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Using Lifestyle Monitoring to Predict Bipolar Episodes Rachel Heath School of Psychology University of Newcastle, Australia SykTek ®

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Presentation on theme: "Using Lifestyle Monitoring to Predict Bipolar Episodes Rachel Heath School of Psychology University of Newcastle, Australia SykTek ®"— Presentation transcript:

1 Using Lifestyle Monitoring to Predict Bipolar Episodes Rachel Heath School of Psychology University of Newcastle, Australia SykTek ®

2 The basic assumptions: Activity measures provide sufficient information to monitor patients diagnosed with bipolar disorder. The activity measures need to be obtained and accumulated at least every 10 minutes for as long as is necessary to observe a relapse, be it depression or mania. A lot of research in various areas of medicine has shown that a measure known as entropy, or uncertainty, is sensitive to relapse in chronic disorders such as heart disease and epilepsy. The entropy measure implies that high values are associated with complex observations whereas low entropy is associated with low complexity. In most cases, we observe a decrease in entropy, or complexity, as measured by tools based on the theory of nonlinear dynamics. The problem is to apply some sensitive test to detect this change in complexity and provide a warning to the patient, carer and medical team.

3 What I have done so far A study published in 2003/2007: Mood ratings obtained over 18 mths for depressed and control subjects showed a lower overall complexity for the depressed subject. A reanalysis of these data in 2015 using a new method for analyzing rating scale data showed that the depressed subject also had a greater level of perichaoticity, the tendency to approach a “mental cliff”. In a talk given in 2014, I showed that data from a subject who suffered a manic episode following 104 days of continuous activity monitoring had a sudden decrease in complexity about three weeks prior to the episode. In our recent submission, we analysed these same activity data using a novel procedure based on multifractal analysis. There was clear evidence of a decline in this patient about three weeks before he was hospitalized. We believe that this method may be sensitive enough to apply to other patients at risk of hospitalization for a manic episode.

4 Detecting the Onset of a Manic Episode Before It Happens ….. Multifractal spectra become wider by week 9 then decreased in width until week 14 when the patient was hospitalized. The greater the width, the greater the complexity.

5 As declining health is associated with a decrease in entropy or complexity, evidence of the impending manic episode may have been available several weeks before hospitalization was required.

6 What I’d like to do next ……………………………….. Obtain activity data from more subjects diagnosed with bipolar disorder who have had at least one relapse in a twelve month period. A properly conducted randomized trial with matched controls would be ideal. Maybe carers??? The activity data can be acquired in any way provided it is precise enough to accumulate over each 10 min period, 24/7. It is handy to measure heart-rate just to be sure when a patient is NOT wearing their device, e.g. Fitbit Charge HR. (swimming, batheing and recharging the FitBit) My technology will allow monitoring of this activity and indicate to the patient, carer and mental health team when intervention is considered necessary. If the activity measurements are precise enough, they can also serve as a sensitive indicator of circadian changes including sleep. All we need is funding for a decent app that works on all mobile devices!


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