Understanding the Correlation Between Boredom Proneness Induced Sleepiness and Microsleep Events Among Drivers
DOI:
https://doi.org/10.70028/dcea.v2i2.71Keywords:
Microsleep, Epworth Sleepiness Scale, Boredom PronenessAbstract
Driving is a crucial daily activity that requires high concentration to ensure safety on road. One of the factors contributing to traffic accidents is the phenomenon of microsleep, a brief involuntary sleep episode that can disrupt a driver’s alertness. In Indonesia, particularly in Province of Aceh, traffic accident rates remain relatively high. The Banda Aceh-Medan Road segment is one of the areas with the highest traffic accident rate in Aceh Province. This highlights the importance of the research. This study aims to analyze the factors affecting the occurrence of microsleep among drivers in Aceh Province, specifically focusing on the ease of falling asleep that was measured using the Epworth Sleepiness Scale (ESS). Whilst, the boredom was measured using the Boredom Proneness Scale (BPS). Data were collected through questionnaires distributed to 310 drivers who had travelled the Banda Aceh - Medan route, both on expressways and national road. The Structural Equation Modelling (SEM) method was used to examine the relationships between the exogenous latent variables (boredom level and sleepiness) and the endogenous latent variable (frequency of microsleep). The model indicates there were relationship between three main latent variables. Boredom Proneness (BP) has a significant positive effect on the Epworth Sleepiness Scale (ESS) with an estimate coefficient of 0.329 (p < 0.001). BP also positively affected Microsleep (MS) with an estimate of 0.242, and the ESS positively influenced the MS with a coefficient of 0.191, respectively. The overall model demonstrated acceptable statistical quality with RMSEA = 0.079, GFI = 0.850, AGFI = 0.813, and CFI = 0.842, indicating a marginal to good fit. However, this study is limited by its cross-sectional and self-reported design, suggesting future research should apply experimental or physiological approaches to better capture driver fatigue dynamics.
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