Biomechanical Characteristics and Predictive Models in Musculoskeletal Trauma: From Prevention to Clinical Management and Rehabilitation

Musculoskeletal trauma remains one of the leading causes of disability and reduced quality of life worldwide, affecting athletes, workers, and the general population across all age groups. Advances in measurement technologies, computational modelling and simulation now allow researchers to identify biomechanical characteristics that predict injury risk, guide clinical decision-making, and monitor recovery with a precision that was not previously possible.For the purposes of this Research Topic, biomechanical characteristics are defined as quantifiable mechanical, movement-related, or neuromuscular features obtained through direct measurement, physics-based modelling, or data-driven inference. These may include movement patterns, joint kinematics and kinetics, external and internal loading, and tissue stress and strain. Such characteristics may be obtained or derived using approaches such as motion capture, wearable sensing, imaging, force measurement, musculoskeletal modelling and simulation, or machine learning. Contributions should clearly describe how the proposed biomechanical characteristics are obtained or derived and explain their biomechanical, clinical, or functional relevance to injury mechanisms, injury risk, diagnosis, prognosis, treatment response, or recovery.This Research Topic aims to bring together current research on biomechanical characteristics and predictive models across the full continuum of musculoskeletal trauma care, from injury prevention and surgical intervention to rehabilitation. We welcome interdisciplinary contributions that explore how clinical phenotyping, together with advanced measurement, modelling, and simulation approaches, can inform the development of actionable tools, technologies, and interventions across trauma prevention, surgical care, and rehabilitation. We particularly encourage collaborative research involving researchers, surgical teams, and healthcare service partners. Submissions should have a clear biomechanical or computational modelling component. Studies that are purely clinical in nature, without a quantifiable biomechanical characteristic, quantitative movement analysis, or modelling component, fall outside the scope of this Research Topic and are better suited to a clinically focused journal. Authors are encouraged to check the fit of their work with the Topic Editors before submission.We invite submissions addressing, but not limited to, the following themes:Identification and characterisation of biomechanical features associated with injury riskPredictive models and machine learning approaches for musculoskeletal injury preventionWearable sensors and motion capture methods for trauma assessment and monitoringBiomechanical analysis of injury mechanisms in sport, occupational, and clinical settingsPersonalised rehabilitation protocols informed by biomechanical dataReturn-to-activity and return-to-sport decision-making frameworksIntegration of imaging, biomechanics, and model-derived characteristics for clinical managementLong-term outcome tracking and re-injury risk prediction Original research articles, reviews, and methodological papers are encouraged. By bridging biomechanics, data science, and clinical practice, this Research Topic seeks to advance evidence-based strategies that improve prevention, treatment, and recovery outcomes for individuals affected by musculoskeletal trauma Topic editors: Ziyun Ding, Andres Diaz Lantada, Yifei Jin Source: https://www.frontiersin.org/research-topics/83984/biomechanical-characteristics-and-predictive-models-in-musculoskeletal-trauma-from-prevention-to-clinical-management-and-rehabilitation