Real-world longitudinal data collected from the SleepHealth mobile app study

Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide. Measurement(s) Demographic Data • Alcohol Consumption • Smoking Behavior • Socioeconomic Factors • Personal Medical History • Sleepiness • Sleep Quality • Objective Alertness Technology Type(s) Survey • Epworth Sleepiness Scale Questionnaire • Pittsburgh Sleep Quality Index • Psychomotor Vigilance Task 3-Minute Version (PVT-B) Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12647108