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.
