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 sensor 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) the duration of social interactions in a student population (Nparticipants = 45, Nobservations = 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.

Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods.

“…We recommend that in future ESM studies also the start time of interactions should be measured so that back-and-forth transitions can be modeled in more detail. In the multistate3 vignette of the dena package, we provide an example analysis using a multistate model on passively sensed social interactions, including start- and end times (also see Elmer & Lodder, 2023). Note, however, that frailties in multistate models with back-and-forth transitions are much more difficult to estimate due to identification issues (Putter & Van Houwelingen, 2015).…”

“…Although this article focuses on event-contingent ESM data of social interaction reports, the statistical modeling approach introduced in this article could also be applied to other types of intensive longitudinal data, such as time-stamped daily diary data (e.g., Kahneman et al, 2004) or continuous monitoring methods, where the timing of (social) behaviors are captured at a fine-grained temporal level using mobile sensors (e.g., see Elmer & Lodder, 2023).…”

Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods

“…We recommend that in future ESM studies also the start time of interactions should be measured so that back and forth transitions can be modeled in more detail. In the multistate vignette of the dena package, we provide an example analysis using a multistate model on passively-sensed social interactions, including start-and end times (also see Elmer & Lodder, 2022). Note, however, that frailties in multistate models with back-and forth transitions are much more difficult to estimate due to identification issues (Putter & Van Houwelingen, 2015).…”

“…Although this article focuses on event-contingent ESM data of social interaction reports, the statistical modeling approach introduced in this article could also be applied to other types of intensive longitudinal data, such as time-stamped daily diary data (e.g., Kahneman et al, 2004) or continuous monitoring methods, where the timing of on-and offline (social) behaviors are captured at a fine-grained temporal level using mobile sensors (e.g., see Elmer & Lodder, 2022).…”

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies

“…With smartphones being omnipresent in our daily lives, they are not only perfectly suited to supersede devices previously used for sending beeps in ESM studies such as paper-and-pencil diaries or personal digital assistants (e.g., PALM). They also offer the possibility of continuously collecting a variety of data types without the active engagement or interruption of participants’ day-to-day behavior, which in turn can be used to derive contextual and behavioral information, even if participants miss certain beeps (Harari et al., 2016 ; Elmer et al., 2022 ; Schoedel et al., 2023 ). Accordingly, scholars have recently pointed out the huge potential of using mobile sensing as a toolbox to gather further insight into compliance in ESM studies (Murray et al., 2023 ; Sun et al., 2020 ), for example, by using GPS data instead of self-reported information on locations (Sokolovsky et al., 2014 ).…”