Abstract
This paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD). The modelling work was part of an EPSRC-funded project, also involving biomedical engineers and computer scientists, to develop a prototype PAM system. BD is a chronic, disabling mental illness associated with recurrent severe episodes of mania and depression, interspersed with periods of remission. Early detection of the onset of an acute episode is crucial for effective treatment and control. The aim of PAM is to enable patients with BD to self-manage their condition, by identifying the person's normal ‘activity signature’ and thus automatically detecting tiny changes in behaviour patterns which could herald the possible onset of an acute episode. PAM then alerts the patient to take appropriate action in time to prevent further deterioration and possible hospitalisation. A disease state transition model for BD was developed, using data from the clinical literature, and then used stochastically in a Monte Carlo simulation to test a wide range of monitoring scenarios. The minimum best set of sensors suitable to detect the onset of acute episodes (of both mania and depression) is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensors.
Highlights
Bipolar disorder (BD) is a severe, chronic form of mental illness associated with two types of recurrent episode, mania and depression, both of which drastically affect quality of life and the ability to function normally (Vojta et al, 2001; Michalak et al, 2007)
This paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD)
The results showed that the PAM system can offer a wider set of personalised prodromal choices to patients who fall into Data set 1 than into Data set 2
Summary
Bipolar disorder (BD) is a severe, chronic form of mental illness associated with two types of recurrent episode, mania and depression, both of which drastically affect quality of life and the ability to function normally (Vojta et al, 2001; Michalak et al, 2007). Based on a total of approximately 12 400 hospital episodes for BD in that year, this gives a rough average cost of over £5000 per admission at 1998 prices (Gupta and Guest, 2002). Surveys have shown that many BD patients are very keen to use psychosocial therapy and self-management approaches in addition to pharmacological treatment (Lish et al, 1994; Hill et al, 1996). Early symptoms of relapse are useful indicators to patients themselves, family members or clinicians, since extra support can be provided to help prevent progression into a full-blown episode. Most self-management interventions have been manual and diary-based. These are time-consuming and expensive, but are unreliable. Automated ambient data collection to identify a BD patient’s daily activity patterns may avoid the drawbacks of manual systems and may detect both aspects of the disorder
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