25premus010 10.3205/25premus010 urn:nbn:de:0183-25premus0105 Meeting Abstract Bao Bao Stephen S

School of Public Health, University of Washington, Seattle, WA, USA

author German Medical Science GMS Publishing House

Düsseldorf

610 20250909 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0625 010 12th International Scientific Conference on the Prevention of Work-Related Musculoskeletal Disorders PREMUS 2025 Keynote SP 4 Tübingen 20250909 20250912 010 20251113The original publication listed an incorrect author. The author information has been replaced. TextOver three decades ago, researchers emphasized the urgent need to address the rising prevalence of work-related musculoskeletal disorders (WMSDs). The term “WMSDs” was introduced to encourage action beyond diagnosis. Epidemiological evidence has linked physical workplace risk factors – such as force, repetition, posture, and vibration – to these disorders, leading to efforts to quantify biomechanical exposures.Various methods have been used, including self-reports, instrumented grip force, electromyography (EMG), and posture analysis. Each approach has strengths and limitations in terms of applicability, reliability, and comparability. Understanding when and how to use different methods is essential to interpreting and comparing exposure data.WMSD development can be conceptualized through a causal pathway. External biomechanical exposures (e.g., task-related force or posture) trigger internal biomechanical responses (e.g., muscle activity), which can result in musculoskeletal outcomes. For example, high grip force may increase forearm EMG activity, contributing to lateral epicondylitis. A single internal exposure (e.g., carpal tunnel pressure) may result from varied external conditions, complicating direct associations.Exposure quantification faces challenges in comparability (different methods yield different representations of the same variable), repeatability (consistency across observers or times), and validity (how well a measure reflects real exposure and risk). For instance, hand force can be self-rated, observed, or measured via sensors—each capturing different aspects. Moreover, force is rarely constant in real work; summarizing it using median, peak, or duty cycle values raises internal validity concerns. External validity is also critical, as cultural or group differences can affect perception and self-reporting.Beyond research, exposure data inform job evaluations aimed at reducing WMSD risk. Methods used by practitioners must balance ease of use with reliability. While researchers may use sophisticated tools, these are not always practical in field settings. Recent advances in wearable tech offer promise for reliable, user-friendly measurements.Effective ergonomics job evaluations must reflect diverse workplace factors and provide weighted scores based on their contributions to WMSD risk. Although epidemiological data currently support limited factors (e.g., object weight, reach distances), tools like REBA and QEC attempt broader inclusion. Ongoing research is needed to improve weighting accuracy and enhance method validity.In conclusion, accurate biomechanical exposure quantification is central to WMSD research and ergonomic practice. Continued efforts are essential to refine measurement methods, improve reliability, and strengthen causal inferences for better prevention and job design. 0 0 0 0