Key Points
Question
What is the effectiveness of nurse-supported self-directed cognitive behavioral therapy for insomnia (CBTi) for reducing insomnia severity and improving sleep outcomes among patients with insomnia disorder?
Findings
In this randomized clinical trial of 178 patients with insomnia disorder, those completing self-directed CBTi with 6 brief (median of 23 minutes each) nurse-supported phone calls achieved greater reductions in insomnia severity, depression, and fatigue at 8 weeks than a health education control group; insomnia severity outcomes were sustained at 6 months.
Meaning
This randomized clinical trial found that patients receiving brief support from a health care clinician for patients engaged in self-directed insomnia treatment had reduced insomnia severity and improved sleep outcomes.
Abstract
Importance
Cognitive behavioral therapy for insomnia (CBTi) is the standard of care for treating insomnia disorder, but access is limited. Alternative approaches are needed to expand access to the standard of care.
Objective
To examine the effectiveness of a nurse-supported, self-directed behavioral insomnia intervention for decreasing insomnia severity and improving sleep outcomes among veterans, a population with considerable mental health comorbidity.
Design, Setting, and Participants
This randomized clinical trial included 178 patients with insomnia disorder who were recruited from a Veterans Affairs hospital (Durham VA Healthcare System) from September 2019 to April 2022 and randomized following baseline assessment; follow-ups were conducted at 8 weeks (primary end point) and 6 months. Data analysis was primarily conducted during the summer of 2023 and concluded in May 2024.
Intervention
Six weekly phone calls from a nurse interventionist plus assigned treatment manual readings covering CBTi treatment components. The health education manual focused on health topics but not sleep.
Main Outcomes and Measures
The primary outcome was the Insomnia Severity Index (score range, 0-28; remission <8; differential improvement of 3 points targeted) score assessed at 8 weeks postrandomization. Secondary outcomes were sleep outcomes, depression, fatigue, treatment response, and remission.
Results
Of 178 study participants, the mean (SD) age was 55.1 (13.2) years, and 128 (71.9%) identified as men. At 8 weeks, Insomnia Severity Index scores decreased by an estimated mean (SE) of 5.7 (0.51) points in the intervention group and 2.0 (0.47) points in the control group, a differential mean improvement of 3.7 points (95% CI, −5.0 to −2.4; P < .001). Differences were sustained at 6 months (mean, −2.8; 95% CI, −4.4 to −1.3; P < .001). The intervention also resulted in greater improvements at 8 weeks postrandomization in diary sleep onset latency, wake after sleep onset, and sleep efficiency and actigraphy sleep efficiency; these differences were sustained at 6 months. At 8 weeks, depression and fatigue were significantly reduced, and the odds of treatment response and remission were greater in the intervention group compared with controls.
Conclusions and Relevance
This randomized clinical trial found that despite greater prevalence of mental health conditions and sleep difficulties among veterans, a nurse-supported self-directed CBTi was more effective than health education control for reducing insomnia severity and improving sleep outcomes. Although less effective than therapist-delivered CBTi, findings were comparable with other trials using modified CBTi protocols.
Trial Registration
ClinicalTrials.gov Identifier: NCT03727438
This randomized clinical trial examines the effectiveness of a nurse-supported, self-directed behavioral insomnia intervention for decreasing insomnia severity and improving sleep outcomes among veterans.
Introduction
Cognitive behavioral therapy for insomnia (CBTi) is the standard of care for treating chronic insomnia disorder1,2,3 but access is limited for multiple reasons. First, there are an inadequate number of CBTi clinicians to match the prevalence of insomnia disorder (6%-10% in the general population),4 and this is particularly true in rural areas of the US.5 Also, health care clinicians are largely uninformed about the developmental trajectory of insomnia disorder, a condition resulting from the use of maladaptive coping strategies that set the stage for and serve to perpetuate the development of the condition.6 Instead, health care clinicians commonly believe that insomnia is a symptom of another condition that would remit if that condition were treated.7 In addition, most health care clinicians have a limited understanding of CBTi and how to discuss it with patients and often do not know that CBTi is the standard of care.8,9 These factors result in low rates of referral to CBTi, increased insomnia disorder chronicity, and inappropriate use of sleep medications.
The prevalence of insomnia disorder is considerably higher among military veterans, with 1 study suggesting a prevalence among Veterans Affairs (VA) health care system (VAHCS) patients up to 50%.10 Although the VA has widely disseminated CBTi training to mental health clinicans,11 these clinicians are in high demand because veterans also have higher mental health morbidity.12 With the high prevalence of insomnia disorder and competing demands for mental health treatment, barriers to receipt of the standard of care are even more pronounced among VAHCS patients. Among veterans using the VAHCS in fiscal year 2021, fewer than 1% received CBTi.13 Some patients may be well suited for self-directed treatments, such as digital interventions, but engagement is low when patients are simply told how to access them,14 and patients have expressed that clinician support is essential for fully engaging in self-directed CBTi approaches.15
Prior studies using self-directed CBTi approaches largely excluded those with mental health conditions,16 and to our knowledge, self-directed CBTi, with or without clinician support, has not been evaluated in veterans, a population having more severe sleep difficulties than nonveterans.17,18 In this study, we assessed an intervention designed to increase CBTi access by leveraging nurses in lieu of mental health clinicians and a self-directed approach in lieu of therapist-delivered treatment. Specifically, we examined the effectiveness of self-directed CBTi combined with nurse support for reducing insomnia severity and improving sleep outcomes.
Methods
This study was a randomized clinical trial comparing supported self-directed CBTi (Tele-Self CBTi) with a health education control (HEC) for improving insomnia severity among veterans with insomnia disorder (Supplement 1). The study protocol was reviewed and approved by the VA health care facility’s (Durham, North Carolina) institutional review board and followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline.19 Informed consent was obtained during a telephone screening. The screening and recruitment flow is depicted in the Figure. A detailed description of study methods and procedures was published previously.20
Figure. CONSORT Diagram.
CBTi indicates cognitive behavioral therapy of insomnia; HEC, health education control.
aOne patient did not complete the intervention but completed both follow-up assessments.
bOne patient did not complete the intervention or 2-month assessment but completed the 6-month assessment.
cOne patient did not complete 2 items of the Insomnia Severity Index (ISI); not included in the ISI analysis (n = 76).
Participants
Participants were patients recruited from a VA health care facility from September 2019 to April 2022, with follow-ups from December 2019 to December 2022, and were identified from electronic medical record data or clinician referrals in response to email solicitations. Among enrolled participants, 90% were identified from an electronic medical record data pull and 10% from clinicians. Eligibility was assessed by medical record review followed by a phone interview. Eligible participants were 18 years or older and (1) prescribed sleep medications, (2) received a diagnosis of insomnia, and/or (3) referred for clinic-based CBTi but were not yet treated. During phone assessment, participants had to have met diagnostic criteria for insomnia disorder,21 as determined by structured clinical interview,22 and not received prior CBTi treatment. Patients with comorbid mental health conditions were not excluded unless the condition represented a CBTi contraindication. Contraindications were psychotic disorders, bipolar disorder, active substance misuse, cognitive impairment, seizure disorder, untreated/undertreated severe sleep apnea, unstable comorbid sleep disorder, excessive daytime sleepiness, shift work, or severe depression or suicidality.
Randomization
Study participants were randomized in a 1:1 ratio to Tele-Self CBTi or HEC using a stratified block randomization with a block size of 4. To assure equivalency across conditions on insomnia severity and mental health diagnosis, assignment to arm was stratified by the Insomnia Severity Index (ISI) score (≤20/>20)11,20 and presence or absence of a preexisting mental health condition as documented in the medical record. The randomization schema was generated by the study’s statistical team a priori.
Interventions
Tele-Self CBTi
Tele-Self CBTi consisted of the combination of 6 weekly nurse phone contacts lasting approximately 20 minutes plus weekly readings from a treatment manual,23 including typical CBTi content: sleep restriction, stimulus control, cognitive therapy, relaxation, and sleep hygiene.1 The participant and nurse interventionist used treatment manuals having parallel content, including review of weekly readings, sleep prescription, troubleshooting nonadherence, and planning.23 The treatment manual was written by subject matter experts, was peer-reviewed, and could be used alone or in combination with an application from the VA (CBTi-Coach).24
Health Education Control
Participants randomized to HEC also received the combination of 6 weekly nurse phone contacts lasting approximately 20 minutes plus a treatment manual focused on a range of health topics. Sleep-focused content was prohibited during HEC calls.
Nurse Interventionist Training and Assessment of Treatment Fidelity
Interventionists included 2 registered nurses (P.G. and C.R.) who were trained using an approach informed by the VA CBTi training model11 to support participants as they progressed through treatment. Nurse interventionists completed 5 web-based CBTi training modules; experiential training involving role plays, dyad practice, and discussion; and web-based video demonstrations. Tele-Self CBTi arm phone contacts were recorded, and 20% were evaluated for treatment fidelity by the principal investigator (C.U.).20 In addition, nurse interventionists met with 2 authors (C.U. and C.V.) monthly for consultation and to address any treatment fidelity concerns identified from review of audio recordings (eg, drift).
Measures
Assessments were conducted at baseline, 8 weeks (primary end point), and 6 months (follow-up) and involved 2 weeks of daily sleep diaries and wearing an actigraphy device. This was followed by completion of a questionnaire battery.
Baseline Characteristics
At baseline, participants reported their age, sex, race, education, marital status, military service details, employment status, sleep medication use, and insomnia treatment history. Mental health diagnostic status (presence/absence) was pulled from the medical record.
Primary Outcome
The primary outcome measure was the ISI,25 a 7-item global measure of perceived insomnia severity. ISI items are rated on a 5-point (0-4) Likert scale and categorized as follows: no clinically significant insomnia (0-7); subthreshold insomnia (8-14); clinical insomnia, moderate (15-21); and clinical insomnia, severe (22-28). A meta-analysis of self-help CBTi16 found an ISI effect size of 0.98 Hedge g. Conservatively, we then based our sample size estimates on an effect size of 0.5; this translated to a relative between-group difference of at least 2.8. Accordingly, the criterion for a minimal important difference was a differential improvement of 3 points in mean ISI score at the 8-week point.20
We also assessed clinically meaningful reductions in symptom severity at the 8-week point by the previously described ISI categories. Treatment response was a reduction in ISI score of at least 8 points (categorical change) and remission, an ISI score of less than 8. In clinical samples, a posttreatment reduction of 8 or more represented a categorical change in insomnia severity status, and an ISI score lower than 8 reflected no clinically significant insomnia.26
Secondary Outcomes
Subjective Sleep Outcomes
Self-reported sleep outcomes, including sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE), were assessed with a sleep diary application (Consensus Sleep Diary; RGANA).27 During each assessment period, participants reported sleep diary data daily using an interactive voice response system (eMethods in Supplement 2).20 During the intervention period, participants in Tele-Self CBTi kept sleep diaries using one of several options offered in the treatment manual, but these data were not collected or used to assess treatment response.
Objective sleep outcomes, including WASO, SE, and total sleep time, were collected using wrist actigraphy devices (Actiwatch Spectrum Plus; Philips). An adapted version of the approach described by Patel and colleagues28 was used to score actigraphy data.
Daytime Consequences of Insomnia and Uptake of CBTi Concepts
The Multi-Dimensional Fatigue Inventory29 was used to assess fatigue and the Patient Health Questionnaire-8 (PHQ-8)30 was used to assess depression. The Insomnia Treatment Knowledge Questionnaire (ITKQ) is a 10-item questionnaire developed for the pilot study in which intervention arm participants reported higher scores (better uptake of treatment concepts) at postintervention relative to controls.
Statistical Analysis
Details of the sample size calculation were previously described.20 To achieve 80% power to detect a differential improvement of 3 points in mean ISI, at least 175 participants needed to be randomized, allowing for 25% dropout. Analyses for the primary and secondary outcomes were conducted using a general linear model for repeated measures, with unstructured covariance (using PROC MIXED in SAS, version 9.4 [SAS Institute]). Model parameters included a common intercept (baseline means constrained to be equal), stratification variables (mental health diagnosis, ISI cutoff of 20), assessment point indicator, and treatment arm interacted with the point indicator. Contrast statements were used to estimate group differences from baseline to each of the follow-up points. The primary hypothesis was tested by examining the 8-week estimated group difference in mean ISI improvement, 95% CIs, and P values. Estimated mean differences, 95% CIs, and P values are reported for all comparisons, with P < .05 considered statistically significant. Primary and secondary outcomes were identified a priori with no adjustments for multiple comparisons.
Analyses were conducted according to the intention-to-treat principle. All available data, including observations from participants who dropped out of the study, were used for primary and secondary analyses. Our modeling estimation approach was conducted with full-likelihood methods, providing unbiased treatment effect estimates within a missing-data framework known as missing at random. For the sensitivity analysis, we examined associations between dropout and baseline variables, including baseline variables differing by dropout in the primary outcome model. Categorized ISI outcomes were analyzed using exact logistic regression including stratification variables.
Results
A total of 178 participants (mean [SD] age, 55.1 (13.2) years; 128 [71.9%] identified as men) were randomized to the 2 experimental arms (Table 1). The study sample comprised 76 Black (42.7%), 11 Hispanic (6.2%), and 87 White (48.9%) veterans; most had received a diagnosis of a mental health condition (147 [82.6%]) and endorsed severe depression symptoms (mean score of 9.9 on the PHQ-8). Participants self-reported at least 1 medical condition (170 [95.5%]), with the most common being back pain (135 [75.8%]) and high blood pressure (99 [55.6%]). Sample insomnia severity was moderate, and most participants endorsed prior treatment with sleep medications (132 [74.2%]).
Table 1. Baseline Sample Characteristics, Overall and Stratified by Intervention.
| Characteristic | No (%) | ||
|---|---|---|---|
| Overall (N = 178) | Tele-Self CBTi (n = 88) | HEC (n = 90) | |
| Demographic | |||
| Age, mean (SD), y | 55.1 (13.2) | 55.1 (13.2) | 55.1 (13.3) |
| Sex | |||
| Female | 50 (28.1) | 26 (29.5) | 24 (26.7) |
| Male | 128 (71.9) | 62 (70.5) | 66 (73.3) |
| Racea | |||
| Black or African American | 76 (42.7) | 34 (38.6) | 42 (46.7) |
| White | 87 (48.9) | 47 (53.4) | 40 (44.4) |
| Otherb | 13 (7.3) | 6 (6.8) | 7 (7.8) |
| Hispanic/Latino(a) ethnicitya | 11 (6.2) | 7 (8.0) | 4 (4.4) |
| Highest education level | |||
| ≤High school graduate | 35 (19.7) | 13 (14.8) | 22 (24.4) |
| Technical school or some college | 50 (28.1) | 25 (28.4) | 25 (27.8) |
| ≥College graduate | 93 (52.2) | 50 (56.8) | 43 (47.8) |
| Married or live-in partner | 49 (55.7) | 59 (65.6) | |
| Employed (full/part-time) | 67 (37.6) | 32 (36.4) | 35 (38.9) |
| Military service | |||
| Branchc | |||
| Army | 88 (49.4) | 45 (51.1) | 43 (47.8) |
| Air Force | 21 (11.8) | 9 (10.2) | 12 (13.3) |
| Coast Guard | 1 (0.6) | 0 (0) | 1 (1.1) |
| Marines | 42 (23.6) | 24 (27.3) | 18 (20.0) |
| Navy | 28 (15.7) | 10 (11.4) | 18 (20.0) |
| Reserves or National Guard | 6 (3.4) | 4 (4.5) | 2 (2.2) |
| Served in combat or operations zone | 93 (52.2) | 46 (52.3) | 47 (52.2) |
| Sleep history | |||
| Shift work during military service | 152 (85.4) | 73 (83.0) | 79 (87.8) |
| Prior treatment for insomnia complaint | 141 (79.2) | 75 (85.2) | 66 (73.3) |
| Type of prior insomnia treatment | |||
| Individual therapy | 7 (3.9) | 4 (4.6) | 3 (3.3) |
| Group therapy | 3 (1.7) | 2 (2.3) | 1 (1.1) |
| Medications | 132 (74.2) | 71 (80.7) | 61 (67.8) |
| Sleep diary | 2 (1.1) | 0 (0) | 2 (2.2) |
| Other | 14 (7.9) | 7 (8.0) | 7 (7.8) |
| Currently using sleep medications | 120 (67.4) | 60 (68.2) | 60 (66.7) |
| Physical activity, median (IQR), METsd | 2134.5 (3459.0) | 2511.0 (4404.0) | 2054.0 (2964.0) |
| Health | |||
| BMI, mean (SD) | 31.2 (5.8) | 31.2 (5.5) | 31.2 (6.0) |
| Mental health diagnosise | 147 (82.6) | 73 (83.0) | 74 (82.2) |
| Medical conditions (self-reported) | |||
| Heart disease, congestive heart failure, or myocardial infarction | 24 (13.5) | 11 (12.5) | 13 (14.4) |
| High blood pressure | 99 (55.6) | 44 (50.0) | 55 (61.1) |
| Lung disease | 27 (15.2) | 16 (18.2) | 11 (12.2) |
| Diabetes | 38 (21.3) | 20 (22.7) | 18 (20.0) |
| Stomach, gastrointestinal disorders | 64 (36.0) | 32 (36.4) | 32 (35.6) |
| Kidney disease | 22 (12.4) | 15 (17.5) | 7 (7.8) |
| Liver disease | 10 (5.6) | 3 (3.4) | 7 (7.8) |
| Anemia or other blood disease | 20 (11.2) | 10 (11.4) | 10 (11.1) |
| Cancer | 19 (10.7) | 12 (13.6) | 7 (7.8) |
| Osteoarthritis | 87 (48.9) | 44 (50.0) | 43 (47.8) |
| Back pain | 135 (75.8) | 69 (78.4) | 66 (73.3) |
| Rheumatoid arthritis | 20 (11.2) | 12 (13.6) | 8 (8.9) |
| Mental health | |||
| Depressive disorders | 92 (51.7) | 49 (55.7) | 43 (47.8) |
| Anxiety disorders | 42 (23.6) | 15 (17.0) | 27 (30.0) |
| Trauma and stress-related disorders | 72 (40.4) | 35 (39.8) | 37 (41.1) |
| Substance and addictive disorders | 9 (5.1) | 3 (3.4) | 6 (6.7) |
| Personality disorders | 0 (0) | 0 (0) | 0 (0) |
| Somatic and related disorders | 3 (1.7) | 2 (2.3) | 1 (1.1) |
| Participant reported outcomes | |||
| ISI score, mean (SD) | 16.1 (4.5) | 16.2 (4.3) | 16.1 (4.7) |
| ISI score >20e | 35 (19.7) | 17 (19.3) | 18 (20.0) |
| Insomnia Treatment Knowledge | 31.6 (4.2) | 31.6 (4.0) | 31.7 (4.4) |
| Fatigue (MFI), mean (SD) | 63.1 (14.2) | 63.0 (14.1) | 63.2 (14.3) |
| Depression (PHQ-8), mean (SD) | 9.9 (5.1) | 9.8 (4.6) | 9.9 (5.6) |
| Sleep diary, mean (SD) | |||
| Sleep onset latency, min | 49.4 (34.8) | 51.2 (34.2) | 47.7 (35.6) |
| Wake after sleep onset, min | 58.8 (41.7) | 58.5 (39.0) | 59.1 (44.5) |
| Sleep efficiency, % | 65.4 (16.4) | 64.7 (16.9) | 66.0 (16.0) |
| Objective outcomes | |||
| Actigraphy, mean (SD) | |||
| Wake after sleep onset, min | 49.4 (22.9) | 47.4 (23.4) | 51.4 (22.5) |
| Total sleep time, h | 6.0 (1.6) | 5.8 (1.8) | 6.1 (1.5) |
| Sleep efficiency, % | 86.2 (4.7) | 86.1 (5.1) | 86.3 (4.3) |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CBTi, cognitive behavioral therapy for insomnia; HEC, health education control; ISI, Insomnia Severity Index; MET, metabolic equivalent of task; MFI, Multi-Dimensional Fatigue Inventory; PHQ-8, Patient Health Questionnaire 8.
Two participants had unknown race or chose not to report their race (1 Tele-Self CBTI, 1 HEC); ethnicity, cancer, depression and osteoarthritis is missing for 1 Tele-Self CBTI participant; serving in combat and diabetes is missing for 1 HEC participant; 3 participants were missing anemia (1 Tele-Self CBTI, 2 HEC), rheumatoid arthritis is missing for 3 participants (2 Tele-Self CBTi, 1 HEC), actigraphy data were not able to be collected for 30 participants (14 Tele-Self CBTi, 16 HEC).
Included American Indian or Native American and multiracial participants.
May serve in more than 1 branch.
MET minutes calculated from the International Physical Activity Questionnaire short form.
Randomization stratification variables.
Primary Outcome
From baseline to 8 weeks, insomnia severity decreased by an estimated mean (SE) of 5.7 (0.51) points in the Tele-Self CBTi arm and 2.0 (0.47) points in the HEC arm, a differential mean improvement of 3.7 points (95% CI, −5.0 to −2.4; P < .001). Differences by arm were sustained at the 6-month follow-up (−2.8; 95% CI, −4.4 to −1.3; P < .001; Table 2). Among those completing the 8-week follow-up assessment, the odds of achieving a response and remission were significantly greater in Tele-Self CBTi than HEC, with 25 of 70 (35.7%) responding and 21 of 70 (30.0%)) remitting in Tele-Self CBTi compared with 4 of 83 (4.8%) responding (odds ratio, 11.0, 95% CI, 3.5-46.7) and 8 of 83 (9.6%) remitting (odds ratio, 4.0; 95% CI, 1.5-11.4) in HEC.
Table 2. Model Estimated Primary and Secondary Outcomesa.
| Characteristic | Baseline estimate (SE) | 8 wk After randomization (posttreatment) | 6 mo After randomization | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Tele-self CBTI estimate (SE) | HEC estimate (SE) | Mean difference in change from baseline between groups (95% CI) | P value | Tele-self CBTi estimate (SE) | HEC estimate (SE) | Mean difference in change from baseline between groups (95% CI) | P value | ||
| Primary outcome | |||||||||
| Insomnia Severity Indexb | 16.1 (0.2) | 10.4 (0.5) | 14.1 (0.5) | −3.7 (−5.0 to −2.4) | <.001 | 10.9 (0.6) | 13.7 (0.6) | −2.8 (−4.4 to −1.3) | <.001 |
| Secondary outcomes | |||||||||
| Sleep diary–sleep onset latencyb | 49.4 (2.6) | 26.5 (3.1) | 46.1 (2.9) | −19.6 (−26.7 to −12.5) | <.001 | 26.6 (2.7) | 40.7 (2.6) | −14.1 (−20.9 to −7.3) | <.001 |
| Sleep diary-wake after sleep onsetb | 58.8 (3.1) | 33.5 (3.6) | 54.0 (3.3) | −20.6 (−29.1 to −12.0) | <.001 | 29.7 (3.6) | 46.9 (3.4) | −17.3 (−26.1 to −8.4) | <.001 |
| Sleep diary sleep efficiencyc | 65.4 (1.2) | 79.0 (1.5) | 68.0 (1.4) | 11.0 (7.7 to 14.3) | <.001 | 80.3 (1.5) | 69.8 (1.4) | 10.5 (7.0 to 14.1) | <.001 |
| Depression (PHQ-8)b | 9.9 (0.4) | 7.3 (0.5) | 9.4 (0.5) | −2.1 (−3.3 to −0.9) | .001 | 7.6 (0.5) | 8.9 (0.5) | −1.3 (−2.7 to 0.1) | .06 |
| Fatigue (MFI)b | 63.1 (1.0) | 58.5 (1.5) | 61.8 (1.3) | −3.8 (−7.3 to −0.3) | .03 | 58.5 (1.4) | 61.8 (1.3) | −3.3 (−6.6 to 0.02) | .05 |
| Insomnia treatment knowledgec | 31.6 (0.3) | 36.1 (0.5) | 31.3 (0.4) | 4.8 (3.6 to 5.9) | <.0001 | 34.4 (0.5) | 31.5 (0.4) | 3.0 (1.8 to 4.1) | <.001 |
Abbreviations: CBTi, cognitive behavioral therapy for insomnia; HEC, health education control; MFI, Multi-Dimensional Fatigue Inventory; PHQ-8, Patient Health Questionnaire 8.
General linear models were used to mean differences in outcomes between the Tele-Self CBTI and HEC arms.
Negative values in mean score differences for these outcomes indicate more improvement in the Tele-Self CBTI group.
Positive values in mean score differences for these outcomes indicate more improvement in the Tele-Self CBTI group.
Secondary Outcomes
From baseline to 8 weeks, the Tele-Self CBTi arm was found to have greater improvements than HEC on subjective and objective sleep outcomes. Sleep diary data SOL decreased in Tele-Self CBTi by an estimated mean (SE) of 23.0 (2.8) minutes compared with 3.4 (2.6) minutes in HEC, a differential mean improvement of 19.6 points (95% CI, −26.7 to −12.5; P < .001). Sleep diary data WASO decreased in Tele-Self CBTi by an estimated mean (SE) of 25.4 (3.5) minutes compared with 4.8 (3.3) minutes in HEC, a differential mean improvement of 20.6 points (95% CI, −29.1 to −12.0; P < .001). Sleep diary SE increased by an estimated 13.7% (1.3%) in Tele-Self CBTi and 2.6% (1.2%) in HEC, a differential mean improvement of 11% (95% CI, 7.7-14.3; P < .001). Sleep diary findings at 6 months paralleled those for 8 weeks, with differential mean improvement between arms for SOL, WASO and SE being 14.1 (95% CI, −20.9 to −7.3; P < .001) minutes, 17.3 (95% CI, −26.1 to −8.4; P < .001) minutes, and 10.5% (95% CI, 7.0%-14.1%; P < .001), respectively. Actigraphy SOL and WASO did not differ between arms at 8 weeks, but actigraphy SE minimally increased (0.65%; SE, 0.51%) in the Tele-Self CBTi arm and minimally decreased (0.87%; SE, 0.47%) in the HEC arm, a differential mean improvement of 1.5% (95% CI, 0.2-2.9; P = .03). Actigraphy findings at 6 months paralleled those for 8 weeks with no differences in SOL and WASO and a minimal difference in SE between groups, with a differential mean improvement between arms of 1.7% (95% CI, 0.02-3.3; P = .05).
From baseline to 8 weeks, scores on fatigue and depression improved in parallel with decreases in insomnia severity in the Tele-Self CBTi arm. The estimated mean (SD) Multi-Dimensional Fatigue Inventory score decreased 4.6 (1.3) points in Tele-Self CBTi and 0.9 (1.2) points in HEC, a differential mean improvement of 3.8 points (95% CI, −7.3 to −0.3; P = .03), and PHQ scores decreased 2.5 (0.47) points in Tele-Self-CBTi and 0.4 (0.43) points in HEC, a differential mean improvement of 2.1 points (95% CI, −3.3 to −0.9; P = .001). However, differences between arms on fatigue and depression were not sustained at 6 months (Table 2).
Uptake of CBTi Concepts
Participants randomized to Tele-Self CBTi were found to have significantly greater increases from baseline at 8 weeks and 6 months than HEC on the ITKQ, suggesting that Tele-Self CBTi imparted the intended knowledge and skills. From baseline to 8 weeks, estimated mean (SE) ITKQ scores increased 4.5 (0.44) points in Tele-Self CBTi compared with a decrease of 0.3 (0.41) points in HEC, a differential mean improvement of 4.8 points (95% CI, 3.6-5.9; P < .001), and this difference between arms was sustained at 6 months (3.0; 95% CI, 1.8-4.1; P < .001; Table 2).
Treatment Completion and Adherence
At 8 weeks, retention (defined as completing an 8-week follow-up assessment) was 79.5% (n = 70) and 92.2% (n = 83) for participants randomized to Tele-Self CBTi vs HEC, respectively (Figure) (eResults in Supplement 2). In addition to treatment arm, age was related to retention, with older patients (mean [SD] age, 55.9 [13.2] years) more likely to complete follow-up than younger patients (mean [SD] age, 50.3 [12.7] years). Sensitivity analyses including age in the primary outcome model did not alter results. Treatment completion (3 or more intervention calls completed) differed somewhat by intervention arm with 77 participants (87.5%) in Tele-Self CBTi and 88 (97.8%) in HEC completing treatment. Conditions also differed in terms of call length, with those in Tele-Self CBTi (mean [SD], 24.2 [9.2] minutes; range, 7-63) having longer calls than those in HEC (mean [SD], 14.0 [5.4] minutes; range, 4-39), but did not differ on the number of sessions completed, with both arms completing a median of 6 sessions.
Discussion
The findings of this randomized clinical trial suggest that veteran patients can benefit from self-directed CBTi when combined with nurse support, even in the context of substantial medical and mental health comorbidity. Moreover, these effects were sustained for at least 6 months and, despite a low intensity intervention, many participants remitted from insomnia disorder. Findings of this trial are comparable with other CBTi trials that used modified CBTi protocols,31,32 similar to standard and modified protocols for sleep diary outcomes, but somewhat less effective than standard CBTi for treatment response and remission.33,34 Given that the nurse interventionists were naive to sleep medicine (behavioral or otherwise) before training, there is reason to believe that various health care professionals could support and coach patients engaged in self-directed CBTi interventions. Future efforts are needed to develop training for health care professionals serving in this role. In accordance with the approach described by Espie and colleagues,35 our findings point to the potential use of supported self-directed CBTi as a strategy for increasing access to guideline-concordant care and as one of several treatment options in a stratified care model.36 In stratified care (a type of stepped care), patients enter treatment at the intensity level matching their personal characteristics (eg, insomnia phenotype or tech literacy). The advantage of stratified care is that patients well suited to self-directed interventions may benefit from support from a range of health care professionals, reserving scarce behavioral sleep medicine expertise for patients having more complex clinical presentations. Future studies should be designed to identify patient characteristics associated with better self-directed treatment outcomes.
Strengths and Limitations
The limitations of this study included greater drop-out in the intervention arm, a finding we might anticipate because CBTi requires substantial behavior change. Although the number of contacts did not differ by arm (median of 6 sessions), we did not achieve equal contact time across arms. Nurse contacts in Tele-Self CBTi were about 10 minutes longer. However, greater uptake of CBTi concepts in the active arm suggests the superior outcomes in Tele-Self CBTi were more likely related to CBTi treatment effects than longer nurse contacts. When implemented in clinical settings, the optimal call duration should be determined. Although HEC calls were administered using a script and therapists reminded to refrain from discussing sleep, HEC calls were not recorded for review. Finally, the relatively small treatment effects in this sample with considerable medical and mental health comorbidity may be improved by targeting patients most likely to benefit from self-directed interventions. Strengths of this study included a trial population that is representative of the typical VA patient seeking insomnia treatment. The effectiveness of our findings is also noteworthy when considered in light of the adverse psychological effects of the COVID-19 pandemic, which began during the study.
Conclusions
Innovations are critical for ensuring access to CBTi, an intervention typically delivered by a mental health clinician. Findings demonstrate that patients can realize significant reductions in insomnia severity when engaged in self-directed CBTi with support from nurses who have no training in sleep medicine or psychotherapy. When access is limited, expertise should be reserved for the most complex clinical presentations. Future studies are needed to identify patient characteristics associated with treatment response and remission. Thereafter, a stratified care model may be used to direct patients to the most accessible and effective intervention for each patient.
Trial protocol
eMethods. Interactive voice response data collection procedures
eResults. Reasons for drop out
Data sharing statement
References
- 1.Edinger JD, Arnedt JT, Bertisch SM, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2021;17(2):255-262. doi: 10.5664/jcsm.8986 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.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. doi: 10.7326/M15-2175 [DOI] [PubMed] [Google Scholar]
- 3.Martin JL, Mysliwiec V, Chowdhuri S, Ulmer CS. The Veterans Administration and Department of Defense clinical practice guidelines for the diagnosis and management of sleep disorders: what does this mean for the practice of sleep medicine? J Clin Sleep Med. 2020;16(8):1377-1381. doi: 10.5664/jcsm.8486 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Morin CM, Jarrin DC. Epidemiology of insomnia: prevalence, course, risk factors, and public health burden. Sleep Med Clin. 2022;17(2):173-191. doi: 10.1016/j.jsmc.2022.03.003 [DOI] [PubMed] [Google Scholar]
- 5.Thomas A, Grandner M, Nowakowski S, Nesom G, Corbitt C, Perlis ML. Where are the behavioral sleep medicine providers and where are they needed? a geographic assessment. Behav Sleep Med. 2016;14(6):687-698. doi: 10.1080/15402002.2016.1173551 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ulmer CS, Bosworth HB, Beckham JC, et al. Veterans Affairs Primary Care Provider Perceptions of Insomnia Treatment. J Clin Sleep Med. 2017;13(8):991-999. doi: 10.5664/jcsm.6702 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Araújo T, Jarrin DC, Leanza Y, Vallières A, Morin CM. Qualitative studies of insomnia: current state of knowledge in the field. Sleep Med Rev. 2017;31:58-69. doi: 10.1016/j.smrv.2016.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Haycock J, Grivell N, Redman A, et al. Primary care management of chronic insomnia: a qualitative analysis of the attitudes and experiences of Australian general practitioners. BMC Fam Pract. 2021;22(1):158. doi: 10.1186/s12875-021-01510-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Davy Z, Middlemass J, Siriwardena AN. Patients’ and clinicians’ experiences and perceptions of the primary care management of insomnia: qualitative study. Health Expect. 2015;18(5):1371-1383. doi: 10.1111/hex.12119 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Colvonen PJ, Almklov E, Tripp JC, Ulmer CS, Pittman JOE, Afari N. Prevalence rates and correlates of insomnia disorder in post-9/11 veterans enrolling in VA healthcare. Sleep. 2020;43(12):zsaa119. doi: 10.1093/sleep/zsaa119 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Karlin BE, Trockel M, Taylor CB, Gimeno J, Manber R. National dissemination of cognitive behavioral therapy for insomnia in veterans: therapist- and patient-level outcomes. J Consult Clin Psychol. 2013;81(5):912-917. doi: 10.1037/a0032554 [DOI] [PubMed] [Google Scholar]
- 12.Trivedi RB, Post EP, Sun H, et al. Prevalence, Comorbidity, and Prognosis of Mental Health Among US Veterans. Am J Public Health. 2015;105(12):2564-2569. doi: 10.2105/AJPH.2015.302836 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Pfeiffer PN, Ganoczy D, Zivin K, Gerlach L, Damschroder L, Ulmer CS. Guideline-concordant use of cognitive behavioral therapy for insomnia in the Veterans Health Administration. Sleep Health. 2023;9(6):893-896. doi: 10.1016/j.sleh.2023.07.002 [DOI] [PubMed] [Google Scholar]
- 14.Lipschitz JM, Pike CK, Hogan TP, Murphy SA, Burdick KE. The engagement problem: a review of engagement with digital mental health interventions and recommendations for a path forward. Curr Treat Options Psychiatry. 2023;10(3):119-135. doi: 10.1007/s40501-023-00297-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ulmer CS, Bosworth HB, Voils CI, et al. Tele-self CBTI: feasibility study findings. Poster presented at: Veterans Affairs HSR&D/QUERI National Conference; July 18-20, 2017; Crystal City, Virginia. [Google Scholar]
- 16.Ho FY, Chung KF, Yeung WF, et al. Self-help cognitive-behavioral therapy for insomnia: a meta-analysis of randomized controlled trials. Sleep Med Rev. 2015;19:17-28. doi: 10.1016/j.smrv.2014.06.010 [DOI] [PubMed] [Google Scholar]
- 17.Faestel PM, Littell CT, Vitiello MV, Forsberg CW, Littman AJ. Perceived insufficient rest or sleep among veterans: Behavioral Risk Factor Surveillance System 2009. J Clin Sleep Med. 2013;9(6):577-584. doi: 10.5664/jcsm.2754 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Cepeda MS, Stang P, Blacketer C, Kent JM, Wittenberg GM. Clinical relevance of sleep duration: results from a cross-sectional analysis using NHANES. J Clin Sleep Med. 2016;12(6):813-819. doi: 10.5664/jcsm.5876 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Schulz KF, Altman DG, Moher D; CONSORT Group . CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Obstet Gynecol. 2010;115(5):1063-1070. doi: 10.1097/AOG.0b013e3181d9d421 [DOI] [PubMed] [Google Scholar]
- 20.Ulmer CS, Bosworth HB, Zervakis J, et al. Provider-supported self-management cognitive behavioral therapy for insomnia (Tele-Self CBTi): protocol for a randomized controlled trial. Contemp Clin Trials. 2023;125:107060. doi: 10.1016/j.cct.2022.107060 [DOI] [PubMed] [Google Scholar]
- 21.American Academy of Sleep Medicine . International Classification of Sleep Disorders. 3rd ed. American Academy of Sleep Medicine; 2014. [Google Scholar]
- 22.Edinger JD, Kirby AC, Lineberger MD, Loiselle MM, Wohlgemuth WK, Means MK. Duke Structured Interview Schedule for DSM-IV-TR and International Classification of Sleep Disorders. 2nd ed. Veterans Affairs and Duke University Medical Centers; 2006. [Google Scholar]
- 23.Ulmer CS, Farrell-Carnahan L, Hughes JM, et al. Improve your Sleep: A Self-Guided Approach for Veterans with Insomnia. Provider Supported Telehealth Session Guide-Veteran Workbook; 2018. [Google Scholar]
- 24.Kuhn E, Weiss BJ, Taylor KL, et al. CBT-I coach: a description and clinician perceptions of a mobile app for cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2016;12(4):597-606. doi: 10.5664/jcsm.5700 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.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. doi: 10.1016/S1389-9457(00)00065-4 [DOI] [PubMed] [Google Scholar]
- 26.Bastien CH, Morin CM, Ouellet MC, Blais FC, Bouchard S. Cognitive-behavioral therapy for insomnia: comparison of individual therapy, group therapy, and telephone consultations. J Consult Clin Psychol. 2004;72(4):653-659. doi: 10.1037/0022-006X.72.4.653 [DOI] [PubMed] [Google Scholar]
- 27.Carney CE, Buysse DJ, Ancoli-Israel S, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35(2):287-302. doi: 10.5665/sleep.1642 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Patel SR, Weng J, Rueschman M, et al. Reproducibility of a standardized actigraphy scoring algorithm for sleep in a US Hispanic/Latino population. Sleep. 2015;38(9):1497-1503. doi: 10.5665/sleep.4998 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Smets EM, Garssen B, Bonke B, De Haes JC. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995;39(3):315-325. doi: 10.1016/0022-3999(94)00125-O [DOI] [PubMed] [Google Scholar]
- 30.Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1-3):163-173. doi: 10.1016/j.jad.2008.06.026 [DOI] [PubMed] [Google Scholar]
- 31.Alessi C, Martin JL, Fiorentino L, et al. Cognitive behavioral therapy for insomnia in older veterans using nonclinician sleep coaches: randomized controlled trial. J Am Geriatr Soc. 2016;64(9):1830-1838. doi: 10.1111/jgs.14304 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Pigeon WR, Funderburk J, Bishop TM, Crean HF. Brief cognitive behavioral therapy for insomnia delivered to depressed veterans receiving primary care services: a pilot study. J Affect Disord. 2017;217:105-111. doi: 10.1016/j.jad.2017.04.003 [DOI] [PubMed] [Google Scholar]
- 33.Martin JL, DeViva J, McCarthy E, et al. In-person and telehealth treatment of veterans with insomnia disorder using cognitive behavioral therapy for insomnia during the COVID-19 pandemic. J Clin Sleep Med. 2023;19(7):1211-1217. doi: 10.5664/jcsm.10540 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Talbot LS, Maguen S, Metzler TJ, et al. Cognitive behavioral therapy for insomnia in posttraumatic stress disorder: a randomized controlled trial. Sleep. 2014;37(2):327-341. doi: 10.5665/sleep.3408 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Espie CA, Henry AL. Disseminating cognitive behavioural therapy (CBT) for insomnia at scale: capitalising on the potential of digital CBT to deliver clinical guideline care. J Sleep Res. 2023;32(6):e14025. doi: 10.1111/jsr.14025 [DOI] [PubMed] [Google Scholar]
- 36.Hingorani AD, Windt DA, Riley RD, et al. ; PROGRESS Group . Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793. doi: 10.1136/bmj.e5793 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Trial protocol
eMethods. Interactive voice response data collection procedures
eResults. Reasons for drop out
Data sharing statement
