BreathSense: A Two-Stage Digital Framework for Student Stress Monitoring Using Personalized Breath-VOC Thresholding...

Student mental health and academic stress are increasingly addressed through digital monitoring, yet evidence for personalized physiological thresholds based on exhaled VOCs, their in-the-wild feasibility, and their trigger-experience correspondence in everyday student life remains limited. This study examines whether exhaled breath signals can support personalized, real-world stress monitoring in university students using a two-stage design that moves from laboratory calibration to daily life validation. A total of 24 university students took part in the laboratory phase (Study 1; N = 24). Under two stress tasks, a social-conflict video task and a Stroop task, we derived an individualized breath-trigger threshold (θ_i) for each participant. We then invited 21 of them to join a three-day field deployment (Study 2; N = 21). Each participant's θ_i from Study 1 was used directly as the trigger threshold for daily monitoring in order to test the association between trigger events and subjectively noticeable emotional deviations and to assess preliminary trigger-experience correspondence in daily life. The results show that 78.6% of paired trigger-EMA records were rated as subjectively salient, with 93.9% of these rated at medium-to-high intensity. These events occurred most frequently during study/work activities (60.6%), in dorm/home settings (57.6%), and when participants were alone (63.6%), suggesting that the triggers captured personally meaningful emotional episodes embedded in routine academic life rather than random physiological fluctuations. Overall, this study presents a portable breath-based emotion sampling device for student academic contexts and a reproducible protocol that combines laboratory thresholding with daily life validation. The findings provide preliminary and exploratory indications of the feasibility and within-person transferability of VOC-based emotion detection in students, and offer methodological support for future digital emotion monitoring and intervention design based on breath signals.