Evidence map›Paper›PMID 26812041›Full record

Trial reportTranslational psychiatry2016

Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series.

E A Holmes, M B Bonsall, S A Hales, H Mitchell, F Renner, S E Blackwell, P Watson, G M Goodwin, M Di Simplicio

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Translational psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
7.0field-weighted citation impact, top 3% 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

38 citing papers in PubMed, 94 citations in OpenAlex.

  1. Trial
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  4. Article
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  7. Review
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  9. Article
  10. Individual-Specific Animated Profiles of Mental Health.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025
    Article
  11. Article
  12. Article
  13. Modelling Identity Disturbance: A Network Analysis of the Personality Structure Questionnaire (PSQ).International journal of environmental research and public health · 2022
    Article
  14. Review
  15. Article
  16. Article
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  18. Review
  19. Profiles of Perfectionistic Ruminations in Undergraduates: Impact on the Spontaneous Use of Mental Images.International journal of environmental research and public health · 2020
    Article
  20. Article
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

9 authors at 3 institutions in 2 countries.

E A HolmesMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
M B BonsallMathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford, UK.
S A HalesUniversity Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.
H MitchellMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
F RennerMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
S E BlackwellMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
P WatsonMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
G M GoodwinUniversity Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.
M Di SimplicioMedical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
MRC Cognition and Brain Sciences Unit · GBUniversity of Oxford · GBMedical Research Council · GB

Funding

Medical Research Council MC-A060-5PR50Medical Research Council MC_U105579212Medical Research Council MC_UP_0901/1Wellcome Trust WT088217
6 · The paper itself

Abstract

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP).

Indexed as

AdultAffectBipolar DisorderCognitive Behavioral TherapyFemaleHumansImagery, PsychotherapyMaleSeverity of Illness IndexTreatment Outcome

Identifiers

PMID26812041
PMCPMC5068881
OpenAlexW2261684155

What OpenQuestion holds

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LicenceCC BY
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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.