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

Wearables and MHealth Reviews (310) mHealth for Wellness, Behavior Change and Prevention (3315) Wearable Devices and Sensors (1133) Evaluation and Research Methodology for mHealth (242) Fitness Trackers and Smart Pedometers/Accelerometers (665) mHealth for Data Collection and Research (1196)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31327, first published .
Personalization of Intervention Timing for Physical Activity: Scoping Review

Personalization of Intervention Timing for Physical Activity: Scoping Review

Personalization of Intervention Timing for Physical Activity: Scoping Review

Authors of this article:

Saurabh Chaudhari1 Author Orcid Image ;   Suparna Ghanvatkar1 Author Orcid Image ;   Atreyi Kankanhalli1 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Hahm J, Choi H, Matsuoka H, Kim J, Byon K. Understanding the relationship between acceptance of multifunctional health and fitness features of wrist-worn wearables and actual usage. International Journal of Sports Marketing and Sponsorship 2023;24(2):333 View
  2. Trumpf R, Schulte L, Schroeder H, Larsen R, Haussermann P, Zijlstra W, Fleiner T. Physical activity monitoring-based interventions in geriatric patients: a scoping review on intervention components and clinical applicability. European Review of Aging and Physical Activity 2023;20(1) View
  3. Vandelanotte C, Trost S, Hodgetts D, Imam T, Rashid M, To Q, Maher C. Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions. Journal of Biomedical Informatics 2023;144:104435 View
  4. Vandelanotte C, Hodgetts D, Peris D, Karki A, Maher C, Imam T, Rashid M, To Q, Trost S. Perceptions and expectations of an artificially intelligent physical activity digital assistant — A focus group study. Applied Psychology: Health and Well-Being 2024;16(4):2362 View
  5. Brons A, Wang S, Visser B, Kröse B, Bakkes S, Veltkamp R. Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview. Journal of Medical Internet Research 2024;26:e47774 View
  6. Tong Z, Zhao X. Practice and Effectiveness Evaluation of Personalized Training Program for Physical Education Teaching Based on Intelligent Algorithm. Applied Mathematics and Nonlinear Sciences 2025;10(1) View
  7. Oba T, Takano K, Sugawara D, Kimura K. Just-in-Time Delivery of Cognitive Behavioral Therapy–Based Exercises: Single-Case Experimental Design With Random Multiple Baselines. JMIR Formative Research 2025;9:e69556 View
  8. Kozan Cikirikci E, Esin M. The impact of machine learning on physical activity–related health outcomes: A systematic review and meta‐analysis. International Nursing Review 2025;72(2) View
  9. Samarawickrama A, Elhindi J, Rhou Y, Melov S, Flood V, McNab J, McLean M, Cheung N, Smith B, Usherwood T, Pasupathy D. Association Between Intensity of Physical Activity in Pregnancy and Gestational Diabetes in a Multi-Ethnic Population: Results from the PROMOTE Cohort Study. Nutrients 2025;17(22):3500 View
  10. Hidayat R, Sumaryanti S, Nugroho S, Ihsan F. Injury prevention and rehabilitation in badminton: an evidence-based approach to player wellbeing - a systematic review. Health, sport, rehabilitation 2024 View

Conference Proceedings

  1. Dong Z, Barrett T, Patil A, Shoda Y, Battle L, Wall E. Proceedings of the 30th International Conference on Intelligent User Interfaces. A Design Space of Behavior Change Interventions for Responsible Data Science View