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

Web-based and Mobile Health Interventions (3340) Innovations and Technology for Physical Activity Education (752) Innovations in Exercise Rehabilitation (215)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28221, first published .
Intelligent Personalized Exercise Prescription Based on an eHealth Promotion System to Improve Health Outcomes of Middle-Aged and Older Adult Community Dwellers: Pretest–Posttest Study

Intelligent Personalized Exercise Prescription Based on an eHealth Promotion System to Improve Health Outcomes of Middle-Aged and Older Adult Community Dwellers: Pretest–Posttest Study

Intelligent Personalized Exercise Prescription Based on an eHealth Promotion System to Improve Health Outcomes of Middle-Aged and Older Adult Community Dwellers: Pretest–Posttest Study

Authors of this article:

Ting Sun1 Author Orcid Image ;   Yang Xu2 Author Orcid Image ;   Hui Xie1 Author Orcid Image ;   Zuchang Ma2 Author Orcid Image ;   Yu Wang2 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Sun T, Zhao H, Ding Z, Xie H, Ma L, Zhang Y, Wang Y, Yang Y, Xu C, Sun Y, Xu X, Ma Z. Evaluating a WeChat-Based Health Behavioral Digital Intervention for Patients With Hypertension: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2023;12:e46883 View
  2. Sun T, Xu X, Zhu N, Zhang J, Ma Z, Xie H. A Service-Learning Project Based on a Community-Oriented Intelligent Health Promotion System for Postgraduate Nursing Students: Mixed Methods Study. JMIR Medical Education 2023;9:e52279 View
  3. Wollesen B, Herden M, Lamberti N, Giannaki C. Defining and reporting exercise intensity in interventions for older adults: a modified Delphi process. European Review of Aging and Physical Activity 2024;21(1) View
  4. Sun T, Xu X, Ding Z, Xie H, Ma L, Zhang J, Xia Y, Zhang G, Ma Z. Development of a Health Behavioral Digital Intervention for Patients With Hypertension Based on an Intelligent Health Promotion System and WeChat: Randomized Controlled Trial. JMIR mHealth and uHealth 2024;12:e53006 View
  5. Yuan Q, Oginni J, Liao N, He H, Gao Z. Promoting precision health using fitness wearable and apps among breast cancer survivors: Protocols of a smart health management trial. Contemporary Clinical Trials 2024;146:107693 View
  6. Xu Y, Liu Q, Pang J, Zeng C, Ma X, Li P, Ma L, Huang J, Xie H. Assessment of Personalized Exercise Prescriptions Issued by ChatGPT 4.0 and Intelligent Health Promotion Systems for Patients with Hypertension Comorbidities Based on the Transtheoretical Model: A Comparative Analysis. Journal of Multidisciplinary Healthcare 2024;Volume 17:5063 View
  7. Sulwarajan K, Jaafar Z, Md Sari N, Hamzah S, Yusop F, Hamid S, Ghani N. A scoping review of the types and features of technology used to deliver exercise prescription and improve exercise adherence. Patient Education and Counseling 2025;131:108580 View
  8. Doherty C, Lambe R, O’Grady B, O’Reilly-Morgan D, Smyth B, Lawlor A, Hurley N, Tragos E. An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial. JMIR mHealth and uHealth 2024;12:e49443 View
  9. Xu X, Zhang G, Xia Y, Xie H, Ding Z, Wang H, Ma Z, Sun T. Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study. JMIR mHealth and uHealth 2024;12:e59610 View

Books/Policy Documents

  1. Ren S, Shen B. Translational Informatics. View