Digital phenotyping of negative symptoms: the relationship to clinician ratings

Abstract

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.

Avolition as the core negative symptom in schizophrenia: relevance to pharmacological treatment development

“…To further improve the assessment of negative symptoms and obtain longitudinal rather than cross-sectional data, a third-generation of measurement tools, based on digital phenotyping (i.e., the use of mobile devices, such as smartphones and smartbands, to initiate data collection in everyday life) is currently under development. Both active (e.g., ecological momentary assessment surveys, ambulatory videos) and passive (e.g., geolocation, accelerometry, acoustic measures) digital phenotyping measures may hold promise for measuring negative symptoms more objectively in the context of everyday life [39][40][41][42] .…”

High Predictive Accuracy of Negative Schizotypy With Acoustic Measures

“…Self-report is largely considered a reliable, valid, and practical method for measuring schizotypy, but there are limitations inherent in self-report. Clinical-rating-based approaches have been used in the general population (Loranger et al, 1997), but they tend to require substantial resources to use, and there are limitations inherent in their use (Cohen, 2019; Cohen, Schwartz, et al, 2021). It has been proposed by several research groups in this area that objective modalities could be used to enhance or replace existing measures (Bedwell et al, 2006; Gooding et al, 2006; Lenzenweger et al, 2007; Minor & Cohen, 2012).…”

“…One approach to digitally phenotype negative schizotypal traits involves quantifying vocal expression using computerized acoustic analysis of natural speech. Vocal expression is an attractive target for measurement because it (a) maps onto the “constricted affect” trait of schizotypy (American Psychiatric Association, 2013); (b) conceptually relates to schizophrenia-spectrum symptoms of blunted vocal affect and alogia (Cohen et al, 2019; Cohen, Schwartz, et al, 2021); (c) reflects a key sociocognitive ability that generally requires psychomotor, social cognitive, and working memory abilities putatively central to negative schizotypy; (d) is based on speech data that can be collected unobtrusively using inexpensive recording technologies; and (e) employs natural language processing, a field of computational linguistics that has been applied to schizophrenia research for more than a decade (Cohen, Auster, McGovern, & MacAulay, 2014; Corcoran et al, 2018; Holshausen et al, 2014; Kuperberg, 2010; Parola et al, 2020). We are aware of seven studies that have examined relationships between self-reported negative schizotypy and acoustic features of speech (Bedwell et al, 2014; Cohen, Auster, McGovern, & MacAulay, 2014; Cohen & Hong, 2011; Cohen, Iglesias, & Minor, 2009; Cohen, Morrison, et al, 2012; Dickey et al, 2012; Tsakanikos & Claridge, 2005).…”

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

“…Second, improved resolution and data scalability can help monitor "within-person" change in ways existing measures cannot. In this regard, people can be tracked over time to best match them to interventions to minimize invasiveness, side-effects, and cost and to maximize their effectiveness (89,90). Third, accounting for signal variation can help identify noise due to technical and recording issues, which can help efforts to optimize recording protocols.…”