Self-experiments allow people to explore what behavioral changes lead to improved health and wellness. However, it is challenging to run such experiments in a scientifically valid way that is also flexible and able to accommodate the realities of daily life. We present a set of design principles for guided self-experiments that aim to lower this barrier to self-experimentation. We demonstrate the value of the principles by implementing them in SleepBandits, an integrated system that includes a smartphone application for sleep experiments. SleepBandits guides users through the steps of a single-case experiment, automatically collecting data from the built-in sensors and user input and calculating and presenting results in real-time. We released SleepBandits to the Google Play Store and people voluntarily downloaded and used it. Based on the data from 365 active users from this in-the-wild study, we discuss opportunities and challenges with the design principles and the SleepBandits system.

We present SleepCoacher, an integrated system implementing a framework for effective self-experiments. SleepCoacher automates the cycle of single-case experiments by collecting raw mobile sensor data and generating personalized, data-driven sleep recommendations based on a collection of template recommendations created with input from clinicians. The system guides users through iterative short experiments to test the effect of recommendations on their sleep. We evaluate SleepCoacher in two studies, measuring the effect of recommendations on the frequency of awakenings, self-reported restfulness, and sleep onset latency, concluding that it is effective: participant sleep improves as adherence with SleepCoacher's recommendations and experiment schedule increases. This approach presents computationally-enhanced interventions leveraging the capacity of a closed feedback loop system, offering a method for scaling guided single-case experiments in real time.