WEARABLE DEVICES IN HYPERTENSION SELF-MANAGEMENT: PATIENT ENGAGEMENT, CLINICAL OUTCOMES, AND BARRIERS TO LONG-TERM ADOPTION
TL;DR
Wearable and connected devices should be treated as components of an organized self-management system rather than stand-alone therapies, and future implementation should prioritize validated measurement, low-burden design, human support, interoperability, and equity-sensitive evaluation over device novelty.
Abstract
Background: Hypertension remains poorly controlled worldwide despite the availability of effective treatment. Wearable devices may support self-management by combining blood pressure monitoring, physical activity feedback, reminders, and digital communication outside clinic visits. Objective: This review evaluates whether wearable and connected devices improve hypertension self-management, which components appear clinically useful, and why long-term adoption often fails. Materials and Methods: A structured narrative review was conducted using English-language publications available through June 2026. Thirty sources were included, comprising randomized trials, systematic reviews, professional statements, validation studies, and qualitative research. Results: Wearable or connected monitoring appears most useful when it is embedded in a care pathway that includes reliable measurement, feedback, education, medication titration, or professional support. Activity tracking or self-measurement alone produces small, inconsistent, or absent blood pressure effects. Cuffless blood pressure devices may reduce measurement burden and capture patterns across daily life, but calibration drift, position effects, proprietary algorithms, and incomplete validation currently limit their use for diagnosis or treatment decisions. Sustained engagement depends on usability, perceived value, trust, tailored feedback, social support, and integration with clinical workflows. Conclusions: Wearables should be treated as components of an organized self-management system rather than stand-alone therapies. Future implementation should prioritize validated measurement, low-burden design, human support, interoperability, and equity-sensitive evaluation over device novelty.