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

New Methods (944) Telehealth and Telemonitoring (1671) Digital Biomarkers and Digital Phenotyping (191) Text-messaging (SMS, WeChat etc)-Based Interventions (650) Hypertension Prevention and Treatment (224)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22493, first published .
Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study

Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study

Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study

Authors of this article:

Anahita Davoudi1 Author Orcid Image ;   Natalie S Lee2, 3, 4 Author Orcid Image ;   Corey Chivers5 Author Orcid Image ;   Timothy Delaney6 Author Orcid Image ;   Elizabeth L Asch3 Author Orcid Image ;   Catherine Reitz6, 7 Author Orcid Image ;   Shivan J Mehta6, 7 Author Orcid Image ;   Krisda H Chaiyachati3, 6, 7 Author Orcid Image ;   Danielle L Mowery1, 8 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Spieker A, Nelson L, Rothman R, Roumie C, Kripalani S, Coco J, Fabbri D, Levy P, Collins S, Wang T, Liu D, McNaughton C. Feasibility and Short‐Term Effects of a Multi‐Component Emergency Department Blood Pressure Intervention: A Pilot Randomized Trial. Journal of the American Heart Association 2022;11(5) View
  2. Davoudi A, Lee N, Luong T, Delaney T, Asch E, Chaiyachati K, Mowery D. Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study. Journal of Medical Internet Research 2022;24(6):e36151 View
  3. Hwang S, Urbanowicz R, Lynch S, Vernon T, Bresz K, Giraldo C, Kennedy E, Leabhart M, Bleacher T, Ripchinski M, Mowery D, Oyer R. Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors. JCO Clinical Cancer Informatics 2023;(7) View
  4. Hare A, Patel M, Volpp K, Adusumalli S. The Role of Behavioral Economics in Improving Cardiovascular Health Behaviors and Outcomes. Current Cardiology Reports 2021;23(11) View
  5. Idris H, Nugraheni W, Rachmawati T, Kusnali A, Yulianti A, Purwatiningsih Y, Nuraini S, Susianti N, Faisal D, Arifin H, Maharani A. How Is Telehealth Currently Being Utilized to Help in Hypertension Management within Primary Healthcare Settings? A Scoping Review. International Journal of Environmental Research and Public Health 2024;21(1):90 View
  6. MacEwan S, Olvera R, Jonnalagadda P, Fareed N, McAlearney A. Patient and Provider Perspectives About the Use of Patient-Generated Health Data During Pregnancy: Qualitative Exploratory Study. JMIR Formative Research 2024;8:e52397 View
  7. Demkina A, Korobeynikova A, Rogoza A, Vladzimirskiy A. SWOT-analysis: Remote monitoring of blood pressure. Digital Diagnostics 2024;5(2):303 View
  8. Eaton C, Vallejo N, McDonald X, Wu J, Rodríguez R, Muthusamy N, Mathioudakis N, Riekert K. User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review. Journal of Medical Internet Research 2024;26:e50508 View
  9. Kasimovskaya N, Fomina E, Krivetskaya M, Diatlova E, Egorova E, Pavlov D. Determination of digital biomarkers of disease progression for digital phenotyping of patients with arterial hypertension. Vasa 2024;53(6):428 View