A Dataset of University Students' Stress and Anxiety Levels based on Questionnaires and Wearable Sensors

Mental health issues such as stress and anxiety are highly prevalent among university students, often affecting their academic performance and overall well-being. Understanding these conditions through objective, real-world data is essential for developing effective monitoring and intervention strategies. We present a multimodal dataset that captures students' daily stress and anxiety levels through self-reports and wearable sensor data. The dataset was collected during one academic semester (February-July 2025) from undergraduate volunteers at two Mexican universities. Participants provided daily ratings of stress and anxiety using a mobile application, while Fitbit Inspire 3 devices continuously recorded physiological and behavioral data including heart rate variability, sleep quality, oxygen saturation, stress score, physical activity, and step count. The dataset features over 80% questionnaire compliance and validated Fitbit measurements. This dataset addresses the scarcity of public, ecologically valid datasets on student mental health and enables reproducible research and analyses in affective computing, wearable sensing, and machine learning for stress and anxiety monitoring.