This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2193) Web-based and Mobile Health Interventions (3092) Methods and Feasibility Studies (367) Chatbots and Conversational Agents (458) Text-messaging (SMS, WeChat etc)-Based Interventions (621) mHealth for Wellness, Behavior Change and Prevention (2728) Behavior Change (652) Diabetes Prevention (77)

Published on in Vol 5, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27956, first published .
Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Authors of this article:

Dhakshenya Ardhithy Dhinagaran1 Author Orcid Image ;   Thirunavukkarasu Sathish2, 3 Author Orcid Image ;   AiJia Soong1 Author Orcid Image ;   Yin-Leng Theng4 Author Orcid Image ;   James Best1 Author Orcid Image ;   Lorainne Tudor Car1, 5 Author Orcid Image
Article Authors Cited by (18) Tweetations (3) Metrics

Journals

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  3. Dhinagaran D, Martinengo L, Ho M, Joty S, Kowatsch T, Atun R, Tudor Car L. Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework. JMIR mHealth and uHealth 2022;10(10):e38740 View
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  10. Martinengo L, Lin X, Jabir A, Kowatsch T, Atun R, Car J, Tudor Car L. Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework. Journal of Medical Internet Research 2023;25:e50767 View
  11. Wutz M, Hermes M, Winter V, Köberlein-Neu J. Factors Influencing the Acceptability, Acceptance, and Adoption of Conversational Agents in Health Care: Integrative Review. Journal of Medical Internet Research 2023;25:e46548 View
  12. Ab Hamid M, Buhari S, Md Noor H, Azizan N, Md Nor N. Web-Based Nutrition and Physical Activity Education Intervention to Ameliorate Cardiometabolic Risks: A Single-Arm and Non-Randomized Feasibility Study. Journal of Primary Care & Community Health 2023;14 View
  13. Visvanathan K, Petry D, McCullough M, May B, Tenkasi R, Sharma N, Klein C, Johnson A, Killian G, Camp M, Paller C, Couzi R, Wilkinson M, Jacobs L, Lange J, Jelovac D, Carducci M, Habibi M, Naik G, Kotwaliwale A. The ENGAGE study: evaluation of a conversational virtual agent that provides tailored pre-test genetic education to cancer patients. Journal of Cancer Survivorship 2023 View
  14. Bak M, Chin J. The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change. Journal of the American Medical Informatics Association 2024 View
  15. Hegde N, Vardhan M, Nathani D, Rosenzweig E, Speed C, Karthikesalingam A, Seneviratne M, Grosan C. Infusing behavior science into large language models for activity coaching. PLOS Digital Health 2024;3(4):e0000431 View
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  17. Bragazzi N, Garbarino S. Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis. JMIR Formative Research 2024;8:e55762 View
  18. Liu J, Ito S, Ngo T, Lawate A, Ong Q, Fox T, Chang S, Phung D, Nair E, Palaiyan M, Joty S, Abisheganaden J, Lee C, Lwin M, Theng Y, Ho M, Chia M, Bojic I, Car J. A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students. DIGITAL HEALTH 2024;10 View