Evidence map›Paper›PMID 34383689›Full record

ArticleJMIR mental health2021

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study.

Jiří Anýž, Eduard Bakštein, Andrea Dally, Marián Kolenič, Jaroslav Hlinka, Tereza Hartmannová, Kateřina Urbanová, Christoph U Correll, Daniel Novák, Filip Španiel

Open access · goldAbstract read
In one paragraph

Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
–field-weighted citation impact, top 94% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
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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

10 authors at 3 institutions in 3 countries.

Jiří AnýžNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0002-8977-1172
Eduard BakšteinNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0002-4672-4923
Andrea DallyMindpax s.r.o, Prague, Czech Republic.ORCID https://orcid.org/0000-0001-5647-8845
Marián KoleničNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0002-2382-3478
Jaroslav HlinkaNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0003-1402-1470
Tereza HartmannováNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0003-0445-0992
Kateřina UrbanováNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0002-8633-951X
Christoph U CorrellDepartment of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0002-7254-5646
Daniel NovákDepartment of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.ORCID https://orcid.org/0000-0001-6996-9344
Filip ŠpanielNational Insitute of Mental Health, Klecany, Czech Republic.ORCID https://orcid.org/0000-0003-3479-696X
National Institute of Mental Health · CZCzech Technical University in Prague · CZDonald & Barbara Zucker School of Medicine at Hofstra/Northwell · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSelf-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care.

objectiveIn this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire-a 10-item, mobile app-based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).

methodsThe content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.

resultsA total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).

conclusionsThe ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

Indexed as

bipolar disorderecological mood assessmentmobile applicationmobile phonerelapse detectionsymptom monitoring

Identifiers

PMID34383689
PMCPMC8386400
OpenAlexW3163303828

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.