SSAQS: A Dataset of University Students’ Stress and Anxiety Levels based on Questionnaires and Wearable Sensors

We present SSAQS, 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. 35 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, sleep quality, oxygen saturation, stress score, physical activity, and step count. The root directory of the SSAQS dataset consists of two .csv files (users-courses.csv, course-details.csv) and a subdirectory for each of the participants. The subdirectory name corresponds to the participant's ID. The specific data for each participant is contained in their corresponding subdirectory. Each subdirectory has seven .csv files: activity_level.csv daily_questions.csv hr.csv oxygen.csv sleep.csv steps.csv stress.csv Except for the three users who did not provide Fitbit data (users 3, 12, and 14). In those cases, only the daily_questions.csv is present. SSAQS 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.