Cognitive Behavioral Therapy for Insomnia: A Self-Management Strategy for Chronic Illness in Heart Failure
Phase: NA
Conditions: Cardiac Failure, Heart Failure, Congestive Heart Failure, Heart Failure, Congestive, Sleep Initiation and Maintenance Disorders, Chronic Insomnia, Disorders of Initiating and Maintaining Sleep, Fatigue, Pain, Depressive Symptoms, Sleep Disorders, Anxiety
Sponsor: Yale University

Title: Insomnia Self-Management in Heart Failure 
 
NCT02660385 
 
Document date: 3/8/2022 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SPECIFIC AIMS 
Almost 75% of HF patients, a population of over 5 million Americans1 who have poor function and high levels 
of morbidity and mortality, report poor sleep.2-7 As many as 25-56%5,8-10 of HF patients report comorbid chronic 
insomnia (difficulty initiating or maintaining sleep or waking early in the morning, with non-restorative sleep that 
persists for at least a month). Chronic insomnia may contribute to the development of HF11 and incident 
mortality11,12 and is associated with disabling symptoms (fatigue, dyspnea, anxiety, depression, excessive 
daytime sleepiness, and pain)4,6,8,10,13-18 and decrements in functional performance.19  
Despite the importance of insomnia to critical HF outcomes, HF patients report that health care 
professionals fail to address it.20 Patients prefer behavioral approaches to hypnotic medications20 that are 
associated with adverse events, such as daytime sleepiness, falls, and altered cognition. Yet, little is known 
about the effects of behavioral treatment for chronic insomnia among HF patients who are especially 
vulnerable to high symptom burden and other negative outcomes.  
Cognitive behavioral therapy for insomnia (CBT-I), a self-management intervention, focuses on 
dysfunctional behaviors and cognitions that perpetuate physical and psychological arousal21-23 and chronic 
insomnia.24 The effects of CBT-I are more durable than hypnotic medications24-26 without negative daytime 
effects.27 In our pilot randomized controlled trial (RCT), CBT-I improved chronic insomnia, sleep quality, 
actigraph-recorded sleep efficiency, and fatigue immediately after treatment in patients with Class II-III HF with 
chronic insomnia, compared with an attention control group who received HF self-management education 
alone.28 Both groups had stable HF at baseline and received evidence-based HF disease management. At six 
months after treatment, the effects on insomnia were sustained in the CBT-I group, with large improvements in 
fatigue and anxiety over baseline and first follow-up and improvement in depression, after controlling for 
baseline symptoms and clinical/demographic characteristics.29 These improvements contrasted with worsening 
fatigue, smaller decreases in insomnia, and no change in anxiety or depression in the attention control group. 
Insomnia remission was associated with mild vs. moderate-severe symptom clusters and functional 
performance.  
Evidence that chronic insomnia predicts incident HF and mortality11,12 and trends suggesting reduced 
hospitalization among HF patients who received CBT-I in our work suggest the potential benefits of CBT-I to 
health care utilization and event free survival  - outcomes of critical importance to HF patients that have not 
been examined in relation to CBT-I. Building on these promising results, the purposes of the proposed RCT 
are to evaluate the sustained effects of self-management of insomnia with CBT-I, compared with HF self-
management education on insomnia severity, objective and subjective sleep characteristics, daytime 
symptoms, functional performance and health care utilization over twelve months among patients who have 
stable chronic HF and comorbid chronic insomnia. We will also explore the effects of CBT-I on event-free 
survival.  
We will address the following specific aims (*primary outcomes): (1) Test the sustained effects (baseline, 3 
months (2 weeks post intervention), 6 and 12 months) of CBT-I provided in 4 group sessions over 8 weeks, 
compared with HF self-management education (attention control condition), on: (1a) *insomnia severity and 
self-reported and actigraph-recorded sleep characteristics (*sleep quality, *sleep efficiency, sleep latency, and 
duration); (1b) symptoms (*fatigue, anxiety, depression, pain, dyspnea, sleepiness, sleep-related impairment), 
and psychomotor vigilance (PVT); and (1c) symptom clusters [membership in clusters characterized by 
severity of specific symptoms; transition between clusters over time]; (2) Test the sustained effects of CBT-I on 
self-reported and objective functional performance; and (3) Examine the cost-effectiveness of CBT-I. 
Exploratory aim: We will explore the effects of CBT-I on event-free survival.  
 
Hypotheses:  CBT-I, compared with an attention-control condition (HF self-management education), leads 
to: (1a) decreased insomnia severity and sleep latency and increased sleep quality, duration and efficiency that 
are sustained at 6,  and 12 months; (1b) decreases in severity of symptoms and improvements in psychomotor 
vigilance that are sustained at 6,  and 12 months; and 1c) transition between symptom clusters that have 
higher symptom severity to those that have lower symptom severity over time. (2) CBT-I will lead to increased 
self-reported and objective functional performance; (3) CBT-I is cost-effective, compared with HF self-
management education alone. 
Support for the proposed hypotheses will lead to future research focused on the dissemination and 
translation of CBT-I into diverse HF settings. Increased access to CBT-I has high potential to improve HF 
patients’ ability to self-manage sleep and daytime symptoms and decrease the costs of health care resource 
utilization.  
 

RESEARCH STRATEGY 
Significance. This study is responsive to PA 14-344 “Self Management for Health in Chronic Conditions” and 
NINR’s strategic plan.30 Our study will address the dearth of efficacious self-management interventions for 
patients who have co-occurring conditions, specifically comorbid chronic insomnia and HF. 
Insomnia occurs in 25-56% of patients with HF,4,5,7-9 a population of over 5 million in the U.S.,1 who suffer 
from high symptom burden, poor function, morbidity, mortality and excessive health care resource utilization. 
Insomnia is more common in HF patients than “healthy” controls7 and the general population.31 It is 
distressing6,20 and associated with symptoms (fatigue, depression, dyspnea, anxiety, pain, sleepiness)4,8,13,17,18 
that decrease quality of life5,20 and functional performance,8 “the day to day corporeal activities people do in the 
normal course of their lives to meet basic needs, fulfill usual roles, and maintain health and wellbeing.”32,p.198  
HF patients think insomnia is critical to daytime function, but clinicians fail to address it.20  
Influences on symptoms and function are multifactorial in HF33,34 and include insomnia. For example, 51% of 
patients with stable chronic HF and evidence-based HF disease management35 had chronic insomnia that was 
not explained by HF severity, comorbidity, or sleep apnea (SA). Insomnia was related to depression, fatigue, 
sleepiness (all p < .05) and function, including a 100-foot decrement in 6 minute walk (6MW) distance (p < 
.001), after controlling for demographics, comorbidity and left ventricular ejection fraction (LVEF).8  
Chronic insomnia predicted incident HF [HR (hazard ratio) = 0.95 (CI95: 0.55 -1.62)] for one insomnia 
symptom to HR= 5.25 (CI95: 2.25 - 12.22) for 3 symptoms, after controlling for clinical/demographics over a 
mean 11 year follow-up (p for trend = .0010) and had a dose-dependent relationship HF mortality.11 These 
findings confirm and extend earlier studies.12 Associations between insomnia-related outcomes (e.g., 
depression,36-38 fatigue,39 symptom clusters,13,20,39,40 function8), hospitalization, death, and event-free survival 
(EFS)5 emphasize the importance of insomnia and the potential benefits of treating this comorbid condition.  
Chronic hyper-arousal, with increased sympathetic and HPA axis activation11,12 may explain the relationships 
between insomnia and EFS. Associations between day/night urinary epinephrine ratio and wake after sleep 
onset (r = -0.53, p <  .01); day/night urinary cortisol ratio, sleep quality (r = 0.39, p = .04) and fatigue (r = -0.54, 
p = .0007)41 in HF patients partially support this explanation. The proposed RCT will contribute to 
understanding the causal relationships between insomnia and EFS, a critical outcome. 
Evidence-based HF disease management and self-care/self-management are standard elements of HF 
care, but typically do not address comorbid conditions (i.e. chronic insomnia) or focus on ameliorating thoughts 
and behaviors that contribute to insomnia.42,43 Cognitive behavioral therapy for insomnia (CBT-I), a multi-modal 
self-management intervention, addresses dysfunctional behaviors and cognitions that perpetuate 
psychological/biological arousal22 and chronic insomnia.24 CBT-I effects are more durable than hypnotic 
medications24-26 without negative daytime effects.27 Our pilot RCT28,44 included adults with stable HF, chronic 
insomnia, and evidence-based HF management.35 It revealed large short-term and sustained CBT-I effects on 
insomnia severity and fatigue and improvements in anxiety and depression, symptoms common to both HF 
and insomnia,45 at six months, compared to the attention control group (HF self-management education46) who 
had less improvement, worsening, or no change. These findings are especially notable, given the frequent 
refractory nature of HF symptoms, especially fatigue, and the dearth of available symptom management 
interventions. The associations of mild vs. moderate/severe symptom clusters with insomnia remission 
underscore the potential benefits of the effects of CBT-I on symptom clusters (patterns of severity of 2 or more 
symptoms) and our plan to examine CBT-I effects on symptom clusters better captures the clustered nature of 
symptoms occurring in real life13,15,47 than past studies of isolated symptoms. 
We will add to CBT-I and self-management science by testing the effects of CBT-I on objective and clinically 
relevant outcomes.48 Use of the psychomotor vigilance test (PVT),49,50 a measure of the neurobiological effects 
of sleep loss and the PROMIS brief sleep impairment items51 will increase understanding of HF patients’ 
reported low levels of sleepiness8,52,53 that may reflect failure to recognize sleep loss, lack of sensitivity of the 
Epworth scale, and/or high levels of arousal.54 The 6 minute walk (6MW), a prognostic measure,55,56 will extend 
self-report. Use of legacy measures of sleep/sleepiness and PROMIS addresses the need to use common 
data elements in symptom science57 and will enable comparability with both past and emerging studies.  
Cost-effectiveness analysis (CEA) will address a gap in self-management and CBT-I science,48 as few, if 
any, studies have addressed it. Promising effects of CBT-I on hospitalization, accounting for 75% of HF 
costs,13,58 suggest its benefits to health care resource utilization, a critical outcome due to the $32 billion costs 
of HF that will rise to $70 billion in 2030.1 High health care resource utilization59-63 and indirect costs (e.g., 
productivity)64 of insomnia13 underscore the need for CEA. Risk of death from HF associated with insomnia11 
and associations between insomnia remission and hospitalization (pilot work) emphasize the need to explore 
CBT-I effects on EFS. 

The results of this study will support future studies of the effectiveness65 and sustainability of CBT-I in diverse 
HF disease management settings (e.g., cardiac rehabilitation, HF support groups). Future delivery modes may 
include interactive www, mobile “apps,” telehealth modalities, or other approaches and will extend the reach of 
the intervention to enable improvement of insomnia and related symptoms in many settings. 
 
Innovation. The primary innovations include: (1) use of a CBT-I to improve chronic insomnia, symptoms, and 
functional performance among patients with comorbid insomnia and HF; (2) the plan to test the effects of CBT-I 
on symptom clusters and functional performance, in contrast to previous CBT-I studies that evaluated isolated 
symptoms and broad quality of life outcomes; (3) use of objective measures of the neurobiological effects of 
chronic sleep loss and functional performance as outcomes of CBT-I; (4) evaluation of direct and indirect costs 
of chronic insomnia treatment in HF; (5) use of latent cluster and latent transition analysis to evaluate the 
effects of CBT-I on symptom clusters; and (6) exploration of the effects of CBT-I on event-free survival (EFS). 
To our knowledge, no investigators have conducted a fully powered RCTs of CBT-I among HF patients, and 
few, if any RCTs evaluated the effects of CBT-I on symptom clusters, EFS, or health care resource utilization. 
 
The organizing framework is based on chronobiologic and homeostatic models of sleep regulation, the “3-p” 
model of insomnia,66-68 the Framework for Self- and Family Management of Chronic Conditions,69,70 and 
research on symptoms/symptom clusters. Sleep is “characterized by reduced awareness of and interaction 
with the environment, lowered motility, and partial or complete abeyance of voluntary behavior and 
consciousness.”71 Chronic insomnia, a sleep disorder, occurs > 3 nights/week for > a month,5,8-10 with 
adequate sleep opportunity, non-restorative sleep and difficulty initiating sleep, maintaining sleep, and/or 
waking too early.72 It is comorbid when it occurs with, but is not explained by, psychiatric or medical disorders 
(e.g., HF)72 and persists with optimal treatment of the comorbid condition.42,43 The 3-p model posits that 
biological, psychological, and social traits (predisposing factors); acute triggers (i.e. illness/treatment - 
precipitating factors); and maladaptive strategies (dysfunctional beliefs, attitudes and behaviors) used to 
compensate for sleep loss (perpetuating factors) contribute to chronic insomnia through chronic physiological 
and biological arousal67 (broken boxes, Fig. 1). Common perpetuating factors lead to somatic and/or 
psychological arousal – the  “final common pathway”42,p.560 to chronic insomnia.  
Predisposing and precipitating factors are poorly understood,68 but multi-factorial.73 Aging and gender often 
predispose to insomnia, but age, gender,5,7-9 left ventricular ejection fraction (LVEF),7,9,73 cardiac 
medications,9,74 and sleep apnea (SA) were not related to chronic insomnia in HF.3,8 Comorbidity was related 
to chronic insomnia,75 but associations with NY Heart Class are conflicting.8 However, because these factors 
may contribute to insomnia and other study outcomes, they will be explored as possible covariates. (Fig. 1). 
HF patients reported perpetuating factors for insomnia. They attributed awakening to modifiable factors (e.g., 
increased time in bed awake,7 insufficient sleep,5 frequent napping,5,7,9 use of TV as white noise at night76). 
Negative associations between sleep hygiene and sleep quality3 and positive associations between insomnia 
severity and dysfunctional beliefs and attitudes about sleep, mental anxiety, and sleep effort (all p < .05)76 
underscore the contributions of perpetuating factors to chronic insomnia among HF patients. 
Self-management (knowledge and skills to manage chronic conditions and related emotions and 
symptoms)69,70 is the conceptual basis for CBT-I and the attention control condition (HF self-management 
education). CBT-I includes cognitive and behavioral strategies that address perpetuating factors for insomnia 
(Table 1, Fig. 1). HF self-management46 includes behaviors needed to manage HF itself. Although self-
care/self-care management77 and symptom clusters13 were associated with EFS in observational studies, 
outcomes of self-management interventions, such as health care resource utilization, are inconsistent.46,78 The 
effects of CBT-I vs. HF self-management education will be compared in the proposed study. 
CBT-I protocols vary in group/individual format, some elements, and session frequency, but address 
perpetuating factors through behavioral (stimulus control, sleep restriction therapy, relaxation training), 
cognitive (cognitive therapy), and educational (sleep hygiene) elements.24 Stimulus control includes re-
associating the bed with sleep; maintaining a consistent sleep/wake schedule; going to bed only when sleepy; 
getting out of bed when unable to sleep; using bed only for sleep and sex; and avoiding naps.24 Sleep 
restriction limits time in bed to actual sleep time. With therapist supervision, patients decrease time in bed to 
maximize sleep efficiency and then gradually increase it until they optimum efficiency and duration. Cognitive 
therapy clarifies misperceptions and misattributions about insomnia and sleep, reframes unrealistic 
expectations, reduces performance anxiety, and increases control over sleep.24,79 Relaxation therapy reduces 
somatic and cognitive arousal. Sleep hygiene focuses on lifestyle and environment,24 but is not efficacious 
alone.24,80 Hypnotic tapering is efficacious when provided gradually with provider supervision.81,82  

CBT-I has consistent effects on chronic insomnia28,42,83,84 and sleep efficiency,28,85-100 and improved some 
symptoms in non-HF groups. Detection of large effects of CBT-I on insomnia severity and fatigue28 and 
associations between symptom clusters, insomnia remission and functional performance among HF patients 
(Fig 1, Table 2) extends past CBT-I trials in other groups to a group who is especially vulnerable to symptom 
burden. Both symptom clusters13 and insomnia contribute to event-free survival (EFS),11 health care resource 
utilization, and indirect costs.60,101 We will examine the effects of CBT-I on these important HF outcomes, while 
considering possible covariates (Fig. 1). 
Fig. 1. Organizing Framework 
Solid-lined boxes/arrow = outcomes; double lined box/arrow = intervention; 
broken boxes/arrows = factors likely to contribute to insomnia, healthcare resource utilization,  
symptoms, functional performance, and EFS  (possible covariates – arrows to these outcomes not shown) 
 
   
 
 
    
 
 
 
   
 
    
 
 
 
 
 
 
 
 
 
 
 
Preliminary Studies. Redeker, PI, conducted studies of sleep, symptoms, and functional performance 
among adults with heart disease for over 20 years and has ongoing collaborations with the study team. Studies 
characterizing sleep and sleep disorders in HF; contributors to chronic insomnia; associations between 
insomnia, sleep, symptoms, and functional performance; HF patients’ perceptions of sleep; and the feasibility 
and preliminary efficacy of CBT-I in HF are summarized below. 
HF patients have poor sleep and insomnia. Our study, funded by the American Heart Association,7 revealed 
poorer self-reported and objective sleep quality, latency and wake after sleep onset, (p < 0.05), but no different 
sleep duration in stable HF patients (M age = 59.10+13.04 years; n = 20/33.9% women; n = 15/26% Black; n = 
5/8.5% Hispanic), than “healthy” adults (M age = 55.00 + 10.29 years; n = 30/50% women; 79% white). In 
another study (R01NR008022), 51% of 173 stable HF patients from HF disease management programs (M 
age = 60.35 + 16.1 years/n = 60/35% women; n = 110/59% white; n = 50/29% Black, n = 10/5.7% Hispanic) 
had one or more chronic insomnia symptoms: difficulty falling asleep (n = 71/42%), staying asleep (n = 
62/36%), or waking too early (n = 41/25%).8 Sleep apnea (SA),53 ejection fraction (LVEF), comorbidity, and NY 
Heart Class did not differ between patients with/without chronic insomnia,8 but insomnia was negatively related 
to polysomnographic sleep duration (p < .01) and efficiency (p < .05).8   
Sleep disturbance/insomnia are associated with symptoms and function in cardiac surgical102,103 and HF 
patients.10,19 Wake time and sleep quality explained 11-21% of the variance in 6 minute walk (6MW), daytime 
activity, and self-reported physical function in stable HF patients (all p < .05),19 controlling for LVEF and NY 
Heart class. Chronic insomnia was associated with physical function (SF36) (p < .001), fatigue (p < .001), 
depression, (p < .001), sleepiness (p = .013)8 and pain (p < .001); and shorter 6MW in people with insomnia [M 
= 875.3 feet (CI95 = 791.0, 959.6)] vs. no insomnia [M = 976.8, (CI95 = 882.9, 1070.7)]8 (R01NR008022).  
Insomnia is important to HF patients. Stable HF patients (N = 11, M age = 73 years, 5 women) described 
critical effects of chronic insomnia on function and mood; unpredictability of a good night’s sleep; need to catch 
up with naps; and clinicians’ failure to attend to sleep. Dislike of hypnotics, interest in behavioral treatment, and 
modifiable perpetuating factors (pain, frequent urination, discomfort while side-lying due to defibrillator, worries, 
poor sleep hygiene: use of TV as a sleep aid), with some use of sleep-promoting behaviors (e.g., music, white 
noise) indicate the importance of behavioral treatment (P30NR008999).20 
CBT-I is feasible and acceptable. We evaluated the effects of CBT-I (“Healthy Sleep:” HS), compared with an 
attention-control [HF self-management education (“Healthy Hearts:” HH)] on insomnia severity, symptoms, and 
functional performance among HF patients in a pilot RCT (R21NR011387).28 (Table 1). Both conditions 
Perpetuating Factors 
Maladaptive Beliefs & 
Behaviors
Predisposing Factors 
Age, Gender, Race 
Symptoms  
(Individual & clustered) 
Fatigue, Sleepiness, 
Anxiety,  
Depression, Dyspnea, Pain 
Functional 
Performance 
Self-report 
Six Minute Walk, 
Psychomotor Vigilance 
CBT-I 
Self-Management of Insomnia 
 
Event-Free Survival 
Hospitalization, ED visits, 
Death,  
LVAD Placement,  
Transplant 
Health Care Resource 
Utilization/Costs 
Direct costs : Hospitalization, Office/Clinic/ED 
visits, Homecare, Medications, Devices, other 
treatments; Indirect Costs: Work & treatment   
Precipitating Factors 
Heart Failure 
Severity/exacerbation 
LV Ejection Fraction 
Comorbid Conditions 
CPAP-treated Sleep Apnea 
Medications 
Adherence/Self-Care/Self-
Management  
Insomnia/Sleep 
Severity of insomnia 
Sleep duration, continuity, latency 

included 4 bi-weekly group sessions with phone follow-up on intervening weeks (total = 8 weeks), followed 
treatment and treatment fidelity protocols, and included guides/workbooks (Appendix). Participants had Class 
II-III HF, were stable at recruitment (no hospitalizations, ED visits, or titration of vasoactive medications in past  
 
Table 1. Content and Schedule for CBT-I and Attention Control Conditions 
 
CBTI: “Healthy Sleep - HS”* 
Attention Control: “Healthy Hearts- HH”* 
Week 1: 
Session 1 
Overview of symptoms of HF and HF self-
management; Weight & symptom monitoring;  
Sleep education: overview of CBT-I; sleep stages; 
circadian rhythm; role of HF & treatment in sleep; 
Sleep scheduling/restriction; Stimulus Control 
Overview of symptom of HF and HF self-
management; definitions of HF; Signs & 
Symptoms and their meaning; Weight & 
symptom monitoring; Diagnostic Testing;  
Medical contact form 
Week 3: 
Session 2 
Review sleep diaries; Adjust sleep schedule; 
Education: Physiological arousal/fight or flight vs. 
relaxation response; Cognitive restructuring; 
Relaxation training; Optional Hypnotic taper 
Review progress & homework; HF symptom 
management: Fluid and sodium 
Management; Effective communication w/ 
healthcare providers 
Week 5: 
Session 3 
Adjust sleep schedule; Review previous strategies; 
Lifestyle factors/sleep hygiene: exercise, sleep 
environment, food, alcohol caffeine; “Mini” relaxation 
HF Medications & Strategies to promote 
adherence; OTC medications/herbal 
remedies; Cardiac devices 
Week 7: 
Session 4 
Reinforce adherence/Call attention to 
progress/review; Relapse prevention advice 
 
*Telephone calls weeks 2, 4, 6, 8 
Healthy Lifestyle: Diet: Lipids, sodium, 
Calories; CVD risk factors; Stress 
Management/relaxation; Exercise; 
Environment and sleep; Review 
Month), had standard evidence-based HF disease management;35 concerns about insomnia for > one month; 
scored > 8 on the Insomnia Severity Index,104 and had no-mild untreated SA or were adherent to SA treatment. 
We randomized to HH or HS in groups of 5, obtained self-reports of insomnia/sleep, symptoms and functional 
performance and 2 weeks of wrist actigraphy/diaries; provided HS/HH interventions; and repeated full 
assessment at 2 weeks post-intervention (T1). Self-report measures included the Insomnia Severity Index, 
Pittsburgh Sleep Quality Index, Centers for the Epidemiological Studies of Depression Scale, Spielberger State 
Anxiety Scale, SF-36 Pain Scale, Multi-dimensional Assessment of Fatigue Scale, and the SF-36 Physical 
Function Component. With the exception of actigraphy (not collected due to budget), these measures, and 
hospitalization were assessed at T2 (6 months). Hospitalization was also elicited at 12 months. 
We enrolled 52 patients (planned n = 40); 48 completed treatment/follow-up in HS (n = 29) or HH groups (n = 
19).28 One HS patient did not complete due to a heart transplant. In the HH, non-completion was due to 
disappointment with randomization (n=1); loss to followup (n=1); and change to shift work (n=1). Eighty-eight 
percent of completers had 100% attendance. Inclement weather, damage to home, and death in family and 
were reasons for missed sessions that were made up by phone. Completers included 23 (47.9%) women, 15 
(32.1%) African Americans, and 32 (66.7%) Whites and had NY Heart Class II or III HF. Age was M = 61.9 
+13.3 (HS group) and 55.2 +16.2 years (HH group) (p =.12). There were no statistically significant group 
differences at baseline on gender, NY Heart Class, comorbidity, symptoms or function. The apnea hypopnea 
index was M = 7.44 + 5.5 in participants not in treatment for SA. The proportion of patients using positive 
airway pressure (PAP) was equivalent between groups [n = 5 (17%) (HS) and n = 3 (16%) (HH)]. OTC or 
prescribed hypnotics were used 3 or more times/week by n = 7 (24%) (HS) and n = 7 (37%) (HH); 72% used 
ACEI/ARBs and 92% used beta-blockers, but there was no statistically significant difference between groups.  
Satisfaction and acceptability were high and averaged 7.5-8.7 (scale: 0-10/10= high). Both groups rated 
scheduling, location, payment, study materials, interventionist effectiveness, and willingness to continue 
treatment as 8.5-9.5. There were no statistically significant differences between groups on ratings, and 
participants had no difficulty completing the measures. PAP users reported that CBT-I was helpful. 
CBT-I had early and sustained effects. Insomnia severity improved in both groups (T1) and was similar to T1 
at T2 (Table 2). The CBT-I effect was moderate-large at T1, with the mean decrease of > 7 points exceeding 
the criterion for improved function,105 while the HH decrease was much smaller. In the HS, 93% improved 
across one or more levels of insomnia severity and only 2 deteriorated, while 68% of the HH group improved, 
and 26% deteriorated. At T1, only 48% of the HS had any insomnia (ISI ≥ 8), compared with 68% of the HH 
group [OR=0.43, CI95=0.13,1.45]. The HS group improved at T1 on sleep quality, compared with HH. Self-
reported (PSQI) and actigraph SE improved more at T1 in HS, and PSQI SE was similar at T2.  
We found moderate-large reductions in fatigue at T1 in HS (p < .01)28 (Table 2). The HS group continued 
reductions in fatigue at T2 (p < .01) (large effect), with no additional intervention, while fatigue increased in HH. 
There were no statistically significant differences in improvements in anxiety or depression at T1; but large 
improvements in anxiety in HS at T2 (effect size = 0.69, p < .01). Depression continued to improve in HS at T2, 

but not HH. The CBT-I effect on depression was significant when adjusted for insomnia remission (p = .02). 
Pain did not improve, but was correlated with insomnia severity (p < .01), fatigue (p = .02), sleepiness (p = 
.07), and physical function (p < .01). Sleepiness was low in both groups (range 6.84-7.55), consistent with past 
research,53 and may reflect sympathetic activation,54 lack of recognition of sleepiness or insufficient sensitivity 
of the Epworth Scale. CBT-I effects on sleepiness will be evaluated with additional measures in this study.  
Cluster analysis performed on symptom severity at T1 (pain, anxiety, depression, fatigue, dyspnea 
sleepiness) revealed 3 clusters: I (all mild: N = 25), II (high pain/dyspnea: N = 9), III (high depression, anxiety, 
sleepiness: N = 14). Of those in insomnia remission at T1 (ISI < 8), 65% were in Cluster I, and only 10% were 
in Cluster III, while 42.3% of those with persistent insomnia were in Cluster III (p = .054). Functional 
performance was higher in Cluster I than II or III (p < .0001). These associations suggest the significance of 
our plan to examine the effects of CBT-I on changes in symptom cluster group membership.  
After controlling for baseline symptoms, the estimated incidence rates of cardiac hospitalizations in one year 
were 11.7% and 18.7% in HS and HH, respectively; 9% of those with insomnia remission, compared with 
27.8% with persistent insomnia were hospitalized. Taken together with fewer cardiac hospitalizations in those 
with mild symptom clusters vs. more severe clusters (Cluster I: 4.8%; Clusters II/III: 35%), these data suggest 
the greater benefits of CBT-I, compared with self-management education, to health care resource utilization.  
Summary. We documented the high prevalence of chronic insomnia and its associations with disabling 
symptoms and functional performance in patients with stable HF and evidence-based HF disease 
management. Our pilot RCT revealed clinically and statistically significant early effects of CBT-I, a self-
management intervention, compared with HF self-management education, on chronic insomnia, SE, and 
fatigue, while controlling for covariates. High acceptability and attendance, sustained effects, completion of all 
measures without burden, and promising effects on hospitalization are strong evidence of the impact of CBT-I 
on critical HF outcomes and the feasibility of our approach. The proposed fully powered RCT will establish a 
strong foundation for future research, including dissemination of CBT-I into HF disease management settings.  
 
Table 2. Means, Standard deviations and Effect Sizes for Selected Outcomes at 2 Weeks (T1) & 6 Months (T2) 
Variable 
GROUP 
Pre  (T0) 
Post (T1) 
Post (T2) 
M (SD) 
M (SD) 
ES# 
M (SD) 
ES# 
Insomnia Severity 
CBT-I (HS)  
15.38 (4.79) 
7.76 (5.21) 
0.65 
8.39 (4.44) 
0.65 
Att Control (HH) 
14.00 (4.33) 
10.32 (5.30) 
10.00 (4.34) 
Sleep Quality:  PSQI * 
CBT-I (HS) 
9.43 (3.37) 
7.25 (3.19) 
0.46 
7.65 (3.20) 
0.02 
Att Control (HH) 
9.78 (3.84) 
9.32 (3.16) 
7.77 (4.64) 
Slp Efficiency (%):  
(PSQI) 
CBT-I (HS) 
76.64 (15.78) 
82.44 (14.45) 
0.38 
79.51 (18.74) 
0.01 
Att Control (HH) 
73.95 (14.18) 
73.89 (11.81) 
74.16 (18.95) 
Slp Efficiency (%)  
Actigraph $ 
CBT-I (HS) 
72.62 (10.25) 
75.92 (8.35) 
0.70 
NA $ 
NA 
Att Control (HH) 
77.35 (7.09) 
76.07 (11.03) 
NA $ 
Fatigue 
CBT-I (HS) 
27.49 (13.16) 
21.49 (10.61) 
0.64 
20.13 (11.52) 
1.22 
Att Control (HH) 
23.90 (11.20) 
24.45 (10.00) 
27.64 (10.43) 
Depression 
CBT-I (HS) 
13.76 (9.18) 
12.17 (10.56) 
0.06 
(**)  
8.87 (5.98) 
0.16 
Att Control (HH) 
16.74 (10.54) 
14.63 (8.63) 
14.00 (7.99) 
Anxiety 
CBT-I (HS) 
35.93 (11.61) 
34.55 (13.73) 
0.12 
30.17 (9.29) 
0.69 
Att Control (HH) 
38.32 (13.68) 
38.05 (10.73) 
37.23 (12.69) 
SF36 Physical Function 
CBT-I (HS) 
26.72 (2.04) 
27.21 (2.01) 
0.26 
26.96 (2.14) 
0.23 
Att Control (HH) 
27.11 (2.07) 
27.30 (2.28) 
27.82 (2.23) 
*higher score = poorer sleep quality; **favors attention-control group. ES# effect size; $actigraph data not collected at T2; PSQI = Pittsburgh Sleep 
Quality Index.  
 
APPROACH 
Design. We will conduct a 2-group randomized controlled trial (RCT). Participants will complete baseline and 
be randomized to CBT-I (4 bi-weekly group sessions/8 weeks: “Healthy Sleep:” HS) or attention control (HF 
self-management education: “Healthy Hearts:” HH) (Table 1). We will measure sleep, sleep-related 
impairment, psychomotor vigilance, symptoms, self-reported and objective functional performance and cost-
effectiveness at baseline (BL) and follow-up (F/U) (wks 2; months 6, , and 12), time frames selected due to the 
dynamic nature of HF severity and symptoms; and track event free survival (EFS) through the first half of year 
5 of the study, allowing a range of 18-45 months of follow-up, based on enrollment times.   
 
The sample will include individuals > 18 years of age with NY Heart Class II-III HF who are cognitively intact 
(clinical impression), speak/read English, concerned about sleep for > 1 month, and score > 8 on the Insomnia 
Severity Index,104 an indicator of ICSD-2 insomnia.106 Participants will have preserved (LVEF > 45%) or 

reduced systolic function (LVEF < 45%). LVEF was not associated with chronic insomnia,8 but may contribute. 
HF patients at the planned sites undergo echocardiography annually and with cardiac changes. We will use the 
most current LVEF. Sleep continuity is negatively related to age, but we will include adults of all ages due to 
the greater effects of insomnia on fatigue in younger adults.8 Participants will receive HF care per AHA/ACCF 
guidelines,35 consistent with practices in the clinical recruitment sites.  
Depressive symptoms are common in HF10 and often comorbid with insomnia. CBT-I improves 
depression,98,107,108 including suicidal ideation.109 Antidepressants94 and depression did not diminish its effects. 
We will include patients with no/moderate depression and those on anti-depressant, anxiolytic and/or hypnotic 
medications. Hypnotic tapering is an effective part of CBT-I,81,82 and will be an option, but not required. 
Exclusion criteria are untreated restless legs syndrome; night/rotating shift work; travel across 2 or more time 
zones within one month of enrollment; contraindications to sleep restriction [seizure disorder, severe 
sleepiness (Epworth Scale >18), bipolar disorder]; neurological/musculoskeletal conditions affecting the non-
dominant arm (use of wrist actigraph), active illicit drug use, dementia, and end-stage renal failure. Participants 
who are hospitalized or have emergency department visits, unstable conditions, or changes in vasoactive 
medications within 4 weeks of recruitment will not be enrolled until 4 weeks elapse without new events. 
Patients with these events after enrollment will be retained; events will be tracked and included in analyses. 
Chronic insomnia and sleep apnea (SA) are often comorbid with HF,110-112 but SA was not associated with 
insomnia in HF patients3,8 and did not affect CBT-I outcomes in older adults.94 Although positive pressure 
(PAP) improves some cardiac variables, lack of rigorous long term RCTs preclude widespread SA treatment in 
HF,113 and the lower treatment threshold is not well defined.113-115 Most studies were small, poorly controlled, 
and included patients with moderate-severe SA (apnea hypopnea index/AHI > 15).115 One study (n = 40 
completers)116 had participants with AHI > 5, but mean AHI was ~ 28, and the proportion with mild SA was not 
reported. Given limited evidence of PAP benefits in very mild SA, we will include patients with AHI < 10 and 
refer those with AHI > 10 for further evaluation. Participants with significant SA will be eligible If adherent to 
PAP > 6 hours/night for at least 3 months. Titration of PAP or sleep studies with PAP use are beyond the study 
scope, but participants will be eligible if they have insomnia once adherent to PAP. The proportion of PAP 
users was equivalent between groups in our pilot.  
We will track clinical/demographic characteristics [age, gender, race, SA, depression, hypnotics, HF 
medications (ACEI/ARB, beta blockers), LVEF, NY heart class] and relevant changes at all study milestones 
and include these indicators in relevant analyses (See data analysis). 
Sample Size. We performed power analyses to detect the effects of CBT-I on primary outcomes [insomnia 
severity, sleep quality (PSQI), self-reported (PSQI) and actigraph sleep efficiency (SE), and fatigue] with pilot 
data in which we randomized HF patients to CBT-I (HS) or attention control (HH). We calculated powers to 
detect differences in changes in outcomes from BL between the two groups to post-intervention (2 wks/T1) 
with 5% type I error.117 We also calculated powers with scenarios of high within-subjects correlations (Rho=0.6, 
0.7, 0.8) for repeated measures during F/U (2 wks, 6, , 12 months) by assuming consistent effect sizes over 
time118 (Table 3). A sample of 100 independent participants per group will yield > 90% power to detect CBT-I 
effects on insomnia severity (ES=0.65), fatigue (ES=0.64), and PSQI sleep quality (ES=0.46) and power of 
76.6% for PSQI SE (ES=0.38), but a power of 99.9% for actigraph-measured SE (ES=0.70). In scenarios 
based on within-subjects correlations of outcomes, we anticipate > 95% power for most outcomes and > 80% 
power for self-reported SE. Inclusion of 200 subjects (100/group) will permit sufficient power to detect the 
association between improved insomnia severity and symptoms [fatigue (>99.9%, r = 0.526), depression 
(99.16%, r = 0.299), anxiety (> 99.9%, r = 0.510), and pain (92.7%, r = -0.238)]. There will be 92.6% power to 
detect risk of hospitalization in participants with insomnia remission (9%), compared to no remission (27.8%). 
We will randomly assign 200 participants in 25-33 clusters of 6-8. Because we do not expect dependency 
among participants in the same clusters, we will not increase the sample for random cluster assignment.   
We computed the power for estimating cost-effectiveness with Willan119 and Glick’s120 methods, based on 
an assumed alpha level of 0.05, expected cost difference of $500, standard deviation for costs of $2,000, 
expected correlation between costs and QALYs of -0.1, and willingness-to-pay of $50,000 per QALY. The 
expected difference in QALYs, based on the pilot study, is estimated to be -0.33. Together, these values 
suggest that the sample of 100 subjects/group yields 89.7% power for detecting cost-effectiveness of CBT-I. 
Setting/Recruitment. We will recruit from the Yale-New Haven Hospital (YNHH) HF Center and the VA CT 
System. Dr. Jacoby will facilitate recruitment at YNHH, and Dr. Yaggi will refer from the VA where he directs 
the sleep clinic and conducts research on sleep in CVD. YNHH and the VA follow ~ 4000 HF patients. 
Because Yale cardiologists practice in both sites with AHA/ACCF guidelines,35  HF management is 
comparable. Based on our pilot, about 55% of screened patients will have chronic insomnia, of whom 50% will 

meet all criteria, and 85-90% will consent. We expect to enroll ~ 23-25% of screened patients, a large number, 
given the prevalence of HF. Likely exclusions will be untreated SA; unstable HF, medical or psychiatric status; 
and cognitive dysfunction. Lack of interest, session frequency, or travel distance may result in refusal. We had 
92% completion in our pilot, but will recruit an additional 20% (total N = 240) to address potential attrition.  
We will obtain IRB approval, and HF clinicians will identify consecutive potential participants by eliciting 
concerns about sleep; explain the study; and obtain agreement for contact. We will promote the study with 
brochures in waiting areas and the Yale CTSA website. The research assistant (RA) will meet with interested 
patients, explain the study, answer questions, and obtain informed consent and a HIPAA waiver for medical 
record review and further screening. The RA will screen patients for health/sleep history, including other sleep 
disorders, and depression (PHQ-9121 121). She/he will screen those with PHQ-9 scores > 15 (moderately 
severe-severe) with the Structured Clinical Interview for DSM-IV Axis Disorders122 (See inclusion/exclusions).  
 
We will also recruit at the Yale Sleep Medicine Clinic.  Participants will be recruited from among those 
patients seen at the Yale Sleep Medicine clinics who carry a diagnosis of chronic HF. The research assistant 
and program manager will work with Dr. Yaggi to identity such patients in clinic medical records.  Medical 
records will be screened to determine if prescreening criteria are met: >18 years of age; physician diagnosis of 
stable heart failure, New York Heart Class II-III Heart Failure; and preserved (LVEF>45%) or reduced systolic 
function (LVEF≤45%). All patients meeting these criteria will be sent an “opt out” letter letting them know that 
they will be called about the study (letter attached). Each week 25 letters will be mailed to allow ample time for 
researchers to make calls. The letter will explain the purpose of the study and notify patients that they will be 
called by a researcher to discuss their potential participation in the study. They will be given the opportunity to 
“opt out” of this contact by leaving a message on an answering machine.  They will be assured that they will 
not receive a call if they follow this procedure and no personally identifiable information will be retained for 
patients who opt out. The research assistant will call all patients who do not “opt out.” Three calls will be made 
to attempt to reach each potential participant. Messages will not be left and personal health information will not 
be shared with family members or others answering the telephone.  
We will also work with YCCI to build an EPIC query to identify all patients with heart failure seen in any Yale 
clinic. This will allow us to identify patients who are not seen in cardiology or sleep clinics. Epic MyChart will be 
used to identify and notify potential candidates for the study.  Patients who have an Epic MyChart account and 
meet basic inclusion /exclusion criteria will be notified of the study through a MyChart message.   The 
notification will provide an overview of the study. Within MyChart, patients can indicate whether or not they are 
interested in the study.   No patient data will be shared with a research coordinator unless requested by the 
patient. If a patient selects yes- they are interested in the study, the research coordinator will receive a 
message requesting that they contact the patient regarding the study.   Research coordinators will then contact 
the patient for eligibility screening.  If a patient selects “no,” they are not interested in the study, they will not 
receive any additional messages about the study within Epic, and their information will not be shared with the 
research coordinator.  We will work with JDAT to send recruitment materials to patients who do not use 
MyChart. JDAT will provide recruitment materials and addresses to the university printing and publishing 
offices who will coordinate the mailing. Interested patients will call us for further information about the study.    
     Research Match is a free online recruitment tool that matches studies to volunteers. Yale University is a 
member institution. We will sign up as a ‘researcher,’ input relevant details about our study, and Research 
Match will query its database of individuals who fit certain criteria, in our case, Heart Failure. We will also 
promote the study by advertising on the Yale Health website’s webpage titled “Research Opportunities”. 
Recruitment will also take place at the VA West Haven (see VA IRB approved protocol, attached) using the 
same process of medical record review and physician referral to identify patients (veterans) with heart failure. 
These patients will be sent an opt-out letter; the screening and opt-out process described above will be used. 
The letters will be sent and the phone calls will be made from the VA campus.  
     A web link to the HIPAA-compliant online survey platform REDCap (https://poa-redcap.med.yale.edu/) 
will be emailed to heart failure patients who receive care by co-investigator, Dr. Jacoby. These patients and 
their email address will be identified through JDAT or Dr. Jacoby. These data will be stored in a secure, 
password protected Yale ITS-managed workstation and only be used to collect survey data about insomnia 
and to inform patients about the study.   
 
 
 
 
 
 

Table 3. Powers to detect the expected effect sizes with a sample size of 100 per group 
Outcome Variables 
Effect Size 
Two weeks post-
intervention 
Post-intervention measures  
(3, 6, 12 months) 
Power 
Rho (*) 
Power 
Insomnia Severity 
0.65 
99.2% 
0.6 
99.9% 
0.7 
99.9% 
0.8 
99.7% 
Sleep quality: PSQI 
0.46 
90.2% 
0.6 
 97.3% 
0.7 
95.9% 
0.8 
94.2% 
Sleep Efficiency (%): PSQI 
0.38 
76.6% 
0.6 
89.5%  
0.7 
86.3% 
0.8 
83.0% 
Sleep Efficiency (%): Actigraph 
0.70 
99.9% 
0.6 
>99.9% 
0.7 
>99.9% 
0.8 
>99.9% 
Fatigue 
0.64 
99.5% 
0.6 
>99.9% 
0.7 
99.9% 
0.8 
99.8% 
We will screen all participants who are not already adherent to positive airway pressure (PAP) for SA with 
the Apnea Risk Evaluation System (ARES) Unicorder (Watermark Medical, Inc.), a wireless recorder worn on 
the forehead http://www.watermarkmedical.com/index.php. (Appendix) ARES measures blood oxygen 
saturation (SpO2) and pulse rate (reflectance pulse oximetry), airflow (nasal cannula/pressure transducer), 
respiratory effort (pressure transducer - forehead venous pressure), venous volume (photo-plethysmography), 
snoring (acoustic microphone), head movement, and position (accelerometers). Pulse rate changes indicate 
arousals. Snoring changes indicate respiratory-related arousals. Forehead venous pressure is a valid measure 
of respiratory effort.123 Head position indicates the positionality of obstructive events. ARES distinguishes REM 
from NREM with EEG, EOG and EMG electrodes at FP1 and FP2 (forehead). The apnea hypopnea index 
(AHI) is the sum of apneas and hypopneas/hour with 4% oxygen desaturation. Respiratory disturbance index 
(RDI) is the sum of apneas and hypopneas/hour with 1% desaturation and at least one arousal indicator. 
ARES has high sensitivity and specificity123-125 and better test-test reliability126 than in-lab PSG. We will 
record 2 nights and calculate respiratory events/total sleep time because of potential night-night variability and 
score the data through the manufacturer’s website with validated automated methods and review by a board 
certified sleep technician. Although forehead venous pressure distinguished central vs. obstructive respiratory 
events,123 it is not the gold standard, and we will report only the AHI/RDI. ARES minimizes participant burden 
and cost, compared with PSG. As in our preliminary study, we will give participants the programmed device 
and instruction, and they will return it personally or by mail. Drs. Redeker and Yaggi will review scored data 
and provide patients and referring providers with results/recommendations for follow up.  
 
Randomization/blinding. We will use block randomization to assign participants to groups of 6-8 to assure 
timely filling of groups and group process. CBT can be provided in groups of 3-12, and groups averaging 5 
were used for CBT-I.86 As groups complete baseline, we will randomize to treatment assignment with a 
computer-generated sequence. Recruitment/screening/data collection/data entry personnel will be blinded to 
group assignment. It is impossible to blind participants, but they will not know the group assignment until the 
first meeting. Interventionists will be blinded to baseline data and specific hypotheses. 
 
Treatment: CBT-I (“Healthy Sleep” Group: HS). Our manualized protocol includes CBT-I elements (Table 1) 
in 4 bi-weekly one-hour sessions;127,128 a call on alternate weeks; the HS Guide, a workbook with CBT-I 
components, with sixth grade reading level, print appropriate for older adults, and images reflecting diversity 
(Appendix); and optional hypnotic tapering. About 80% of hypnotic users selected tapering in our pilot, and we 
expect hypnotic use to decrease, based on the literature81,82 and our experience.20 Four bi-weekly sessions of 
CBT-I were superior to other frequencies.129 The group format was feasible in our pilot, and will enhance social 
support and learning. Use of a CBT-I trained APRN interventionist is consistent with our29 and other94 studies 
and will promote sustainability due to the greater availability of APRNs than sleep psychologists.  
The advanced practice nurse (APRN) interventionist will briefly review the HH Guide (HF self-management). 
Homework includes CBT-I skills practice and daily tracking of CBT-I use, sleep, symptoms, and weights in a 
HS Log. We will provide an electronic scale, the HS/HH Guides, instructions, schedules, progressive muscle 
relaxation recordings, videos of sessions, and HS logs programmed with Qualtrics ™ software on a mini-tablet 
computer that participants may keep. Logs will be time stamped and uploaded from the tablet to the Yale 
server when WI-FI is available, but WI-FI is not necessary for data entry or the time stamp. Therefore, 

participants do not need WI-FI in their homes. We piloted this method and found it feasible. Completion of the 
logs takes < 10 min/day. However, to accommodate participant preferences88,130 and to promote adherence131 
we will provide both paper and electronic options that produce equivalent results when training and reminders 
are provided,130,131 in contrast to “batch effects” when paper diaries are used without reminders or 
training.132,133 We will text message or phone participants every other day to remind them. Group differences in 
paper/electronic use are unlikely due to randomization, careful training, reminders, and equal study team 
contacts, but we will explore the possible effects of unbalanced usage. Web applications are available for CBT-
I,134-136 but we will not use them because of the need for consistent interactions/social support between groups. 
Most CBT-I studies used usual care, wait-list, or information-only controls, but we will use attention-control 
because time and attention may improve symptoms and function. The Attention Control (“Healthy Hearts:” 
HH) includes didactic sessions with HF self-management education46,137 (Table 1), supported with the HH 
Guide (Appendix), developed from the “Fight against Heart Failure Workbook.137 Sessions, homework, and the 
HH Guide are equivalent to CBT-I in frequency, duration, and interventionist time. Sleep hygiene is included, 
but not the active ingredient24,80,138 and was the control in past trials.93,139 There is information on 
relaxation/stress, but no relaxation homework. We will explain that HF self-management may improve sleep by 
improving HF management. Homework includes practice of HF self-management and tracking these skills, 
symptoms, and daily weights in a HH Log. We will provide all materials, including HH session videos, HH 
Guide, HH Logs (except the relaxation script and CBT-I video) formatted identically to those for the HS group. 
 
We will hold group sessions in the Biobehavioral Research Laboratory at Yale School of Nursing where there 
is ample parking, shuttles to/from clinical sites, video conferencing, and recording capability. We will provide 
rest periods and healthy refreshments and review HH/HS logs at each session to guide practice. We will use 
scripted telephone boosters of ~10 minutes each on weeks alternate to group sessions to elicit skills use and 
provide review, problem solving, and reminders to complete logs and attend future sessions. We had no 
difficulty with these methods that were highly acceptable in our preliminary study.28  
As in our pilot, we will send birthday/holiday cards to maintain contact and promote retention. We will provide 
$50 for ARES screening and $250 disbursed over data collection/treatment for travel/time and taxi fare if 
needed. To promote retention and adherence after intervention, we will use brief scripted monthly phone calls 
until the12 month F/U, with content identical to the earlier calls, except meeting reminders. Some attrition is 
unavoidable, but our strategies will minimize it. We will resume data collection at later milestones if participants 
miss data points and track reasons for missing data and/or attrition. 
We will run group series simultaneously to offer options for time of day/day of week and offer makeup 
sessions with another ongoing group, but telephone or Skype make-ups will be used for those unable to attend 
in person due to weather or other reasons. However, participants will be encouraged to continue with the same 
group. Make-ups will be held within one week of missed sessions, as in our pilot. Tele-health CBT-I was 
successful in non-HF patients,136 and phone-delivered CBT is efficacious in cardiopulmonary140 and mental 
health disorders.141,142 We will compare groups on the phone/Skype vs. face-face sessions and attendance and 
include the number and types of sessions in analyses if groups are unbalanced.  
The study is designed to assure treatment fidelity (consistent dose, frequency, and contacts), and we will use 
similar materials/homework in both manualized conditions (Appendix). We will use protocols we developed to 
train study personnel. Separate interventionists, blinded to baseline/outcomes data and study hypotheses, will 
provide both treatment conditions to avoid contamination, and single interventionists will provide each 
condition. To assure protocol adherence, we will record sessions and script and record phone calls. With 
published methods,143 we will randomly select and evaluate the fidelity of 20% of recordings and give feedback 
to the therapist. We will promote treatment receipt by reinforcing instruction (return demonstration, workbooks, 
and phone calls) and providing study information to take home for referral. 
 
Data collection. We will conduct medical record review and interviews for clinical and demographic data. 
Participants will complete questionnaires and 2 weeks of continuous wrist actigraphy/diaries at home and a Six 
Minute Walk Test (6MW) and Psychomotor Vigilance Test (PVT) with standard methods at the YSN 
Biobehavioral Research Laboratory at the end of the 2-week monitoring. These measures will be obtained at 
baseline (B/L) and each follow-up (F/U) (2 wks, 6, , 12 months). We will obtain actigraphy/diaries for 2 weeks 
as recommended due to high variability in sleep characteristics (e.g., sleep latency, efficiency) in insomnia,144 
consistent with many past CBT-I trials (e.g.,95). Sleep diaries (as the logs) will be offered in paper and 
electronic format and used to assist in interpreting the actigraph data (reasons for removal, lights “out/on” 
times), but not to generate outcomes. Participants had no difficulty with 2 weeks of diaries/actigraphy in our 

pilot and found procedures acceptable, so burden is not a concern. As in our pilot, we will deliver materials by 
overnight mail or home visit, phone participants within a day of receipt; and review instructions (remove 
actigraphs only for bathing, depress event markers at “lights out/on” and removal times). Time for form 
completion, 6MW and PVT are ~1 hour/collection period and actigraphy/diary time: < 10 minutes/day. To elicit 
event free survival (EFS), we will interview participants and conduct medical record review during scheduled 
contacts for 12 months and call every 2 months thereafter through the first half of study year 5. 
 
Independent and dependent study variables/measures. We will use self-report and objective measures of 
insomnia/sleep (Table 4). The Insomnia Severity Index (ISI), based on ICSD-2 criteria,106  is internally 
consistent (0.74 – 0.88)104,145 and sensitive to treatment.104,145,146 The Pittsburgh Sleep Quality Index (PSQI),147 
a legacy measure,51 has diagnostic sensitivity (89.6%) and specificity (86.5%) for distinguishing “good” vs. 
“poor” sleepers147 (Alpha = 0.83 in HF)8 and will be used to calculate self-reported sleep duration, sleep 
efficiency (SE) [(total sleep duration/time in bed) X 100], latency, and global sleep quality. To allow comparison 
with PROMIS studies, we will administer the PROMIS sleep quality brief item bank, a validated and precise 
measure.51 We will use the Respironics Minimitter Actiwatch AW2, a wrist-worn actigraph, to elicit objective 
sleep variables. Correlations between actigraphy and polysomnography (PSG) range from 0.82-0.98 (SE) and 
0.90-0.97 (duration) in normal sleepers;148-154 correspondence is good in insomnia.155 Actigraphs are sensitive 
to treatment156 and cardiac patients were adherent.19,157 Actigraph-recorded sleep latency is not always stable, 
but is associated with NY Heart class (unpublished). We will use the AW2, rather than a publicly marketed 
accelerometer, because it is valid and reliable. Per recommendations,158 participants will complete a daily diary 
based on the Consensus sleep diary159 to identify lights out/on, times/purpose of removal, and hypnotic use. 
The diary will be provided in the same format as the logs (See treatment). PSG is not diagnostic for insomnia 
and increases burden and cost.   
Symptoms will include fatigue, sleepiness, sleep-related impairment, pain, anxiety, dyspnea, and depression 
because these are common in insomnia and HF (Table 4); cluster together and were associated with insomnia 
remission in our pilot. The Multi-Dimensional Assessment of Fatigue Scale (MAFS)160 has concurrent (r = 0.84) 
and divergent (r = -0.62) validity and internal consistency (alpha= 0.84 in HF).10 The Multi-Dimensional 
Assessment of Dyspnea Scale (MADS), adapted from the MAFS, is reliable (alpha = 0.94) and valid.10 The 
Epworth Sleepiness Scale (ESS)161-163 is reliable and valid162,163 (alpha = 0.87 in HF).10 The PROMIS Sleep-
Related Impairment brief item bank,51 a valid and precise measure, will enable comparison with emerging 
studies. We will measure Psychomotor Vigilance (PVT)49 with the PVT-192 (Ambulatory Monitoring, Inc. 
Ardsley NY) with the 10 minute protocol,50 detecting lapses/reaction time. PVT is valid when administered as a 
single measure in the clinic setting,50 used in HF patients,3 and associated with fatigue and quality of life.164,165 
The PROMISTM 8-item short-form elicits fatigue and anxious and depressive symptoms. The PROMISTM Pain 
intensity and Pain Interference item banks will be used to measure pain. The PROMISTM measures are based 
on item-response theory and are responsive to clinical change.  
We will measure functional performance with the Medical Outcomes Study (MOS) SF36v2 Physical Function 
Component175 (reliable and valid in HF);175-177 and the Six Minute Walk Test (6MW)178 (correlated with oxygen 
consumption,179 cycle ergometry, HF prognoses, and self-reported function)178 with standard methods.180 With 
the exception of PROMIS, BPI, MW and PVT, the measures were used in our preliminary work and 
participants did not view them as burdensome. We have experience with the 6 MW53,181 and PVT.181  
We will conduct cost-effectiveness analysis (CEA) from a societal perspective where all costs are 
considered, regardless of who incurs them,182,183 document resource use in units, and apply prices to units. 
Data will be obtained by self-report and validated through the EMR, hospital cost accounting database, and the 
Medicare fee schedule. Variables will include direct costs for health care resources used [office and 
emergency department visits, homecare, hospitalizations (and reasons, e.g., HF), cardiac devices, over the 
counter (OTC) and prescribed medications]. A sub-set of our team, blinded to group assignment, will review 
hospitalization/ED/office visits and evaluate causes. We will use the electronic pharmacy system to obtain 
prescription drug utilization and self-report for OTC medications. We will determine costs for prescription and 
OTC medications with Redbook average wholesale prices and public market prices from drugstore.com. We 
will include payments to the interventionists and costs of intervention materials. Deaths and causes of death 
will be ascertained with the EMR. If not found, we will query the National Death Index. Indirect costs will 
include self-reported time missed from work, travel, and self-management and CBT-I activities. We will 
measure effectiveness as quality adjusted life years (QALY), estimated from the EQ-6D algorithm from SF-36 
data184 and track research-related costs (monitoring, data collection, personnel costs, and subject payments). 

 
Table 4. Primary Study Variables and Measures: Baseline, 3 months, 6, 12 months 
Variable 
Measure 
Sleep/Insomnia 
  Global Sleep Quality 
 
Pittsburgh Sleep Quality Index (PSQI);  
PROMIS Sleep Disturbance Brief Item Bank 
  Sleep latency (SL), wake after sleep onset (WASO),  
  efficiency (SE), time in bed (TIB), total sleep time (TST) 
Subjective: PSQI 
Objective: Actiwatch2/Wrist Actigraph 
  Insomnia Severity 
Insomnia Severity Index (ISI) 
Symptoms  
 
  Excessive Daytime Sleepiness 
Epworth Sleepiness Scale (ESS);  
PROMIS Sleep Impairment Brief Item Bank 
  Fatigue (total, severity, distress, interference, timing)  
Multi-dim Assessment of Fatigue Scale (MAFS) 
  Depressive Symptoms 
PROMIS Depression Short Form 8a 
  Anxious Symptoms 
PROMIS Anxiety Short Form 8a 
  Pain (severity, location, and interference) 
PROMIS Pain intensity-3 item; PROMIS Pain Interference 
  Dyspnea (total, severity, distress, interference, timing) 
Multi-dim Assessment of Dyspnea Scale (MADS) 
  Neurobiological effects of sleep loss/sleepiness 
Psychomotor Vigilance Test (PVT-192) 
Functional Performance 
Self-report: SF36 v2 Physical Function Component; Objective: 
Six Minute Walk Test (6 MW) 
Health Care Resource Utilization 
Event free survival 
Direct and Indirect Costs (See narrative) 
Absence of: death (all cause), HF hospitalizations; HF ED 
visits; heart transplantation, LVAD 
EFS  = time to first event 
Event-free survival (EFS) will include time without events listed in table 4 and will be tracked through the 
second quarter of study year 5, enabling monitoring of up to 18-45 months/subject, depending on enrollment 
times. We will assess events through participant/family report and confirm with the electronic medical record 
(EMR); a sub-set of our team blinded to group assignment will review data to determine cause of 
death/hospitalization. We will query the National Death Index if information is otherwise unavailable.  
 
Clinical and demographic variables. We will use interview and EMR to track demographic/clinical data: sleep 
and medical history, comorbidity (e.g., osteoarthritis); prescribed and over-the-counter (OTC) medications 
(especially ACEI/ARBs and beta blockers that improve survival and prescribed/OTC hypnotics); SA; PAP use; 
LVEF; NY Heart class; cardiac devices; and HF stage. Self-report data will be validated through EMR and vice-
versa. The Charlson Comorbidity Index185 and Seattle HF Risk Model, a predictor of death,186 will be computed. 
Due to the possible influence of self-care/self-management, including adherence to HF disease management, 
we will use the 8-item Morisky Scale,187 a reliable and valid measure of medication adherence, and the reliable 
and valid self-care maintenance and management scales of the Self Care in Heart Failure Index (SCHFI 
v.6)188,189 These variables will be measured at baseline and all f/u periods. 
Satisfaction/acceptability. As in our pilot, we will elicit satisfaction/acceptability with the treatment and 
control, materials, and logistics with an adapted version of the Treatment Evaluation Questionnaire 
(TEQ).129,190 We will use a 0-10 (10 = high) rating scale for each item (e.g., treatment helpfulness; willingness 
to continue; degree of “cure;” satisfaction with scheduling, provider, delivery methods, and take home 
materials, including electronic or paper format). This evaluation will occur after the 8 week treatment. 
We will evaluate treatment enactment/adherence (CBT-I and HF self-management skills use) with bi-weekly 
calls and review of the HH and HS logs (See treatment section). We will use the Morisky Scale and SCHFI V.6. 
to assess self-care/self-management (see variables/measures) and evaluate participant flow with 
CONSORT,191 attendance, and reasons for non-participation, and attrition.  
 
Data Management and Statistical Analysis Plan. We will use REDCap, an NIH-supported electronic data 
capture application192 to develop data collection forms. The Yale Program on Aging (POA) hosts Yale's 
implementation of REDCap (See resources). Data will be entered directly into REDCap with tablets, but we will 
provide printed forms for all instruments if tablets are not available or fail. Blinded staff will not have access to 
intervention forms or data. REDCap and study-specific tools and data will be the basis of an administrative 
system that will support field operations. This blinded tool will enable tracking of recruitment, retention and 
study progress, scheduling and managing telephone calls, and managing/reporting adverse events. We will 
develop a separate unblinded database tool for managing intervention schedules and adherence. We will 
maintain a read-only, de-identified SAS data mart that will be updated daily and will be the basis for conduct-of-
study, error-check, and data safety/monitoring reports. Snapshots of the data mart will be used for interim and 
final analyses.   

Data Analysis. We will use Actiware v.6 software (Respironics Minimitter, Inc.) to score actigraph data. 
Personnel blinded to group assignment will use event markers, light meter and diary recordings to identify 
“lights on/out” and removal times and to summarize actigraph variables over the 2-week intervals. Use of three 
methods will minimize problems with missing data (e.g., occlusion of the light meter) for single indicators. 
Wrist-worn light meters are valid measures of ambient light.193 
To identify outliers and data errors, we will use logical data checks and handle missing data with the 
assumption of missing at random (MAR), an approach that assumes missing values have the same patterns as 
complete data. We will impute missing data on survey items with Rubin’s194 multiple imputation method, 
replacing missing values with simulated sets of imputed values from Monte Carlo simulation, and analyze each 
simulated completed dataset with standard methods. Results will include estimates and confidence intervals 
that incorporate the uncertainty of imputed values. Generalized Linear mixed Models (GLM) and General 
Estimating Equations (GEE) allow the use of incomplete repeated measures due to dropout, If there is at least 
one baseline and follow-up data point. This approach will minimize the effects of sample loss due to attrition 
 
We will compute univariate statistics to examine frequency distributions of categorical variables and normal 
distributions for continuous variables with box plots and the Shapiro Wilkes test to assess normality. We will 
test differences in demographic and clinical variables (e.g., SA, LVEF), and baseline adherence, self-care, and 
self-management (see inclusion/exclusion criteria; clinical/demographic variables) between the two 
randomized groups to determine the success of randomization. Because some clinical and demographic 
variables, including PAP use, may influence outcomes, we will include them as covariates in multivariate 
models if groups are unbalanced on these variables at baseline. 
We will use descriptive statistics to describe clinical and demographic characteristics by treatment group and 
time. To evaluate differences in insomnia severity, sleep characteristics (Aim 1a), symptoms, PVT (Aim 1b) 
and functional performance (Aim 2) over the four time points (baseline, 3, 6, and 12 months), we will use 
GLMM with a repeated measures statement to examine group, time, and group by time effects. Nonlinear time 
effect will be controlled by transforming a continuous time variable with cube root form, which allows to 
estimate relatively rapid change in early at post-intervention than the follow-ups. We will choose the best 
covariance structure (compound symmetry vs. unstructured vs. moving average) by comparing the Akaike 
Information Criterion (AIC) and Bayesian Information Criterion (BIC). In multivariate models, adjusted 
intervention effects will be estimated by including covariates that might differ by group assignment (e.g., LVEF, 
comorbidity, age, SA with CPAP use). We will assess multi-collinearity among covariates with the variance 
inflation factor (VIF) and eliminate variables with high VIF (> 10) if the intervention effect is not modified after 
elimination. We will identify influential outliers with DFFITS, DFBETAS, and Cook’s distance. If there are non-
normally distributed residuals, the outcome variable will be transformed to ordinal form or fitted with more 
flexible distributions (e.g., Gamma, Poisson). We will use GEE with a robust working correlation structure to 
address within-subjects correlations for changes in outcomes with clinical cutoffs (i.e., ISI≥, ESS≥10, PSQI≥5, 
SMW≥1000) over time. To avoid the inflation of type 1 error, False Discovery Rate (FDR) will be calculated for 
post-intervention effects on 13 outcome variables.  
For cluster analysis (Aim 1c), we will use Latent Transition Analysis (LTA)195,196 to produce a latent variable 
categorizing a symptom cluster at baseline and follow-ups. Consistent with methods in cardiac samples166 
each participant will be stratified into a symptom cluster group, based on dichotomized symptom severity. In 
our pilot, we found 3 clusters (I: mild); (II: high pain/dyspnea); (III: severe – high depression. anxiety, 
sleepiness). We expect LTA to produce two or more clusters of 2 or more symptoms characterized by 
symptom severity and patterns of correlated symptoms. LTA provides the item-response probability of having a 
specific symptom in each symptom profile group and the probabilities of membership in each symptom profile 
at each time and transitioning from one symptom profile to another. SEM and other similar latent variable 
methods do not quantify symptom cluster membership and transitions between clusters over time. The LTA 
model will include a group variable as a covariate to test the effects of CBT-I on membership in a symptom 
cluster and transition between clusters over time.   
We will use GLMM or GEE to determine the extent to which the effects of CBT-I on insomnia, sleep 
characteristics, symptoms and functional performance at 6 and 12 months are non-inferior to outcomes at two 
weeks. (Aims 1a, 1b, 2). We will identify potential barriers to sustained intervention effects (e.g., LVEF, 
comorbidity, PAP treated SA, self-care/self-management) by testing interactions between time and covariates 
associated with outcomes. 
 To address Aim 3, we will perform stochastic cost-effectiveness analysis (CEA) from the perspective of 
society. We will document resource utilization in units for direct and indirect costs (above) and apply prices to 
each unit. Costs will be the sum of the product of units and prices for both groups. The goal of stochastic CEA 

is to estimate the incremental cost per additional unit of effectiveness, measured as QALYs, the standard in 
cost-effectiveness research and economic evaluations of sleep disorders.101 We will estimate QALYs by 
applying the utility measure derived from the EQ-5D to the time spent during the study. We will consider the 
limited life expectancy of HF patients by including only time spent alive in the study, adjusted by utility. We will 
summarize the cost-effectiveness of CBT-I relative to HH with an incremental cost-effectiveness ratio (ICER):  
 
(CCBT-I: expected cost of the intervention; CHH: expected costs for the HH group; ECBT-I: effectiveness of the 
intervention, measured as QALYs;EHH: effectiveness in the HH group). ICER represents the incremental cost 
to gain an additional QALY with CBT-I. We will discount costs over the 3 years of data collection and estimate 
uncertainty around the ICER by bootstrapping, yielding a 95% confidence ellipse around the ICER. In the usual 
cost-effectiveness framework, a willingness to pay threshold is used to determine cost-effectiveness of a 
treatment. However, in a stochastic cost-effectiveness framework, we can estimate the probability that CBT-I is 
cost-effective over a wide range of willingness to pay thresholds with the cost-effectiveness acceptability curve 
(CEAC). We will perform sensitivity analyses for costs, life expectancy, and utility.  
We will use survival analysis for exploratory aim 1 and compare time to EFS between CBT-I and the 
attention control condition with a competing risk approach.197 This approach accounts for the relationship of the 
occurrence of one event with others. Participants who do not experience events during f/u will be right-
censored. As a secondary analysis, we will examine time to specific events (e.g., hospitalization, death). We 
will adjust the survival model by including baseline Seattle Heart Failure score,186 depression, LVEF, 
adherence,36 and self-care/self-care management,77 as they predicted EFS.  
Table 5. Timeline for study activities 
 
Quarters of Years 1-5 
Activity 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
Hire & train personnel 
X 
X 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Prepare study materials 
X 
X 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruit & screen 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
 
 
 
Provide treatment 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
 
 
Collect data 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
Analyze data 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
Present/Publish 
 
 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
Challenges/rationale. Although desirable to include non-English speaking patients, translation is beyond 
available resources. Inclusion of patients with mild SA is safe and will yield a sufficient sample. We 
successfully included PAP-adherent SA patients in our pilot, with an equivalent proportion in each group. 
Attrition was low, but we expect more in a longer study due to HF exacerbation and other events. We will over-
recruit and use retention and intent-to-treat strategies. If a patient misses a follow-up, we will follow at the next 
time point if possible. We will include patients who are stable at B/L and have evidence-based HF disease 
management because uncompensated HF and/or poor disease management may confound outcomes. 
Randomization will promote group equivalence, and we will explore possible confounding effects of clinical and 
demographic factors on the outcome variables. Participants had no difficult completing the intervention and 
measures in our pilot and very few participants may be unable to complete the 6MW. Duration of EFS tracking 
will vary (18-45 months) based on enrollment time but is unavoidable due to total study duration. The five-year 
study will provide sufficient time to obtain meaningful results. It is possible that HF self-management education 
may improve health care resource utilization and EFS, but our pilot suggests that CBT-I had a larger effect, 
and a previous study78 showed no effect of HF self-management on this outcome. Comparison of group effects 
on this outcome is an important goal of the study. Because of the plan for extended follow-up, we will not offer 
cross-over from the attention-control into the CBT-I condition, and we will track changes in insomnia and self-
management behavior over time. The results will be generalizable to HF patients who have chronic comorbid 
insomnia – approximately half of HF patients.8 Future studies will address effectiveness, dissemination and 
translation of CBT-I into diverse HF settings. 
 
PROTECTION OF HUMAN SUBJECTS 
Risks to Human Subjects 
a. Human Subjects Involvement and Characteristics 
1. Involvement of human subjects 
 

Recruitment & screening: 
Health care providers will identify potential candidates for the study and elicit their consent to be contacted by a 
member of the study team, or potential participants could respond to advertisements to the study and contact 
the study team.  A member of the study team will contact participants. Screening for participation will include 
medical record review and patient interview to determine whether participants meet the enrollment criteria (see 
characteristics of the study population). We will also use the PHQ-9 depression scale, a component of the 
PRIME-MD, to screen for depression. Those who score in the moderate-severe range (score of 15 or higher) 
will be further evaluated for depression using the Structured Clinical Interview for Depression. Those who have 
a suicidal ideation or have severe depression or bipolar disorder will not be included in the study. All others will 
be invited to participate in the study, because several studies have shown that CBT-I improves mild and 
moderate depression. Medical records will be reviewed with patient consent to elicit health and medical history, 
and medications. (See characteristics of study population). Participants will be consented for further screening 
and study participation. All participants who are not already in treatment for sleep disordered breathing will be 
screened in their homes with a portable sleep recorder (ARES), after detailed instruction. Participants with an 
apnea hypopnea index < 10 or who have sleep apnea and are adherent to positive airway pressure for > 6 
hours/night will be included in the next phase of data collection. 
 
Data collection: Participants who meet the study criteria and agree to participate will complete a battery of 
questionnaires to elicit self-reported symptoms, functional performance, and characteristics of sleep. They will 
also complete forms to indicate their use of heart failure self-management and self-care behaviors. Participants 
will wear wrist actigraphs (accelerometer device that is approximately the size of a sports watch) for 2 
continuous weeks, removing them only for bathing. They will be reminded not to cover the actigraph with 
clothing or bedclothes because this will obscure the light measurements. Participants will simultaneously 
complete a diary to note characteristics of sleep, use of hypnotic medications, and times of lights out and lights 
on related to sleep time, as well as removal (and reasons) of the actigraph. These data will be used to assist in 
scoring the actigraphy data, and participants will have the option to complete the diary on paper or on an 
electronic device. At the end of this screening period, participants will visit the Yale School of Nursing 
Biobehavioral Laboratory where they will complete a six-minute walk test and a psychomotor vigilance test.  
 
The questionnaires, diaries, actigraphy, six minute walk, and psychomotor vigilance test will be repeated at 
baseline, 3 months (2 weeks after participating in the treatment/control conditions), and at 6 and 12 months.  
 
In addition to the primary study variables noted above, medical records will be reviewed and participants will be 
interviewed over each of the study time periods to elicit changes in health status, medication use, any 
additional treatments, and use of health care resources (e.g., visits to health care providers, hospitalizations, 
emergency department visits, medications, as well as time off from work, time associated with use of the CBT-I 
and self-management skills practice, and other possible costs associated with the intervention and heart 
failure. These data will be obtained through the 12-month follow-up period.  
 
After the 12-month follow-up, a member of the study team will contact participants every 2 months until the end 
of the 5 year study period to obtain information on health status, hospitalization/emergency department visits 
(and their causes), possible heart transplants, or implantation of a left ventricular assist device in order to 
measure events used to calculate event free survival. This may result in between 18 and 45 months of follow-
up depending on when participants are recruited into the study. 
 
Intervention phase: 
Participants will be randomized in groups of 6-8 into the intervention group (cognitive behavioral therapy for 
insomnia – CBT-I: “Healthy Sleep Group:”HH) or into a comparison group condition that will include information 
on self-management of heart failure and sleep hygiene information (“Healthy Hearts Group:” HH). Participants 
will not know the group to which they have been randomized until they arrive at the meeting. Participants in 
both groups will attend 4 bi-weekly face-face one-hour sessions and participate in 4 15-minute telephone calls 
(each of the 4 weeks alternating with the face-face sessions). The calls will consist of 1) reminders to continue 
using the strategies taught in the sessions 2) clarification of questions or problems with the prescribed activities 
(attention or control); 3) reminders/clarification regarding completion of the logs, and 4) reminders about the 
time and place for the next session. If unable to attend isolated group meetings, they will participate by 
telephone conference call or Skype. They will also be able to make up single sessions by telephone or Skype. 

 
The CBT-I group will receive instruction on techniques to improve their sleep and be expected to practice these 
techniques at home. These techniques will be included over the 4 face-face meetings in which the therapist will 
teach and provide examples and feedback on individual progress with each of the activities. There will be an 
opportunity for questions and discussion. The therapist will provide feedback at each session. CBT-I includes 
stimulus control; sleep restriction, cognitive therapy, relaxation, and optional tapering of hypnotic medications. 
They will participate in a brief discussion covering HF medications, symptoms, management of fluids and when 
to call their health care provider about HF symptoms. 
Stimulus control utilizes instructions designed to re-associate the bed/bedroom with sleep and to re-
establish a consistent sleep-wake schedule. Patients are instructed to: go to bed only when sleepy; get out of 
bed when unable to sleep; use the bed only for sleeping; arise at the same time each morning; and avoid 
napping.  
Sleep restriction is designed to limit the actual sleep time to improve sleep efficiency. Under the supervision 
of the therapist, the patient decreases time in bed to maximize the percentage of time asleep. Time in bed is 
gradually increased based on sleep efficiency, until the optimum sleep duration/efficiency are obtained.  
Cognitive therapy addresses misperceptions about insomnia, clarifies misattributions of consequences of 
sleep, clarifies and reframes unrealistic expectations about sleep, reduces performance anxiety, increases 
control, and clarifies faulty beliefs about sleep. The therapist will work with participants to adopt 6 basic 
strategies: (1) “keep realistic expectations; (2) do not blame insomnia for all impairments; (3) never try to sleep; 
(4) do not give too much importance to sleep; (5) do not catastrophize after a poor night’s sleep; and (6) 
develop tolerance for the effects of insomnia.26, p. 37 
Relaxation therapy is focused on reducing somatic and cognitive arousal. We will use progressive muscle 
relaxation to reduce somatic arousal, combined with a cognitive focus on diaphragmatic breathing through a 
scripted relaxation protocol. Participants will be provided a recording of the scripted relaxation exercise on a 
min-tablet computer or an MP3 player if they prefer.  
Sleep hygiene education focuses on managing external factors that may affect sleep. Although there is 
variation between studies on elements used,198 the core elements are avoidance of caffeine, tobacco, and 
alcohol during the evening hours, modification of the sleeping environment (comfortable bed, temperature, 
noise, lighting),and avoidance of exercise near bedtime. Sleep hygiene often includes information about 
normal sleep and age-associated changes.24 We will include this and information about the potential 
interactions between HF, its treatment and sleep/insomnia  
 
Hypnotic tapering is an option for the CBT-I group. This includes gradual reduction in use over the counter 
or prescribed hypnotics guided by the therapist. Participants may choose or not choose this option. 
  Participants will be expected to practice the HS behavior at home and record them, their daily weights, sleep 
and symptoms in a Health Sleep Log that will be available in paper or electronic format. They will receive an 
electronic bathroom scale and videos of the CBT-I session, the recorded relaxation exercise, the HS Log, 
instructions for all study elements, and study schedules on a mini-tablet computer. If they prefer, these 
materials can be used on an electronic device (smart phone, tablet, computer) of their choice. If they prefer, 
paper copies of all materials will be made available. In addition to the Healthy Sleep Guide, the CBT-I group 
will receive the Healthy Hearts Guide, a workbook that includes strategies for self-management of heart failure 
(See below). They will be encouraged to use the tablet for recording into the logs. The information will be 
uploaded to the Yale server when WIFI is available but can also be completed offline.  
 
The participants who are randomized to the heart failure self-management group (“Healthy Hearts”) will receive 
4 bi-weekly one hour sessions of classes on aspects of managing heart failure, including information about the 
disease process, symptoms, medications, exercise, managing weight, fluid, and diet, and basic information on 
stress management and sleep hygiene. Improvement of disease management, including sleep hygiene, 
physical activity, use of medications, avoidance of sodium etc. may improve sleep. Therefore, patients will be 
informed that this may be the case. They will be encouraged to use the techniques learned while at home. 
Participants in the “Healthy Hearts” group will receive the Healthy Hearts Guide, based on the “Fight against 
Heart Failure Handbook” with information on aspects of HF disease management, and a digital scale for 
monitoring of daily weights. The duration and scheduling of the HH group will be identical to the HS group. 
Participants will complete HH Logs to track their daily weights, and use of the self-management strategies and 
symptoms. Like the Healthy Sleep Group, the participants will receive information materials on a mini-tablet 
computer or in paper format. They will receive the Healthy Hearts Guide, a video of the HH intervention 

sessions, the HH logs, instructions for all elements of the study, and schedules.  They will not receive the CBT-
I video, HS Logs, HS Guide or the relaxation script. 
 
Follow-up data collection 
Data collection will proceed during the follow-up periods as noted above. Participants will be called monthly 
after the intervention phase until the 12-month follow-up. These calls will elicit any changes in health status 
and medications, health care utilization (hospitalization, provider visits, emergency department visits, home 
care use, changes in work status, time associated with the self-management/CBT-I activities). The caller will 
also elicit concerns about the HH or HS skills, provide problem solving, and remind participants about the next 
follow-up period. After the 12-month measurement period, participants or their designated family member will 
be called bi-monthly to elicit changes in health status (hospitalizations, heart transplant, implantation of cardiac 
devices or artificial hearts, or death) until the end of the study duration. Participants will be informed of the 
potential duration of follow-up, based on when they were recruited into the study (18-45 months).  
 
Participant will receive $50 for the sleep apnea screening; and an additional $250 disbursed over the study 
milestones. They be able to keep the mini-tablet (estimated value $60), and the study will provide taxi fare if 
needed. 
 
2. Characteristics of the study population 
 
The sample will include 200 individuals > 18 years of age with NY Heart Class II-III HF who are cognitively 
intact by clinical impression, speak/read English, concerned about their sleep for > month, and score > 8 on 
the Insomnia Severity Index,104 an indicator of ICSD-2 insomnia.106 Participants will have preserved (LVEF > 
45%) or reduced ejection fraction (LVEF < 45%). LVEF was not associated with chronic insomnia,8 but may 
contribute. HF patients at the recruitment sites undergo annual echocardiography and with cardiac changes. 
We will use the most current LVEF. Although sleep continuity is negatively related to age, we will include adults 
of all ages because of the greater effects of insomnia on fatigue in younger adults.8 Participants will receive HF 
care per the AHA/ACCF guidelines,35 as provided in the study recruitment sites.  
Depressive symptoms are common in HF10 and often comorbid with insomnia. CBT-I improves 
depression,98,107,108 including suicidal ideation,109 and antidepressants94 and depression199 did not diminish its 
effects. We will include patients with no/moderate depression and those on anti-depressant, anxiolytic and/or 
hypnotic medications because hypnotic tapering is an effective part of CBT-I.81,82  
Exclusion criteria are untreated restless legs syndrome; night/rotating shift work; travel across 2 or more 
time zones within one month of enrollment; contraindications to sleep restriction [seizure disorder, severe 
sleepiness (Epworth Scale >18), bipolar disorder]; neurological/musculoskeletal conditions affecting the non-
dominant arm (use of wrist actigraph), active illicit drug use, dementia, and end-stage renal failure. Participants 
who are hospitalized or have emergency department visits, unstable conditions, or changes in vasoactive 
medications within 4 weeks of recruitment will not be enrolled until 4 weeks elapse without new events. 
Patients with these events after enrollment will be retained; events will be tracked and included in analyses. 
Chronic insomnia and sleep apnea (SA) are often comorbid with HF,110-112 but SA was not associated with 
insomnia in HF patients3,8 and did not affect CBT-I outcomes in older adults.94 Although positive pressure 
(PAP) improves some cardiac variables, lack of rigorous long term RCTs preclude widespread SA treatment in 
HF,113 and the lower treatment threshold is not well defined.113-115 Most studies were small, poorly controlled, 
and included patients with moderate-severe SA (apnea hypopnea index/AHI > 15).115 One study (n = 40 
completed)116 had participants with AHI > 5, but mean AHI was ~ 28, and the authors did not report the 
proportion with mild SA. Given limited evidence of PAP benefits in very mild SA, we will include patients with 
AHI < 10 and refer those with AHI > 10 for further evaluation in collaboration with Dr. Yaggi. Participants with 
significant SA will be eligible If adherent to PAP > 6 hours nightly for at least 3 months. Titration of PAP or 
conduct of home sleep studies during PAP treatment is beyond the scope of this study, but participants will be 
eligible if they continue to have insomnia once adherent to PAP. 
 
 
Vulnerable groups  
No fetuses, neonates, pregnant women, prisoners, institutionalized individuals, or others who may be 
considered vulnerable populations will be included. In the state of CT, individuals 18 years of age and older are 
considered to be adults and are able to provide written informed consent. 
 

3. Recruitment  
Participants will be recruited through the Yale New Haven Hospital (YNHH) Heart Failure and Transplantation 
Center, and the VA Connecticut health Care System. We will also have access to the Yale Primary Care 
Center, a community based primary care program and the Yale Sleep Medicine Clinic. These sites use the 
AHA/ACC guidelines for management of HF. Potential participants will be identified by health care providers in 
these settings. Providers will elicit permission for the study team to contact the participants, and a member of 
the team will approach the patient, explain the study, and obtain informed consent for initial screening. We will 
also screen medical charts and sent invitation letters by mail. Epic MyChart will also be used to identify and 
notify potential candidates for the study.  Patients who have an Epic MyChart account and meet basic inclusion 
/exclusion criteria will be notified of the study through a MyChart message.  The online recruitment tool 
“Research Match” will be used to advertise the study, along with Yale websites. 
 
b. Sources of Materials 
1. Research material 
The sources of materials will include medical records (including electronic medical records), the hospital cost-
accounting database, patient self-report and recordings of sleep, Psychomotor vigilance, and six-minute walk 
test performance. 
 
2. Description of data to be collected  
Data collection will include self-reported and actigraph-recorded characteristics of sleep, insomnia, fatigue, 
depressive symptoms, anxious symptoms, excessive daytime sleepiness, pain, dyspnea, and sleep-related 
impairment. Functional performance (ability to complete daily roles and activities) will be assessed by self-
report and through a 6 minute walk test (a test of the distance walked in 6 minutes). Measures of physiological 
data, including oxygenation, respiratory effort, and airflow will be obtained in a screening evaluation for sleep 
apnea. Participants will also complete a psychomotor vigilance test to evaluate reaction time and vigilance 
associated with sleep loss.  We will obtain information on participants’ perceptions about their satisfaction with 
treatment and acceptability of the study activities. We will review medical and administrative records, as well as 
death certificates if necessary to determine health care utilization and survival. 
 
3. Access to private information provided by the participants 
Only members of the study team will have access to private information collected from the study participants. 
 
4. Collection of the information specifically for this study  
Data collection will include self-reported and actigraph-recorded characteristics of sleep, insomnia, fatigue, 
depressive symptoms, anxious symptoms, excessive daytime sleepiness, pain, dyspnea, and sleep-related 
impairment. Functional performance (ability to complete daily roles and activities) will be assessed by self-
report and through a 6 minute walk test (a test of the distance walked in 6 minutes). Measures of physiological 
data, including oxygenation, respiratory effort, and airflow will be obtained in a screening evaluation for sleep 
apnea. Participants will also complete a psychomotor vigilance test to evaluate reaction time and vigilance 
associated with sleep loss.  We will obtain information on participants’ perceptions about their satisfaction with 
treatment and acceptability of the study activities. We will review medical and administrative records, as well as 
death certificates if necessary to determine health care utilization and survival. The initial interview will take 
approximately ½ hour, completion of the actigraphy and diaries will take approximately 10 minutes each day 
during the 5 data collection periods. The actigraphy will be completed over a 2-week period of time at each 
time point. The questionnaires will take no more than an hour at each of the 5 time points. 
We will screen for SDB with the Apnea Risk Evaluation System (ARES) Unicorder (Advanced Brain 
Monitoring, Carlsbad, CA), a wireless physiological recorder worn on the forehead and measures blood oxygen 
saturation (SpO2) and pulse rate (reflectance pulse oximetry), airflow (nasal cannula/ pressure transducer), 
respiratory effort (pressure transducer sensing forehead venous pressure) and venous volume 
(photoplethysmography), snoring levels (calibrated acoustic microphone), head movement and position 
(accelerometers). It is small and comfortably worn in all sleep positions, and is easily applied by the user with 
simple instruction. Participants will wear the recorder for 2 consecutive nights. 
 
Participants will wear the actigraph continuously for 2 weeks, removing it only for bathing, depress the event 
marker at “lights out” and “lights on” times to demarcate time in bed, and keep the light meter on the actigraph 
uncovered. Instructions will be provided.  Sleep diaries will be completed daily to assess time of lights out/on 

and times of removal of the actigraph, as well as hypnotic use. Detailed verbal and written instructions will be 
provided prior to the assessments. Study materials will be delivered directly through home visits, overnight 
mail, or handed directly to the study participants at the time of the study sessions, depending on the time 
frame. 
 
c. Potential Risks 
1. Potential risks and their likelihood 
Because study participants will engage in group discussions, there is some possibility that others in the group 
may disclose information to outsiders that may be confidential. The interveners will establish strict guidelines 
and rules for each group session to discourage disclosure outside of the group setting. Participants will be 
asked to enter their daily weights, symptoms, sleep and practice of skills learned in an electronic log. They will 
also be asked to complete an electronic sleep diary prior to the intervention and at each of the 5 data collection 
periods. The electronic logs will be included as links on password protected and encrypted mini-tablet 
computers. These data can be uploaded directly by the participants to Yale’s HIPAA compliant website or 
completed offline and uploaded when WIFI is available. However, due to password protection and encryption, 
the risk of a violation of privacy is minimal. If participants decide to use their own electronic devices (smart 
phone, computer, tablet), the log data will not reside on the device, but will be directly entered onto the Yale 
site with a password. It will not be possible to view data submitted by other participants. We will provide video-
recordings of intervention sessions (HH and HS), but these will not be recorded with identifiable study 
participants. 
Risks are highly unlikely. However, stimulus control therapy, a component of CBT-I requires the patient to 
get out of bed when sleepy. For frail patients, it is possible that this could result in falls. Sleep restriction may 
result in excessive daytime sleepiness and lower the seizure threshold for people who have undiagnosed 
epilepsy or bipolar disorder. However, we do not expect this to occur, as these are exclusion criteria. In 
addition, paradoxical anxiety reactions have rarely been reported as a result of relaxation therapy. It is 
possible, but unlikely that the group sessions, telephone calls, or interviews may elicit psychosocial distress in 
study participants. The clinicians leading the sessions include experienced advanced practice nurses who will 
refer participants for follow-up care as appropriate.  
We may uncover sleep disordered breathing or psychiatric disorders, such as depression in the screening 
process. Participants with these conditions will be notified and suggestions will be made for follow-up care. All 
patients and referring providers will receive a written report of their sleep evaluations and depressive and 
anxious symptoms with recommendations for follow-up as needed. We will not send these reports to providers 
other than those referring to the trial because that would be a HIPAA violation. If our screening suggests that 
any patient may be suicidal, we will refer them immediately for follow-up. 
 
2. Alternative Treatment 
An alternative treatment for study participants is the use of hypnotic drugs. Other forms of behavioral treatment 
are available that include components similar to those in this study (relaxation, sleep restriction, sleep hygiene, 
etc.). Patients who are depressed or anxious and have insomnia may also benefit from pharmacological or 
behavioral treatment for these conditions. However, several studies have shown that CBT-I improves mood 
disorders with and or without additional pharmacological treatment. 
 
Adequacy of Protection Against Risks 
a. Recruitment and Informed Consent 
1. Recruitment 
Participants will be recruited through the Yale-New Haven Hospital (YNHH) Heart Failure and Transplantation 
Center, and the VA CT Health Care System. We will also have access to the Yale Primary Care Center. We 
will place brochures and/or posters in medical offices and post information on the Yale CTSA website to 
provide information about the study and with a telephone number to contact the study coordinator. In addition, 
the clinicians in these practices will identify potential participants during routine care, briefly explain the study 
and elicit their permission to be contacted by a member of the study team. The research coordinator will meet 
with potential study participants and explain the study in detail, answering questions. Written information will be 
provided. We will also screen medical charts and sent invitation letters by mail. Epic MyChart will also be used 
to identify and notify potential candidates for the study.  Patients who have an Epic MyChart account and meet 
basic inclusion /exclusion criteria will be notified of the study through a MyChart message.  The online 

recruitment tool “Research Match” will be used to advertise the study, along with Yale websites. All recruitment 
methods will be approved by Yale University Human Investigations Committee (IRB).  
 
 
2. Consent procedure 
The study research coordinator or research assistant will obtain consent. After explaining the study 
procedures, all risks and benefits, as well as alternative treatments, protection of confidentiality and contact 
information for the IRB, participants will be provided a written informed consent document to read. If they 
prefer, the study personnel will read the document to them. Participants will sign 2 copies of the consent, retain 
one, and the researchers will keep the second copy on file. Potential participants will be told that HF self-
management strategies provided in the attention-control condition may also improve their sleep, based on 
improvements in HF.  
 
b. Protections against Risk 
1. Procedures for minimizing risk 
We will protect against the risks of the effects of the experimental condition by excluding patients who have 
bipolar disorder or seizure disorders, as well as those with Epworth Sleepiness Scale scores >18 due to the 
potential for sleep restriction to increase sleepiness and potential injury. In addition, we will not use sleep 
restriction below 4.5 hours to avoid extreme sleepiness. We will not include individuals with severe depression 
in either group. Participants will be carefully monitored for these and any changes in emotional distress, 
particularly anxiety and depression. The clinicians leading the groups are health professionals who are trained 
to observe for these issues. Participants will be instructed to call the research coordinator if they have 
worsening of their sleep, emotional distress, or increased severity of any symptoms over the course of the 
study. Participants will be referred to a mental health clinicians or the cardiology health care provider if they 
have any of these concerns. If new or exacerbated health problems, such as sleep apnea or depression, are 
detected during the study assessments, participants will be referred to their referring providers (or a provider of 
their choice) for follow-up evaluation and possible treatment.  
All study participants and their referring health care providers will receive a report of their ARES sleep 
studies (measure of sleep disordered breathing) and a report of the depression screening with appropriate 
recommendations for treatment if indicated. 
The risk of violation of confidentiality will be minimized by using documents with code numbers, linked only 
to the master list of participants that will be maintained in a physically locked file. Electronic records, including 
the patient logs, will be password protected and encrypted, consistent with Yale University policies. Only 
members of the study team will have access to the physical or electronic data. 
 
Potential Benefits of the Proposed Research to Human Subjects and Others 
a. Potential Benefits to Participants 
Potential benefits include learning more about sleep and improving insomnia, as well as self-management of 
heart failure. It is also possible that symptoms, including sleepiness, fatigue, anxiety, and depression, and 
daytime functioning may improve, especially in the experimental arm. Participants in the comparison group will 
learn about self-management of heart failure, including increased understanding of the disease process, 
medications, fluids, diet, sodium management, stress management and exercise, as well as basic information 
about sleep. It is possible that improvement in these behaviors may improve sleep/insomnia, as well as 
symptoms and functional performance. HF self-management information will be available to both groups 
through the Fight against Heart Failure Handbook. 
 
b. Balance of Risks and Benefits 
The risks of this study are minimal relative to the potential benefits. 
 
Importance of the Knowledge to be gained 
The knowledge obtained in this study may lead to important advances in understanding the outcomes of 
cognitive behavioral therapy for insomnia, an efficacious treatment in many populations that has been under-
studied among people with HF. Based on our preliminary work, we expect that CBT-I will reduce insomnia 
severity and lead to improvements and fatigue and possibly other symptoms related to insomnia and often 
experienced by HF patients. This study will also evaluate the impact of CBT-I on health care utilization and 
cost, as well as event-free survival. If, as we hypothesize, CBT-I has sustained efficacy and is cost-effective, 

this study will provide critical evidence to support of future translational work in which we will conduct an 
effectiveness study focused on enhancing reach, adoption, implementation, and long term maintenance65 
(factors associated with sustainability) of CBTI-into diverse HF disease management settings. For example, 
this study may include structured online training for HF registered nurses; education on use of acceptable 
billing codes for insomnia and fatigue treatment; and strategies to integrate CBT-I into cardiac rehabilitation 
programs or HF support groups, among others. 
 
Data Safety and Monitoring Plan 
The risks are very minimal in this single-site study, therefore a data and safety monitoring board is not 
required.200 We had no adverse events in our previous study. 
  
a. Monitoring entity or who will monitor the study (i.e., PI, ISM, SMC, or DSMB).  
The PI will have primary responsibility for overseeing all aspects of the study, including compliance with all 
human subjects requirements, ongoing monitoring for adverse events, and submitting reports to the Yale IRB 
and NINR. An Adverse Events Committee, comprised of the PI, a senior scientist with expertise in symptom 
management from the school of nursing and a physician sleep specialist will review all possible adverse events 
and make recommendations. Aside from the PI, neither of the other 2 members will be affiliated with the study. 
The PI will report the following to NINR:  Unanticipated problems or unexpected serious adverse events that 
may be related to the study protocol; IRB-approved revisions to the study protocol that indicate a change in risk 
for participants; A summary of recommendations made by the Adverse Events Committee or other monitoring 
entity as appropriate and (if applicable) the action plan for response; Notice of any actions taken by the IRB or 
regulatory bodies regarding the research and any responses to those actions.  
   
b. 
Procedures for 1) monitoring study safety to include monitoring schedule, auditing selected 
cases for compliance with IRB requirements, conformance with informed consent requirements, 
verification of source documents, and investigator compliance; 2) minimizing research-associated risk, 
and 3) protecting the confidentiality of participant data.  
 
1) The PI will audit selected cases on a monthly basis for conformance with informed consent requirements; 
take responsibility for assuring the confidentiality of the data collected, verify the source documents, and 
submit reports to the NINR. The Adverse Events Committee will review all possible adverse events and make 
recommendations. The Adverse Events Committee will meet quarterly and as needed to review events related 
to the study. The PI will report adverse events promptly to this group and to the Yale University Institutional 
Review Board’s Human Investigation Committee (HIC).  
2) The PI will continuously monitor all study personnel, data and all study procedures to minimize research-
associated risk and assure that personnel are adequately trained and follow procedures as per the human 
subjects research protection program. Specific strategies within the study protocol will include the following: 
We will protect against the risks of the effects of the experimental condition by excluding patients who have 
bipolar disorder or seizure disorders, as well as those with Epworth Sleepiness Scale scores >18 due to the 
potential for sleep restriction to increase sleepiness and potential injury. In addition, we will not use sleep 
restriction below 4.5 hours to avoid extreme sleepiness. We will not include individuals with severe depression 
in either group. Participants will be carefully monitored for these and any changes in emotional distress, 
particularly anxiety and depression. The clinicians leading the groups are health professionals who are trained 
to observe for these issues. Participants will be instructed to call the research coordinator if they have 
worsening of their sleep, emotional distress, or increased severity of any symptoms over the course of the 
study. Participants will be referred to a mental health clinicians or the cardiology health care provider if they 
have any of these concerns. If new or exacerbated health problems, such as sleep apnea or depression, are 
detected during the study assessments, participants will be referred to their referring providers (or a provider of 
their choice) for follow-up evaluation and possible treatment.  
All study participants and their referring health care providers will receive a report of their ARES sleep studies 
(measure of sleep disordered breathing) and a report of the depression screening with appropriate 
recommendations for treatment if indicated. 

 
3) The risk of violation of confidentiality will be minimized by using documents with code numbers, linked only 
to the master list of participants that will be maintained in a physically locked file. Electronic records, including 
the patient logs, will be password protected and encrypted, consistent with Yale University policies. Only 
members of the study team will have access to the physical or electronic data. 
 
c. Procedures for identifying, reviewing, and reporting adverse events and unanticipated problems to 
the IRB, NINR, and FDA (if applicable).  
 
The PI will report adverse events promptly to the Adverse Events Committee and to the Yale University 
Institutional Review Board’s Human Investigation Committee (HIC). The PI will report serious adverse events 
or deaths encountered during the study immediately by phone to the HIC and to the NINR. The PI will follow-up 
immediately to gather more information about the event. Additional information will be acquired as necessary. 
If the PI is unavailable, there will be an on-call schedule that includes the co-investigators who will follow-up in 
place of the PI.  
 
d. For multi-site studies, procedures to ensure compliance with the monitoring plan and reporting 
requirements across study sites.  
This is not a multi-site study. 
e. An assessment of external factors or relevant information (e.g., developments in the literature, 
results of related studies) that may have an impact on the safety of participants or on the ethics for 
the research study.  
To our knowledge there are currently no external factors or relevant studies that may have an impact on the 
safety of participants or on the ethics of the research study. The PI will continue to monitor the literature and 
reports of studies disseminated through other venues (e.g., conferences) throughout the duration of the study. 
If changes are indicated, she will discuss with the Adverse Events Monitoring Committee and with NINR staff. 
f. The advanced plans for interim and/or futility analysis as appropriate.  
 
Interim analyses will not be needed, as this is a low risk study. Conduct of interim analysis would introduce 
undue Type I error. 
 
 
 
  
 
 
 
 

 
 
References 
 
1. 
Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics--2014 update: a report from 
the American Heart Association. Circulation. 2014;129(3):e28-e292. 
2. 
Principe-Rodriguez K, Strohl KP, Hadziefendic S, Pina IL. Sleep symptoms and clinical markers of 
illness in patients with heart failure. Sleep Breath. 2005;9(3):127-133. 
3. 
Riegel B, Glaser D, Richards K, et al. Modifiable factors associated with sleep dysfunction in adults with 
heart failure. Eur J Cardiovasc Nurs. 2012;11(4):402-409. 
4. 
Gau FY, Chen XP, Wu HY, Lin ML, Chao YF. Sleep-related predictors of quality of life in the elderly 
versus younger heart failure patients: a questionnaire survey. Int J Nurs Stud. 2011;48(4):419-428. 
5. 
Brostrom A, Stromberg A, Dalstrom U, Fridlund B. Sleep difficulties, daytime sleepiness, and health-
related quality of life in patients with chronic heart failure. J Cardiovasc Nurs. 2004;19(4):234-242. 
6. 
Zambroski CH, Moser DK, Bhat G, Ziegler C. Impact of symptom prevalence and symptom burden on 
quality of life in patients with heart failure. Eur J Cardiovasc Nurs. 2005;4(3):198-206. 
7. 
Redeker NS, Stein S. Characteristics of sleep in patients with stable heart failure versus a comparison 
group. Heart Lung. 2006;35(4):252-261. 
8. 
Redeker NS, Jeon S, Muench U, Campbell D, Walsleben J, Rapoport DM. Insomnia symptoms and 
daytime function in stable heart failure. Sleep. 2010;33(9):1210-1216. 
9. 
Erickson VS, Westlake CA, Dracup KA, Woo M, Hage A. Sleep disturbance symptoms in patients with 
heart failure. AACN Clin Issues. 2003;14(4):477-487. 
10. 
Redeker NS. Somatic symptoms explain differences in psychological distress in heart failure patients vs 
a comparison group. Prog Cardiovasc Nurs. 2006;21(4):182-189. 
11. 
Laugsand LE, Strand LB, Platou C, Vatten LJ, Janszky I. Insomnia and the risk of incident heart failure: 
a population study. Eur Heart J. 2014;35(21):1382-1393. 
12. 
Spiegelhalder K, Scholtes C, Riemann D. The association between insomnia and cardiovascular 
diseases. Nat Sci Sleep. 2010;2:71-78. 
13. 
Lee KS, Song EK, Lennie TA, et al. Symptom clusters in men and women with heart failure and their 
impact on cardiac event-free survival. J Cardiovasc Nurs. 2010;25(4):263-272. 
14. 
Song EK, Moser DK, Rayens MK, Lennie TA. Symptom clusters predict event-free survival in patients 
with heart failure. J Cardiovasc Nurs. 2010;25(4):284-291. 
15. 
Jurgens CY, Moser DK, Armola R, Carlson B, Sethares K, Riegel B. Symptom clusters of heart failure. 
Res Nurs Health. 2009;32(5):551-560. 
16. 
Blinderman CD, Homel P, Billings JA, Portenoy RK, Tennstedt SL. Symptom distress and quality of life 
in patients with advanced congestive heart failure. J Pain Symptom Manag. 2008;35(6):594-603. 
17. 
Johansson P, Riegel B, Svensson E, et al. The contribution of heart failure to sleep disturbances and 
depressive symptoms in older adults. J Geriatric Psychiatry Neurol.. 2012;25(3):179-187. 
18. 
Conley S, Feder S, Redeker NS. The relationship between pain, fatigue, depression and functional 
performance in stable heart failure. Heart Lung. 2015;44(2):107-112. 
19. 
Redeker NS, Hilkert R. Sleep and quality of life in stable heart failure. J Card Fail. 2005;11(9):700-704. 
20. 
Andrews LK, Coviello J, Hurley E, Rose L, Redeker NS. "I'd eat a bucket of nails if you told me it would 
help me sleep:" perceptions of insomnia and its treatment in patients with stable heart failure. Heart 
Lung. 2013;42(5):339-345. 
21. 
Riemann D. Hyperarousal and insomnia: state of the science. Sleep Med Rev. 2010;14(1):17. 
22. 
Perlis ML, Giles DE, Mendelson WB, Bootzin RR, Wyatt JK. Psychophysiological insomnia: the 
behavioural model and a neurocognitive perspective.  J Sleep Res. 1997;6(3):179-188. 
23. 
Perlis ML, Merica H, Smith MT, Giles DE. Beta EEG activity and insomnia. Sleep Med Rev. 
2001;5(5):363-374. 
24. 
Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA, Lichstein KL. Psychological and behavioral 
treatment of insomnia:Update of the recent evidence (1998-2004). Sleep. 2006;29(11):1398-1414. 
25. 
Smith MT, Perlis ML, Park A, et al. Comparative meta-analysis of pharmacotherapy and behavior 
therapy for persistent insomnia. Am J Psychiatry. 2002;159(1):5-11. 

26. 
Morin CM. Cognitive-behavioral approaches to the treatment of insomnia.  J Clin Psychiatry. 2004;65 
Suppl 16:33-40. 
27. 
Morin CM, Hauri PJ, Espie CA, Spielman AJ, Buysse DJ, Bootzin RR. Nonpharmacologic treatment of 
chronic insomnia. An American Academy of Sleep Medicine Review. Sleep. 1999;22(8):1134-1156. 
28. 
Redeker NS, Jeon S, Andrews L, Cline J, Jacoby D, Mohensin V. Feasibility, Acceptability and Efficacy 
of Cognitive Behavioral Therapy for Insomnia in Stable Heart Failure. J Clin Sleep Med. Manuscript in 
press. 
29. 
Redeker NS, Conley, S.,, Alexander N, et al. Pilot clinical effectiveness study of behavioral treatment 
for insomnia in primary care. Eastern Nursing Research Annual Scientific Sessions. 2013. 
30. 
National Institute of Nursing Research. Bringing Science to Life: NINR Strategic Plan. 2011. 
31. 
Foundation NS. 2005 Sleep in American Poll. 2005; 
http://www.sleepfoundation.org/hottopics/index.php?secid=16&id=24. Accessed November 21, 2006. 
32. 
Leidy NK. Functional status and the forward progress of merry-go-rounds:  Toward a coherent 
analytical framework. Nurs Res. 1994;43(4):196-202. 
33. 
Skotzko CE. Symptom perception in CHF: (why mind matters). Heart Fail Rev. 2009;14(1):29-34. 
34. 
Fritschi C, Redeker NS. Contributions of Comorbid Diabetes to Sleep Characteristics, Daytime 
Symptoms, and Physical Function Among Patients With Stable Heart Failure.  J Cardiovasc Nurs.. 
2014. [Epub ahead of print]. 
35. 
Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: 
executive summary: a report of the American College of Cardiology Foundation/American Heart 
Association Task Force on practice guidelines. Circulation. 2013;128(16):1810-1852. 
36. 
Wu JR, Lennie TA, Dekker RL, Biddle MJ, Moser DK. Medication adherence, depressive symptoms, 
and cardiac event-free survival in patients with heart failure. J Card Fail. 2013;19(5):317-324. 
37. 
Albert NM, Fonarow GC, Abraham WT, et al. Depression and clinical outcomes in heart failure: an 
OPTIMIZE-HF analysis. Am J Med. 2009;122(4):366-373. 
38. 
Sayers SL, Hanrahan N, Kutney A, Clarke SP, Reis BF, Riegel B. Psychiatric comorbidity and greater 
hospitalization risk, longer length of stay, and higher hospitalization costs in older adults with heart 
failure. J Am Geriatrics Soc. 2007;55(10):1585-1591. 
39. 
Fink AM, Gonzalez RC, Lisowski T, et al. Fatigue, inflammation, and projected mortality in heart failure. 
J Card Fail. 2012;18(9):711-716. 
40. 
Freedland KE, Carney RM, Rich MW. Effect of depression on prognosis in heart failure. Heart Fail Clin.  
2011;7(1):11-21. 
41. 
Redeker NS, Jeon S, Pacelli, J.,, Anderson G. Sleep disturbance, sleep-related symptoms, and 
biological rhythms in heart failure patients who have insomnia. Sleep. 2014;37:A248-A249. 
42. 
Smith MT, Huang MI, Manber R. Cognitive behavior therapy for chronic insomnia occurring within the 
context of medical and psychiatric disorders. Clin Psychol Rev. 2005;25(5):559-592. 
43. 
Katz DA, McHorney CA. Clinical correlates of insomnia in patients with chronic illness. Arch Int Med. 
1998;158(10):1099-1107. 
44. 
Redeker NS, Jeon S, Andrews L, Cline J, Pacelli J, Jacoby D. Cognitive behavioral therapy for 
insomnia has sustained effects on daytime symptoms and hospitalization in patients with stable heart 
failure. Sleep. 2013;36:A297. 
45. 
Tully PJ, Selkow T, Bengel J, Rafanelli C. A dynamic view of comorbid depression and generalized 
anxiety disorder symptom change in chronic heart failure: the discrete effects of cognitive behavioral 
therapy, exercise, and psychotropic medication. Disabil Rehabil. 2014:1-8. 
46. 
Boren SA, Wakefield BJ, Gunlock TL, Wakefield DS. Heart failure self-management education: a 
systematic review of the evidence. Int J Evidence-based Healthcare. 2009;7(3):159-168. 
47. 
Hertzog MA, Pozehl B, Duncan K. Cluster analysis of symptom occurrence to identify subgroups of 
heart failure patients: a pilot study. J Cardiovasc Nurs. 2010;25(4):273-283. 
48. 
Vitiello MV, McCurry SM, Rybarczyk BD. The future of cognitive behavioral therapy for insomnia: what 
important research remains to be done? J Clin Psychol. 2013;69(10):1013-1021. 
49. 
Dinges DF, Pack F, Williams K, et al. Cumulative sleepiness, mood disturbance, and psychomotor 
vigilance performance decrements during a week of sleep restricted to 4-5 hours per night. Sleep. 
1997;20(4):267-267. 
50. 
Sunwoo BY, Jackson N, Maislin G, et al. Reliability of a single objective measure in assessing 
sleepiness. Sleep. 2012;35(1):149-158. 

51. 
Yu L, Buysse DJ, Germain A, et al. Development of short forms from the PROMIS sleep disturbance 
and Sleep-Related Impairment item banks. Behav Sleep Med.  2011;10(1):6-24. 
52. 
Arzt M, Young T, Finn L, et al. Sleepiness and sleep in patients with both systolic heart failure and 
obstructive sleep apnea. Arch Internal Med. 2006;166(16):1716-1722. 
53. 
Redeker NS, Muench U, Zucker MJ, et al. Sleep disordered breathing, daytime symptoms, and 
functional performance in stable heart failure. Sleep. 2010;33(4):551-560. 
54. 
Taranto Montemurro L, Floras JS, Millar PJ, et al. Inverse relationship of subjective daytime sleepiness 
to sympathetic activity in patients with heart failure and obstructive sleep apnea. Chest. 
2012;142(5):1222-1228. 
55. 
Myers J, Zaheer N, Quaglietti S, Madhavan R, Froelicher V, Heidenreich P. Association of functional 
and health status measures in heart failure. J Card Fail. 2006;12(6):439-445. 
56. 
Rostagno C, Olivo G, Comeglio M, et al. Prognostic value of 6-minute walk corridor test in patients with 
mild to moderate heart failure: comparison with other methods of functional evaluation. Eur J Heart Fail. 
2003;5(3):247-252. 
57. 
Redeker NS, Anderson R, Bakken S, et al. Advancing Symptom Science Through Use of Common 
Data Elements. Manuscript under review. 
58. 
Zaya M, Phan A, Schwarz ER. The dilemma, causes and approaches to avoid recurrent hospital 
readmissions for patients with chronic heart failure. Heart Fail Rev. 2012;17(3):345-353. 
59. 
Sivertsen B, Krokstad S, Mykletun A, Overland S. Insomnia symptoms and use of health care services 
and medications: the HUNT-2 study. Behav Sleep Med. 2009;7(4):210-222. 
60. 
Sarsour K, Kalsekar A, Swindle R, Foley K, Walsh JK. The association between insomnia severity and 
healthcare and productivity costs in a health plan sample. Sleep. 2011;34(4):443-450. 
61. 
Pollack M, Seal B, Joish VN, Cziraky MJ. Insomnia-related comorbidities and economic costs among a 
commercially insured population in the United States. Curr Med Res Opin.  2009;25(8):1901-1911. 
62. 
Bin YS, Marshall NS, Glozier N. The burden of insomnia on individual function and healthcare 
consumption in Australia. Austral NZ J Pub Health. 2012;36(5):462-468. 
63. 
Valgus JM, Faso A, Gregory KM, et al. Integration of a clinical pharmacist into the hematology-oncology 
clinics at an academic medical center. Am J Health Syst Pharm. 2011;68(7):613-619. 
64. 
Daley M, Morin CM, LeBlanc M, Gregoire JP, Savard J, Baillargeon L. Insomnia and its relationship to 
health-care utilization, work absenteeism, productivity and accidents. Sleep Med. 2009;10(4):427-438. 
65. 
Glasgow RE, McKay HG, Piette JD, Reynolds KD. The RE-AIM framework for evaluating interventions: 
what can it tell us about approaches to chronic illness management? Patient Educ Couns. 
2001;44(2):119-127. 
66. 
Spielman AJ, Caruso LS, Glovinsky PB. A behavioral perspective on insomnia treatment. Psychiatric 
Clin No Amer. 1987;10(4):541-553. 
67. 
Perlis ML, Smith MT, Pigeon WR. Etiology and pathophysiology of insomnia. In: Kryger MH, Roth, T.H., 
Dement, W.C., ed. Principles and Practice of Sleep Medicine. 4th ed. Philadelphia: Elsevier Saunders; 
2005:714-725. 
68. 
Schwartz DR, Carney CE. Mediators of cognitive-behavioral therapy for insomnia: a review of 
randomized controlled trials and secondary analysis studies. Clin Psychol Rev. 2012;32(7):664-675. 
69. 
Grey M, Knafl K, McCorkle R. A framework for the study of self- and family management of chronic 
conditions. Nurs Outlook. 2006;54(5):278-286. 
70. 
Schulman-Green D, Jaser S, Martin F, et al. Processes of self-management in chronic illness. J Nurs 
Scholarsh. 2012;44(2):136-144. 
71. 
Anch M, Browman CP, Mitler MM, Walsh JK. Sleep: A scientific perspective. Englewood Cliffs, NJ: 
Prentice Hall; 1988. 
72. 
National Institutes of of Health. National Institutes of Health State of the Science Conference 
Statement. Manifestations and management of chronic insomnia in adults. Sleep. 2005;28:1049-1057. 
73. 
Hayes D, Jr., Anstead MI, Ho J, Phillips BA. Insomnia and chronic heart failure. Heart Fail Rev. 
2009;14(3):171-182. 
74. 
Redeker NS, Adams L, Berkowitz R, et al. Nocturia, Sleep and Daytime Function in Stable Heart 
Failure. J Cardiac Fail. 2012;18(7):569-575. 
75. 
Wesley Y, Smeltzer SC, Redeker NS, Walker S, Palumbo P, Whipple B. Reproductive decision making 
in mothers with HIV-1. Health Care Women Int. 2000;21(4):291-304. 
76. 
Redeker NS, Jeon S, Pacelli J, Andrews L, Cline J. Perpetuating factors for insomnia in stable HF. 
Manuscript in preparation. in progress. 

77. 
Lee CS, Moser DK, Lennie TA, Riegel B. Event-free survival in adults with heart failure who engage in 
self-care management. Heart Lung. 2011;40(1):12-20. 
78. 
Shao JH, Chang AM, Edwards H, Shyu YI, Chen SH. A randomized controlled trial of self-management 
programme improves health-related outcomes of older people with heart failure. J Adv Nurs. 
2013;69(11):2458-2469. 
79. 
Bootzin RR, Perlis ML. Nonpharmacologic treatments of insomnia. J Clin Psychiatry. 1992;53 Suppl:37-
41. 
80. 
McCrae CS, Rowe MA, Dautovich ND, et al. Sleep hygiene practices in two community dwelling 
samples of older adults. Sleep. 2006;29(12):1551-1560. 
81. 
Lichstein KL, Nau SD, Wilson NM, et al. Psychological treatment of hypnotic-dependent insomnia in a 
primarily older adult sample. Behav Res Ther. 2013;51(12):787-796. 
82. 
Belanger L, Belleville G, Morin C. Management of Hypnotic Discontinuation in Chronic Insomnia. Sleep 
Med Clin. 2009;4(4):583-592. 
83. 
McCrae CS, McGovern R, Lukefahr R, Stripling AM. Research Evaluating Brief Behavioral Sleep 
Treatments for Rural Elderly (RESTORE): a preliminary examination of effectiveness. Am J Geriatric 
Psychiatry. 2007;15(11):979-982. 
84. 
Lovato N, Lack L, Wright H, Kennaway DJ. Evaluation of a brief treatment program of cognitive 
behavior therapy for insomnia in older adults. Sleep. 2014;37(1):117-126. 
85. 
Lichstein KL, Wilson NM, Johnson CT. Psychological treatment of secondary insomnia. Psychol Aging. 
2000;15(2):232-240. 
86. 
Rybarczyk B, Stepanski E, Fogg L, Lopez M, Barry P, Davis A. A placebo-controlled test of cognitive-
behavioral therapy for comorbid insomnia in older adults. J Consult Clin Psychol. 2005;73(6):1164-
1174. 
87. 
Perlis ML, Sharpe M, Smith MT, Greenblatt D, Giles D. Behavioral treatment of insomnia: treatment 
outcome and the relevance of medical and psychiatric morbidity. J Behav Med. 2001;24(3):281-296. 
88. 
Edinger JD, Olsen MK, Stechuchak KM, et al. Cognitive behavioral therapy for patients with primary 
insomnia or insomnia associated predominantly with mixed psychiatric disorders: a randomized clinical 
trial. Sleep. 2009;32(4):499-510. 
89. 
Currie SR, Wilson KG, Pontefract AJ, deLaplante L. Cognitive-behavioral treatment of insomnia 
secondary to chronic pain. J Consult Clin Psychol. 2000;68(3):407-416. 
90. 
Currie SR, Clark S, Hodgins DC, El-Guebaly N. Randomized controlled trial of brief cognitive-
behavioural interventions for insomnia in recovering alcoholics. Addiction. 2004;99(9):1121-1132. 
91. 
Rybarczyk B, Lopez M, Benson R, Alsten C, Stepanski E. Efficacy of two behavioral treatment 
programs for comorbid geriatric insomnia. Psychol Aging. 2002;17(2):288-298. 
92. 
Espie CA, MacMahon KM, Kelly HL, et al. Randomized clinical effectiveness trial of nurse-administered 
small-group cognitive behavior therapy for persistent insomnia in general practice. Sleep. 
2007;30(5):574-584. 
93. 
Germain A, Moul DE, Franzen PL, et al. Effects of a brief behavioral treatment for late-life insomnia: 
preliminary findings. J Clin Sleep Med. 2006;2(4):403-406. 
94. 
Buysse DJ, Germain A, Moul DE, et al. Efficacy of brief behavioral treatment for chronic insomnia in 
older adults. Arch Int Med. 2011;171(10):887-895. 
95. 
Espie CA, Fleming L, Cassidy J, et al. Randomized controlled clinical effectiveness trial of cognitive 
behavior therapy compared with treatment as usual for persistent insomnia in patients with cancer. J 
Clin Oncol. 2008;26(28):4651-4658. 
96. 
Epstein DR, Dirksen SR. Randomized trial of a cognitive-behavioral intervention for insomnia in breast 
cancer survivors. Oncol Nurs Forum. 2007;34(5):E51-59. 
97. 
Chen HY, Chiang CK, Wang HH, et al. Cognitive-behavioral therapy for sleep disturbance in patients 
undergoing peritoneal dialysis: a pilot randomized controlled trial. Am J Kidney Dis. 2008;52(2):314-
323. 
98. 
Manber R, Edinger JD, Gress JL, San Pedro-Salcedo MG, Kuo TF, Kalista T. Cognitive behavioral 
therapy for insomnia enhances depression outcome in patients with comorbid major depressive 
disorder and insomnia. Sleep. 2008;31(4):489-495. 
99. 
Ulmer CS, Edinger JD, Calhoun PS. A multi-component cognitive-behavioral intervention for sleep 
disturbance in veterans with PTSD: a pilot study. J Clin Sleep Med. 2011;7(1):57-68. 

100. 
Kapella MC, Herdegen JJ, Perlis ML, et al. Cognitive behavioral therapy for insomnia comorbid with 
COPD is feasible with preliminary evidence of positive sleep and fatigue effects. Int J. Chron Obstr 
Pulm Dis. 2011;6:625-635. 
101. 
Botteman M. Health economics of insomnia therapy: implications for policy. Sleep Med. 2009;10 Suppl 
1:S22-25. 
102. 
Redeker NS, Ruggiero J, Hedges C. Sleep is related to physical function and emotional wellbeing after 
cardiac surgery. Nurs Rese. 2004;53:154-162. 
103. 
Redeker NS, Mason DJ, Wykpisz E, Glica B, Miner C. First postoperative week activity patterns and 
recovery in women after coronary artery bypass surgery. Nurs Res. 1994;43(3):168-173. 
104. 
Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure 
for insomnia research. Sleep Med. 2001;2(4):297-307. 
105. 
Yang M, Morin CM, Schaefer K, Wallenstein GV. Interpreting score differences in the Insomnia Severity 
Index: using health-related outcomes to define the minimally important difference. Curr Med Res 
Opinion. 2009;25(10):2487-2494. 
106. 
American Academy of Sleep Medicine. The International Classification of Sleep Disorders. 
Westchester, IL: American Academy of Sleep Medicine; 2005. 
107. 
Riemann D. Does effective management of sleep disorders reduce depressive symptoms and the risk 
of depression? Drugs. 2009;2:43-64. 
108. 
Howland RH. Sleep interventions for the treatment of depression. J Psychosoc Nurs Mental Health 
Serv. 2011;49(1):17-20. 
109. 
Manber R, Bernert RA, Suh S, Nowakowski S, Siebern AT, Ong JC. CBT for insomnia in patients with 
high and low depressive symptom severity: adherence and clinical outcomes. J Clin Sleep Med. 
2011;7(6):645-652. 
110. 
Al-Jawder SE, Bahammam AS. Comorbid insomnia in sleep-related breathing disorders: an under-
recognized association. Sleep Breath. 2011;29:29. 
111. 
Luyster FS, Buysse DJ, Strollo PJ, Jr. Comorbid insomnia and obstructive sleep apnea: challenges for 
clinical practice and research. J Clin Sleep Med. 2010;6(2):196-204. 
112. 
Gooneratne NS, Gehrman PR, Nkwuo JE, et al. Consequences of comorbid insomnia symptoms and 
sleep-related breathing disorder in elderly subjects. Arch Int Med.  2006;166(16):1732-1738. 
113. 
Somers VK, White DP, Amin R, et al. Sleep apnea and cardiovascular disease: an American Heart 
Association/American College of Cardiology Foundation Scientific Statement from the American Heart 
Association Council for High Blood Pressure Research Professional Education Committee, Council on 
Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing. J Am Coll Cardiol. 
2008;52(8):686-717. 
114. 
Kasai T. Sleep apnea and heart failure. J Cardiol. 2012;60(2):78-85. 
115. 
Chowdhury M, Adams S, Whellan DJ. Sleep-disordered breathing and heart failure: focus on 
obstructive sleep apnea and treatment with continuous positive airway pressure. J Card Fail. 
2010;16(2):164-174. 
116. 
Mansfield DR, Gollogly NC, Kaye DM, Richardson M, Bergin P, Naughton MT. Controlled trial of 
continuous positive airway pressure in obstructive sleep apnea and heart failure. Am J Resp Crit Care 
Med. 2004;169(3):361-366. 
117. 
Fleiss JL. The design and analysis of clinical experiments. New York: John Wiley & Sons; 1986. 
118. 
Diggle P, Heagerty P, Liang KY, Zeger S. Analysis of longitudinal data. New York: Oxford University 
Press; 2002. 
119. 
Willan AR. Analysis, sample size, and power for estimating incremental net health benefit from clinical 
trial data. Controll Clin Trials. 2001;22(3):228-237. 
120. 
Glick HA. Sample size and power for cost-effectiveness analysis (Part 2): the effect of maximum 
willingness to pay. PharmacoEconomics. 2011;29(4):287-296. 
121. 
Kroenke K, Spitzer RL. The PHQ-9: A New depression diagnostic and severity measure. Psychiatric 
Ann. 2002;32(9):1-7. 
122. 
First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview For DSM-IV-TR Axis I 
Disorders Research Version. New York: State Psychiatric Institute;2002. 
123. 
Popovic D, King C, Guerrero M, Levendowski DJ, Henninger D, Westbrook PR. Validation of forehead 
venous pressure as a measure of respiratory effort for the diagnosis of sleep apnea. J Clin Monit 
Comput. 2009;23(1):1-10. 

124. 
Westbrook PR, Levendowski DJ, Cvetinovic M, et al. Chest. 2005;128(Description and Validation of the 
Apnea Risk Evaluation System: A Novel Method To Diagnose Sleep Apnea-Hypopnea in the 
Home):2166-2175. 
125. 
Ayappa I, Norman RG, Seelall V, Rapoport DM. Validation of a self-applied unattended monitor for 
sleep disordered breathing. J Clin Sleep Med. 2008;4(1):26-37. 
126. 
Levendowski D, Steward D, Woodson BT, Olmstead R, Popovic D, Westbrook P. The impact of 
obstructive sleep apnea variability measured in-lab versus in-home on sample size calculations. Int 
Archiv Med.  2009;2(1):2. 
127. 
Lichstein KL. Secondary insomnia: a myth dismissed. Sleep medicine reviews. 2006;10(1):3-5. 
128. 
Stepanski EJ, Rybarczyk B. Emerging research on the treatment and etiology of secondary or comorbid 
insomnia. Sleep Med Rev. 2006;10(1):7-18. 
129. 
Edinger JD, Wohlgemuth WK, Radtke RA, Coffman CJ, Carney CE. Dose-response effects of 
cognitive-behavioral insomnia therapy: a randomized clinical trial. Sleep. 2007;30(2):203-212. 
130. 
Muller S, Hemmi MH, Wilhelm FH, Barr RG, Schneider S. Parental report of infant sleep behavior by 
electronic versus paper-and-pencil diaries, and their relationship to actigraphic sleep measurement. J 
Sleep Res. 2011;20(4):598-605. 
131. 
Green AS, Rafaeli E, Bolger N, Shrout PE, Reis HT. Paper or plastic? Data equivalence in paper and 
electronic diaries. Psychol Methods. 2006;11(1):87-105. 
132. 
Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient non-compliance with paper 
diaries. BMJ. 2002;324(7347):1193-1194. 
133. 
Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient compliance with paper and 
electronic diaries. Controll Clin Trials. 2003;24(2):182-199. 
134. 
Thorndike FP, Ritterband LM, Gonder-Frederick LA, Lord HR, Ingersoll KS, Morin CM. A randomized 
controlled trial of an internet intervention for adults with insomnia: effects on comorbid psychological 
and fatigue symptoms. J Clin Psychol. 2013;69(10):1078-1093. 
135. 
Ritterband LM, Thorndike FP, Gonder-Frederick LA, et al. Efficacy of an Internet-based behavioral 
intervention for adults with insomnia. Arch Gen Psychiatry. 2009;66(7):692-698. 
136. 
Holmqvist M, Vincent N, Walsh K. Web- vs. telehealth-based delivery of cognitive behavioral therapy 
for insomnia: a randomized controlled trial. Sleep Med. 2014;15(2):187-195. 
137. 
American Association of Heart Failure Nurses PCNA, Society of Chest Pain Centers. Fight Against 
Heart Failure Handbook. 2008. 
138. 
Chesson AL, AM AW, Littner M, et al. Practice parameters for the nonpharmacologic treatment of 
chronic insomnia. Sleep. 1999;22(8):1128-1133. 
139. 
Edinger JD, Sampson WS. A primary care "friendly" cognitive behavioral insomnia therapy. Sleep. 
2003;26(2):177-182. 
140. 
Cully JA, Stanley MA, Deswal A, Hanania NA, Phillips LL, Kunik ME. Cognitive-behavioral therapy for 
chronic cardiopulmonary conditions: preliminary outcomes from an open trial. Prim Care Companion J 
Clin Psychiatry. 2010;12(4). 
141. 
Himelhoch S, Mohr D, Maxfield J, et al. Feasibility of telephone-based cognitive behavioral therapy 
targeting major depression among urban dwelling African-American people with co-occurring HIV. 
Psychol Health Med. 2011;16(2):156-165. 
142. 
Piette JD, Richardson C, Himle J, et al. A Randomized Trial of Telephonic Counseling Plus Walking for 
Depressed Diabetes Patients. Med Care. 2011;6:6. 
143. 
Edinger JD, Wohlgemuth WK, Radtke RA, Marsh GR, Quillian RE. Cognitive behavioral therapy for 
treatment of chronic primary insomnia: a randomized controlled trial. JAMA. 2001;285(14):1856-1864. 
144. 
EJ van Someren. Improving actigraphic sleep estimates in insomnia and dementia: how many nights? J 
Sleep Res. 2007;16(3):269-275. 
145. 
Blais FC, Gendron L, Mimeault V, Morin CM. [Evaluation of insomnia: validity of 3 questionnaires]. 
Encephale. 1997;23(6):447-453. 
146. 
Smith S, Trinder J. Detecting insomnia: comparison of four self-report measures of sleep in a young 
adult population. J Sleep Res. 2001;10(3):229-235. 
147. 
Buysse DJ, Reynolds CF, 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a 
new instrument for psychiatric practice and research. Psychiatry Reseasrch. 1989;28(2):193-213. 
148. 
Mullaney DJ, Kripke DF, Messin S. Wrist-actigraphic estimation of sleep time. Sleep. 1980;3:83-92. 
149. 
Kripke DF, Mullaney DJ, Messin S, Wyborney VG. Wrist actigraph measures of sleep and rhythms. 
Electroencephalography Clin Neurophys. 1978;44:674-676. 

150. 
Webster JB, Kripke DF, Messin S, Mullaney DJ, Wyborney G. An activity-based sleep monitor system 
for ambulatory use. Sleep. 1982;5:389-399. 
151. 
Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, Dement WC. Comparison of actigraphic, 
polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. Sleep 
Med. 2001;2:389-396. 
152. 
Blood MR, Sack RL, Percy DC, Pen J. A comparison of sleep detection by wrist actigraph, behavioral 
response, and polysomnography. Sleep. 1997;20:388-395. 
153. 
Edinger JD, Means MK, Stechuchak KM, Olsen MK. A pilot study of inexpensive sleep-assessment 
devices. Behav Sleep Med. 2004;2(1):41-49. 
154. 
Benson K, Friedman L, Noda A, Wicks D, Wakabayashi E, Yesavage J. The measurement of sleep by 
actigraphy: direct comparison of 2 commercially available actigraphs in a nonclinical population. Sleep. 
2004;27(5):986-989. 
155. 
Lichstein KL, Stone KC, Donaldson J, et al. Actigraphy validation with insomnia. Sleep. 2006;29(2):232-
239. 
156. 
Littner M, Kushida CA, Anderson WM, et al. Practice parameters for the role of actigraphy in the study 
of sleep and circadian rhythms: an update for 2002. Sleep. 2003;26(3):337-341. 
157. 
Redeker NS, Mason DJ, Wykpisz E, Glica B. Sleep patterns in women after coronary artery bypass 
surgery. Appl Nurs Res. 1996;9(3):115-122. 
158. 
Stone KC, Ancoli-Israel S. Actigraphy. In: Kryger M, Roth AJ, Dement W, eds. Principles and Practice 
of Sleep Medicine. 5th ed. New York: Elsevier; 2011:1668-1675. 
159. 
Carney CE, Buysse DJ, Ancoli-Israel S, et al. The consensus sleep diary: standardizing prospective 
sleep self-monitoring. Sleep. 2012;35(2):287-302. 
160. 
Belza BL. Multidimensional Assessment of Fatigue (MAF)  Scale Users Guide. Seattle:: University of 
Washington; 1990. 
161. 
Johns MW. A new method for measuring daytime sleepiness:  The Epworth Sleepiness Scale. Sleep. 
1991;14(6):540-545. 
162. 
Johns MW. Daytime sleepiness, snoring, and obstructive sleep apnea, the Epworth Sleepiness Scale. 
Chest. 1993;103:30-36. 
163. 
Johns MW. Reliability and factor analysis of the Epworth Sleepiness Scale. Sleep. 1992;15(4):376-381. 
164. 
Lee IS, Bardwell WA, Ancoli-Israel S, Dimsdale JE. Number of lapses during the psychomotor vigilance 
task as an objective measure of fatigue. J Clin Sleep Med. 2010;6(2):163-168. 
165. 
Lee IS, Bardwell W, Ancoli-Israel S, Natarajan L, Loredo JS, Dimsdale JE. The Relationship between 
psychomotor vigilance performance and quality of life in obstructive sleep apnea. J Clin Sleep Med 
2011;7(3):254-260. 
166. 
Radloff LS, Teri L. Use of the Center for Epidemiological Studies-Depression Scale with older adults. 
Clin Gerontol. 1986;5(1/2):119-136. 
167. 
Devins G, Orme C. Center for Epidemiologic Studies Depression Scale. In: Keyser D, Sweetland R, 
eds. Test Critiques. Kansas City, MO: Test Corporation of American, a susidiary of Westport 
Publishers, Inc.; 1985:144-160. 
168. 
Badger TA. Physical health impairment and depression among older adults. Image, J Nurs Scholarsh. 
1993;25:325-330. 
169. 
Mulrow CD, Williams JW, Jr., Gerety MB, Ramirez G, Montiel OM, Kerber C. Case-finding instruments 
for depression in primary care settings. Ann Int Med. 1995;122(12):913-921. 
170. 
Spielberger CD. Manual for the State-Trait Anxiety Inventory STAI-Form Y. Palo Alto, CA: Consulting 
Psychologists Press;1983. 
171. 
Garfield LD, Scherrer JF, Hauptman PJ, et al. Association of anxiety disorders and depression with 
incident heart failure. Psychosom Med. 2014;76(2):128-136. 
172. 
Rossi V, Pourtois G. Transient state-dependent fluctuations in anxiety measured using STAI, POMS, 
PANAS or VAS: a comparative review. Anxiety, stress, and coping. 2012;25(6):603-645. 
173. 
Cleeland CS. Brief Pain Inventory User Guide. Houston, TX: MD Anderson Cancer Center;2009. 
174. 
Mendoza T, Mayne T, Rublee D, Cleeland C. Reliability and validity of a modified Brief Pain Inventory 
short form in patients with osteoarthritis. Eur J Pain. 2006;10(4):353-361. 
175. 
Ware JE. SF-36 Physical and Mental Health Summary Scales:  A User's Manual. Boston: The Health 
Institute, New England Medical Center; 1994. 
176. 
Ware JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I.Conceptual framework 
and item selection. Med Care. 1992;30(6):473-483. 

177. 
Ware Jr. JE. SF-36® Health Survey Update. 2002; http://www.sf-36.org/tools/SF36.shtml. Accessed 
February 8,, 2006. 
178. 
Guyatt GH, Thompson PJ, Berman LB, et al. How should we measure function in patients with chronic 
heart and lung disease? J Chron Dis. 1985;38(6):517-524. 
179. 
Lipkin DP, Scriven AJ, Crake T, Poole-Wilson PA. Six minute walking test for assessing exercise 
capacity in chronic heart failure. B Med J. 1986;292(6521):653-655. 
180. 
Guyatt GH, Sullivan MJ, Thompson PJ, et al. The 6-minute walk: a new measure of exercise capacity in 
patients with chronic heart failure. Can Med Assoc J. 1985;132(8):919-923. 
181. 
Ruggiero JS, Redeker NS, Fiedler N, Avi-Itzhak T, Fischetti N. Sleep and psychomotor vigilance in 
female shiftworkers. Biol Res Nurs. 2012;14(3):225-235. 
182. 
Russell LB, Siegel JE, Daniels N, et al. Cost-effectiveness analysis as a guide to resource allocation in 
health : roles and limitations. New York: Oxford University Press; 1996. 
183. 
Gold MR. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996. 
184. 
Kharroubi SA, Brazier JE, Roberts J, O'Hagan A. Modelling SF-6D health state preference data using a 
nonparametric Bayesian method. J Health Econ. 2007;26(3):597-612. 
185. 
Charlson ME, Pompei P, Ales KL, McKenzie CR. A new method of classifying prognostic comorbidity in 
longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373-383. 
186. 
Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model: prediction of survival in 
heart failure. Circulation. 2006;113(11):1424-1433. 
187. 
Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure 
in an outpatient setting. J Clin Hypertens. 2008;10(5):348-354. 
188. 
Riegel B, Dickson VV. A situation-specific theory of heart failure self-care. J Cardiovasc Nurs. 
2008;23(3):190-196. 
189. 
Riegel B, Lee CS, Dickson VV, Carlson B. An update on the self-care of heart failure index. J 
Cardiovasc Nurs. 2009;24(6):485-497. 
190. 
Borkovec TD, D. NS. Credibility of analogue therapy rationales. J Behav Ther Exper Psychiatry. 
1972;3:257-260. 
191. 
Moher D, Schulz KF, Altman D. The CONSORT statement: revised recommendations for improving the 
quality of reports of parallel-group randomized trials. JAMA. 2001;285(15):1987-1991. 
192. 
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture 
(REDCap)--a metadata-driven methodology and workflow process for providing translational research 
informatics support. J Biomed Informatics. 2009;42(2):377-381. 
193. 
Jardim AC, Pawley MD, Cheeseman JF, Guesgen MJ, Steele CT, Warman GR. Validating the use of 
wrist-level light monitoring for in-hospital circadian studies. Chronobiol Int. 2011;28(9):834-840. 
194. 
Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987. 
195. 
Lanza ST, Collins LM. A new SAS procedure for latent transition analysis: transitions in dating and 
sexual risk behavior. Devel Psychol. 2008;44(2):446-456. 
196. 
Lanza ST, Collins LM, Lemmon DR, Schafer JL. PROC LCA: A SAS Procedure for Latent Class 
Analysis. Structural Equation Modeling. 2007;14(4):671-694 PMC - PMC2785099. 
197. 
Arriagada R, Rutqvist, L. E., Kramar, A., & Johansson, H. . Competing risks determining event-free 
survival in early breast cancer. Brit J Cancer. 1992;66(5):951. 
198. 
Stepanski EJ, Wyatt JK. Use of sleep hygiene in the treatment of insomnia. Sleep Med Rev. 
2003;7(3):215-225. 
199. 
Redeker NS, Andrews L, Cline J, Pacelli J, Mohsenin V, Jacoby D. Effects of Cognitive Behavioral 
Therapy for Insomnia in Patients with Heart Failure. Manuscript under review. 2011. 
200. 
National Institute of Nursing Research. NINR Data And Safety Monitoring for Extramural Clinical Trials. 
2014. 
 
 
 
 
 
 
 
 
 

Statistical Analysis Plan 
 
Data Analysis. We will use Actiware v.6 software (Respironics Minimitter, Inc.) to score actigraph data. 
Personnel blinded to group assignment will use event markers, light meter and diary recordings to identify 
“lights on/out” and removal times and to summarize actigraph variables over the 2-week intervals. Use of three 
methods will minimize problems with missing data (e.g., occlusion of the light meter) for single indicators. 
Wrist-worn light meters are valid measures of ambient light.193 
To identify outliers and data errors, we will use logical data checks and handle missing data with the 
assumption of missing at random (MAR), an approach that assumes missing values have the same patterns as 
complete data. We will impute missing data on survey items with Rubin’s194 multiple imputation method, 
replacing missing values with simulated sets of imputed values from Monte Carlo simulation, and analyze each 
simulated completed dataset with standard methods. Results will include estimates and confidence intervals 
that incorporate the uncertainty of imputed values. Generalized Linear mixed Models (GLM) and General 
Estimating Equations (GEE) allow the use of incomplete repeated measures due to dropout, If there is at least 
one baseline and follow-up data point. This approach will minimize the effects of sample loss due to attrition 
 
We will compute univariate statistics to examine frequency distributions of categorical variables and normal 
distributions for continuous variables with box plots and the Shapiro Wilkes test to assess normality. We will 
test differences in demographic and clinical variables (e.g., SA, LVEF), and baseline adherence, self-care, and 
self-management (see inclusion/exclusion criteria; clinical/demographic variables) between the two 
randomized groups to determine the success of randomization. Because some clinical and demographic 
variables, including PAP use, may influence outcomes, we will include them as covariates in multivariate 
models if groups are unbalanced on these variables at baseline. 
We will use descriptive statistics to describe clinical and demographic characteristics by treatment group and 
time. To evaluate differences in insomnia severity, sleep characteristics (Aim 1a), symptoms, PVT (Aim 1b) 
and functional performance (Aim 2) over the four time points (baseline, 3, 6, and 12 months), we will use 
GLMM with a repeated measures statement to examine group, time, and group by time effects. Nonlinear time 
effect will be controlled by transforming a continuous time variable with cube root form, which allows to 
estimate relatively rapid change in early at post-intervention than the follow-ups. We will choose the best 
covariance structure (compound symmetry vs. unstructured vs. moving average) by comparing the Akaike 
Information Criterion (AIC) and Bayesian Information Criterion (BIC). In multivariate models, adjusted 
intervention effects will be estimated by including covariates that might differ by group assignment (e.g., LVEF, 
comorbidity, age, SA with CPAP use). We will assess multi-collinearity among covariates with the variance 
inflation factor (VIF) and eliminate variables with high VIF (> 10) if the intervention effect is not modified after 
elimination. We will identify influential outliers with DFFITS, DFBETAS, and Cook’s distance. If there are non-
normally distributed residuals, the outcome variable will be transformed to ordinal form or fitted with more 
flexible distributions (e.g., Gamma, Poisson). We will use GEE with a robust working correlation structure to 
address within-subjects correlations for changes in outcomes with clinical cutoffs (i.e., ISI≥, ESS≥10, PSQI≥5, 
SMW≥1000) over time. To avoid the inflation of type 1 error, False Discovery Rate (FDR) will be calculated for 
post-intervention effects on 13 outcome variables. .  
For cluster analysis (Aim 1c), we will use Latent Transition Analysis (LTA)195,196 to produce a latent variable 
categorizing a symptom cluster at baseline and follow-ups. Consistent with methods in cardiac samples166 
each participant will be stratified into a symptom cluster group, based on dichotomized symptom severity. In 
our pilot, we found 3 clusters (I: mild); (II: high pain/dyspnea); (III: severe – high depression. anxiety, 
sleepiness). We expect LTA to produce two or more clusters of 2 or more symptoms characterized by 
symptom severity and patterns of correlated symptoms. LTA provides the item-response probability of having a 
specific symptom in each symptom profile group and the probabilities of membership in each symptom profile 
at each time and transitioning from one symptom profile to another. SEM and other similar latent variable 
methods do not quantify symptom cluster membership and transitions between clusters over time. The LTA 
model will include a group variable as a covariate to test the effects of CBT-I on membership in a symptom 
cluster and transition between clusters over time.   
We will use GLMM or GEE to determine the extent to which the effects of CBT-I on insomnia, sleep 
characteristics, symptoms and functional performance 6, 9, and 12 months are non-inferior to outcomes at two 
weeks. (Aims 1a, 1b, 2). We will identify potential barriers to sustained intervention effects (e.g., LVEF, 
comorbidity, PAP treated SA, self-care/self-management) by testing interactions between time and covariates 
associated with outcomes. 

 To address Aim 3, we will perform stochastic cost-effectiveness analysis (CEA) from the perspective of 
society. We will document resource utilization in units for direct and indirect costs (above) and apply prices to 
each unit. Costs will be the sum of the product of units and prices for both groups. The goal of stochastic CEA 
is to estimate the incremental cost per additional unit of effectiveness, measured as QALYs, the standard in 
cost-effectiveness research and economic evaluations of sleep disorders.101 We will estimate QALYs by 
applying the utility measure derived from the EQ-5D to the time spent during the study. We will consider the 
limited life expectancy of HF patients by including only time spent alive in the study, adjusted by utility. We will 
summarize the cost-effectiveness of CBT-I relative to HH with an incremental cost-effectiveness ratio (ICER):  
 
(CCBT-I: expected cost of the intervention; CHH: expected costs for the HH group; ECBT-I: effectiveness of the 
intervention, measured as QALYs;EHH: effectiveness in the HH group). ICER represents the incremental cost 
to gain an additional QALY with CBT-I. We will discount costs over the 3 years of data collection and estimate 
uncertainty around the ICER by bootstrapping, yielding a 95% confidence ellipse around the ICER. In the usual 
cost-effectiveness framework, a willingness to pay threshold is used to determine cost-effectiveness of a 
treatment. However, in a stochastic cost-effectiveness framework, we can estimate the probability that CBT-I is 
cost-effective over a wide range of willingness to pay thresholds with the cost-effectiveness acceptability curve 
(CEAC). We will perform sensitivity analyses for costs, life expectancy, and utility.  
We will use survival analysis for exploratory aim 1 and compare time to EFS between CBT-I and the 
attention control condition with a competing risk approach.197 This approach accounts for the relationship of the 
occurrence of one event with others. Participants who do not experience events during f/u will be right-
censored. As a secondary analysis, we will examine time to specific events (e.g., hospitalization, death). We 
will adjust the survival model by including baseline Seattle Heart Failure score,186 depression, LVEF, 
adherence,36 and self-care/self-care management,77 as they predicted EFS.