Modeling social interaction dynamics measured with smartphone sensors

An ambulatory assessment study on social interactions and loneliness

Bibliographic Data

ID2918471
AuthorsTimon Elmer (0000-0003-4354-4457, University of Groningen, corresponding author), Gerine Lodder (Tilburg University), Gerine M A Lodder (0000-0002-9253-7504, Tilburg University)
Year2023
Volume40
Issue2
Pages654-669
Publication date2023-02-01
Open AccessYes
TypeARTICLE
VenueJournal of Social and Personal Relationships (JOURNAL)
Journal identifiersISSN: 0265-4075 • E-ISSN: 1460-3608
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/02654075221122069
PMID36844896
OpenAlexW4292544975
LanguageEN
Citations received2
References cited44

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

Ambulatory · Dynamics (music · Loneliness · Social relation · Social relationship · Applied Psychology · Digital Mental Health Interventions · Health disparities and outcomes · Medicine · Mental Health Research Topics · Psychology · Social Psychology

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Open DOIOpen Access