Smith Robert J, Crutchley Patrick, Schwartz H Andrew, Ungar Lyle, Shofer Frances, Padrez Kevin A, Merchant Raina M
Penn Medicine Social Media and Health Innovation Lab, Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.
Positive Psychology Center, Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States.
J Med Internet Res. 2017 Jan 6;19(1):e7. doi: 10.2196/jmir.6486.
Social media is emerging as an insightful platform for studying health. To develop targeted health interventions involving social media, we sought to identify the patient demographic and disease predictors of frequency of posting on Facebook.
The aims were to explore the language topics correlated with frequency of social media use across a cohort of social media users within a health care setting, evaluate the differences in the quantity of social media postings across individuals with different disease diagnoses, and determine if patients could accurately predict their own levels of social media engagement.
Patients seeking care at a single, academic, urban, tertiary care emergency department from March to October 2014 were queried on their willingness to share data from their Facebook accounts and electronic medical records (EMRs). For each participant, the total content of Facebook posts was extracted. Using the latent Dirichlet allocation natural language processing technique, Facebook language topics were correlated with frequency of Facebook use. The mean number of Facebook posts over 6 months prior to enrollment was then compared across validated health outcomes in the sample.
A total of 695 patients consented to provide access to their EMR and social media data. Significantly correlated language topics among participants with the highest quartile of posts contained health terms, such as "cough," "headaches," and "insomnia." When adjusted for demographics, individuals with a history of depression had significantly higher posts (mean 38, 95% CI 28-50) than individuals without a history of depression (mean 22, 95% CI 19-26, P=.001). Except for depression, across prevalent health outcomes in the sample (hypertension, diabetes, asthma), there were no significant posting differences between individuals with or without each condition.
High-frequency posters in our sample were more likely to post about health and to have a diagnosis of depression. The direction of causality between depression and social media use requires further evaluation. Our findings suggest that patients with depression may be appropriate targets for health-related interventions on social media.
社交媒体正成为一个研究健康问题的有见地的平台。为了开展涉及社交媒体的针对性健康干预措施，我们试图确定在脸书上发帖频率的患者人口统计学特征和疾病预测因素。
旨在探讨在医疗环境中的一群社交媒体用户中，与社交媒体使用频率相关的语言主题，评估不同疾病诊断的个体在社交媒体发帖数量上的差异，并确定患者是否能够准确预测自己的社交媒体参与水平。
对2014年3月至10月在一家单一的学术性城市三级护理急诊科寻求治疗的患者询问其是否愿意分享脸书账户和电子病历（EMR）的数据。对于每位参与者，提取脸书帖子的全部内容。使用潜在狄利克雷分配自然语言处理技术，将脸书语言主题与脸书使用频率相关联。然后在样本中根据经过验证的健康结果比较入组前6个月脸书帖子的平均数量。
共有695名患者同意提供对其电子病历和社交媒体数据的访问权限。发帖量处于最高四分位数的参与者之间显著相关的语言主题包含健康术语，如“咳嗽”“头痛”和“失眠”。在对人口统计学因素进行调整后，有抑郁症病史的个体的发帖量（平均38，95%可信区间28 - 50）显著高于无抑郁症病史的个体（平均22，95%可信区间19 - 26，P = 0.001）。除抑郁症外，在样本中的常见健康结果（高血压、糖尿病、哮喘）方面，有或无每种疾病的个体之间在发帖量上没有显著差异。
我们样本中的高频发帖者更有可能发布有关健康的内容并且被诊断为患有抑郁症。抑郁症与社交媒体使用之间的因果关系方向需要进一步评估。我们的研究结果表明，抑郁症患者可能是社交媒体上与健康相关干预措施的合适目标人群。
