Assessment of an App-Based Sleep Program to Improve Sleep Outcomes in a Clinical Insomnia Population: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Insomnia is the most commonly reported sleep disturbance and significantly impacts mental health and quality of life. Traditional treatments for insomnia include pharmacological interventions or cognitive behavioral therapy for insomnia (CBT-I), but these options may not be accessible to everyone who needs treatment. OBJECTIVE: This study aims to assess the effectiveness of the app-based Headspace Sleep Program in adults with clinical insomnia on sleep disturbance and mental health outcomes, compared with a waitlist control group. METHODS: This randomized controlled trial included 132 adults with clinical insomnia who were assigned to either the Headspace Sleep Program (an 18-session self-guided, in-app program utilizing CBT-I techniques augmented by mindfulness) or a waitlist control group. Sleep disturbance outcomes were assessed by changes in insomnia symptoms (measured using the Insomnia Severity Index) and sleep efficiency (measured via sleep diary and actigraphy). Mental health outcomes included perceived stress (measured by the 10-item Perceived Stress Scale), depressive symptoms (measured by the 8-item Patient Health Questionnaire), sleep quality (measured by the Pittsburgh Sleep Quality Index), anxiety symptoms (measured by the 7-item Generalized Anxiety Disorder Scale), and mindfulness (measured by the Mindful Attention Awareness Scale). Changes from baseline to postintervention and follow-up were assessed for each outcome. RESULTS: Participants had a mean (SD) age of 37.2 (10.6) years, with 69 out of 132 (52.3%) identifying as female. Those randomized to the Headspace Sleep Program group experienced significantly greater improvements in insomnia symptoms from baseline to postintervention and follow-up compared with participants in the waitlist control group (P<.001, η²p=0.107). Improvements from baseline to postintervention and follow-up were also observed in the Headspace Sleep Program group for sleep efficiency, as measured by both sleep diary (P=.01, η²p=.03) and actigraphy outcomes (P=.01, η²p=.03). Participants in the Headspace Sleep Program group showed greater improvements in insomnia remission rates (8/66, 12%, at postintervention and 9/66, 14%, at follow-up) and treatment response (11/66, 17%, at postintervention and 15/66, 23%, at follow-up) compared with the control group (remission rate 2/66, 3%, at postintervention and 0/66, 0%, at follow-up; treatment response 3/66, 5%, at postintervention and 1/66, 2%, at follow-up). The results suggest significant improvements in depressive symptoms (P=.01, η²p=.04), anxiety symptoms (P=.02, η²p=.02), and mindfulness (P=.01, η²p=.03) in the Headspace Sleep Program group. CONCLUSIONS: The Headspace Sleep Program is an effective intervention for improving sleep disturbances in adults with clinical insomnia. TRIAL REGISTRATION: ClinicalTrials.gov NCT05872672; https://clinicaltrials.gov/ct2/show/NCT05872672.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".