Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.
Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.
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DOI：
10.2196/30833
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发表时间：
2021-09-15
影响因子：
5.2
通讯作者：
Perez-Rodriguez MM
中科院分区：
文献类型：
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作者：
Ryu J;Sükei E;Norbury A;H Liu S;Campaña-Montes JJ;Baca-Garcia E;Artés A;Perez-Rodriguez MM
公共卫生危机期间的焦虑症状与不良精神病后果和健康决策受损有关。传染病爆发期间实时社交媒体使用模式与临床焦虑之间的相互作用尚未得到充分研究。我们旨在评估西班牙马德里COVID-19激增和封锁期间门诊精神病治疗患者使用2种类型社交媒体应用程序（通信和社交网络）的模式及其临床随访时的短期焦虑症状（7项广泛性焦虑障碍量表）。使用重复测量方差分析总结了2020年2月1日至5月3日期间社交媒体使用行为中位数的个人水平变化，该分析解释了锁定（锁定前与锁定后），组（临床焦虑组与非临床焦虑组），锁定和组的相互作用以及用户的随机效应的固定效应。基于包括通信和社交网络应用程序使用的纵向时间序列数据，应用基于机器学习的方法，结合隐马尔可夫模型和逻辑回归来预测临床焦虑（n=44）和非临床焦虑（n=51）（以秒为单位）以及与焦虑相关的临床调查变量，包括家庭中重要工作者的存在，对生活不稳定的担忧，在封锁期间社会交往频率的变化、同居状态和健康状况。对日常社交媒体使用情况的个人层面分析显示，临床焦虑组从封锁前到封锁期的通信应用程序使用量的增加显著小于非临床焦虑组（F1，72=3.84，P= 0.05）。在识别临床焦虑组的10倍交叉验证中，机器学习模型的平均准确率为62.30%（SD 16%），受试者工作曲线下面积为0.70（SD 0.19）。报告严重焦虑症状的患者在强制封锁后不太活跃于通信应用程序，而在整个期间更多地参与社交网络应用程序，这表明适应危机的数字社交行为模式不同。使用数字生物标记的预测建模被动感知在封锁期间基于类别的社交媒体应用程序使用的变化可以识别出有精神后遗症风险的个人。
Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.
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影响因子：
15.2
作者：
Guntuku SC;Klinger EV;McCalpin HJ;Ungar LH;Asch DA;Merchant RM
通讯作者：
Merchant RM
影响因子：
7.4
作者：
Jacobson, Nicholas C.;Summers, Berta;Wilhelm, Sabine
通讯作者：
Wilhelm, Sabine
影响因子：
9.9
作者：
Choi, Doo-Hun;Yoo, Woohyun;Park, Keeho
通讯作者：
Park, Keeho
DOI：
10.1007/7854_2020_170
发表时间：
2020-01-01
期刊：
BEHAVIORAL NEUROBIOLOGY OF SUICIDE AND SELF HARM
影响因子：
--
作者：
Carretero, Patricia;Jose Campana-Montes, Juan;Artes-Rodriguez, Antonio
通讯作者：
Artes-Rodriguez, Antonio
影响因子：
3.7
作者：
Lin, Suwen;Faust, Louis;Chawla, Nitesh V.
通讯作者：
Chawla, Nitesh V.