AI-Enabled Digital Monitoring for Musculoskeletal Health and Injury Prevention Across Physically Active Populations

Digital public health is increasingly shaped by advances in scalable, real-world monitoring systems that capture detailed data on human movement, activity, and physiology. Musculoskeletal conditions are a leading contributor to disability and lost productivity worldwide, and physical inactivity compounds that burden, making musculoskeletal health, injury prevention, and the promotion of safe physical activity a public-health priority. Across physically active populations , including recreational exercisers, occupational and manual workers, military and tactical personnel, older active adults, and the general physically-active public, the challenge lies in accurately measuring musculoskeletal health, activity exposure, and injury risk outside laboratory settings and at scale. Field-based assessment tools, wearable sensors, short functional tests, and biochemical markers are becoming central to large-scale, population-level data collection. However, critical questions persist: How valid and reliable are these digital assessments in diverse, uncontrolled environments? Can artificial intelligence and machine learning ensure rigorous quality control, calibration, and unbiased data interpretation across heterogeneous populations? And what frameworks best safeguard privacy, consent, equity, and transparency as data-driven tools become embedded in public health practice? Recent studies have demonstrated the feasibility of integrating multimodal sensor data and AI algorithms for injury surveillance and risk prediction. Still, widespread adoption is hindered by poorly standardized methodologies, uncertain generalizability across settings and subgroups, and limited attention to ethical governance and health-promotion outcomes.This Research Topic aims to explore the frontiers of AI-enabled digital monitoring for musculoskeletal health, with a focus on translating measurement innovation into actionable, ethically sound, and equitable decision support for injury prevention and activity promotion across active populations. The central objectives include evaluating and validating field-based assessment tools in real-world populations; strengthening AI/ML methods for signal quality, calibration, bias mitigation, and generalizability; building and validating population-level surveillance and cohort/longitudinal monitoring approaches against real health outcomes; and examining the human, ethical, and health-promotion dimensions of decision-support systems. Key questions to be addressed include: What are the most robust methods for ensuring validity, reliability, and calibration in low-resource, real-world, and population-scale settings? How can AI/ML models be validated on longitudinal cohort data while controlling for bias and demonstrating generalizability across subgroups? How can data privacy, consent, algorithmic fairness, and equity be built into population monitoring systems? How do human-in-the-loop approaches balance automation with expert oversight, workforce readiness, and behavior change?To gather further insights into the boundaries of scalable, AI-driven monitoring and ethical, equitable decision-making for musculoskeletal health in active populations, we welcome articles addressing, but not limited to, the following themes:Validation and reliability of portable, field-based musculoskeletal assessment tools (e.g., dynamometry, camera-aided mobility checks) across occupational, military, older-active, and general physically-active populationsDevelopment of AI/ML methods for data quality assurance, artifact detection, and device calibration in uncontrolled, real-world environmentsPopulation-level surveillance, cohort, and longitudinal monitoring designs, including rigorous validation of AI/ML models against real health outcomes, with attention to bias, calibration, and generalizabilityCreation and validation of multimodal models combining activity and load exposures with functional and neuromuscular testing for population-level risk assessment and injury preventionImplementation and evaluation of decision-support dashboards, including human-in-the-loop systems, alarm thresholds, workforce readiness, and operational trade-offs at scaleData governance, privacy, fairness, equity, and transparency in monitoring technologies for active and at-risk populationsAcceptability, equity, and health-promotion/behavior-change outcomes of digital monitoring, including qualitative and mixed-methods studiesMethodological papers on reporting standards, managing bias, and enhancing the generalizability and reproducibility of AI/ML research on population health data Topic editors: George Panayiotou, DIMITRIS PANARETOS, Thalia Bellali Source: https://www.frontiersin.org/research-topics/80679/ai-enabled-digital-monitoring-for-musculoskeletal-health-and-injury-prevention-across-physically-active-populations