A concise, machine learning-based questionnaire that screens for insomnia and apnoea in the general population

Abstract

ABSTRACTPoor sleep is a major public health problem with implications for a wide range of critical health outcomes, including cardiovascular disease, obesity, mental health, and neurodegenerative disease.1,2 The most prevalent sleep disorders are insomnia and sleep apnoea. While questionnaires aimed at detecting and quantifying sleep problems have been used for years and proven to be reliable,3-6 they are often very extensive and scientifically worded. Here, we propose that the general population can use the SleepHubs Check-up (SHC), a concise questionnaire as a screening tool for sleep apnoea and insomnia. We validated the SHC against widely-used sleep questionnaires. These include the Insomnia Sleep Index (ISI)5 for detection of insomnia risk, as well as STOP-Bang3 and Multivariable Apnoea Prediction Index (MAPI)7,8 for the detection of sleep apnoea risk. We built a multivariate linear model to predict the ISI score based on the SHC questions and obtained an R2 of 0.60. For the detection of sleep apnoea, we constructed a convoluted neural network to predict the risk of apnoea from the SHC questions, and obtained an accuracy of 0.91. The SHC is therefore a reliable and accessible tool for the detection of latent sleep problems in the general public. Future work will aim at increasing the input data to improve the accuracy.