Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness

Abstract

More and more data are being collected using combined active (e.g., surveys) and passive (e.g., smartphone sensors) ambulatory assessment methods. Fine-grained temporal data, such as smartphone-sensors data, allow gaining new insights into the dynamics of social interactions in day-to-day life and how these are associated with psychosocial phenomena – such as loneliness. So far, however, smartphone sensor data have often been aggregated over time, thus, not doing justice to the fine-grained temporality of these data. In this article, we demonstrate how time-stamped sensor data of social interactions can be modeled with multistate survival models. We examine how loneliness is associated with (a) the time between social interaction (i.e., interaction rate) and (b) duration of social interactions in a student population (N_participants = 45, N_observations = 74,645). Before a 10-week ambulatory assessment phase, participants completed the UCLA loneliness scale, covering subscales on intimate, relational, and collective loneliness. Results from the multistate survival models indicated that loneliness subscales were not significantly associated with differences in social interaction rate and duration– only relational loneliness predicted shorter social interaction encounters. These findings illustrate how the combination of new measurement and modeling methods can advance knowledge on social interaction dynamics in daily life settings and how they relate to psychosocial phenomena such as loneliness.

Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API

“…For instance, Site et al [8] studied a combination of the domains of gerontology, social psychology, architecture, and portable wireless technology to monitor and attempt to predict loneliness. Elmer et al [9] use passive sensing collecting data from smartphone data to analyze time-stamped sensor data of social interactions. With that, they generate multistate survival models.…”