ArticlePLOS digital health2026
Predicting adherence to fully-automated, chatbot-delivered digital cognitive behavioral therapy for insomnia (dCBT-I) using machine learning: A pilot real-world study.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital cognitive behavioral therapy for insomnia (dCBT-I) is effective in treating insomnia, but adherence remains a major challenge in real-world applications. Machine learning (ML) offers potential in predicting healthcare utilization. This study applied ML techniques to predict adherence to dCBT-I based on participant baseline characteristics. This pilot real-world study included 75 individuals (69% female; 41% aged 35-44 years) with insomnia symptoms (Insomnia Severity Index, ISI ≥ 8) who participated in a 28-day chatbot-delivered dCBT-I program. ML models, including logistic regression with elastic-net penalty, support vector machine, random forest, and gradient boosting, analyzed participant baseline characteristics to predict adherence to dCBT-I in terms of session completion, usage duration, and response volume. These models were fine-tuned using grid search and evaluated with cross-validation. The synthetic minority over-sampling technique was applied to address data imbalances in the training set. Baseline depressive symptoms were the most predictive of non-adherence. Higher depressive symptoms were associated with shorter overall usage duration (β = -3.57, 95% CI: -5.82 to -1.33, p = .002). Longer sleep onset latency and wake time after sleep onset from the previous night increased the number of responses and longer usage duration on the following day (β = 0.01-0.05, p < .05). No significant associations were found between daytime and bedtime usage and sleep parameters for that specific night. ML models predicted overall adherence, with AUCs of 0.65-0.91 (p < .05). ML models also predicted next-day adherence, with AUCs of 0.56-0.74 (p < .05). This real-world study demonstrates the potential of ML to predict user adherence to dCBT-I and provides clinical insights for personalizing sleep-focused treatments. The study also investigated daily usage and adherence patterns in dCBT-I to predict next-day adherence.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.