Digital and AI-Enhanced Cognitive Behavioral Therapy for Insomnia: Neurocognitive Mechanisms and Clinical Outcomes

被引:6
作者
Gkintoni, Evgenia [1 ]
Vassilopoulos, Stephanos P. [1 ]
Nikolaou, Georgios [1 ]
Boutsinas, Basilis [2 ]
机构
[1] Univ Patras, Dept Educ Sci & Social Work, Patras 26504, Greece
[2] Univ Patras, Dept Business Adm, Patras 26504, Greece
关键词
CBT; sleep disorders; artificial intelligence; neurocognitive profile; insomnia; personalized therapy; AI-driven CBT; sleep improvement; mental health; digital health; RANDOMIZED-CONTROLLED-TRIAL; OBSTRUCTIVE SLEEP-APNEA; ARTIFICIAL-INTELLIGENCE; CBT-I; DISORDER; PILOT; DEPRESSION; SCIENCE; ADULTS;
D O I
10.3390/jcm14072265
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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页数:94
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