State of the Science: The Transdiagnostic Intervention for Sleep and Circadian Dysfunction.

• Sleep and circadian problems are often comorbid with mental disorders. • Sleep and circadian problems are mechanistic contributors to mental disorders. • TranS-C treats sleep and circadian problems across mental disorders via one protocol. • TranS-C is grounded in basic sleep and circadian science and existing EBPTs. • TranS-C includes cognitive-behavioral therapy for insomnia (CBT-I) The transdiagnostic intervention for sleep and circadian dysfunction (TranS-C) was developed to provide one protocol that treats a range of sleep and circadian problems across a range of mental disorders. The focus of TranS-C includes, and goes beyond, categorically defined sleep and circadian disorders to facilitate healthy sleep along empirically derived "sleep health" dimensions. In this State of the Science review, we highlight key advantages of a transdiagnostic approach to sleep and circadian problems, including (a) the potential to better understand and treat comorbidity between various sleep and circadian problems and mental disorders, as well as the potential to better understand and treat the heterogeneous sleep and circadian problems that are present within a specific mental disorder; (b) the opportunity to explore the hypothesis that sleep and circadian problems are an important transdiagnostic mechanism in the multifactorial maintenance of mental disorders; (c) the potential to transfer breakthroughs made across siloed areas of research and practice; (d) its suitability for dissemination into a broad range of settings, particularly lower resource settings; and (e) the opportunity to improve a range of important outcomes. We also explain the theoretical underpinnings of TranS-C, including the two-process model of sleep regulation and the Sleep Health Framework. TranS-C includes cognitive-behavioral therapy for insomnia (CBT-I) and we offer recommendations for when to use CBT-I versus TranS-C. The process for developing TranS-C is discussed along with outcome data, applications to underserved communities, and future directions for research. [ABSTRACT FROM AUTHOR]