Predicting Discomfort from Workload Parameters – Towards the Design of Comfortable Work

OCCUPATIONAL APPLICATIONS We present empirical data on how perceived discomfort varies under different loading situations based on analyzed experimental data. We propose mathematical models to calculate endurance time and recovery needs based on employees’ perceived physical discomfort. While this modeling approach needs further research and field trials, it opens the door for a novel, positively oriented approach for proactive ergonomics, supporting job design aimed at creating comfortable work. TECHNICAL ABSTRACT Rationale: Physical overload is one of the causes of ill-health which can manifest as discomfort, pain, fatigue, and musculoskeletal disorders (MSDs). Insufficient recovery during or after a work shift is considered to play an important role in developing MSDs. A few studies have addressed discomfort as being associated with fatigue and performance, but models predicting discomfort in occupational settings remain rare. There is a lack of information that includes perceived discomfort in both the loading and recovery phases of work. Such information would be required to model changes in discomfort levels over time in a work context. Purpose: The overall objective of this paper is to increase our understanding of how perceived discomfort (PD) varies under different loading conditions during and after static efforts. Methods: Based on regression analysis of data from an empirical dataset, in which the parameters loading time, load level, and trial number were varied, mathematical models were developed to describe how PD varies during and after static loading tasks. Results: Mathematical models for PD are presented as a novel way to establish associations for endurance time and resumption time based on PD ratings. Further, a potential use of such models is illustrated in hypothetical work design examples in which the development of PD over time is predicted. Conclusions: This novel, positively oriented approach (as opposed to injury avoidance approaches) offers a new way to estimate PD levels in repetitive work. Further research is needed to extend and validate the use of comfort prediction approaches in real world job design settings.