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

Non-Randomized Studies (funded, eHealth) (109) Web-based and Mobile Health Interventions (3681) Smoking Cessation (595) Prevention and Health Promotion (1147) Substance Abuse (474) Generative Language Models Including ChatGPT (521) Chatbots and Conversational Agents (645)

Published on in Vol 12 (2023)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53556, first published .
AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study

AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study

AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study

Authors of this article:

Nadia Minian1, 2, 3, 4, 5 Author Orcid Image ;   Kamna Mehra1 Author Orcid Image ;   Mackenzie Earle1 Author Orcid Image ;   Sowsan Hafuth1 Author Orcid Image ;   Ryan Ting-A-Kee1 Author Orcid Image ;   Jonathan Rose1, 6 Author Orcid Image ;   Scott Veldhuizen1, 2 Author Orcid Image ;   Laurie Zawertailo1, 3, 4 Author Orcid Image ;   Matt Ratto7, 8 Author Orcid Image ;   Osnat C Melamed1, 2, 3, 5 Author Orcid Image ;   Peter Selby1, 2, 3, 9, 10 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Minian N, Wong M, Hafuth S, Rodak T, Rahimi A, Gjomema D, Rose J, Zawertailo L, Ratto M, Selby P. Identifying determinants of varenicline adherence using the Theoretical Domains framework: a rapid review. BMC Public Health 2024;24(1) View
  2. Olawade D, Aienobe-Asekharen C. Artificial intelligence in tobacco control: A systematic scoping review of applications, challenges, and ethical implications. International Journal of Medical Informatics 2025;202:105987 View