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

Non-randomized Protocols and Methods (ehealth) (571) mHealth for Treatment Adherence (402) Artificial Intelligence (1407) Hypertension Prevention and Treatment (228) Medication Adherence (88) mHealth for Wellness, Behavior Change and Prevention (2910) Web-based and Mobile Health Interventions (3342)

Published on in Vol 10, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24494, first published .
Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study

Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study

Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study

Authors of this article:

Kobra Etminani1 Author Orcid Image ;   Carina Göransson2 Author Orcid Image ;   Alexander Galozy1 Author Orcid Image ;   Margaretha Norell Pejner2, 3 Author Orcid Image ;   Sławomir Nowaczyk1 Author Orcid Image
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Journals

  1. Gupta K, Bajaj V, Ansari I, Rajendra Acharya U. Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals. Biocybernetics and Biomedical Engineering 2022;42(3):784 View