Evidence map›Paper›PMID 34757319›Full record

ArticleJMIR formative research2021

Development of a Severity Score and Comparison With Validated Measures for Depression and Anxiety: Validation Study.

William Lynch, Michael L Platt, Adam Pardes

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Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

William Lynch *NeuroFlow, Inc, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-2711-6834
Michael L Platt *Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-3912-8821
Adam Pardes *NeuroFlow, Inc, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-4040-5670

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLess than 10% of the individuals seeking behavioral health care receive measurement-based care (MBC). Technology has the potential to implement MBC in a secure and efficient manner. To test this idea, a mobile health (mHealth) platform was developed with the goal of making MBC easier to deliver by clinicians and more accessible to patients within integrated behavioral health care. Data from over 3000 users of the mHealth platform were used to develop an output severity score, a robust screening measure for depression and anxiety.

objectiveThe aim of this study is to compare severity scores with scores from validated assessments for depression and anxiety and scores from clinician review to evaluate the potential added value of this new measure.

methodsThe severity score uses patient-reported and passively collected data related to behavioral health on an mHealth platform. An artificial intelligence-derived algorithm was developed that condenses behavioral health data into a single, quantifiable measure for longitudinal tracking of an individual's depression and anxiety symptoms. Linear regression and Bland-Altman analyses were used to evaluate the relationships and differences between severity scores and Personal Health Questionnaire-9 (PHQ-9) or Generalized Anxiety Disorder-7 (GAD-7) scores from over 35,000 mHealth platform users. The severity score was also compared with a review by a panel of expert clinicians for a subset of 250 individuals.

resultsLinear regression results showed a strong correlation between the severity score and PHQ-9 (r=0.74; P<.001) and GAD-7 (r=0.80; P<.001) changes. A strong positive correlation was also found between the severity score and expert panel clinical review (r=0.80-0.84; P<.001). However, Bland-Altman analysis and the evaluation of outliers on regression analysis showed that the severity score was significantly different from the PHQ-9.

conclusionsClinicians can reliably use the mHealth severity score as a proxy measure for screening and monitoring behavioral health symptoms longitudinally. The severity score may identify at-risk individuals who are not identified by the PHQ-9. Further research is warranted to evaluate the sensitivity and specificity of the severity score.

Indexed as

anxiety assessmentdepression assessmentGAD-7integrated behavioral healthmeasurement-based carePHQ-9

Identifiers

PMID34757319
PMCPMC8663615

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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.