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

Web-based and Mobile Health Interventions (3313) Smoking Cessation (559) Prevention and Health Promotion (991) mHealth for Wellness, Behavior Change and Prevention (2883) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (494)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54248, first published .
Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study

Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study

Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study

Authors of this article:

Linda Q Yu1 Author Orcid Image ;   Michael S Amato1, 2 Author Orcid Image ;   George D Papandonatos3 Author Orcid Image ;   Sarah Cha1 Author Orcid Image ;   Amanda L Graham1, 2, 4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Linda Q Yu   1 , PhD ;   Michael S Amato   1, 2 , PhD ;   George D Papandonatos   3 , PhD ;   Sarah Cha   1 , MSPH ;   Amanda L Graham   1, 2, 4 , PhD

1 Innovations Center, Truth Initiative, Washington, DC, United States

2 Department of Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, United States

3 Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI, United States

4 Department of Oncology, Georgetown University Medical Center, Washington, DC, United States

Corresponding Author: