Abstract

Abstract

Abstract
Study Objectives Unsupervised machine learning-an approach that identifies patterns and structures within data without relying on labels-has demonstrated remarkable success in various domains of sleep research. This underscores the broader utility of machine learning, suggesting that its capabilities extend beyond current applications and warrant further exploration for novel insights in sleep studies, focusing specifically on unsupervised machine learning.Methods This paper outlines a scoping review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews. A comprehensive search covering various search terms focusing on the intersection between unsupervised machine learning and sleep led to 3960 publications. After screening all titles and abstracts with two independent reviewers, ultimately, 356 publications were included in the full-text review. The data extracted from the full texts included information about the machine learning methods and types of sleep data, as well as the study population.Results There has been a steep increase in the number of publications in this research area in the past 10 years. Clustering is the most commonly used method, but other methods are gaining popularity. Apart from classical polysomnography, data from wearable devices, nearables, video, audio, and medical imaging techniques have been used as input to unsupervised machine learning. The broad search allowed us to explore various applications within sleep research, ranging from the general population to populations with various sleep disorders.Conclusion The review mapped existing research on unsupervised learning in sleep research, identified gaps in the literature, and derived directions for future research. Statement of Significance Sleep is a transdisciplinary research field. With the rise of unsupervised machine learning and its emergence in sleep research, there is a pressing need to cultivate a mutual understanding across disciplinary boundaries to curate meaningful applications of unsupervised machine learning. This scoping review aims to serve as a foundation to facilitate collaboration across disciplines and ultimately contribute to the elevation of sleep research, by identifying novel ways of applying unsupervised machine learning.

Abstract
This viewpoint highlights the critical need for proactive and strategic integration of digital health tools into heat-health action plans (HHAPs) across Europe. Drawing insights from the digital health surge during the COVID-19 pandemic and recent heat-related health impacts, we identify response gaps and suggest specific strategies to strengthen current plans. Key recommendations include leveraging mobile health communication, expanding telemedicine usage, adopting wearable health monitoring devices, and using advanced data analytics to improve responsiveness and equity. This perspective aims to guide policymakers, health authorities, and health care providers in systematically enhancing heat-health preparedness through digital health innovation.