Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis.
Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis.
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DOI：
10.5993/ajhb.42.2.11
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发表时间：
2018-03-01
影响因子：
2.3
通讯作者：
Primack BA
中科院分区：
文献类型：
--
作者：
Shensa A;Sidani JE;Dew MA;Escobar-Viera CG;Primack BA
个人使用的社交媒体具有不同的数量、情感和行为依恋，这可能与心理健康结果有不同的关联。在这项研究中，我们试图确定不同的社交媒体使用模式(SMU)，并评估这些模式与抑郁和焦虑症状之间的关联。2014年10月，具有全国代表性的1730名19岁至32岁的美国成年人完成了一项在线调查。采用聚类分析的方法确定SMU的类型。抑郁和焦虑分别使用患者报告的结果测量信息系统(PROMIS)的4个条目进行测量。多变量Logistic回归模型被用来评估集群成员与抑郁和焦虑之间的关联。聚类分析得到了一个5-聚类解。参与者被描述为“有线的”、“连接的”、“分散的涉猎者”、“集中的涉猎者”和“不插电的”。“有线”和“联网”两个集群的成员身份增加了抑郁和焦虑症状的风险(AOR=2.7，95%CI=1.5~4.7；AOR=3.7，95%CI=2.1~6.5；AOR=2.0，95%CI=1.3~3.2；AOR=2.0，95%CI=1.3~3.1)。对大量人群的SMU模式特征表明，两种模式与抑郁和焦虑的风险相关。制定针对SMU使用模式而不是单一方面(例如数量)的教育干预措施可能是有用的。
Individuals use social media with varying quantity, emotional, and behavioral attachment 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. 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 cluster membership and depression and anxiety. Cluster analysis yielded a 5-cluster solution. 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). SMU pattern characterization of a large population suggests 2 patterns 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.