Encouraging Behavior Changes and Preventing Cardiovascular Diseases Using the Prevent Connect Mobile Health App: Conception and Evaluation of App Quality.

Background: Cardiovascular diseases are a major cause of death worldwide. Mobile health apps could help in preventing cardiovascular diseases by improving modifiable risk factors such as eating habits, physical activity levels, and alcohol or tobacco consumption.

Objective: The aim of this study was to design a mobile health app, Prevent Connect, and to assess its quality for (1) assessing patient behavior for 4 cardiovascular risk factors (unhealthy eating, sedentary lifestyle, alcohol, and tobacco consumption) and (2) suggesting personalized recommendations and mobile health interventions for risky behaviors.

Decision Support System for Selection of e-Health Interventions.

The main goal of this work was to design a decision support system for effective personalized cardiovascular risk prevention: i) to identify behavioral groups associated with clinical risk factors, ii) to provide recommendations associated with the objective to be achieved and iii) to determine the decision-making rules assigning each group to the type of mobile health intervention conveying the most appropriate prevention messages, to help patients to achieve attainable goals. The system is based on an existing data prediction model taking into account specific risky behaviors, clinical risk factors and social status, and it is embedded in a new e-health application. The system is operational.

Design of schistosomiasis ontology (IDOSCHISTO) extending the infectious disease ontology.

Epidemiological monitoring of the schistosomiasis' spreading brings together many practitioners working at different levels of granularity (biology, host individual, host population), who have different perspectives (biology, clinic and epidemiology) on the same phenomenon. Biological perspective deals with pathogens (e.g.

The issue of building generative terminologies for automatic medical data aggregation.

This work addresses the problem of medical terminologies building. Starting from a set of elementary knowledge, a multi-hierarchical terminology is generated according to formal principles that are based on explicit classificatory points of view. The experimentation has been carried out on kidney diseases.