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Area of Science:

  • Digital Health
  • Sleep Medicine
  • Machine Learning

Background:

  • Sleep disorders affect up to 40% of the population.
  • Diagnosis is often delayed due to specialist availability.

Purpose of the Study:

  • To evaluate the use of search engine activity combined with a web-based questionnaire for large-scale screening of sleep disorders.
  • To assess the predictive power of search query data for identifying specific sleep disorders.

Main Methods:

  • Web-based sleep disorder screening questionnaires were advertised via search engine ads.
  • Participants clicking ads and completing questionnaires were screened for sleep disorders.
  • Machine learning algorithms analyzed past search queries to predict suspected sleep disorders.

Main Results:

  • 397 users completed the questionnaire; 132 had usable search query data.
  • Sleep disorder patients exhibited diurnal pattern shifts of 2-3 hours compared to controls.
  • Search query activity showed predictive capability for sleep disorders (AUC 0.62-0.69).

Conclusions:

  • Targeted search advertisements can serve as an initial screening tool for sleep disorders.
  • Search engine data alone is insufficient for comprehensive screening.
  • Web-based information collection can aid diagnosis and encourage earlier patient assessment.