Unlike traditional randomized controlled trials that generalize relationships in large groups of people, single-case experiments seek to quantify an individual's reaction to an intervention by measuring an independent variable's effect on a dependent variable (i.e., an intervention's effect on an outcome behavior). These single-case experiments are then combined back together using Bayesian Statistics methods in order to learn more general patterns about a population. We are interested in single-case experiments that test the causal relationships between behaviors that have been observed to be correlated with higher wellbeing.
Thus, instead of using an RCT to find what works for the imaginary "average" person, we can learn what works for each individual and then carefully combine data to generalize the results to other real individuals.
To our knowledge, single-case experiments have not been implemented in a smartphone app format. We believe that a successful app will allow researchers to dramatically scale the number of participants in these studies.
Code available on GitHub!