ArticleSleep2022
The Sleep Well Baby project: an automated real-time sleep-wake state prediction algorithm in preterm infants.
Article in Sleep, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06201962 (Effect of Massage and Foot Reflexology on Newborns' Sleep), which is not on this map. Cited by 19 papers.
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.
Effect of Massage and Foot Reflexology on Newborns' Sleep
Who cites it
19 citing papers in PubMed, 28 citations in OpenAlex.
- Skin-to-skin contact improves sleep stability in preterm infants.Pediatric research · 2026Article
- Towards implementation of continuous biologically relevant light measurements at the bedside in the Neonatal Intensive Care Unit.Npj biological timing and sleep · 2026Article
- The developing relationship between circadian rhythm and heart rate variability in premature infants.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2026Article
- Sleep During NICU Care Is Associated With Proportional Cerebellar Size at Term-Equivalent Age in Extrem Infantsmely Preter.Cerebellum (London, England) · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach.JMIR pediatrics and parenting · 2026Article
- From Cradle to Early-Life Sleep: A Bibliometric Analysis of Global Research on Neonatal Sleep.Nature and science of sleep · 2026Article
- Mother-Preterm Infant Contingent Interactions During Supported Infant-Directed Singing in the NICU-A Feasibility Study.Children (Basel, Switzerland) · 2025Article
- Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Sleep as a driver of pre- and postnatal brain development.Pediatric research · 2024Review
- Near-Infrared Spectroscopy for Neonatal Sleep Classification.Sensors (Basel, Switzerland) · 2024Article
- Circadian rhythm development in preterm infants. The role of postnatal versus postmenstrual age.Early human development · 2024Article
- An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States.Children (Basel, Switzerland) · 2023Article
- Effects of the neonatal intensive care environment on circadian health and development of preterm infants.Frontiers in physiology · 2023Review
- Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar.Frontiers in sleep · 2023Article
- A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care.Frontiers in digital health · 2022Article
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 at 1 institution in 1 country.
Funding
Abstract
STUDY
objectivesSleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep-wake states of preterm infants in real-time at the bedside.
methodsIn this study, sleep-wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep-wake states. Independent training (n = 37) and validation datasets were validation n = 9) datasets were used. Finally, a setup was designed for real-time implementation at the bedside.
resultsThe macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77).
conclusionsWith this study, to the best of our knowledge, a reliable, nonobtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants' sleep-wake states, potentially promoting the early brain development and well-being of preterm infants.
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.