Submit a Manuscript to the Journal
Nature and Science of Sleep
For an Article Collection on
The Intersection of Wearables and Sleep Medicine
31 December 2023
The Intersection of Wearables and Sleep Medicine
Physiological and behavioral sensing from wearable devices provides a unique opportunity to capture an individual’s condition and health status in situ, passively, over time. As a result, wearable technology has provided unprecedented insight and progress to both clinical care and consumer wellness. Sleep science is one of the first disciplines to harvest the potential of these advanced non-invasive technologies to accurately screen, diagnose, and monitor for abnormalities or illness.
Sleep affects most aspects of well-being, from cognitive function and mental health to physical performance and disease susceptibility. Despite its importance, sleep disorders remain widespread and often undiagnosed. The physiological changes that occur during sleep and are passively measured through wearables can serve as indicators of numerous health conditions, including neural and cardiac events. Moreover, wearables allow for measurement during the daytime, extending the interpretation of sleep beyond the sleep period. These insights can guide interventions, lifestyle changes, and even medical treatments, ultimately leading to improved sleep and overall health.
To highlight the critical uses of wearables for sleep science, we are providing a platform for groundbreaking research. We hope the articles in this Collection will serve to offer insights into, and foster a dialogue around, this rapidly evolving interdisciplinary field. We welcome all original research articles, reviews, and perspectives that use wearable technology to better understand sleep. Our scope includes the following:
- Studies using wearable devices for the diagnosis, prognosis, and treatment related to sleep disorders
- Applications of continuous physiological data collection using wearable devices during sleep
- Sleep-related studies leveraging new wearables for sleep or daytime usage
- Validation of sleep-related data collection and/or derived metrics from wearable devices using gold standard procedures
- New algorithms or machine learning models that use data from wearable devices with sleep-related applications
- The positive and negative impact of wearables on sleep habits and behavior
All manuscripts submitted to this Article Collection will undergo full peer-review; Guest Advisors will not be handling submitted articles. Please review the journal’s aims and scope and author instructions prior to submission.
Please submit your manuscript through the Dovepress website. During submission, enter the promo code YJVGW to indicate that your article should be considered for this Collection.
The manuscript submission deadline is 31 December 2023.
If you have any questions about this Article Collection, please contact Krista Thom at [email protected].
Aarti Sathyanarayana’s research strives to improve human health and performance through digital phenotyping and biomarker discovery. She aims to translate enigmatic digital health data collected from smartphones, wearables, and biomedical devices, into actionable insights for clinical care and personal wellness. Her work has developed new signal processing and machine learning algorithms for time variant health data analysis. In addition to her role at Northeastern, Dr. Sathyanarayana also holds appointments in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, and the Clinical Data Animation Center at Massachusetts General Hospital and Harvard Medical School.
Matheus Araújo actively researches the emerging area of Computer Science applied in healthcare at Cleveland Clinic Foundation. In his interdisciplinary research, he addresses sleep medicine challenges by using clinical and wearable devices as raw sources of information to build solutions using machine learning models, natural language processing, and time-series analyses. Before joining Cleveland Clinic Foundation, Matheus received his Ph.D. in Computer Science from the University of Minnesota, where he developed machine learning models for predicting patients' adherence to medical therapies such as Upper-Airway stimulation (UAS/Inspire), Continuous Positive Airway Pressure (CPAP) and growth hormone therapy (Merck).
Disclosure Statement: Prof. Sathyanarayana and Prof. Araujo declare no conflict of interest.
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All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Guest Advisors for this collection will not be involved in peer-reviewing manuscripts unless they are an existing member of the Editorial Board. Please review the journal Aims and Scope and author submission instructions prior to submitting a manuscript.