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利用数字表型准确检测抑郁症严重程度。

Using Digital Phenotyping to Accurately Detect Depression Severity.

作者信息

Jacobson Nicholas C, Weingarden Hilary, Wilhelm Sabine

机构信息

Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

出版信息

J Nerv Ment Dis. 2019 Oct;207(10):893-896. doi: 10.1097/NMD.0000000000001042.

Abstract

Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder (MDD). The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with MDD. Using over 1 week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855; 95% confidence interval [CI], 0.610-0.950; p = 4.95 × 10) and clinician-rated (r = 0.604; 95% CI, 0.133-0.894; p = 0.017) symptom severity. Pending replication, the present data suggest that the use of passive wearable sensors to inform healthcare decisions holds considerable promise.

摘要

数字生物标志物的发展有望实现可扩展、对时间敏感且具有成本效益的策略,以监测重度抑郁症(MDD)患者的症状严重程度。本研究调查了利用可穿戴设备的被动运动和光照数据来评估15名MDD患者的抑郁严重程度。通过超过1周的运动数据,我们能够以高精度显著评估自我报告的(r = 0.855;95%置信区间[CI],0.610 - 0.950;p = 4.95 × 10)和临床医生评定的(r = 0.604;95% CI,0.133 - 0.894;p = 0.017)症状严重程度。在等待重复验证的情况下,目前的数据表明,使用被动可穿戴传感器为医疗决策提供依据具有很大的前景。

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