Digital health interventions (i.e., interventions implemented through digital technologies such as smartphones, websites, emails, wearable devices) have the potential to provide effective, safe, and scalable interventions to improve health, support independent living, and reduce healthcare costs .
Digital health interventions have been shown to improve outcomes in people living with chronic diseases, allow remote access to effective treatments and support changing health risk behaviors such as inactivity, unhealthy diet or substance abuse.
Mechanical learning (ML) is often used to personalize interventions. To facilitate intervention compliance and maximize its effectiveness, it is important to provide the right support at the right time.
At the seminar, I will present recent work on designing and personalizing digital health interventions that I carried out in collaboration with Prof. Szymon Wilk (Poznań University of Technology) and Prof. Mor Peleg (University of Haifa). We will explore different machine learning approaches to find the best time to intervene. We will also examine the factors affecting the effectiveness of the intervention based on the actual outcome of the real-world intervention. Finally, I will discuss future research avenues.