Validation of full-length radiograph based musculoskeletal modeling method to estimate medial and lateral knee contact forces.

BACKGROUND: Anatomical parameters of the pelvis, femur, and tibia derived from the full-length radiograph can be used to create a more accurate musculoskeletal model compared to marker-based linear scaling method. However, whether this model leads to more accurate estimations of medial knee contact force (MCF) and lateral knee contact force (LCF) than marker-based linear scaling method is still unknown. RESEARCH QUESTION: This main purpose of this study was to determine whether musculoskeletal model generated from full-length radiograph improves the estimations of MCF and LCF. METHODS: An open-source dataset including marker trajectories, ground reaction forces, in vivo knee contact forces, and full-length radiograph was used to evaluate the accuracy of full-length radiograph musculoskeletal modeling method. Subject-specific musculoskeletal models were created using anatomical parameters derived from the full-length radiograph or marker-based linear scaling methods. MCF and LCF were estimated using musculoskeletal simulations of normal walking trails. The accuracy of modeling methods was determined by comparing the estimated and in vivo measured MCF and LCF. RESULTS: Compared to the marker-based linear scaling approach, the full-length radiograph musculoskeletal modeling method exhibited decreases of 38.3 % and 41.3 % in root mean square error for MCF and LCF respectively, as well as reductions of 50.0 % and 49.3 % in mean peak errors for MCF and LCF respectively. SIGNIFICANCE: The full-length radiograph musculoskeletal modeling method provides a more accurate way to estimate MCF and LCF compared to the traditional maker-based linear scaling approach, which may contribute to understand the initiation, progression, and treatment of OA.

Telehealth-Supported Exercise or Physical Activity Programs for Knee Osteoarthritis: Systematic Review and Meta-Analysis.

BACKGROUND: The integration of telehealth-supported programs in chronic disease management has become increasingly common. However, its effectiveness for individuals with knee osteoarthritis (KOA) remains unclear. OBJECTIVE: This study aimed to assess the effectiveness of telehealth-supported exercise or physical activity programs for individuals with KOA. METHODS: A comprehensive literature search encompassing Embase, MEDLINE, CENTRAL, Web of Science, PubMed, Scopus, PEDro, GreyNet, and medRxiv from inception to September 2023 was conducted to identify randomized controlled trials comparing telehealth-supported exercise or physical activity programs to a control condition for KOA. Data were extracted and qualitatively synthesized across eligible studies, and a meta-analysis was performed to evaluate the effects. The study was reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020. RESULTS: In total, 23 studies met eligibility criteria, with 20 included in the meta-analysis. Results showed that telehealth-supported exercise or physical activity programs reduced pain (g=-0.39; 95% CI -0.67 to -0.11; P<.001), improved physical activity (g=0.13; 95% CI 0.03-0.23; P=.01), and enhanced physical function (g=-0.51; 95% CI -0.98 to -0.05; P=.03). Moreover, significant improvements in quality of life (g=0.25; 95% CI 0.14-0.36; P<.001), self-efficacy for pain (g=0.72; 95% CI 0.53-0.91; P<.001), and global improvement (odds ratio 2.69, 95% CI 1.41-5.15; P<.001) were observed. However, self-efficacy for physical function (g=0.14; 95% CI -0.26 to 0.53; P=.50) showed insignificant improvements. Subgroup analyses based on the World Health Organization classification of digital health (pain: χ22=6.5; P=.04 and physical function: χ22=6.4; P=.04), the type of teletechnology in the intervention group (pain: χ24=4.8; P=.31 and function: χ24=13.0; P=.01), and active or inactive controls (pain: χ21=5.3; P=.02 and physical function: χ21=3.4; P=.07) showed significant subgroup differences. CONCLUSIONS: Telehealth-supported exercise or physical activity programs might reduce knee pain and improve physical activity, physical function, quality of life, self-efficacy, and global improvement in individuals with KOA. Future research should consider longer implementation durations and assess the feasibility of incorporating wearables and standardized components into large-scale interventions to evaluate the effects. TRIAL REGISTRATION: PROSPERO CRD42022359658; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=359658.

A Novel Balance Control Strategy Based on Enhanced Stability Pyramid Index and Dynamic Movement Primitives for a Lower Limb Human-Exoskeleton System.

The lower limb exoskeleton is playing an increasing role in enabling individuals with spinal cord injury (SCI) to stand upright, walk, turn, and so on. Hence, it is essential to maintain the balance of the human-exoskeleton system during movements. However, the balance of the human-exoskeleton system is challenging to maintain. There are no effective balance control strategies because most of them can only be used in a specific movement like walking or standing. Hence, the primary aim of the current study is to propose a balance control strategy to improve the balance of the human-exoskeleton system in dynamic movements. This study proposes a new safety index named Enhanced Stability Pyramid Index (ESPI), and a new balance control strategy is based on the ESPI and the Dynamic Movement Primitives (DMPs). To incorporate dynamic information of the system, the ESPI employs eXtrapolated Center of Mass (XCoM) instead of the center of mass (CoM). Meanwhile, Time-to-Contact (TTC), the urgency of safety, is used as an automatic weight assignment factor of ESPI instead of the traditional manual one. Then, the balance control strategy utilizing DMPs to generate the gait trajectory according to the scalar and vector values of the ESPI is proposed. Finally, the walking simulation in Gazebo and the experiments of the human-exoskeleton system verify the effectiveness of the index and balance control strategy.