Imperial College London
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Dr Martina Di Simplicio

Faculty of Medicine, Department of Brain Sciences

Clinical Senior Lecturer in Psychiatry
 
 
 
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Contact

 

+44 (0)20 7594 1071m.di-simplicio

 
 
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Assistant

 

Ms Nicole Hickey +44 (0)20 3313 4161

 
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Location

 

7N11ACommonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Holmes:2016:10.1038/tp.2015.207,
author = {Holmes, EA and Bonsall, MB and Hales, SA and Mitchell, H and Renner, F and Blackwell, SE and Watson, P and Goodwin, GM and Di, Simplicio M},
doi = {10.1038/tp.2015.207},
journal = {TRANSLATIONAL PSYCHIATRY},
title = {Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series},
url = {http://dx.doi.org/10.1038/tp.2015.207},
volume = {6},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 (
AU  - Holmes,EA
AU  - Bonsall,MB
AU  - Hales,SA
AU  - Mitchell,H
AU  - Renner,F
AU  - Blackwell,SE
AU  - Watson,P
AU  - Goodwin,GM
AU  - Di,Simplicio M
DO  - 10.1038/tp.2015.207
PY  - 2016///
SN  - 2158-3188
TI  - Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series
T2  - TRANSLATIONAL PSYCHIATRY
UR  - http://dx.doi.org/10.1038/tp.2015.207
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000368586600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/61859
VL  - 6
ER  -
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