Authors:
Mark Matthews
;
Saeed Abdullah
;
Geri Gay
and
Tanzeem Choudhury
Affiliation:
Cornell University, United States
Keyword(s):
Serious Mental Illness, Bipolar Disorder, Sensing, Smartphones, Mhealth.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Collaboration and e-Services
;
Devices
;
e-Business
;
Enterprise Information Systems
;
Health Monitoring Devices
;
Human-Computer Interaction
;
Physiological Computing Systems
;
Usability
;
Usability and Ergonomics
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Serious mental illnesses, including bipolar disorders (BD), account for a large share of the worldwide
healthcare burden—estimated at $62.7B in the U.S. alone. Bipolar disorders represent a family of common,
lifelong illnesses associated with poor functional and clinical outcomes, high suicide rates, and huge societal
costs. Interpersonal and Social Rhythm Therapy (IPSRT), a validated treatment for BD, helps patients lead
lives characterized by greater stability of daily rhythms, using a 5 item paper-and-pencil self-monitoring
instrument called the Social Rhythm Metric (SRM). IPSRT has been shown to improve patient outcomes,
yet many patients struggle to monitor their daily routine or even access the treatment. In this paper we
describe how biological characteristics of bipolar disorder can be taken into consideration when developing
systems to detect and stabilize mood episodes. We describe the co-design of MoodRhythm, a smartphone
and web app, with patients and therapists. It is de
signed to support patients in tracking their health passively
and actively over a long period of time. MoodRhythm uses the phone’s onboard sensors to automatically
track sleep and social activity patterns. We report results of a small clinical pilot with experienced IPSRT
clinicians and patients with bipolar disorder and finish by describing the role physiological computing could
have not just in monitoring psychiatric illnesses according to existing broad categories of diagnosis but in
helping radically tailor diagnoses to each individual patient and develop interventions that take advantage of
idiosyncratic characteristics of each person’s illness in order to increase patient engagement in and
adherence to treatment.
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