Objective: This narrative review summarizes some of the evidence guiding current intensive care unit (ICU) design, focussing on environmental factors impacting on sleep, and compares available evidence and recommendations to current ICU designs and builds. Background: The importance of sleep for recovery after illness is well known. However, hospitalized patients frequently experience poor and disrupted sleep. This is especially true for patients admitted to the ICU. There are many factors negatively impacting on ICU patients' ability to sleep. Some relate to their illness or pre-existing sleep problems; others relate to patient care activities. While the ICU bedspace may facilitate 24h care, there is growing awareness of the detrimental impact the bedspace environment (especially suboptimal lighting and excessive sound/noise) has on sleep quality, and important questions raised regarding how this may impact on recovery and health outcomes. Multiple guidelines and recommendations exist to guide ICU bedspace design. However, questions have been raised whether contemporary ICUs are evidence-based, and whether the available evidence is effectively translated into the built ICU. Methods: A comprehensive literature review was conducted, exploring the evidence supporting current ICU bedspace design and the impact of ICU design and environmental factors on patient sleep. Results and conclusion: This review summarizes the impact of the ICU bedspace environment on patient outcomes and describes features of the ICU bedspace design that may not adhere to best evidence and contribute to poor sleep. Suggestions on how ICU bedspaces can be improved to optimize sleep are provided.

To review causes, risk factors, and consequences of sleep disruption in critically ill patients; evaluate the role of nonpharmacological and pharmacological therapies for management of sleep in the intensive care unit (ICU); and discuss the role of pharmacists in implementation of sleep bundles.

Critically ill patients often have disrupted sleep and circadian rhythm alterations that cause anxiety, stress, and traumatic memories. This can be caused by factors such as critical illness, environmental factors, mechanical ventilation, and medications. Methods to evaluate sleep, including polysomnography and questionnaires, have limitations that should be considered. Multicomponent sleep bundles with a focus on nonpharmacological therapy aiming to reduce nocturnal noise, light, and unnecessary patient care may improve sleep disorders in critically ill patients. While pharmacological agents are often used to facilitate sleep in critically ill patients, evidence supporting their use is often of low quality, which limits use to patients who have sleep disruption refractory to nonpharmacological therapy. Dedicated interprofessional teams are needed for implementation of sleep bundles in the ICU. Extensive pharmacotherapeutic training and participation in daily patient care rounds make pharmacists vital members of the team who can help with all components of the bundle. This narrative review discusses evidence for elements of the multicomponent sleep bundle and provides guidance on how pharmacists can help with implementation of nonpharmacological therapies and management of neuroactive medications to facilitate sleep.

Sleep bundles are necessary for patients in the ICU, and dedicated interprofessional teams that include pharmacists are vital for successful creation and implementation.

Sepsis is defined as life-threatening organ injury induced by infection, with high incidence and mortality. Sleep disorder is prevalent in septic patients and approximately 50% of patients with sepsis may develop atypical sleep patterns, but many of them may have been underdiagnosed by physicians. Sleep disorders and sepsis exhibit a close bidirectional relationship, with each condition significantly influencing the other. Conversely, sleep deprivation, sleep dysrhythmia and sleep fragmentation have been shown to impact the outcome of sepsis. This review endeavors to offer a comprehensive understanding of the intricate mechanisms that underpin the interplay between sepsis and sleep disorders, in addition to exploring potential clinical intervention strategies that could enhance outcomes for patients suffering from sepsis.

Background: Sleep deprivation is reported in 80% of patients in the intensive care unit (ICU) and is associated with delirium. Guidelines recommend implementing a sleep-promoting protocol in critically ill patients which may increase the quantity and quality of sleep and may decrease delirium. Our objective was to implement a pharmacist-led interdisciplinary sleep-promoting protocol and analyze its impact on delirium in ICU patients receiving mechanical ventilation (MV). Methods: The study involved pre-implementation education, protocol development, and post-implementation analysis. ICU pharmacists completed prospective patient chart reviews to reduce exposure to deliriogenic medications and assess the need for a pharmacologic sleep aid. The primary outcome was the incidence of delirium and delirium-free days. Secondary outcomes included ICU length of stay (LOS), incidence of MV, and pharmacist medication interventions. Results: Post-protocol patients (n = 185) had a higher incidence of delirium compared to pre-protocol patients (n = 237) (51.3% vs 39.0%; P = .01). Post-protocol patients had a higher average APACHE III score (P = <.001). Delirium-free days were not significantly different between groups (P = .97). Difference in ICU LOS was not significant (P = .80). More patients received MV post-protocol implementation (55.7% vs 36.1%; P < .001). Pharmacists documented a total of 113 medication interventions. Conclusion and Relevance: A pharmacist-led, ICU sleep-promoting protocol was successfully implemented but did not reduce the incidence of delirium or the administration of insomnia agents. Post-protocol patients had higher disease severity and were more likely to receive MV. Incidence of delirium was consistent with the national reported prevalence of ICU delirium. ICU pharmacists on all shifts had an active role in optimizing sleep.

Exercise has been recommended to enhance sleep. However, there is a paucity of studies investigating the relationships between exercise and sleep problems in patients with bladder cancer. The authors explored the effects of a single bout of light-intensity walking on the sleep quality of patients with bladder cancer who have sleep disorders.

A total of 14 patients with bladder cancer with sleep disorders were recruited for this trial. The participants were randomly assigned to the walking or control condition in a cross-over design to explore the effects of a single light-intensity walking session on objectively measured sleep quality. A two-way repeated measures analysis of variance and a nonparametric permutation test were used to examine intervention effects. Twelve participants (85.7%) completed the trial. A significant group × time interaction for sleep latency (P = .023) was identified. The pairwise comparison showed significant results (P = .012) for the difference between the post-test sleep latency and the pre-test. No significant group × time interactions were observed for the remaining seven sleep parameters. Additionally, only the main effects of time on length of awakening and time in bed were significant (P < .001).

A single bout of light-intensity walking has a positive effect on shortening the sleep latency of patients with bladder cancer who have sleep disorders.

Oncology nurses can encourage patients with bladder cancer to exercise, even light-intensity walking, which may improve sleep quality.

Noise and lighting are prime factors of poor sleep quality in critically ill patients, which impair recovery and increase the risk of delirium or complications.

To identify and rank the effectiveness of sound and darkness interventions on the sleep quality of critically ill patients.

This systematic review and component network meta-analysis was based on the Preferred Reporting Items for Systematic Reviews incorporating the Network Meta-Analyses (PRISMA-NMA) Statement. The Embase, MEDLINE, Cochrane CENTRAL, CINAHL, Airiti Library, and Google Scholar databases were searched from inception to August 10, 2021, for randomized controlled trials (RCTs) on sound and darkness interventions targeting critically ill patients' sleep quality. We applied standard and component NMA to determine the effects of interventions. The certainty of evidence was evaluated using the Cochrane risk-of-bias tool (V.2.0) and the online Confidence in Network Meta-Analysis (CINeMA) application.

Twenty-four RCTs with 1507 participants who used combined interventions constituting seven competing interventions were included in the standard NMA. The combination of earplugs, eye masks, and music; eye masks alone; earplugs combined with eye masks; and music alone had beneficial intervention effects. The combination of earplugs, eye masks, and music was the best intervention, and these components had no interaction effect. An eye mask had the best relative effect, followed by music, quiet time, and earplugs.

This study provides clinical evidence of the effectiveness of using eye masks, music, and earplugs to improve sleep quality in critically ill patients. We also recommend future research using bedtime music, nocturnal eye masks, and quiet time, which had the best relative effects on sleep quality.

This study provides recommendations for interventions that nurses can use to improve critically ill patients' sleep quality.

Background: The Society of Critical Care Medicine Clinical Practice Guidelines for Management of Pain, Agitation, Delirium, Immobility, and Sleep recommend protocolized non-pharmacologic sleep improvement. Pharmacologic interventions are frequently initiated to promote sleep but the evidence supporting these strategies remains controversial. Purpose: To systematically search and synthesize evidence evaluating pharmacologic sleep promotion modalities in critically ill adults. Methods: A rapid systematic review protocol was used to search Medline, Cochrane Library, and Embase for reports published through October 2022. We included randomized controlled trials (RCTs) and before-and-after cohort studies evaluating pharmacologic modalities intended to improve sleep in adult intensive care unit (ICU) patients. Sleep-related endpoints were the primary outcome of interest. Study and patient characteristics and relevant safety and non-sleep outcome data were also collected. The Cochrane Collaboration Risk of Bias or Risk of Bias in Non-Randomized Studies of Interventions were used to assess the risk of bias for all included studies. Results: Sixteen studies (75% RCTs) enrolling 2573 patients were included; 1207 patients were allocated to the pharmacologic sleep intervention. Most studies utilized dexmedetomidine (7/16; total n = 505 patients) or a melatonin agonist (6/16; total n = 592 patients). Only half of the studies incorporated a sleep promotion protocol as standard of care. Most (11/16, 68.8%) studies demonstrated a significant improvement in ≥1 sleep endpoint (n = 5 dexmedetomidine, n = 3 melatonin agonists, n = 2 propofol/benzodiazepines). Risk of bias was generally low for RCTs and moderate-severe for cohort studies. Conclusions: Dexmedetomidine and melatonin agonists are the most studied pharmacologic sleep promotion modalities, but current evidence does not support their routine administration in the ICU to improve sleep. Future RCTs evaluating pharmacologic modalities for ICU sleep should consider patients' baseline and ICU risks for disrupted sleep, incorporate a non-pharmacologic sleep improvement protocol, and evaluate the effect of these medication interventions on circadian rhythm, physiologic sleep, patient-perceived sleep quality, and delirium.

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Critical illness and stays in the Intensive Care Unit (ICU) have significant impact on sleep. Poor sleep is common in this setting, can persist beyond acute critical illness, and is associated with increased morbidity and mortality. In the past 5 years, intensive care clinical practice guidelines have directed more focus on sleep and circadian disruption, spurring new initiatives to study and improve sleep complications in the critically ill. The global SARS-COV-2 (COVID-19) pandemic and dramatic spikes in patients requiring ICU level care also brought augmented levels of sleep disruption, the understanding of which continues to evolve. This review aims to summarize existing literature on sleep and critical illness and briefly discuss future directions in the field.

Sleep deprivation is expected in the intensive care unit (ICU) and is associated with delirium and increased mortality. Polysomnography (PSG) is the gold standard for sleep assessment, but practical issues limit the method. Hence, many ICUs worldwide use subjective sleep assessment (SSA) for sleep monitoring, but the agreement between SSA and PSG is unknown. The hypothesis was that the level of agreement between SSA and PSG was low and that total sleep time (TST) assessed with SSA would be overestimated compared to PSG in this existing cohort database. In this sub-analysis, 30 consecutive study participants underwent 15-h PSG recordings during two consecutive nights. The attending nurse performed an hourly subjective observer rating of sleep quantity during both nights, and the agreement between SSA and PSG was determined along with mean TST. Primary outcome: The level of agreement between SSA and PSG determined by Bland-Altman analysis. Secondary outcome: (1) The overall mean TST estimated by SSA compared to PSG in all study participants enrolled in the main study during both study nights, (2) TST for all study participants evaluated hourly during both study nights, (3) TST assessed with SSA compared to PSG in study participants sedated with dexmedetomidine during the second night and for study participants treated with placebo or non-sedation the first and second nights. The level of agreement between SSA and PSG was low. Mean TST estimated by SSA during the time interval 4.00 p.m. to 7.00 a.m. was 481 min (428;534, 95% CI) vs. PSG at 437 min (386;488, 95% CI) (p = .05). When sedated with dexmedetomidine, TST estimated using SSA was 650 min (571;729, 95% CI) versus PSG which was 588 min (531;645, 95% CI) (p = 0.56). For participants treated with placebo or non-sedation TST estimated with SSA was 397 min (343;450, 95% CI) versus PSG at 362 min (302;422, 95% CI) versus (p = 0.17). In mechanically ventilated critically ill ICU patients, the level of agreement between SSA and PSG was low, and there was a significant overestimation of mean TST. SSA should only be used under awareness that it is imprecise and overestimates TST.

Sleep is particularly important for critically ill patients. Here, we review the latest evidence on how sleep and circadian disruption in the intensive care unit (ICU) affects physiology and clinical outcomes, as well as the most recent advances in sleep and circadian rhythm promoting interventions including therapeutics.

On a molecular level, clock genes dysrhythmia and altered immunity are clearly linked, particularly in sepsis. Melatonin may also be associated with insulin sensitivity in ICU patients. Clinically, changes in sleep architecture are associated with delirium, and sleep-promoting interventions in the form of multifaceted care bundles may reduce its incidence. Regarding medications, one recent randomized controlled trial (RCT) on melatonin showed no difference in sleep quality or incidence of delirium.

Further investigation is needed to establish the clinical relevance of sleep and circadian disruption in the ICU. For interventions, standardized protocols of sleep promotion bundles require validation by larger multicenter trials. Administratively, such protocols should be individualized to both organizational and independent patient needs. Incorporating pharmacotherapy such as melatonin and nocturnal dexmedetomidine requires further evaluation in large RCTs.

Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits. Unfortunately, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. The data in this work pertains to 30 patients that were admitted to the stroke unit of the Udine University Hospital, Italy. Unlike previous studies, exclusion criteria are minimal. As a result, data are strongly affected by noise, and individuals may suffer from several comorbidities. Each patient instance is composed of overnight vital signs data deriving from multi-channel ECG, photoplethysmography and polysomnography, and related domain expert's OSAS annotations. The dataset aims to support the development of automated methods for the detection of OSAS events based on just routinely monitored vital signs, and capable of working in a real-world scenario.