Abstract
Multifractal dynamics of activity data in bipolar disorder: Towards automated early warning of manic relapse
Highlights
Bipolar disorder, a severe psychiatric condition with a lifetime prevalence of about 2% [1] is characterised by episodes of severe depression and mania interspersed with euthymic periods during which the patient is relatively well
Owing to a small amount of missing data, the multifractal spectrum parameters were not computed for Week 6
Slopes close to –1 suggest long-term correlations in the activity data, often associated with a complex timescale invariant process, a more detailed multifractal analysis of these data offered greater insight into the temporal structure of activity data to facilitate its application in preventative mental health
Summary
A severe psychiatric condition with a lifetime prevalence of about 2% [1] is characterised by episodes of severe depression and mania interspersed with euthymic periods during which the patient is relatively well. The burden from bipolar depression can be extremely debilitating, manic episodes are the most functionally impairing phase of the disorder, frequently accompanied by psychosis, hyperactivity and sleep disturbance, leading to protective hospitalisation [2]. By identifying manic episodes early, less aggressive interventions, such as stimulation reduction and an increase in antipsychotic medication, may lead to euthymia. Bipolar patients who respond swiftly to early indicators of an episode have better outcomes [3]. As commonly-used daily mood monitoring is not ideal because of patient burden and impaired insight, 24-hour activity monitoring can provide automated, objective information about episode prodromes [4]
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