Feb 2022: Two research projects on UNIGE’s P3: Call for Students
Two QoL lab’s projects are listed under the UNIGE’s Programme de Projects Partenaires (P3) and accepting applicants – students or other interested collaborators.
The two projects are the following.
Your phone knows everything: better quality of life and health in Geneva!
Problem — The world is now more connected than ever, and smart devices are closer to us than we ever imagined. Whether it’s through a smartphone, smartwatch, smart bracelet, smart ring, or any other type of smart and ubiquitous device, it is now possible to collect an enormous amount of data about our physical, psychological, social, and contextual state. Many health problems could then be detected early and prevented by looking at only small changes over time. Brain diseases, such as dementia, are an example. If developed with precision, the tools that make this health improvement possible would ultimately improve our overall quality of life.
Solution — The QoL Lab aims to leverage smartphones and wearables to collect data from everyday life, in time and context, over long periods of time. This project focuses on the development and implementation of a mobile application to collect data and build using Flutter, based on the existing mQoL-Lab application on Android. The ultimate goal is to deploy it in a longitudinal study with up to 5000 participants over 5 years. The final results will help us understand how predictive models can be implemented in the coming years and guide us towards better quality of life practices in our daily lives.
Requirements — Good knowledge of Flutter and Dart; Good communication in English and/or French.
Want to join this project? Get in touch with us at katarzyna.wac@unige.ch or igor.matias@unige.ch.
If you continue like this… Computational Models of Scenarios for Chronic Disease Development
Problem — More than 40% of the risk of chronic diseases is due to unhealthy behaviors, such as physical inactivity, bad nutrition, poor sleep, or smoking status. However, there is no precise cause-effect formula that translates all these behavioral “markers” to the risk factors used in the risk assessment models from the literature. In this project, we will leverage the models from the literature and computationally model different alternative scenarios for the future.
Solution — Scope: Research, development, and deployment of computational models and interactive scenario generator (web) based on the latest results in chronic disease risk assessment, longitudinal behavioral and health data from a large sample of the Swiss, Danish and US populations, and behavioral and health data from a participant.
Requirements — Good knowledge of Python and/or R; Good communication in English and/or French.
Want to join this project? Get in touch with us at katarzyna.wac@unige.ch.
The full list of P3 projects can be accessed here.