Development and Validation of the Manual Integrated Ergonomics Risk Assessment Tool (MIERAT): Field Application in a Small-Scale Automotive Service Environment

Work-related musculoskeletal disorders (WRMSDs) remain a major occupational health challenge, and automotive service work is a high-risk context involving awkward postures, repetitive tasks, and sustained static loading. Conventional ergonomic tools capture static posture snapshots and do not reflect how musculoskeletal stress accumulates over time. This study developed the Manual Integrated Ergonomics Risk Assessment Tool (MIERAT), a low-cost, observation-based framework for resource-constrained automotive settings, and conducted an initial field validation. MIERAT combines REBA-based posture severity (PS), exposure duration (D), and a calibrated Time Modifier (TM; 1.0–2.5) into a Cumulative Risk Index (CRI = PS × D × TM), grounded in biomechanical loading theory and aligned with ISO 11226, ISO 11228-1, and ISO 45001. Expert panel review yielded an S-CVI/Ave of 0.95, indicating strong content validity. Duration–CRI associations were very strong for under-vehicle servicing (ρ = 0.910), tyre fitment (ρ = 0.961), and brake/suspension repair (ρ = 0.945), all p < 0.001. Because duration enters the CRI equation directly and also determines the TM, these correlations are mathematically expected; they show that the index behaves as designed but are not independent evidence of construct validity. Inter-rater reliability across three observers was excellent (ICC = 0.975–0.997), with moderate-to-perfect categorical agreement (weighted κ = 0.557–1.000). Task-level profiling identified tyre demounting, wheel alignment, and drain plug loosening as dominant ergonomic hotspots. These findings provide preliminary support for MIERAT as a content-valid and reliable tool for profiling cumulative postural exposure and planning ergonomic interventions in South African automotive workshops. Validation against independent outcomes, such as musculoskeletal symptoms, fatigue, biomechanical loading, or injury data, is needed to establish its construct and predictive validity.