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

mHealth for Wellness, Behavior Change and Prevention (3315) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Registered Report (791) Mobile Health (mhealth) (3464) Public (e)Health, Digital Epidemiology and Public Health Informatics (1008) Behavior Change (863) Alcohol Abuse, Alcoholism Prevention and Recovery (340) Prevention and Health Promotion (1362)

Published on in Vol 11 (2023)

This is a member publication of University College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38342, first published .
How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

Authors of this article:

Lauren Bell1, 2 Author Orcid Image ;   Claire Garnett3 Author Orcid Image ;   Yihan Bao4 Author Orcid Image ;   Zhaoxi Cheng5 Author Orcid Image ;   Tianchen Qian6 Author Orcid Image ;   Olga Perski3 Author Orcid Image ;   Henry W W Potts7 Author Orcid Image ;   Elizabeth Williamson1 Author Orcid Image
Article Authors Cited by (18) Tweetations (13) Metrics

Journals

  1. Alnooh G, Alessa T, Noorwali E, Albar S, Williams E, de Witte L, Hawley M. Identification of the Most Suitable Mobile Apps to Support Dietary Approaches to Stop Hypertension (DASH) Diet Self-Management: Systematic Search of App Stores and Content Analysis. Nutrients 2023;15(15):3476 View
  2. 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
  3. Prentice C, Peven K, Zhaunova L, Nayak V, Radovic T, Klepchukova A, Potts H, Ponzo S. Methods for evaluating the efficacy and effectiveness of direct-to-consumer mobile health apps: a scoping review. BMC Digital Health 2024;2(1) View
  4. 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
  5. Patra E, Kokkinopoulou A, Wilson-Barnes S, Hart K, Gymnopoulos L, Tsatsou D, Solachidis V, Dimitropoulos K, Rouskas K, Argiriou A, Lalama E, Csanalosi M, Pfeiffer A, Cornelissen V, Decorte E, Dias S, Oikonomidis Y, María Botana J, Leoni R, Russell D, Mantovani E, Aleksić M, Brkić B, Hassapidou M, Pagkalos I. Personal Goals, User Engagement, and Meal Adherence within a Personalised AI-Based Mobile Application for Nutrition and Physical Activity. Life 2024;14(10):1238 View
  6. Pretolesi D, Motnikar L, Bieg T, Gafert M, Uhl J. Exploring user preferences: customisation and attitudes towards notifications in mobile health and well-being applications. Behaviour & Information Technology 2025;44(1):2 View
  7. Zhang Z, Sun S, Moradbakhti L, Hall A, Mougenot C, Chen J, Calvo R. Health Care Professionals' Engagement With Digital Mental Health Interventions in the United Kingdom and China: Mixed Methods Study on Engagement Factors and Design Implications. JMIR Mental Health 2025;12:e67190 View
  8. Zamora A, Zeitlin A, Moore J, Oppezzo M. Exploring Perceptions, Barriers, and Facilitators of Participation in an Exercise Snack Intervention Among U.S. Office Workers: Findings From a Pilot Study. American Journal of Health Promotion 2025;39(8):1147 View
  9. Linardon J, Torous J. Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders. International Journal of Eating Disorders 2025;58(8):1415 View
  10. 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
  11. Navarro-Ovando V, van Schie S, Garrelfs I, Rijksbaron J, Rivero C, Mathôt R, Dumont G. Current approaches using remote monitoring technology in alcohol use disorder (AUD): an integrative review. Alcohol and Alcoholism 2025;60(4) View
  12. Adorni R, Zanatta F, Serino S, Vanutelli M, Caso D, D’Addario M, Steca P. Efficacy of a theory-based and tailored mHealth intervention promoting walking behavior: a preliminary randomized controlled trial. Scientific Reports 2025;15(1) View
  13. Khoziasheva A. Development of push notification-based brief interventions for youth engaged in problematic mobile gaming. Social Sciences & Humanities Open 2025;12:101921 View
  14. Yu J, Qian T. Modeling time-varying effects of mobile health interventions using longitudinal functional data from HeartSteps micro-randomized trial. The Annals of Applied Statistics 2025;19(3) View
  15. Kheirdoust A, Mazaheri Habibi M, Emadzadeh A, Jafarzadeh Esfehani A, Agha Seyyed Esmaeil Amiri F, Ghaddaripouri K, Eslami S. Investigating the approach of using behavior change techniques in the field of mobile applications: a systematic review. BMC Health Services Research 2025;25(1) View

Books/Policy Documents

  1. Hsueh C, Chen C. Human Interface and the Management of Information. View

Conference Proceedings

  1. Telang S, Barth C, Huang E. Companion Publication of the 2025 ACM Designing Interactive Systems Conference. “Can you stop beeping!”: Designing Notifications that Empathise with Fatigue in Diabetes Technology View
  2. Paradeda R, Silva E, Santos L. Anais do XVII Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP 2025). Balancing Notification Frequency and User Satisfaction in Task Management Tools View