From Hazard Detection to Pre-Symptomatic Prediction: Can Wearable Sensors Identify Trajectories of Work-Related Musculoskeletal Risk Before Symptoms Develop in Healthcare Workers?

Abstract

Background: Work-related musculoskeletal disorders (WRMSDs) remain common among healthcare workers, especially in roles that involve patient handling, sustained posture, repetitive movement, and prolonged physical workload. Conventional ergonomic tools are useful for identifying hazardous tasks, but they usually describe exposure at a single point in time. Wearable sensors offer a different possibility by allowing posture, movement, muscle activity, and workload to be measured repeatedly during real work. Objective: This integrative review examines whether wearable sensing can move occupational health beyond hazard detection toward earlier identification of workers whose biomechanical profile is changing before pain or functional impairment appears. It also proposes a Pre-Symptomatic Musculoskeletal Risk Trajectory (PMRT) Framework to guide prospective research. Methods: A structured PubMed search was conducted, supported by a Scopus-style cross-database search strategy using equivalent title, abstract, and keyword concepts. Searches combined terms for healthcare workers, WRMSDs, patient handling, wearable sensors, inertial measurement units, electromyography, posture, ergonomics, machine learning, fatigue, recovery, and prediction. Priority was given to systematic reviews, meta-analyses, prospective studies, field studies, and healthcare-specific wearable investigations. Reference lists of key reviews were also screened. The review is integrative rather than systematic and does not report pooled effect estimates. Results: Current evidence shows that wearable systems can quantify non-neutral posture, movement, muscular activation, cumulative physical exposure, and some aspects of fatigue and recovery. Inertial sensors and surface electromyography are the most established technologies in healthcare ergonomics. Machine-learning studies can classify current posture, task, or musculoskeletal status with promising accuracy. However, prospective evidence showing that wearable-derived changes predict future symptom onset in initially asymptomatic healthcare workers remains scarce. Conclusion: Wearables are already useful for exposure measurement, but true pre-symptomatic prediction has not yet been demonstrated. The proposed PMRT Framework treats musculoskeletal risk as a longitudinal process involving exposure, response, recovery, adaptation, and eventual symptoms. Prospective cohorts with worker-level validation and clinically meaningful outcomes are needed before wearable-based prediction can be used in occupational health practice.