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

Wearable Devices and Sensors (1645) Human Factors and Usability Case Studies (1327) Usability of Apps and User Perceptions of mHealth (1831) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2433) mHealth for Wellness, Behavior Change and Prevention (4310) Novel Sensors and Data Acquisition Methods in Cardiology (267) Design and Usability of Consumer Health Tech and Home Monitoring Devices (475) Hypertension Prevention and Treatment (357) Development of Novel Medical Devices and Innovations for Existing Devices (156)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18012, first published .
Smartwatch, blood pressure monitor, and smartphone displaying health data

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Authors of this article:

Luis J Mena1 Author Orcid Image ;   Vanessa G Félix1 Author Orcid Image ;   Rodolfo Ostos1 Author Orcid Image ;   Armando J González1 Author Orcid Image ;   Rafael Martínez-Peláez2 Author Orcid Image ;   Jesus D Melgarejo3 Author Orcid Image ;   Gladys E Maestre4 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Holyoke P, Yogaratnam K, Kalles E. Web-Based Smartphone Algorithm for Calculating Blood Pressure From Photoplethysmography Remotely in a General Adult Population: Validation Study. Journal of Medical Internet Research 2021;23(4):e19187 View
  2. Brophy E, De Vos M, Boylan G, Ward T. Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach. Sensors 2021;21(18):6311 View
  3. Lee P, Kim H, Kim Y, Choi W, Zitouni M, Khandoker A, Jelinek H, Hadjileontiadis L, Lee U, Jeong Y. Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management. JMIR mHealth and uHealth 2022;10(6):e38614 View
  4. Rivera S, Bernal C, Martínez-Peláez R, Robledo-Nolasco R, De León-Larios G, Félix V, Ostos R, Maestre G, Melgarejo J, Mena L. Computer Simulation of Catheter Cryoablation for Pulmonary Vein Isolation. Healthcare 2024;12(15):1508 View
  5. Qiao M, Chang L, Zhou Z, Jun S, He L, Zhang J. A two-branch framework for blood pressure estimation using photoplethysmography signals with deep learning and clinical prior physiological knowledge. Physiological Measurement 2025;46(2):025004 View
  6. Shi Y, Mandizadza O, Ji C. Home blood pressure monitoring: technology, digitisation and future development. BMJ Supportive & Palliative Care 2025;15(4):436 View
  7. Cai Y, Yao L, Masaki H, Shi T. Evaluation methods of internet of things in nursing care interventions for older adults: a scoping review protocol. BMJ Open 2025;15(10):e101839 View
  8. Neri L, Orro A, Paolocci N, Borghi C, Bagha M. Blood pressure monitoring via wearable devices and artificial intelligence in hypertension detection. Journal of Hypertension 2026 View
  9. Buszko J, Dyś M, Buż A, Danilczuk D, Bajkowska-Piterak M, Szlachta-Gubernat M, Baran N, Piterak P, Adamiec W. CONTACTLESS AND CONTACT-BASED SMARTPHONE PHOTOPLETHYSMOGRAPHY AS A MULTI-PARAMETER CARDIOMETABOLIC HOME SCREEN: BEYOND ATRIAL FIBRILLATION TOWARD BLOOD PRESSURE AND DYSGLYCEMIA ESTIMATION. International Journal of Innovative Technologies in Social Science 2026;3(3(51)) View