Using Health Chatbots for Behavior Change: A Mapping Study

  • Juanan Pereira
  • Óscar DíazEmail author
Mobile & Wireless Health
Part of the following topical collections:
  1. Mobile & Wireless Health


This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest in the health domain? We identify 30 articles related to health chatbots from 2014 to 2018. We analyze the selected articles qualitatively and extract a triplet <technicalEnablers, competence, illness> for each of them. This data serves to provide a first overview of chatbot-mediated behavior change on the health domain. Main insights include: nutritional disorders and neurological disorders as the main illness areas being tackled; “affect” as the human competence most pursued by chatbots to attain change behavior; and “personalization” and “consumability” as the most appreciated technical enablers. On the other hand, main limitations include lack of adherence to good practices to case-study reporting, and a deeper look at the broader sociological implications brought by this technology.


Chatbots Mobile healthcare Instant messaging Software agents 


Compliance with Ethical Standards

Conflict of Interest

Juanan Pereira declares that he has no conflict of interest. Óscar Díaz declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Abashev, A., Grigoryev, R., Grigorian, K., and Boyko, V., Programming tools for messenger-based chatbot system organization: Implication for outpatient and translational medicines. BioNanoScience. 7(2):403–407, 2017. Scholar
  2. 2.
    Andersson, G., and Cuijpers, P., Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cognit. Behav. Ther. 38(4):196–205, 2009. Scholar
  3. 3.
    Atay, C., Ireland, D., Liddle, J., Wiles, J., Vogel, A., Angus, D., Bradford, D., Campbell, A., Rushin, O., and Chenery, H. J., Can a smartphone-based chatbot engage older community group members? The impact of specialised content. Alzheimer’s Dement.: J. Alzheimer’s Assoc. 12(7):P1005–P1006, 2016. Scholar
  4. 4.
    Beun, R.J., Brinkman, W.-P., Fitrianie, S., Griffioen-Both, F., Horsch, C., Lancee, J., and Spruit, S.: Improving adherence in automated e-coaching. In: International conference on persuasive technology. pp. 276–287. Springer, 2016).Google Scholar
  5. 5.
    Bickmore, T. W., Puskar, K., Schlenk, E. A., Pfeifer, L. M., and Sereika, S. M., Maintaining reality: Relational agents for antipsychotic medication adherence. Interact. Comput. 22(4):276–288, 2010. Scholar
  6. 6.
    Brinkman, P., Virtual health agents for behavior change: Research perspectives and directions. Proceedings of the workshop on graphical and robotic embodied agents for therapeutic systems (GREATS16) held during the international conference on intelligent virtual agents (IVA16), 2016.Google Scholar
  7. 7.
    Brixey, J., Hoegen, R., Lan, W., Rusow, J., Singla, K., Yin, X., Artstein, R., and Leuski, A., SHIHbot: A Facebook chatbot for sexual health information on HIV/AIDS. Proceedings of the 18th annual SIGdial meeting on discourse and dialogue. 370–373, 2017.Google Scholar
  8. 8.
    Callejas, Z., Griol, D., McTear, M.F., López-Cózar, R.: A virtual coach for active ageing based on sentient computing and m-health. International workshop on ambient assisted living: 59–66. Springer, 2014.Google Scholar
  9. 9.
    Cameron, G., Cameron, D., Megaw, G., Bond, R., Mulvenna, M., O’Neill, S., Armour, C., and McTear, M., Towards a chatbot for digital counselling. J. Med. Internet Res. 4(1):e3, 2017.Google Scholar
  10. 10.
    Cheng, A., Raghavaraju, V., Kanugo, J., Handrianto, Y.P., and Shang, Y., Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE annual. Pp. 1–5. IEEE, 2018.Google Scholar
  11. 11.
    Chung, K., and Park, R.C., Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing. 1–13,2018. doi:
  12. 12.
    Crawford, E., Bots are awesome! Humans? Not so much, .
  13. 13.
    Cruz-Sandoval, D., and Favela, J., Semi-autonomous conversational robot to Deal with problematic behaviors from people with dementia. International conference on ubiquitous computing and ambient intelligence. 677–688. Springer, 2017.Google Scholar
  14. 14.
    Cruzes, D.S., and Dyba, T., Recommended steps for thematic synthesis in software engineering. In: 2011 international symposium on empirical software engineering and measurement. pp. 275–284. IEEE, 2011.Google Scholar
  15. 15.
    Dale, R., The return of the chatbots. Nat. Lang. Eng. 22(5):811–817, 2016. Scholar
  16. 16.
    Dubosson, F., Schaer, R., Savioz, R., and Schumacher, M., Going beyond the relapse peak on social network smoking cessation programmes: ChatBot opportunities. Swiss Med. Inform. 33, 2017.Google Scholar
  17. 17.
    Elmasri, D., and Maeder, A., A conversational agent for an online mental health intervention. International conference on brain and health informatics. 243–251. Springer, 2016.Google Scholar
  18. 18.
    Eysenbach, G., and Group, C.-E., CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. Journal of medical Internet research. 13(4), 2011. doi: .
  19. 19.
    Fadhil, A., A conversational Interface to improve medication adherence: Towards AI support in Patient’s treatment. arXiv preprint arXiv:1803.09844. 2018.Google Scholar
  20. 20.
    Fadhil, A., and Gabrielli, S., Addressing challenges in promoting healthy lifestyles: The al-chatbot approach. Proceedings of the 11th EAI international conference on pervasive computing Technologies for Healthcare. 261–265. ACM, 2017.Google Scholar
  21. 21.
    Fadhil, A., Villafiorita, A.: An adaptive learning with gamification & conversational UIs: The rise of CiboPoliBot. Adjunct publication of the 25th conference on user modeling, adaptation and personalization: 408–412. ACM, 2017.Google Scholar
  22. 22.
    Fernandez-Luque, L., Lattab, A., Hors, S., and Ahmedna, M., Implementation and feasibility study of a tailored health education bot in telegram for mothers of children with obesity and overweight. Qatar Foundation annual research conference proceedings. p. HBPD506. HBKU press Qatar, 2018.Google Scholar
  23. 23.
    Fogg, B. J., Persuasive technology: Using computers to change what we think and do. Ubiquity. 2002(December):5, 2002. Scholar
  24. 24.
    Gabrielli, S., Marie, K., and Corte, C.D., SLOWBot (Chatbot) lifestyle assistant. In: Proceedings of the 12th EAI international conference on pervasive computing Technologies for Healthcare. 367–370. ACM, New York, NY, USA, 2018.Google Scholar
  25. 25.
    Griol, D., and Molina, J.M., An ambient assisted living Mobile application for helping people with Alzheimer. International conference on practical applications of agents and multi-agent systems: 3–14. Springer, 2015.Google Scholar
  26. 26.
    Hsu, P., Zhao, J., Liao, K., Liu, T., Wang, C.: AllergyBot: A Chatbot technology intervention for young adults with food allergies dining out. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. pp. 74–79. ACM, 2017.Google Scholar
  27. 27.
    Huang, C., Yang, M., Huang, C., Chen, Y., Wu, M., Chen, K.: A Chatbot-supported smart wireless interactive healthcare system for weight control and health promotion. 2018 IEEE international conference on industrial engineering and engineering management (IEEM): 1791–1795, 2018.Google Scholar
  28. 28.
    Inkster, B., Sarda, S., and Subramanian, V., An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth. 6:e12106, 2018. Scholar
  29. 29.
    Isern, D., and Moreno, A., A systematic literature review of agents applied in healthcare. J. Med. Syst. 40(2):43, 2016. Scholar
  30. 30.
    Ivarsson, M., and Gorschek, T., A method for evaluating rigor and industrial relevance of technology evaluations. Empiric. Softw. Eng. 16:365–395, 2011. Scholar
  31. 31.
    Jeong, S., and Breazeal, C., Toward robotic companions that enhance psychological wellbeing with smartphone technology. Proceedings of the companion of the 2017 ACM/IEEE international conference on human-robot interaction. pp. 345–346. ACM, 2017.Google Scholar
  32. 32.
    Kimani, E., Bickmore, T., Trinh, H., Ring, L., Paasche-Orlow, M.K., and Magnani, J.W., A smartphone-based virtual agent for atrial fibrillation education and counseling. International conference on intelligent virtual agents. 120–127. Springer, 2016.Google Scholar
  33. 33.
    Kitchenham, B.A., Budgen, D., and Brereton, P., Evidence-based software engineering and systematic reviews. CRC Press, 2015.Google Scholar
  34. 34.
    Klopfenstein, L.C., Delpriori, S., Malatini, S., and Bogliolo, A., The rise of bots: A survey of conversational interfaces, patterns, and paradigms. Proceedings of the 2017 conference on designing interactive systems. 555–565. ACM, New York, NY, USA, 2017.Google Scholar
  35. 35.
    Kobori, Y., Osaka, A., Soh, S., and Okada, H., Novel application for sexual transmitted infection screening with an ai chatbot. J. Urol. 199(4, Supplement):e189–e190, 2018. Scholar
  36. 36.
    Kowatsch, T., Ni’s sen, M., Shih, C.-H.I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Hung, S., and Büchter, D., Text-based healthcare Chatbots supporting patient and health professional teams: Preliminary results of a randomized controlled trial on childhood obesity. Persuasive embodied agents for behavior change (PEACH2017). ETH Zurich, 2017.Google Scholar
  37. 37.
    Kowatsch, T., Volland, D., Shih, I., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., and Heldt, K., Design and evaluation of a Mobile chat app for the open source behavioral health intervention platform MobileCoach. International conference on design science research in information systems: 485–489. Springer, 2017.Google Scholar
  38. 38.
    Kozinakova, B., Analysis of chatbot systems focusing on the elderly as users. Master Thesis, Politecnico de Milano, 2017.Google Scholar
  39. 39.
    Lambert, A.O.C., Montañez, C.H.T., Martinez, M.B., and Funes-Gallanzi, M., A conversational agent for use in the identification of rare diseases. In: Applications for future internet. 128–139. Springer, 2017.Google Scholar
  40. 40.
    Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., and Lau, A., Conversational agents in healthcare: A systematic review. J. Am. Med. Inform. Assoc., 2018.
  41. 41.
    Lisetti, C., Amini, R., and Yasavur, U., Now all together: Overview of virtual health assistants emulating face-to-face health interview experience. KI-Künstliche Intelligenz. 29(2):161–172, 2015. Scholar
  42. 42.
    Ly, K. H., Ly, A.-M., and Andersson, G., A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet Interven. 10:39–46, 2017. Scholar
  43. 43.
    Miner, A., Chow, A., Adler, S., Zaitsev, I., Tero, P., Darcy, A., and Paepcke, A., Conversational agents and mental health: Theory-informed assessment of language and affect. Proceedings of the fourth international conference on human agent interaction. 123–130. ACM, 2016.Google Scholar
  44. 44.
    Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., and Linos, E., Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern. Med. 176(5):619–625, 2016. Scholar
  45. 45.
    Oh, K.-J., Lee, D., Ko, B., and Choi, H.-J., A Chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. Mobile data management (MDM), 2017 18th IEEE international conference on. 371–375. IEEE, 2017.Google Scholar
  46. 46.
    Pereira, J., Díaz, Ó.: Chatbot dimensions that matter: Lessons from the trenches. In: Web engineering, lecture notes in computer science, pp. 129–135. Springer, Cham (2018). doi: CrossRefGoogle Scholar
  47. 47.
    Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M., Systematic mapping studies in software engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering. pp. 68–77. BCS Learning & Development Ltd., Swindon, UK, 2008.Google Scholar
  48. 48.
    Richards, D., and Caldwell, P., Improving health outcomes sooner rather than later via an interactive website virtual specialist. IEEE Journal of Biomedical and Health Informatics. 1–1, 2017. doi: .
  49. 49.
    Richards, D., Caldwell, P.H.: Gamification to improve adherence to clinical treatment advice. Health literacy: Breakthroughs in research and practice: Breakthroughs in research and practice. 80, 2017. doi: .
  50. 50.
    Roniotis, A., and Tsiknakis, M., Detecting depression using voice signal extracted by Chatbots: A feasibility study. In: Interactivity, game creation, design, learning, and innovation. Springer, 2017, 386–392.Google Scholar
  51. 51.
    Schueller, S. M., Tomasino, K. N., and Mohr, D. C., Integrating human support into behavioral intervention technologies: The efficiency model of support. Clin. Psychol.: Sci. Pract. 24(1):27–45, 2017. Scholar
  52. 52.
    Stratou, G., Morency, L.P., DeVault, D., Hartholt, A., Fast, E., Lhommet, M., Lucas, G., Morbini, F., Georgila, K., Scherer, S., Gratch, J., Marsella, S., Traum, D., and Rizzo, A., A demonstration of the perception system in SimSensei, a virtual human application for healthcare interviews. 2015 international conference on affective computing and intelligent interaction (ACII). pp. 787–789, 2015.Google Scholar
  53. 53.
    van Heerden, A., Ntinga, X., and Vilakazi, K., The potential of conversational agents to provide a rapid HIV counseling and testing services. The Frontiers and advances in data science (FADS), 2017 international conference on. 80–85. IEEE, 2017.Google Scholar
  54. 54.
    Van Vuuren, S., and Cherney, L.R., A virtual therapist for speech and language therapy. International conference on intelligent virtual agents. 438–448. Springer, 2014.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.ONEKIN Research GroupUniversity of the Basque Country, UPV/EHULeioaSpain

Personalised recommendations