Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors
Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors
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DOI：
10.2196/16875
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发表时间：
2020-05-29
影响因子：
7.4
通讯作者：
Wilhelm, Sabine
中科院分区：
文献类型：
--
作者：
Jacobson, Nicholas C.;Summers, Berta;Wilhelm, Sabine
背景：社交焦虑障碍是一种非常普遍且负担沉重的疾病。患有社交焦虑的人经常避免寻求医生的支持，也很少接受治疗。社交焦虑症状经常被低估和低估，这对准确评估这些症状造成了障碍。因此，需要更多的研究来确定社交焦虑症状严重程度的被动生物标志物。数字表型，即利用被动传感器数据为卫生保健决策提供信息，为解决这一评估障碍提供了一种可能的方法。目的：本研究旨在确定从智能手机数据中获取的被动传感器数据是否可以使用公开的数据集准确预测社交焦虑症状的严重程度。方法：在本研究中，参与者（n=59）完成了社交焦虑症状严重程度、抑郁症状严重程度、积极影响和消极影响的自我报告评估。接下来，参与者安装了一个应用程序，该应用程序在两周内被动地收集他们的运动（加速度计）和社交联系（来电和短信）的数据。之后，这些被动传感器数据被用来形成数字生物标志物，并与机器学习模型配对，以预测参与者的社交焦虑症状严重程度。结果：被动性传感器数据可准确预测被试社交焦虑症状严重程度（预测与观察的r=0.702），且抑郁、消极情绪和积极情绪具有判别效度。结论：这些结果表明，智能手机传感器数据可以准确地检测社交焦虑症状严重程度，并将社交焦虑症状严重程度与抑郁症状、消极情绪和积极情绪区分开来。
Background: Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.Objective: This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.Methods: In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.Results: The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.