User Adaptive Systems for Behaviour Change in Health And Wellness

There has been a dramatic increase in digital well-being products in recent years and there is a market saturated with ineffective user experiences and little to no sustainable, desired behavioural change. By assessing and enhancing the effectiveness of a personalized approach to digital well-being app interaction through machine learning and emotion-driven adaptive computing, we can develop a new, intelligent and highly effective digital well-being platform that can support lifestyle wellness behaviour change. We will monitor the user experience using our lifestyle wellness application (the Q-life) to integrate enhanced user experiences for a digital well-being platform (i.e. JackHabbit). This ‘next-generation’ intelligent digital well-being platform will disrupt the currently unstandardized market of digital well-being products and support various health and well-being applications for several user markets.

Faculty Supervisor:

Jonathan Fowles;Jeffery Zahavich;Rita Orji;Shannon Johnson;David Russell

Student:

Julia Koppernaes;Cristina Forcione;Yasmeen Ibrahim;Ashfaq A Zamil Adib;Oladapo Oyebode;Rose Scoville

Partner:

Habit Forming Technologies

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Program: