Poor 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.

中文翻译：

---

简洁的，基于机器学习的问卷调查，可以筛查一般人群的失眠和呼吸暂停

睡眠不足是一个主要的公共健康问题，对许多关键的健康后果都有影响，包括心血管疾病，肥胖症，心理健康和神经退行性疾病。1,2最普遍的睡眠障碍是失眠和睡眠呼吸暂停。虽然旨在检测和量化睡眠问题的问卷已经使用了多年，并且被证明是可靠的，[3-6]但它们通常是非常广泛的，并且用科学的措辞来表达。在这里，我们建议普通人群可以使用简洁的问卷“ SleepHubs Check-up（SHC）”作为睡眠呼吸暂停和失眠的筛查工具。我们根据广泛使用的睡眠问卷对SHC进行了验证。其中包括用于检测失眠风险的失眠睡眠指数（ISI）5，以及STOP-Bang3和多变量呼吸暂停预测指数（MAPI）7，8用于检测睡眠呼吸暂停风险。我们基于SHC问题构建了一个多元线性模型来预测ISI得分，并获得0.60的R2。为了检测睡眠呼吸暂停，我们构建了一个卷积神经网络，以从SHC问题中预测呼吸暂停的风险，并获得0.91的准确性。因此，SHC是检测普通大众潜在睡眠问题的可靠且可访问的工具。未来的工作将旨在增加输入数据以提高准确性。因此，SHC是检测普通大众潜在睡眠问题的可靠且可访问的工具。未来的工作将旨在增加输入数据以提高准确性。因此，SHC是检测普通大众潜在睡眠问题的可靠且可访问的工具。未来的工作将旨在增加输入数据以提高准确性。