An ergonomic and systemic evaluation of the effect of sleep quality and lifestyle behaviors on academic performance among industrial engineering students

Mira Rahayu; Ni Nyoman Prema Mega; Yusuf Nugroho Doyo Yekti

doi:10.30656/jsmi.v10i1.11335

Authors

Mira Rahayu Universitas Telkom https://orcid.org/0000-0002-9342-6478
Ni Nyoman Prema Mega Universitas Telkom
Yusuf Nugroho Doyo Yekti Universitas Telkom https://orcid.org/0000-0002-8140-5485

DOI:

https://doi.org/10.30656/jsmi.v10i1.11335

Keywords:

Ergonomics, Health-promoting lifestyle, Higher education system, Human factors, Sleep quality, Student performance

Abstract

University life is a demanding transitional period in which academic workload, social change, and financial pressure often disrupt students’ sleep and daily health habits, with direct implications for learning and academic outcomes. This research examines how sleep quality and lifestyle habits affect the academic success of undergraduate students in industrial engineering, using a human factors and ergonomics framework. Utilizing the Pittsburgh Sleep Quality Index (PSQI) alongside the health-Promoting Lifestyle Profile II (HPLP-II), data from 293 students were paired with institutional cumulative Grade Point Average (GPA) records. The results indicate that 58% of the participants suffered from poor sleep quality (PSQI > 5). A significant negative correlation was observed between PSQI metrics and cumulative GPA (r=−0.312, p<0.01). Conversely, stress management (r=0.267, p<0.01) and health responsibility (r=0.231, p<0.01) positively correlated with GPA. Overall, sleep quality and lifestyle behaviors explained 29.6% of the GPA variance (adjusted R2 =0.296, p<0.001), with all predictors meeting multicollinearity thresholds (VIF < 3.0). No significant gender disparities were observed for sleep quality (p = 0.339) or GPA (p = 0.066), although males scored higher in overall lifestyle habits (p = 0.008). These findings demonstrate that lifestyle and sleep are critical determinants of the performance of industrial engineering students, suggesting that universities should deploy data-driven strategies, including schedule revisions and wellness initiatives, prior to longitudinal causal validation.

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