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Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders.

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Title: Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders.
Authors: Kim, Ko Woon (AUTHOR), Byun, Andrew Jin Soo (AUTHOR), Castillo, Juan (AUTHOR), Youn, Young Chul (AUTHOR), Torous, John (AUTHOR)
Source: Harvard Review of Psychiatry. Mar/Apr2026, Vol. 34 Issue 2, p85-94. 10p.
Subjects: Metadata, Cognitive testing, Mental depression, Mental illness, Bipolar disorder, Schizophrenia, Mental health
Abstract: Smartphone-based cognitive assessments have emerged as promising tools for frequent and ecologically valid monitoring of cognitive function in real-world settings. These tools enable continuous capture of cognitive and behavioral patterns, including intra-individual variability, practice-related improvement, and contextual influences. Repeated assessments offer a unique opportunity to detect subtle cognitive changes over time. The interpretability and clinical utility of the metadata generated by such assessments, however, remain underexplored. In this review, we consider the current landscape of smartphone-derived cognitive metadata in the context of cognitive and affective disorders. We focus on emerging evidence linking metadata features to functional outcomes and symptom fluctuations across conditions such as schizophrenia, bipolar disorder, and depression. Additionally, we discuss methodological considerations for optimizing metadata analysis, including test design, sampling frequency, and analytical strategies. We propose that cognitive metadata may serve as sensitive indicators of early cognitive change and support personalized mental health monitoring and targeted intervention. [ABSTRACT FROM AUTHOR]
Copyright of Harvard Review of Psychiatry is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders.
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  Data: <searchLink fieldCode="JN" term="%22Harvard+Review+of+Psychiatry%22">Harvard Review of Psychiatry</searchLink>. Mar/Apr2026, Vol. 34 Issue 2, p85-94. 10p.
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  Data: Smartphone-based cognitive assessments have emerged as promising tools for frequent and ecologically valid monitoring of cognitive function in real-world settings. These tools enable continuous capture of cognitive and behavioral patterns, including intra-individual variability, practice-related improvement, and contextual influences. Repeated assessments offer a unique opportunity to detect subtle cognitive changes over time. The interpretability and clinical utility of the metadata generated by such assessments, however, remain underexplored. In this review, we consider the current landscape of smartphone-derived cognitive metadata in the context of cognitive and affective disorders. We focus on emerging evidence linking metadata features to functional outcomes and symptom fluctuations across conditions such as schizophrenia, bipolar disorder, and depression. Additionally, we discuss methodological considerations for optimizing metadata analysis, including test design, sampling frequency, and analytical strategies. We propose that cognitive metadata may serve as sensitive indicators of early cognitive change and support personalized mental health monitoring and targeted intervention. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Harvard Review of Psychiatry is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=192146697
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        Value: 10.1097/HRP.0000000000000453
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              Text: Mar/Apr2026
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