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

Journals

  1. Chaikijurajai T, Laffin L, Tang W. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. American Journal of Hypertension 2020;33(11):967 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
  5. Chiang P, Wong M, Dey S. Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure. IEEE Journal of Translational Engineering in Health and Medicine 2021;9:1 View
  6. Bayes J, Peng W, Adams J, Sibbritt D. The effect of the Mediterranean diet on health outcomes in post-stroke adults: a systematic literature review of intervention trials. European Journal of Clinical Nutrition 2023;77(5):551 View
  7. McElfish P, Felix H, Bursac Z, Rowland B, Yeary K, Long C, Selig J, Kaholokula J, Riklon S. A Cluster Randomized Controlled Trial Comparing Diabetes Prevention Program Interventions for Overweight/Obese Marshallese Adults. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2023;60:004695802311520 View
  8. Nomura A, Tanigawa T, Kario K, Igarashi A. Cost-effectiveness of digital therapeutics for essential hypertension. Hypertension Research 2022;45(10):1538 View
  9. Huh K, Oh J, Lee S, Yu K. Clinical Evaluation of Digital Therapeutics: Present and Future. Healthcare Informatics Research 2022;28(3):188 View
  10. Nakagami H. New wave of digital hypertension management for clinical applications. Hypertension Research 2022;45(10):1549 View
  11. Chekka L, Cooper‐DeHoff R, Gums J, Chapman A, Johnson J. Pairwise comparison of hydrochlorothiazide and chlorthalidone responses among hypertensive patients. Clinical and Translational Science 2022;15(12):2858 View
  12. Wilson-Anumudu F, Quan R, Cerrada C, Juusola J, Castro Sweet C, Bradner Jasik C, Turken M. Pilot Results of a Digital Hypertension Self-management Program Among Adults With Excess Body Weight: Single-Arm Nonrandomized Trial. JMIR Formative Research 2022;6(3):e33057 View
  13. Kario K, Nomura A, Harada N, Okura A, Nakagawa K, Tanigawa T, Hida E. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial. European Heart Journal 2021;42(40):4111 View
  14. Alkhouri N, Edwards K, Berman M, Finn H, Escandon R, Lupinacci P, Guthrie N, Coste A, Topete J, Noureddin M. A Novel Prescription Digital Therapeutic Option for the Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease. Gastro Hep Advances 2024;3(1):9 View
  15. Katz M, Mszar R, Grimshaw A, Gunderson C, Onuma O, Lu Y, Spatz E. Digital Health Interventions for Hypertension Management in US Populations Experiencing Health Disparities. JAMA Network Open 2024;7(2):e2356070 View

Books/Policy Documents

  1. Koshimizu H, Okuno Y. Artificial Intelligence in Medicine. View
  2. Koshimizu H, Okuno Y. Artificial Intelligence in Medicine. View