Sleep to Prevent Evolving Affecting Disorders
Phase: NA
Conditions: Insomnia, Primary, Depression
Sponsor: Henry Ford Health System

 
 
 
 
 
 
Sleep to prevent evolving 
affecting disorders (SPREAD) 
 
NCT02988375 
 
Approved on March 8th 2016 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
1. PURPOSE: State in one or two sentences the purpose or objective of this project. The purpose of this 
study is to determine the effectiveness of Digital Cognitive Behavior Therapy for Insonmia (dCBTI) in 
preventing the incidence of depression among individuals with insomnia.  
2. SPECIFIC AIMS: Number your aims so that the aims can be referred to in the Project Design and Data 
Analysis sections of this outline. Specific aim 1: to assess the efficacy of dCBTI for the prevention of 
incident depression among individuals with insomnia. Specific aim 2: to determine the moderating 
effects of health/SES disparities on the efficacy of the dCBTI intervention.  
3. RATIONALE FOR THE PROJECT: a. State the rationale for the project and support it with background 
information about the project. Critically evaluate existing knowledge, and specifically identify the gaps in 
knowledge the project will fill. A growing body of research indicates that treatment for insomnia may 
also help prevent the onset of depression (Falussy, Balla & Frecska, 2014). However, CBTI, the current 
first-line treatment for insomnia, is under-utilized due to a shortage of providers, time- and cost-
constraints, and the stigma associated with mental-health treatment (Fields, Schutte-Rodin, Perlis & 
Meyer, 2013). Digital, internet-delivered forms of CBTI (dCBTI) can overcome these obstacles to make 
access more widespread, reduce health-care costs, and serve as a sustainable population-level 
prophylactic treatment for depression. b. State the applicant's prior research and experience in this 
research area. Dr. Drake has a broad background in clinical psychology, with specific training and 
expertise in sleep medicine. He is an Associate Editor for the journal SLEEP and has authored over 100 
peer-reviewed original articles, reviews, and chapters, focused on sleep. He recently served as Chairman 
of the National Sleep Foundation. Dr. Drake has been funded by the NIH to study vulnerability to 
insomnia and triggers of insomnia (i.e. life stressors). Dr. Drake is board certified in CBT-I Treatment to 
successfully treat insomnia with the long-term goal of preventing incident depression. His experience in 
this area will make it possible to recruit and retain participants for this research study focusing on 
insomniacs and depression prevention efforts.  
4. SIGNIFICANCE: State concisely the importance of this project by relating the purpose to broader, long- 
range objectives. Depression is among the most prevelant mental health disorders and carries the 
heaviest burden of disability among the mentally ill, accounting for roughly 8% of all U.S. years lived with 
disability (Murray, Atkinson, Bhalla, et al., 2010). Thus, prevention efforts are a major public health 
concern. According to a recent meta-analysis of over 20-longitudinal studies, the risk of depressive onset 
among individuals with insomnia is nearly three times that for healthy sleepers (Baglioni, Battagliese, 
Feige, et al., 2011). Specifically, CBTI is recognized as an effective treatment for insomnia and has also 
been shown to improve comorbid depression (Manber, Bernert, Suh, Seibern & Ong, 2011; Wu, 
Appleman, Salazar & Ong, 2015). Combined, these results indicate that the early treatment for Insomnia 
via CBTI has strong potential for the prevention of depression (Falussy, Balla & Frecska, 2014). However, 
there are a number of barriers that hinder widespread access to CBTI. Two main problems include 
access to CBTI health care providers as well as the availability of enough health care providers to meet 

he population demands for face-to-face therapy sessions. Thus, digital CBTI (dCBTI), which relies on web 
and mobile technology, can overcome these obstacles to widespread delivery, reduce patient load on 
current providers, and reduce health-care costs. Through dissemination of dCBTI, health care will have 
the potential to treat mental illness post-onset. Overall, this study will serve to determine the 
effectiveness of using an online CBT-I Treatment to successfully treat insomnia with the long-term goal 
of preventing incident depression.  
5. SUBJECTS IN THE PROJECT: a. State the inclusion and exclusion criteria for enrollment of subjects. 
Inclusion factors for the patient group will be subjects who have Insomnia and have opted for behavioral 
treatment for insomnia as therapy at recruitment. Exclusion criteria for subject group includes the 
presence of unstable chronic medical conditions or mental health conditions that would interfere with 
the study protocol as well as the presence of moderate depression (QIDS > 15) at recruitment. The 
history of sleep disorders other than insomnia by self-report will be assessed and used as covariates in 
analyses; subjects presenting with a history consistent with obstructive sleep apnea (OSA) or restless leg 
syndrome (RLS) or other sleep disorders will be identified based on standardized questionnaires. b. 
Describe the control population (if utilized) and justify its selection. The control for this study will be an 
"information control" treatment group. Subjects will be emailed sleep tips and sleep-related 
information. c. Support the likelihood of recruiting the number of subjects required to complete the 
project. Relate this to other projects recruiting similar subjects. Participants will be recruited from 
several well-developed sources, including the HFHS Sleep Center Clinical database. We maintain a large 
and representative database (N=7608) of prior participants who have volunteered to be contacted for 
clinical trials. Preliminary analyses suggest that a large proportion of this database (N=2590) meets 
primary inclusion criteria for this proposal (current insomnia but no depression). These individuals will 
be contacted by phone and/or email for potential participation pending a screening assessment. We 
believe that based on the success we have seen in previous studies (IRB#5544 and #8447), we will be 
able to complete this protocol within two years following IRB approval.  
6. PROJECT DESIGN AND PROTOCOL: a. Describe the experimental design/methodology. This is a 
randomized parallel group study with group 1 getting CBT-I treatment for insomnia by an online 
program (Sleepio) and group 2 getting information control sleep tips. Insomnia and other sleep-related 
symptoms will be assessed at pre treatment, post treatment and at 1-year follow-up. Sleepio can be 
accessed at www.sleepio.com. Click "Log In" at top right corner and enter email "test@sleepio.com" and 
password "password". b. Outline the protocol, corresponding it to the specific aims; identify the data or 
endpoints to be analyzed to reach the specific aims. Our primary aim is to maximize and/or improve 
sleep efficiency in patients (number of hours asleep over number of hours in bed) and to shorten sleep 
latency (amount of time taken to fall asleep). The primary aim will be to reduce the incidence of 
depression. Other secondary measures will be taken regarding aspects other than sleep in patients. 
These will be recorded through the surveys mentioned above and will measure symptom improvements 
in severity of depression and work productivity. c. Discuss potential limitations and difficulties in the 
protocol. We do not anticipate any limitations to the current protocol. d. Provide a tentative schedule 
for conducting and completing this project and, if applicable, the multicenter study. We plan on starting 
data collection between February 25 and March 1, pending IRB approval. e. Data collection: Submit a 
copy of the data collection tool or list the data fields to be collected (review IRB policy, Access to 
Medical Records for Research).Attached in appendix.  

7. DATA ANALYSIS: Describe the analysis of the data and relate this to the specific aims in detail. Case 
reports and medical record reviews also require a description of the planned data analysis (ie. 
descriptive, observational). The Committee recommends free consultation with the Division of 
Biostatistics and Research Epidemiology before IRB submission. The following data analysis plan was 
developed in conjunction with Ed Peterson, biostatistics section. A logistic regression will be used to 
assess the association between treatment group and depression status at 1 year. This approach allows 
inclusion of covariates in the model. We plan on enrolling a total of 300 subjects per group. This results 
in the analysis having 85% power, with a two-sided alpha level of 0.05, to detect an odds ratio of 2.7 
corresponding to rates of depression at 1 year of 10% and 4%. Stratified analysis assuming an equal split 
of the population, will have 85% power to detest an odds ratio of 10.0. This corresponds to a difference 
in rates from 10% to 1% which is what we anticipate. 
8. JUSTIFICATION FOR NUMBER OF SUBJECTS OR DATA: a. State the number of subjects or data points to 
be analyzed in the project at this institution and the total number for multicenter studies. The 
Committee recommends consultation with the Division of Biostatistics and Research Epidemiology (313) 
874-6360. The proposed sample size of 300 per group (n=600) will yield > 85% power to detect effects 
with a two-tailed 0.05 alpha level. b. Describe the statistical justification for this number of subjects or 
data points. The primary hypothesis i.e., risk of depression incidence in the dCBTI group relative to the 
control group will be analyzed using a multivariate logistic regression model. Group differences in 
continuous outcomes (e.g., SOL, TST, WASO; see STRATEGY section for a full list of quantitative 
measures) will be tested using ANCOVA models. Power. We anticipate a 14.6% rate of incident 
depression in the control group, compared to 6.7% in the treatment group.21 The proposed sample size 
of 300 per group (n=600) will yield > 85% power to detect effects with a two-tailed 0.05 alpha level.