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Monitoring and understanding affective states are important aspects ofhealthy functioning and treatment of mood-based disorders. Recent advancementsof ubiquitous wearable technologies have increased the reliability of suchtools in detecting and accurately estimating mental states (e.g., mood, stress,etc.), offering comprehensive and continuous monitoring of individuals overtime. Previous attempts to model an individual's mental state were limited tosubjective approaches or the inclusion of only a few modalities (i.e., phone,watch). Thus, the goal of our study was to investigate the capacity to moreaccurately predict affect through a fully automatic and objective approachusing multiple commercial devices. Longitudinal physiological data and dailyassessments of emotions were collected from a sample of college students usingsmart wearables and phones for over a year. Results showed that our model wasable to predict next-day affect with accuracy comparable to state of the artmethods.

Objective Prediction of Tomorrow's Affect Using Multi-Modal  Physiological Data and Personal Chronicles: A Study of Monitoring College  Student Well-being in 2020