↓ Skip to main content

A transdiagnostic sleep and circadian treatment to improve severe mental illness outcomes in a community setting: study protocol for a randomized controlled trial

Overview of attention for article published in Trials, December 2016
Altmetric Badge

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
147 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A transdiagnostic sleep and circadian treatment to improve severe mental illness outcomes in a community setting: study protocol for a randomized controlled trial
Published in
Trials, December 2016
DOI 10.1186/s13063-016-1690-9
Pubmed ID
Authors

Allison G. Harvey, Kerrie Hein, Lu Dong, Freddie L. Smith, Michael Lisman, Stephanie Yu, Sophia Rabe-Hesketh, Daniel J. Buysse

Abstract

Severe mental illness (SMI) is common, chronic and difficult to treat. Sleep and circadian dysfunctions are prominent correlates of SMI, yet have been minimally studied in ways that reflect the complexity of the sleep problems experienced. Prior treatment studies have been disorder-focused-they have treated a specific sleep problem in a specific diagnostic group. However, real life sleep and circadianproblems are not so neatly categorized, particularly in SMI where features of insomnia overlap with hypersomnia, delayed sleep phase and irregular sleep-wake schedules. Accordingly, the aim of this studyprotocol is to test the hypothesis that a Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) will improve functional impairment, disorder-focused symptoms and sleep and circadian functioning. Participants across DSM diagnoses and across common sleep and circadian problems are eligible. The elements of TranS-C are efficacious across SMI in research settings with research-based providers. The next step is to test TranS-C in a community setting. Accordingly, this study is being conducted within Alameda County Behavioral Health Care Services (ACBHCS), the Community Mental Health Centre (CMHC) for Alameda County. 120 adults diagnosed with SMI and sleep and circadian dysfunction within ACBHCS will be randomly allocated to TranS-C (n = 60) or 6-months of Usual Care followed by Delayed Treatment with TranS-C (UC-DT; n = 60). TranS-C is modularized and delivered across eight to twelve 50-minute, weekly, individual sessions. All participants will be assessed before and immediately following treatment and again 6 months later. Primary analysis will examine whether TranS-C significantly improves functional impairment, disorder-specific symptoms and sleep and circadian functioning, relative to UC-DT. Exploratory analysis will examine whether improvements in sleep and circadian functioning predict reduction in functional impairment and disorder-specific symptoms, and whether the intervention effects are mediated by improved sleep and circadian functioning and moderated by previously reported risk factors (demographics, symptom severity, medications, psychiatric and medical comorbidity). This trial tests an important and understudied mechanism-dysregulated sleep and circadian rhythms-in SMI, a novel transdiagnostic treatment approach, in a community setting so as to contribute to the goal of bridging the gap between research and practice. ClinicalTrials.gov identifier: NCT02469233 . Registered on 9 June 2015.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 146 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 14%
Student > Master 20 14%
Student > Ph. D. Student 17 12%
Student > Bachelor 13 9%
Student > Doctoral Student 11 7%
Other 26 18%
Unknown 39 27%
Readers by discipline Count As %
Psychology 49 33%
Medicine and Dentistry 13 9%
Nursing and Health Professions 12 8%
Neuroscience 8 5%
Social Sciences 5 3%
Other 10 7%
Unknown 50 34%