Digital phenotyping of negative symptoms: the relationship to clinician ratings
Digital phenotyping of negative symptoms: the relationship to clinician ratings
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DOI：
10.1093/schbul/sbaa065
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发表时间：
2021-01-01
影响因子：
6.6
通讯作者：
Strauss, Gregory P.
中科院分区：
文献类型：
--
作者：
Cohen, Alex S.;Schwartz, Elana;Strauss, Gregory P.
阴性症状是精神分裂症的一个关键但鲜为人知的方面。对阴性症状的测量主要依赖于临床医生的评分，这是一项具有既定可靠性和有效性的努力。已经有越来越多的尝试使用客观的生物行为技术来数字化表型阴性症状，例如，使用对声音、语音、面部、手和其他行为的计算机分析。令人惊讶的是，生物行为技术和临床医生评级只有适度的相互关联，来自个别研究的发现往往不重复或违反直觉。在这篇文章中，我们在4个案例研究中，在877个音频/视频样本的档案数据集中，以及在现有的文献中，记录和评估这种缺乏收敛的情况。然后，我们从“分辨率”的角度来解释这种差异，“分辨率”是生物医学、工程和计算科学中的一种重要的心理测量学属性，定义为在区分信号的各个方面时的精确度。我们演示了如何通过在不同分辨率上扩展数据来实现临床评级和生物行为数据之间的融合。临床评级反映了一种不可或缺的工具，它将大量信息整合到可操作但“低分辨率”的顺序评级中。这样就可以看到负面症状的“森林”。不幸的是，它们的分辨率不能足够精确地缩放或分解，以分离出负性症状的时间、背景和性质，以用于许多目的(例如，看到“树”)。生物行为测量为了解何时、何地以及为什么出现阴性症状提供了精确的信息，尽管需要做很多工作来验证它们。阴性症状的数字表型可以为跟踪、理解和治疗它们提供前所未有的机会，但需要考虑解决问题。
Negative symptoms are a critical, but poorly understood, aspect of schizophrenia. Measurement of negative symptoms primarily relies on clinician ratings, an endeavor with established reliability and validity. There have been increasing attempts to digitally phenotype negative symptoms using objective biobehavioral technologies, eg, using computerized analysis of vocal, speech, facial, hand and other behaviors. Surprisingly, biobehavioral technologies and clinician ratings are only modestly inter-related, and findings from individual studies often do not replicate or are counterintuitive. In this article, we document and evaluate this lack of convergence in 4 case studies, in an archival dataset of 877 audio/video samples, and in the extant literature. We then explain this divergence in terms of "resolution"-a critical psychometric property in biomedical, engineering, and computational sciences defined as precision in distinguishing various aspects of a signal. We demonstrate how convergence between clinical ratings and biobehavioral data can be achieved by scaling data across various resolutions. Clinical ratings reflect an indispensable tool that integrates considerable information into actionable, yet "low resolution" ordinal ratings. This allows viewing of the "forest" of negative symptoms. Unfortunately, their resolution cannot be scaled or decomposed with sufficient precision to isolate the time, setting, and nature of negative symptoms for many purposes (ie, to see the "trees"). Biobehavioral measures afford precision for understanding when, where, and why negative symptoms emerge, though much work is needed to validate them. Digital phenotyping of negative symptoms can provide unprecedented opportunities for tracking, understanding, and treating them, but requires consideration of resolution.