Smartphone Monitoring of Mood Instability in Young Depressed Patients: A Latent-class Analyses.

Vibha Anand, Bo Hu, Amit Anand

Open access · greenAbstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

0.3field-weighted citation impact, top 36% of its field

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

2 citing papers in PubMed, 4 citations in OpenAlex.

Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026
Article
Efficacy of digital technologies aimed at enhancing emotion regulation skills: Literature review.Frontiers in psychiatry · 2022
Review

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

3 authors at 2 institutions in 1 country.

Vibha AnandCenter for Computational Health, IBM T.J. Watson Research Center, Cambridge, MA, USA.

Bo HuQuantitative Health Sciences, Cleveland Clinic, Ohio, USA.

Amit AnandCenter for Behavioral Health, Cleveland Clinic Foundation, Ohio, USA.

Cleveland Clinic · USIBM (United States) · US

Funding

Differentiating Unipolar and Bipolar Depression in Young Adults Using fMRIR01MH093420 · NIMH · INDIANA UNIVERSITY INDIANAPOLIS · PI ANAND, AMIT · 2011 to 2016

$2.3M

NIMH NIH HHS R01 MH093420

6 · The paper itself

Abstract

This study captured daily and weekly mood ratings using a smartphone from bipolar disorder (BD) and unipolar major depression disorder (MDD) subjects at high (HRMDD) and low risk (LRMDD) for developing Bipolar Disorder (BD) and healthy controls (HC).

method40 subjects (18 - 30 yr) (6 BD, 13 HRMDD, 16 LRMDD and 5 HC) were studied and a total of 2401 daily and 744 weekly ratings were collected. HRMDD and LRMDD subjects were naturalistically treated with antidepressants. We investigate if latent-class analyses of ratings can detect mood instability among MDD and BD groups.

resultsOur analyses revealed four underlying mood states correlating with clinical mood states. There was a trend for greater number of state changes in BD and HRMDD subjects compared to LRMDD and HC groups.

conclusionSmartphone ratings may adequately capture mood instability in BD subjects and at risk HRMDD subjects and offers a prudent way for monitoring development of serious manic symptoms.

Indexed as

Mobile ApplicationsSmartphoneAdolescentAdultAntidepressive AgentsBipolar DisorderFemaleHumansLatent Class AnalysisMajor Depressive DisorderMaleSelf ReportYoung AdultAntidepressive Agents

Identifiers

PMID32308814

PMCPMC7153103

OpenAlexW3019795608

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.