Digital biomarkers of mood disorders and symptom change
Digital biomarkers of mood disorders and symptom change
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DOI：
10.1038/s41746-019-0078-0
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发表时间：
2019-02-01
影响因子：
15.2
通讯作者：
Wilhelm, Sabine
中科院分区：
文献类型：
--
作者：
Jacobson, Nicholas C.;Weingarden, Hilary;Wilhelm, Sabine
目前的精神病评估方法是资源密集型的，需要经过培训的临床医生进行耗时的评估。数字生物标志物的开发有望实现对精神病诊断和症状变化的可扩展，时间敏感和成本效益的评估。本研究旨在通过重新分析在重度抑郁症或双相情感障碍患者和健康对照者中收集的公共使用腕动记录数据，确定诊断状态和症状严重程度变化的稳健数字生物标志物，时间约为2周。结果表明，参与者的诊断组状态（即，情绪障碍，控制）可以高准确度（正确预测89%的时间，Kappa = 0.773）进行预测。结果还表明，体动记录数据可用于预测2周内的症状变化（r = 0.782，p = 1.04e-05）。通过在我们的统计模型中纳入数字生物标志物，这些数字生物标志物可推广到新样本，其他研究小组可以复制这些结果，以验证和扩展这项工作。
Current approaches to psychiatric assessment are resource-intensive, requiring time-consuming evaluation by a trained clinician. Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective assessment of both psychiatric diagnosis and symptom change. The present study aimed to identify robust digital biomarkers of diagnostic status and changes in symptom severity over similar to 2 weeks, through re-analysis of public-use actigraphy data collected in patients with major depressive or bipolar disorder and healthy controls. Results suggest that participants' diagnostic group status (i.e., mood disorder, control) can be predicted with a high degree of accuracy (predicted correctly 89% of the time, kappa = 0.773), using features extracted from actigraphy data alone. Results also suggest that actigraphy data can be used to predict symptom change across similar to 2 weeks (r = 0.782, p = 1.04e-05). Through inclusion of digital biomarkers in our statistical model, which are generalizable to new samples, the results may be replicated by other research groups in order to validate and extend this work.