ReviewNPJ digital medicine2020
The future of sleep health: a data-driven revolution in sleep science and medicine.
Review in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06606691 (Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders), which is not on this map. Cited by 111 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders
Who cites it
111 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Consumer-oriented (wearable) sleep technology: a systematic SWOT analysis and recommendations for athletes and fitness enthusiasts.Frontiers in sports and active living · 2026Pooled it
- A patient first perspective of sleep disturbance across therapeutic areas: a systematic literature review of qualitative studies.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Pooled it
- Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Trial
- The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System.JMIR mHealth and uHealth · 2022Trial
- Loneliness and social isolation is associated with sleep problems among older community dwelling women and men with complex needs.Scientific reports · 2021Trial
- The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG.bioRxiv : the preprint server for biology · 2026Article
- SleepDepNet: A multi-task transformer model for assessing sleep quality and depression risk from social media narratives.PLOS digital health · 2026Article
- Exploring bed sensor technology: interdisciplinary insights in a geriatric assessment inpatient setting.The Gerontologist · 2026Article
- Digital Sleep Disruption: Unraveling the Network Structure of Technology Use and Sleep Problems Through Network Analysis.CNS neuroscience & therapeutics · 2026Article
- Global Prevalence of Sleep-Disordered Breathing in Intracerebral Hemorrhage Survivors: A Meta-Analysis and Systematic Review.Neurology international · 2026Review
- Article
- Editorial: Methods to modulate sleep with neurotechnology, devices, or wearables.Frontiers in neuroscience · 2026Article
- Reliability and validity of the Children's Sleep Habits Questionnaire-toddler in Chinese preschoolers.Frontiers in psychology · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Developing a Data Trust Model (Not Only) for Sleep Research: Conceptual Study and Quantitative Survey.JMIR human factors · 2025Article
- Co-production in sleep research: A scoping review of current practices and future directions.Journal of sleep research · 2025Article
- Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2025Article
- Circadian Regulation of Neuronal Membrane Capacitance-Mechanisms and Implications for Neural Computation and Behavior.International journal of molecular sciences · 2025Review
- The Role of the Sleep Centre in the Future of Sleep Medicine. How Should It Be Organised? Will There Still Be Sleep Inpatients in the Future?Journal of sleep research · 2025Review
51 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.