Could sleep app data help identify respiratory disease trends in England?

摘要

UKHSA and Sleep Cycle launch innovative research study exploring how coughing and sleep-related signals from smartphone app data can support earlier detection of respiratory disease trends in the UK.

詳細內容

UKHSA and Sleep Cycle are launching a research study focusing on how coughing and sleep-related signals can support earlier detection of respiratory trends in the UK. This innovative approach represents a novel application of digital health data for public health surveillance.

Research objectives:

Assess whether sleep app data can detect respiratory disease trends
Evaluate cough detection as an early warning signal
Compare app-based signals with traditional surveillance systems
Explore real-time disease activity monitoring potential

How it works: Sleep Cycle app users who opt-in will contribute anonymized data on:

Coughing detected during sleep
Sleep quality and duration
Other sleep disturbances potentially related to illness
Geographic location (aggregated for privacy)

Potential benefits:

Earlier outbreak detection: Digital signals may appear before clinical diagnoses
Geographic granularity: Localized disease activity monitoring
Real-time data: Faster than traditional surveillance lag times
Large-scale participation: Broad population coverage
Cost-effective: Leverages existing technology infrastructure

Privacy and ethics:

Fully anonymized data
Opt-in participation only
GDPR compliant
No individual-level health tracking

Syndromic surveillance innovation: This study builds on UKHSA’s existing syndromic surveillance systems (NHS 111, GP consultations, emergency departments) by adding a novel data stream. If successful, sleep app data could complement traditional surveillance methods, particularly for:

Influenza and respiratory syncytial virus (RSV)
COVID-19 and emerging respiratory pathogens
Seasonal respiratory illness patterns

Research significance: This partnership demonstrates how digital health technologies can be harnessed for public health good, potentially transforming disease surveillance with earlier, more granular detection of respiratory disease trends.

摘要

詳細內容

相關疾病