EAGER: Computationally and Socially Guided Self-Experiments

Brown University, Providence RI

Investigators

Abstract

This research will develop a fully automated system that guides users in adapting health tracking and behavior improvement information technology systems to suit their personal needs. Many people lose interest in health tracking devices after the novelty wears off. While the goal of empowering users to improve their own health and wellness with data is impactful, current methods which rely heavily on displaying charts and providing summary statistics about steps taken and heart rate, are not sufficiently motivating for most people in the long term. Personalized recommendations are a key factor to improve engagement. This project poses a new paradigm for societal improvement that has not been tested before - computationally guided self-experiments. It enables people to conduct rigorous conclusive analysis of behavior change without having to know details about statistical analysis and experimental design, at no cost by using their existing mobile devices. The underlying technology is an intervention-based experimental design that observes and learns what works for each individual. The complete cycle includes generating interventions through actionable behavior change suggestions, causal analysis to learn from outcomes, and then back to generating a new intervention. The experiment is constantly evolving from month to month, so the user is always being asked to make actionable changes and informed of ongoing results. The experimental design is guided by standards from single-case intervention research methods, but modified to take advantage of Bayesian statistics to eliminate inconclusive or excessively long experiments. The research comprises an investigation into the efficacy of guided self-experiments as a new paradigm for improving people?s lives. One condition tested will be whether people grouped into anonymous cohorts to share ongoing results can help motivate them to continue lengthy experiments. The social nature of the groups allow people to observe each other?s progress for self-motivation, and gain ideas for new self-experiments to run.