ABSTRACT
Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.
ABSTRACT
OBJECTIVE: To determine factors affecting inpatient sleep and assess the range and effectiveness of non-pharmacological interventions aimed at improving the sleep of patients admitted to regular care wards. METHODS: A systematic literature search was conducted in five scientific databases, including articles published from inception to June 23rd, 2023. Eligible studies evaluated sleep disturbing factors or the effect of non-pharmacological intervention(s). Meta-analyses on intervention studies were conducted using a random effects model. Certainty of evidence was assessed using the GRADE approach. RESULTS: Out of 591 potentially eligible studies, 229 were included in this review. Sleep disturbers were identified in 153 studies, and 102 studies were eligible for meta-analysis. Common factors contributing to poor sleep included noise, light, care-related interruptions, pain, and anxiety. The meta-analyses revealed large pooled effects in favor of sleep for the use of eye masks and earplugs, headphones and white noise, aromatherapy, massage, muscle relaxation and breathing exercises, and advanced nursing strategies. However, the certainty of the evidence ranged from moderate to very low. CONCLUSION: Inpatient sleep is often disturbed by patient-related, care-related, and environmental factors. While there are promising non-pharmacological interventions, the overall quality of studies, heterogeneity in study populations, and differences in outcome measures present challenges for drawing definitive conclusions.
