Research shows that people who sleep poorly tend to have brain age that is older than their actual age. Chronic inflammation in the body caused by poor sleep likely plays a part. While the link between poor sleep and dementia has long been known, it was unclear whether poor sleep habits could cause dementia or whether poor sleep was an early symptom of dementia. However, new research has revealed that sleep quality may have a direct impact on the rate at which the brain ages . Our findings provide evidence that poor sleep may contribute to accelerated brain aging, explains Abigail Dove, a neuroepidemiologist at the Karolinska Institute in Sweden, and point to inflammation as one of the underlying mechanisms.

Sleep quality significantly impacts well-being. Therefore, healthcare providers and individuals need accessible and reliable forecasting tools for preventive interventions. This paper introduces an interpretable, individualized two-stage adaptive spatial-temporal model for predicting sleep quality scores. Our proposed framework combines multi-scale convolutional layers to model spatial interactions across multiple input variables, recurrent layers and attention mechanisms to capture long-term temporal dependencies, and a two-stage domain adaptation strategy to enhance generalization. The first adaptation stage is applied during training to mitigate overfitting on the training set. In the second stage, a source-free test-time adaptation mechanism is employed to adapt the model to new users without requiring labels. We conducted various experiments with five input window sizes (3, 5, 7, 9, and 11 days) and five prediction window sizes (1, 3, 5, 7, and 9 days). Our model consistently outperformed time series forecasting baseline approaches, including Long Short-Term Memory (LSTM), Informer, PatchTST, and TimesNet. The best performance was achieved with a three-day input window and a one-day prediction window, yielding a root mean square error (RMSE) of 0.216. Furthermore, the model demonstrated good predictive performance even for longer forecasting horizons (e.g, with a 0.257 RMSE for a three-day prediction window), highlighting its practical utility for real-world applications. We also conducted an explainability analysis to examine how different features influence sleep quality. These findings proved that the proposed framework offers a robust, adaptive, and explainable solution for personalized sleep forecasting using sparse data from commercial wearable devices.

In this study we analyzed average sleep durations across 61 countries to examine the impact of Daylight Saving Time (DST) practices. Key metrics influencing sleep were identified, and statistical correlation analysis was applied to explore relationships among these factors. Countries were grouped based on DST observance, and visualizations compared sleep patterns between DST and non-DST regions. Results show that, on average, countries observing DST tend to report longer sleep durations than those that do not. A more detailed pattern emerged when accounting for latitude: at lower latitudes, DST-observing countries reported shorter sleep durations compared to non-DST countries, while at higher latitudes, DST-observing countries reported longer average sleep durations. These findings suggest that the influence of DST on sleep may be moderated by geographical location.

Despite the prevalence of sleep-tracking devices, many individuals struggle to translate data into actionable improvements in sleep health. Current methods often provide data-driven suggestions but may not be feasible and adaptive to real-life constraints and individual contexts. We present HealthGuru, a novel large language model-powered chatbot to enhance sleep health through data-driven, theory-guided, and adaptive recommendations with conversational behavior change support. HealthGuru's multi-agent framework integrates wearable device data, contextual information, and a contextual multi-armed bandit model to suggest tailored sleep-enhancing activities. The system facilitates natural conversations while incorporating data-driven insights and theoretical behavior change techniques. Our eight-week in-the-wild deployment study with 16 participants compared HealthGuru to a baseline chatbot. Results show improved metrics like sleep duration and activity scores, higher quality responses, and increased user motivation for behavior change with HealthGuru. We also identify challenges and design considerations for personalization and user engagement in health chatbots.

Continuous monitoring and accurate detection of complex sleep patterns associated to different sleep-related conditions is essential, not only for enhancing sleep quality but also for preventing the risk of developing chronic illnesses associated to unhealthy sleep. Despite significant advances in research, achieving versatile recognition of various unhealthy and sub-healthy sleep patterns with simple wearable devices at home remains a significant challenge. Here, we report a robust and durable ultrasensitive strain sensor array printed on a smart garment, in its collar region. This solution allows detecting subtle vibrations associated with multiple sleep patterns at the extrinsic laryngeal muscles. Equipped with a deep learning neural network, it can precisely identify six sleep states-nasal breathing, mouth breathing, snoring, bruxism, central sleep apnea (CSA), and obstructive sleep apnea (OSA)-with an impressive accuracy of 98.6%, all without requiring specific positioning. We further demonstrate its explainability and generalization capabilities in practical applications. Explainable artificial intelligence (XAI) visualizations reflect comprehensive signal pattern analysis with low bias. Transfer learning tests show that the system can achieve high accuracy (overall accuracy of 95%) on new users with very few-shot learning (less than 15 samples per class). The scalable manufacturing process, robustness, high accuracy, and excellent generalization of the smart garment make it a promising tool for next-generation continuous sleep monitoring.

Artificial intelligence has made its way into drug development, surgery and medical advice -- and now it's helping people improve the quality of their sleep. The Artificial Intelligence in Sleep Medicine Committee, which is part of the American Academy of Sleep Medicine, recently published a paper that highlights how AI is contributing to the field of sleep medicine. The committee looked at how AI is assisting in three areas: clinical applications, lifestyle management and population health. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Clinical applications involve the use of AI to diagnose and treat sleep disorders, while lifestyle management focuses on the use of consumer technology to track sleep data.

Sleep detection and annotation are crucial for researchers to understand sleep patterns, especially in children. With modern wrist-worn watches comprising built-in accelerometers, sleep logs can be collected. However, the annotation of these logs into distinct sleep events: onset and wakeup, proves to be challenging. These annotations must be automated, precise, and scalable. We propose to model the accelerometer data using different machine learning (ML) techniques such as support vectors, boosting, ensemble methods, and more complex approaches involving LSTMs and Region-based CNNs. Later, we aim to evaluate these approaches using the Event Detection Average Precision (EDAP) score (similar to the IOU metric) to eventually compare the predictive power and model performance.

A smart ring designed exclusively for women will do what no husband would ever dream of – tell them how to snap out of their mood. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best'. Rather than provide the data in complex graphs and charts, the results will instead be simplified into'actionable insights' for the user to change their lifestyle. The Californian-based firm behind the smart ring, Movano, is aiming for it to become the first wearable to also be approved as a medical device. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best' Alongside monitoring heart rate, respiration rate, and skin temperature, the ring will also track users' ovulation, periods, and menstrual symptoms.

While Apple is reportedly working on AI technology that can detect mental health states and emotion, some are skeptical. It is unclear and still unproven whether AI is reliable for producing clear diagnoses and uncertain how such "emotion AI" would be used in the field, according to Jorge Barraza, assistant professor of the practice of psychology at the University of Southern California and CSO at Immersion, a neuroscience tech vendor. "When we infer things from emotion AI at the macro level -- meaning that we tend to see patterns at the macro level -- at the individual level it starts becoming a little more dubious," Barraza said. Outside of a social context, "it's unclear how much meaning [emotion] has in order for us to understand what people's psychological experiences are," he added. "Different types of expressions or emoting might have different meanings whether it's in a social context or whether it is not."

Technological innovations offer a lot of advancements in the healthcare field, especially as we are looking for more personalized and effective treatments. From artificial intelligence technology to virtual reality technology and many other technologies are finding their application in the healthcare sector. In this article, we will provide an overview of some of the most important tech trends and how they shape this sector. Virtual reality technology is associated with the gaming sector, and for a good reason. There are actually a lot of VR games, and VR headsets have definitely progressed over the years.