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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (491) Registered Report (560) Innovations and Technology for Physical Activity Education (742) Innovations and Technology for Healthy Eating Education (414) Web-based and Mobile Health Interventions (3297) mHealth for Wellness, Behavior Change and Prevention (2871)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26421, first published .
Influence of Baseline User Characteristics and Early Use Patterns (24-Hour) on Long-Term Adherence and Effectiveness of a Web-Based Weight Loss Randomized Controlled Trial: Latent Profile Analysis

Influence of Baseline User Characteristics and Early Use Patterns (24-Hour) on Long-Term Adherence and Effectiveness of a Web-Based Weight Loss Randomized Controlled Trial: Latent Profile Analysis

Influence of Baseline User Characteristics and Early Use Patterns (24-Hour) on Long-Term Adherence and Effectiveness of a Web-Based Weight Loss Randomized Controlled Trial: Latent Profile Analysis

Authors of this article:

Andre Q Andrade1 Author Orcid Image ;   Alline Beleigoli2, 3, 4 Author Orcid Image ;   Maria De Fatima Diniz4 Author Orcid Image ;   Antonio Luiz Ribeiro5, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations (11) Metrics

Journals

  1. Lavikainen P, Mattila E, Absetz P, Harjumaa M, Lindström J, Järvelä-Reijonen E, Aittola K, Männikkö R, Tilles-Tirkkonen T, Lintu N, Lakka T, van Gils M, Pihlajamäki J, Martikainen J. Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial. Journal of Medical Internet Research 2022;24(2):e31530 View
  2. Qu H, Shewchuk R, Richman J, Andreae L, Safford M. Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis. Risk Management and Healthcare Policy 2022;Volume 15:1055 View
  3. Wenger F, Allenhof C, Schreynemackers S, Hegerl U, Reich H. Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data. JMIR Formative Research 2024;8:e53768 View