The UK Health Security Agency (UKHSA) and AI sleep technology company Sleep Cycle have published the results of a research study evaluating if cough data from a sleep app could give early indications of increasing respiratory illness in England
The study found that the passive data collected can provide a robust and regionally consistent indicator of community respiratory illness while also providing early signals for influenza and COVID-19 activity, supporting the role of digital health data in public health surveillance
Researchers found that cough data collected through the app closely reflected levels of respiratory illness reported through NHS 111, with rises in coughing often seen around a week before increases in flu and COVID-19 cases
Existing surveillance systems rely on people seeking care through the NHS, which can be influenced by factors including public awareness, service availability and demographic or socioeconomic differences, and they are also impacted by reporting and laboratory processing times
Sleep Cycle is a smartphone app designed to help people understand and improve their sleep through AI-powered sound analysis. Unlike traditional surveillance, Sleep Cycle’s cough signal is generated automatically during normal sleep using privacy-preserved, passively collected data and updated daily, providing a near real-time view of respiratory illness activity
The study found that increases in coughing were often observed around one week before increases in influenza and COVID-19 activity, highlighting the potential of passive digital health data to provide earlier situational awareness alongside established surveillance systems
The findings of the study suggest that used alongside established surveillance systems, passive sleep monitoring could provide a fuller picture of respiratory surveillance data, helping public health experts better understand seasonal trends sooner
Professor Steven Riley, Chief Data Officer at UKHSA, said:
These findings suggest that combining established surveillance approaches with novel digital health signals could contribute to an earlier, richer and more resilient understanding of population respiratory health
No single surveillance system provides a complete picture of respiratory disease activity, but this shows that passive nocturnal cough monitoring can complement other surveillance systems to provide a timely population-level signal of upcoming disease trends, without being affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling and reporting delays
Dr. Emil Carlsson, Research Scientist & Co-lead Author, said:
This study demonstrates that passively collected nightly cough data captures meaningful changes in community respiratory illness
Equally important, it shows that consumer-generated health data can be transformed into epidemiologically meaningful surveillance signals using rigorous scientific methods while maintaining strong privacy protections
Dr. Mikael Kågebäck, Chief Technology Officer and Acting Chief Executive Officer at Sleep Cycle, said:
This study validates a completely new category of health data
For the first time, we’ve demonstrated that passively generated smartphone data can produce robust population-level health intelligence at national scale, while also providing earlier signals for influenza and COVID-19 activity
That creates opportunities to strengthen public health surveillance and enable researchers, healthcare organisations and industry partners to build new services for situational awareness and operational decision support
Updates to this page
Published 3 September 2026
