Abstract
Chatbots, i.e., a digital system that provides an interface for interaction with users that is based on natural language, are becoming increasingly more common in our daily lives. Potential benefits of using conversational agents for health-related purposes. Moreover, interactions with chatbots can be deeply social and elicit social responses that can lead to improved engagement, making this a promising field of exploration in mental health. The purpose of this chapter is to present an overview of the use of chatbots in mental health care, highlighting its potential contributions to clinical practice in the field. To that, the chapter presents operational definitions and key terms in the chatbot field, expanding it to an overview of a taxonomy of chatbots. Then, current applications of bots in mental health and related fields are presented. The benefits of using chatbots in clinical settings are discussed, including gains related to increasing access to mental health care, facilitating data collection and management, and promoting patient disclosure. Possible concerns about the use of chatbots are also discussed, highlighting the importance of accounting for patient safety, privacy and confidentiality of data, and the management of emergencies in this context. Finally, we reflect on dilemmas and new areas of exploration regarding future applications of chatbots in mental health research and practice.
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References
Nass C, Steuer J, Tauber ER. Computers are Social Actors. Proceedings. Boston, USA; 1994. p. 24–8.
Nass C, Moon Y. Machines and mindlessness: social responses to computers. J Soc Issues. 2000;56:81–103.
Weizenbaum J. Computer power and human reason: from judgment to calculation. San Francisco: W. H. Freeman & Co.; 1976.
Weizenbaum J. ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9:36–45.
Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatr. 2019;64(7):456–64.
Van Grove J. Robot love? Why people are falling for Amazon’s Echo. San Diego Union-Trib [Internet]. 2016 Apr 22 [cited 2020 Mar 5]; Available from: https://www.sandiegouniontribune.com/business/technology/sdut-amazon-echo-alexa-love-2016apr22-story.html
Waytz A, Gray K, Epley N, Wegner DM. Causes and consequences of mind perception. Trends Cogn Sci. 2010;14:383–8.
Lee S, Lee N, Sah YJ. Perceiving a mind in a chatbot: effect of mind perception and social cues on co-presence, closeness, and intention to use. Int J Human-Computer Interact. 2020;36:930–40.
Heider F, Simmel M. An experimental study of apparent behavior. Am J Psychol. 1944;57:243.
Heyselaar E, Bosse T. Using Theory of Mind to Assess Users’ Sense of Agency in Social Chatbots. Chatbot Research and Design, Third International Workshop, CONVERSATIONS 2019, Amsterdam, The Netherlands, November 19–20, 2019, Revised Selected Papers. Switzerland: Springer International Publishing; 2020. p. 158–69.
Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. 2018;25:1248–58.
Adamopoulou E, Moussiades L. Chatbots: history, technology, and applications. Mach Learn Appl. 2020;2:100006.
Wang Y. Your Next New Best Friend Might Be a Robot. Nautilus [Internet]. 2016 Feb 4 [cited 2021 Apr 5]; Available from: https://nautil.us/issue/33/attraction/your-next-new-best-friend-might-be-a-robot
Shevat A. Designing bots: creating conversational experiences. Sebastopol, California: O'Reilly Media, Inc.; 2017.
Instant Messaging Users to Reach 4.3 Billion in 2020, as New Payment Services Emerge [Internet]. [cited 2021 Apr 5]. Available from: https://www.juniperresearch.com/press/press-releases/instant-messaging-users-4-point-3-billion-2020
Roger V, Farinas J, Pinquier J. Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data. ArXiv200304241 Cs Eess Stat [Internet]. 2020 [cited 2021 Apr 5]; Available from: http://arxiv.org/abs/2003.04241
Shen J, Pang R, Weiss RJ, Schuster M, Jaitly N, Yang Z, et al. Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions. 2018 IEEE Int Conf Acoust Speech Signal Process ICASSP. 2018. p. 4779–83.
Gardner S. sophgdn/SignBot [Internet]. 2021 [cited 2021 Apr 5]. Available from: https://github.com/sophgdn/SignBot
Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. ArXiv200514165 Cs [Internet]. 2020 [cited 2021 Apr 6]; Available from: http://arxiv.org/abs/2005.14165
Miner AS, Milstein A, Schueller S, Hegde R, Mangurian C, Linos E. Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern Med. 2016;176:619–25.
Zhou L, Gao J, Li D, Shum H-Y. The design and implementation of XiaoIce, an empathetic social chatbot. Comput Linguist. 2020;46:53–93.
Abd-alrazaq AA, Alajlani M, Alalwan AA, Bewick BM, Gardner P, Househ M. An overview of the features of chatbots in mental health: a scoping review. Int J Med Inf. 2019;132:103978.
Philip P, Micoulaud-Franchi J-A, Sagaspe P, Sevin ED, Olive J, Bioulac S, et al. Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Sci Rep. 2017;7:42656.
Auriacombe M, Moriceau S, Serre F, Denis C, Micoulaud-Franchi J-A, de Sevin E, et al. Development and validation of a virtual agent to screen tobacco and alcohol use disorders. Drug Alcohol Depend. 2018;193:1–6.
Philip P, Dupuy L, Auriacombe M, Serre F, de Sevin E, Sauteraud A, et al. Trust and acceptance of a virtual psychiatric interview between embodied conversational agents and outpatients. Npj Digit Med. 2020;3:1–7.
Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health. 2017;4:e19.
Prochaska JJ, Vogel EA, Chieng A, Kendra M, Baiocchi M, Pajarito S, et al. A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study. J Med Internet Res. 2021;23:e24850.
Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health. 2018;5:e64.
So R, Furukawa TA, Matsushita S, Baba T, Matsuzaki T, Furuno S, et al. Unguided chatbot-delivered cognitive Behavioural intervention for problem gamblers through messaging app: a randomised controlled trial. J Gambl Stud. 2020;36:1391–407.
Oh J, Jang S, Kim H, Kim J-J. Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder. Int J Med Inf. 2020;140:104171.
Jang S, Kim J-J, Kim S-J, Hong J, Kim S, Kim E. Mobile app-based chatbot to deliver cognitive behavioral therapy and psychoeducation for adults with attention deficit: a development and feasibility/usability study. Int J Med Inf. 2021;150:104440.
Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental Well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018;6:e12106.
Ly KH, Ly A-M, Andersson G. A fully automated conversational agent for promoting mental Well-being: a pilot RCT using mixed methods. Internet Interv. 2017;10:39–46.
Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. 2018;5:e10454.
Oladeji BD, Gureje O. Brain drain: a challenge to global mental health. BJPsych Int. 2016;13:61–3.
National Collaborating Centre for Mental Health (UK). Common Mental Health Disorders: Identification and Pathways to Care [Internet]. Leicester (UK): British Psychological Society; 2011 [cited 2021 Mar 9]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK92266/
Bickmore TW, Puskar K, Schlenk EA, Pfeifer LM, Sereika SM. Maintaining reality: relational agents for antipsychotic medication adherence. Interact Comput. 2010;22:276–88.
Hoermann S, McCabe KL, Milne DN, Calvo RA. Application of synchronous text-based dialogue systems in mental health interventions: systematic review. J Med Internet Res. 2017;19(8):e267. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5595406/
Fukkink R, Hermanns J. Counseling children at a helpline: chatting or calling? J Community Psychol. 2009;37:939–48.
Kretzschmar K, Tyroll H, Pavarini G, Manzini A, Singh I. Can Your Phone Be Your Therapist? Young People’s Ethical Perspectives on the Use of Fully Automated Conversational Agents (Chatbots) in Mental Health Support. Biomed Inform Insights. 2019;11:1178222619829083. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402067/
Shinozaki T, Yamamoto Y, Tsuruta S. Context-based counselor agent for software development ecosystem. Computing. 2015;97:3–28.
Lucas GM, Rizzo A, Gratch J, Scherer S, Stratou G, Boberg J, et al. Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Front Robot AI. 2017;4:51.
Gardiner PM, McCue KD, Negash LM, Cheng T, White LF, Yinusa-Nyahkoon L, et al. Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: a feasibility randomized control trial. Patient Educ Couns. 2017;100:1720–9.
Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H. Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study. J Med Internet Res. 2016;18:e72.
Gentzler A, Kerns K. Adult attachment and memory of emotional reactions to negative and positive events. Cogn Emot. 2006;20:20–42.
Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.
Lucas GM, Gratch J, King A, Morency L-P. It’s only a computer: virtual humans increase willingness to disclose. Comput Hum Behav. 2014;37:94–100.
Grice T. Factors Affecting Disclosure of Mental Health Problems [Doctorate in Clinical Psychology]. [London, England]: University College London; 2016.
Weisband S, Kiesler S. Self-disclosure on computer forms: meta-analysis and implications. Proc SIGCHI Conf Hum Factors Comput Syst Common Ground - CHI 96 [Internet]. Vancouver, British Columbia, Canada: ACM Press; 1996. p. 3–10. Available from: http://portal.acm.org/citation.cfm?doid=238386.238387
Joinson AN. Self-disclosure in computer-mediated communication: the role of self-awareness and visual anonymity. Eur J Soc Psychol. 2001;31:177–92.
Kim S, Lee J, Gweon G. Comparing Data from Chatbot and Web Surveys: Effects of Platform and Conversational Style on Survey Response Quality. Proc 2019 CHI Conf Hum Factors Comput Syst. Glasgow Scotland UK: ACM; 2019 p. 1–12. https://doi.org/10.1145/3290605.3300316
Ravichander A, Black AW. An Empirical Study of Self-Disclosure in Spoken Dialogue Systems. Proc 19th Annu SIGdial Meet Discourse Dialogue. Melbourne, Australia: Association for Computational Linguistics; 2018. p. 253–63. Available from: http://aclweb.org/anthology/W18-5030
Lee Y-C, Yamashita N, Huang Y, Fu W. I Hear You, I Feel You: Encouraging Deep Self-disclosure through a Chatbot. Proc 2020 CHI Conf Hum Factors Comput Syst. Honolulu HI USA: ACM; 2020 p. 1–12. https://doi.org/10.1145/3313831.3376175
Lukoff K, Li T, Zhuang Y, Lim BY. TableChat: Mobile food journaling to facilitate family support for healthy eating. Proc ACM Hum-Comput Interact. 2018;2:1–28.
Kelley C, Lee B, Wilcox L. Self-tracking for Mental Wellness: Understanding Expert Perspectives and Student Experiences. Proc 2017 CHI Conf Hum Factors Comput Syst. Denver Colorado USA: ACM; 2017. p. 629–41. https://doi.org/10.1145/3025453.3025750
Ho A, Hancock J, Miner AS. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. J Commun. 2018;68:712–33.
Chatbot e a Lei Geral de Proteção de Dados: o que muda? [Internet]. X2 Intel. Digit. 2020 [cited 2021 Mar 15]. Available from: https://x2inteligencia.digital/2020/10/08/chatbot-e-a-lei-geral-de-protecao-de-dados/
Beck B. 5 Ways to Maintain Compliance As Chatbot Marketing Adapts to New Laws [Internet]. ClearVoice. 2019 [cited 2021 Mar 15]. Available from: https://www.clearvoice.com/blog/how-to-keep-chatbots-compliant-with-new-laws/
Bickmore T, Gruber A, Picard R. Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Educ Couns. 2005;59:21–30.
Rodrigues M. Microsoft explica episódio com chatbot racista e diz que Tay deve voltar [Internet]. 2016 [cited 2021 Mar 15]. Available from: https://www.tecmundo.com.br/inteligencia-artificial/102835-microsoft-explica-episodio-chatbot-racista-diz-tay-deve-voltar.htm
Abd-Alrazaq AA, Rababeh A, Alajlani M, Bewick BM. Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis. J Med Internet Res. 2020;17:e16021.
Lee M, Ackermans S, van As N, Chang H, Lucas E, IJsselsteijn W. Caring for Vincent: a chatbot for self-compassion. Proc 2019 CHI conf hum factors Comput syst. New York, NY, USA: Association for Computing Machinery; 2019. p. 1–13. https://doi.org/10.1145/3290605.3300932.
Duffy BR. Anthropomorphism and the social robot. Robot Auton Syst. 2003;42:177–90.
Policy guidance on AI for children. United Nations Children’s Fund (UNICEF); 2020.
Druga S, Williams R, Breazeal C, Resnick M. “Hey Google is it OK if I eat you?”: initial explorations in child-agent interaction. Proc 2017 conf interact des child. New York, NY, USA: Association for Computing Machinery; 2017. p. 595–600. https://doi.org/10.1145/3078072.3084330.
Leung W. How will AI technologies affect child development? Globe Mail [Internet]. 2018 Jul 22 [cited 2021 Mar 15]; Available from: https://www.theglobeandmail.com/life/article-how-will-ai-technologies-affect-child-development/
Hetrick SE, Robinson J, Burge E, Blandon R, Mobilio B, Rice SM, Simmons MB, Alvarez-Jimenez M, Goodrich S, Davey CG. Youth codesign of a Mobile phone app to facilitate self-monitoring and Management of Mood Symptoms in young people with major depression, suicidal ideation, and self-harm. JMIR Mental Health. 2018;5(1):e9. https://doi.org/10.2196/mental.9041.
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Viduani, A., Cosenza, V., Araújo, R.M., Kieling, C. (2023). Chatbots in the Field of Mental Health: Challenges and Opportunities. In: Passos, I.C., Rabelo-da-Ponte, F.D., Kapczinski, F. (eds) Digital Mental Health. Springer, Cham. https://doi.org/10.1007/978-3-031-10698-9_8
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