BreathSense: A Two-Stage Digital Framework for Student Stress Monitoring Using Personalized Breath-VOC Thresholding and In-the-Wild Validation

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 (θi) for each participant. We then invited 21 of them to join a three-day field deployment (Study 2; N = 21). Each participant’s θi 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.