An Evaluation of Insomnia Treatment to Reduce Cardiovascular Risk in Patients With Posttraumatic Stress Disorder
Phase: NA
Conditions: Insomnia, Posttraumatic Stress Disorder, Cardiovascular Risk Factor
Sponsor: Duke University

 
 
 
 
An Evaluation of Insomnia Treatment to Reduce Cardiovascular 
Risk in Patients With Posttraumatic Stress Disorder 
 
 
 
 
 
NCT04498754 
 
 
 
 
Study Protocol and Statistical Analysis Plan 
Document Date: 8/26/2024 

 
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Full Protocol: An evaluation of insomnia treatment to reduce cardiovascular risk in patients with 
posttraumatic stress disorder 
Principal Investigators: Jean C. Beckham, Ph.D. and Andrew Sherwood, Ph.D. 
 
Posttraumatic stress disorder (PTSD) is a disabling, chronic, and costly psychiatric disorder that is 
estimated to occur in 20% of individuals who are exposed to a traumatic event (Kessler, Chiu, Demler, 
& Walters, 2005). In addition to its negative impact on quality of life, there is substantial evidence that 
PTSD is associated with a markedly increased risk of morbidity and mortality due to cardiovascular 
disease (CVD) even after controlling for depression and other risk factors (Flood et al., 2010; Dedert et 
al., 2012; Edmondson, Kronish, Shaffer, Falzon, & Burg, 2013). Although the mechanisms for the 
association between PTSD and CVD risk are not well understood, sleep quality (insomnia, sleep 
efficiency, reduced wake after sleep onset and/or sleep duration) has emerged as being strongly 
associated with many atherosclerotic CVD risk factors (Irwin, Omstead, & Carroll, 2016; Irwin et al., 
2014; Carroll et al., 2015; Meng, Zheng, & Hui, 2013; Li, Zhang, Hou, & Tang, 2014; Aziz et al., 2016; Sofi 
et al.., 2014; Matthews et al., 2014; Hoevenaar-Blom, Spijkerman, Kromhount, van den Berg, & 
Verschuren, 2011; Haack et al., 2013; Ferrie et al., 2007; Wang, Xi, Liu, Zhang, & Fu, 2012), and is a 
prominent complaint among those with PTSD. In fact, most individuals with PTSD also meet criteria for 
co-morbid Insomnia Disorder. Our pilot data, as well as the work of others, suggest that sleep may 
mediate the association between PTSD and CVD biomarkers (Irwin, Omstead, & Carroll, 2016; Irwin et 
al., 2014; Carroll et al., 2015; Meng, Zheng, & Hui, 2013; Li, Zhang, Hou, & Tang, 2014; Aziz et al., 2016; 
Sofi et al.., 2014; Matthews et al., 2014; Hoevenaar-Blom et al., 2011; Haack et al., 2013; Ferrie et al., 
2007; Wang et al., 2012).  
  
Our long-term goal is to understand how PTSD increases risk of CVD morbidity and mortality so that 
preventative and therapeutic strategies can be developed. Impaired sleep is a hallmark symptom of 
PTSD (American Psychiatric Association, 2013), with insomnia reported by as many as 90% of patients 
who have received first-line PTSD treatment (Zayfert & DeViva, 2004; Ohayon & Shapiro, 2000). It is 
well established that insomnia is associated with increased CVD risk (Bonnet & Arand, 2007), but this 
association has not been studied in those with PTSD (Sofi et al., 2014). The objective of this application 
is to establish the contribution of insomnia to the markedly increased atherosclerotic CVD risk among 
those with PTSD, and to examine whether this risk can be ameliorated.  
 
Based upon recent evidence, including our own preliminary data, our central hypothesis is that 
insomnia in those with PTSD is a potentially modifiable feature of PTSD that contributes to CVD. 
Unfortunately, even following intensive evidence-based psychotherapy for PTSD, insomnia typically 
remains an unresolved problem (Belleville, Guay, & Marchand, 2011). Recent evidence suggests that a 
behavioral sleep intervention, Cognitive Behavioral Therapy for Insomnia (CBT-I), can increase improve 
sleep quality and duration, and promote consolidated sleep among individuals with PTSD (Talbor et al., 
2014); however, it is currently unknown whether these improvements would have a favorable impact 
on CVD risk.  
  
SPECIFIC AIMS 

 
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We will test our central hypothesis in a randomized controlled trial (RCT) designed to determine if 
reduced insomnia symptoms result in improved CVD risk among those with co-morbid PTSD and 
Insomnia Disorder (ID). Specific and established CVD morbidity and mortality biomarkers of risk will be 
examined, including vascular endothelial function measured by brachial artery flow-mediated dilation 
(FMD), nighttime blood pressure (BP) dipping measured using 24-hour ambulatory blood pressure 
monitoring (ABPM), arterial stiffness measured by central pulse wave velocity (PWV), and 
atherosclerotic cardiovascular disease (ASCVD) risk score (Bonnet & Arand, 2007; Bonnet & Arand, 
2010; Routledge & McFetridge-Durdle, 2007; Tsuji et al., 1996; Verdecchia, Angeli, Gentile, Mazzotta, & 
Reboldi, 2011). Insomnia will be measured via a multi-modal assessment strategy, including self-report 
and actigraphy. The rationale for the proposed research is if it is established that insomnia-related 
sleep impairment is a modifiable CVD risk factor among those with PTSD, new innovative approaches 
to integrating sleep treatment with PTSD focused interventions can be developed and implemented.  
 
We will randomize 150 individuals with co-morbid PTSD and ID to either an 8-week CBT-I intervention 
or to a minimal contact control condition. Insomnia severity and objective sleep parameters, quality of 
life measures, and biomarkers of CVD risk will be assessed at baseline, post-intervention, and at a 6-
month follow-up. Specific Aims include: 
 
AIM 1: Determine whether CBT-I improves (a) CVD risk biomarkers, (b) 10-year ASCVD risk, and (c) 
quality of life (QoL) in persons with PTSD. Hypothesis 1: Compared to the control condition, CBT-I will 
be associated with increased sleep-efficiency and decreased insomnia severity (manipulation check), 
improved CVD risk biomarkers (Hypothesis 1a) including (i) lower nighttime BP and enhanced nighttime 
BP dipping, (ii) augmented vascular endothelial function (FMD), (iii) reduced sympathetic nervous 
system (SNS) activity (catecholamines), lower 10-year ASCVD risk score (Hypothesis 1b) and improved 
quality of life (Hypothesis 1c). 
 
AIM 2: Determine whether within-person changes in sleep efficiency are accompanied by improved 
CVD risk biomarkers and 10-year ASCVD risk. Hypothesis 2: Within-person improvements in sleep 
efficiency (N=150) will be associated with improvements in CVD risk biomarkers and reduced ASCVD 
risk score.  
 
Supplemental Aim: Explore the heterogeneity of CBT-I treatment response on sleep and CVD risk. We 
will explore moderators of improvements in sleep and CVD risk biomarkers following CBT-I including 
primary trauma type, insomnia disorder phenotype (objectively measured short sleep duration vs. 
objective normal sleep duration), medication use, biological sex, and minority status. 
 
With respect to expected outcomes, the proposed work should establish whether unconsolidated sleep 
contributes to mechanisms conveying CVD risk burden in persons with PTSD. Identification of 
modifiable determinants of the relationship between PTSD and CVD risk is expected to have an 
important positive impact on the medical management of PTSD. 
 
A. RESEARCH STRATEGY: SIGNIFICANCE AND INNOVATION 
Several studies have now linked PTSD to increased cardiovascular disease (CVD) morbidity and 

 
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mortality (Boscarino, 2006; Edmondson et al., 2013; Flood et al., 2010), including a two-fold increase in 
the risk of premature death from CVD (Boscarino, 2008). While the distinct mechanisms underlying this 
association remain unclear, evidence suggests that PTSD contributes to CVD morbidity and mortality 
independent of health risk behaviors (e.g., smoking) and comorbid depression (Boscarino, 2008; 
Vaccarino et al., 2013). A recent review of the available evidence described PTSD as “a fast-track to 
premature cardiovascular disease” concluding that there was a lack of mechanistic evidence regarding 
the progression of atherosclersosis in PTSD, and a need to examine CVD risk biomarkers (Wentworth et 
al., 2013). Establishing the mechanism(s) linking PTSD to CVD is an essential next step in identifying 
therapeutic targets to reduce cardiovascular risk in this vulnerable population (Committee on 
Veterans’ Affairs House of Representatives Subject, 2010).  
 
We hypothesize that deficits in sleep are causally related to increased CVD risk in persons with PTSD 
and expect that improving sleep in this population will significantly improve quality of life and 
cardiovascular risk parameters. The potential benefit of demonstrating that CBT-I is an effective 
treatment for insomnia in PTSD (AIM 1), and a treatment that also improves QoL (AIM 2) and reduces 
CVD risk (AIM 3) in this vulnerable population, could be tremendous.  The scientific premise of the 
proposed project is built solidly upon evidence that (1) PTSD is associated with increased rates of 
insomnia and sleep disturbance even after successful PTSD treatment, (2) insomnia is associated with 
increased CVD risk in non-clinical populations, and (3) our preliminary data and that from others (Irwin, 
Omstead, & Carroll, 2016; Irwin et al., 2014; Carroll et al., 2015; Meng, Zheng, & Hui, 2013; Li, Zhang, 
Hou, & Tang, 2014; Aziz et al., 2016; Sofi et al.., 2014; Matthews et al., 2014; Hoevenaar-Blom et al., 
2011; Haack et al., 2013; Ferrie et al., 2007; Wang et al., 2012; McCubbin et al., 2016) suggest that the 
association between PTSD and CVD risk parameters is mediated by poor sleep. Note that: 
 An estimated 7.7 million adults age 18 or older in the United States suffer from PTSD in a given year 
(Kessler et al., 2005).  
 In addition to the deleterious impact of PTSD on emotional, occupational and social functioning, 
PTSD is a significant risk factor for poor physical health (Beckham et al., 1998; Friedman & Schnurr, 
1995; Schnurr & Green, 2004; Spiro, Hankin, Mansell, & Kazis, 2006).  
 Studies have linked PTSD to increased cardiovascular disease (CVD) morbidity and mortality 
(Boscarino, 2006; Edmondson et al., 2013; Flood et al., 2010) including a two-fold increase in the 
risk of premature death from CVD (Boscarino, 2008).  
 PTSD and insomnia are twice as likely to occur in women compared to men (Krishnan & Collop, 
2006; Perkonigg, Kessler, Storz, & Wittchen, 2000).  
 Over $57 billion is spent annually on mental illness in medical costs, disability payments, and lost 
productivity (Soni, 2009); the healthcare costs of PTSD exceed costs associated with major 
depressive disorder (Ivanova et al., 2011).  
 Sleep disturbance has been labeled the “hallmark of PTSD” (Ross, Ball, Sullivan, & Caroff, 1989).  
 Insomnia among persons with PTSD is associated with increased physical health complaints (Clumb, 
Nishith, & Resick, 2011; Mohr et al., 2003), and is characterized by self-reported poor sleep quality, 
objectively measured short sleep duration, increased intermittent wakefulness, and decreased 
sleep efficiency. 
 Insomnia persists following evidence-based PTSD treatments that have otherwise been successful 
in improving PTSD symptoms (Ohayon & Shapiro, 2000; Belleville, Guay, & Marchand, 2011).  

 
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 Our preliminary studies, as well as data from others (McCubbin et al., 2016), suggest that poor 
sleep is a significant mediator of the relationship between PTSD and CVD risk biomarkers. 
 
Most of the previous work examining insomnia and CVD risk in individuals with PTSD has relied on 
cross-sectional designs, thus limiting the ability to establish a temporal relationship between PTSD 
sleep-related symptoms and CVD risk biomarkers. Despite compelling evidence of the link between 
insomnia and CVD risk, no studies have been specifically designed to examine the role that insomnia 
plays in the link between PTSD and CVD risk. Further, the current study provides a novel and innovative 
approach to assessing the direct effect of impaired sleep on CVD risk biomarkers. Since the majority of 
individuals with PTSD also meet criteria for co-morbid Insomnia Disorder (Talbot et al., 2014), we 
propose to use CBT-I to treat insomnia with the hypothesis that among those receiving CBT-I, 
improvements in sleep will result in improved diurnal blood pressure regulation and biomarkers of CVD 
risk compared to individuals receiving either sleep education alone or usual care. These findings would 
establish for the first time a direct link between insomnia symptoms (total sleep time [TST], wake after 
sleep onset [WASO], and sleep efficiency [SE]) and CVD risk among individuals with co-morbid PTSD 
and ID independent of other symptoms and health risk behaviors. The innovative use of CBT-I to 
specifically target CVD risk stands to make considerable contributions to research in other at-risk 
populations (e.g., depression). It is expected that insomnia treatment may be a novel and cost effective 
approach to reducing the CVD risk burden evident in this population. 
 
 
B. RESEARCH STRATEGY: APPROACH 
B.1. Insomnia and CVD risk 
The scientific premise for this work is based in part on a growing body of evidence that insomnia is 
associated with increased CVD risk (Aziz et al., 2016; Carroll et al., 2015; Ferrie et al., 2007; Haack et al., 
2013; Hoevenaar-Blom et al., 2011; Li et al., 2014; Matthews et al., 2014; Meng, Zheng, & Hui, 2013; 
Sofi et al., 2014; Wang et al., 2012). Observational studies employing objective sleep measures are 
consistent with this larger literature as well, and show that those with insomnia have higher CVD, even 
after controlling for co-morbid sleep disorders, depression and anxiety (Taylor et al., 2007). Short sleep 
duration, in either insomniacs or healthy adults, has also been associated with an increased risk of 
heart disease, higher blood pressure, metabolic syndrome, greater coronary calcification, and higher 
levels of stress hormones (Bonnet & Arand, 2007).  
 
B.2. Insomnia and PTSD 
Sleep disturbance has been labeled the “hallmark of PTSD” (Ross et al., 1989) Insomnia Disorder is 
defined as difficulty initiating or maintaining sleep (Motivala, 2011), is the most frequently reported 
symptom of PTSD, and is reported by up to 90% of individuals with PTSD. Insomnia among persons 
with PTSD is associated with increased physical health complaints (Mohr et al., 2003; Clum, Nishith, & 
Resick, 2001), and is characterized by self-reported poor sleep quality, objectively measured short 
sleep duration, increased intermittent wakefulness, and decreased sleep efficiency.  
 
Importantly, insomnia typically persists following evidence-based PTSD treatments that have otherwise 
been successful in improving PTSD symptoms (Belleville, Guay, & Marchand, 2011; Zayfert & DeViva, 

 
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2004). Although most RCTs of PTSD treatments have failed to report effects on sleep, there is 
increasing evidence that insomnia remains prevalent among patients who were otherwise successfully 
treated for PTSD (Galovski, Monson, Bruce, & Resick, 2009; Spoormaker & Montgomery, 2008; Zayfert 
& DeViva, 2004).  
 
B.3. Insomnia Symptoms Fail to Remit Following PTSD Treatment 
Importantly, considerable insomnia symptoms persist following evidence-based PTSD treatments that 
have otherwise been successful in improving PTSD symptoms (Belleville, Guay & Marchand, 2011; 
Zayfert & DeVIva, 2004). Although most RCTs of PTSD treatments have failed to report effects on sleep, 
there is increasing evidence that insomnia remains prevalent among patients who were otherwise 
successfully treated for PTSD (Galovski, Monson, Bruce & Resick, 2009; Spoormaker & Mongomery, 
2008; Zayfert & DeVIva, 2004). For example, in an important trial comparing two evidence-based 
therapies for PTSD, none of the participants randomized to prolonged exposure therapy or cognitive 
processing therapy achieved reductions in sleep quality on the Pittsburgh Sleep Quality Index (PSQI) 
that placed them below clinical cutoffs, suggesting none of the sample returned to normal sleep 
functioning despite treatment gains (Galovski et al., 2009). These results are consistent with prior trials 
that suggest the majority of patients successfully treated for PTSD (who are no longer reporting 
nightmares and hypervigilance) still report clinically significant insomnia (Schnurr & Lunney, 2019; 
Zafert & DeViva, 2004).  
 
B.4. Insomnia may be a Modifiable CVD Risk Factor in those with PTSD 
Based upon recent evidence, including our own preliminary data, our central hypothesis is that sleep 
disturbance in those with PTSD is a potentially modifiable feature of PTSD that contributes to CVD risk. 
While the evidence that insomnia is a risk for CVD is convincing, the scientific premise for this work is 
further supported by two recent studies conducted in our laboratory, indicating that impaired sleep 
was a significant mediator of the association between PTSD and cardiovascular risk mechanisms (see 
C.8. Preliminary Studies). In order to demonstrate mediation with high scientific rigor, however, 
randomized trials are needed (Kraemer, 2016). With few exceptions, randomized studies designed to 
examine physiological response to insomnia treatment are largely absent from the literature. We 
propose to move the field forward by improving insomnia symptoms via CBT-I in an at-risk population 
and measuring changes in key cardiovascular risk biomarkers. We expect that improvements in 
objectively measured sleep efficiency will be accompanied by reduced cardiovascular risk in individuals 
with PTSD. This premise is supported by recent evidence that improved sleep can reduce blood 
pressure. In a randomized trial among prehypertensive or Stage 1 hypertensive adults, improved sleep 
was associated with a concurrent reduction in both systolic and diastolic blood pressure (Haack et al., 
2013). Importantly, another recent trial of adults (N=123; aged 55+), has demonstrated that CBT-I 
treatment results in significant reductions in CVD risk biomarkers (Irwin et al., 2014; Irwin, Olmstead, & 
Carroll, 2016) including a multisystem CVD risk score (Carroll et al., 2015) where effects persisted at 16-
months post-intervention. 
 
B.5. Cognitive Behavioral Therapy for Insomnia (CBT-I) CBT-I has well-established efficacy in improving 
sleep (Edinger et al., 2001; Geiger-Brown et al., 2015; Morin et al., 2006; Morin et al., 2015; Morin, 
Culbert, & Schwartz, Morin, Vallieres, & Guay, 2009; 1994; Murtagh & Greenwood, 1995) and can be 

 
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delivered by nonspecialist providers (Buysse, Germain, & Moul, 2011; Manber, Carney, & Edinger, 
2012; Neylan, 2011). CBT-I is delivered as a short-term intervention and produces long term gains in 
sleep (Morin et al., 1999; Murtagh & Greenwood, 1995). CBT-I has been shown to be effective even 
among individuals with other medical and psychiatric morbidity including cancer, depression, alcohol 
dependence and chronic pain (Smith, Huang, & Maber, 2005). Theoretically, a behavioral model of 
sleep is applicable to PTSD in that the traumatic event can serve as an initiating event for insomnia 
while behavioral factors (e.g., time in bed extension) may lead to chronic insomnia(Spielman, Saskin, & 
Thorpy, 1987). An 8-week course of CBT-I has recently been shown to improve objective and subjective 
sleep in individuals with PTSD (Talbot et al., 2004), and gains were maintained at 6-months. Bonnet 
and Arand (2007) have recommended that future research is needed to determine whether sleep 
interventions that result in improved sleep are also temporally related to reduced CVD risk. 
 
B.6. Biomarkers of Cardiovascular Risk 
The logistical and financial obstacles (e.g., large sample sizes, extended follow-up periods of years) 
associated with infrequent "hard" clinical endpoints (e.g., myocardial infarction, stroke, death) has 
proven to be a major challenge to furthering our understanding of the optimal ways to treat vulnerable 
patients because of the obvious logistical and financial obstacles such investigations present. One 
solution to this problem is to first study intermediate markers of risk in vulnerable patients to 
untoward cardiac events (Rozanski, Blumenthal, Davidson, Saab, & Kubzansky, 2005). Examination of 
changes in intermediate endpoints can provide important insights into the mechanisms and potential 
value of clinical interventions and has a number of important advantages over “hard endpoints,” in 
that fewer patients are required to detect treatment effects and changes in cardiac risk can be reliably 
and objectively measured over short follow-up intervals. Following the guidelines advanced by a panel 
of prominent cardiovascular scientists (Naghavi et al., 2003), we propose to examine intervention 
effects on biomarkers of atherosclerotic cardiovascular risk (i.e., "intermediate endpoints") in 
“vulnerable” PTSD patients with insomnia. 
 
B.6.1. Ambulatory Blood Pressure (ABP) and Blunted Nighttime Blood Pressure (BP) Dipping. 
Measurement of BP throughout the day and night by 24-hour ABP monitoring has been shown to be of 
superior diagnostic and prognostic value than multiple clinic BP readings (Clement, De Buyzere, & De 
Bacquer, 2003; Dolan, Stanton, & Thom, 2009; Pickering, Schwartz, & James, 1995; Staesson, Asmar, & 
De Buyzere, 2001;White, Giles, Bakris, Neutel, Davidai, & Weber, 2006). Twenty-four-hour ABP 
monitoring also allows characterization of the diurnal pattern of BP variation, including daytime BP, 
nighttime BP, and BP “dipping” (the relative fall in BP from day to night). Nighttime BP is a stronger 
predictor of cardiovascular risk than clinic or daytime ABP (Hanson, Li, Boggia, Thijs, Richart, & 
Staessen, 2001). Blunted nighttime BP dipping, including BP non-dipping (typically defined as <10% fall 
in systolic BP from day to night), is a strong independent prognostic indicator of cardiovascular 
morbidity and mortality for both hypertensive and non-hypertensive individuals (Ben-Dov, Kark, Ben-
Ishay, Mekler, Ben-Arie, & Bursztyn, 2007; Clement, De Buyzere, & De Bacquer, 220; Dolan, Stanton, & 
Thom, 2009; Fagard, Celis, Thijs, Staessen, Clement, & De Buyzere, 2008; Hansen et al., 2011; 
Ingelssson, Bjorklund-Bodegard, Lind, Arnlov, & Sundstrom, 2006; Mancia, Facchetti, Bombelli, Grassi, 
& Sega, 2006; Ohkubo et al., 2002; Staessen et al., 1999). Despite the established adverse 
consequences of blunted nighttime BP dipping, its causes are less well understood. In terms of 

 
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modifiable causes, a number of studies have now shown that impaired sleep quality and insomnia are 
accompanied by blunted nighttime BP dipping (Hinderliter et al., 2013; Hughes, 2007; Lanfranchi et al., 
2009; Loredo, Neleson, Ancoli-Israel, & Dimsdale, 2004; Routledge & McFetridge-Durdle, 2007; 
Sherwood, Bower, & Routledge, 2012; Sherwood et al., 2011; Ulu, Ulu, & Ulasli, 2013). A recent study 
linked poor sleep quality to high levels of stress, with both factors contributing to blunted BP dipping 
(Huang, Mai, & Hu, 2011). PTSD is characterized by both sleep impairment and high levels of stress, but 
to our knowledge, only two studies have evaluated nighttime BP dipping in individuals with PTSD. 
Mellman and colleagues (2009) studied 30 young African American men and women with a history of 
trauma, including 17 individuals with full or sub-threshold lifetime PTSD, with the remainder never 
having evidenced significant PTSD symptoms. PTSD was found to be associated with nighttime BP non-
dipping. A second study by our group (Ulmer, Calhoun, Bosworth, Dennis, & Beckham, 2013) found 
that sleep quality mediated the association between PTSD symptoms and nighttime BP non-dipping. 
 
B.6.2. Vascular Endothelial Function assessed by Flow Mediated Dilation (FMD). Vascular endothelial 
dysfunction plays a vital role in the development, progression, and clinical manifestations of 
atherosclerosis (Bonetti, Lerman, & Lerman, 2003; Brevetti, Silvestro, Schiano, & Chiariello, 2003). It 
can be assessed non-invasively using standardized vascular ultrasound techniques to determine FMD 
of the brachial artery (Celermajer, Sorenson, Bull, Robinson, & Deanfield, 1994; Corretti et al., 2002), , 
and is prognostic of the clinical manifestation of CVD. It has recently been identified as a mechanism by 
which posttraumatic stress could contribute to atherosclerosis (Von Kanel et al., 2006). Also, in a 
recently published study of 496 men and women, impaired FMD was related to poor sleep quality and 
insomnia (Routledge et al., 2015). Impaired FMD has been linked to elevated depression symptoms 
(Sherwood, Hinderliter, Watkins, Waugh, & Blumenthal, 2005), elevated anxiety symptoms (Munk et 
al., 2012), acute mental stress (Ghiadoni et al., 2000; Lind, Johansson, & Hall, 2002; Jambrik, Sebastiani, 
Picano, Ghelarducci, & Santarcangelo, 2005), and most recently to insomnia (Routledge et al., 2015). 
Impaired FMD is responsive to treatments designed to reduce CVD risk, and behavioral interventions 
designed to reduce stress have resulted in improved FMD (Blumenthal et al., 2005).  
 
B.6.3 24-hour Urinary Catecholamines (Epinephrine and Norepinphrine). The SNS is involved in the 
pathogenesis of hypertension, as well as various manifestations of CBD (Esler, Lambert, Schlaich, & 
Navar, 2010; Grassi, Mark, & Esler, 2015). SNS activity can be assessed noninvasively by the 
measurement of catecholamines (epinephrine and norepinephrine) excreted in the urine. Using 24-
hour urinary catecholamine assays, partitioned in awake and nighttime sleep-period samples, our own 
work has shown that blunted nighttime BP dipping is related to nighttime SNS hyperactivity in men and 
women with hypertension (Sherwood, Steffen, Blumenthal, Kuhn, & Hinderliter, 2002; Sherwood et al., 
2011). Data from the Multi-Ethnic Study of Atherosclerosis (MESA) suggest that heightened SNS 
activity is linked to low sleep efficiency (Castro-Diehl et al., 2016). SNS hyperactivity assessed by 24-
hour urinary catecholamine excretion has been found to be associated with low sleep efficiency, 
measured by actigraphy, in healthy middle-aged men and women (Zhang et al., 2011). These 
observations are consistent with our hypothesis that SNS hyperactivity is a potential mechanism of 
CVD risk related to low sleep efficiency. Urinary catecholamines have also been used to document that 
insomnia disorder is often characterized by a state of hyperarousal (Roehrs & Roth, 2016). 
 

 
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B.6.. 10-year Atherosclerotic Cardiovascular Disease (ASCVD) Risk. ASCVD risk will be based on the 
Pooled Cohort Equations recently validated and recommended by the clinical practice guideline expert 
panel of the American College of Cardiology/American Heart Association (Goff et al., 2013). These 
equations estimate the 10-year risk of a primary ASCVD event (including myocardial infarction and 
stroke) among patients without pre-existing CVD who are between 40 and 79 years of age. Patients are 
considered to be at "elevated" risk if the Pooled Cohort Equations predicted risk is ≥ 7.5%. Because 
several large population studies have shown that insomnia and short sleep duration are linked to 
hyperlipidemia (Lin, Tsai, & Yeh, 2016), the CBT-I intervention holds promise for both lowering BP and 
improving the abnormal lipid profiles seen in PTSD (Solter, Thaller, Karlovic, & Crnkovic, 2002).  
 
C. RESEARCH DESIGN AND METHODS 
 
C.1. Study Design Overview  
In an innovative use of an established and well validated 
intervention for insomnia, we propose to examine the direct 
effects of sleep on biomarkers of CVD risk outcomes among 
persons with PTSD and comorbid insomnia disorder (ID; see 
Figure 1). In this 2-arm RCT, 150 participants with PTSD and ID 
will be randomized at a 2:1 ratio to either CBT-I, or a minimal 
contact control condition (CCC). We anticipate that we may 
need to consent and screen as many as 500 participants to 
reach this goal. Sleep efficiency as measured by actigraphy will 
be the primary sleep outcome measure. Other parameters 
[total sleep time (TST) wake after sleep onset (WASO), sleep 
fragmentation) will be measured with a multi-modal 
assessment strategy including validated self-report measures, 
sleep diaries collected via Qualtrics, and objective sleep 
measurement (i.e., actigraphy). Sleep diaries and actigraphy 
will be continuously collected for 10-weeks (including a week-
long baseline, 8-week period for CBT-I or CCC, and one-week 
post treatment) and for a week-long period at the 6-month 
follow-up. The primary outcomes for this clinical trial are 
established biomarkers of CVD risk, including flow mediated 
dilation, SNS activity, nighttime BP, nighttime BP dipping, and 
ASCVD risk. These biomarkers will be assessed at three time 
points: baseline, post-intervention (8-weeks), and 6-month 
follow-up. The 6-month follow-up period will allow us to 
evaluate whether the intervention effects on CVD risk are 
sustained beyond the immediate post-intervention period. 
Timing for the 6-month follow-up visit will be 6 months after 
the baseline assessment or three months after the post-intervention assessment, whichever is later. 
Participants in both arms will continue with usual medical care.  
 

 
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C.2. Recruitment 
Participants (N=150) will be recruited from among outpatients in the primary care and PTSD clinics at 
the Duke University Medical Center (DUMC), and in the community. This procedure has been used in 
our previous studies with strong success for recruitment of individuals with PTSD. Over the past five 
years, approximately 1,200 males and females have participated in our ongoing clinical research 
studies and there has never been difficulty with recruitment of PTSD participants for our studies. The 
samples we have collected include men and women with and without PTSD; men and women who 
have trauma exposure but no PTSD; PTSD resulting from a wide range of trauma exposures including 
sexual assault, physical assault, accidents, and combat; and significant minority representation.  
 
On recruitment materials, we will plan to use an acronym to refer to the study. That acronym is UPLIFT, 
Understanding PTSD Risk by Lessening Insomnia From Treatment.  
 
Recruitment materials such as IRB-approved study flyers and brochures will be placed in outpatient 
clinic areas at DUMC. In addition, study flyers will be placed throughout DUMC in centrally located 
posting areas. Study recruitment materials will advertise a research study that includes insomnia 
treatment for persons with PTSD. This recruitment method will be used over the course of the entire 
recruitment period. We will also contract with a third-party company, BuildClinical, to enhance 
recruitment. BuildClinical is a data-driven platform that helps academic researchers recruit participants 
for research studies more efficiently using social media, software, and machine learning. We have 
included for your review the recruitment materials that they have developed. BuildClinical utilizes 
study-specific advertisements to engage participants on digital platforms such as Facebook, Google, 
WebMD, etc., and redirect them to a study-specific landing page should they click it. On the landing 
page, the person can complete an online pre-screen questionnaire. Duke staff can log into the 
BuildClinical portal to retrieve information about potential participants.  
 
We will identify potential participants using Duke’s Deduce and Maestro Care systems. We will identify 
potentially eligible participants, using diagnostic and contact information in those systems. Potentially 
eligible participants will be sent information and invitation to participate via 1) MyChart, 2) an 
introductory letter, or 3) email asking them to consider participation, and they will be provided contact 
information for the study coordinator. Potential participants may also be called about a week after 
they are sent the letter to determine interest. Potential participants will be contacted no more than 
three times, as indicated by DOCR guidelines. They will also be provided information about how to opt 
out of additional contact from the study team re: this study.  
 
We will also identify potential participants using University of North Carolina (UNC) medical center’s 
messaging health care system, My UNC Care. Our team at UNC will use PHI including diagnostic 
information, medical record numbers, and participant names. In accordance with UNC’s policies 
regarding this recruitment type, the UNC study team will reach out to potential participants via My 
UNC Chart to inform them of the Duke study, and to ask them to consider participation. Interested 
participants will be provided the name, telephone number, and email address of the study coordinator, 
and will be asked to contact them about participation. They will also be provided a link to a Qualtrics 

 
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survey that will screen them for participation. No staff at Duke will have access to the PHI of UNC 
patients unless those patients contact the study team about participation. Only UNC staff will access 
PHI of UNC patients. The UNC site will rely on Duke’s IRB for oversight, please see the reliance 
agreement provided. Please note that Dr. Patrick Smith, the UNC investigator who will be assisting with 
this recruitment effort, is already listed on our project as key personnel outside Duke. Please note that 
although Dr. Smith will have access to the PHI of UNC patients, he will not have any access to Duke PHI 
after participants provide consent at Duke.  
 
Maestro Care MyChart messaging:  This study will use a MaestroCare MyChart recruitment invitation 
to help identify potential participants pre-consent. Potential participants are identified via a report 
generated within Maestro Care by the DOCR Maestro Care Analyst team. A recruitment invitation will 
be sent by a DOCR analyst to potential participants via MyChart. The patient will indicate if they are 
interested or not interested, and they will be asked to complete a survey designed to evaluate 
eligibility. The study coordinator will be sent an Inbasket message (Maestro Care internal message) 
indicating the response. Only key personnel who are delegated the task of patient 
identification/recruitment will have access to the Inbasket messages. Only patients who express 
interest and meet basic study eligibility will be contacted by key personnel, who will then follow the 
recruitment process approved by the IRB for this study.  If a patient expresses interest and the study 
team determines the patient is not qualified for the study prior to contacting them, they will be sent an 
approved email letting them know.  If a patient does not open their MyChart message, the Maestro or 
Epic Analyst can send up to two additional "tickler" notifications, at least a week apart, to let the 
patient know they have an unread message.   
 
We will plan to use a recruitment method referred to as respondent-driven sampling, or “seed 
recruitment” (Christina Meade, Ph.D., personal communication). Seed recruitment is suitable for 
sampling “hidden populations” of participants who are best known by their own peers (Heckathorn, 
1997).  It includes providing incentives to participants for referral of other eligible participants. In our 
model, each participant, or seed, will receive three coupons to recruit other people in his/her social 
networks. The recruitment coupons will provide a brief description of the survey and a phone number 
for contacting the study coordinator. The coupon will be marked with a unique identification number 
(not the study identification number) so that when the coupons are returned to us, the ID number can 
be used to provide payment ($20) to the participant who made the referral. The key connecting the 
participant’s study ID number with the seed ID number will be kept in a database separate from other 
PHI, creating two layers of separation between the seed ID and the already-participating person’s 
identifying information. Any participant who does not wish to recruit in this manner will not be 
required to do so. 
 
The study will be registered at clinicaltrials.gov, which will allow for online recruitment during the 
course of the study. As needed in years one through five, we will advertise the study in local area 
newspapers, on Duke University’s clinical research website, and on online classified advertising 
websites such as Craigslist.com, DukeList, or JobFinder.com. In addition, we will post study recruitment 
materials at local community areas such as laundromats, Bull Connector bus lines, substance use 
treatment centers, rape crisis centers, battered women shelters/programs, restaurants, and grocery 

 
Full Protocol, PI version date 8/26/24 
11
stores.  
 
Our study team will use social media to reach potentially eligible participants. We have developed a 
Facebook page for posting IRB-approved study flyers and information for this and other studies in the 
Traumatic Stress and Health Research Laboratory, https://www.facebook.com/Duke-Traumatic-Stress-
and-Health-Research-Lab-379366159145563/. We plan to place pictures of our study flyers on the 
Facebook page, and use Facebook’s post boost to draw attention to the post. The post itself will say 
“Enroll now!” or “Now enrolling!” We will also plan to use Facebook ads to target potential participants 
within a 50-mile radius of Duke. If any participant contacts the email associated with the Facebook 
page (TSHRLab@dm.duke.edu, he/she will be sent an automatic email response. 
 
Any participant who contacts by telephone the study coordinator or other study staff regarding the 
study will be provided more information, and will be interviewed using an IRB-approved telephone 
screening. If a participant is deemed potentially eligible in the telephone screen, he/she will be 
scheduled to attend a formal screening visit. We will send, via Duke secured email, appointment 
reminders to any participant who wants to receive them. Prior to beginning study procedures, the 
study staff member obtaining consent will explain the study in detail, provide the participant with an 
IRB-approved written consent form explaining the procedures and risks, and answer any questions. 
The consent form will be signed via RedCap. Participants are given the chance to thoroughly read the 
consent prior to participation. Participants are given a copy of the signed informed consent form, and 
are given phone numbers to call if they have additional questions about the consent form or the 
research, if they have any problems during the study, or if they have questions about participating in 
research studies in general. With regards to determination of decision making capacity of potential 
participants, our laboratory has a standard procedure for determining understanding of the study 
procedures, risks, and benefits. We utilize this procedure if we have any reason to suspect that the 
participant may have difficulty in the consent process (e.g., traumatic brain injury impacting cognitive 
function, active psychotic symptoms). In this procedure, the study coordinator providing the informed 
consent information evaluates understanding of the procedures at several different time points during 
the process by asking questions like “Do you understand what we’re asking you to do?” and “Do you 
have any questions about the risks of the study? Can you tell me what you understand the risks to be?” 
Prior to having a participant sign consent, the study coordinator, who has clinical experience in working 
with persons with psychotic disorders, may ask the potential participant to outline the study 
procedures, risks, and benefits so that he can make sure that the participant is aware of them. If the 
participant is unable to summarize these, he/she will not be allowed to sign the informed consent 
form, and may be referred for other treatment. No study procedures will begin until informed consent 
has been obtained. 
 
C.3. Patient Eligibility and Randomization 
Participants must meet each of the following inclusion criteria: 
 Are between 40-59 years old; 
 Has a current diagnosis of chronic PTSD (at least 3 months duration) based on the Clinician 
Administered PTSD Scale DSM-5 version (Weathers et al., 2013);  
 Has a current diagnosis of ID as defined in the International Classification of Sleep Disorders 

 
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12
(ICSD-3; American Academy of Sleep Medicine, 2014) 
 
Participants who meet any of the following criteria will be excluded from participation: 
 Has a history of CVD events, including myocardial infarction, stroke, transient ischemic attack, 
or coronary revascularization; 
 Has diagnosis of congestive heart failure or coronary artery disease based on results of 
diagnostic testing; 
 Has a current alcohol use or substance use disorder (those who meet lifetime but not current 
alcohol or substance use disorder will be included); 
 Is currently participating in  an evidence-based trauma focused therapy for PTSD (i.e., 
Prolonged Exposure, Cognitive Processing Therapy, or Eye Movement Desensitization and 
Reprocessing); 
 Has cognitive impairment as evidenced by less than 20 on the Montreal Cognitive Assessment 
scale (M0CA; Nasreddine et al., 2005);  
 Meets criteria for a psychotic spectrum disorder or bipolar disorder; 
 Has severely impaired hearing or speech;  
 Is pregnant;  
 Does not use benzodiazepines for sleep, and if prescribed benzodiazepines for some other use 
(e.g., anxiety, panic attacks), uses them fewer than four times in a one month period.; 
 Is not stable (medications and dose stable for one month) on any other current psychoactive 
and/or cardiovascular medications or will not be stable on these medications during the course 
of the study;  
 Works night shift;  
 Is participating in another interventional study to address insomnia;  
 Has prominent suicidal or homicidal ideation (as assessed through a clinical interview);  
 Has nonclinically significant or sub-threshold insomnia as indicated by a score of <8 on the 
Insomnia Severity Index;  
 Has seizures (based on clinical interview and self-report); 
 Has a body mass index of 45 or greater;  
 Has sleep apnea (based on the overnight assessment described below) or a positive sleep apnea 
screen;  
 Has restless leg syndrome (based on the Duke Structured Interview for Sleep Disorders (DSISD); 
Edinger, Wyatt, & Olsen, 2009), and that sleep disorder is the primary cause of their sleep 
complaint (participants with restless legs syndrome who also have insomnia disorder can be 
included in the study); 
 Has an organic cause of sleep disruption that cannot be addressed by cognitive-behavioral 
changes (e.g., hyperthyroidism), as determined by the DSISD;  
 Has excessive daytime sleepiness, defined as a score >15 on the Epworth Sleepiness Scale (ESS) 
or as determined by the DSISD;  
 Does not complete sleep diary assessments within 6 hours of rising on at least 5 of the 7 days of 
the initial assessment period; or 
 Cancels or no-shows for two or more Time 1 assessment appointments 

 
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13
 Has uncontrolled hypertension (screening office BP > 160/100 mm Hg) 
 
Participants who cannot complete study procedures may be withdrawn by the PIs. If any participant is 
excluded due to uncontrolled hypertension seeks treatment and their blood pressure becomes 
adequately controlled {e.g., with a stable antihypertensive medication regimen), they will become 
eligible to participate if their clinic/office blood pressure is lower than 160/100 mm Hg and they have 
been stable on their antihypertensive medication(s) for one month. 
 
Because the physiological changes of pregnancy may impact study outcome, we will exclude women 
who self-report pregnancy. Urine samples will be assayed by a trained staff member using a Quidel 
QuickVue pregnancy test. These pregnancy tests will be completed for women of childbearing 
potential. We have developed a short interview for female participants; this interview will help us 
determine which female participants must have a urine pregnancy test, and when the test should be 
done. Female participants of childbearing potential who are not pregnant must agree to use 
appropriate contraception during the course of the study, and to notify study staff if they become 
pregnant during the study. 
 
Dropout rates across psychological and pharmacologic studies in our clinic that involved 3 to 15 visits 
have ranged from 0 to 20%. Participants who drop out will receive partial reimbursement for the time 
they donate. Eligible participants will be randomized to either CBT-I, or CCC+ in a 2:1 ratio. 
Randomization will be stratified by gender and smoking. Participants will be paid $1060 for their 
complete participation. 
 
During the study, study staff members will communicate with participants via phone, email, standard 
mail, or texts sent from a Duke-owned phone, based on participant preferences. Participants will be 
allowed to opt out of texting or emailing if they wish.  
 
C.4. Interventions  
C.4.1. Cognitive Behavior Therapy for Insomnia (CBT-I). The approach to CBT-I for the proposed study 
is based on CBT-I programs that have been practiced and studied by research team members (Karlin, 
Trockel, Tylor, Gimeno, & Manber, 2013; Manber, Carney, & Edinger, 2012). CBT-I is manualized in a 
session-by-session guide (Perlis, Jungquist, Smith, & Posner, 2005). Dr. Ulmer serves as a consultant 
and trainer on the nationwide dissemination of CBT-I. Dr. Ulmer will train study therapists to deliver 
CBT-I in accordance with the training approach adopted by the CBT-I dissemination effort (Karlin, 
Trockel, Tylor, Gimeno, & Manber, 2013). Further, Dr. Manber will be available for consultation 
regarding the training procedures and fidelity measures and methods.  
 
C.4.2. Minimal Contact Control Condition. Participants assigned to the CCC will be contacted via 
WebEx or Zoom every week for eight weeks. To monitor their insomnia symptoms and mood, they will 
be asked to complete the PTSD Checklist and Insomnia Severity Index. Check-in calls will be audio-
recorded. This will allow us to do fidelity ratings to ensure that participants are not receiving any sleep 
interventions, advice, etc. Please note that our lab has an SOP related to suicide risk assessment, and 
the study coordinator who makes the check-in calls has been trained in the use of the SOP. Participants 

 
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14
will be asked about any insomnia treatments they use/receive during usual care. At the end of the 6-
month follow-up, participants randomized to this condition will be offered CBT-I. 
 
C.4.3. Intervention Training and Fidelity. Study therapists will include Dr. Ulmer and two masters’ level 
mental health providers. Each therapist will provide treatment in both arms. Masters’ level therapists 
will be trained by Dr. Ulmer. Initial didactic training (covering session by session procedures, core 
components of CBT-I and SH+) will last three days and be followed by training cases. Therapists will be 
allowed to provide the intervention once they have demonstrated 100% competence and adherence 
with all required treatment elements. Therapists will meet weekly for supervision throughout the trial. 
All therapy sessions will be audio-recorded. A random selection of twenty percent will be rated for 
fidelity. In order to protect against drift, the frequency of fidelity checks will occur equally across the 
beginning, middle, and end of the intervention period. Dr. Ulmer will rate therapist treatment 
fidelity/adherence for recorded sessions using the adapted version of the Yale Adherence and 
Competence Scale (Carroll, 2000). In the CBT-I condition, recordings will be rated on the 
presence/absence of CBT-I elements. In the comparison condition, recordings will be rated on the 
presence of proscribed elements of CBT-I. In addition, recordings from both conditions will be rated on 
the presence/absence of nonspecific treatment elements (e.g., therapeutic alliance, patient 
expectancies). Feedback from fidelity checks will be provided to therapists. In addition, emergency 
procedures for suicidal and homicidal ideation have been well developed in our laboratory and these 
will be carefully followed for each participant. Dr. Manber will also provide consultation to Dr. Ulmer. 
 
C.5. Measures 
We have experience assessing all the proposed measures and items discussed below. Table 1 
summarizes the measures and the timepoints at which they are given. 
 
Table 2. Study Measures
Measure 
Completer
1=Beckham team,  
2=Sherwood team 
Time 
Screen*
Baseline
PostTxt
6-Mon
Clinician Administered PTSD Scale
Trained interviewer1
X2
Structured Clinical Interview for DSM5
Trained interviewer1
X2
Duke Structured Interview for Sleep Disorders
Trained interviewer2
X1
Montreal Cognitive Assessment
Trained interviewer1
X1
Traumatic Life Events Questionnaire
Participant
X1
PTSD Checklist
Participant
X1
X
X
X
Beck Depression Inventory – II
Participant
X1
X
X
X
WHO Disability Assessment Schedule
Participant
X1
WHO Quality of Life 
Participant
X
X
X
Everyday Discrimination Scale
Participant
X1
Short Form-36 Health Survey
Participant
X
X
X
Daily sleep diaries
Participant
X
X
X
X
Insomnia Severity Index
Participant
X1
X
X
X
Pittsburgh Sleep Quality Index with PTSD 
addendum 
Participant 
 
X 
X 
X 

 
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15
Epworth Sleepiness Scale
Participant
X1
Medical Problems Checklist 
Study coordinator1
X1
STOP-BANG
Participant
X1
Pre-Sleep Arousal Scale
Participant
X
X
X
Demographics measure
Participant
X1
Fagerström Test for Nicotine Dependence
Participant
X1
AUDIT-C
Participant
X1
Leisure Time Exercise Questionnaire
Participant
X
X
X
Menopausal status
Participant
X
X
PEG
Participant
X1
Medications List
Participant
X1
X
X
X
Insomnia Treatment Measure
Participant
X
Xau
Over-the-Counter Medications List
Participant
X1
X
X
X
Physiological Measures
Actigraphy
Study staff2
X
X
X
WatchPAT One sleep apnea assessment
Participant
X2
24-hour blood pressure assessment
Study staff2
X
X
X
Flow mediated dilation
Study staff2
X
X
X
24-hour urinary catecholamines
Participant2
X
X
X
ASCVD risk
Study staff2
X
X
X
Body Mass Index
Study staff2
X
X
X
* Superscript 1 indicates that these procedures are completed at the first screen visit, and superscript 2 items are completed at the second screen visit. 
 
C.5.1. PTSD, Depression, Alcohol Use Disorder and Quality of Life Measures. The Clinician-
Administered PTSD Scale (CAPS; Weathers, Blake, et al., 2013) will be utilized to establish PTSD 
diagnosis. The CAPS is considered to be the gold standard for determining PTSD diagnosis. The 
Traumatic Life Events Questionnaire (TLEQ; Kubany, Hill, & Owens, 2003) is designed to assess 
exposure to traumatic events, and has an item to assess exposure to combat. Individual 
items/exposures can be summed to reflect a total exposure type score. PTSD symptom severity will be 
measured by the PTSD Checklist (PCL5) with criterion A (Weathers, Litz et al., 2013). Diagnoses other 
than PTSD will be assessed using the Structured Clinical Interview for DSM-5 (SCID). The SCID has been 
shown to have good reliability (Williams, Gibbon, First, 1992) and includes modules that determine 
current and lifetime psychiatric disorders. PTSD symptom severity will be measured by the PTSD 
Checklist (PCL5 with criterion A; Weathers, Litz et al., 2013). The Everyday Discrimination Scale will 
measure the perceived frequency of chronic, routine and subtle experiences of unfair treatment that 
could be one dimension of perceived stress in our sample (Williams, Yu, Jackson, & Anderson, 
1997).Depressive symptoms will be measured with the Beck Depression Inventory-II (Beck, Steer, & 
Brown, 1996). Functional status will be measured using the World Health Organization Disability 
Assessment Schedule 2.0 (WHO DAS-II), a 36-item self-report of functional disability designed to 
measure functioning and impairment over the past 30 days. The WHO DAS-II has exhibited excellent 
reliability and validity in prior research (Garin et al., 2010). Quality of life will be measured using the 
Short Form-36 Health Survey (SF-36; Ware & Sherbourne, 1992), which provides a physical health and 
mental health QoL summary score. The SF-36 has demonstrated reliability and validity in PTSD 
populations (Shiner, Watts, Pomerantz, Young-Xu, & Schnurr, 2011). We will also use the World Health 
Organization Quality of Life Scale, Brief Version (WHOQOL-BREF).  

 
Full Protocol, PI version date 8/26/24 
16
 
C.5.2. Insomnia and Sleep Measurement. Sleep measurement will include both subjective and 
objective measures. Subjective measures will include sleep diaries collected via interactive voice 
technology and self-report measures of sleep quality, insomnia severity, and perceived daytime 
sleepiness. Actigraphy will be employed as an objective measure of the sleep outcomes (TST, WASO, 
and SE). 
  
C.5.2.1 Interview-Based Assessment. The Duke Structured Interview for Sleep Disorders (DSISD) is 
currently being revised to be consistent with DSM-V and the International Classification of Sleep 
Disorders, and will be administered to identify those meeting criteria for ID and rule out individuals 
having excluded co-morbid sleep disorders. The DSISD has been shown to have good reliability and 
validity (Edinger, Wyatt, & Olsen, 2009). The DSISD will be given during the first screening session. 
 
C.5.2.2. Sleep Apnea Assessment. Following the DSISD and (completed at the enrollment visit), if a 
participant has scored a three or greater on the STOP BANG, the potential study participant will be sent 
home with a portable apnea screening device to wear for 1 complete night to rule out sleep apnea. The 
WatchPAT device (Itamar Medical) is a sensitive and specific assessment for identifying the presence of 
obstructive sleep apnea. The device requires that participants download an app that will sync with the 
device and transfer the data to WatchPAT’s servers. Participants will be given the option of using their 
own personal phone or using a Duke-owned iPhone that they then return. In the event that the sleep 
evaluation reveals that a participant shows an apnea-hypopnea index (AHI) > 15 (i.e., more than 5 
sleep apnea-hypopneas per hour of sleep) (Walczewska et al., 2011), participant will be notified that 
they are no longer eligible to continue, and will be encouraged to seek additional evaluation and 
treatment through their physician (Epstein et al., 2009).  
 
In the event of any equipment malfunction, participants may be asked to repeat the WatchPAT home 
study. In order to reliably rule out obstructive sleep apnea, a minimum of 4 hours of recording time is 
required. Therefore, if participants notice any of the equipment coming off in the middle of the night, 
they will be instructed to reattach. If it is found that there is less than 4 hours of recording time, the 
participant will be asked to repeat the in-home study. To prevent this from occurring, participants will 
be given detailed instructions and staff will provide an in-person demonstration to show them how to 
properly use the device. 
 
C.5.2.3. Actigraphy. Actiwatch Spectrum Plus® (Respironics, Inc.) are wristwatch style actigraphs 
containing a calibrated accelerometer that samples, digitizes, and stores movement activity. When 
interfaced with a computer, a scoring algorithm provides estimates of various sleep parameters. When 
costly PSG is not feasible and sleep staging outcomes are not the focus of research, wrist actigraphy is 
recommended as an essential measure of rest-activity patterns in insomnia research (Buysse, Ancoli-
Israel, Edinger, Lichstein, & Morin, 2006). The following sleep metrics will be collected over a one week 
period at baseline, post-treatment, and 6-month follow-up: Time in Bed (TIB), TST, WASO, and sleep 
fragmentation (Ancoli-Israel, Cole, Alessi, Chambers, Moorcroft, & Pollack, 2003; Blackwell et al., 2008; 
Littner et al., 2003; Marino et al., 2013; Morgenthaler et al., 2007; Sadeh & Acebo, 2002; Sadeh, Hauri, 
Kripke, & Lavie, 1995; Thorpy et al., 1995). These parameters will also be collected during the entire 8-

 
Full Protocol, PI version date 8/26/24 
17
week treatment period.  
 
Sleep duration will be defined as TST (calculated by subtracting all periods of wakefulness from the 
time spent in bed). WASO is calculated by summing wakefulness periods after initial sleep onset and 
before final awakening. Sleep efficiency is defined as the ratio of total sleep time divided by TIB 
(TST/TIB). Sleep fragmentation (a secondary outcome) is defined as the sum of percent mobile and 
percent immobile bouts less than 1-minute duration to the number of immobile bouts for a given 
interval (Respironics Incorporated, 2008). Actigraphy will be used as the primary source for these 
variables (Lauderdale, Knutson, Yan, Liu, & Rathouz, 2008). Scoring of actigraphy data will proceed in 
accordance with the guidelines by the Society of Behavioral Sleep Medicine (Ancoli-Israel et al., 2015). 
The sleep diary will include queries about removal of the actigraphy watch from the wrist, and unusual 
activities during the monitoring period (e.g., travel), and these factors will be considered when scoring. 
The event marker will be used as the primary method for establishing “into bed” and “out of bed” 
times. However, when study participants fail to press the event marker, (i.e., no marker on actigraphy 
output), light sensor data and sleep diaries will be combined with accelerometery data to establish 
“into and out of bed” times.  
 
C.5.2.4. Daily Sleep Diaries. Subjective sleep estimates will be obtained concurrent with actigraphy, 
using a Qualtrics survey. Participants will record their sleep using the recently developed consensus 
sleep diary (Carney et al., 2012) to assure comparability across insomnia research studies. Sleep diaries 
will be used to calculate averages for: TIB, sleep onset latency (SOL) (Marino et al., 2013), TST, WASO, 
and SE. The sleep diary is considered the gold standard of self-reported sleep (Buysse et al., 2006). We 
will use Duke Qualtrics to gather the daily sleep diaries.   
 
C.5.2.5. Subjective Insomnia Severity Measures. Subjective insomnia severity will be evaluated utilizing 
the Insomnia Severity Index (Bastien, Vallieres, & Morin, 2001). The ISI provides a global measure of 
perceived insomnia severity. The total scores range from 0-28, with higher scores indicating higher 
severity. The ISI has excellent internal consistency (Cronbach alpha = 0.74) and temporal stability (r = 
0.80). The ISI has been validated with sleep diaries and PSG (Bastien, Vallieres, & Morin, 2001). In 
clinical samples, a cut off score of 11 was shown to have the greatest sensitivity and specificity for 
correctly identifying study participants meeting criteria for insomnia diagnosis (Morin, Belleville, 
Belanger, & Ivers, 2011). Because we are using the ISI as an screening measure and as a within-subject 
outcome, we will use a cut score of 8, which is indicative of subthreshold insomnia. Diagnosis of 
insomnia will be determined by a structured sleep interview.  Other sleep measures to be used, each of 
which has been shown to have internal consistency and validity, include the Pittsburgh Sleep Quality 
Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989), a 19-item measure of sleep quality and 
disturbances over the past month, the Pittsburgh Sleep Quality Index-Addendum (PSQI-A; Germain, 
Hall, Krakow, Katherine, & Buysse, 2005), which assesses other disruptive nocturnal behaviors related 
to PTSD including hot flashes, nightmares and episodes of terror during sleep; and the Epworth 
Sleepiness Scale (ESS; Johns, 1991), an 8-item measure of perceived daytime sleepiness. 
 
C.5.2.6. Sleep-Related Arousal. The Pre-Sleep Arousal Scale (PSAS; Jansson-Fröjmark & Norell-Clarke, 
2012) is a widely used measure of pre-sleep arousal with cognitive and somatic arousal subscales. 

 
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18
Scores on the 13-item measure range from 8-40. The PSAS demonstrated acceptable internal 
consistency on both the cognitive and somatic arousal subscales, and it effectively discriminated 
between normal sleepers, poor sleepers, and those with insomnia disorder. The PSAS demonstrated 
convergent validity with measures of sleep-related worry, sleep-related beliefs, anxiety, depression, 
sleep parameters, and daytime impairment.  
 
C.5.3. Demographic Variables, Medical Screen and Health Risk Behaviors. Each participant’s medical 
history will be reviewed, and clinic BP will be determined by standard sphygmomanometry. We will 
collect demographic, health risk, and other clinical information that will be used in part to characterize 
study participants’ 10-year risk for ASCVD (Goff et al., 2013). Education, occupation and household 
income will be added to provide a composite measure. Education will be measured by highest degree 
earned. Occupation will be coded into three categories. This summary variable has been reported in 
studies examining the relationship between social status and psychological and physiological 
functioning (Adler, Epel, Castellazzo, & Ickovics, 2000). We will collect information about participants’ 
current medications and current/lifetime medical diagnoses. In addition, we will collect data on body 
mass index (BMI), calculated using participant’s weight and height. This measurement will be done at 
the baseline lab visit. The Fagerström Test of Nicotine Dependence (FTND; Heatherton, Kozlowski, 
Frecker, & Fagerström, 1991) and a smoking history questionnaire (e.g., number of cigarettes 
smoked/day, age of first smoking, number of previous quit attempts) will be used to measure smoking 
behaviors. Self-reported alcohol use will be assessed with the three-item AUDIT-C (Bush, Kivlanhan, 
McDonnel, Fihn, & Bradley, 1998), which has demonstrated reliability and validity (Crawford et al., 
2013). The Leisure Time Exercise Questionnaire (Godin & Shephard, 1985) will be collected via 
Qualtrics during the day of ABP assessment). We will also check for the presence of cognitive 
impairment using the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005). This tool has 
been used as a short cognitive screening tool for identifying impaired cognitive functioning in the 
general population; scores range from 0 to 30, and any individual who scores below 20 will be excluded 
from participation due to the presence of cognitive impairment (Gallagher et al., 2013). Although the 
cut score of 26 has good specificity and sensitivity to diagnose mild cognitive impairment (MCI; Hoops 
et al., 2009), we do not wish to rule out participants with MCI, but rather those with dementia. A cut 
score of 26 will likely otherwise suitable participants, as CPT has been successfully used in patients with 
traumatic brain injury (TBI) and MCI. Any participant who is excluded due to low MoCA scores will be 
informed of the results and encouraged to discuss the findings with their personal physician or care 
provider for further evaluation and treatment if necessary. 
 
C.5.4. Measurement of Other Potential Covariates. Menopausal status will be documented by self-
reported menstrual bleeding, using the criteria described by Tom and colleagues (2010). Status 
categories are postmenopausal (natural or surgical), perimenopausal, and premenopausal. 
Menopausal status and lifetime alcohol use disorder (as well as current alcohol consumption measured 
as drinks per week) will be considered as potential covariates in analytic models. Pain will be measured 
with the 3-item PEG, a reliable and valid brief version of the Brief Pain Inventory (Krebs et al., 2009). 
For descriptive and analytic purposes, each patient’s medications will be recorded and classified as: 
anticholinergic; alpha-adrenergic blockade; beta-blockade and other anti-hypertensive medications 
(diuretics, ACE inhibitors, and calcium channel blockers); specific serotonin re-uptake inhibitor (SSRI); 

 
Full Protocol, PI version date 8/26/24 
19
heterocyclic antidepressant; lithium; MAOI anti-depressant; other antidepressant; neuroleptic; or 
sedative-hypnotic. This classification system is based on our previous work involving the input and 
consensus of our team physicians (Beckham et al., 2000).  
 
C.6. Measures of Cardiovascular Disease Risk  
C.6.1. Assessment of 24-hour Ambulatory Blood Pressure (ABP). We have experience measuring 24-
hour ABP and will use the Oscar 2 ABP monitor (Suntech Medical Inc., Raleigh, NC), which has been 
validated by previous investigators (Goodwin, Bilous, Winship, Finn, & Jones, 2007; Jones, Bilous, 
Winship, Finn, & Goodwin, 2004). Clinic BP will be determined according to the new ACC/AHA 
guidelines (Whelton et al., 2018) with three readings taken after five minutes of quiet rest in a seated 
posture, each separated by 2 minutes, using the Oscar 2 BP monitor together with appropriate cuff 
size, and averaged to represent clinic BP. Subsequently, he ABP monitor will be programmed to take BP 
measurements every 20 minutes throughout the waking hours and every 30 minutes during the 
nighttime sleep period. We have used this procedure with excellent participant acceptability and 
compliance in numerous previous studies (Blumenthal, Sherwood, & Gullette, 2000; Carels, Sherwood, 
Szczepanski, & Blumenthal, 2000; Prather, Blumenthal, Hinderliter, & Sherwood, 2011; Sherwood et 
al., 2011; Sherwood, Thurston, Steffen, Blumenthal, Waugh, & Hinderliter, 2001). A successful 24-hour 
study will require at least 80% of the total readings to be valid, with no more than two consecutive 
hours lacking a valid reading (White et al., 2006). Unsuccessful ABP studies will be repeated.  
 
In addition to mean 24-hour ABP, mean waking and nighttime ABP will be computed, with the 
nighttime sleep period defined by self-report and confirmed by actigraphy (Sherwood et al., 2001; 
Sherwood et al., 2011; Sherwood et al., 2002). Nighttime BP dipping will be defined as the percentage 
of the decline in nighttime systolic blood pressure (SBP) [(mean daytime SBP- mean nighttime 
SBP/mean daytime SBP) × 100]; SBP dipping defined in this way is a continuous variable that will be a 
primary outcome measure in this proposed RCT. In addition, for descriptive purposes, a SBP dip ≥ 10% 
will be classified as normal “dipper”, while <10% will be classified as “non-dipper”, with secondary 
analyses evaluating intervention effects on dipping status. Twenty-four-hour ABP assessments will be 
obtained at pre-randomization baseline, upon completion of the 8-week intervention, and at 6-month 
follow-up, in order to evaluate intervention effects on ABP and nighttime BP dipping.  
 
C.6.2. Flow Mediated Dilation (FMD). Our approach for assessing endothelial function conforms to the 
recently published guidelines for assessment of flow-mediated arterial vasodilatation (Thijssen et al., 
2011). Longitudinal B-mode ultrasound images of the brachial artery, 4-6 cm proximal to the 
antecubital crease, will be obtained at end-diastole (ECG R-wave gated digital image capture) using a 
dedicated Acuson Aspen ultrasound platform. All images will be acquired with participants supine, 
utilizing an 11 MHz linear array probe with stereotactic holder in our temperature-controlled clinical 
research laboratory, by Michael Ellis, RDMS, RVT, who has over 15 years of experience performing the 
standardized image acquisition protocols for our ultrasound FMD assessments. In an unpublished 
evaluation of 20 healthy men and women who underwent our FMD assessment protocol on two 
consecutive days, repeat FMD values showed a correlation of r=0.81, p<.001, a mean absolute 
difference of 0.64%. Images will be obtained and stored digitally at resting baseline, as well as during 
and following inflation to 250 mm Hg of an occlusion cuff placed around the forearm, 2 cm below the 

 
Full Protocol, PI version date 8/26/24 
20
elbow. All arterial diameter measurements will be performed by the same experienced member of the 
research team (AS), blinded to participant identity and treatment condition, using edge detection 
software (Brachial Analyzer, MIA-LLC, Coralville, IA). FMD response will be assessed from 10-120 
seconds post-deflation of the forearm cuff, with peak arterial diameter quantified using polynomial 
curve fitting, and FMD thereby defined as the maximum percent change in arterial diameter relative to 
pre-inflation resting baseline. As others have reported, using this rigorous standardization of FMD 
methodology, our FMD assessments will be obtained with optimal reproducibility, reflected in a 
coefficient of variation of approximately 10% or less (Donald et al., 2008; Ghiadoni et al., 2012; 
Charakida et al., 2013). Peak hyperemic flow and shear stress will be derived by standard formulae 
based upon Doppler velocity measurements during the first 10 seconds following deflation of the 
occlusion cuff (Mitchell et al., 2004; Pyke, Hartnett, & Tschakovsky, 2008).  
 
C.6.3. 24-hour Urinary Catecholamines (Epinephrine and Norepinephrine). Patients will be asked to 
collect urine over a 24-hour period (Blumenthal, Sherwood, & Gullette, 2000; Hughes, Watkins, 
Blumenthal, Kuhn, & Sherwood, 2004; Sherwood, Steffen et al., 2002; Zhang et al., 2011, White, 
Brunner, & Barron, 1995;. Urine samples will be kept cold by storage in a portable cooler throughout 
each 24-hour sample period.  Urine will be collected in three containers provided to each participant in 
a portable cooler, with careful instructions to collect daytime/awake urine in containers labeled 
accordingly, including the last collection before bedtime; any nighttime/sleep period urine collection 
will be collected in the container labelled “nighttime”, and participants will be instructed to void using 
that collection container as soon as they get up in the morning.   Samples will be assayed for 
norepinephrine, epinephrine, and creatinine.  Catecholamine levels will be expressed as urine 
concentration (µg/ml) per urine concentration of creatinine (mg/ml), yielding norepinephrine and 
epinephrine values of µg per mg creatinine for each sample.  This provides catecholamine excretion 
indices that are corrected for individual differences in body size and urine volume (White, Brunner, & 
Barron, 1995). In prior studies of circadian changes in sympathetic activity, urinary catecholamine data 
have proven informative, with low subject burden and excellent compliance (Blumenthal, Sherwood, & 
Gullette, 2000; Hughes et al., 2004; Sherwood, Steffen et al., 2002; Steffen, Hinderliter, Blumenthal, & 
Sherwood, 2001). 24-urinary catecholamines (epinephrine and norepinephrine) have also been shown 
to be inversely related to sleep efficiency measured by actigraphy (Zhang et al., 2011).  
 
C.6.4. ASCVD – 10-Year Consensus Markers. We will collect demographic and clinical information 
required to characterize study participants’ 10-year risk for ASCVD, and calculate it at baseline, post 
CBT-I, and at 6-month follow-up. The variables that are included in risk assessment equations are age, 
race, total and HDL-cholesterol, systolic BP (including treated or untreated status), diabetes, and 
current smoking status. Information on age, gender, race, and treatment of hypertension will be 
collected. Clinical information will be obtained from medical record review. Lipids and glucose levels 
will be determined from blood specimens drawn between 8:00-10:00 am following overnight fasting. 
All blood samples will be kept refrigerated until transfer to the appropriate laboratories. Lipids will be 
determined by enzymatic methods.  
 
C.7. Risk/Benefit Ratio and Protections Against Risk 
C.7.1. Study Risks. In both conditions, with regards to completing study measures, there is a risk of 

 
Full Protocol, PI version date 8/26/24 
21
discomfort or distress in answering questions, especially questions related to traumatic experiences. 
However, distress and discomfort related to questionnaire completion are usually temporary and well-
tolerated. With the blood draw, there is a risk of bruising at the site of the draw and rarely, fainting 
and/or infection. There is a minimal risk of disrupted sleep associated with the sleep apnea 
assessment. The Oscar 2 ABPM has built in safe-guards to avoid over-inflation or prolonged inflation of 
the cuff, as well as a measurement abort key that can be activated by the participant. The FMD 
assessment will include blood pressure cuff inflation for 5 minutes, which has the risk of causing 
tingling and discomfort in the arm. There are no known risks associated with use of the actigraphy or 
the Qualtrics sleep diary. There is a potential risk associated with the loss of confidentiality of study 
data, although all due precautions will be taken to minimize such risk. Participants are provided 
information during the informed consent process about risks inherent with mobile app download and 
use, and use of a Duke-owned, loaned device.  
 
C.7.2. Potential Benefits. While participants may benefit from improved sleep and/or reduced 
insomnia complaints, there are no guaranteed benefits to the individual participant and no immediate 
benefits of the proposed research to others. Information gathered from participants may help us to 
establish a link between sleep impairment and autonomic and inflammatory markers of CVD risk 
among individuals with PTSD. In our opinion, the anticipated benefits of this study outweigh the 
potential risks. 
 
C.7.3. Protection from Risk. Participants are informed clearly during the initial informed consent 
process that the study is completely voluntary, and that they may refuse to answer any items that they 
do not wish to answer on the questionnaires and interviews. They are also informed that they are free 
to decline participation in any procedure and can withdraw from the study at any time. Potential risks 
will be minimized by carefully screening potential participants according to the inclusion/exclusion 
criteria, closely monitoring symptom levels, and following established laboratory procedures. Patients 
will complete the MoCA to assess cognitive function. Patients who score <20, which suggests the 
presence of cognitive impairment, will be informed of the results and encouraged to discuss the 
findings with their personal care physician for further evaluation and treatment if necessary. With 
regards to determination of decision making capacity of potential participants, our laboratory has a 
standard procedure for determining understanding of the study procedures, risks, and benefits. We 
utilize this procedure if we have any reason to suspect that the participant may have difficulty in the 
consent process (e.g., traumatic brain injury impacting cognitive function, active psychotic symptoms). 
In this procedure, the study coordinator providing the informed consent information evaluates 
understanding of the procedures at several different time points during the process by asking 
questions like “Do you understand what we’re asking you to do?” and “Do you have any questions 
about the risks of the study? Can you tell me what you understand the risks to be?” 
 
C.7.4. Data Storage and Security. Data that links participants to information collected in the course of 
a given study will be kept separately from identifying information in an electronic, password-protected 
MS Access database stored at duhsnas-pri\dusom_psych\private\irb\beckham\PTSD Insomnia; the key 
connecting identifying information and data will be stored here as well. Hard copy paper records will 
be stored in a locked filing cabinet in the study coordinator’s locked office within Dr. Beckham’s 

 
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22
laboratory space at Duke University Medical Center. Information from the interview and/or 
questionnaires may be entered into a computerized database that will be stored on the DUMC server 
at duhsnas-pri\dusom_psych\private\irb\beckham\PTSD Insomnia in a password-protected database. 
This database is accessible only by Dr. Beckham, Dr. Sherwood, and study staff. Any staff members who 
leave the study for any reason will have access to study resources, including data, removed 
immediately. 
 
Audio recordings of therapy sessions and check-in calls will be made via Duke Zoom or WebEx. We will 
record directly to a Duke-owned laptop, and then transfer recordings to the Duke shared server  In the 
event that we move back to in-person visits post-pandemic, we will use a Duke-owned iPhone or iPad 
that is encrypted at levels compliant with FIPS 140-2 standards. The device to be used will be hardened 
such that only the recording capability will remain. Recordings will be moved from the device to the 
Duke shared server (file path above) as soon as possible after the recordings have been made, and will 
then be deleted from the mobile device. Any device that is not in use will be stored in a locked filing 
cabinet in the study coordinator’s office. Recordings will remain on the Duke shared drive until they 
are reviewed for fidelity by Dr. Ulmer. The recordings will be copied to an encrypted hard drive for 
permanent storage, and the copies on the Duke shared drive will be deleted. The encrypted hard drive 
will be stored in a locked file cabinet in Dr. Beckham’s lab space at Duke. Only the key personnel listed 
on the staff listing will have access to the encryption password and/or the hard drive.  
 
 
C.7.5. Data and Safety Monitoring Plan 
We believe that provision of treatment for insomnia should enhance rather than jeopardize health 
status, and potential serious adverse events (SAEs) for participants in this project are not expected. 
Regardless, we will minimize potential risk by carefully screening potential participants and following 
established laboratory procedures for physiological data acquisition and data safety monitoring. The 
individuals responsible for data safety and monitoring will be the PIs, the co-Investigators, and the 
study coordinator.  
 
Dr. Beckham will serve as the primary safety monitor for the proposed study. There will be several 
ongoing mechanisms for monitoring and reporting of adverse events (AEs), including serious adverse 
events (SAEs): 1) ongoing participant contact via study personnel; 2) a telephone number provided to 
participants to report concerns related to study participation; 3) weekly meetings between Dr. 
Beckham and study personnel. In order to monitor possible adverse events, participants will be 
instructed to report any adverse effects of study participation as soon as possible to research staff; 
they will have contact information needed to report these problems to study personnel.  The PI will 
meet at least weekly with study personnel to discuss participants’ reactions to the intervention, proper 
delivery of the intervention, and any adverse events or unanticipated problems. Monthly meetings 
between the investigators and the study coordinator will allow for ongoing progress reports, including 
the number of participants currently involved in the study groups, attrition rates, and scheduled data 
collection from participants, as well as notification and review of any AEs. Safety monitoring for 
adverse events will be conducted in real time by Dr. Beckham and/or study coordinator. The following 
information about adverse events will be collected: 1) the onset and resolution of the AE, 2) an 

 
Full Protocol, PI version date 8/26/24 
23
assessment of the severity or intensity, 3) an assessment of the relationship of the event to the study 
(definitely, probably, possibly or not related), and 4) action taken (e.g., none, referral to physician, start 
or increase concomitant medication). Dr. Beckham will determine the severity of the event, will assign 
attribution to the event, and will monitor the event until its resolution. 
 
Any adverse events (AEs) will be reported to the Duke University Medical Center (DUMC) Institutional 
Review Board (IRB) in accordance with the local Human Research Protection Program’s Standards of 
Practice. These guidelines require immediate reporting of any SAEs that are potentially study related. 
All research projects conducted at DUMC are required to have yearly IRB review. Reports of non-
serious AEs or unanticipated problems that may increase risk for study participants or others are 
required as part of the annual progress reports. Additionally, any changes to the project between 
review periods must be approved by the IRB prior to implementation. All communication with the 
DUMC IRB will occur using Duke’s electronic IRB system (IRIS).  
 
C.7.6. Plans for Responding to Psychiatric Emergencies. Given the nature of PTSD symptoms, it is not 
unexpected that participants will experience increased distress associated with the assessment 
procedures and diagnostic clinical interviews. Our extensive clinical and research experience suggest 
that there is no serious risk in these patients associated with assessment and interview procedures as 
proposed. Over the past several years, several hundred patients with PTSD have participated in our 
research team’s clinical trials, and there have been only rare occurrences of serious adverse events 
(i.e., hospitalization only) due to PTSD symptom increases. 
 
The Traumatic Stress and Health Research Laboratory (TSHRL) has established, IRB-approved standards 
of practice (SOP) for the evaluation of risk of suicide and homicide. All study staff members are trained 
in use of the psychiatric emergencies SOP by a Ph.D. or Masters’ level clinician with years of experience 
in working with persons with PTSD. The SOP includes a thorough risk assessment including evaluation 
of risk factors and protective factors associated with both suicide and homicide. Also included in the 
policy are differential recommendations for action based on determinations of low, moderate, or high 
risk. Any staff member conducting an interview in which moderate or high risk is determined will 
contact a senior staff person with clinical expertise in risk assessment [including the PI, co-
investigator(s), and/or DUMC’s Emergency Room]. At several time points throughout the study, 
participants are reminded that they are asked to inform the study therapist, study coordinator, or 
study PI if they experience a psychiatric emergency such as homicidal or suicidal ideation. In addition, 
participants are provided the telephone number of study staff who they can call in the case of 
psychiatric emergencies, including an after-hours contact number. 
 
C.7.7. Post-Study Referrals for Participants. It is not unexpected that participants in both conditions 
may complete the study with residual PTSD and/or major depressive disorder symptoms. Because 
participants complete the PTSD Checklist (PCL5) and the Beck Depression Inventory-II (BDI-II) at 
multiple time points, we will refer to these scores at the end of the study as a gauge of residual 
symptoms. We will provide referrals for continued treatment to anyone with a score of 38 or higher on 
the PCL5 and/or a BDI-II score of 20 or higher (i.e., moderate to severe depression range). Additionally, 
referrals will be provided to participants who self-report distress at symptoms of PTSD or depression 

 
Full Protocol, PI version date 8/26/24 
24
(regardless of measure scores) and wish to continue with another form of treatment. The TSHRL has 
developed patient educational materials regarding available resources, including portable resource 
cards for participants. Participant resources may include local community mental health centers, local 
private mental health providers, Duke Adult Psychiatry Clinic, Duke’s Psychology Clinic, and other 
resources such as emergency clinics 
and substance abuse treatment 
centers. Where applicable and 
convenient to the participant, direct 
referral will be made in addition to 
providing resource cards. 
 
C.7.8. Subject Reimbursement. 
Reimbursement to participants will 
offset the costs in time, effort, and 
possible lost wages associated with 
commitment to the study. Participants 
are paid as indicated in Table 2. 
Participants who are eligible for 
treatment will not receive payment for study participation until completion of the post-treatment 
actigraphy monitoring and labs. At that time, they will be paid $700. If participants are excluded prior 
to randomization, or withdraw from CBT-I, they will be paid for their study participation to date. 
 
Participants will be provided parking passes and/or bus passes to encourage attendance at all sessions. 
 
 
D. STATISTICAL ANALYSES 
 
D.1. General Considerations and Descriptive Analyses 
The study is designed as a two-arm trial to examine the effects of CBT-I on objective sleep parameters 
and CVD risk biomarkers. Statistical analyses will be performed using SAS, version 9.4 (SAS Institute, 
Cary, NC) and Mplus, version 7 (Muthén & Muthén, 2012). The first step in the analysis will be to 
calculate descriptive statistics, including graphical displays, to summarize all study variables. We will 
construct individual and mean trajectory plots of the longitudinal outcome variables to understand 
their general trends from baseline to post-intervention and 6-month follow-up. In addition, we will 
explore the variability and correlation structure of the longitudinal outcome variables. We will stratify 
by trauma exposure (combat or non-combat) and objectively measured sleep duration (<6 hours or >6 
hours).   
 
Each of the study hypotheses will be examined using data collected at baseline, immediately following 
the 8-week intervention phase (i.e., post-intervention), and again at 6-month follow-up. Given nesting 
of these multiple observations within each participant, multilevel modeling (MLM) will be used to 
examine each set of hypotheses. MLM is a technique for analyzing repeated observations of data 
across multiple individuals (Searle, Casella, & McCulloch, 1992). Unlike repeated-measures ANOVA, 
Table 2. Participant Reimbursement 
Task Completed 
Payment 
Screening Visit 
Up to $75
Sleep Apnea Screen and Actigraphy Monitoring 
$100
Baseline Labs (FMD, ABP) 
$225
Treatment Sessions (8) 
$0
Posttreatment Actigraphy Monitoring (1 week) 
$50
Posttreatment Labs (FMD, ABP) 
$250
Follow-up Actigraphy Monitoring (1 week) 
$50
Follow-up Labs (FMD, ABP) 
$250
 Seed recruitment 
$60
TOTAL
$1060

 
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25
MLM uses all available data and can handle data missing at random. MLM can also accommodate 
imbalanced data and unequal variances. 
 
To manage the problem of random positive findings when making multiple comparisons within each 
hypothesis (i.e., random significant findings occurring due to multiple proposed analyses), we will 
correct alpha using the “false discovery rate approach” described by Glickman (Glickman, Rao, & 
Schultz, 2014). This methodology differentiates random findings from hypothesis-driven outcomes and 
is more powerful than Bonferroni-type alpha adjustments that control the false-positive rate 
(Benjamini, 2010; Glickman, Rao, & Schultz, 2014; Schafer & Olsen, 1988; Searle, Casella, & McCulloch, 
1992; Simes, 1986). Specifically, alpha correction will be applied to Aims 1 and 2 but not to the 
Supplemental Aim, given that any exploratory hypothesis that is supported would require validation 
with further research. 
 
D.2. Missing Data Considerations 
MLM can accommodate data missing at random, using maximum likelihood-derived estimates based 
on all available data. That said, data may be missing not at random (MNAR). That is, the outcome data 
may be systematically missing, for instance, from participants who experience no benefit from CBT-I. 
Nevertheless, even in the case of MNAR data, estimates derived from analyses examining Hypothesis 2 
(addressing the within-person associations of change in insomnia with change in CVD risk) and the 
Supplemental Aim are unlikely to be affected by data missingness. After all, even if participants who 
benefit the least from CBT-I drop out of the study, we still anticipate observing the same magnitude of 
association between improved sleep and changes in cardiovascular biomarkers and QoL. By contrast, 
Hypothesis 1, which examines clinical response to CBT-I, would be affected by MNAR data. As such, we 
will conduct sensitivity analysis on three missing data approaches for Aim 1. First, we will conduct a 
completer analysis, analyzing only data from participants who complete each of the three assessments. 
Second, we will allow the maximum-likelihood estimation to generate estimates for all available data. 
In a third approach, we will use an intent-to-treat approach, assuming that missing post-intervention 
and/or 6-month follow-up data are evidence of stagnation. Specifically, we will use last observation 
carried forward in the cases of missing data. 
 
D.3. Primary and Secondary Analyses 
To examine the degree to which CBT-I is associated with improved sleep efficiency and insomnia 
severity index score (manipulation check), linear MLM will be used to model sleep outcomes as a 
function of treatment (CBT-I, control), time (baseline, post-intervention, 6-month follow-up), and the 
interaction between treatment and time. Planned treatment contrasts in change from baseline to post-
intervention and 6-month follow-up will be examined within the context of the model. To determine 
the degree to which CBT-I is associated with improved CVD risk biomarkers (Hypotheses 1a), each of 
the 6 CVD risk variables (24-hr SBP, nighttime SBP dipping, FMD, 24-hr epinephrine, and 
norepinephrine) will also be modeled as a function of treatment, time, and their interaction, with 
planned treatment contrasts in change from baseline at post-intervention and 6-month follow-up. 
Dichotomized 10-year ASCVD risk scores (Hypothesis 1b) and QoL scores (Hypothesis 1c) will similarly 
be modeled via logistic MLM.  
 

 
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26
To examine whether increased sleep efficiency is associated with decreased CVD risk (Hypothesis 2), 
lagged values will be used to create changes scores, defined by subtracting values for sleep efficiency 
and each of the CVD risk parameters measured at time t-1 from those measured at time t. Thus, 
decreases will be reflected by negative change scores and increases by positive change scores. Linear 
MLM will then be used to model each of the change scores corresponding to the 6 aforementioned 
CVD risk variables using the general equation below:  
CVD riskΔt = B0 + B1*Sleep efficiencyΔt + B2*CVD riskt-1 + B3*Sleep efficiency t-1 
where change in the CVD risk outcome from t-1 to t will be modeled as a function of corresponding 
change in sleep efficiency, covarying for sleep efficiency measured at t-1 and the outcome in question 
measured at t-1. To explore the heterogeneity of CBT-I treatment response on sleep (Supplemental 
Aim), potential moderators (trauma type, insomnia phenotype, medication use, biological sex, and 
minority status) of the effect of CBT-I treatment on sleep outcomes will be examined via interaction 
effects. Specifically, sleep efficiency and sleep quality measured at baseline, post-intervention, and 6-
month follow-up will be modeled in separate analyses as a function of treatment, time, a given 
moderator, and their second- and third-order interactions. To avoid the effects of multicollinearity, 
each moderator will be tested in a separate analysis. To determine whether the effect of changes in 
sleep efficiency on changes in CVD risk parameters vary by each of the aforementioned moderators, 
the analyses specified above for Hypothesis 2 will be modified to examine the main effect of a given 
moderator as well as the interaction effect of the moderator and sleep efficiency on each of the CVD 
risk variables. 
 
D.4. Sample Size and Power Considerations  
Calculations are based on Aims 1 and 2. Because each of the analyses will make use of repeated-
measures data, statistical power will depend on the effective sample size (ESS). For Hypotheses 1, 
which will examine treatment effects associated with differences in sleep and cardiovascular 
outcomes, the ESS will be equivalent to the total number of observations (number of participants x 
number of data collections) adjusted for intra-individual correlations. As the intra-individual, or intra-
class, correlation (ICC) decreases, the ESS increases. Given that we anticipate substantial intra-
individual variability on account of the intervention, an ICC of .50, representing an equal allotment of 
variance attributable to inter-individual differences and intra-individual variability, provides a 
conservative estimate of the ESS. With a sample of 150 contributing 3-time points of data each and an 
ICC of .50, the ESS would be 225. Even with 10% attrition post-intervention and then again at the 6-
month follow-up, the ESS would be 215. Assuming equivalent attrition rates among the 100 CBT-I 
participants and 50 controls, this would have 80% power to detect treatment effects equivalent to 
Cohen’s d of 0.41 via a two-tailed test with an alpha of .05. For reference, a meta-analysis on CBT-I 
(Okajima, Komada, & Inoue, 2011) found 12-month treatment effects on SOL, WASO, and sleep 
efficiency equivalent to d = 0.45, 0.47, and 0.54, respectively. Six-month follow-up CBT-I treatment 
effects in Talbot et al.’s (2014) RCT of CBT-I in persons with PTSD far exceeded these for SE (d = 1.48), 
SOL (d = 1.31), and TST (d = 0.76). Moreover, Talbot et al. (2014) recorded large effects of CBT-I on 6-
month increases in psychosocial functioning (d = 1.02). Although the specific impact of CBT-I on CVD 
risk remains untested, comparable behavioral-health interventions, such as stress management, have 
shown robust effects. For instance, in one study, 16 weeks of weekly 1.5-hour stress-management 
training compared to usual care was sufficient to achieve large increases in FMD (d = 2.32; Blumenthal, 

 
Full Protocol, PI version date 8/26/24 
27
Sherwood et al., 2005). In another study, a similar regimen of weekly group stress-management 
sessions was associated with significantly reduced time to adverse cardiac events, HR = 0.26 (95% CI: 
0.07-0.90), in comparison to usual care. Given these effect sizes, the present study should have 
adequate power to detect treatment effects similar to those found in previous trials. 
  
The second aim, which will examine within-person associations of changes in sleep efficiency with 
concomitant changes in cardiovascular indices, will have an ESS that is roughly equivalent to the total 
number of observations. With 150 participants contributing up to 2 change scores each, even with 10% 
attrition at each time point, the ESS is projected to be 256, which would have 80% power to detect an 
association equivalent to β = .15. A number of studies have established cross-sectional associations of 
sleep disturbance with cardiovascular risk factors (Spiegelhalder, Scholtes, & Riemann, 2010). In our 
own research, we have found that self-reported sleep disturbance is modestly associated with reduced 
heart-rate variability (β = -.18) and dyslipidemia (equivalent β = .11). We anticipate that these 
associations will be stronger in the proposed within-subjects analysis given the absence of random 
effects from between-person differences. Thus, the current trial should be sufficiently powered to 
detect even modest associations between concurrent changes in sleep efficiency and CVD risk 
parameters. 
 
D.5. Data Sharing with Key Personnel Outside Duke  
Patrick Smith, PhD, at University of North Carolina at Chapel Hill, will perform some of the above-
described statistical analyses in accordance with a sub-award to him. De-identified data will be shared 
with Dr. Smith via Box@Duke.  No PHI, including dates, will be shared with Dr. Smith.  
 
 
E. STUDY TIMELINE 
The overall data collection and analysis timeline is outlined in Table 3. Recruitment will occur in years 1 
through 5. Intervention delivery and follow-ups will occur in years 1-5.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3. Project Timeline 
ACTIVITY 
Year 
1 
Year 
2 
Year 
3 
Year 
4 
Year
5 
Totals 
Hiring/Training/Development 
X 
 
 
 
 
 
Screening 
26 
48 
47 
47 
31 
199 
Recruitment 
20 
35 
35 
35 
25 
150 
DATA COLLECTION 
 
 
 
CVD & Sleep Measures 
20 
35 
35 
35 
25 
150 
Treatment Waitlist 
20 
35 
35 
35 
25 
150 
Post Assessment 
15 
38 
34 
32 
23 
142 
6-month Assessment 
0 
40 
38 
41 
23 
142 
Data Processing 
 
 
 
X 
X 
 
Data Analysis 
 
 
 
X 
 

 
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28
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