Imagine, you had to change your lifestyle radically tomorrow! What would you do? – How would you do it? Why would you do it?
For many people ‘why’ they would change their lifestyle is simple, they do not have a choice. Type 2 diabetes (T2DB) is a chronic disease that effects a significant number of people and if left untreated has a significant number of complications, such as damage to eyes. However, some of these can be
avoided or delayed by adherence to a healthy lifestyle and medicine.
In recent years, a large number of mobile health (mHealth) applications have sprung up to support different uses, such as long-term chronic illnesses in this case T2DB. Many applications are focused on tracking health data, however tracking alone often does not provide enough feedback to sustain long term user engagement and to achieve user health goals.
One promising solution to improve engagement and sustained intervention impact is to tailor interventions to individuals in context specific situations. These micro-interventions: “aim to deliver the right component at the most effective dose and time to avoid delivering an intervention component when it is not necessary” (Miller 2019).
Micro-interventions have in recent studies shown great promise in creation, development and use of personalized algorithms for effective feedback delivery. Conceptually, this relationship can be described as: "monitoring-analysis-intervention" in real-time. Therefore, we aim to collect behavioral user data in close to real-time and process it, providing motivational, educational, or other personalized feedback to users in a context aware fashion when they need it. An example of how we envision a micro-intervention based system could be: That the system supports the user to start exercising, and here the system could initially help the user by providing knowledge, even suggesting exercises. Next the users might undertake the exercises and the system may automatically provide feedback and encouragement, ensuring correct intensity of the exercise. Lastly the system could transition to support adherence to the lifestyle change, by motivation.
The overall research question of this project is thus, how technology for micro-intervention can be effectively designed with a focus on diabetes self-management? This is divided into three sub-questions:
1. What is the system design of the micro-intervention technology based on recommender technologies?
2. What is the UX (hardware, software, use scenario) design of micro-intervention technology that involves and engages the user?
3. What is the feasibility of using micro-intervention technology in the self-management of diabetes?
Miller CK. Adaptive intervention designs to promote behavioral change in adults: what is the evidence? Current Diabetes Reports. 2019 Feb 1;19(2):7.