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

Mobile Health (mhealth) (3464) Smoking Cessation (629) mHealth for Wellness, Behavior Change and Prevention (3315) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Web-based and Mobile Health Interventions (4018)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43629, first published .
Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Authors of this article:

Jonathan Bricker1, 2 Author Orcid Image ;   Zhen Miao3 Author Orcid Image ;   Kristin Mull1 Author Orcid Image ;   Margarita Santiago-Torres1 Author Orcid Image ;   David M Vock4 Author Orcid Image
Article Authors Cited by (18) Tweetations (8) Metrics

Journals

  1. Ardito V, Golubev G, Ciani O, Tarricone R. Evaluating Barriers and Facilitators to the Uptake of mHealth Apps in Cancer Care Using the Consolidated Framework for Implementation Research: Scoping Literature Review. JMIR Cancer 2023;9:e42092 View
  2. Willingham T, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. International Journal of Environmental Research and Public Health 2024;21(1):79 View
  3. Krotter A, Aonso-Diego G, González-Menéndez A, González-Roz A, Secades-Villa R, García-Pérez Á. Effectiveness of acceptance and commitment therapy for addictive behaviors: A systematic review and meta-analysis. Journal of Contextual Behavioral Science 2024;32:100773 View
  4. McCool M, Schwebel F, Pearson M, Tonigan J. Examining early adherence measures as predictors of subsequent adherence in an intensive longitudinal study of individuals in mutual help groups: One day at a time. Alcohol, Clinical and Experimental Research 2024;48(8):1552 View
  5. Santiago-Torres M, Mull K, Sullivan B, Prochaska J, Zvolensky M, Bricker J, Perovic M. Can an Acceptance and Commitment Therapy‐Based Smartphone App Help Individuals with Mental Health Disorders Quit Smoking?. Depression and Anxiety 2024;2024(1) View
  6. Jakob R, Narauskas J, Fleisch E, König L, Kowatsch T. Factors associated with adherence to a public mobile nutritional health intervention: Retrospective cohort study. Computers in Human Behavior Reports 2024;15:100445 View
  7. Yu L, Amato M, Papandonatos G, Cha S, Graham A. Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study. Journal of Medical Internet Research 2024;26:e54248 View
  8. Zantvoort K, Nacke B, Görlich D, Hornstein S, Jacobi C, Funk B. Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions. npj Digital Medicine 2024;7(1) View
  9. Sanchez Ortuño M, Pecune F, Coelho J, Micoulaud-Franchi J, Salles N, Auriacombe M, Serre F, Levavasseur Y, De Sevin E, Sagaspe P, Philip P. Determinants of Dropout From a Virtual Agent–Based App for Insomnia Management in a Self-Selected Sample of Users With Insomnia Symptoms: Longitudinal Study. JMIR Mental Health 2025;12:e51022 View
  10. Morikawa M, Harada K, Kurita S, Nishijima C, Fujii K, Kakita D, Yamashiro Y, Takayanagi N, Sudo M, Shimada H. Multivariable Prediction Model Development and Validation for Dropout in Community-Based Going-Out Program for Older Adults. Journal of Physical Activity and Health 2025;22(7):793 View
  11. Ash G, Mak S, Haughton A, Augustine M, Bodurtha P, Axtell R, Borsari B, Liu J, Lou S, Xin X, Fucito L, Jeon S, Stults-Kolehmainen M, Gerstein M. College Community–Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association. JMIR mHealth and uHealth 2025;13:e51707 View
  12. Hoogland C, Sutton S, Fennell B, Simmons V, Vinci C, Naqa I, Salloum R, Shete S, Hembree T, Vidrine D, Vidrine J. Early Treatment Engagement and Long-Term Smoking Abstinence Among Women With Cervical Intraepithelial Neoplasia or Cervical Cancer. AJPM Focus 2025;4(4):100361 View
  13. Bul K, Holliday N, Luhanga E. Editorial: Designing and evaluating digital health interventions. Frontiers in Digital Health 2025;7 View
  14. Liu C, Messer M, Linardon J, Fuller-Tyszkiewicz M. Applying models of self-regulated learning to understand engagement with digital health interventions: a narrative review. Frontiers in Digital Health 2025;7 View
  15. Liu C, Linardon J, Fuller-Tyszkiewicz M, Messer M. Perceptions on Drivers of Self-regulated Learning for Shaping Engagement with Digital Health Interventions : An Interview Study with Digital Health Users. Technology, Knowledge and Learning 2025 View
  16. Santiago-Torres M, Mull K, Sullivan B, Bricker J. Does living near a tobacco retailer impact the efficacy of smoking cessation treatments?: Analysis from a randomized trial. Addictive Behaviors Reports 2025;22:100635 View

Conference Proceedings

  1. Jakob R, Lepper N, Fleisch E, Kowatsch T. Proceedings of the CHI Conference on Human Factors in Computing Systems. Predicting early user churn in a public digital weight loss intervention View
  2. Oewel B. Companion Publication of the 2025 ACM Designing Interactive Systems Conference. Designing for Engagement in Digital Mental Health Services View