Christopher B. Miller

While digital cognitive behavioral therapy for insomnia (dCBT-I) is highly effective, accessibility and adherence challenges persist, especially among vulnerable groups. To highlight unaccounted gaps in dCBT-I treatment adherence, a qualitative interview and case study were conducted to explore the experience of a deaf individual engaging in a six-week dCBT-I program. The patient showed strong adherence and improved sleep efficiency from 63% at Session 1 to 87% at Session 6, with feedback highlighting key accessibility barriers (eg, translation of audio content, inaccessible treatment recommendations) and facilitators (eg, telehealth sessions, motivational approach). This case serves as a foundation for future research and innovation, aiming to reduce treatment disparities in the digital mental health sphere.

Background: Insomnia affects up to one-third of US adults and is a significant health challenge with an estimated economic burden of up to $100 billion annually. Cognitive behavioral therapy (CBT) for insomnia (CBT-I) is the recommended first-line treatment, but access is limited due to a shortage of trained therapists. Digital CBT-I offers an effective alternative that may enhance accessibility and reduce higher healthcare costs associated with insomnia. Objective: To evaluate the US healthcare cost-savings of digital CBT-I compared with standard-of-care control. Methods: A retrospective difference-in-differences analysis compared 1-year preinitiation and post-initiation healthcare costs for 11 027 individuals receiving SleepioRx (FDA-cleared digital CBT treatment for insomnia disorder) compared with 1:1 exact matched controls with insomnia receiving standard care (n = 10 770). Commercial and Medicare claims were adjusted for comorbidities, index year, and baseline utilization. Results: Digital CBT-I was associated with statistically significant mean annual total cost savings of $2083 (95% CI, $1508-$2657, P < .001) per person, equating to a 42% reduction in costs with SleepioRx relative to matched controls who received standard of care (medications for insomnia). Discussion: Digital CBT-I was associated with substantial cost savings for payers. The integration of guideline-concordant treatment through digital delivery into standard care pathways offers a promising strategy to address the clinical and economic challenges of insomnia, supporting more efficient resource allocation. Conclusions: Findings suggest that implementing digital CBT-I at scale may lead to decreased costs for healthcare payers, relative to the current standard of care, while improving access to effective insomnia treatment.

Background Insomnia affects up to one-third of US adults and is a significant health challenge with an estimated economic burden of up to $100 billion annually. Cognitive behavioral therapy (CBT) for insomnia (CBT-I) is the recommended first-line treatment, but access is limited due to a shortage of trained therapists. Digital CBT-I offers an effective alternative that may enhance accessibility and reduce higher healthcare costs associated with insomnia. Objective To evaluate the US healthcare cost-savings of digital CBT-I compared with standard-of-care control. Methods A retrospective difference-in-differences analysis compared 1-year preinitiation and post-initiation healthcare costs for 11 027 individuals receiving SleepioRx (FDA-cleared digital CBT treatment for insomnia disorder) compared with 1:1 exact matched controls with insomnia receiving standard care (n = 10 770). Commercial and Medicare claims were adjusted for comorbidities, index year, and baseline utilization. Results Digital CBT-I was associated with statistically significant mean annual total cost savings of $2083 (95% CI, $1508-$2657, P < .001) per person, equating to a 42% reduction in costs with SleepioRx relative to matched controls who received standard of care (medications for insomnia). Discussion Digital CBT-I was associated with substantial cost savings for payers. The integration of guideline-concordant treatment through digital delivery into standard care pathways offers a promising strategy to address the clinical and economic challenges of insomnia, supporting more efficient resource allocation. Conclusions Findings suggest that implementing digital CBT-I at scale may lead to decreased costs for healthcare payers, relative to the current standard of care, while improving access to effective insomnia treatment.

Insomnia disorder is co-morbid with and predictive of developing pain conditions and a key factor in pain interference (PI) - the extent to which pain impedes daily living. Emerging literature suggests treating insomnia with cognitive-behavioral therapy for insomnia reduces co-occurring PI. This secondary data analysis tested the extent to which digital CBT-I (dCBT-I) vs. sleep education reduces and prevents significant PI by treating insomnia. Insomnia disorder participants were randomized into dCBT-I (n = 697) and sleep education (n = 623) and reported pre- and post-treatment insomnia and PI. Logistic regressions evaluated intervention effects: 1) reduction of insomnia severity changes in PI and 2) prevention of treatment condition on PI. The reduction model showed that dCBT-I participants with moderate-to-severe pre-treatment PI experienced 17% odds increase in reduced PI for each one-point reduction in insomnia severity compared to control, OR = 1.17, 95% CI [1.01, 1.35]. In the prevention model, dCBT-I participants with little-to-no pre-treatment PI exhibited a 32% odds reduction of post-treatment progression to moderate-to-severe PI compared to control, OR = 0.68, 95% CI [0.51, 0.90]. dCBT-I demonstrated significant and clinically meaningful reduction and prevention effects against PI in a large sample. dCBT-I may help providers address sleep issues to restore pain-related impairments to daytime function, quality of life, and overall sleep.

Insomnia prevalence increases with age. Although cognitive behavioral therapy (CBT) for insomnia is the first-line treatment, limited accessibility leaves many older adults with few effective treatment options. This study assessed the efficacy of digital CBT (dCBT) for treating insomnia, anxiety, and depression symptoms in adults aged 65 and older. Data from three published randomized controlled trials of dCBT for insomnia (Sleepio) versus controls were combined for those aged 65+ with insomnia disorder (N = 315). Insomnia, anxiety, and depression scores were standardized into z-scores for comparison. Mixed-effects models estimated the treatment effect on insomnia, depression, and anxiety outcomes at post-treatment (8-10 wk) and follow-up (24 wk). Chi-squared tests of Sleep Condition Indicator (SCI-8) scores evaluated post-treatment insomnia remission rates between groups. Compared with controls, dCBT generated significantly greater improvements at post-treatment and follow-up in insomnia (gs ≤ -1.88, ps < 0.001) and depression (gs ≤ -0.44, ps ≤ 0.001) and significantly greater improvements in anxiety at post-treatment (g = -0.33, p < 0.001). dCBT generated higher post-treatment insomnia remission rates (60% vs. 16%, p < 0.001). dCBT can effectively improve symptoms of insomnia, anxiety, and depression in adults aged 65+. Digital CBT may serve as an accessible means for older adults to receive guideline-concordant treatment at scale and avoid adverse side effects from common pharmacologic interventions.

Background: Chronic insomnia disorder affects 10–15% of adults, causing significant individual and societal burden. Despite Cognitive Behavioral Therapy for Insomnia (CBT-I) being the recommended first-line, sleep medications remain more common due to limited access to trained providers. Digital CBT-I offers a scalable solution, but evidence of its real-world impact in U.S. clinical settings is lacking. Method: This study evaluates real-world implementation and impact of digital CBT-I in U.S. clinical settings, using Normalization Process Theory (NPT) to guide integration at Henry Ford Health, Detroit, Michigan. Implementation success was assessed through order rates, patient sign-ups and workflow acceptability. We assess the effect on healthcare utilization through a propensity-matched observational treatment-control design. Results: Implementation was successful, with 1,162 patients offered digital CBT-I. From this cohort, we analyzed a sample of 340 patients with sufficient chart data and established care (120 days) who utilized digital CBT-I, comparing them to 340 matched standard care controls. Patients who used digital CBT-I had a 64% reduction in the odds of any medication fill during the postwindow period ( p < .001) and were 53% less likely to fill insomnia medication prescriptions compared with the preperiod ( p = .013). Controls did not have any significant reductions in medication fill rates. Time-varied analysis showed digital CBT-I patients had transiently higher outpatient visit odds at 30–60 days, followed by sustained reductions of 28% (120–150 days) and 31% (150–180 days). After covariate adjustment, early differences were nonsignificant while later reductions remained significant. Conclusions: NPT facilitated integration of digital CBT-I into existing workflows, allowing immediate access while minimizing disruption to routine practice. Provider training sessions and reminders effectively promoted suitable patient uptake. Digital CBT-I was associated with reduced medication fills pre-to-post with an initial rise and then sustained reduction in outpatient service utilization patterns over time. A key limitation is the use of individuals who declined digital CBT-I as comparators, which may introduce selection bias. Generalizability may be limited as the study was conducted within a single healthcare system. Trial Registration: Not applicable—the assignment of the medical intervention to patients was not at the discretion of the investigators.

This study examines the cost-effectiveness and cost-benefit of a fully automated smartphone-delivered digital cognitive behavioral therapy (CBT) intervention for Generalized Anxiety Disorder (GAD). In a simulated Markov model, 100,000 individuals with GAD were studied under one of five (n = 20,000 per arm) treatments (digital CBT [Daylight], individual CBT, group CBT, pharmacotherapy, or no GAD treatment). Model inputs were determined from the literature and included direct treatment costs and disease costs. Net monetary benefit (NMB) determined whether digital CBT is cost-beneficial from both a private payer and societal perspective in the United States in 2020. Digital CBT was found to generate the lowest 12-month total cost ($167.02m) and the second highest number of total quality-adjusted life years (14,711.86). Digital CBT showed a positive NMB relative to each alternative treatment and to no treatment for GAD in both a payer and societal perspective. Relative to no treatment, the average NMB of digital CBT was $1,836.83 from the payer perspective and $4,126.88 from the societal perspective. Digital CBT generates the most value in both a payer and societal perspective, and results were robust to sensitivity analysis with respect to effectiveness, pricing, and attrition parameters.

Summary: Insomnia is a highly prevalent sleep disorder. It is the most frequent sleep complaint among Higher Education students. The Sleep Condition Indicator is a self‐report tool aimed at assessing insomnia based on the DSM‐5 criteria. The principal goal of this study was to establish preliminary psychometric properties of the European Portuguese version of the Sleep Condition Indicator in a sample of Higher Education students. Data from a diverse pool of Higher Education students (N = 537) were collected online over a month. Most participants were women (75%) and aged approximately 27 years. The Sleep Condition Indicator demonstrated good internal consistency (α = 0.85), with all the items accounting significantly for the scale reliability. The most appropriate factor structure considering the ordinal nature of the items was unidimensional, with all items explaining 64% of the total variance. However, a two‐factor structure (sleep pattern and sleep‐related impact) was also plausible when other statistical estimators were used. The Sleep Condition Indicator correlated significantly with insomnia severity, vulnerability to stress‐related sleep disturbance, and self‐reported daytime sleepiness. The optimal cut‐off point established based on the receiver operating characteristic curve analysis was ≤ 16. A short version comprising only two items was also viable as suggested by the literature. The Sleep Condition Indicator is a reliable and valid tool for screening for insomnia. More studies with other groups are now required, specifically with clinical samples.

Purpose: We aimed to adapt the Turkish Sleep Condition Indicator (SCI) version and examine its psychometric properties among the general population. Methods: This study was a cross-sectional study. The item-total correlation, standard error of measurement, Cronbach’s α, and McDonald’s ω were used for internal consistency. We ran confirmatory factor analysis (CFA) and network analysis to confirm the factor structure. Multigroup CFA was run to assess the measurement invariance across gender, whether clinical insomnia or not, and poor sleep quality. We correlated SCI scores with Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Index (PSQI) scores to evaluate construct validity. A receiver operating characteristic (ROC) curve analysis was conducted to calculate the cut-off score of the SCI. The temporal stability was examined with the intraclass correlation coefficient. Results: Eight hundred thirty-four participants attended. Over half of the participants were women (63.2% n = 527); the mean age was 36.15 ± 9.64. Confirmatory factor and network analysis results show that the two-factor correlated model had a good model fit for the SCI. The SCI had scalar level invariance across gender, having clinical insomnia and poor sleep quality in the Multigroup CFA. ROC curve analysis shows that the SCI has good sensitivity (90.3%) and specificity (91.8%) for cut-off ≤ 15. The intraclass correlation coefficient computed between the first and second SCI total scores was significant (r=0.80 with a 95% confidence interval from 0.78 to 0.87; p < 0.001). Conclusion: The Turkish SCI is a practical self-reported insomnia scale with good psychometric properties that can be used to screen for insomnia disorder.