Evidence map›Paper›PMID 32467967›Full record

ArticleSchizophrenia bulletin2021

Digital phenotyping of negative symptoms: the relationship to clinician ratings.

Alex S Cohen, Elana Schwartz, Thanh P Le, Tovah Cowan, Brian Kirkpatrick, Ian M Raugh, Gregory P Strauss

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Schizophrenia bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Humanizing "multimodal" digital phenotyping of serious mental illness.Journal of psychopathology and clinical science · 2026
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Social network reductions are associated with negative symptoms in schizophrenia.Social psychiatry and psychiatric epidemiology · 2025
    Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alex S CohenDepartment of Psychology, Louisiana State University, Baton Rouge, LA.
Elana SchwartzDepartment of Psychology, Louisiana State University, Baton Rouge, LA.
Thanh P LeDepartment of Psychology, Louisiana State University, Baton Rouge, LA.
Tovah CowanDepartment of Psychology, Louisiana State University, Baton Rouge, LA.
Brian KirkpatrickDepartment of Psychiatry and Behavioral Sciences, University of Nevada, Reno School of Medicine, Reno, NV.
Ian M RaughDepartment of Psychology, University of Georgia, Athens, GA.
Gregory P StraussDepartment of Psychology, University of Georgia, Athens, GA.

Funding

Identifying the Vocal Markers of Schizophrenia-Spectrum DisordersR03MH092622 · NIMH · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI COHEN, ALEX S · 2011 to 2012
$148k
NIMH NIH HHS R03 MH092622
6 · The paper itself

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.

Indexed as

Behavior Rating ScalePsychiatric Status Rating ScalesPsychometricsFemaleHumansInterview, PsychologicalMaleMiddle AgedPhenotypeSchizophreniabiobehavioralcomputationaldeficitdigital phenotypingnegativepsychiatryschizophrenia

Identifiers

PMID32467967
PMCPMC7825094

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.