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 · 91 citas
Cómo se estudió
- Diseño
- Estudio de cohortes (indexado por PubMed)
- Estudiado en
- Personas
- Resultado principal
- Marcadores de salud y funcionamiento
Quién lo pagó
- Financiación
- Financiación independiente
- Gobierno
- National Cancer Institute
- Gobierno
- NCI NIH HHS
- Subvenciones
- National Cancer Institute (R01-CA140150)
Según 2 financiadores declarados.
Publicación
- Publicado
- 2018-02-19 · Am J Health Behav · vol. 42 · número 2 · págs. 116–128
- Editorial
- PNG Publications
- Citado
- 237 citas · más que el 100% de estudios similares · 41.1× el promedio de su campo
- Impacto
- Entre el 10 % más citado de su campo
- Referencias
- 42 trabajos
- Acceso
- Acceso abierto (copia en repositorio)
- Áreas de investigación
- Impact of Technology on Adolescents · Mental Health via Writing · Digital Mental Health Interventions
- Palabras clave
- 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 autores
- Ariel S. Shensa🇺🇸University of Pittsburgh
- Jaime E. Sidani🇺🇸University of Pittsburgh
- Mary Amanda Dew🇺🇸University of Pittsburgh
- César G. Escobar-Viera🇺🇸University of Pittsburgh
- Brian A. Primack🇺🇸University of Pittsburgh
Resumen
El texto original del estudio, en el idioma en que se publicó.
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.
Resumen vía Europe PMC. Los derechos de autor siguen siendo de los autores o la editorial.
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