An academic study to provide real time adaptive personal messaging to adults with type 2 diabetes and hypertension.
What is 4Me?
4Me is a program that provides messaging on healthy behaviours and diet by asking questions, passively tracking FitBit data while also utilizing data inputs from Bitesnap to determine the type of content to send, the right time to send it and the right frequency to send it.
The program begins with a 2-week monitoring period to obtain a baseline for the participant and what areas they need support in. The main areas of focus are exercise, diet, sleep, and stress. The specific breakdown of messages will depend on the participants barriers and facilitators, which are determined through surveys conducted by the research team. These include self-efficacy, health literacy, busyness, motivation, and social support. Participants receive a total of 10 per week; 2 messages from each barrier and 1 message from each facilitator.
Participants also have access to a website that provides them with visualizations of their progress and resources to help them achieve their goals.
Diabetes and Hypertension
Approximately 20 – 70% of adults have hypertension and type 2 diabetes and nearly 30% have prehypertension and 38% have prediabetes.
The type of solutions offered to the public are one size fits all, hard to follow and not always possible based on the individual’s lifestyle. 4Me looks to adapt to individuals’ social routines and environments. The goal is to learn what factors might impact healthy eating choices, ability to exercise or get enough sleep and use the information to provide a solution that will address these factors.
Dr. Azizi Seixas, Ph.D. is an associate professor at The University of Miami Miller School of Medicine in the Department of Psychiatry and Behavioural Sciences. He is Founding Director of the Media and Innovation Lab and Associate Director for the Center for Translational Sleep and Circadian Sciences. Dr. Seixas’ focus on 1) biological, behavioral, clinical, environmental, and psychosocial determinants of cardiovascular and cardiometabolic disease/conditions, mental health, and brain health (dementia and aging), and 2) developing adaptive, precise, and personalized behavioral interventions to improve health and well-being with the use of machine learning, translational artificial intelligence, and digital technology.