Smart Coping with Stress: Biofeedback via Smart Phone for Stress Reduction and Relapse Prevention in Alcohol Dependent Subjects

The paper presents the design plan for a mobile solution aimed at stress reduction. The solution will be developed by a team of medics, psychotherapists, HCI experts and knowledge engineers and will provide continuous data sensing and feedback about personal stress levels. At the same time contextual and activity information will be captured. Stress management is particularly important for high-risk populations such as former alcoholics to reduce the risk of relapse; they will therefore test and validate the solution. By combining and correlating psycho-physiological data with data on activities (e.g. walking or social interactions) and environment/location (e.g. ambient light) it is expected that sources of stress can be recognised which in turn will allow individuals to either avoid stress-inducing factors or develop appropriate coping strategies. To make sense of the data captured, it is proposed to use intelligent algorithms to recognise patterns in the data streams and semantic technologies to interpret the text messages of users. People with other stress-related health problems such as burn-out, smoking, depression or sleeping problems will also benefit from our research.

[1]  Walter Ritter,et al.  Measuring Psychophysiological Signals in Every-Day Situations , 2009, HCI.

[2]  S. Hunt,et al.  Stress-related neuropeptides and alcoholism: CRH, NPY, and beyond. , 2009, Alcohol.

[3]  Joel J. P. C. Rodrigues,et al.  Body Sensor Network Mobile Solutions for Biofeedback Monitoring , 2011, Mob. Networks Appl..

[4]  Ulrich Reimer,et al.  SEMPER: A Web-Based Support System for Patient Self-Management , 2010, Bled eConference.

[5]  R.J.M. Hartog,et al.  Evaluation in Design-Oriented Research , 2005 .

[6]  M. Reivich,et al.  ACTIVATION , 1980, The Social Value of Zoos.

[7]  G. Schumann,et al.  Gene–environment interactions resulting in risk alcohol drinking behaviour are mediated by CRF and CRF1 , 2009, Pharmacology Biochemistry and Behavior.

[8]  J. Felix Hampe,et al.  Exploring e/mHealth Potential for Health Improvement: A Design Analysis for Future e/mHealth Impact , 2010, Bled eConference.

[9]  Diane M. Strong,et al.  AIMQ: a methodology for information quality assessment , 2002, Inf. Manag..

[10]  Dave Chaffey,et al.  Business Information Management , 2006 .

[11]  M. Rietschel,et al.  Multiple polymorphisms in genes of the adrenergic stress system confer vulnerability to alcohol abuse , 2012, Addiction biology.

[12]  D. Goldman,et al.  Functional CRH variation increases stress-induced alcohol consumption in primates , 2009, Proceedings of the National Academy of Sciences.

[13]  Rosalind W. Picard Emotion Research by the People, for the People , 2010 .

[14]  Regan L. Mandryk,et al.  A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies , 2007, Int. J. Hum. Comput. Stud..

[15]  Rosalind W. Picard,et al.  A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity , 2010, IEEE Transactions on Biomedical Engineering.

[16]  M. Dawson,et al.  The electrodermal system , 2007 .

[17]  Ulrich Reimer,et al.  Learning a Lightweight Ontology for Semantic Retrieval in Patient-Centered Information Systems , 2011, Int. J. Knowl. Manag..

[18]  T. E. Thiele,et al.  Pre-clinical evidence that corticotropin-releasing factor (CRF) receptor antagonists are promising targets for pharmacological treatment of alcoholism. , 2010, CNS & neurological disorders drug targets.

[19]  Wendy Demark-Wahnefried,et al.  Lifestyle interventions in cancer survivors: designing programs that meet the needs of this vulnerable and growing population. , 2007, The Journal of nutrition.

[20]  R. Harris,et al.  Exaggerated response to mild stress in rats fed high-fat diet. , 2006, American journal of physiology. Regulatory, integrative and comparative physiology.

[21]  M. Laclavik,et al.  Expanding the Knowledge Economy : Issues , Applications , Case Studies , 2007 .

[22]  Gari D. Clifford,et al.  ECG Statistics , Noise , Artifacts , and Missing Data , 2006 .