Despite research into the development of digital cognitive behavioral therapy for insomnia (dCBT-I), research into the outcomes of dCBT-I on insomnia and the associated clinical conditions of depression and anxiety have been limited. The PubMed, PsycINFO (Ovid), Embase, and Cochrane databases were searched for randomized controlled trials (RCTs) on adult patients with insomnia also having reported measures of depressive or anxiety symptoms. In total, 2504 articles were identified after duplicate removal, and 22 RCTs were included in the final meta-analysis. At the post-treatment assessment, the dCBT-I group had a small to moderate effect in alleviating depressive (standardized mean difference (SMD) = −0.42; 95% CI: −0.56, −0.28; p < 0.001; k = 21) and anxiety symptoms (SMD = −0.29; 95% CI: −0.40, −0.19; p < 0.001; k = 18), but had a large effect on sleep outcome measures (SMD = −0.76; 95% CI: −0.95, −0.57; p < 0.001; k = 22). When considering treatment adherence, the treatment effects of those in the high adherent groups identified a more robust outcome, showing greater effect sizes than those in the low adherent groups for depression, anxiety, and sleep outcomes. Furthermore, additional subgroup analysis on studies that have used the fully automated dCBT-I treatment without the support of human therapists reported significant treatment effects for depression, anxiety, and sleep outcomes. The results demonstrated that digital intervention for insomnia yielded significant effects on alleviating depressive and anxiety symptoms as well as insomnia symptoms. Specifically, the study demonstrated significant effects on the above symptoms when considering treatment adherence and implementing fully automated dCBT-I.

This study determined whether cognitive behavioral therapy for insomnia (CBT-i) decreased the need for sleep medications and produced better treatment outcomes than pharmacotherapy alone. We reviewed data from patients with insomnia in the outpatient clinic of a general hospital between 2009 and 2015. We compared 41 patients who received five sessions of CBT-i with 100 age- and sex-matched patients who received pharmacotherapy only. We evaluated the change in prescription for sleep (i.e., antidepressants, hypnotics, and others) between the first and last visits using repeated measures analysis of variance (ANOVA). Clinical global impressions and completion status at the last visit were assessed using the chisquare test. We found a significant decrease in the prescription rate and the dosage of hypnotics among patients who received CBT-i when compared with control patients. There was no significant change in the dosage of antidepressants between the two groups. Achievement of case closure was better in the CBT-i group at the trend level. Clinical global impression at the last visit was not significantly different. These results show that CBT-i reduces the need for hypnotics among insomnia patients. Our results indicate that CBT-i offers additional benefits beyond improving sleep characteristics and thus provides another reason for recommending CBT-i as a first-line treatment for insomnia.

The early identification and prevention of dementia is important for reducing its worldwide burden and increasing individuals’ quality of life. Although several dementia prediction models have been developed, there remains a need for a practical and precise model targeted to middle-aged and Asian populations. Here, we used national Korean health examination data from adults (331,126 individuals, 40–69 years of age, mean age: 52 years) from 2002–2003 to predict the incidence of dementia after 10 years. We divided the dataset into two cohorts to develop and validate of our prediction model. Cox proportional hazards models were used to construct dementia prediction models for the total group and sex-specific subgroups. Receiver operating characteristics curves, C-statistics, calibration plots, and cumulative hazards were used to validate model performance. Discriminative accuracy as measured by C-statistics was 0.81 in the total group (95% confidence interval (CI) = 0.81 to 0.82), 0.81 in the male subgroup (CI = 0.80 to 0.82), and 0.81 in the female subgroup (CI = 0.80 to 0.82). Significant risk factors for dementia in the total group were age; female sex; underweight; current hypertension; comorbid psychiatric or neurological disorder; past medical history of cardiovascular disease, diabetes mellitus, or hypertension; current smoking; and no exercise. All identified risk factors were statistically significant in the sex-specific subgroups except for low body weight and current hypertension in the female subgroup. These results suggest that public health examination data can be effectively used to predict dementia and facilitate the early identification of dementia within a middle-aged Asian population.

Digital therapeutics (DTx) are products that provide evidence-based interventions driven by high-quality software programs to prevent, manage, or treat a medical disease. DTx are receiving increasing attention as a new therapeutic approach. Several DTx for insomnia are on the market, some of which have received approval by national regulatory agencies. DTx for insomnia are usually based on cognitive behavioral therapy for insomnia. No DTx for other sleep disorders, such as narcolepsy or sleep-related breathing disorders, have received regulatory authority approval as a medical device. DTx have the substantial benefits of being accessible and relatively low-cost. However, several issues related to DTx have not yet been fully resolved, and discussions regarding DTx are still in the early stages. To use DTx for sleep disorders as an effective treatment option in the future, considering the current status of DTx is necessary. This review discusses definitions and background of DTx; specific DTx for insomnia that have been developed; use of DTx for sleep and related psychiatric comorbid symptoms; global regulatory processes for DTx, including prescribing and medical billing issues; and remaining challenges regarding the use of DTx.