Key PointsQuestion
What is the efficacy of a fully automated internet-delivered cognitive behavior therapy for insomnia intervention compared with an insomnia patient education website with respect to the primary sleep outcomes of Insomnia Severity Index, sleep onset latency, and wake after sleep onset in a heterogeneous sample?
Findings
In this randomized clinical trial of 303 adults with chronic insomnia, those who received the internet cognitive behavior therapy for insomnia intervention (Sleep Healthy Using the Internet [SHUTi]) had significantly improved sleep compared with those who received access to the patient education website, with 56.6% achieving insomnia remission status and 69.7% deemed treatment responders at 1 year.
Meaning
Achieving improved sleep outcomes using an internet intervention without human support that are similar in magnitude to those reported in trials with face-to-face cognitive behavior therapy for insomnia has important implications for public health.
Importance
Although cognitive behavior therapy for insomnia (CBT-I) has been established as the first-line recommendation for the millions of adults with chronic insomnia, there is a paucity of trained clinicians to deliver this much needed treatment. Internet-delivered CBT-I has shown promise as a method to overcome this obstacle; however, the long-term effectiveness has not been proven in a representative sample with chronic insomnia.
Objective
To evaluate a web-based, automated CBT-I intervention to improve insomnia in the short term (9 weeks) and long term (1 year).
Design, Setting, and Participants
A randomized clinical trial comparing the internet CBT-I with internet patient education at baseline, 9 weeks, 6 months, and 1 year. Altogether, 303 adults with chronic insomnia self-referred to participate, of whom 151 (49.8%) reported at least 1 medical or psychiatric comorbidity.
Interventions
The internet CBT-I (Sleep Healthy Using the Internet [SHUTi]) was a 6-week fully automated, interactive, and tailored web-based program that incorporated the primary tenets of face-to-face CBT-I. The online patient education program provided nontailored and fixed online information about insomnia.
Main Outcomes and Measures
The primary sleep outcomes were self-reported online ratings of insomnia severity (Insomnia Severity Index) and online sleep diary–derived values for sleep-onset latency and wake after sleep onset, collected prospectively for 10 days at each assessment period. The secondary sleep outcomes included sleep efficiency, number of awakenings, sleep quality, and total sleep time.
Results
Among 303 participants, the mean (SD) age was 43.28 (11.59) years, and 71.9% (218 of 303) were female. Of these, 151 were randomized to the SHUTi group and 152 to the online patient education group. Results of the 3 primary sleep outcomes showed that the overall group × time interaction was significant for all variables, favoring the SHUTi group (Insomnia Severity Index [F3,1063 = 20.65, P < .001], sleep-onset latency [F3,1042 = 6.01, P < .001], and wake after sleep onset [F3,1042 = 12.68, P < .001]). Within-group effect sizes demonstrated improvements from baseline to postassessment for the SHUTi participants (range, Cohen d = 0.79 [95% CI, 0.55-1.04] to d = 1.90 [95% CI, 1.62-2.18]). Treatment effects were maintained at the 1-year follow-up (SHUTi Insomnia Severity Index d = 2.32 [95% CI, 2.01-2.63], sleep-onset latency d = 1.41 [95% CI, 1.15-1.68], and wake after sleep onset d = 0.95 [95% CI, 0.70-1.21]), with 56.6% (69 of 122) achieving remission status and 69.7% (85 of 122) deemed treatment responders at 1 year based on Insomnia Severity Index data. All secondary sleep outcomes, except total sleep time, also showed significant overall group × time interactions, favoring the SHUTi group.
Conclusions and Relevance
Given its efficacy and availability, internet-delivered CBT-I may have a key role in the dissemination of effective behavioral treatments for insomnia.
Trial Registration
clinicaltrials.gov Identifier: NCT01438697
Insomnia is a significant public health problem with substantial medical, psychiatric, and financial ramifications. Simply defined, insomnia is difficulty falling asleep or maintaining sleep, and it is one of the most common health concerns today.1 Approximately 35% to 50% of the general adult population experience insomnia symptoms,2 with 12% to 20% meeting criteria for insomnia as a disorder.3,4 Insomnia is highly comorbid with medical5 and psychiatric6 disorders. The combined direct and indirect costs associated with insomnia exceed $100 billion in the United States alone.7 Given the high prevalence and detrimental effect of insomnia, finding effective, accessible, and affordable treatment is critical.
Cognitive behavior therapy (CBT) and pharmacotherapy are the only 2 treatments with sufficient empirical support to be recommended for treating chronic insomnia (>1 month).8,9 While medication use is readily accessible and effective in the short term, CBT for insomnia (CBT-I) has longer-lasting benefits, with fewer adverse effects.10 It is now recommended as a first-line treatment.1,9 Most important, CBT-I addresses the problematic thoughts and behaviors believed to have developed in response to acute insomnia or a short-term sleep problem (days or weeks).
Although there is strong empirical support for CBT-I, the lack of trained clinicians and expense, while cost-effective,11 limits access. To overcome these barriers, innovative methods of delivering CBT-I have been developed, and initial evidence supports their feasibility and efficacy.12 To date, internet-based trials with published outcomes have been small, homogeneous, and limited in their generalizability and have excluded individuals with insomnia co-occurring with medical and psychiatric disorders.13
The present study was designed to address several limitations that exist within the internet-delivered CBT-I literature.12 This large-scale randomized clinical trial tested the long-term effect of a fully automated internet CBT-I intervention in a heterogeneous sample representing geographic, psychiatric, and medical diversity. In contrast to those with access to an insomnia education website, the internet CBT-I participants were hypothesized to show greater improvements in the primary sleep outcomes of Insomnia Severity Index (ISI), sleep-onset latency (SOL), and wake after sleep onset (WASO). It was also hypothesized that the internet CBT-I participants would demonstrate greater improvements in the secondary sleep outcomes, including sleep efficiency, number of awakenings, sleep quality, and total sleep time. Intervention use and clinical significance are also described.
This single-blind (participant) randomized clinical trial included 303 adults aged 21 to 65 years with chronic insomnia. Outcome data were collected online in this 2-group (internet-delivered CBT-I vs online patient education [PE]) trial at 4 assessment points, including baseline, 9 weeks, 6 months, and 1 year. All participants gave informed consent by providing a digital signature to the online form after discussion with study staff (including one of us, C.F.) by telephone. The study design and protocol (Supplement 1) were approved by the Institutional Review Board for Health Sciences Research at the University of Virginia, Charlottesville.
Adults with regular internet access were eligible to participate if they met the following criteria: (1) they required more than 30 minutes to fall asleep at the beginning of the night (sleep-onset insomnia) or more than 30 minutes of time awake after initially falling asleep (sleep maintenance insomnia) for at least 3 nights per week for at least 6 months, (2) their total sleep time averaged 6.5 hours or less, and (3) they manifested sleep disturbances (or associated daytime symptoms) causing significant distress or impairment in social, occupational, or other areas of functioning. Exclusion criteria included the following: (1) the presence of another untreated sleep disorder, (2) an irregular schedule that would prevent adoption of intervention strategies (ie, work schedule resulting in usual bedtime earlier than 8 pm or later than 2 am or arising time earlier than 4 am or later than 10 am), (3) pregnancy, (4) current or past behavioral treatment for insomnia, and (5) initiation of any psychological treatment within the previous 3 months. Comorbid psychiatric disorders were included unless the individual met criteria for any of the following: moderate to high current risk of suicide, severe major depression, bipolar disorder, or alcohol or drug abuse within the past year. Comorbid medical conditions were included unless they were deemed active, unstable, and degenerative (eg, congestive heart failure or multiple sclerosis) in a manner that could worsen the insomnia. Participants could be taking medications, including sleep medications, if the medication regimen had not been changed in the previous 3 months.
Measures used in this study included the ISI,14 a 7-item global index of self-reported insomnia symptom severity that has been shown to be valid, reliable, and sensitive to changes in insomnia treatment14,15 and validated for online use.16 Daily sleep diaries17 based on consensus sleep diary questions17 were collected online prospectively during a 2-week period at each of the 4 assessment periods. A semistructured clinical telephone interview was conducted to obtain a detailed sleep and health history, screen for other sleep disorders, and assess for psychiatric comorbidity using the Mini-International Neuropsychiatric Interview18 and included components of the Diagnostic Interview for Insomnia.19 Finally, intervention use and adherence were measured using login frequency, diary count, and number of cores completed. More details are available in the eAppendix in Supplement 2.
Internet CBT-I (Sleep Healthy Using the Internet)
Sleep Healthy Using the Internet (SHUTi) is a fully automated, interactive, and tailored web-based program that incorporates the primary tenets of face-to-face CBT-I, including sleep restriction, stimulus control, cognitive restructuring, sleep hygiene, and relapse prevention.20-22 Other content areas that are occasionally included in CBT-I, such as relaxation and medication titration, were not included (details are available in the eAppendix in Supplement 2).
The online PE program provided nontailored and fixed material about insomnia symptoms and the effect, prevalence, and causes of insomnia. It also discussed when to see a physician and basic lifestyle, environmental, and behavioral strategies to improve sleep (details are available in the eAppendix in Supplement 2).
Participants were recruited online (Figure 1 and Figure 2). Those enrolled completed a baseline assessment that included questionnaires and 10 days of sleep diaries. They then received access to their assigned web program for 9 weeks, followed by postassessment data collection after the intervention period and again at 6 months and 1 year that included questionnaires and sleep diaries. Participants received online gift certificates for completing the postassessment and 6-month follow-up ($50 each) and the 1-year follow-up ($100).
Sample size was calculated based on an effect size between groups of Cohen d = 0.20 on each of the 3 primary sleep outcomes (ISI, SOL, and WASO) and an anticipated 35.0% attrition rate across the 1-year study. Powering the study at 80% to detect pre-post differences between groups with α = .05 (using G*Power 3 software23), a sample size of 300 individuals was planned to achieve 220 participants after expected attrition.
Differences by treatment group in baseline characteristics, rates of postassessment and follow-up nonadherence, and posttreatment ISI response and remittance were analyzed with t tests for continuous variables, Wilcoxon rank sum tests for skewed continuous variables, and χ2 tests for categorical variables. Statistical significance was set at α = .05. Sleep-onset latency, WASO, and number of awakenings were skewed and were therefore log-transformed before analysis. The diaries of participants were averaged at each time point to create summary scores for the diary variables. The primary and secondary sleep outcomes, missing data, and sleep medication use were modeled with multilevel mixed-effects regression (linear or logistic as appropriate) using a software program (Stata, version 14; StataCorp LP). For all outcomes, group × time interactions were examined, accounting for baseline group differences. Group × time × comorbidity interactions were also examined. Time was treated as a categorical variable to model its nonlinear effects, and an unstructured covariance matrix was assumed. Random effects were modeled for time. Within-group effect sizes were computed as the modeled mean difference between baseline and each postassessment divided by the full sample baseline SD. To compare postassessment ISIs by completers and noncompleters, analysis of covariance was conducted with core completion as predictor and baseline ISI as covariate. An intent-to-treat approach included all participants in the mixed model. The Consolidated Standards of Reporting Trials diagram (Figure 1) shows details of assessment completion rates. Group type was not significantly associated with attrition from preassessment to postassessment or overall.
Between October 19, 2011, and July 2, 2013, a total of 1212 individuals submitted online forms that indicated interest in participating in this trial. Of them, 303 were enrolled, living in 41 different states in the United States and the District of Columbia, and were instructed to complete the online baseline assessment that included questionnaires and prospective sleep diaries. After baseline data collection, 151 were randomized to receive SHUTi, and 152 were randomized to receive access to online PE.
Table 1 lists baseline characteristics by group. The groups differed on 1 baseline characteristic, with the SHUTi group reporting more years of sleep problems than the PE group. Across both groups, participants were mostly female (71.9% [218 of 303]), of white race and non-Hispanic ethnicity (83.8% [254 of 303]), and well educated (77.6% [235 of 303] with at least a college degree). Participants also reported experiencing chronic insomnia, with a median of 9 years of sleep difficulties occurring most nights of the week (median, 6 nights per week). In addition, 29.0% (88 of 303) of participants had a medical comorbidity, 30.7% (93 of 303) had a psychiatric comorbidity, and 49.8% (151 of 303) had a medical or psychiatric comorbidity or both.
During baseline diary collection, of 151 participants, 51.0% (n = 77) of the SHUTi group and 45.7% (n = 69) of the PE group reported taking a sleep aid (prescription or nonprescription) at least 1 night during the baseline period. Among those who reported using a sleep aid, an average of 56% of baseline nights were classified as medicated nights. Numbers of participants reporting at least 1 medicated night decreased during the postassessment, 6-month follow-up, and 1-year follow-up periods, with values of 38.3% (49 of 128), 37.2% (42 of 113) and 33.1% (40 of 121) for the SHUTi group and 42.5% (57 of 134), 34.4% (43 of 125), and 33.9% (43 of 127) for the PE group, respectively. A logistic mixed-model analysis examining sleep medication use over time by group shows that, while the groups did not differ over time (χ2 = 3.00, P = .39), there was an effect for time (χ2 = 18.32, P < .001), indicating that both groups reduced sleep aid use over time. Participants also reported a high rate of comfort in using the internet, and most indicated that they check email and use the internet daily.
Results of the 3 primary sleep outcomes are shown in Figure 3. The overall group × time interactions were significant for the ISI (F3,1063 = 20.65, P < .001), SOL (F3,1042 = 6.01, P < .001), and WASO (F3,1042 = 12.68, P < .001). Within-group time effects, from baseline to postassessment, were large for the SHUTi group (range, 0.79-1.90) and small to large for the PE group (range, 0.37-0.77). At the 1-year follow-up, the SHUTi group experienced significant improvements from baseline on the ISI (Cohen d = 2.32; 95% CI, 2.01-2.63), SOL (Cohen d = 0.95; 95% CI, 0.70-1.21), and WASO (Cohen d = 1.41; 95% CI, 1.15-1.68). The PE group also experienced improvements (although smaller than those of the SHUTi group) on the ISI (Cohen d = 1.53; 95% CI, 1.26-1.79), SOL (Cohen d = 0.64; 95% CI, 0.40-0.88), and WASO (Cohen d = 0.86; 95% CI, 0.61-1.11). These findings are similar to those reported in studies of face-to-face CBT-I.24
An evaluation of clinical significance also demonstrated the superiority of SHUTi, with 52.6% (70 of 133) of the SHUTi group deemed treatment responders (defined by a reduction of >7 points on the ISI15) from baseline to postassessment compared with only 16.9% (24 of 142) of the PE group. At the 6-month follow-up, 59.7% (68 of 114) of the SHUTi group and 35.7% (46 of 129) of the PE group were considered responders, while 69.7% (85 of 122) of the SHUTi group and 43.0% (55 of 128) of the PE group were deemed responders at the 1-year follow-up. A similar pattern was observed using criteria of insomnia remittance (<8 on the ISI15): 40.6% (54 of 133) (baseline to postassessment), 49.1% (56 of 114) (baseline to 6-month follow-up), and 56.6% (69 of 122) (baseline to 1-year follow-up) of the SHUTi group were deemed insomnia remitters compared with 11.3% (16 of 142) (baseline to postassessment), 24.0% (31 of 129) (baseline to 6-month follow-up), and 27.3% (35 of 128) (baseline to 1-year follow-up) of the PE group.
Primary Sleep Outcomes by Comorbidity
Additional mixed-model interactions were used to examine whether the presence of a comorbid condition influenced the treatment effect on the primary sleep outcomes. The 3 comorbidity groupings were categorized as (1) the presence or absence of medical comorbidity, (2) the presence or absence of psychiatric comorbidity, and (3) the presence or absence of any comorbidity. In all cases, those with a comorbidity did not show significantly different changes over time by group in sleep outcomes than those without a comorbidity.
Significant group × time interaction effects were found on the secondary sleep outcomes of sleep efficiency (F3,1042 = 8.39, P < .001), number of awakenings (F3,1042 = 3.41, P = .02), and sleep quality (F3,1042 = 2.93, P = .03) but not total sleep time (F3,1042 = 0.40, P = .76). Effect sizes showed patterns similar to those of the primary sleep outcomes (Table 2). Specifically, the SHUTi group experienced larger effects from baseline to the postassessment, 6-month follow-up, and 1-year follow-up compared with the PE group for all secondary sleep variables except total sleep time, for which effects increased similarly in both groups.
The SHUTi group logged in between 0 and 142 times during the 9-week intervention period, with a median login count of 25. The SHUTi group submitted a median of 44 sleep diaries during this same intervention period, ranging from 0 to 70. A total of 60.3% (91 of 151) of the SHUTi group completed all 6 cores of the program. In contrast, 3.3% (5 of 151) of the SHUTi group did not complete any of the cores, 3.3% (5 of 151) completed only the tutorial core explaining how the program works, 5.3% (8 of 151) completed through the first core, 11.3% (17 of 151) completed through the second core, 9.3% (14 of 151) completed through the third core, 0.7% (1 of 151) completed through the fourth core, and 6.6% (10 of 151) completed through the fifth core.
For those in the SHUTi group who completed all 6 cores, the estimated marginal mean (EMM) (SD) ISI at postassessment was 7.99 (0.47) compared with 12.24 (0.69) (P < .001 for group difference) for those who completed less than 6 cores. However, during the 1-year follow-up period, the difference in EMM (SD) ISIs for those who completed all 6 cores and those who did not diminished. At the 6-month follow-up and 1-year follow-up, those who completed all 6 cores had EMM (SD) ISIs of 7.90 (0.53) and 6.91 (0.57), respectively, while those who did not complete all 6 cores had EMM (SD) ISIs of 9.98 (0.97) and 8.56 (0.93), respectively.
This randomized clinical trial is the first to evaluate the long-term efficacy of a fully automated internet-delivered CBT-I intervention to improve sleep in a heterogeneous sample of adults with chronic insomnia. The SHUTi group experienced significant improvements in ISI, SOL, and WASO—the 3 primary sleep outcomes. With all participants averaging in the moderate severity insomnia range of 15 to 21 on the ISI at baseline, those in the SHUTi group had a mean ISI of less than 8 (denoting no insomnia) at the 1-year follow-up, whereas the PE group was in the subthreshold insomnia range of 8 to 14.
The study design was of particular importance in evaluating the efficacy of online CBT-I. The PE condition formed an active control, offering high-quality information similar to that found in a PE booklet or in patient-oriented online materials, and some improvements were expected. The finding of greater improvement in all measures among the SHUTi groups demonstrates the value of the metered, interactive, and tailored format analogous to what typically occurs in face-to-face CBT-I.
Previous studies have documented the efficacy of online CBT-I to improve insomnia, but these early investigations used small samples, homogeneous populations, and limited comparison conditions and relied on costly human support to guide users through the program or failed to include long-term follow-up.12 The present study addresses these issues and demonstrates that positive outcomes can be achieved and maintained when using a robust internet intervention for insomnia in a general population of adults with insomnia.
Participants in this trial represented a symptomatic group with a long history of insomnia. Consistent with previous trials of SHUTi25,26 but in contrast to the general internet intervention literature,27 dropout attrition (participants not completing follow-up measures) was low, with 9.2% (28 of 303) lost at the postassessment and 17.5% (53 of 303) lost at the 1-year follow-up. Nonuse attrition (participants who do not complete all cores of the intervention) was low at 39.7% (60 of 151). This finding is notable given that no clinical support was provided as part of the intervention. Although there is limited research examining why completion rates differ across programs, many researchers speculate that increased interactivity and tailoring may foster engagement and ultimately program adherence.13,28
Although this study addresses many shortcomings of previous internet-delivered CBT-I studies, our findings must still be considered in light of several limitations. The sample is primarily of white race and non-Hispanic ethnicity, well educated, and comfortable using the internet. With that said, this sample is more heterogeneous than other internet-delivered CBT-I studies because it included a national (US) sample of participants in which 16.2% (49 of 303) were of nonwhite race; 7.9% (24 of 303) were of Hispanic ethnicity, and 49.8% (151 of 303) had a comorbid condition. However, the full range of medical and psychiatric conditions that co-occur with insomnia is not represented in this study. Another limitation is that the SHUTi group had sleep difficulties for a significantly longer time (10 years) compared with the PE group (7 years). That said, this difference would likely result in more conservative findings if it had any effect on outcomes. In addition, minimal masking occurred because savvy participants could deduce the web program to which they were randomized based on their online experience. Finally, no objective measure of sleep was used; however, while some researchers may question the validity of self-reported sleep data, daily sleep diaries are used in more than 90% of treatment studies of insomnia,29 and tracking sleep variables during multiple weeks provides a more comprehensive understanding of sleep problems than polysomnography, which is usually only administered for 1 to 3 nights.29 Sleep diaries are reliable and valid measures of sleep and correlate strongly with findings using other objective measures of sleep, such as polysomnography,30-34 actigraphy,35-39 and spousal reports.33,39
Internet-delivered CBT-I provides a less expensive, scalable treatment option that could reach previously unimaginable numbers of people. Future studies are necessary to determine who may be best served by this type of intervention and how the next steps of dissemination should occur. Ensuring that these interventions work with different patient populations, whether tailored or not for those groups, should also be examined. In addition, exploring the use of these interventions with lower educated, less technologically experienced, and older populations will be critical to broad dissemination efforts.
This study provides compelling evidence that the self-guided, web-based CBT-I intervention SHUTi can effectively treat insomnia. It extends findings that internet-delivered CBT-I can meaningfully improve insomnia symptoms and sleep variables, including when insomnia is comorbid with other conditions.
Corresponding Author: Lee M. Ritterband, PhD, Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health & Technology, University of Virginia School of Medicine, PO Box 801075, Charlottesville, VA 22908 (leer@virginia.edu).
Accepted for Publication: October 5, 2016.
Published Online: November 30, 2016. doi:10.1001/jamapsychiatry.2016.3249
Author Contributions: Drs Ingersoll and Lord had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Ritterband, Thorndike, Ingersoll, Gonder-Frederick, Quigg, Cohn, Morin.
Acquisition, analysis, or interpretation of data: Ritterband, Thorndike, Ingersoll, Lord, Gonder-Frederick, Frederick, Quigg, Cohn.
Drafting of the manuscript: Ritterband, Thorndike, Ingersoll, Lord, Frederick.
Critical revision of the manuscript for important intellectual content: Ritterband, Thorndike, Ingersoll, Lord, Gonder-Frederick, Quigg, Cohn, Morin.
Statistical analysis: Lord.
Obtained funding: Ritterband, Thorndike.
Administrative, technical, or material support: Thorndike, Frederick, Cohn.
Study supervision: Ritterband, Thorndike, Ingersoll, Gonder-Frederick, Quigg, Morin.
Conflict of Interest Disclosures: Drs Ritterband, Thorndike, Gonder-Frederick, and Morin reported having equity ownership in BeHealth Solutions, LLC, a company that develops and makes available products related to the research reported in this article. Specifically, BeHealth Solutions, LLC, has licensed the Sleep Healthy Using the Internet (SHUTi) program and the software platform on which it was built from the University of Virginia. The terms of this arrangement have been reviewed and approved by the University of Virginia in accord with its conflict of interest policy. No other disclosures were reported.
Funding/Support: This study was supported by grant R01 MH86758 from the National Institute of Mental Health. Drs Ritterband, Ingersoll, Gonder-Frederick, Quigg, Cohn, and Morin received funding from the National Institutes of Health.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: All study procedures were performed at the University of Virginia School of Medicine. Rachael L. Maynard, MPH, CCRC, Emory University Winship Cancer Institute, assisted in coordinating this study (without compensation outside of her usual salary). Numerous team members from the University of Virginia School of Medicine provided their design, development, and clinical expertise in preparing and maintaining Sleep Healthy Using the Internet (SHUTi) as part of the conducted randomized clinical trial, including Michelle Hilgart, MEd, PhD, Steve P. Johnson, BA, Gabe D. Heath, BA, and Jacklyn A. Shepard, PsyD, all of whom received compensation for their contributions. We thank Performant Software Solutions for their support in the technical development of SHUTi, as well as the numerous University of Virginia students who helped test the program and provided ongoing daily support needs to study staff during the trial.
2.Walsh
JK, Coulouvrat
C, Hajak
G,
et al. Nighttime insomnia symptoms and perceived health in the America Insomnia Survey (AIS).
Sleep. 2011;34(8):997-1011.
PubMedGoogle Scholar 3.Morin
CM, LeBlanc
M, Bélanger
L, Ivers
H, Mérette
C, Savard
J. Prevalence of insomnia and its treatment in Canada.
Can J Psychiatry. 2011;56(9):540-548.
PubMedGoogle Scholar 4.Roth
T, Coulouvrat
C, Hajak
G,
et al. Prevalence and perceived health associated with insomnia based on
DSM-IV-TR;
International Statistical Classification of Diseases and Related Health Problems,
Tenth Revision; and
Research Diagnostic Criteria/
International Classification of Sleep Disorders, criteria: results from the America Insomnia Survey.
Biol Psychiatry. 2011;69(6):592-600.
PubMedGoogle ScholarCrossref 5.Budhiraja
R, Roth
T, Hudgel
DW, Budhiraja
P, Drake
CL. Prevalence and polysomnographic correlates of insomnia comorbid with medical disorders.
Sleep. 2011;34(7):859-867.
PubMedGoogle ScholarCrossref 6.Wu
JQ, Appleman
ER, Salazar
RD, Ong
JC. Cognitive behavioral therapy for insomnia comorbid with psychiatric and medical conditions: a meta-analysis.
JAMA Intern Med. 2015;175(9):1461-1472.
PubMedGoogle ScholarCrossref 7.Fullerton
DS. The economic impact of insomnia in managed care: a clearer picture emerges.
Am J Manag Care. 2006;12(8)(suppl):S246-S252.
PubMedGoogle Scholar 8.National Institutes of Health. NIH State-of-the-Science Conference Statement on manifestations and management of chronic insomnia in adults.
NIH Consens State Sci Statements. 2005;22(2):1-30.
PubMedGoogle Scholar 9.Qaseem
A, Kansagara
D, Forciea
MA, Cooke
M, Denberg
TD; Clinical Guidelines Committee of the American College of Physicians. Management of chronic insomnia disorder in adults: a clinical practice guideline from the American College of Physicians.
Ann Intern Med. 2016;165(2):125-133.
PubMedGoogle ScholarCrossref 10.Morin
CM, Vallières
A, Guay
B,
et al. Cognitive behavioral therapy, singly and combined with medication, for persistent insomnia: a randomized controlled trial.
JAMA. 2009;301(19):2005-2015.
PubMedGoogle ScholarCrossref 11.Wickwire
EM, Shaya
FT, Scharf
SM. Health economics of insomnia treatments: the return on investment for a good night’s sleep.
Sleep Med Rev. 2015;30:72-82.
PubMedGoogle ScholarCrossref 12.Zachariae
R, Lyby
MS, Ritterband
LM, O’Toole
MS. Efficacy of internet-delivered cognitive-behavioral therapy for insomnia: a systematic review and meta-analysis of randomized controlled trials.
Sleep Med Rev. 2015;30:1-10.
PubMedGoogle ScholarCrossref 13.Cheng
SK, Dizon
J. Computerised cognitive behavioural therapy for insomnia: a systematic review and meta-analysis.
Psychother Psychosom. 2012;81(4):206-216.
PubMedGoogle ScholarCrossref 14.Bastien
CH, Vallières
A, Morin
CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research.
Sleep Med. 2001;2(4):297-307.
PubMedGoogle ScholarCrossref 15.Morin
CM, Belleville
G, Bélanger
L, Ivers
H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response.
Sleep. 2011;34(5):601-608.
PubMedGoogle Scholar 16.Thorndike
FP, Ritterband
LM, Saylor
DK, Magee
JC, Gonder-Frederick
LA, Morin
CM. Validation of the Insomnia Severity Index as a web-based measure.
Behav Sleep Med. 2011;9(4):216-223.
PubMedGoogle ScholarCrossref 17.Carney
CE, Buysse
DJ, Ancoli-Israel
S,
et al. The Consensus Sleep Diary: standardizing prospective sleep self-monitoring.
Sleep. 2012;35(2):287-302.
PubMedGoogle Scholar 18.Sheehan
DV, Lecrubier
Y, Sheehan
KH,
et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for
DSM-IV and
ICD-10.
J Clin Psychiatry. 1998;59(suppl 20):22-33.
PubMedGoogle Scholar 19.Morin
CM. Insomnia: Psychological Assessment and Management. New York, NY: Guilford Press; 1993.
21.Schutte-Rodin
S, Broch
L, Buysse
D, Dorsey
C, Sateia
M. Clinical guideline for the evaluation and management of chronic insomnia in adults.
J Clin Sleep Med. 2008;4(5):487-504.
PubMedGoogle Scholar 22.Thorndike
FP, Saylor
DK, Bailey
ET, Gonder-Frederick
L, Morin
CM, Ritterband
LM. Development and perceived utility and impact of an internet intervention for insomnia.
E J Appl Psychol. 2008;4(2):32-42.
PubMedGoogle ScholarCrossref 23.Faul
F, Erdfelder
E, Buchner
A, Lang
AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.
Behav Res Methods. 2009;41:1149-1160.
Google ScholarCrossref 24.Geiger-Brown
JM, Rogers
VE, Liu
W, Ludeman
EM, Downton
KD, Diaz-Abad
M. Cognitive behavioral therapy in persons with comorbid insomnia: a meta-analysis.
Sleep Med Rev. 2015;23:54-67.
PubMedGoogle ScholarCrossref 25.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.
PubMedGoogle ScholarCrossref 26.Ritterband
LM, Bailey
ET, Thorndike
FP, Lord
HR, Farrell-Carnahan
L, Baum
LD. Initial evaluation of an internet intervention to improve the sleep of cancer survivors with insomnia.
Psychooncology. 2012;21(7):695-705.
PubMedGoogle ScholarCrossref 28.Strecher
V. Internet methods for delivering behavioral and health-related interventions (eHealth).
Annu Rev Clin Psychol. 2007;3:53-76.
PubMedGoogle ScholarCrossref 29.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.
PubMedGoogle Scholar 30.Morin
CM, Colecchi
C, Stone
J, Sood
R, Brink
D. Behavioral and pharmacological therapies for late-life insomnia: a randomized controlled trial.
JAMA. 1999;281(11):991-999.
PubMedGoogle ScholarCrossref 32.Hauri
PJ, Percy
L, Hellekson
C, Hartmann
E, Russ
D. The treatment of psychophysiologic insomnia with biofeedback: a replication study.
Biofeedback Self Regul. 1982;7(2):223-235.
PubMedGoogle ScholarCrossref 33.Morin
CM, Kowatch
RA, Barry
T, Walton
E. Cognitive-behavior therapy for late-life insomnia.
J Consult Clin Psychol. 1993;61(1):137-146.
PubMedGoogle ScholarCrossref 34.Jacobs
GD, Benson
H, Friedman
R. Home-based central nervous system assessment of a multifactor behavioral intervention for chronic-onset insomnia.
Behav Ther. 1993;24(1):159-174.
Google ScholarCrossref 35.Espie
CA, Lindsay
WR, Brooks
DN, Hood
EM, Turvey
T. A controlled comparative investigation of psychological treatments for chronic sleep-onset insomnia.
Behav Res Ther. 1989;27(1):79-88.
PubMedGoogle ScholarCrossref 36.Edinger
JD, Hoelscher
TJ, Marsh
GR, Lipper
S, Ionescu-Pioggia
M. A cognitive-behavioral therapy for sleep-maintenance insomnia in older adults.
Psychol Aging. 1992;7(2):282-289.
PubMedGoogle ScholarCrossref 37.Guilleminault
C, Clerk
A, Black
J, Labanowski
M, Pelayo
R, Claman
D. Nondrug treatment trials in psychophysiologic insomnia.
Arch Intern Med. 1995;155(8):838-844.
PubMedGoogle ScholarCrossref 38.Hoelscher
TJ, Edinger
JD. Treatment of sleep-maintenance insomnia in older adults: sleep period reduction, sleep education, and modified stimulus control.
Psychol Aging. 1988;3(3):258-263.
PubMedGoogle ScholarCrossref