Monitoring sleep using smartphone data in a population of college students

Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not affordable or accessible to the majority of the population. As consumer devices like smartphones become increasingly powerful and accessible, monitoring sleep using smartphone patterns offers a feasible and scalable alternative. In this study, we analyze the sleep behavior of 67 college students with elevated stress over 28 days. While using the open-source mindLAMP smartphone app to complete daily and weekly sleep and mental health surveys, participants also passively collected phone sensor data. We used these passive sensor data streams to estimate sleep duration. These sensor-based sleep duration estimates, when averaged for each participant, were correlated with self-reported sleep duration (r=0.83). We constructed a simple predictive model using both sensor-based sleep duration estimates and surveys as predictors that predicted Pittsburgh Sleep Quality Index (PSQI) scores within 1 point. Overall, smartphone-derived sleep duration estimates offer practical results for estimating sleep duration and can serve useful functions in digital phenotyping.