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

Formative Evaluation of Digital Health Interventions (2208) Hypertension Prevention and Treatment (207) Innovations and Technology for Healthy Eating Education (400) mHealth for Wellness, Behavior Change and Prevention (2743) Artificial Intelligence (1198)

Published on in Vol 6, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38215, first published .
Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App–Based Hypertension Care Program: Observational Study

Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App–Based Hypertension Care Program: Observational Study

Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App–Based Hypertension Care Program: Observational Study

Authors of this article:

OraLee H Branch1 Author Orcid Image ;   Mohit Rikhy1 Author Orcid Image ;   Lisa A Auster-Gussman1 Author Orcid Image ;   Kimberly G Lockwood1 Author Orcid Image ;   Sarah A Graham1 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Auster-Gussman L, Rikhy M, Lockwood K, Branch O, Graham S. The Effects of Providing a Connected Scale in an App-Based Digital Health Program: Cross-sectional Examination. JMIR mHealth and uHealth 2023;11:e40865 View
  2. Seffah K, Zaman M, Awais N, Satnarine T, Haq A, Hernandez G, Khan S. Exploring the Role of Wearable Electronic Medical Devices in Improving Cardiovascular Risk Factors and Outcomes Among Adults: A Systematic Review. Cureus 2023 View
  3. Lockwood K, Pitter V, Kulkarni P, Graham S, Auster-Gussman L, Branch O, Ayatollahi H. Predictors of program interest in a digital health pilot study for heart health. PLOS Digital Health 2023;2(7):e0000303 View
  4. Graham S, Pickus S, Lockwood K, Buch L, Dunn P, Paruthi J. Business vs After-Hours Use of an Artificial Intelligence–Powered Digital Health Platform Among Insured Patients. JAMA Network Open 2023;6(9):e2333511 View
  5. Paz E, Pargaonkar V, Roach B, Meadows M, Roberts J, Gazit T, Zaleski A, Craig K, Serra S, Dunn P, Michos E. Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self‐Management Program. Journal of the American Heart Association 2024;13(10) View
  6. Bucher A, Blazek E, Symons C. How Are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024 View
  7. Lockwood K, Kulkarni P, Paruthi J, Buch L, Chaffard M, Schitter E, Branch O, Graham S. Evaluating a New Digital App–Based Program for Heart Health: Feasibility and Acceptability Pilot Study. JMIR Formative Research 2024;8:e50446 View
  8. Leitner J, Chiang P, Agnihotri P, Dey S. The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial. JMIR Cardio 2024;8:e51916 View
  9. Maeda T, Sakamoto Y, Hosoki S, Satoh A, Koyoshi R, Yamashita S, Arima H. Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review. Hypertension Research 2024 View