Real-Time Stress Profiling in University Students During Post-COVID-19 Recovery and PM2.5 Exposure Using a Web Application

Authors

  • Pongjan Yoopat Ergonomics Unit, Department of Medical Science, Faculty of Science, Rangsit University, Pathum Thani 12000, Thailand
  • Karn Yongsiriwit College of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand
  • Thannob Aribarg College of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand
  • Nisakorn Julraksa Ergonomics Unit, Department of Medical Science, Faculty of Science, Rangsit University, Pathum Thani 12000, Thailand
  • Weerawat Liemmaneee Ergonomics Unit, Department of Medical Science, Faculty of Science, Rangsit University, Pathum Thani 12000, Thailand

DOI:

https://doi.org/10.59796/jcst.V16N2.2026.167

Keywords:

progressive web application, digital stress monitoring, Subjective workload index, university students, PM2.5 air pollution, post-pandemic stress, environmental stressors, ergonomic assessment, physical performance

Abstract

This study compared stress profiles of Thai university students during post-COVID-19 recovery (2024) and peak PM2.5 exposure (2025) using the Find My Stress Progressive Web Application (PWA). A cross-sectional design enrolled 613 students (post-COVID-19: n = 303; PM2.5: n = 310). Participants completed PWA-based assessments including demographic profiling, task-related stressor ratings (0–10 scale), Subjective Workload Index (SWI) computation, and activity-based evaluations across four daily domains. Handgrip strength normalized by BMI (HG/BMI) was measured in the PM2.5 cohort. Usability was assessed via a 14-item questionnaire (n = 372). Data were analyzed using independent-samples t-tests, Pearson correlations, and stepwise regression (p < .05). The post-COVID-19 cohort exhibited significantly higher SWI (M = 3.09, SD = 0.85) than the PM2.5 cohort (M = 2.37, SD = 0.99; p < .001, Cohen’s d = 0.78), reflecting elevated psychosocial strain. The PM2.5 cohort reported greater environmental discomfort (air quality, dust, illumination) and biomechanical burden (adverse posture, restricted movement). Stepwise regression identified six predictors of HG/BMI: time, noise, dust, vibration, organizational factors, and gender (r = 0.674, p < .001). SWI correlated positively with fatigue and task complexity and negatively with motivation and autonomy. The PWA demonstrated excellent reliability (Cronbach’s α = 0.957). The Find My Stress PWA effectively captured context-specific stress patterns: elevated psychosocial workload during post-pandemic recovery and heightened environmental strain under PM2.5 exposure. These findings support the integration of scalable digital ergonomics tools into university health systems for real-time stress monitoring.

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Published

2026-03-30

How to Cite

Yoopat, P., Yongsiriwit, K., Aribarg, T., Julraksa, N., & Liemmaneee, W. (2026). Real-Time Stress Profiling in University Students During Post-COVID-19 Recovery and PM2.5 Exposure Using a Web Application. Journal of Current Science and Technology, 16(2), 167. https://doi.org/10.59796/jcst.V16N2.2026.167

Issue

Vol. 16 No. 2 (2026): April-June

Section

Research Article

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