Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis
Shensa A, Sidani JE, Dew MA, Escobar-Viera CG, Primack BA
American journal of health behavior · 90 citations
How it was studied
- Design
- Cohort study (indexed by PubMed)
- Studied in
- People
- Main outcome
- Health markers and function
Who paid for it
- Funding
- Independent funding
- Government
- National Cancer Institute
- Government
- NCI NIH HHS
- Grants
- National Cancer Institute (R01-CA140150)
Based on 2 listed funder(s).
Publication
- Published
- 2018-02-19 · Am J Health Behav · vol. 42 · issue 2 · pp. 116–128
- Publisher
- PNG Publications
- Cited
- 237 citations · more than 100% of similar papers · 41.1× the field average
- Impact
- Top 10% most cited in its field
- References
- 42 works
- Access
- Open access (repository copy)
- Research areas
- Impact of Technology on Adolescents · Mental Health via Writing · Digital Mental Health Interventions
- Keywords
- social media use, depressive symptoms, anxiety symptoms, mental health, young adults
- MeSH
- humans, cluster analysis, longitudinal studies, depression, emotions, anxiety, social support, adult, female, male, young adult, social media
5 authors
From US
- Ariel S. ShensaUniversity of Pittsburgh
- Jaime E. SidaniUniversity of Pittsburgh
- Mary Amanda DewUniversity of Pittsburgh
- César G. Escobar-VieraUniversity of Pittsburgh
- Brian A. PrimackUniversity of Pittsburgh
Abstract
Objectives
Individuals use social media with varying quantity, emotional, and behavioral at- tachment that may have differential associations with mental health outcomes. In this study, we sought to identify distinct patterns of social media use (SMU) and to assess associations between those patterns and depression and anxiety symptoms.
Methods
In October 2014, a nationally-representative sample of 1730 US adults ages 19 to 32 completed an online survey. Cluster analysis was used to identify patterns of SMU. Depression and anxiety were measured using respective 4-item Patient-Reported Outcome Measurement Information System (PROMIS) scales. Multivariable logistic regression models were used to assess associations between clus- ter membership and depression and anxiety.
Results
Cluster analysis yielded a 5-cluster solu- tion. Participants were characterized as "Wired," "Connected," "Diffuse Dabblers," "Concentrated Dabblers," and "Unplugged." Membership in 2 clusters - "Wired" and "Connected" - increased the odds of elevated depression and anxiety symptoms (AOR = 2.7, 95% CI = 1.5-4.7; AOR = 3.7, 95% CI = 2.1-6.5, respectively, and AOR = 2.0, 95% CI = 1.3-3.2; AOR = 2.0, 95% CI = 1.3-3.1, respectively).
Conclusions
SMU pattern characterization of a large population suggests 2 pat- terns are associated with risk for depression and anxiety. Developing educational interventions that address use patterns rather than single aspects of SMU (eg, quantity) would likely be useful.
Abstract via Europe PMC. Copyright remains with the authors or publisher.
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