Background/Objectives: This systematic review explores the integration of digital and AI-enhanced cognitive behavioral therapy (CBT) for insomnia, focusing on underlying neurocognitive mechanisms and associated clinical outcomes. Insomnia significantly impairs cognitive functioning, overall health, and quality of life. Although traditional CBT has demonstrated efficacy, its scalability and ability to deliver individualized care remain limited. Emerging AI-driven interventions-including chatbots, mobile applications, and web-based platforms-present innovative avenues for delivering more accessible and personalized insomnia treatments. Methods: Following PRISMA guidelines, this review synthesized findings from 78 studies published between 2004 and 2024. A systematic search was conducted across PubMed, Scopus, Web of Science, and PsycINFO. Studies were included based on predefined criteria prioritizing randomized controlled trials (RCTs) and high-quality empirical research that evaluated AI-augmented CBT interventions targeting sleep disorders, particularly insomnia. Results: The findings suggest that digital and AI-enhanced CBT significantly improves sleep parameters, patient adherence, satisfaction, and the personalization of therapy in alignment with individual neurocognitive profiles. Moreover, these technologies address critical limitations of conventional CBT, notably those related to access and scalability. AI-based tools appear especially promising in optimizing treatment delivery and adapting interventions to cognitive-behavioral patterns. Conclusions: While AI-enhanced CBT demonstrates strong potential for advancing insomnia treatment through neurocognitive personalization and broader clinical accessibility, several challenges persist. These include uncertainties surrounding long-term efficacy, practical implementation barriers, and ethical considerations. Future large-scale longitudinal research is necessary to confirm the sustained neurocognitive and behavioral benefits of digital and AI-powered CBT for insomnia.

Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths.

This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs.

A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted.

ChatGPT labeled a significant portion (n=17, 85%) of the statements as "false" (n=9, 45%) or "generally false" (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about "sleep timing," "sleep duration," and "behaviors during sleep," while it had varying degrees of success with other categories such as "pre-sleep behaviors" and "brain function and sleep." ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations.

ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors.

Sleep and well-being have been intricately linked, and sleep hygiene is paramount for developing mental well-being and resilience. Although widespread, sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments. Current technologies have allowed various devices to diagnose sleep disorders at home. However, these devices are in various validation stages, with many already receiving approvals from competent authorities. This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications. This is expected to be integrated with patients' Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

Chronic insomnia is the most common sleep disorder, occurring in ~32 million people in the United States per annum. Acute insomnia is even more prevalent, affecting nearly half of adults at some point each year. The prevalence of insomnia among primary care patients is even higher. The problem, however, is that most primary care providers do not feel adequately knowledgeable or equipped to treat sleep-related concerns. Many providers have never heard of or have not been trained in cognitive behavioral therapy for insomnia or CBT-I (the first line treatment for insomnia). The focus of the current review is to summarize the factors contributing to why sleep health and insomnia treatment have been mostly neglected, identify how this has contributed to disparities in sleep health among certain groups, particularly racial and ethnic minorities and discuss considerations or potential areas of exploration that may improve access to behavioral sleep health interventions, particularly in primary care.

Healthy sleep and optimal ventilatory control begin in early development and are crucial for positive child outcomes. This paper summarizes information presented at the Sleep and Ventilatory Control sessions of the National Heart, Lung, and Blood-sponsored 2021 Defining and Promoting Pediatric Pulmonary Health workshop. These sessions focused on pediatric sleep health, screening for sleep health and sleep disorders in primary care using the electronic health record, infant sleep and ventilatory control, and home sleep testing. Throughout this summary, we discuss key gaps in and barriers to promoting sleep and ventilatory health that were identified during the workshop sessions. We conclude with strategies to address these gaps and barriers and directions for future multidisciplinary research, patient care, and training.

Military service inherently includes frequent periods of high-stress training, operational tempo, and sustained deployments to austere far-forward environments. These occupational requirements can contribute to acute and chronic sleep disruption, fatigue, and behavioral health challenges related to acute and chronic stress and disruption of team dynamics. To date, there is no centralized mobile health platform that supports self- and supervised detection, monitoring, and management of sleep and behavioral health issues in garrison and during and after deployments.

The objective of this study was to adapt a clinical decision support platform for use outside clinical settings, in garrison, and during field exercises by medics and soldiers to monitor and manage sleep and behavioral health in operational settings.

To adapt an existing clinical decision support digital health platform, we first gathered system, content, and context-related requirements for a sleep and behavioral health management system from experts. Sleep and behavioral health assessments were then adapted for prospective digital data capture. Evidence-based and operationally relevant educational and interventional modules were formatted for digital delivery. These modules addressed the management and mitigation of sleep, circadian challenges, fatigue, stress responses, and team communication. Connectivity protocols were adapted to accommodate the absence of cellular or Wi-Fi access in deployed settings. The resulting apps were then tested in garrison and during 2 separate field exercises.

Based on identified requirements, 2 Android smartphone apps were adapted for self-monitoring and management for soldiers (Soldier app) and team supervision and intervention by medics (Medic app). A total of 246 soldiers, including 28 medics, received training on how to use the apps. Both apps function as expected under conditions of limited connectivity during field exercises. Areas for future technology enhancement were also identified.

We demonstrated the feasibility of adapting a clinical decision support platform into Android smartphone-based apps to collect, save, and synthesize sleep and behavioral health data, as well as share data using adaptive data transfer protocols when Wi-Fi or cellular data are unavailable. The AIRE (Autonomous Connectivity Independent System for Remote Environments) prototype offers a novel self-management and supervised tool to augment capabilities for prospective monitoring, detection, and intervention for emerging sleep, fatigue, and behavioral health issues that are common in military and nonmilitary high-tempo occupations (eg, submarines, long-haul flights, space stations, and oil rigs) where medical expertise is limited.

This review synthesizes the pediatric behavioral sleep intervention (BSI) evidence base, summarizes challenges in translating BSIs from research to practice, and provides recommendations for integrating implementation science methods to advance pediatric BSI research.

We briefly review the common behavioral sleep disturbances among youth, discuss the pediatric BSIs with well-established evidence, and identify gaps in pediatric BSI research. We then identify contributors to the dearth of research evaluating pediatric BSIs in accessible settings and present a model for applying implementation science strategies to address identified gaps across the continuum of translational research.

Relatively few BSI trials include older children and adolescents. Similarly, there is limited research evaluating BSIs among racially and ethnically minoritized children and families and/or those of lower socioeconomic status backgrounds. Access to scalable and easily disseminable tools to treat pediatric sleep disturbances early in their development is crucial for promoting positive child outcomes. To address these gaps, researchers should apply implementation science theories, models, and frameworks to design new interventions for implementation, adapt existing interventions with end users and settings in mind, conduct hybrid effectiveness-implementation trials, and test implementation strategies.

Given the prevalence and consequences of poor sleep across developmental periods, pediatric BSIs must be effective as well as adaptable, scalable, and easily disseminable. Implementation science theories, models, and frameworks can enhance access to, engagement in, and the implementation and dissemination of scalable BSIs across diverse pediatric care settings and heterogeneous populations.

Levenson JC, Williamson AA. Bridging the gap: leveraging implementation science to advance pediatric behavioral sleep interventions. J Clin Sleep Med. 2023;19(7):1321-1336.

Hybrid effectiveness-implementation studies allow researchers to combine study of a clinical intervention's effectiveness with study of its implementation with the aim of accelerating the translation of evidence into practice. However, there currently exists limited guidance on how to design and manage such hybrid studies. This is particularly true for studies that include a comparison/control arm that, by design, receives less implementation support than the intervention arm. Lack of such guidance can present a challenge for researchers both in setting up but also in effectively managing participating sites in such trials. This paper uses a narrative review of the literature (Phase 1 of the research) and comparative case study of three studies (Phase 2 of the research) to identify common themes related to study design and management. Based on these, we comment and reflect on: (1) the balance that needs to be struck between fidelity to the study design and tailoring to emerging requests from participating sites as part of the research process, and (2) the modifications to the implementation strategies being evaluated. Hybrid trial teams should carefully consider the impact of design selection, trial management decisions, and any modifications to implementation processes and/or support on the delivery of a controlled evaluation. The rationale for these choices should be systematically reported to fill the gap in the literature.

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.