Smartphones as mood barometers: Predicting mood in daily life using different sensing modalities

Momentary experiences of positive and negative emotionality are core components of well-being and performance. This study investigates whether passively sensed smartphone data can be used to recognize individuals’ mood (i.e. Valence and Arousal (Russell, 1980)) based on their smartphone sensing data. The exploratory analysis uses data generated from N = 453 participants in a two-week experience sampling wave which was part of the Smartphone Sensing Panel Study (SSPS; Schödel & Oldemeier, 2020). Different cross-validated machine learning algorithms are compared to predict participants’ current mood given a variety of situational and behavioral variables, reflected by different smartphone sensing modalities. Moreover, the impact of different time perspectives (i.e. daily versus hourly) on the predictive performance is investigated. unknown other