Abstract
Recommender systems have been applied in several areas, including e-Health systems, which refers to information and health services enhanced through technology. However, most studies aim at imposing rules to improve lifestyle, rather than recommending nutrition and physical activities. In this context, this study aims to develop a system for recommending physical activities for hypertensive patients to create opportunities for the patients so they can search for and create a healthy lifestyle. To achieve this goal, we elaborated on a hypertensive user profile model, called HyperModel2PAR, and a physical activity recommender system for hypertensive patients, called HyperRecSysPA. The model resulting from this study is composed of 32 elements divided into three groups, which were used in the modeling of user profiles within the system for generating HyperRecSysPA recommendations. The developed system was validated by physicians who answered a specific questionnaire. As a result, ~75% of the recommendations generated were approved. Therefore, this study has prospective contributions to the literature, since both models obtained conclusive results in the assessments performed.
Highlights
The health topic has been an object of study of many researchers and scholars from different areas, especially when considering technology devices and tools that integrate with healthcare service platforms, known as eHealth [1]
Our research question is: What recommendation mechanism is necessary to give hypertensive patients the opportunity to search for a healthy lifestyle in the context of eHealth? To answer it, we propose a recommender system that generates recommendations of physical activities for hypertensive patients
The 7th Brazilian Guideline on Hypertension [21] recommends that hypertensive patients with high blood pressure or who have more than three risk factors, diabetes, target organ damage, or heart disease undergo an exercise test before performing physical exercises at moderate intensity
Summary
The health topic has been an object of study of many researchers and scholars from different areas, especially when considering technology devices and tools that integrate with healthcare service platforms, known as eHealth [1]. These systems attract an increasing number of users, motivated mainly by the advent and diffusion of mobile devices, as well as by the search for a better quality of life [2], [3]. Among a number of systems developed for health management, there is the eLifeStyle platform [4], [5].
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