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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. (More)


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Paper citation in several formats:
Matthews, M.; Abdullah, S.; Gay, G. and Choudhury, T. (2016). Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness. In Proceedings of the 3rd International Conference on Physiological Computing Systems - PhyCS, ISBN 978-989-758-197-7; ISSN 2184-321X, pages 98-104. DOI: 10.5220/0005952600980104

author={Mark Matthews. and Saeed Abdullah. and Geri Gay. and Tanzeem Choudhury.},
title={Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness},
booktitle={Proceedings of the 3rd International Conference on Physiological Computing Systems - PhyCS,},


JO - Proceedings of the 3rd International Conference on Physiological Computing Systems - PhyCS,
TI - Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness
SN - 978-989-758-197-7
IS - 2184-321X
AU - Matthews, M.
AU - Abdullah, S.
AU - Gay, G.
AU - Choudhury, T.
PY - 2016
SP - 98
EP - 104
DO - 10.5220/0005952600980104