JMIR Publications

ABSTRACT

Background:

Visual analytics enables efficient analysis and understanding of large data sets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating the risk of cardiovascular diseases (CVDs).

Objective:

This review aims to present the most used visualisation techniques used to estimate CVD risk.

Methods:

In this scoping review, the search strategy involved searching databases including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and including grey literature from Google Scholar. This review includes English articles on digital health, mHealth, mobile applications (apps), images, charts, and decision support systems for estimating CVD risk and empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews.

Results:

A scoping review included 17 studies using different methodologies, including descriptive, quantitative, and population-based studies. The most frequently used prognostic risk factors were age, sex, blood pressure (16/17 each; 94%), smoking status (14/17 each; 82%), diabetes status (11/17 each; 65%), family history (10/17 each; 59%), HDL and total cholesterol (9/17 each; 53%), and triglycerides and LDL cholesterol (6/17 each; 35%). The most frequently used visualization techniques used in studies were "visual cues" (10/17; 59%), followed by "bar charts" (5/17; 29%) and "graphs" (4/17; 24%).

Conclusions:

Technology-based interventions improve healthcare worker performance, knowledge, motivation and compliance by integrating machine learning and visual analytics into applications to identify and respond to CVD risk. Visualisation aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mHealth's effectiveness in improving CVD outcomes is limited.