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

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).

Trial registration: ClinicalTrials.gov NCT01981018.

Conflict of interest statement

GMG declares shares in P1vital and in the last 2 years having served as consultant, advisor or continuing medical education speaker for AstraZeneca, Abbvie, Cephalon/Teva, Convergence, Eli Lilly and Co, GSK, Lundbeck, Medscape, Merck, Otsuka, P1vital, Servier, Sunovion and Takeda. The remaining authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The QIDS-SR daily mood scores for 28 days pre-treatment (left hand side) and 28 days post-treatment (right hand side), per participant. Participants presented in order of starting mood monitoring. Individual mood plots show the QIDS-SR score (black points and black line), best model fit from time-series analysis (purple points). Predicted values (from the overall time-series model pre- and post-treatment) are shown with an approximate 95% CI band in grey. Note, differing y axis are used for visibility of any change in variability of the daily ratings (and see Supplementary Figure S1 for mean weekly values pre- and post-treatment). CI, confidence interval; QIDS-SR, Quick Inventory of Depressive Symptomatology.
Figure 2
Figure 2
Markov chain analysis of changes in QIDS-SR daily scores for individual participants pre- and post-treatment. Circle size represents the probability of a patient being in a certain mood state: red circles represent moderate levels of depression (QIDS-SR⩾9); orange circles represent mild levels of depression (QIDS-SR⩽9 and not equal to 0); green circles represent the absence of any depressive symptoms (QIDS-SR=0). For a given participant, this gives a picture of transition between states during their 28-day baseline phase (front triangle) which can be compared with their 28-day post-treatment phase (back triangle).

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