscle demands and joint forces. This study compared two previously published generic MSK models and a modified model to estimate tibiofemoral reaction forces (TFRF) during gait, sit-to-stand, and stand-to-sit. The estimated tibiofemoral reaction forces were compared with an in vivo dataset from six patients using an instrumented knee prosthesis. A correlation and root mean square error (RMSE) in the time-series analysis and relative peak error (RPE) were evaluated. The results showed that the three MSK models were similar in estimating the vertical forces, with a large correlation, and RPE was found around 20 % during gait. The RMSE and the RPE indicated that the modified model had lower total and lateral compartment forces errors for sit-to-stand and stand-to-sit, showing the best performance. The shear forces for all tasks and models showed significant errors. Future MSK studies should consider these findings when researching functional tasks. The modified model was found to be more effective in estimating the vertical tibiofemoral joint reaction forces in tasks that impose greater demands on muscle forces and require high knee and hip flexion.
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Affiliation(s)
- Alexandre R M Pelegrinelli
- Laboratory of Applied Biomechanics, State University of Londrina, Brazil; Human Movement Biomechanics Laboratory, University of Ottawa, Canada.
| | - Danilo S Catelli
- Human Movement Biomechanics Laboratory, University of Ottawa, Canada; Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Belgium
| | - Erik Kowalski
- Human Movement Biomechanics Laboratory, University of Ottawa, Canada
| | - Mario Lamontagne
- Human Movement Biomechanics Laboratory, University of Ottawa, Canada
| | - Felipe A Moura
- Laboratory of Applied Biomechanics, State University of Londrina, Brazil
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Sabashi K, Chiba T, Yamanaka M, Tohyama H. Effect of toe-out gait modification on patellofemoral joint loading. Gait Posture 2023; 104:135-139. [PMID: 37419054 DOI: 10.1016/j.gaitpost.2023.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 06/24/2023] [Accepted: 06/27/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND Toe-out gait has been proposed as a conservative treatment to reduce medial tibiofemoral joint loading. However, patellofemoral joint loading during toe-out gait is not yet understood. RESEARCH QUESTION Does the toe-out gait modification affect patellofemoral joint loading? METHODS Sixteen healthy adults were enrolled in this study. The natural gait and toe-out gait were measured using a three-dimensional motion analysis and a force plate. The knee flexion angle and external knee flexion moment during the stance phase were calculated. Thus, dynamic knee joint stiffness, a proxy of patellofemoral joint loading, was defined as a linear regression of the knee flexion moment and knee flexion angle during the early stance. Additionally, the peak patellofemoral compressive force during the early stance was calculated using a musculoskeletal simulation. A paired t-test was used to compare these biomechanical parameters during the natural gait and toe-out gait. RESULTS The toe-out gait significantly increased the peak patellofemoral compressive force (mean difference = 0.37 BW, P = 0.017) and dynamic knee joint stiffness (mean difference = 0.07%BW*Ht/°, P = 0.001). The 1st peak of the knee flexion moment also significantly increased in the toe-out gait (mean difference = 1.01%BW*Ht, P = 0.003); however, the knee flexion angle did not change significantly (initial contact: mean difference = 1.7°, P = 0.078; peak: mean difference = 1.3°, P = 0.224). SIGNIFICANCE Toe-out gait increased the patellofemoral compressive force and dynamic knee joint stiffness because of increasing knee flexion moment, but not the knee flexion angle. When the toe-out gait is adapted, clinicians should pay attention to an increase in the patellofemoral joint loading.
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Affiliation(s)
- Kento Sabashi
- Department of Rehabilitation, Hokkaido University Hospital, Kita 14, Nishi 5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Faculty of Health Sciences, Hokkaido University, Kita 12, Nishi 5, Kita-Ku, Sapporo, Hokkaido 060-0812, Japan.
| | - Takeshi Chiba
- Department of Rehabilitation, Hokkaido University Hospital, Kita 14, Nishi 5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Faculty of Health Sciences, Hokkaido University, Kita 12, Nishi 5, Kita-Ku, Sapporo, Hokkaido 060-0812, Japan
| | - Masanori Yamanaka
- Faculty of Health Science, Hokkaido Chitose College of Rehabilitation, Satomi 2-10, Chitose, Hokkaido 066-0055, Japan
| | - Harukazu Tohyama
- Faculty of Health Sciences, Hokkaido University, Kita 12, Nishi 5, Kita-Ku, Sapporo, Hokkaido 060-0812, Japan
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