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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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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 |
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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 |
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