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

Hypertension Prevention and Treatment (111) mHealth for Wellness, Behavior Change and Prevention (1404)

Published on in Vol 3, No 1 (2019): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13030, first published .
Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study

Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study

Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study

Authors of this article:

Nicole L Guthrie 1 Author Orcid Image ;   Mark A Berman 1 Author Orcid Image ;   Katherine L Edwards 1 Author Orcid Image ;   Kevin J Appelbaum 1 Author Orcid Image ;   Sourav Dey 2 Author Orcid Image ;   Jason Carpenter 2 Author Orcid Image ;   David M Eisenberg 3 Author Orcid Image ;   David L Katz 1, 4 Author Orcid Image
Article Authors Cited by (4) Tweetations (15) Metrics

Journals

  1. Chaikijurajai T, Laffin L, Tang W. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. American Journal of Hypertension 2020 View
  2. Nordyke R, Appelbaum K, Berman M. Estimating the Impact of Novel Digital Therapeutics in Type 2 Diabetes and Hypertension: Health Economic Analysis. Journal of Medical Internet Research 2019;21(10):e15814 View
  3. Wechkunanukul K, Parajuli D, Hamiduzzaman M. Utilising digital health to improve medication-related quality of care for hypertensive patients: An integrative literature review. World Journal of Clinical Cases 2020;8(11):2266 View
  4. Rafferty A, Hall R, Johnston C. A Novel Mobile App (Heali) for Disease Treatment in Participants With Irritable Bowel Syndrome: Randomized Controlled Pilot Trial. Journal of Medical Internet Research 2021;23(3):e24134 View