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22-05-2021 | Original Article

Idiographic Network Models of Social Media Use and Depression Symptoms

Auteurs: Micaela Rodriguez, George Aalbers, Richard J. McNally

Gepubliceerd in: Cognitive Therapy and Research | Uitgave 1/2022

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Abstract

Background

Disentangling the impact of social media use on well-being is a priority for psychological research. Numerous studies suggest that active social media use (ASMU) enhances well-being, whereas passive social media use (PSMU) undermines it. However, such research has conducted group-level analyses, potentially obscuring individual differences. We examined person-centered relationships between SMU and depression symptoms by using a publicly available experience sampling dataset (Aalbers Journal of Experimental Psychology: General 148: 1454–1462, 2019)

Methods

Dutch undergraduate students (N = 125) reported PSMU, ASMU, and depression symptoms 7 times daily for 14 days. We (a) visualized interindividual variability in temporal associations between social media use and individual depression symptoms, (b) compared the aggregate network model to idiographic models, and (c) determined the distribution of person-specific temporal associations.

Results

Overall, we found that associations between social media use and depression symptoms differed substantially from individual to individual in both strength and kind. In addition, PSMU and ASMU were very weakly to weakly associated with depression symptoms for most individuals.

Conclusions

Studying idiographic relationships between social media use and depression may help us (1) determine which individuals are most at risk of experiencing elevated depression symptoms after using social media and (2) personalize therapeutic treatments to alleviate symptoms.
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Metagegevens
Titel
Idiographic Network Models of Social Media Use and Depression Symptoms
Auteurs
Micaela Rodriguez
George Aalbers
Richard J. McNally
Publicatiedatum
22-05-2021
Uitgeverij
Springer US
Gepubliceerd in
Cognitive Therapy and Research / Uitgave 1/2022
Print ISSN: 0147-5916
Elektronisch ISSN: 1573-2819
DOI
https://doi.org/10.1007/s10608-021-10236-2