Validating the effectiveness of a self-report tool to predict unsafe behavior of industrial workers: a QEEG analysis

Abstract

Objectives. Unsafe behavior (UB) is defined as the likelihood of intentionally or unintentionally deviating from pre-defined plans. This study aims to investigate the validation of a self-report tool for measuring workers’ cognitive-based UB using quantitative electroencephalography (QEEG). Methods. The cognitive-based unsafe behavior questionnaire (CUBQ) was completed by 632 front-line workers in a manufacturing industry to identify differences in the backgrounds of the subjects regarding UBs. Two groups were then selected as extreme groups and QEEG was conducted based on the international 10–20 electrode placement. Results. The mean values of absolute power (AP), alpha/beta ratio (ABR) and alpha/gamma ratio (AGR) from brain oscillations in different regions of the cortex were significantly different between the studied groups (p < 0.05). Additionally, these values were found to be significantly correlated with slips, lapses and mistakes, as measured by certain scales of the CUBQ (p < 0.05). Conclusions. The findings of this study indicated differences in brain oscillation activities among industrial workers with different UB backgrounds. These results confirm the effectiveness of CUBQ as a proactive tool for safety practitioners to predict industrial workers’ UBs.

KEYWORDS:

• quantitative electroencephalography
• industrial workers
• unsafe behavior
• cognitive factors

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Shiraz University of Medical Sciences [Post Doctoral Grant Number 21442].