Evidence map›Paper›PMID 27449555›Full record

ArticleJournal of affective disorders2016

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder.

A Tsanas, K E A Saunders, A C Bilderbeck, N Palmius, M Osipov, G D Clifford, G Μ Goodwin, M De Vos

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of affective disorders, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 6 pooled it
16.0field-weighted citation impact, top 1% 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

83 citing papers in PubMed, 6 syntheses or guidelines pooled it, 171 citations in OpenAlex.

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  9. Using a generative model of affect to characterize affective variability and its response to treatment in bipolar disorder.Proceedings of the National Academy of Sciences of the United States of America · 2022
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23 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

A TsanasInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK; Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, UK. Electronic address: athanasios.tsanas@eng.ox.ac.uk.
K E A SaundersDepartment of Psychiatry, University of Oxford, UK.
A C BilderbeckDepartment of Psychiatry, University of Oxford, UK.
N PalmiusInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
M OsipovInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
G D CliffordDepartment of Biomedical Informatics, Emory University, Atlanta, GA, USA; Department of Biomedical Engineering, Georgia Institute of Technology, USA.
G Μ GoodwinDepartment of Psychiatry, University of Oxford, UK.
M De VosInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, UK.
University of Oxford · GBEmory University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose.

methodsWe introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools.

resultsMZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. LIMITATIONS: All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings.

conclusionsMZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

Indexed as

AffectIrritable MoodAdolescentAdultAngerAnxietyBipolar DisorderBorderline Personality DisorderDepressionFemaleHumansMaleMiddle AgedQuality of LifeSelf-AssessmentSelf ReportBipolar disorderBorderline personality disorderDigital healthMood assessmentMood monitoringPatient reported outcome measures

Identifiers

PMID27449555
PMCPMC5296237
OpenAlexW2466601107

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.