For Taro Ueno, a psychiatrist and president of Susmed, the idea to develop an app for insomnia came from observing how doctors in Japan overprescribe sleeping pills. Japan's medical industry has generally been slow to embrace digital technology, with many clinics still keeping patient records and writing prescriptions on paper. But a few domestic startups have recently launched chatbot-powered apps designed to help treat a range of conditions, such as hypertension, alcohol addiction and insomnia. Unlike the plethora of lifestyle apps anyone with a smartphone can download, these are prescription-only medical apps whose efficacy has been demonstrated in clinical trials. For Taro Ueno, a psychiatrist and brain researcher, the idea to develop an app for insomnia came from observing how doctors in Japan overprescribe sleeping pills. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Insomnia affects a vast population of the world and can have a wide range of causes. Existing treatments for insomnia have been linked with many side effects like headaches, dizziness, etc. As such, there is a clear need for improved insomnia treatment. Brain modelling has helped with assessing the effects of brain pathology on brain network dynamics and with supporting clinical decisions in the treatment of Alzheimer's disease, epilepsy, etc. However, such models have not been developed for insomnia. Therefore, this project attempts to understand the characteristics of the brain of individuals experiencing insomnia using continuous long-duration EEG data. Brain networks are derived based on functional connectivity and spatial distance between EEG channels. The power spectral density of the channels is then computed for the major brain wave frequency bands. A graph convolutional neural network (GCNN) model is then trained to capture the functional characteristics associated with insomnia and configured for the classification task to judge performance. Results indicated a 50-second non-overlapping sliding window was the most suitable choice for EEG segmentation. This approach achieved a classification accuracy of 70% at window level and 68% at subject level. Additionally, the omission of EEG channels C4-P4, F4-C4 and C4-A1 caused higher degradation in model performance than the removal of other channels. These channel electrodes are positioned near brain regions known to exhibit atypical levels of functional connectivity in individuals with insomnia, which can explain such results.

Continuing advances in Large Language Models (LLMs) in artificial intelligence offer important capacities in intuitively accessing and using medical knowledge in many contexts, including education and training as well as assessment and treatment. Most of the initial literature on LLMs in medicine has emphasized that LLMs are unsuitable for medical use because they are non-deterministic, may provide incorrect or harmful responses, and cannot be regulated to assure quality control. If these issues could be corrected, optimizing LLM technology could benefit patients and physicians by providing affordable, point-of-care medical knowledge. Our proposed framework refines LLM responses by restricting their primary knowledge base to domain-specific datasets containing validated medical information. Additionally, we introduce an actor-critic LLM prompting protocol based on active inference principles of human cognition, where a Therapist agent initially responds to patient queries, and a Supervisor agent evaluates and adjusts responses to ensure accuracy and reliability. We conducted a validation study where expert cognitive behaviour therapy for insomnia (CBT-I) therapists evaluated responses from the LLM in a blind format. Experienced human CBT-I therapists assessed responses to 100 patient queries, comparing LLM-generated responses with appropriate and inappropriate responses crafted by experienced CBT-I therapists. Results showed that LLM responses received high ratings from the CBT-I therapists, often exceeding those of therapist-generated appropriate responses. This structured approach aims to integrate advanced LLM technology into medical applications, meeting regulatory requirements for establishing the safe and effective use of special purpose validated LLMs in medicine.

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia.

A machine learning model can effectively predict a patient's risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values, according to a new study published this week in the open-access journal PLOS ONE by Samuel Y. Huang of Virginia Commonwealth University School of Medicine, and Alexander A. Huang of Northwestern Feinberg University School of Medicine, U.S. The prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade. This trend is important to better understand and reverse since sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression. In the new work, the researchers used the machine learning model XGBoost to analyze publicly available data on 7,929 patients in the U.S. who completed the National Health and Nutrition Examination Survey. The data contained 684 variables for each patient, including demographic, dietary, exercise and mental health questionnaire responses, as well as laboratory and physical exam information. Overall, 2,302 patients in the study had a physician diagnosis of a sleep disorder.

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Sleep is an essential part of our biology. Not getting enough of it harms our decision-making skills, our reasoning, even our social interactions. However, sleep has recently become yet another aspect of our lives that causes endless worry, rather than being enjoyable. We've become obsessed with getting the perfect night's sleep. As a consequence, many of us are now afflicted by sleep anxiety, a condition in which people worry about not falling asleep or staying asleep.

The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availability and reduce the relative costs of mental health support. The provided support can be divided into two main types: cognitive and emotional support. Existing work on this topic mainly focuses on constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles. Such agents operate based on pre-defined templates and exercises to provide cognitive support. However, research on emotional support using such agents is limited. In addition, most of the constructed agents operate in English, highlighting the importance of conducting such studies in China. In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress. Emohaa is a conversational agent that provides cognitive support through CBT-based exercises and guided conversations. It also emotionally supports users by enabling them to vent their desired emotional problems. The study included 134 participants, split into three groups: Emohaa (CBT-based), Emohaa (Full), and control. Experimental results demonstrated that compared to the control group, participants who used Emohaa experienced considerably more significant improvements in symptoms of mental distress. We also found that adding the emotional support agent had a complementary effect on such improvements, mainly depression and insomnia. Based on the obtained results and participants' satisfaction with the platform, we concluded that Emohaa is a practical and effective tool for reducing mental distress.

An app which uses cognitive behavioural therapy techniques to help people overcome insomnia has received recommendation from the National Institute for Health and Care Excellence (NICE). Sleepio, from Big Health, uses an artificial intelligence (AI) algorithm to provide people with tailored therapy and provides a digital six-week self-help programme involving a sleep test, weekly interactive sessions with users encouraged to keep a diary about their sleeping patterns. Sleepio was rolled out in the south of England towards the end of 2018 and in 2019 was made available across London. NICE is recommending that the Sleepio app is used as cost-effective alternative to prescribed medication after is Medical Technologies Advisory Committee evaluated the platform. The committee concluded that Sleepio is more effective than conventional treatment options (sleep hygiene and medication) in reducing symptoms of insomnia in adults.

A new artificial intelligence sleep app has been developed that might be able to replace sleeping pills for insomnia sufferers. Sleepio uses an AI algorithm to provide individuals with tailored cognitive behavioural therapy for insomnia (CBT-I). The National Institute for Health and Care Excellence (Nice) said it would save the NHS money as well as reduce prescriptions of medicines such as zolpidem and zopiclone, which can be dependency forming. Its economic analysis found healthcare costs were lower after one year of using Sleepio, mostly because of fewer GP appointments and sleeping pills prescribed. The app provides a digital six-week self-help programme involving a sleep test, weekly interactive CBT-I sessions and keeping a diary about sleeping patterns.