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Introduction: External loads applied to the hand during work are transmitted through the upper extremity to the shoulder joint (SJ), where they are countered by internal muscle forces. An imbalance between load and recovery can lead to musculoskeletal disorders. Systematic reviews link occupational risk factors – such as working at or above SJ level, repetitive SJ movements, and force application – to shoulder complaints. To date, few studies have presented measurement-based objective shoulder load assessment approaches. Consequently, a comprehensive approach to shoulder load assessment was derived and developed from the literature as a component of the CUELA risk assessment framework.

Methods: Risk factors were identified from biomechanical and epidemiological research. Assessment procedures were adopted from occupational physiological and biomechanical studies using measurement-based data. Additionally, frequency distributions from the IFA’s exposure database informed the development. The method was validated using data from baggage handling and aircraft assembly tasks.

Results: The SJ orientation assessment follows standardised principles but defines the neutral range for flexion and adduction starting at -5°. The SJ motion intensity is categorised via angular velocity (ω) as static, dynamic, and highly dynamic, with static postures defined as ≥10s. A ω of 55°/s, derived from P90 values of the work shifts studied, marks the threshold for highly dynamic motion. A cumulative exposure metric – % of time in non-recommended postures/movements (%NRPM) – combines movement intensity with joint angle. Four risk categories are classified according to the MEGAPHYS risk approach (1: low risk to 4: high risk). The muscle moment that counteracts the external loads is normalised to maximum voluntary contraction to give the Muscular Strength Utilization Ratio (MUR, (Svensson 1987)). The MUR reflects the situational load intensity and is classified as light (<25%), moderate (<50%), heavy (<75%), or very heavy (≥75%). Cumulative shoulder loads are visualised using load distribution curves that reflect work-specific muscle use patterns. Fatigue is accounted for using literature-based maximum acceptable exertion (MAE) thresholds (Potvin 2012). Baggage handling showed very heavy situational shoulder muscle strain, but the MUR distribution curve remained below the MAE threshold and the %NRPM values were within acceptable limits. In contrast, situational heavy shoulder muscle strain was observed during aircraft assembly, but the MUR distribution exceeded the MAE and %NRPM values were in an unacceptable range.

Discussion: The assessment relies on recorded movements and external forces. If force data is missing, joint loads can be estimated based on knowledge of the loads being moved. The shoulder strain determined appears to be caused by frequent load handling during baggage handling and by prolonged work above shoulder height during aircraft assembly.

Conclusion: This measurement-based approach allows for individual and cumulative assessment of shoulder load risks across tasks or full shifts. It is a robust tool for identifying and quantifying work-related musculoskeletal risks objectively.