Real-world evidence from users of a behavioral digital therapeutic for chronic insomnia
Bibliographic record
Abstract
BACKGROUND: There have been many research trials of various digital therapeutics, but few real world evaluations of their efficacy. This type of data, however, can provide a more rounded understanding of their impact, utility, reach, and adoption. Findings presented here focus on outcome and patient engagement data of SHUTi (Sleep Health Using the Internet), a digital therapeutic delivering Cognitive Behavioral Therapy for insomnia (CBT-I), in a large real-world dataset of adults with insomnia. METHODS: 7216 adults who purchased access to SHUTi between December 2015 and February 2019 are included in the analysis. The Insomnia Severity Index (ISI) was administered at the beginning of each of six treatment Cores of the intervention. Users entered sleep diaries between Cores to track changes in sleep over time and obtain tailored sleep recommendations. Number of Cores completed and sleep diaries entered indicate program usage. RESULTS: Users showed a reduction in mean ISI scores and a corresponding increase in effect size at the start of each subsequent Core (compared to Core 1) (range: d = 0.3-1.9). Effect sizes at the last Core relative to the first were moderate-to-large for diary-derived sleep onset latency and wake after sleep onset. A reduction in number of medicated nights was also found, with those with severe insomnia showing the largest reduction from last-to-first week of treatment (d = 0.3). At the last Core, 61% met criteria for meaningful treatment response (reduction of >7 points on ISI) and 40% met criteria for remission (ISI<8). Engagement was comparable to SHUTi research trials. CONCLUSION: Consistent with controlled trials, real-world data suggest that digital therapeutics can result in relatively high levels of engagement and clinically meaningful sleep improvements.
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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 | 0.016 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".