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

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 ho...

Descripción completa

Detalles Bibliográficos
Autores principales: Elmer, Timon, Lodder, Gerine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941651/
https://www.ncbi.nlm.nih.gov/pubmed/36844896
http://dx.doi.org/10.1177/02654075221122069
_version_ 1784891333633114112
author Elmer, Timon
Lodder, Gerine
author_facet Elmer, Timon
Lodder, Gerine
author_sort Elmer, Timon
collection PubMed
description 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 (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.
format Online
Article
Text
id pubmed-9941651
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-99416512023-02-22 Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness Elmer, Timon Lodder, Gerine J Soc Pers Relat Articles 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 (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. SAGE Publications 2022-08-20 2023-02 /pmc/articles/PMC9941651/ /pubmed/36844896 http://dx.doi.org/10.1177/02654075221122069 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Articles
Elmer, Timon
Lodder, Gerine
Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title_full Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title_fullStr Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title_full_unstemmed Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title_short Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness
title_sort modeling social interaction dynamics measured with smartphone sensors: an ambulatory assessment study on social interactions and loneliness
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941651/
https://www.ncbi.nlm.nih.gov/pubmed/36844896
http://dx.doi.org/10.1177/02654075221122069
work_keys_str_mv AT elmertimon modelingsocialinteractiondynamicsmeasuredwithsmartphonesensorsanambulatoryassessmentstudyonsocialinteractionsandloneliness
AT loddergerine modelingsocialinteractiondynamicsmeasuredwithsmartphonesensorsanambulatoryassessmentstudyonsocialinteractionsandloneliness