Text-based Healthcare Chatbots Supporting Patient and Health Professional Teams: Preliminary Results of a Randomized Controlled Trial on Childhood Obesity

Health professionals have limited resources and are not able to personally monitor and support patients in their everyday life. Against this background and due to the increasing number of self-service channels and digital health interventions, we investigate how text-based healthcare chatbots (THCB) can be designed to effectively support patients and health professionals in therapeutic settings beyond on-site consultations. We present an open source THCB system and how the THCP was designed for a childhood obesity intervention. Preliminary results with 15 patients indicate promising results with respect to intervention adherence (ca. 13.000 conversational turns over the course of 4 months or ca. 8 per day and patient), scalability of the THCB approach (ca. 99.5% of all conversational turns were THCB-driven) and over-average scores on perceived enjoyment and attachment bond between patient and THCB. Future work is discussed.

[1]  Tobias Kowatsch,et al.  Design And Lab Experiment Of A Stress Detection Service Based On Mouse Movements , 2017, MCIS.

[2]  T. Kowatsch,et al.  Moderators of outcome in a technology-based intervention to prevent and reduce problem drinking among adolescents. , 2017, Addictive behaviors.

[3]  F. Wahle,et al.  stressOUT: Design, Implementation and Evaluation of a Mouse-based Stress Management Service , 2017 .

[4]  Tobias Kowatsch,et al.  Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach , 2017, International Conference on Design Science Research in Information Systems and Technology.

[5]  Tobias Kowatsch,et al.  Efficacy of a Web- and Text Messaging-Based Intervention to Reduce Problem Drinking in Adolescents: Results of a Cluster-Randomized Controlled Trial , 2017, Journal of consulting and clinical psychology.

[6]  Joseph S. Valacich,et al.  How Is Your User Feeling? Inferring Emotion Through Human-Computer interaction Devices , 2017, MIS Q..

[7]  Patricia Michelle Troxell Klingenbjerg.  Smartphone-Based Conversational Agents and Responses to Questions about Mental Health, Interpersonal Violence, and Physical Health , 2016 .

[8]  Ambuj Tewari,et al.  Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. , 2015, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[9]  Tom H. Luan,et al.  Predictive data mining for Converged Internet of Things: A Mobile Health perspective , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[10]  Adriana M. Seelye,et al.  Computer mouse movement patterns: A potential marker of mild cognitive impairment , 2015, Alzheimer's & dementia.

[11]  Joel J. P. C. Rodrigues,et al.  Mobile-health: A review of current state in 2015 , 2015, J. Biomed. Informatics.

[12]  Elgar Fleisch,et al.  Towards Short-term Detection of Job Strain in Knowledge Workers with a Minimal-invasive Information System Service: Theoretical Foundation and Experimental Design , 2015, ECIS.

[13]  Elgar Fleisch,et al.  MobileCoach: A novel open source platform for the design of evidence-based, scalable and low-cost behavioral health interventions: Overview and preliminary evaluation in the public health context , 2015, 2015 Wireless Telecommunications Symposium (WTS).

[14]  Wei Peng,et al.  Effects of visual and linguistic anthropomorphic cues on social perception, self-awareness, and information disclosure in a health website , 2015, Comput. Hum. Behav..

[15]  Nancy Viola Wünderlich,et al.  The Value of Self-Service: Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention , 2015, MIS Q..

[16]  Monique W. M. Jaspers,et al.  Using personas to tailor educational messages to the preferences of coronary heart disease patients , 2015, J. Biomed. Informatics.

[17]  Byungtae Lee,et al.  Detecting Depression of Cancer Patients with Daily Mental Health Logs from Mobile Applications , 2015, ICIS.

[18]  Brian D. Kiluk,et al.  Only connect: The working alliance in computer-based cognitive behavioral therapy. , 2014, Behaviour research and therapy.

[19]  Jesse Dallery,et al.  Behavioral Health Care and Technology: Using Science-Based Innovations to Transform Practice , 2014 .

[20]  Ran Zhao,et al.  Towards a Computational Architecture of Dyadic Rapport Management for Virtual Agents , 2014, IVA.

[21]  David Sun,et al.  MouStress: detecting stress from mouse motion , 2014, CHI.

[22]  Naphtali Rishe,et al.  I Can Help You Change! An Empathic Virtual Agent Delivers Behavior Change Health Interventions , 2013, TMIS.

[23]  E. Cottrell,et al.  Using simple telehealth in primary care to reduce blood pressure: a service evaluation , 2012, BMJ Open.

[24]  Jay F. Nunamaker,et al.  Predicting Users' Perceived Trust in Embodied Conversational Agents Using Vocal Dynamics , 2012, 2012 45th Hawaii International Conference on System Sciences.

[25]  C. Quinn,et al.  Cluster-Randomized Trial of a Mobile Phone Personalized Behavioral Intervention for Blood Glucose Control , 2011, Diabetes Care.

[26]  Jay F. Nunamaker,et al.  Embodied Conversational Agent-Based Kiosk for Automated Interviewing , 2011, J. Manag. Inf. Syst..

[27]  Jay F. Nunamaker,et al.  Design Principles for Special Purpose, Embodied, Conversational Intelligence with Environmental Sensors (SPECIES) Agents , 2011 .

[28]  Ipke Wachsmuth,et al.  Modeling empathy for a virtual human: how, when and to what extent? , 2011, AAMAS.

[29]  Ritu Agarwal,et al.  The Digital Transformation of Healthcare: Current Status and the Road Ahead , 2010 .

[30]  Timothy W. Bickmore,et al.  Response to a relational agent by hospital patients with depressive symptoms , 2010, Interact. Comput..

[31]  International Foundation for Autonomous Agents and MultiAgent Systems ( IFAAMAS ) , 2007 .

[32]  Arnold Kamis,et al.  Using an Attribute-Based Decision Support System for User-Customized Products Online: An Experimental Investigation , 2008, MIS Q..

[33]  J. Gillaspy,et al.  Development and validation of a revised short version of the working alliance inventory , 2006 .

[34]  Christian Rauh,et al.  The Influence of the Avatar on Online Perceptions of Anthropomorphism, Androgyny, Credibility, Homophily, and Attraction , 2005, J. Comput. Mediat. Commun..

[35]  Rosalind W. Picard,et al.  Establishing the computer-patient working alliance in automated health behavior change interventions. , 2005, Patient education and counseling.

[36]  J. Cassell,et al.  Social Dialongue with Embodied Conversational Agents , 2005 .

[37]  J. Cassell,et al.  SOCIAL DIALOGUE WITH EMBODIED CONVERSATIONAL AGENTS , 2005 .

[38]  Susan C. Herring,et al.  Slouching Toward the Ordinary: Current Trends in Computer-Mediated Communication , 2004, New Media Soc..

[39]  Justine Cassell,et al.  Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents , 2003, User Modeling and User-Adapted Interaction.

[40]  P. Barrett,et al.  The scale of perceived interpersonal closeness (PICS) , 2003 .

[41]  Jonathan Klein,et al.  This computer responds to user frustration: Theory, design, and results , 2002, Interact. Comput..

[42]  Joseph B. Walther,et al.  The Impacts of Emoticons on Message Interpretation in Computer-Mediated Communication , 2001 .

[43]  J. Cassell,et al.  Embodied conversational agents , 2000 .

[44]  木村 和夫.  Pragmatics , 1997, Language Teaching.

[45]  A. Aron,et al.  Inclusion of Other in the Self Scale and the structure of interpersonal closeness , 1992 .

[46]  A. Horvath,et al.  Development and validation of the Working Alliance Inventory. , 1989 .

[47]  Joan Laird,et al.  Effective helping: Interviewing and counseling techniques , 1983 .

[48]  J. Laver,et al.  Linguistic Routines and Politeness in Greeting and Parting , 1981 .