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

RCTs - Protocols/Proposals (eHealth) (609) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (553) Alcohol Abuse, Alcoholism Prevention and Recovery (325)

Published on in Vol 9, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18690, first published .
Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial

Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial

Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial

Authors of this article:

Lauren Bell1 Author Orcid Image ;   Claire Garnett2 Author Orcid Image ;   Tianchen Qian3 Author Orcid Image ;   Olga Perski2 Author Orcid Image ;   Henry W W Potts4, 5 Author Orcid Image ;   Elizabeth Williamson1, 5 Author Orcid Image
Article Authors Cited by (15) Tweetations (12) Metrics

Journals

  1. Garnett C, Perski O, Beard E, Michie S, West R, Brown J. The impact of celebrity influence and national media coverage on users of an alcohol reduction app: a natural experiment. BMC Public Health 2021;21(1) View
  2. Bell L, Garnett C, Qian T, Perski O, Williamson E, Potts H. Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(12):e23369 View
  3. Goldstein S, Zhang F, Klasnja P, Hoover A, Wing R, Thomas J. Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial. JMIR Research Protocols 2021;10(12):e33568 View
  4. Nahum-Shani I, Dziak J, Wetter D. MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions. Frontiers in Digital Health 2022;4 View
  5. Bell L, Garnett C, Bao Y, Cheng Z, Qian T, Perski O, Potts H, Williamson E. How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial. JMIR mHealth and uHealth 2023;11:e38342 View
  6. Cohn E, Qian T, Murphy S. Sample size considerations for micro‐randomized trials with binary proximal outcomes. Statistics in Medicine 2023;42(16):2777 View
  7. Leong U, Chakraborty B. Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review. JMIR mHealth and uHealth 2023;11:e44685 View
  8. Nolan J, Gutman L. Process evaluation of a UK school-based alcohol intervention using the Behaviour Change Wheel Framework. International Journal of Health Promotion and Education 2023:1 View
  9. Kim M, Patrick K, Nebeker C, Godino J, Stein S, Klasnja P, Perski O, Viglione C, Coleman A, Hekler E. The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring. Journal of Medical Internet Research 2024;26:e49208 View
  10. Liu X, Qian T, Bell L, Chakraborty B. Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes. Biometrics 2024;80(2) View
  11. Bao Y, Bell L, Williamson E, Garnett C, Qian T. Estimating causal effects for binary outcomes using per-decision inverse probability weighting. Biostatistics 2024;26(1) View
  12. Liu X, Deliu N, Chakraborty T, Bell L, Chakraborty B. Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. The Annals of Applied Statistics 2025;19(2) View
  13. Švihrová R, Dei Rossi A, Marzorati D, Tzovara A, Faraci F. Designing digital health interventions with causal inference and multi-armed bandits: a review. Frontiers in Digital Health 2025;7 View
  14. Valle C, Nezami B, O’Shea N, Hatley K, Tate D. Outcomes of a pilot microrandomized trial of just-in-time adaptive intervention messages nudging weight-related behaviors in young adults. DIGITAL HEALTH 2025;11 View

Books/Policy Documents

  1. Deliu N, Chakraborty B. Frontiers of Statistics and Data Science. View