This paper is in the following e-collection/theme issue:

Insomnia and Sleep Hygiene (177) Web-based and Mobile Health Interventions (3357) Sleep Monitoring, Sleep Quality, Sleep Disorders (191) Formative Evaluation of Digital Health Interventions (2491) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50516, first published .
Efficacy of an Internet-Delivered Intervention for Improving Insomnia Severity and Functioning in Veterans: Randomized Controlled Trial

Efficacy of an Internet-Delivered Intervention for Improving Insomnia Severity and Functioning in Veterans: Randomized Controlled Trial

Efficacy of an Internet-Delivered Intervention for Improving Insomnia Severity and Functioning in Veterans: Randomized Controlled Trial

Authors of this article:

Sarra Nazem1, 2 Author Orcid Image ;   Sean M Barnes2, 3 Author Orcid Image ;   Jeri E Forster2, 4 Author Orcid Image ;   Trisha A Hostetter2 Author Orcid Image ;   Lindsey L Monteith2, 3, 4 Author Orcid Image ;   Emily B Kramer2 Author Orcid Image ;   Laurel A Gaeddert2 Author Orcid Image ;   Lisa A Brenner2, 3, 4, 5 Author Orcid Image
Article Authors Cited by (1) Tweetations (12) Metrics

    Original Paper

    1Dissemination & Training Division, National Center for Posttraumatic Stress Disorder, Menlo Park, CA, United States

    2Veterans Affairs Rocky Mountain Mental Illness Research, Education and Clinical Center, Aurora, CO, United States

    3Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    4Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    5Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    Corresponding Author:

    Sarra Nazem, PhD

    Dissemination & Training Division

    National Center for Posttraumatic Stress Disorder

    795 Willow Road

    Building 334

    Menlo Park, CA, 94025

    United States

    Phone: 1 650 796 8208

    Fax:1 650 617 2701

    Email: Sarra.Nazem@va.gov


    Background: Despite a growing evidence base that internet-delivered cognitive behavioral therapy for insomnia (iCBT-I) is associated with decreased insomnia severity, its efficacy has been minimally examined in veterans.

    Objective: The objective of this study was to evaluate the efficacy of an unguided iCBT-I (Sleep Healthy Using the Internet [SHUTi]) among veterans.

    Methods: We conducted a single-blind, randomized controlled trial in Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn veterans eligible for Veterans Health Administration care. Participants were randomly assigned (1:1) to receive SHUTi (a self-guided and interactive program) or an Insomnia Education Website (IEW) that provided nontailored and fixed insomnia information. Web-based assessments were administered at baseline, postintervention, 6 months postintervention, and 1 year postintervention. The primary outcome was self-reported insomnia severity (Insomnia Severity Index [ISI]). Secondary outcomes were self-reported mental and physical health functioning (Veterans RAND 36-item Health Survey). Exploratory outcomes comprised sleep diary parameters.

    Results: Of the 231 randomized participants (mean age 39.3, SD 7.8 years; 170/231, 73.5% male sex; 26/231, 11.3% Black; 172/231, 74.5% White; 10/231, 4.3% multiracial; and 17/231, 7.4% other; 36/231, 15.6% Hispanic) randomized between April 2018 and January 2019, a total of 116 (50.2%) were randomly assigned to SHUTi and 115 (49.8%) to the IEW. In intent-to-treat analyses, SHUTi participants experienced significantly larger ISI decreases compared with IEW participants at all time points (generalized η2 values of 0.13, 0.12, and 0.10, respectively; all P<.0001). These corresponded to estimated larger differences in changes of –3.47 (95% CI –4.78 to –2.16), –3.80 (95% CI –5.34 to –2.27), and –3.42 (95% CI –4.97 to 1.88) points on the ISI for the SHUTi group. SHUTi participants experienced significant improvements in physical (6-month generalized η2=0.04; P=.004) and mental health functioning (6-month and 1-year generalized η2=0.04; P=.009 and P=.005, respectively). Significant sleep parameter improvements were noted for SHUTi (all P<.05), though the pattern and magnitude of these reductions varied by parameter. No adverse events were reported.

    Conclusions: Self-administered iCBT-I was associated with immediate and long-term improvements in insomnia severity. Findings suggest that leveraging technology to meet insomnia treatment demands among veterans may be a promising approach.

    Trial Registration: ClinicalTrials.gov NCT03366870; https://clinicaltrials.gov/ct2/show/NCT03366870

    JMIR Ment Health 2023;10:e50516

    doi:10.2196/50516

    Keywords

    cognitive behavioral therapy; insomnia; internet intervention; online intervention; randomized controlled trial; RCT; RCTs; sleep; treatment; veteran; veterans; veterans’ health 


    Insomnia is a public health and clinical problem affecting US veterans. In a nationally representative sample of US veterans, 35% endorsed symptoms consistent with clinical or subthreshold insomnia [1]. This is especially concerning given that insomnia precedes and exacerbates functionally impairing mental health concerns and is associated with suicide risk [2-7]. Chronic insomnia is also linked to an increased risk of chronic diseases (eg, diabetes, cardiovascular diseases, and cancer) [8,9]. To improve veterans’ mental and physical health, it is imperative that veterans receive evidence-based psychotherapies (EBPs) that reduce insomnia.

    Cognitive behavioral therapy for insomnia (CBT-I) is one of the most efficacious treatments for insomnia [10-12]. Long-term effects surpass those of sleep medications, with the added benefit of being associated with few adverse side effects [13,14]. Despite successful Veterans Health Administration (VHA) efforts that have improved access to CBT-I, the resources necessary to implement provider-delivered CBT-I outstrip treatment demands [15-19]. Self-guided, internet-delivered cognitive behavioral therapy (iCBT) offers a potentially efficacious means of delivering insomnia treatments by providing individuals with the opportunity to engage in an EBP on the web. Veterans have an interest in digital health technologies, including internet-delivered CBT-I (iCBT-I), and leveraging technology is critical to meeting treatment demand and patient preferences [20-24].

    From both policy and clinical perspectives, iCBTs have garnered attention due to their potential to overcome barriers to treatment (eg, cost, scheduling and travel demands, stigma, and lack of trained EBP clinicians) [25,26]. Recent meta-analyses found that iCBT-I improved insomnia severity, sleep parameters, and subjective sleep quality, with effect sizes comparable to those found in face-to-face (F2F) CBT-I (ie, Hedges g ranging from 0.21 to 1.09) [27,28]. iCBT-I programs that included more CBT-I elements, had longer intervention periods, and used highly interactive program designs optimized outcomes and facilitated better treatment engagement and adherence [29].

    Despite veterans’ interest in digital health technologies and a growing evidence base for iCBT-I interventions, relatively few studies have examined iCBT-I interventions in veteran populations. Initial, formative studies examined the use of the patient mobile app, CBT-I Coach, which was developed and designed to help facilitate patients’ completion of CBT-I tasks while receiving F2F CBT-I. A pilot randomized controlled trial (RCT; n=18) demonstrated the feasibility and acceptability of integrating CBT-I Coach with individual F2F CBT-I [23]. A total of 2 additional small studies (n<35) found that self-administered use of CBT-I Coach paired with supplemental app-delivered worksheets and a self-management guide was associated with a reduction of insomnia symptoms in veterans reporting chronic insomnia [30,31]. Researchers have also demonstrated the feasibility and potential efficacy of stand-alone, self-guided iCBT-I interventions in 3 studies. First, in an uncontrolled pre-post intervention study of a 6-session self-administered iCBT-I, veterans seeking care in a Department of Veterans Affairs (VA) substance use disorder outpatient clinic achieved clinically significant improvements in insomnia severity following the iCBT-I intervention [32]. In a pilot RCT, veterans with probable insomnia disorder (n=50) were randomized to Insomnia Coach, a self-management CBT-I app, or a waitlist control. Veterans found Insomnia Coach to be feasible and acceptable, with preliminary efficacy data suggesting that app use was associated with clinically significant improvement in insomnia severity [33]. Finally, using a cohort trial design, Hermes and colleagues [34] examined the feasibility of implementing Sleep Healthy Using the Internet (SHUTi) in VA primary care clinics. Although the primary aim of this study was to examine differences in implementation outcomes based on low-intensity implementation strategy modifications, improved clinical outcomes were observed, with 20% of veterans achieving clinically meaningful change in their insomnia symptoms.

    Previous studies examining iCBT-I interventions, including apps, in veteran populations have established the acceptability and feasibility of digital health interventions, with preliminary evidence suggesting that iCBT-I interventions are associated with improved insomnia outcomes as observed in nonveteran populations. Most of these studies, however, were not powered for efficacy outcomes, and no RCT has compared an evidence-based iCBT-I with a nonwaitlist control in a veteran population. Further, no study has conducted long-term follow-up beyond 12 weeks, and most studies have enrolled veterans without a confirmed insomnia diagnosis. To strengthen the evidence base, this study was designed to evaluate the efficacy of an evidence-based iCBT-I intervention, SHUTi, to decrease insomnia severity among Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND) veterans. Although SHUTi is one of the most empirically supported iCBT-I programs in nonveteran populations [35-44], to our knowledge, no RCT has been conducted to examine the efficacy of SHUTi in a veteran sample. We hypothesized that SHUTi participants would show greater improvements in insomnia severity and mental and physical health functioning across all time points, compared with participants randomized to an Insomnia Education Website (IEW) control. We also evaluated whether SHUTi was associated with improvements across sleep diary parameters (exploratory objective).


    Ethical Considerations

    The study design and protocol (Multimedia Appendix 1 [2,15,25,29,40-75]) were approved by the following supporting agencies: Colorado Multiple Institutional Review Board (COMIRB 17-0920), Rocky Mountain Regional Veterans Affairs Medical Center Research and Development Committee, and the US Army Medical Research and Development Command Human Research Protection Office (HRPO Log Number A-19051.2; ORHO Log Number E03947.2). Additional information on the informed consent process, data privacy and security measures, and compensation are provided below and in Multimedia Appendix 1.

    Trial Design

    This is a single-blind (participants blinded to treatment allocation) 2-group (SHUTi vs IEW) longitudinal (4 time points) RCT. Assessments were administered at baseline (T1), 9 weeks postintervention (T2), 6 months postintervention (T3), and 1 year postintervention (T4). Data were collected on the web, and participants provided informed consent before study enrollment. The RCT was registered with ClinicalTrials.gov (NCT03366870).

    Randomization

    Due to the empirical association between insomnia and suicide risk, randomization was stratified by self-reported suicide attempt history (“yes” or “no”). A statistically generated allocation sequence, using random blocks of 4 and 6, stratified by suicide attempt history, was conducted using SAS PROC PLAN (SAS Institute). Randomization tables were generated by the trial’s statistician and programmed to automatically assign a participant to the next allocation immediately after T1 completion.

    Participants

    OEF, OIF, and OND veterans aged between 18 and 55 years were eligible to participate if they met the following criteria: current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [45] insomnia diagnosis; reliable access to the internet; eligibility to receive VHA care; English-speaking; and ability to provide informed consent. Exclusion criteria included the following: being currently enrolled in another intervention research study, the presence of another untreated sleep disorder, currently receiving psychological treatment for insomnia (excluding sleep medications), a change in schedule or dosage of sleep medications in the past 3 months, and the presence of a condition contraindicated with CBT-I. Examples of conditions contraindicated with CBT-I included untreated seizures or seizure disorder, pregnancy or a plan to become pregnant, irregular shift work, and significant cognitive impairment. Veterans with comorbid psychiatric presentations were eligible to participate in the RCT unless they had used any nonalcohol substances (excluding cannabis) more than once within the past 3 months; endorsed a problematic pattern of alcohol use associated with significant impairment or distress (past 3 months); or had ever been diagnosed with bipolar I disorder, schizophrenia, schizoaffective, or a psychotic disorder. Those with comorbid medical conditions were included unless conditions were deemed active, unstable, or degenerative in a manner expected to influence sleep. Research staff received extensive training on conducting semistructured interviews focused on clinical sleep history to inform differential diagnoses (eg, untreated sleep apnea, periodic limb movement disorder, parasomnia, and circadian rhythm disorder) and structured clinical interviews for DSM-5 Research Version [76] modules (eg, insomnia disorder and alcohol use disorder) to determine RCT eligibility; cases were regularly reviewed by the team and principal investigator to ensure accuracy.

    Total compensation for completion of all study assessments was US $208, and the participant payment for each study assessment was as follows: US $1 for each preintervention sleep diary (up to US $14 maximum), US $50 for T1 and T2 assessments, US $1 for each postintervention diary (up to US $14 maximum), and US $40 for T3 and T4 assessments.

    Outcomes

    The primary outcome measure was the Insomnia Severity Index (ISI) [77], one of the most empirically examined and clinically used self-report scales assessing insomnia symptoms. The ISI is comprised of 7 items that assess current (ie, past 2 weeks) insomnia severity. Each item is scored on a 0-4 scale, with total scores ranging from 0-28; each 7-point increment is associated with increasing levels of insomnia severity. The ISI has been shown to have adequate test-retest reliability over 3 months and concurrent validity with sleep diaries and polysomnography [46] and has been validated for web-based use [78]. In the current sample, the ISI demonstrated adequate internal consistency (Cronbach α ranged from .78 to .90 across the 4 time points).

    Our secondary outcome measure was the Veterans RAND 36-item Health Survey (VR-36) [79]. The VR-36 assesses perceived general health-related quality of life; respondents indicate the impact of health on physical functioning, role limitations due to physical problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. The VR-36 was formerly called the Short Form Health Survey for Veterans (Veterans SF-36) [80]. The VR-36 has been thoroughly validated and has strong psychometric properties [79,80]. Scores are used to derive 2 summary scales (ie, Physical Component Summary [PCS] and Mental Component Summary [MCS]). Scores range from 0-100, with a mean score of 50 (SD 10). Lower scores indicate poorer health-related quality of life.

    Exploratory Outcomes

    Daily sleep diaries based on consensus sleep diary [81] questions were collected for 2 weeks before T1 (preintervention sleep diaries) and immediately following T2 (postintervention sleep diaries). Sleep parameters included sleep onset latency (SOL; time to fall asleep), wake after sleep onset (WASO) + early morning awakenings (EMA; combined time awake after falling asleep and before final awakening), time in bed (TIB), total sleep time (TST), and sleep efficiency (percentage of time asleep while in bed).

    Clinical Characteristics

    A total of 4 psychometrically sound assessments (Multimedia Appendix 2 [47,48,82-85]) were used to characterize baseline clinical characteristics: (1) Adult Suicidal Ideation Questionnaire [47], a 25-item self-report instrument that assesses past-month suicidal ideation frequency; (2) Beck Anxiety Inventory [48], a 21-item self-report rating inventory that measures past-week subjective, somatic, and panic-related symptoms of anxiety; (3) Beck Depression Inventory II [82], a 21-item self-report rating inventory that measures past 2-week characteristic attitudes and symptoms of depression; and (4) Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) [83], a 20-item self-report measure that assesses past-month severity of posttraumatic stress disorder symptoms.

    Interventions

    SHUTi Program

    SHUTi is a self-guided, interactive, and tailored internet-delivered program modeled on the primary tenants of F2F CBT-I [44]. Intervention content is delivered through 6 “cores” where users obtain access to a new core based on a time- and event-based schedule. The SHUTi program relies on user-entered web-based sleep diaries to track progress and tailor treatment recommendations. Each SHUTi core acts as a web-based analog for the weekly sessions typically used when delivering CBT-I in a F2F format, following the same general structure: (1) core objectives (what will be learned and why this information is important), (2) review of previous week’s homework and sleep diary data, (3) new intervention material, (4) assignment of homework (treatment strategies for the coming week), and (5) a summary of the core’s main points. Intervention content is enhanced through a variety of interactive features, including personalized goal setting, graphical feedback based on inputted symptoms, animations and illustrations to enhance comprehension, quizzes to test users’ knowledge, patient vignettes, and video-based expert explanation. The SHUTi program relies on user-entered web-based sleep diaries to track progress and tailor treatment recommendations. Automated emails are sent throughout the intervention to encourage program adherence.

    IEW Control

    The IEW used in this RCT has been previously used in SHUTi trials [37,44]. Informed by what typically constitutes “treatment as usual” for patient education websites targeting insomnia, the IEW provided static information about insomnia symptoms, causes, and strategies to improve sleep (ie, sleep hygiene, stimulus control, and behavioral recommendations). In contrast to SHUTi, IEW strategies were presented as text with no tailoring, interactivity, or feedback.

    Procedure

    Veterans were screened over the phone for eligibility. Of the 844 veterans who expressed interest in the study, a total of 61 were unable to be reached for screening, and 494 were ineligible for participation. The top reasons for ineligibility were identification of another untreated sleep disorder (162/494, 32.8%); not serving in the OEF, OIF, or OND conflict (112/494, 22.7%); multiple exclusion criteria (54/494, 10.9%); and not meeting criteria for insomnia disorder (52/494, 10.5%). After screening, a total of 289 veterans were eligible for the study, and 250 were enrolled.

    Most participants (234/250, 93.6%) enrolled were recruited from targeted mailings sent to veterans with diagnosed sleep problems, as identified by medical codes in the VA’s Corporate Data Warehouse. An additional 3.6% (9/250) of enrolled participants were recruited based on their expressed interest in learning more about the study. Participants were enrolled in the RCT between April 2018 and January 2019. The final T4 assessment was completed in June 2020.

    After providing informed consent, participants were given instructions on how to complete a web-based, 2-week sleep diary assessment and informed that they had to complete 10 out of 14 sleep diaries to move forward in the study; a total of 6% (15/250) of enrolled veterans did not complete the sleep diary requirement. Baseline assessments (T1) were unlocked once the requirement was fulfilled. A total of 3 participants did not complete T1 assessments, resulting in a total of 231 participants (92.4%) who were randomized.

    A total of 84.8% (196/231) of all randomized participants completed at least 1 postintervention assessment, of which 151 participants completed all 3 assessments (77%), another 21 completed 2 assessments (10.7%), and 24 completed 1 assessment (12.2%). A total of 9 participants asked to be withdrawn from the study: 1 during the intervention, 2 at T2, 5 at T3, and 1 at T4. A total of 2 participants randomized to the IEW died during the study (deaths unrelated to research procedures); these deaths occurred between T2 and T3, and T3 and T4. Figure 1 depicts the study enrollment flow.

    Participants received reminder calls from study staff to complete initial sleep diaries and the T1 assessment. During the 9-week intervention window, study staff did not initiate any contact with participants, so participants only received automated emails built into the interventions during the intervention window. Participants received reminder calls, automated emails, and reminder postcards to support postintervention (T2-T4) assessment completion.

    Participants were compared across demographic variables, ISI, and VR-36 summary scale scores to examine whether there were differences between participants who did not complete a postintervention assessment (n=35) and participants who completed at least 1 postintervention assessment (n=196). The only statistically significant difference observed was that participants who did not complete a postintervention assessment were younger (mean 35.4, SD 6.4 years) than participants who completed at least 1 postintervention assessment (mean 40.0, SD 7.8 years; 2-tailed t229=3.33; P=.001).

    Of the 35 participants who did not complete a postintervention assessment, a total of 20 randomized to SHUTi demonstrated significantly lower VR-36 MCS scores (mean 31.9, SD 8.2) than the 15 randomized to the IEW (mean 40.1, SD 12.6; 2-tailed t33=2.35; P=.02). No other statistically significant differences were observed between groups.

    ‎
    Figure 1. CONSORT (Consolidated Standards of Reporting Trials) diagram of study enrollment flow. IEW: Insomnia Education Website; SHUTi: Sleep Healthy Using the Internet.

    Statistical Methods

    Because data were collected on the web, steps were taken to verify data quality before conducting analyses, including examining abnormally low survey completion times and surveys with potentially biased or inconsistent responses. No inconsistent, biased, or inaccurate response patterns were identified in the data set. Missing data was minimal across measures and was handled in accordance with manual instructions. Sleep diaries were excluded from analyses if participants provided too few (less than 7) usable sleep diaries during the sleep diary assessment window (T1, n=2; and T2, n=4).

    We used any pair power and Holm sequential procedure [86] to control the familywise error rate, considering all 3 primary (ISI: T2-T4) and 6 secondary outcomes (PCS: T2-T4, and MCS: T2-T4). A final sample size of at least 170 completing T2 assessments (85 per group) would provide 80% power to detect an effect size of 0.6 for each outcome. Assuming an attrition rate of 25%, we sought to randomize at least 226 participants to achieve at least 85 per group completing T2 assessments.

    The analysis of covariance was used to model the change from T1 to T2, from T1 to T3, and from T1 to T4 for each outcome as a function of group, the baseline value of the respective outcome, and suicide attempt (stratification variable). Generalized η2 [87] is reported with all analysis of covariance results, where values of 0.01 are considered small, 0.06 as medium, and 0.14 as large. Fisher exact tests were used for post hoc analyses that examined the clinical significance of ISI changes at each postintervention assessment. All analyses were conducted using SAS (version 9.4; SAS Institute) statistical software [88].


    Overview

    There were no significant differences between groups on demographic or baseline clinical characteristics (Table 1 and Multimedia Appendix 2). At T1, participants on average endorsed moderate insomnia and reported deficits in functioning, with PCS and MCS scores about 1 SD below the normative US population sample but consistent with the VHA normative sample. Table 2 contains the ISI, PCS, and MCS descriptive statistics over time.

    Table 1. Demographic and baseline clinical characteristics.

    		Sleep Healthy Using the Internet (n=116)Insomnia Education Website (n=115)P value
    Age (years), mean (SD)39.1 (8.0)39.5 (7.6).72
    Male sex, n (%)86 (74)86 (7).91
    Gendera, n (%).88b

    		Man84 (73)86 (75)

    		Woman30 (26)29 (25)

    		Transwoman1 (1)0 (0)
    Racec, n (%).92

    		Black or African American13 (12)13 (12)

    		White or Caucasian85 (76)87 (77)

    		Multiracial6 (5)4 (4)

    		Other8 (7)9 (8)
    Hispanicd, n (%)16 (14)20 (18).45
    Relationship status, n (%).74

    		Married67 (58)67 (58)

    		Single16 (14)21 (18)

    		Cohabitating11 (9)9 (8)

    		Widowed, divorced, or separated22 (19)18 (16)
    Employment, n (%).96

    		Full time68 (59)65 (57)

    		Part time9 (8)8 (7)

    		Unemployed and not seeking10 (9)10 (9)

    		Unemployed and seeking13 (11)12 (10)

    		Retired16 (14)20 (17)
    Deployed99 (85)106 (92).10
    Combata87 (76)87 (76)>.99
    Era (not mutually exclusive), n (%)

    		Post-Vietnam10 (9)9 (8).83

    		Desert Storm34 (29)29 (25).48

    		OEFe or OIFf113 (97)113 (98)>.99b

    		Other1 (1)1 (1)>.99b
    History of a suicide attempt, mean (SD)23 (20)23 (20).97
    ISIg total score, mean (SD)16.9 (4.6)16.3 (4.3).35
    Veterans SF-36h PCSi, mean (SD)41.8 (10.1)42.9 (9.7).40
    Veterans SF-36 MCSj, mean (SD)37.0 (12.4)37.3 (11.6).86
    ASIQk total scored, mean (SD)14.4 (20.4)13.2 (18.4).64
    BAIl total score, mean (SD)15.6 (13.4)15.3 (12.5).83
    BDIm total scoren, mean (SD)20.8 (12.6)19.7 (12.1).49
    PCL-5o total sore, mean (SD)34.6 (18.6)33.0 (19.3).51
    Provisional PTSDp, n (%)66 (57)53 (46).10
    Probable PTSD, n (%)61 (53)53 (46).32

    an=115, Sleep Healthy Using the Internet.

    bFisher exact test.

    cn=112, Sleep Healthy Using the Internet; n=113, Insomnia Education Website.

    dn=115, Sleep Healthy Using the Internet; n=114, Insomnia Education Website.

    eOEF: Operation Enduring Freedom.

    fOIF: Operation Iraqi Freedom.

    gISI: Insomnia Severity Index.

    hVeteran SF-36: Short Form Health Survey for Veterans.

    iPCS: Physical Component Summary.

    jMCS: Mental Component Summary.

    kASIQ: Adult Suicidal Ideation Questionnaire.

    lBAI: Beck Anxiety Inventory.

    mBDI: Beck Depression Inventory.

    nn=114, Insomnia Education Website.

    oPCL-5: Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

    pPTSD: posttraumatic stress disorder.

    Table 2. Primary and secondary outcome descriptive statistics over time.
    OutcomeTime 1Time 2Time 3Time 4

    		nFrequencynFrequencynFrequencynFrequency
    Sleep Healthy Using the Internet

    		ISIa, mean (SD)11616.9 (4.6)9111.6 (6.7)7710.5 (6.8)7910.7 (6.7)

    		ISI categorical, n (%)116
    		91
    		77
    		79


    		0-7
    		1 (1)
    		28 (31)
    		29 (38)
    		32 (41)


    		8-14
    		40 (34)
    		33 (36)
    		27 (35)
    		26 (33)


    		15-21
    		54 (47)
    		23 (25)
    		15 (19)
    		15 (19)


    		22-28
    		21 (18)
    		7 (8)
    		6 (8)
    		6 (8)

    		PCSb, mean (SD)11641.8 (10.1)9142.5 (10.5)7743.7 (10.6)7842.6 (10.2)

    		MCSc, mean (SD)11637.0 (12.4)9142.5 (13.4)7743.9 (13.8)7843.7 (13.2)
    Insomnia Education Website

    		ISI, mean (SD)11516.3 (4.3)9814.9 (4.9)8813.9 (5.1)8713.8 (4.8)

    		ISI categorical, n (%)115
    		98
    		88
    		87


    		0-7
    		2 (2)
    		4 (4)
    		6 (7)
    		11 (13)


    		8-14
    		40 (35)
    		45 (46)
    		48 (55)
    		40 (46)


    		15-21
    		57 (50)
    		41 (42)
    		25 (28)
    		29 (33)


    		22-28
    		16 (14)
    		8 (8)
    		9 (10)
    		7 (8)

    		PCS, mean (SD)11542.9 (9.7)9741.8 (9.7)8842.3 (8.9)8742.0 (10.4)

    		MCS, mean (SD)11537.3 (11.6)9738.7 (12.5)8838.6 (11.7)8738.2 (12.2)

    aISI: Insomnia Severity Index.

    bPCS: Physical Component Summary of the Veterans RAND 36-item Health Survey.

    cMCS: Mental Component Summary of the Veterans RAND 36-item Health Survey.

    Primary and Secondary Outcomes

    SHUTi participants experienced a significantly larger T1 to T2 (–3.47, 95% CI –4.78 to –2.16 points; P<.0001), T1 to T3 (–3.80, 95% CI –5.34 to –2.27 points; P<.0001) and T1 to T4 (–3.42, 95% CI –4.97 to –1.88 points; P<.0001) decreases on the ISI compared with IEW participants. The magnitude of the results was maintained across all time points (generalized η2 values of 0.13, 0.12, and 0.10; Table 3).

    SHUTi participants experienced a significantly greater T1 to T3 improvement on the VR-36 PCS (3.22, 95% CI 1.07-5.36 points; generalized η2=0.04; P=.004) compared with IEW participants (Table 3). Significance was not observed for the change from T1 to T2 (P=.03) or from T1 to T4 (P=.14). For the VR-36 MCS, SHUTi participants had a significantly greater T1 to T3 (4.33, 95% CI 1.11-7.55 points; generalized η2=0.03; P=.009) and T1 to T4 (4.53, 95% CI 1.39-7.67 points; generalized η2=0.04; P=.005) improvement, but the difference in change from T1 to T2 did not achieve statistical significance (2.58, 95% CI –0.07 to 5.24 points; P=.06; Table 3).

    Table 3. Estimated difference in change between groups. All models control for the baseline value of the outcome and history of suicide attempt (“yes” or “no”). Dependent variables were calculated as the later time point minus baseline, and the difference in change was estimated as Sleep Healthy Using the Internet minus Insomnia Education Website. The italicized text indicates statistical significance based on the Holm sequential procedure, considering all 9 outcomes.
    ModelsChange time 1 to time 2a,bChange time 1 to time 3cChange time 1 to time 4d,e

    		Estimated difference between groups, SEGeneralized η2P valueEstimated difference between groups, SEGeneralized η2P valueEstimated difference between groups, SEGeneralized η2P value
    ISIf–3.47 (0.66)0.13<.0001–3.80 (0.78)0.12<.0001–3.42 (0.78)0.10<.0001
    PCSg2.07 (0.96)0.02.033.22 (1.08)0.04.0041.81 (1.22)0.01.14
    MCSh2.58 (1.35)0.02.064.33 (1.63)0.04.0094.53 (1.59)0.04.005

    an=98, Insomnia Education Website (for ISI); n=91, Sleep Healthy Using the Internet (for ISI).

    bn=97, Insomnia Education Website (for PCS and MCS); n=91, Sleep Healthy Using the Internet (for PCS and MCS).

    cn=88, Insomnia Education Website (for all outcomes); n=77, Sleep Healthy Using the internet (for all outcomes).

    dn=87, Insomnia Education Website (for ISI); n=79, Sleep Healthy Using the Internet (for ISI).

    en=87, Insomnia Education Website (for PCS and MCS); n=78, Sleep Healthy Using the Internet (for PCS and MCS).

    fISI: Insomnia Severity Index.

    gPCS=Physical Component Summary of the Veterans RAND 36-item Health Survey.

    hMCS=Mental Component Summary of the Veterans RAND 36-item Health Survey.

    Exploratory Outcomes: Sleep Diary Parameters

    Results for the sleep diary parameters are displayed in Table 4. SHUTi participants experienced a significantly greater T1 to T2 decrease in SOL (P=.0001), WASO+EMA (P=.006), TIB (P<.0001), and TST (P=.04), compared with IEW participants, and a significantly greater improvement in sleep efficiency (P=.02). Specifically, SOL decreased by 16.9 (95% CI –25.6 to –8.3; generalized η2=0.04) more minutes for SHUTi participants, WASO+EMA decreased by 15.3 (95% CI –26.1 to –4.4; generalized η2=0.03) minutes more, TIB decreased by 48 (95% CI –65.4 to –30.0; generalized η2=0.12) minutes more, and TST decreased by 18.6 (95% CI –36.6 to –0.60; generalized η2=0.02) minutes more. Sleep efficiency improved for SHUTi participants 3.74% (95% CI 0.72%-6.75%; generalized η2=0.03) more than for the IEW participants.

    Table 4. Exploratory sleep parameter models. All models control for the baseline value of the outcome and history of suicide attempt (“yes” or “no”). Dependent variables were calculated as the later time point minus baseline, and the difference in change was estimated as Sleep Healthy Using the Internet minus Insomnia Education Website. The italicized text indicates statistical significance at the P<.05 level.
    OutcomeChange Time 1 to Time 2a
    Group parameter
    estimate, SE    		Generalized η2P value
    Sleep onset latency (minutes)–16.95 (4.37)0.04.0001
    Wake after sleep onset +
    early morning awakenings (minutes)    		–15.26 (5.51)0.03.006
    Time in bed (hours)–0.80 (0.15)0.12<.0001
    Total sleep time (hours)–0.31 (0.15)0.02.04
    Sleep efficiency (%)3.74 (1.53)0.03.02

    an=89, Insomnia Education Website; n=83, Sleep Healthy Using the Internet.

    Post Hoc Analyses

    A significantly greater proportion of the SHUTi group were deemed treatment responders (reduction of greater than 7 points on the ISI) at all time points compared with IEW participants (31% vs 4% at T2; 43% vs 10% at T3, and 37% vs 11% at T4; all P<.001; Figure 2). Additionally, a greater proportion of the SHUTi group met criteria for insomnia remittance (change from ≥8 at T1 to <8 on the ISI at T2, T3, and T4) at all time points compared with IEW participants (30% vs 4% at T2; 37% vs 7% at T3, and 40% vs 13% at T4; all P<.001; Figure 2).

    ‎
    Figure 2. Treatment responders and insomnia remittance by group. Treatment responders are defined as a reduction of >7 points on the Insomnia Severity Index (ISI) between T2, T3, and T4, respectively, and T1. Insomnia remittance is defined as an ISI change from ≥8 at T1 to <8 at T2, T3, and T4. IEW: Insomnia Education Website; SHUTI: Sleep Healthy Using the Internet. *P<.001; **P<.0001.

    Principal Findings

    Despite the empirical support for CBT-I as an efficacious first-line treatment for insomnia and evidence that iCBT-I improves insomnia severity with comparable effect sizes to those found in F2F CBT-I, there is a paucity of RCTs examining iCBT-I in veterans. Data from this RCT confirmed the efficacy of SHUTi for the treatment of insomnia in OEF, OIF, and OND veterans, and this is the first study to demonstrate that improvements in insomnia severity are sustained up to 1 year postintervention. SHUTi participants reported significant changes in insomnia severity across all time points compared with IEW participants. These effects were medium-large and consistent with civilian SHUTi trials, an iCBT-I trial [89] in a military sample, and self-guided iCBT-I interventions in veteran samples [32-34]. SHUTi participants also experienced significant improvements in sleep diary parameters compared with IEW participants. Although SHUTi participants experienced a reduction in total sleep time compared with IEW participants, they spent less time in bed and improved their sleep efficiency, which are consistent with CBT-I desired outcomes.

    IEW participants completed pre- and postintervention sleep diaries and had access to high-quality, patient-oriented insomnia materials, constituting a more “active” control compared with waitlist designs. For example, behavioral tracking can facilitate awareness of patterns that foster insight and promote behavior change. Given this, the fact that SHUTi participants significantly outperformed IEW participants in reducing their insomnia severity demonstrates the value of metered, interactive, and tailored iCBT-I content. These findings add to the robust empirical data that supports the superiority of CBT-I interventions over approaches such as sleep hygiene education. This study extends those by demonstrating the efficacy of self-administered iCBT-I in a veteran population.

    SHUTi participants were also more likely than IEW participants to report significant improvements in health-related quality of life as assessed by the VR-36 during follow-up, though effect sizes were small. These findings suggest that mental and physical health functioning may require sustained improvements in insomnia to ultimately improve functioning. Furthermore, given the many health factors impacting veterans’ health-related quality of life, it is possible that small effect sizes were due to improved sleep but the continued negative impact of comorbid issues. Our findings were consistent with those observed in a military sample [90] but differed slightly from findings reported in a systematic review on F2F CBT-I and functioning [91], suggesting potential differences in the insomnia-functioning association across populations. Although our findings fall below the change typically indicative of clinical significance at an individual level, they are above the cutoff for population clinical significance [79,92]. Paired with the insomnia severity findings, this suggests that health care implementation of iCBT-I could be an especially promising approach to improving veteran outcomes (eg, systems level).

    SHUTi participants were more likely to achieve clinically meaningful changes in insomnia severity compared with IEW participants across all time points, consistent with improvements observed at posttreatment by veterans following use of Insomnia Coach [33]. The proportion of treatment responders (31%) and those who achieved remittance (30%) at posttreatment was greater in our SHUTi sample compared with an implementation-focused study of SHUTi in VA primary care (20% responders, 18.6% remitted) [34]. These differences may have been driven by sample characteristics, as participants in this study were younger (mean age of 39.3, SD 7.8 years) than those in the study of Hermes et al [34] (mean age of 67, SD 16.9 years). Furthermore, participants in this study may have been more motivated to engage in the iCBT-I as enrollment originated from self-interest versus provider referral. Despite SHUTi participants achieving clinically meaningful changes in insomnia, responder and remittance percentages were lower than what had been observed in nonveteran SHUTi trials. IEW participants, however, were also less likely to naturally recover or receive benefit from the IEW when compared with nonveteran IEW participants, suggesting that differences may be attributable to methodology and sample clinical complexity. Given that this study was not designed to specifically examine comorbidity, future studies are necessary to investigate how clinical comorbidity impacts iCBT-I engagement and benefit.

    For self-administered approaches to be a viable health care option, individuals must be interested in using technology to receive an intervention and effectively engage. OEF, OIF, and OND veterans’ interest in this study was high, with almost 900 veterans reporting interest in approximately 9 months. Engagement was also high, with 58% of SHUTi participants engaging in the intervention to receive an adequate dose (ie, completion of more than 4 cores) [44]. This finding is notable given there was no clinician or research staff support during the intervention, demonstrating that some veterans can achieve positive outcomes from a self-administered iCBT-I without clinician or coaching support.

    Limitations

    Although this trial was adequately powered and included a longitudinal design with a high retention rate in an important clinical population, several limitations are important to note. To mitigate primary outcome confounding, there was no ISI inclusion requirement. As such, about one-third of randomized participants reported insomnia severity below the ISI clinically significant threshold. Although CBT-I has been shown to provide benefit in subthreshold insomnia [93], this has not been widely studied in veteran populations and may have resulted in a floor effect. Most participants in this study identified as male, White, and non-Hispanic. Thus, our results may not generalize to veterans with different identities who face additional barriers to accessing medical care. To ensure safety, participants were ineligible if they had untreated sleep apnea (a commonly cooccurring sleep disorder with insomnia) [30,94]; our findings may not generalize to veterans with insomnia and untreated sleep apnea. During the consent process, participants were told that the purpose of the study was to learn whether participation in computerized insomnia programs may reduce insomnia symptoms and improve functioning in veterans (ie, SHUTi and IEW were not described or compared). Although participants were blinded to their treatment allocation, based on their experiences (eg, access to static IEW information vs access to SHUTi, seeking information on ClinicalTrials.gov), they may have speculated that their assigned condition was better or worse than the other. To better understand how participant perceptions of assigned conditions impact outcomes, asking participants what condition they believe they received would be of benefit to future iCBT-I trials. Given the RCT design (ie, no initiated contact during the intervention), we do not have a reliable way to ascertain the duration or engagement in SHUTi or the IEW. Although this design improves the ecological validity of our findings, it limits our ability to determine whether there were moderating factors associated with trajectories of improvement. Future research examining levels of support will be integral in determining CBT-I treatment options (ie, clinician-delivered CBT-I and clinician-supported and self-administered iCBT-I) [33,95-97]. Although participants must have experienced insomnia symptoms for at least 3 months to meet the inclusion criteria, we did not systematically collect information on insomnia symptom duration, sleep medication use, or medical comorbidity; this information would have further characterized the sample and will be important to include in future iCBT-I research. Finally, although commonly used as an important indicator, sleep outcomes were based on self-report, with no objective indicators. Future research examining potential differences in subjective versus objective outcomes following SHUTi will be critical to understanding the mechanisms of change.

    Conclusions

    This study is the first known longitudinal RCT comparing an evidence-based iCBT-I to a nonwaitlist control in a veteran population meeting diagnostic criteria for insomnia disorder. In this RCT, we observed significant reductions in the primary outcome of insomnia severity between groups immediately after the intervention and up to 1 year postintervention. Although the secondary outcomes of improved physical and mental health functioning were not detected immediately after the 9-week intervention, SHUTi was efficacious in improving mental health functioning at both follow-up time points and physical health functioning at 6 months. In exploratory analyses, participants randomized to SHUTi experienced significant improvements across sleep parameters. The results of this RCT support the use of a self-administered iCBT-I in OEF, OIF, and OND veterans who meet criteria for insomnia disorder.

    Acknowledgments

    The authors wish to acknowledge Christine Jackson, BA, for her contributions to the success of this research, and Lee Ritterband, PhD, for his consultation and support in the execution of this research.

    This research was funded by the Military Suicide Research Consortium (principal investigator SN; W81XWH-16-2-0004) and supported with resources and the use of facilities at the Rocky Mountain Mental Illness Research, Education, and Clinical Center (MIRECC), which is located within the Department of Veterans Affairs (VA) Rocky Mountain Regional Medical Center.

    Conflicts of Interest

    SN reports grants from the VA and editorial board membership and remuneration from Wiley Periodicals, LLC. LAB reports grants from the VA, the Department of Defense (DOD), the National Institutes of Health (NIH), and the State of Colorado; editorial remuneration from Wolters Kluwer and the Rand Corporation; and royalties from the American Psychological Association and Oxford University Press. In addition, she consults with sports leagues through her university affiliation. JEF reports grants from the VA, DOD, NIH, and the State of Colorado. SMB reports grants from the VA. LLM reports grants from the VA and DOD.

    Multimedia Appendix 1

    Trial protocol.

    DOCX File , 82 KB
    Multimedia Appendix 2

    Exploratory measures.

    DOCX File , 17 KB
    Multimedia Appendix 3

    CONSORT-EHEALTH checklist (V 1.6.1).

    PDF File (Adobe PDF File), 1196 KB
    1. Byrne SP, McCarthy E, DeViva JC, Southwick SM, Pietrzak RH. Prevalence, risk correlates, and health comorbidities of insomnia in US military veterans: results from the 2019-2020 national health and resilience in veterans study. J Clin Sleep Med. 2021;17(6):1267-1277. [FREE Full text] [CrossRef] [Medline]
    2. Ohayon MM, Roth T. Place of chronic insomnia in the course of depressive and anxiety disorders. J Psychiatr Res. 2003;37(1):9-15. [CrossRef] [Medline]
    3. Ulmer CS, van Voorhees E, Germain AE, Voils CI, Beckham JC, VA Mid-Atlantic Mental Illness Research Education and Clinical Center Registry Workgroup. A comparison of sleep difficulties among Iraq/Afghanistan theater veterans with and without mental health diagnoses. J Clin Sleep Med. 2015;11(9):995-1005. [FREE Full text] [CrossRef] [Medline]
    4. Pigeon WR, Britton PC, Ilgen MA, Chapman B, Conner KR. Sleep disturbance preceding suicide among veterans. Am J Public Health. 2012;102(Suppl 1):S93-S97. [FREE Full text] [CrossRef] [Medline]
    5. Malik S, Kanwar A, Sim LA, Prokop LJ, Wang Z, Benkhadra K, et al. The association between sleep disturbances and suicidal behaviors in patients with psychiatric diagnoses: a systematic review and meta-analysis. Syst Rev. 2014;3(1):18. [FREE Full text] [CrossRef] [Medline]
    6. Littlewood D, Kyle SD, Pratt D, Peters S, Gooding P. Examining the role of psychological factors in the relationship between sleep problems and suicide. Clin Psychol Rev. 2017;54:1-16. [FREE Full text] [CrossRef] [Medline]
    7. Zuromski KL, Cero I, Witte TK. Insomnia symptoms drive changes in suicide ideation: a latent difference score model of community adults over a brief interval. J Abnorm Psychol. 2017;126(6):739-749. [CrossRef] [Medline]
    8. Zhang Y, Jiang X, Liu J, Lang Y, Liu Y. The association between insomnia and the risk of metabolic syndrome: a systematic review and meta-analysis. J Clin Neurosci. 2021;89:430-436. [FREE Full text] [CrossRef] [Medline]
    9. Taylor DJ, Mallory LJ, Lichstein KL, Durrence HH, Riedel BW, Bush AJ. Comorbidity of chronic insomnia with medical problems. Sleep. 2007;30(2):213-218. [FREE Full text] [CrossRef] [Medline]
    10. Siebern AT, Manber R. New developments in cognitive behavioral therapy as the first-line treatment of insomnia. Psychol Res Behav Manag. 2011;4:21-28. [FREE Full text] [CrossRef] [Medline]
    11. Mysliwiec V, Martin JL, Ulmer CS, Chowdhuri S, Brock MS, Spevak C, et al. The management of chronic insomnia disorder and obstructive sleep apnea: synopsis of the 2019 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guidelines. Ann Intern Med. 2020;172(5):325-336. [FREE Full text] [CrossRef] [Medline]
    12. Taylor DJ, Pruiksma KE. Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric populations: a systematic review. Int Rev Psychiatry. 2014;26(2):205-213. [FREE Full text] [CrossRef] [Medline]
    13. 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. [FREE Full text] [Medline]
    14. Sweetman A, Putland S, Lack L, McEvoy RD, Adams R, Grunstein R, et al. The effect of cognitive behavioural therapy for insomnia on sedative-hypnotic use: a narrative review. Sleep Med Rev. 2021;56:101404. [FREE Full text] [CrossRef] [Medline]
    15. Edinger JD, Means MK. Cognitive-behavioral therapy for primary insomnia. Clin Psychol Rev. 2005;25(5):539-558. [FREE Full text] [CrossRef] [Medline]
    16. Manber R, Carney C, Edinger J, Epstein D, Friedman L, Haynes PL, et al. Dissemination of CBTI to the non-sleep specialist: protocol development and training issues. J Clin Sleep Med. 2012;8(2):209-218. [FREE Full text] [CrossRef] [Medline]
    17. Trockel M, Karlin BE, Taylor CB, Manber R. Cognitive behavioral therapy for insomnia with veterans: evaluation of effectiveness and correlates of treatment outcomes. Behav Res Ther. 2014;53:41-46. [FREE Full text] [CrossRef] [Medline]
    18. Koffel E, Farrell-Carnahan L. Feasibility and preliminary real-world promise of a manualized group-based cognitive behavioral therapy for insomnia protocol for veterans. Mil Med. 2014;179(5):521-528. [FREE Full text] [CrossRef]
    19. Sarmiento K, Rossettie J, Stepnowsky C, Atwood C, Calvitti A, VA Sleep Network. The state of Veterans Affairs sleep medicine programs: 2012 inventory results. Sleep Breath. 2016;20(1):379-382. [FREE Full text] [CrossRef] [Medline]
    20. Bramoweth AD, Germain A. Deployment-related insomnia in military personnel and veterans. Curr Psychiatry Rep. 2013;15(10):401. [FREE Full text] [CrossRef] [Medline]
    21. Gutner CA, Pedersen ER, Drummond SPA. Going direct to the consumer: examining treatment preferences for veterans with insomnia, PTSD, and depression. Psychiatry Res. 2018;263:108-114. [FREE Full text] [CrossRef] [Medline]
    22. Epstein DR, Babcock-Parziale JL, Haynes PL, Herb CA. Insomnia treatment acceptability and preferences of male Iraq and Afghanistan combat veterans and their healthcare providers. J Rehabil Res Dev. 2012;49(6):867-878. [FREE Full text] [CrossRef] [Medline]
    23. Koffel E, Kuhn E, Petsoulis N, Erbes CR, Anders S, Hoffman JE, et al. A randomized controlled pilot study of CBT-I coach: feasibility, acceptability, and potential impact of a mobile phone application for patients in cognitive behavioral therapy for insomnia. Health Informatics J. 2018;24(1):3-13. [FREE Full text] [CrossRef] [Medline]
    24. Vincent N, Lionberg C. Treatment preference and patient satisfaction in chronic insomnia. Sleep. 2001;24(4):411-417. [FREE Full text] [CrossRef] [Medline]
    25. Cartreine JA, Ahern DK, Locke SE. A roadmap to computer-based psychotherapy in the United States. Harv Rev Psychiatry. 2010;18(2):80-95. [CrossRef] [Medline]
    26. Simon L, Steinmetz L, Feige B, Benz F, Spiegelhalder K, Baumeister H. Comparative efficacy of onsite, digital, and other settings for cognitive behavioral therapy for insomnia: a systematic review and network meta-analysis. Sci Rep. 2023;13(1):1929. [FREE Full text] [CrossRef] [Medline]
    27. 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. 2016;30:1-10. [FREE Full text] [CrossRef] [Medline]
    28. Soh HL, Ho RC, Ho CS, Tam WW. Efficacy of digital cognitive behavioural therapy for insomnia: a meta-analysis of randomised controlled trials. Sleep Med. 2020;75:315-325. [FREE Full text] [CrossRef] [Medline]
    29. Cheng SK, Dizon J. Computerised cognitive behavioural therapy for insomnia: a systematic review and meta-analysis. Psychother Psychosom. 2012;81(4):206-216. [FREE Full text] [CrossRef] [Medline]
    30. Reilly ED, Robinson SA, Petrakis BA, Kuhn E, Pigeon WR, Wiener RS, et al. Mobile app use for insomnia self-management: pilot findings on sleep outcomes in veterans. Interact J Med Res. 2019;8(3):e12408. [FREE Full text] [CrossRef] [Medline]
    31. Reilly ED, Robinson SA, Petrakis BA, Gardner MM, Wiener RS, Castaneda-Sceppa C, et al. Mobile intervention to improve sleep and functional health of veterans with insomnia: randomized controlled trial. JMIR Form Res. 2021;5(12):e29573. [FREE Full text] [CrossRef] [Medline]
    32. Hermes EDA, Rosenheck RA. Implementing computer-based psychotherapy among veterans in outpatient treatment for substance use disorders. Psychiatr Serv. 2016;67(2):176-183. [FREE Full text] [CrossRef] [Medline]
    33. Kuhn E, Miller KE, Puran D, Wielgosz J, YorkWilliams SL, Owen JE, et al. A pilot randomized controlled trial of the insomnia coach mobile app to assess its feasibility, acceptability, and potential efficacy. Behav Ther. 2022;53(3):440-457. [FREE Full text] [CrossRef] [Medline]
    34. Hermes EDA, Rosenheck RA, Burrone L, Dante G, Lukens C, Martino S. The implementation and effectiveness of digital cognitive behavioral therapy for insomnia in primary care: a pilot study. Implement Res Pract. 2021;2:26334895211053659. [FREE Full text] [CrossRef] [Medline]
    35. Cavanagh RC, Mackey R, Bridges L, Gleason A, Ciulla R, Micheel L, et al. The use of digital health technologies to manage insomnia in military populations. J Technol Behav Sci. 2019;5(1):61-69. [FREE Full text] [CrossRef]
    36. Vedaa Ø, Kallestad H, Scott J, Smith ORF, Pallesen S, Morken G, et al. Effects of digital cognitive behavioural therapy for insomnia on insomnia severity: a large-scale randomised controlled trial. Lancet Digit Health. 2020;2(8):e397-e406. [FREE Full text] [CrossRef] [Medline]
    37. Hagatun S, Vedaa Ø, Nordgreen T, Smith ORF, Pallesen S, Havik OE, et al. The short-term efficacy of an unguided internet-based cognitive-behavioral therapy for insomnia: a randomized controlled trial with a six-month nonrandomized follow-up. Behav Sleep Med. 2019;17(2):137-155. [FREE Full text] [CrossRef] [Medline]
    38. Shi X, Buysse DJ, Ritterband LM, Sereika SM, Strollo PJ, Wenzel SE, et al. Solving insomnia electronically: sleep treatment for asthma (SIESTA): a study protocol for a randomized controlled trial. Contemp Clin Trials. 2019;79:73-79. [FREE Full text] [CrossRef] [Medline]
    39. Zhou ES, Ritterband LM, Bethea TN, Robles YP, Heeren TC, Rosenberg L. Effect of culturally tailored, internet-delivered cognitive behavioral therapy for insomnia in Black women: a randomized clinical trial. JAMA Psychiatry. 2022;79(6):538-549. [FREE Full text] [CrossRef] [Medline]
    40. Ritterband LM, Thorndike FP, Gonder-Frederick LA, Magee JC, Bailey ET, Saylor DK, et al. Efficacy of an internet-based behavioral intervention for adults with insomnia. Arch Gen Psychiatry. 2009;66(7):692-698. [FREE Full text] [CrossRef] [Medline]
    41. 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. [FREE Full text] [CrossRef] [Medline]
    42. 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. [FREE Full text] [CrossRef] [Medline]
    43. 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. [FREE Full text] [CrossRef] [Medline]
    44. Ritterband LM, Thorndike FP, Ingersoll KS, Lord HR, Gonder-Frederick L, Frederick C, et al. Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: a randomized clinical trial. JAMA Psychiatry. 2017;74(1):68-75. [FREE Full text] [CrossRef] [Medline]
    45. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed. Arlington, VA. American Psychiatric Publishing; 2013.
    46. 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. [FREE Full text] [CrossRef] [Medline]
    47. Reynolds WM. Psychometric characteristics of the adult suicidal ideation questionnaire in college students. J Pers Assess. 1991;56(2):289-307. [FREE Full text] [CrossRef] [Medline]
    48. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56(6):893-897. [FREE Full text] [CrossRef] [Medline]
    49. Armed Forces Health Surveillance Center. Insomnia, active component, US Armed Forces, January 2000-December 2009. MSMR. 2010;17(5):12-15. [CrossRef]
    50. Lew HL, Pogoda TK, Hsu PT, Cohen S, Amick MM, Baker E, et al. Impact of the "polytrauma clinical triad" on sleep disturbance in a Department of Veterans Affairs outpatient rehabilitation setting. Am J Phys Med Rehabil. 2010;89(6):437-445. [FREE Full text] [CrossRef] [Medline]
    51. Alexander M, Ray MA, Hébert JR, Youngstedt SD, Zhang H, Steck SE, et al. The national veteran sleep disorder study: descriptive epidemiology and secular trends, 2000-2010. Sleep. 2016;39(7):1399-1410. [FREE Full text] [CrossRef] [Medline]
    52. Germain A, Richardson R, Stocker R, Mammen O, Hall M, Bramoweth AD, et al. Treatment for insomnia in combat-exposed OEF/OIF/OND military veterans: preliminary randomized controlled trial. Behav Res Ther. 2014;61:78-88. [FREE Full text] [CrossRef] [Medline]
    53. Vasterling JJ, Verfaellie M, Sullivan KD. Mild traumatic brain injury and posttraumatic stress disorder in returning veterans: perspectives from cognitive neuroscience. Clin Psychol Rev. 2009;29(8):674-684. [FREE Full text] [CrossRef] [Medline]
    54. Gilbert KS, Kark SM, Gehrman P, Bogdanova Y. Sleep disturbances, TBI and PTSD: implications for treatment and recovery. Clin Psychol Rev. 2015;40:195-212. [FREE Full text] [CrossRef] [Medline]
    55. Vincent N, Lewycky M. Logging on for better sleep: RCT of the effectiveness of online treatment for insomnia. Sleep. 2009;32(6):807-815. [FREE Full text] [CrossRef] [Medline]
    56. Okajima I, Komada Y, Inoue Y. A meta-analysis on the treatment effectiveness of cognitive behavioral therapy for primary insomnia. Sleep Biol Rhythms. 2011;9(1):24-34. [CrossRef]
    57. Morin CM, Culbert JP, Schwartz SM. Nonpharmacological interventions for insomnia: a meta-analysis of treatment efficacy. Am J Psychiatry. 1994;151(8):1172-1180. [FREE Full text] [CrossRef] [Medline]
    58. Murtagh DR, Greenwood KM. Identifying effective psychological treatments for insomnia: a meta-analysis. J Consult Clin Psychol. 1995;63(1):79-89. [CrossRef] [Medline]
    59. Smith MT, Perlis ML, Park A, Smith MS, Pennington J, Giles DE, et al. Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. Am J Psychiatry. 2002;159(1):5-11. [FREE Full text] [CrossRef] [Medline]
    60. Espie CA. "Stepped care": a health technology solution for delivering cognitive behavioral therapy as a first line insomnia treatment. Sleep. 2009;32(12):1549-1558. [FREE Full text] [CrossRef] [Medline]
    61. Christensen H, Batterham PJ, Gosling JA, Ritterband LM, Griffiths KM, Thorndike FP, et al. Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight study): a randomised controlled trial. Lancet Psychiatry. 2016;3(4):333-341. [FREE Full text] [CrossRef] [Medline]
    62. Woosley JA, Lichstein KL, Taylor DJ, Riedel BW, Bush AJ. Insomnia complaint versus sleep diary parameters: predictions of suicidal ideation. Suicide Life Threat Behav. 2016;46(1):88-95. [CrossRef] [Medline]
    63. Kazis LW. The Veterans SF-36 Health Status Questionnaire: development and application in the Veterans Health Administration. Med Outcomes. 2000;5(1):1-18.
    64. Tarlov AR, Ware JE, Greenfield S, Nelson EC, Perrin E, Zubkoff M. The medical outcomes study. an application of methods for monitoring the results of medical care. JAMA. 1989;262(7):925-930. [FREE Full text] [CrossRef] [Medline]
    65. Brown GK. A Review of Suicide Assessment Measures for Intervention Research with Adults and Older Adults. Pennsylvania. University of Pennsylvania; 2001.
    66. Beck AT, Ward CH, Mendelson J, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4(6):561-571. [CrossRef] [Medline]
    67. Holmes TH, Rahe RH. The social readjustment rating scale. J Psychosom Res. 1967;11(2):213-218. [FREE Full text] [CrossRef] [Medline]
    68. Horne JA, Ostberg O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int J Chronobiol. 1976;4(2):97-110. [Medline]
    69. Weathers FW, Litz BT, Keane TM, Palmieri PA, Marx BP, Schnurr PP. Posttraumatic stress disorder checklist for DSM-5 (PCL-5). U.S. Department of Veterans Affairs. 2013. URL: https://www.ohsu.edu/sites/default/files/2022-08/%28PCL-5%29% 20PTSD%20Checklist%20for%20DSM5.pdf [accessed 2015-12-06]
    70. Morin CM. Insomnia: Psychological Assessment and Management. New York, NY. Guilford Publications; 1993.
    71. 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. [FREE Full text] [CrossRef] [Medline]
    72. Hasson-Ohayon I, Roe D, Kravetz S. A qualitative approach to the evaluation of psychosocial interventions for persons with severe mental illness. Psychol Serv. 2006;3(4):262-273. [CrossRef]
    73. Nguyen TD, Attkisson CC, Stegner BL. Assessment of patient satisfaction: development and refinement of a service evaluation questionnaire. Eval Program Plann. 1983;6(3-4):299-313. [CrossRef] [Medline]
    74. Bagalman E. The number of veterans that use VA health care services: a fact sheet. Congressional Research Service. 2014. URL: https://sgp.fas.org/crs/misc/R43579.pdf [accessed 2015-12-06]
    75. 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. [FREE Full text] [CrossRef] [Medline]
    76. First MB, Williams JB, Karg RS, Spitzer RL. Structured clinical interview for DSM-5-Research version (SCID-5 for DSM-5, research version; SCID-5-RV). Arlington, VA. American Psychiatric Association; 2015.
    77. 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. [FREE Full text] [CrossRef] [Medline]
    78. 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. [FREE Full text] [CrossRef] [Medline]
    79. Kazis LE, Miller DR, Clark JA, Skinner KM, Lee A, Ren XS, et al. Improving the response choices on the veterans SF-36 health survey role functioning scales: results from the veterans health study. J Ambul Care Manage. 2004;27(3):263-280. [CrossRef] [Medline]
    80. Kazis LE, Ren XS, Lee A, Skinner K, Rogers W, Clark J, et al. Health status in VA patients: results from the veterans health study. Am J Med Qual. 1999;14(1):28-38. [FREE Full text] [CrossRef] [Medline]
    81. Carney CE, Buysse DJ, Ancoli-Israel S, Edinger JD, Krystal AD, Lichstein KL, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35(2):287-302. [FREE Full text] [CrossRef] [Medline]
    82. Beck AT, Steer RA, Brown GK. BDI-II, Beck Depression Inventory. San Antonio, Tex, Boston. Psychological Corporation; 2011.
    83. Weathers FW, Litz B, Herman D, Juska J, Keane T. PTSD checklist-civilian version. American Psychological Association. 2012. URL: https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft02622-000 [accessed 2023-11-08]
    84. Batterham PJ, Ftanou M, Pirkis J, Brewer JL, Mackinnon AJ, Beautrais A, et al. A systematic review and evaluation of measures for suicidal ideation and behaviors in population-based research. Psychol Assess. 2015;27(2):501-512. [CrossRef] [Medline]
    85. Marx BP, Lee DJ, Norman SB, Bovin MJ, Sloan DM, Weathers FW, et al. Reliable and clinically significant change in the clinician-administered PTSD scale for DSM-5 and PTSD checklist for DSM-5 among male veterans. Psychol Assess. 2022;34(2):197-203. [FREE Full text] [CrossRef] [Medline]
    86. Eichstaedt KE, Kovatch K, Maroof DA. A less conservative method to adjust for familywise error rate in neuropsychological research: the Holm's sequential bonferroni procedure. NeuroRehabilitation. 2013;32(3):693-696. [CrossRef] [Medline]
    87. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. [FREE Full text] [CrossRef] [Medline]
    88. SAS Institute Inc. 2016. URL: https://www.sas.com/en_in/home.html [accessed 2023-11-08]
    89. Taylor DJ, Peterson AL, Pruiksma KE, Young-McCaughan S, Nicholson K, Mintz J, et al. Internet and in-person cognitive behavioral therapy for insomnia in military personnel: a randomized clinical trial. Sleep. 2017;40(6):1-12. [FREE Full text] [CrossRef] [Medline]
    90. Taylor DJ, Peterson AL, Pruiksma KE, Hale WJ, Young-McCaughan S, Wilkerson A, et al. Impact of cognitive behavioral therapy for insomnia disorder on sleep and comorbid symptoms in military personnel: a randomized clinical trial. Sleep. 2018;41(6):1-11. [FREE Full text] [CrossRef] [Medline]
    91. Xu D, Cardell E, Broadley SA, Sun J. Efficacy of face-to-face delivered cognitive behavioral therapy in improving health status of patients with insomnia: a meta-analysis. Front Psychiatry. 2021;12:798453. [FREE Full text] [CrossRef] [Medline]
    92. Ware J, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. [FREE Full text] [CrossRef] [Medline]
    93. Denis D, Eley TC, Rijsdijk F, Zavos HMS, Keers R, Espie CA, et al. Is digital cognitive behavioural therapy for insomnia effective in treating sub-threshold insomnia: a pilot RCT. Sleep Med. 2020;66:174-183. [FREE Full text] [CrossRef] [Medline]
    94. e Cruz MM, Kryger MH, Morin CM, Palombini L, Salles C, Gozal D. Comorbid insomnia and sleep apnea: mechanisms and implications of an underrecognized and misinterpreted sleep disorder. Sleep Med. 2021;84:283-288. [FREE Full text] [CrossRef] [Medline]
    95. Luik AI, Kyle SD, Espie CA. Digital Cognitive Behavioral Therapy (dCBT) for insomnia: a state-of-the-science review. Curr Sleep Med Rep. 2017;3(2):48-56. [FREE Full text] [CrossRef] [Medline]
    96. Luik AI, van der Zweerde T, van Straten A, Lancee J. Digital delivery of cognitive behavioral therapy for insomnia. Curr Psychiatry Rep. 2019;21(7):50. [FREE Full text] [CrossRef] [Medline]
    97. Hermes EDA, Lyon AR, Schueller SM, Glass JE. Measuring the implementation of behavioral intervention technologies: recharacterization of established outcomes. J Med Internet Res. 2019;21(1):e11752. [FREE Full text] [CrossRef] [Medline]

    ‎
    CBT-I: Cognitive behavioral therapy for insomnia
    DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
    EBP: evidence-based psychotherapy
    EMA: early morning awakenings
    F2F: face-to-face
    iCBT: internet-delivered cognitive behavioral therapy
    iCBT-I: internet-delivered cognitive behavioral therapy for insomnia
    IEW: Insomnia Education Website
    ISI: Insomnia Severity Index
    MCS: Mental Component Summary
    OEF: Operation Enduring Freedom
    OIF: Operation Iraqi Freedom
    OND: Operation New Dawn
    PCL-5: Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
    PCS: Physical Component Summary
    RCT: randomized controlled trial
    SHUTi: Sleep Healthy Using the Internet
    SOL: sleep onset latency
    TIB: time in bed
    TST: total sleep time
    VA: Department of Veterans Affairs
    Veterans SF-36: Short Form Health Survey for Veterans
    VHA: Veterans Health Administration
    VR-36: Veterans RAND 36-item Health Survey
    WASO: wake after sleep onset

    Edited by J Torous; submitted 03.07.23; peer-reviewed by H Sun, L Yang; comments to author 05.08.23; revised version received 26.09.23; accepted 06.10.23; published 24.11.23.

    Copyright

    ©Sarra Nazem, Sean M Barnes, Jeri E Forster, Trisha A Hostetter, Lindsey L Monteith, Emily B Kramer, Laurel A Gaeddert, Lisa A Brenner. Originally published in JMIR Mental Health (https://mental.jmir.org), 24.11.2023.

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