Mobile Psychiatry: Toward Improving the Care for Bipolar Disorder

Int J Ment Health Syst. 2012;6:5. (doi:10.1186/1752-4458-6-5)

Clinical psychiatry has, to date, depended heavily on patients’ own recollections of their symptoms for gathering data. Intuitively, though, it is clear that memories about one’s own mental state would often be colored by one’s current mood and by whether the answers have positive associations or are socially acceptable. So an objective and quantitative method of collecting data on a patient’s environment and mental states would be ideal, to provide a more accurate basis for treatment decisions.

Ambulatory assessment systems are already used to collect data on physical activity: applications range from detecting falls in the elderly1 to researching the causes of obesity.2 Some study of ambulatory systems in the area of psychiatry has already been undertaken (in, for example, panic disorder3 and Alzheimer’s disease4). However, the full potential for ambulatory monitoring in mental health applications has yet to be explored.

Prociow and colleagues developed a prototype of an “early warning system” aimed at gathering objective, real-time data on a number of parameters in patients with bipolar disorder. Their research question focused on whether the system could point to possible signals and triggers of a mood episode in bipolar patients and then successfully cue an early intervention to stop that episode—and its attendant consequences—before it starts.

Their prototype incorporated a range of environmental and wearable sensors (Table 1), and they tested it first in 4 healthy volunteers. For the second phase of their study, end-user testing, 1 person with bipolar disorder agreed to full installation of the system. The participant remained euthymic during the trial, and no large changes were noted in her routine.

Table 1. Elements of the Personalized Ambient Monitoring Prototype

Sensor Parameters Monitored
Environmental
Motion Detector Indoor mobility or unusual activity (psychomotor retardation or agitation)
Door switches (eg, cupboard doors) Eating habits
Light detector Sleep patterns (either mania or depression detection)
Remote control monitor Faster or harder pressing of remote control buttons on, eg, a TV (mania detection)
Wearable (worn on a belt)
Accelerometer Physical activity, posture, unusual sleep patterns (either mania or depression detection)
GPS receiver (Bluetooth-enabled) Social encounters, precise physical location

The researchers were able to demonstrate collection of a number of valuable types of behavioral data via both the wearable and environmental components of the prototype. The wearable sensors included an accelerometer, which showed fluctuations in the participant’s physical activity, such as exercise habits (eg, a Sunday workout was noted). Periods of exposure to high levels of natural light were tracked by the light sensor, indicating the subject’s time spent outdoors. The GPS data provided insights into locations frequented by the participant (eg, work, the gym, a friend’s house, a bar/restaurant). Bluetooth encounters were analyzed to show how often the study participant interacted with other users of these devices, and where these encounters occurred. Many Bluetooth encounters in a single location might indicate being near a crowd of people.

The environmental sensors successfully provided insights into the participant’s sleep patterns, which can be of high importance in predicting the onset of a manic or depressive episode. Bed sensors indicated how long the subject slept as well as how many physical movements occurred. The authors note that these events could point to restlessness or night terrors, possibly predicting the onset of depression.

Environmental sensors also included indoor motion detectors, which revealed spikes or valleys in activity level (possibly indicating psychomotor retardation or activation), as well as door switches that measured cupboard door openings (possibly indicating excessive appetite or loss of appetite).

The researchers noted that far fewer data were gathered during the patient trial than in the healthy volunteer trial. The patient herself identified barriers to compliance with the monitoring routine: (1) discomfort of carrying extra devices, (2) forgetfulness with regard to charging the devices, and (3) a baseline unfamiliarity with personal technology devices.

Despite the limited trial size and the issues with compliance, Prociow and colleagues’ study represents a significant contribution to the literature on technology-based solutions in psychiatry. Their project demonstrated that a “personalized ambient monitoring system” can successfully provide data on a number of parameters that could point to the development of a mood episode. Rather than focusing on a single parameter in isolation, such as the number of steps taken in a day, their model takes an integrative approach, gathering objective data on a constellation of factors that point toward impending bipolar symptoms.

The authors point out that although the technology for monitoring patient behavior is rapidly becoming more sophisticated, psychiatrists and psychologists have yet to adopt it into general practice. Further, they assert, realization of the potential of technology-based approaches such as the one they developed will not happen without collaboration among researchers, clinicians, and policy makers.

References

  1. Noury N. Fleury, Rumeau, et al. Fall detection: principles and methods. Proceedings of the 29th Annual International Conference of the IEEE EMBS; August 23–26, 2007; Lyon, France.
  2. Tudor-Locke C, Brashear MM, Johnson WD, et al. Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese US men and women. Int J Behav Nutr Phys Act. 2010;7(1):60. doi:10.1186/1479-5868-7-60 PubMed
  3. Hoehn-Saric R, McLeod DR, Funderburk F, et al. Somatic symptoms and physiologic responses in generalized anxiety disorder and panic disorder: an ambulatory monitor study. Arch Gen Psychiatry. 2004;61(9):913–921. doi:10.1001/archpsyc.61.9.913 PubMed
  4. Volicer L, Harper DG, Manning BC, et al. Sundowning and circadian rhythms in Alzheimer’s disease. Am J Psychiatry. 2001;158(5):704–711. doi:10.1176/appi.ajp.158.5.704 PubMed