Insomnia is a disorder characterized by both nocturnal and daytime symptoms. The main symptoms are unsatisfactory sleep quality or duration, accompanied by difficulty falling asleep before bedtime, frequent or prolonged awakenings, or an inability to fall back asleep after waking in the early morning. Our previous investigation has confirmed that during the period of home isolation of the epidemic, the community people suffered from acute insomnia induced by the epidemic. In order to comprehensively, efficiently and scientifically respond to major public health emergencies such as the COVID-19 epidemic and its long-term impact, it is necessary to carry out in-depth and systematic research on insomnia related issues of medical staff under the COVID-19 epidemic. In summary, insomnia is a widespread problem among medical staff during the epidemic, which greatly reduces the work efficiency of medical staff and damages their physical and mental health. Without timely and effective early identification and effective intervention, allowing the disease to continue to develop will bring a series of concurrent diseases, threaten the lives of medical staff and bring a series of negative social effects. At the same time, the diagnosis and intervention of large-scale acute insomnia for medical staff under the epidemic face some scenario limitations, and it is necessary to consider the spread of the virus to reduce direct contact. Especially for some medical staff in isolation, it is more difficult to implement face-to-face evaluation, diagnosis and treatment. Under the COVID-19 pandemic, there are two main contradictions in the acute insomnia of medical staff. The first is the lack of a diagnostic cloud platform based on artificial intelligence for large-scale acute insomnia. The second is the lack of an effective remote intervention for acute insomnia suitable for the epidemic scenario. Based on the results and deficiencies of the previous research, this project intends to further study and improve in three aspects. First, a large-scale and more accurate artificial intelligence-based automatic screening and diagnosis model research was carried out in combination with CPC equipment for acute insomnia screening of medical staff under the epidemic situation. The second is to use epidemic insomnia acute insomnia CPR to intervene the acute insomnia and other psychiatric symptoms of medical staff on a large scale and verify its effectiveness through follow-up. Third, for the epidemic scenario, further build an intelligent screening and remote intervention system platform for acute insomnia for the majority of medical staff, and continue to provide an assessment, intervention and consultation platform for medical staff under the epidemic. Therefore, in order to comprehensively cope with the increase in the incidence of acute insomnia among medical staff under the COVID-19 epidemic and its resulting disease, social and economic burden, we should pay attention to the mental health of medical staff in the first-level key susceptible population, and improve the response experience of major public health emergencies in the future. This project aims to establish a portable and efficient artificial intelligent-based diagnosis cloud platform method and remote intervention system for medical staff with acute insomnia under the epidemic situation, which is suitable for large-scale development. Based on the data collected by portable devices and electronic scales, a risk assessment model for acute insomnia and other psychiatric symptoms of medical staff in the epidemic situation is constructed, and effective intervention is carried out on this basis. To promote the establishment of a comprehensive prevention and treatment system for insomnia after the epidemic, comprehensively carry out systematic work from multiple perspectives, improve mental health, summarize and form China's experience in dealing with major public emergencies, and promote it internationally, so as to reduce the impact and loss caused by the COVID-19 epidemic on a global scale.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
evaluation of scale
Timeframe: 7 days