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

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).

SUBMITTER: Holmes EA

PROVIDER: S-EPMC5068881 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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

Holmes E A EA Bonsall M B MB Hales S A SA Mitchell H H Renner F F Blackwell S E SE Watson P P Goodwin G M GM Di Simplicio M M

Translational psychiatry 20160126

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 ...[more]

PMID: 26812041

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Project description:ObjectiveThe Course and Outcome of Bipolar Youth study found that children and adolescents with bipolar spectrum disorders followed 1 of 4 distinct mood trajectories over 8 years of follow-up, with as many as 25% of participants showing a predominantly euthymic course. We evaluated whether similar patterns of illness course are observed in adolescents with bipolar I and II disorder who participated in a 2-year clinical trial.MethodA total of 144 adolescents with bipolar I or II disorder, identified shortly after a mood episode, were assessed over a 2-year period. Participants were randomly assigned to one of 2 psychosocial family treatments during the first 9 months of the study, and pharmacotherapy was provided throughout the 2 years. Using latent class growth analyses, we classified participants into distinct courses of illness based on mood ratings collected over the 2 years. We examined demographic and illness variables as predictors of these course classifications.ResultsLatent class growth analyses indicated four mood trajectories: "predominantly euthymic" (29.9% of sample), "ill with significantly improving course" (11.1%), "moderately euthymic" (26.4%), and "ill with moderately improving course" (32.6%). Adolescents in these classes were euthymic 77.7%, 53.6%, 44.1%, and 18.6% of the weeks of follow-up, respectively. Psychosocial treatment condition and baseline medication exposure were not associated with trajectories. However, youth with more severe baseline depressive symptoms, suicidality, lower quality of life scores, and minority race/ethnicity had more symptomatic courses of illness over time.ConclusionA substantial proportion (25%-30%) of youth with bipolar I or II disorder maintain euthymic states over extended periods of follow-up. Identifying youth who are more and less likely to remain stable over time may help guide psychosocial and pharmacological treatments after an illness episode.Clinical trial registration informationEffectiveness of Family-Focused Treatment Plus Pharmacotherapy for Bipolar Disorder in Adolescents; https://clinicaltrials.gov/; NCT00332098.

Dataset Information

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

Publications

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

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