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

Area of Science:

• Digital Health
• Mobile Health Research
• Biomedical Data Collection

Background:

• Smartphone-based research offers a novel, practical method for large-scale data acquisition in health and disease.
• This approach allows for the collection of real-world, real-time data, including contextual and environmental information through device sensors and wearables.
• Previous research has not fully tapped into the potential of mobile technology for tracking disease manifestation and long-term health trajectories.

Purpose of the Study:

• To investigate the relationship between sleep habits and daytime functioning using a novel digital health approach.
• To assess the feasibility of a fully remote clinical data collection strategy.
• To evaluate data validity, user retention, and data-sharing preferences in a large cohort study.

Main Methods:

• Utilized a mobile application to collect data from a large cohort of 7,250 participants across the United States.
• Employed surveys and sensor-based activities integrated into participants' daily routines.
• Collected contextual and environmental data via device sensors, data aggregators, and connected wearable devices.

Main Results:

• Successfully gathered extensive data from 7,250 participants, demonstrating the feasibility of a large-scale, remote digital health study.
• The SleepHealth Mobile App Study (SHMAS) provided a rich dataset for understanding sleep-health relationships.
• Participant data sharing was broad, including with qualified researchers globally.

Conclusions:

• Smartphone-based research is a viable and practical method for large-scale biomedical data collection.
• Digital health approaches, like SHMAS, can effectively study complex relationships such as sleep and daytime functioning.
• The study highlights the potential for remote data collection to advance health research and understanding of disease.