The Effectiveness of Digital Cognitive Behavioral Therapy to Treat Insomnia Disorder in US Adults: Nationwide Decentralized Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioral therapy (CBT) is recommended as the first-line treatment for insomnia; however, few patients have access to it. A new class of Food and Drug Administration (FDA)-regulated digital CBT treatments has the potential to address this unmet need. These treatments are ordered or prescribed by health care providers and are fully automated, delivering CBT directly to patients without human coaches. This trial builds upon promising earlier digital cognitive behavioral therapy for insomnia (CBT-I) research by using a decentralized design to recruit a sample with greater representation of the US general population, including individuals from lower socioeconomic status groups who often face greater barriers to care. OBJECTIVE: This decentralized trial evaluated the effectiveness of a fully automated digital CBT-I program (SleepioRx) for treating insomnia disorder compared with online sleep hygiene education (SHE) in a sample of participants recruited from across the United States. METHODS: A decentralized, parallel-group randomized controlled trial was conducted between November 2022 and August 2023. Participants were recruited nationally from across the United States, and a total of 336 adults aged 22 and older, diagnosed with the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) insomnia disorder via structured clinical interview, were allocated 1:1 to either digital CBT-I (SleepioRx) or online SHE. The primary end points were insomnia severity, assessed using the Insomnia Severity Index (ISI), and sleep diary measures of sleep onset latency (SOL) and wake after sleep onset (WASO) at 10 weeks, with follow-up assessments at 16 and 24 weeks postrandomization. RESULTS: Compared with SHE, SleepioRx showed statistically and clinically significant improvements on the ISI at posttreatment (10 weeks; Cohen d=0.60, P<.001), with effects sustained at follow-up (16 weeks; d=0.65, P<.001; and 24 weeks, d=0.77, P<.001). SleepioRx led to significant reductions in WASO at all time points (10 weeks, P=.003; 16 and 24 weeks, P<.001); however, effects on SOL were not statistically significant at an adjusted α (10 weeks, P=.01; 16 weeks, P=.07; 24 weeks, P=.27). SleepioRx participants had 2.5 times (odds ratio 2.52; P<.001, 99% CI 1.33-4.75) and 5.8 times (odds ratio 5.78; P<.001, 99% CI 2.11-15.84) greater odds of response and remission at week 10, respectively, with statistically and clinically significant differences in rates sustained at follow-up assessments (P<.001). SleepioRx also demonstrated sustained improvements in secondary sleep and broader mental health outcomes. CONCLUSIONS: The results of this trial demonstrate the effectiveness of digital CBT-I (SleepioRx) for treating insomnia, with gains sustained at 6 months, and support the FDA authorization of SleepioRx for the treatment of insomnia disorder. These findings underscore the potential of a new class of FDA-authorized, fully automated digital treatments to provide first-line, guideline-recommended CBT at scale. Efforts should now focus on expanding access to these evidence-based treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT05541055; https://clinicaltrials.gov/ct2/show/NCT05541055.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".