Cough Data Captured During Sleep Could Flag Flu and COVID-19 Surges a Week Early
Key Takeaways:
- The UK Health Security Agency (UKHSA) and AI sleep technology company Sleep Cycle have published research showing that cough data collected passively by a smartphone sleep app closely reflects levels of respiratory illness reported through NHS 111 in England.
- Rises in night-time coughing were often observed around a week before increases in influenza and COVID-19 activity, offering an early signal that is not shaped by healthcare-seeking behaviour, laboratory turnaround times or reporting delays.
- The cough signal was found to be robust and regionally consistent, supporting a role for privacy-preserved consumer digital health data alongside, rather than instead of, established public health surveillance systems.
A new kind of respiratory signal
A study published by the UK Health Security Agency (UKHSA) and the AI sleep technology company Sleep Cycle has evaluated whether cough data gathered by a sleep app can give early indications of rising respiratory illness in England. The full results are available on medRxiv.
The researchers concluded that passively collected data of this kind can provide a robust and regionally consistent indicator of community respiratory illness, while also providing early signals for influenza and COVID-19 activity. Taken together, the authors argue, the findings support a wider role for digital health data in public health surveillance.
Crucially, the cough data was not gathered by asking anyone to report symptoms. It was generated automatically overnight, from the sound analysis that the app already performs as part of its everyday function.
Why existing surveillance has blind spots
Current respiratory surveillance in England depends heavily on people seeking care through the NHS. That dependence introduces a series of variables that have nothing to do with how much illness is actually circulating in a community.
Levels of public awareness can change how quickly people make contact with a service. The availability of that service matters too, as do demographic and socioeconomic differences between the populations being observed. Two areas with similar levels of illness may therefore generate very different surveillance figures.
Timing is a further constraint. Established systems are also affected by reporting and laboratory processing times, which means the picture available to public health teams is always, to some degree, a picture of the recent past rather than the present moment.
How the sleep app signal is generated
Sleep Cycle is a smartphone app designed to help people understand and improve their sleep through AI-powered sound analysis. The app listens during the night and interprets the sounds it detects, including coughing.
Unlike traditional surveillance, Sleep Cycle’s cough signal is generated automatically during normal sleep using privacy-preserved, passively collected data, and it is updated daily. That combination produces a near real-time view of respiratory illness activity, refreshed without anyone needing to complete a form, book an appointment or wait for a test result.
Because the data is produced as a by-product of ordinary app use, it sidesteps several of the behavioural and administrative filters that sit between illness in a community and the figures that reach public health teams.
A week’s head start on flu and COVID-19
The researchers found that cough data collected through the app closely reflected levels of respiratory illness reported through NHS 111. The two measures moved together, which is the first thing any candidate surveillance signal has to demonstrate.
More significantly, the two measures did not move together at the same moment. Increases in coughing were often observed around one week before increases in influenza and COVID-19 activity. The authors highlight this as evidence that passive digital health data can provide earlier situational awareness alongside established surveillance systems.
A week is a meaningful interval in respiratory season planning. It is time that can be used to prepare staffing, communicate with the public, and anticipate pressure on services rather than respond to it.
The findings 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.
What the researchers said
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 and 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.”
Complement, not replacement
Both organisations frame the work in the same way. The cough signal is not presented as a substitute for laboratory confirmation, clinical reporting or NHS 111 data. It is presented as an additional layer that behaves differently from the others, and whose value lies precisely in that difference.
As Professor Riley notes above, no single surveillance system provides a complete picture. A signal that is unaffected by whether people choose to seek care, and unaffected by how long a laboratory takes to process a sample, fails in different ways from the systems already in place. Combining sources with different weaknesses is a well-established way of building a more resilient overall view.
What it means for clinicians and health organisations
For practitioners, the study is a practical illustration of a broader shift. Data generated by consumer devices is increasingly being assessed against the standards applied to conventional health data, and in this case it held up well enough to warrant serious attention from a national public health body.
That shift raises questions clinicians and managers are likely to encounter more often: how such signals are validated, what privacy protections are in place, how to judge the reliability of a consumer-generated dataset, and where these tools genuinely add value to decision-making. Professionals looking to build confidence in this area may find structured CPD useful, and The College of Contemporary Health’s short course AI Essentials for Primary Care is designed to help healthcare professionals understand how AI-driven tools are being applied in practice and how to appraise them critically.
A note on the status of this research
This was reported in medRxiv, The Preprint Server for Health Sciences. The following caution applies, as it does to all preprints:
Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The findings described here should therefore be read as promising and carefully conducted early-stage research rather than as settled evidence. Peer review may yet refine the conclusions, the size of the reported lead time, or the conditions under which the signal performs best.
CCH insight
Digital health signals are moving quickly from novelty to everyday practice, and healthcare professionals are increasingly expected to interpret them with the same rigour they apply to any other source of clinical or population data.
AI Essentials for Primary Care is a CPD short course from The College of Contemporary Health, developed for healthcare professionals who want a clear, practical grounding in how artificial intelligence is being used across healthcare settings and how to evaluate it responsibly.
Explore AI Essentials for Primary Care →
Source: GOV.UK
