Effectiveness of unguided digital cognitive behavioral therapy for insomnia on depressive symptoms: a systematic review and meta-analysis of randomized controlled trials

Objective: This study aims to assess the intervention effects of unguided digital cognitive behavioral therapy for insomnia (dCBT-I) on patients with comorbid depression and insomnia through a systematic review and meta-analysis, in order to determine its effectiveness as a treatment strategy for comorbid depression and insomnia.

Methods: We conducted a systematic search across PubMed, Cochrane, Embase, ClinicalTrials.gov, and PsycINFO.databases to identify randomized controlled trials (RCTs) focusing on adults with comorbid insomnia and depressive symptoms, excluding those with severe physical illnesses or psychiatric disorders. Following the removal of duplicates, 1842 articles were screened, resulting in the inclusion of 16 RCTs in the final meta-analysis.

Results: Post-treatment evaluations revealed that digital cognitive behavioral therapy for insomnia (dCBT-I) demonstrated a significant impact on insomnia (SMD = -0.94; 95% CI: -1.40 to -0.48; p < 0.001; k = 16), though substantial heterogeneity was observed (I² = 98.63%; Q = 1250.89, df = 15, p < 0.05). For depressive symptoms, dCBT-I demonstrated a moderate effect (SMD = -0.63; 95% CI: -0.81 to -0.46; p < 0.05; k = 16), with high heterogeneity (I² = 81%; Q = 78.31, df = 15, p < 0.05).Strong outcomes were observed in both the Web and APP cohorts when analyzing program carriers, with notable findings in the moderately depressed subgroup.

Conclusions: This meta-analysis evaluated the efficacy of therapist-unguided digital cognitive behavioral therapy (dCBT-I) for insomnia and depression, dCBT-I demonstrated significant reductions in insomnia severity and depressive symptoms within the studied population, despite substantial heterogeneity. It also demonstrated moderate to large effects for moderate-to-severe depression, further supporting the effectiveness of unguided dCBT-I.

Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251044042, identifier CRD420251044042.

1 Introduction

Insomnia, as one of the most common sleep disorders, is associated with the occurrence and development of many diseases. Among the general adult population, approximately 10% to 50% have experienced short-term or long-term insomnia (1). According to the Diagnostic and Statistical Manual of Mental Disorders-V (DSM-V) diagnostic criteria, insomnia disorder is defined as subjective reports of difficulty falling asleep, sleep maintenance disorders, or inability to achieve restorative sleep quality, and the sleep difficulty occurs at least 3 nights per week (2). Insomnia is also associated with daytime dysfunction such as fatigue, depression, anxiety, irritability, and decreased cognitive function (3).

Epidemiological investigations have shown that there is a relatively high incidence of comorbidity between insomnia and depression, and possess a degree of correlation and bidirectionality (4). Among patients meeting the diagnostic criteria for insomnia, the prevalence of depression is significantly higher than that in patients without insomnia (5).Over 90% of individuals with depression experience a decline in sleep quality. Insomnia is a prevalent residual symptom of depression, and individuals with depression who have persistent insomnia face a notably higher risk of relapse (6, 7). The prevalence of comorbid insomnia and depression has emerged as a critical concern in clinical psychology (8). These two mental health conditions perpetuate a mutually reinforcing pathological cycle through complex mechanisms involving neural circuitry, endocrine function, inflammatory processes, and structural brain changes (9, 10). This interplay significantly undermines individual mental resilience and social adaptability, presenting a crucial public health challenge in the 21st century that requires urgent attention and intervention (11).

In clinical settings, insomnia symptoms are typically addressed through pharmacotherapy (12). However, hypnotics inadequately mitigate depressive comorbidities and often result in significant daytime side effects (13). Cognitive Behavioral Therapy for Insomnia (CBT-I) is recognized as the primary treatment recommended by the American Academy of Sleep Medicine (AASM, 2021) due to its comprehensive intervention benefits (14). Through the implementation of evidence-based strategies—including sleep restriction, stimulus control, and cognitive restructuring—this therapy enhances sleep efficiency, rectifies dysfunctional sleep-related cognitions, and synergistically regulates the central nervous system mechanisms underlying the sleep-wake rhythm (15). Clinical studies have demonstrated that cognitive-behavioral therapy for insomnia (CBT-I) significantly ameliorated insomnia symptoms and markedly reduced depression scale scores in patients with comorbid depression (16–18).

Although the efficacy of traditional cognitive behavioral therapy for insomnia (CBT-I) in treating insomnia and depression has been confirmed, it also has some limitations, such as a shortage of therapist resources, high treatment costs, and restrictions in terms of time and location (19, 20). With the development of technology, digital cognitive behavioral therapy for insomnia (dCBT-I) has been developed and studied in the past decade (21). This technology is relevant to various vehicles, including computers, the Internet, and smartphone applications. Programs for dCBT-I encompass not just the essential elements of conventional CBT-I, but also offer services tailored to individual needs (22–24).

A portion of dCBT-I includes a human feedback component that gives personalized advice based on what the participant has completed and is supervised to increase the participant’s compliance (25). There are also unguided dCBT-I programs that are algorithmically based to provide feedback from a virtual therapist without any human involvement component (26, 27). In groups that show greater prejudice against mental health problems, there is a clear preference for self-help psychological interventions and an increasing acceptance of low-intensity, easily accessible treatments (28, 29).

There are subgroup analyses of fully automated dCBT-I in previous studies that have shown small to moderate therapeutic effects on depression, but these studies did not differentiate between populations and included populations with other co-morbidities (such as cancer or pain), which may have influenced the results of the study (30). Therefore, the effects of dCBT-I on insomnia and depression need to be further investigated in a generalized population without the involvement of a therapist. This meta-analysis was intended to evaluate the efficacy of unguided digital cognitive behavioral therapy for insomnia (dCBT-I) for the treatment of insomnia and depressive symptoms in the adults with comorbid insomnia and depressive symptoms, excluding those with severe physical illnesses or psychiatric disorders, with subgroup analyses of dCBT for both web and app vehicles, and a study of patients with moderate and higher levels of depression to evaluate the efficacy of dCBT-I.

2 Method

2.1 Data sources and search strategies

This work follows the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (31, 32). The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251044042, with no prior publication of the protocol. The RCTs reporting depression outcomes in adult insomnia patients were searched in the PubMed, Cochrane, Embase, ClinicalTrials.gov, and PsycINFO databases up to May 25, 2025. These trials compared unguided digital cognitive behavioral therapy for insomnia (dCBT-I) against placebo or standard care. Further studies or follow-up studies from the original trials were also eligible if they met the predefined inclusion criteria. The search query was applied in PubMed as follows: (“sleep initiation and maintenance disorders”[MeSH Terms] OR “insomnia”[Title/Abstract] OR “early awakening” [Title/Abstract] OR “sleeplessness”[Title/Abstract] OR “sleep initiation dysfunction”[Title/Abstract] OR “DIMS”[Title/Abstract] OR “sleep”[Title/Abstract]) AND (“depressive disorder”[MeSH Terms] OR “melancholia”[All Fields] OR “endogenous depression”[All Fields] OR “depression unipolar”[All Fields] OR “neurotic depression”[All Fields] OR “psychological”[All Fields] OR “depression”[All Fields]) AND (“CBT”[Title/Abstract] OR “cognitive behavioral therapy”[Title/Abstract] OR “cognitive therapy”[Title/Abstract] OR “behavioral therapy”[Title/Abstract]) AND (“digital”[Title/Abstract] OR “internet”[Title/Abstract] OR “online”[Title/Abstract] OR “web”[Title/Abstract] OR “telephone”[Title/Abstract] OR “mobile”[Title/Abstract] OR “app”[Title/Abstract] OR “application”[Title/Abstract] OR “smartphone”[Title/Abstract] OR “computer”[Title/Abstract] OR “computerized”[Title/Abstract] OR “ehealth”[Title/Abstract] OR “AI”[Title/Abstract]).

2.2 Inclusion and exclusion criteria

The inclusion criteria followed the Participant, Intervention, Comparison, Outcome, and Study (PICOS) guidelines. Studies meeting the following criteria were included in this meta-analysis: (1) Participants: The study population consisted of adult patients ≥18 years of age, with clinician-diagnosed or symptomatic insomnia (per DSM/ICSD criteria), regardless of gender/education (2, 33, 34); (2) Intervention: Unguided digital CBT-I (computer/internet/apps) incorporating ≥1 cognitive (e.g., cognitive restructuring) and ≥1 behavioral component (e.g., stimulus control/sleep restriction); (3) Comparison: Placebo, active control, waitlist, or standard care; (4) Outcomes: Baseline and endpoint depressive symptoms measured by validated scales (e.g., HAMD, PHQ-9); studies including anxiety comorbidities were not excluded; (5) Study design: Randomized controlled trials (RCTs).

The following exclusion criteria were used: (1) Serious physical illness (e.g., cancer, Parkinson’s), substance abuse, organic/primary sleep disorders (e.g., sleep apnea, RLS), psychiatric disorders (e.g., bipolar disorder, schizophrenia), neurological disorders, acute suicide risk, severe cognitive impairment, or pregnancy; (2) Non-dCBT-I therapies (e.g., pharmacotherapy alone), unstandardized dCBT-I, or combined interventions without isolated dCBT-I effect analysis; (3) Incomplete outcome reporting (e.g., single-scale data) or irretrievable data; (4) Non-RCTs, case reports, protocols, reviews, duplicate publications, or conference abstracts (incomplete methods); (5) The cognitive behavioral therapy for depression (CBT-D).

2.3 Data extraction and quality assessment

Two researchers (PXZ, QZ) independently extracted and reviewed studies for consideration based on the eligibility criteria. If there was disagreement, the decision was reviewed by a third researcher (HJW). Any discrepancies were resolved through discussion between all authors. Duplicate articles were removed; titles and abstracts of potential studies were screened; and full texts that met the criteria were further evaluated. We identified publications of the same study so that each study, rather than each report, was the unit of analysis in the review. From the included studies, we extracted: sample size, mean age, and control group. Detailed information, including title, authors, year of publication, study design, number of dCBT-I sessions, and treatment duration, was extracted. In addition, relevant study variables were assessed using an assessment tool, and pre- and post scores were extracted for the intervention and control groups. Two reviewers independently assessed the methodological quality of the included RCTs using the Cochrane Risk of Bias Assessment Tool (RoB 2.0) (35). The assessment included randomization sequence generation, allocation concealment, participant and investigator blinding, completeness of outcome data, selective outcome reporting, and other biases. The results showed that of the 16 studies, all 16 had a low risk of bias in terms of “Incomplete outcome data” and “Selective reporting”. Regarding other bias types, 1 study was judged to have an unclear risk of bias in “Random sequence generation” due to “not mentioned”, 2 studies were judged to have an unclear risk of bias in “Allocation concealment” due to “not mentioned”, 3 studies were judged to have an unclear risk of bias in “Blinding of participants and personnel” due to “not mentioned”, 3 studies were judged to have an unclear risk of bias in “Blinding of outcome assessment” due to “not mentioned”, and 2 studies were judged to have an unclear risk of bias in “Other bias” due to “not mentioned”. No studies were found to have a high overall risk of bias.

2.4 Statistical analysis

Standardized Mean Difference (SMD) was used as an indicator of the combined effect sizes of the continuous variables, and the overall between-group SMD was calculated based on the difference in post-intervention outcome measures between the dCBT-I intervention and control groups, with 95% confidence intervals (CI) reported. If the study did not provide the relevant data, they were obtained by graphical extraction or by contacting the original authors for additional information. Consistent with prior research, analyses were conducted to compare post-treatment values between groups (30).

Heterogeneity was assessed via the Cochrane Q test and the I² statistic.I² values ranging from 0% to 100%, with larger values indicating higher heterogeneity. Generally, I² ≤ 25% is considered low heterogeneity, 25%≤ I² ≤ 50% as moderate heterogeneity, and I² > 50% as substantial heterogeneity. The choice of meta-analytic model was based on the degree of statistical heterogeneity. A random-effects model was used if I² > 50% or if the p-value for Cochran’s Q test was < 0.10, otherwise, a fixed-effects model was applied. Publication bias was assessed using Egger’s test. Possible reporting bias and small study effects were assessed using contour-enhanced funnel plots.

2.5 Subgroup analysis

The effects of the interventions on the combined outcomes were analyzed by grouping the studies according to the type of intervention (different dCBT-I vectors) included in the study, comparing differences in effect sizes between subgroups. And the effect of dCBT-I on different levels of depression was assessed by grouping them according to the level of depression at baseline. We also conducted a separate analysis of studies with moderate to severe depression levels. Subgroup analyses also used a random effects model to calculate the combined SMD and 95% CI for each subgroup, and to determine the effect of grouping factors on outcomes by comparing the differences in effect sizes and statistical significance between subgroups. Statistical differences between subgroups were assessed at α = 0.05 significance level. All analyses were performed in Review Manager 5.4 and Stata 17.0.

3 Results

3.1 Study flow

The flow of study selection is presented in the PRISMA flow diagram (Figure 1). A total of 1842 articles were identified after duplicate removal, of which 225 articles were assessed for full-text review. A final sample of 16 RCTs was included in the meta-analysis.

Figure 1

Figure 1. PRISMA flow diagram of study selection.

3.2 Study characteristics

Table 1 describes the methodological and demographic characteristics of the 16 included studies. A total of 7488 participants were involved in the meta-analysis, with 3803 individuals allocated to the dCBT-I group. The median sample size of study was 100 participants (range: 46 to 3352). The mean age of the dCBT-I group and the control group was 36.40 ± 14.96 and 36.30 ± 14.94 years, respectively. Subjects in the control group received either active interventions, including sleep education or general health education (not sleep-specific), or passive controls, including routine treatment and waiting lists. All studies used a parallel design, with 15 studies using a two-arm trial and one using a three-arm trial, but one of the three-arm studies did not meet the inclusion criteria, so only two of the data sets were included. For the treatment group, dCBT-I treatment sessions ranged from 5 to 18, with a mean of 7.31 sessions for the included studies. These studies were conducted in Australia (n=3), China (n=2), Germany (n=3), Iranian (n=1), the Netherlands (n=2), Switzerland (n=1), the United Kingdom (n=2), and the United States (n=2).

Table 1

Table 1. Summary characteristics of the included studies.

3.3 Risk and bias assessment

The risk of bias for the included studies was assessed using the Cochrane Risk of Bias Tool (version 226), and the results are presented in Figure 2. Ten studies had an overall low risk of bias, six had a moderate risk of bias. Significant risk of bias was detected for measures from the outcome domains, mainly due to blinding of study participants and researchers, which may also lead to deviation from the intended intervention. The included studies were generally of high methodological quality, with a low overall risk of bias.

Figure 2

Figure 2. Risk of bias assessment.

3.4 Treatment effects

3.4.1 Sleep measures

Sleep outcome measures varied from study to study and included the Insomnia Severity Index (ISI), the Sleep Condition Index (SCI), and the Sleep-50 measure. The ISI was chosen as the primary sleep outcome measure. All studies used the ISI as an outcome of insomnia severity, except for two studies, which were Lancee et al., 2012 (49) and Henry et al., 2020 (40), one study used the SCI, and the other used the SLEEP-50 measure.

For the severity of insomnia after treatment, we found a large effect (Figure 3; SMD = -0.94; 95% confidence interval (CI): -1.40, -0.48; p < 0.001; k = 16). Statistical heterogeneity of effect sizes between studies was very high (I² = 98.63; Q = 1250.89; df = 15; p < 0.05). In studies that included only ISI, we found that dCBT-I still had a substantial advantage (Supplementary Figure 1, SMD = -1.07; 95% CI: -1.51, -0.63; p < 0.05; I² = 97.33; k = 14).

Figure 3

Figure 3. Effects of dCBT-I on sleep outcomes and sleep efficiency.

For sleep diary outcomes, the effect was significant with a moderate to large effect size for SE (Figure 4; SMD = 0.59; 95% CI: 0.37, 0.81; p < 0.05; I² = 67; k = 6), SOL (Supplementary Figure 2, SMD = −0.42; 95% CI: −0.54 −0.30; p < 0.05; I² = 3.05; k = 6) and WASO (Supplementary Figure 3; SMD = −0.49; 95% CI: −0.85, −0.13; p < 0.05; I² = 84.68; k = 5), while the effect was significant with a small effect size for TST (Supplementary Figure 4; SMD = 0.28; 95% CI: 0.16, 0.40; p < 0.05; I² = 1.12; k = 6).

Figure 4

Figure 4. Effect of dCBT-I on sleep efficiency.

3.4.2 Depression measures

All 16 studies reported the severity of depressive symptoms. The result measurement of depressive symptoms varies in different studies. Including the Center for Epidemiologic Studies Depression Scale (CES-D) and Patient Health Questionnaire-9 (PHQ-9), Allgemeiner Depressions-Skala (ADS-K), Hospital Anxiety and Depression Scale-Depression (HADS-D), Beck Depression Inventory (BDI-II), and Quick Inventory of Depressive Symptomatology (QIDS). In the post-treatment assessment, we found that digital cognitive behavioral therapy (dCBT-I) produced a moderate positive effect in treating depression. (Figure 5; SMD = -0.63; 95% CI: -0.81, -0.46; p < 0.05; k = 16). The statistical heterogeneity in effect sizes among studies was high (I² = 81; Q = 78.31; df = 15; p < 0.05).

Figure 5

Figure 5. Effects of dCBT-I on depression.

3.5 Subgroup analysis

An additional subgroup analysis was performed on 16 studies that reported relevant applications of the dCBT-I session. To compare the effects of different applied treatments, we categorized the 16 studies into two groups: (1) the web platform group, dCBT-I completers who applied the online web platform; and (2) the app group, dCBT-I completers who applied the mobile app. The treatment effect of the web group was significant for insomnia (Figure 6; SMD = -0.97; 95% CI: -1.18, -0.13; p < 0.05; I² = 99.47; k = 9) and medium for depression (Figure 7; SMD = -0.63; 95% CI: -0.97, -0.29; p < 0.05; I² = 96.85; k = 9). The treatment effect of the app group was also significant for insomnia (SMD = -0.93; 95% CI: -1.25, -0.61; p < 0.05; I² = 84.89; k = 7), and medium for depression (SMD = -0.69; 95% CI: -0.85, -0.53; p < 0.05; I² = 46.3; k = 7).

Figure 6

Figure 6. Subgroup analysis (Insomnia): Web vs. App.

Figure 7

Figure 7. Subgroup analysis (Depression): Web vs. App.

Conduct a separate analysis of the 7 studies that used the PHQ-9/PHQ-8 as the outcome measure for depression and had a baseline value greater than 9 in the intervention group (50). The dCBT-I has shown moderate to large effects in moderate and severe depression (Figure 8; SMD = -0.78; 95% CI: -1.19, -0.38; p < 0.05; I² = 94.2; k = 7).

Figure 8

Figure 8. Subgroup analysis (Outcomes: PHQ-8 and PHQ-9).

3.6 Sensitivity analysis and publication bias

Sensitivity analyses were performed by removing two studies with insomnia whose outcomes were not ISI and one study in which the intervention was a self-developed app. After the exclusion of these studies, the therapeutic effect of dCBT-I was demonstrated for sleep (Supplementary Figure 1; SMD = -1.07; 95% CI: -1.51, -0.63; p < 0.05; I² = 97.33; k = 14).

Visual inspection of funnel plots (Supplementary Figure 5 for sleep outcome, Supplementary Figure 6 for depression) and the Egger test was conducted under the random-effects model with the REML method, and the results showed no evidence of publication bias (p = 1.000).

4 Discussion

The current meta-analysis aims to evaluate the efficacy of dCBT-I without therapist guidance on insomnia and depression, and to examine whether there are differences in the efficacy among different forms of dCBT-I. By aggregating the data obtained from eligible RCTs, our results indicated that, compared with the control group, the intervention group was effective in alleviating insomnia, depressive symptoms, and the SE, TST, SOL, and WASO indicators in the sleep diary after unguided dCBT-I treatment. Among them, there is a large effect on insomnia symptoms and a medium effect on depressive symptoms. This is somewhat different from the results reported in the previous meta-analysis examining the impact of dCBT-I on depression, which indicated a mild to moderate effect on depression (30). However, previous studies were limited by the relatively small number of available studies and included other co-patient populations, which might have masked some of the effects of dCBT-I on depression. Due to an increase in sample size to 16 RCTS and the exclusion of the influence of other comorbidities, this updated meta-analysis further supports the effectiveness of self-service dCBT-I.

Although the pooled effect of dCBT-I on depressive symptoms was moderate, there was considerable heterogeneity in the magnitude of the observed effect. The diversity observed aligns with prior research, foreseeable due to the varied participant pool in these studies, the outcome metrics used, the types of CBT-I administered, and the initial severity of depression. Although dCBT-I was developed for the treatment of insomnia, current research results show that dCBT-I intervention is beneficial for reducing clinical depressive symptoms and can effectively complement therapy. In addition to alleviating depressive symptoms, the improvement in the severity of insomnia in this study was usually consistent with the improvement reported in previous meta-analysis reviews of the severity of insomnia in dCBT-I, SE, TST, SOL, and WASO (15).

Subgroup analyses revealed that both online-delivered and app-based dCBT-I demonstrated favorable outcomes on insomnia and depression, without notable distinctions in treatment efficacy between the two modalities. This similarity in effectiveness could be attributed to the convergence of online platforms and apps in user experience aspects, including interface design and interaction modalities, facilitated by advancements in mobile Internet technology. Consequently, the differences in user experience between different delivery media may be minimal, thereby exerting limited influence on treatment outcomes.

For patients with moderate and severe depression, dCBT-I shows moderate to large effects, approaching the “large effect” threshold. This differs from previous research on major depressive disorder, which found moderate effects (51). Patients with moderate and severe depression are often accompanied by chronic insomnia, and the two form a vicious cycle. The dCBT-I directly breaks this vicious cycle by improving insomnia: after sleep is restored, neurotransmitter secretion rhythms are stabilized, and the overactivation of the Hypothalamic axis is suppressed, which in turn alleviates the core symptoms of depression (e.g., lack of energy, low mood) (52). It contains cognitive reframing techniques that can correct the patient’s negative cognitions about sleep, and this thought adjustment will migrate to negative cognitions about depression; concurrently, while sleep restriction therapy may initially induce transient fatigue, it reduces emotional reactivity by enhancing sleep efficiency and restoring circadian rhythms and undermines the cognitive triad of depression; sleep restriction therapy indirectly increases patients’ daytime activity, which is often accompanied by behavioral withdrawal in patients with moderate-to-severe depression, and this passive activation breaks behavioral inhibition and enhances a sense of mastery, which in turn alleviates the core symptom of depression of helplessness. Given the significant heterogeneity between studies, further research is needed to validate these findings. Prior research suggests that therapist guided dCBT-I has superior outcomes in the treatment of depression compared to self-directed interventions (49). Future clinical trials could explore the effectiveness of virtual therapist interventions in this context.

This study is subject to certain limitations, such as small sample sizes potentially leading to inflated effect estimates and variations in control group compositions across studies, impacting result comparability. Subsequent research avenues may delve into the involvement of virtual therapists in dCBT-I, undertake extended follow-up assessments to gauge sustained efficacy, and juxtapose the efficacy of dCBT-I against CBT-I. Although statistical results indicate that dCBT-I demonstrates a relatively significant effect on both insomnia and depression, the extremely high heterogeneity renders the pooled estimates less stable. Digital interventions frequently encounter challenges related to low participant adherence. To enhance adherence, several studies have employed multiple strategies, such as personalized assessments and tailored treatment recommendations, incentive mechanisms, behavioral prompts for small daily actions, and fully automated reminder systems that deliver periodic prompts via push notifications or email. Furthermore, the vast majority of studies employ intention-to-treat (ITT) analysis to minimize bias arising from participant attrition, thereby ensuring robust results.

5 Conclusion

Unguided digital cognitive behavioral therapy for insomnia (dCBT-I) is an effective intervention for improving insomnia and depressive symptoms in adults with comorbid insomnia and depressive symptoms. The findings may not be generalizable to individuals at extremely high risk of suicide, those with severe mental comorbidities, or those with serious physical illnesses. The automated nature of dCBT-I not only overcomes geographical constraints but also diminishes treatment expenses, thereby affording patients improved treatment results.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

PZ: Conceptualization, Data curation, Formal Analysis, Validation, Visualization, Writing – original draft, Writing – review & editing. QZ: Data curation, Formal Analysis, Supervision, Writing – review & editing. TL: Data curation, Supervision, Writing – review & editing. LT: Supervision, Writing – review & editing. HW: Methodology, Supervision, Writing – review & editing. WP: Funding acquisition, Supervision, Writing – review & editing, Project administration.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Natural Science Foundation of China (Grant No. 72364032, U24A20774) ; National Key Research Development Program of China (2025YFC3507505) ; Qinghai University Postgraduate Supervisor Innovation Team Development Project (qddt-2511).

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1718949/full#supplementary-material

References

1. Rocha RB, Bomtempo FF, Nager GB, Cenci GI, and Telles JPM. Dual orexin receptor antagonists for the treatment of insomnia: systematic review and network meta-analysis. Arq Neuropsiquiatr. (2023) 81:475–83. doi: 10.1055/s-0043-1768667

PubMed Abstract | Crossref Full Text | Google Scholar

2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Am J Med. (2013) 132:292–9. doi: 10.1176/appi.books.9780890425787

PubMed Abstract | Crossref Full Text | Google Scholar

3. Riedel BW and Lichstein KL. Insomnia and daytime functioning. Sleep Med Rev. (2000) 4:277–98. doi: 10.1053/smrv.1999.0074

PubMed Abstract | Crossref Full Text | Google Scholar

4. Sutton EL. Insomnia. Ann Internal Med. (2021) 174:ITC33–48. doi: 10.7326/AITC202103160

PubMed Abstract | Crossref Full Text | Google Scholar

5. Olufsen IS, Sørensen ME, and Bjorvatn B. New diagnostic criteria for insomnia and the association between insomnia, anxiety and depression. Tidsskr Nor Laegeforen. (2020) 140. doi: 10.4045/tidsskr.19.0041. English, Norwegian.

PubMed Abstract | Crossref Full Text | Google Scholar

6. Kwaśny A, Włodarczyk A, Dywel A, Szarmach J, Strandberg O, and Cubała WJ. Residual insomnia in major depressive disorder: a systematic review. Front Psychiatry. (2023) 14:1190415. doi: 10.3389/fpsyt.2023.1190415

PubMed Abstract | Crossref Full Text | Google Scholar

7. Alamoudi D, Breeze E, Crawley E, and Nabney I. The feasibility of using smartphone sensors to track insomnia, depression, and anxiety in adults and young adults: narrative review. JMIR Mhealth Uhealth. (2023) 11:e44123. doi: 10.2196/44123

PubMed Abstract | Crossref Full Text | Google Scholar

8. Fang H, Yao T, Li W, Pan N, Xu H, Zhao Q, et al. Efficacy and safety of fecal microbiota transplantation for chronic insomnia in adults: a real world study. Front Microbiol. (2023) 14:1299816. doi: 10.3389/fmicb.2023.1299816

PubMed Abstract | Crossref Full Text | Google Scholar

9. Liang X, Guo Y, Zhang H, Wang X, Li D, Liu Y, et al. Neuroimaging signatures and a deep learning modeling for early diagnosing and predicting non-pharmacological therapy success for subclinical depression comorbid sleep disorders in college students. Int J Clin Health Psychol. (2024) 24:100526. doi: 10.1016/j.ijchp.2024.100526

PubMed Abstract | Crossref Full Text | Google Scholar

10. Oh JW, Kim SM, Lee D, Son NH, Uh J, Yoon JH, et al. Evaluating the modified patient health questionnaire-2 and insomnia severity index-2 for daily digital screening of depression and insomnia: validation study. JMIR Ment Health. (2023) 10:e45543. doi: 10.2196/45543

PubMed Abstract | Crossref Full Text | Google Scholar

11. Gao T, Xiang H, Wu QN, Zhu LS, Pei WJ, Fu WJ, et al. Advances in the research of comorbid insomnia and depression: mechanisms, impacts, and interventions. Front Psychiatry. (2025) 16:1468212. doi: 10.3389/fpsyt.2025.1468212

PubMed Abstract | Crossref Full Text | Google Scholar

12. Carvalhas-Almeida C, Serra J, Moita J, Cavadas C, and Álvaro AR. Understanding neuron-glia crosstalk and biological clocks in insomnia. Neurosci Biobehav Rev. (2023) 147:105100. doi: 10.1016/j.neubiorev.2023.105100

PubMed Abstract | Crossref Full Text | Google Scholar

13. Shukla M and Vincent B. Melatonin as a harmonizing factor of circadian rhythms, neuronal cell cycle and neurogenesis: additional arguments for its therapeutic use in Alzheimer’s disease. Curr Neuropharmacol. (2023) 21:1273–98. doi: 10.2174/1570159X21666230314142505

PubMed Abstract | Crossref Full Text | Google Scholar

14. Edinger JD, Arnedt JT, Bertisch SM, Carney CE, Harrington JJ, Lichstein KL, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. (2021) 17:255–62. doi: 10.5664/jcsm.8986

PubMed Abstract | Crossref Full Text | Google Scholar

15. Soh HL, Ho RC, Ho CS, and Tam WW. Efficacy of digital cognitive behavioural therapy for insomnia: a meta-analysis of randomised controlled trials. Sleep Med. (2020) 75:315–25. doi: 10.1016/j.sleep.2020.08.020

PubMed Abstract | Crossref Full Text | Google Scholar

16. Coteur K, Henrard G, Schoenmakers B, Laenen A, Van den Broeck K, De Sutter A, et al. Blended care to discontinue benzodiazepine receptor agonists use in patients with chronic insomnia disorder: a pragmatic cluster randomized controlled trial in primary care. Sleep. (2023) 46:zsac278. doi: 10.1093/sleep/zsac278

PubMed Abstract | Crossref Full Text | Google Scholar

17. Bei B, Pinnington DM, Quin N, Shen L, Blumfield M, Wiley JF, et al. Improving perinatal sleep via a scalable cognitive behavioural intervention: findings from a randomised controlled trial from pregnancy to 2 years postpartum. Psychol Med. (2023) 53:513–23. doi: 10.1017/S0033291721001860

PubMed Abstract | Crossref Full Text | Google Scholar

18. Vollert B, Müller L, Jacobi C, Trockel M, and Beintner I. Effectiveness of an app-based short intervention to improve sleep: randomized controlled trial. JMIR Ment Health. (2023) 10:e39052. doi: 10.2196/39052

PubMed Abstract | Crossref Full Text | Google Scholar

19. Wang F, He J, Zhou Y, Ye L, Li B, Ma Z, et al. A phase 1 study of dimdazenil to evaluate the pharmacokinetics, food effect and safety in Chinese healthy subjects. Front Pharmacol. (2023) 14:1226014. doi: 10.3389/fphar.2023.1226014

PubMed Abstract | Crossref Full Text | Google Scholar

20. Steinmetz L, Simon L, Feige B, Riemann D, Johann AF, Ell J, et al. Network meta-analysis examining efficacy of components of cognitive behavioural therapy for insomnia. Clin Psychol Rev. (2024) 114:102507. doi: 10.1016/j.cpr.2024.102507

PubMed Abstract | Crossref Full Text | Google Scholar

21. Werner-Seidler A, O’Dea B, Shand F, Johnston L, Frayne A, Fogarty AS, et al. A smartphone app for adolescents with sleep disturbance: development of the sleep ninja. JMIR Ment Health. (2017) 4:e28. doi: 10.2196/mental.7614

PubMed Abstract | Crossref Full Text | Google Scholar

22. Cheng P, Kalmbach DA, Hsieh HF, Castelan AC, Sagong C, and Drake CL. Improved resilience following digital cognitive behavioral therapy for insomnia protects against insomnia and depression one year later. Psychol Med. (2023) 53:3826–36. doi: 10.1017/S0033291722000472

PubMed Abstract | Crossref Full Text | Google Scholar

23. Cheng P, Luik AI, Fellman-Couture C, Peterson E, Joseph CLM, Tallent G, et al. Efficacy of digital CBT for insomnia to reduce depression across demographic groups: a randomized trial. Psychol Med. (2019) 49:491–500. doi: 10.1017/S0033291718001113

PubMed Abstract | Crossref Full Text | Google Scholar

24. Schuffelen J, Maurer LF, Lorenz N, Rötger A, Pietrowsky R, and Gieselmann A. The clinical effects of digital cognitive behavioral therapy for insomnia in a heterogenous study sample: results from a randomized controlled trial. Sleep. (2023) 46:zsad184. doi: 10.1093/sleep/zsad184

PubMed Abstract | Crossref Full Text | Google Scholar

25. Kallestad H, Scott J, Vedaa Ø, Lydersen S, Vethe D, Morken G, et al. Mode of delivery of Cognitive Behavioral Therapy for Insomnia: a randomized controlled non-inferiority trial of digital and face-to-face therapy. Sleep. (2021) 44:zsab185. doi: 10.1093/sleep/zsab185

PubMed Abstract | Crossref Full Text | Google Scholar

26. Zhang C, Liu Y, Guo X, Liu Y, Shen Y, and Ma J. Digital cognitive behavioral therapy for insomnia using a smartphone application in China: A pilot randomized clinical trial. JAMA Netw Open. (2023) 6:e234866. doi: 10.1001/jamanetworkopen.2023.4866

PubMed Abstract | Crossref Full Text | Google Scholar

27. Specht A, Betz LT, Riepenhausen A, Jauch-Chara K, Jacob GA, Riemann D, et al. Effectiveness and safety of an interactive internet-based intervention to improve insomnia: Results from a randomised controlled trial. J Sleep Res. (2025) 34:e14409. doi: 10.1111/jsr.14409

PubMed Abstract | Crossref Full Text | Google Scholar

28. Kazdin AE, Fitzsimmons-Craft EE, and Wilfley DE. Addressing critical gaps in the treatment of eating disorders. Int J Eat Disord. (2017) 50:170–89. doi: 10.1002/eat.22670

PubMed Abstract | Crossref Full Text | Google Scholar

29. Ormel J and Emmelkamp PMG. More treatment, but not less anxiety and mood disorders: why? Seven hypotheses and their evaluation. Psychother Psychosom. (2023) 92:73–80. doi: 10.1159/000528544

PubMed Abstract | Crossref Full Text | Google Scholar

30. Lee S, Oh JW, Park KM, Lee S, and Lee E. Digital cognitive behavioral therapy for insomnia on depression and anxiety: a systematic review and meta-analysis. NPJ Digit Med. (2023) 6:52. doi: 10.1038/s41746-023-00800-3

PubMed Abstract | Crossref Full Text | Google Scholar

31. Green S, Higgins JPT, Alderson P, Clarke M, Mulrow CD, and Oxman AD. Cochrane handbook for systematic reviews of interventions. Chichester, West Sussex, United Kingdom: John Wiley & Sons (2019). doi: 10.1002/9781119536604

Crossref Full Text | Google Scholar

32. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71

PubMed Abstract | Crossref Full Text | Google Scholar

33. Sateia MJ. International classification of sleep disorders-third edition: highlights and modifications. Chest. (2014) 146:1387–94. doi: 10.1378/chest.14-0970

PubMed Abstract | Crossref Full Text | Google Scholar

34. World Health Organization. International Classification of Diseases, Eleventh Revision (ICD-11). Geneva, Switzerland: World Health Organization (2019).

Google Scholar

35. Sterne JAC, Savović J, Page MJ, Fairbairn RG, Balk IMH, Caldwell IR, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. (2019) 366:I4898. doi: 10.1136/bmj.l4898

PubMed Abstract | Crossref Full Text | Google Scholar

36. Lancee J, van den Bout J, van Straten A, and Spoormaker VI. Internet-delivered or mailed self-help treatment for insomnia?: a randomized waiting-list controlled trial. Behav Res Ther. (2012) 50:22–9. doi: 10.1016/j.brat.2011.09.012

PubMed Abstract | Crossref Full Text | Google Scholar

37. Christensen H, Batterham PJ, Gosling JA, Ritterband LM, Griffiths KM, Thorndike FP, et al. Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial. Lancet Psychiatry. (2016) 3:333–41. doi: 10.1016/S2215-0366(15)00536-2

PubMed Abstract | Crossref Full Text | Google Scholar

38. Horsch CH, Lancee J, Griffioen-Both F, Spruit S, Fitrianie S, Neerincx MA, et al. Mobile phone-delivered cognitive behavioral therapy for insomnia: A randomized waitlist controlled trial. J Med Internet Res. (2017) 19:e70. doi: 10.2196/jmir.6524

PubMed Abstract | Crossref Full Text | Google Scholar

39. Glozier N, Christensen H, Griffiths KM, Hickie IB, Naismith SL, Biddle D, et al. Adjunctive Internet-delivered cognitive behavioural therapy for insomnia in men with depression: A randomised controlled trial. Aust N Z J Psychiatry. (2019) 53:350–60. doi: 10.1177/0004867418797432

PubMed Abstract | Crossref Full Text | Google Scholar

40. Lorenz N, Heim E, Roetger A, Birrer E, and Maercker A. Randomized controlled trial to test the efficacy of an unguided online intervention with automated feedback for the treatment of insomnia. Behav Cognit Psychother. (2019) 47:287–302. doi: 10.1017/S1352465818000486

PubMed Abstract | Crossref Full Text | Google Scholar

41. Henry AL, Miller CB, Emsley R, Sheaves B, Freeman D, Luik AI, et al. Insomnia as a mediating therapeutic target for depressive symptoms: A sub-analysis of participant data from two large randomized controlled trials of a digital sleep intervention. J Sleep Res. (2021) 30:e13140. doi: 10.1111/jsr.13140

PubMed Abstract | Crossref Full Text | Google Scholar

42. Rajabi Majd N, Broström A, Ulander M, Lin CY, Griffiths MD, Imani V, et al. Efficacy of a theory-based cognitive behavioral technique app-based intervention for patients with insomnia: randomized controlled trial. J Med Internet Res. (2020) 22:e15841. doi: 10.2196/15841

PubMed Abstract | Crossref Full Text | Google Scholar

43. Chan CS, Wong CYF, Yu BYM, Hui VKY, Ho FYY, and Cuijpers P. Treating depression with a smartphone-delivered self-help cognitive behavioral therapy for insomnia: a parallel-group randomized controlled trial. Psychol Med. (2023) 53:1799–813. doi: 10.1017/S0033291721003421

PubMed Abstract | Crossref Full Text | Google Scholar

44. Rötger A, Schuffelen J, Maurer LF, Lorenz N, Pollok B, and Gieselmann A. The clinical effect of digital cognitive behavioural therapy for insomnia in subgroups with depressive and anxiety symptoms: A secondary analysis of a randomized-controlled trial. J Sleep Res. (2024) 33:e14173. doi: 10.1111/jsr.14173

PubMed Abstract | Crossref Full Text | Google Scholar

45. Sweetman A, Reynolds C, and Richardson C. Digital cognitive behavioural therapy for insomnia versus digital sleep education control in an Australian community-based sample: a randomised controlled trial. Intern Med J. (2024) 54:1838–48. doi: 10.1111/imj.16521

PubMed Abstract | Crossref Full Text | Google Scholar

46. Tamm S, Tse KYK, Hellier J, Saunders KEA, Harmer CJ, Espie CA, et al. Emotional processing following digital cognitive behavioral therapy for insomnia in people with depressive symptoms: A randomized clinical trial. JAMA Netw Open. (2025) 8:e2461502. doi: 10.1001/jamanetworkopen.2024.61502

PubMed Abstract | Crossref Full Text | Google Scholar

47. Staiano W, Callahan C, Davis M, Tanner L, Coe C, Kunkle S, et al. Assessment of an app-based sleep program to improve sleep outcomes in a clinical insomnia population: randomized controlled trial. JMIR Mhealth Uhealth. (2025) 13:e68665. doi: 10.2196/68665

PubMed Abstract | Crossref Full Text | Google Scholar

48. Schuffelen J, Maurer LF, and Gieselmann A. Digital CBT-I in comorbid insomnia and depression: clinical outcomes from a pragmatic randomized controlled trial. Depress Anxiety. (2025) 2025:2171041. doi: 10.1155/da/2171041

PubMed Abstract | Crossref Full Text | Google Scholar

49. Lancee J, van den Bout J, Sorbi MJ, and van Straten A. Motivational support provided via email improves the effectiveness of internet-delivered self-help treatment for insomnia: a randomized trial. Behav Res Ther. (2013) 51:797–805. doi: 10.1016/j.brat.2013.09.004

PubMed Abstract | Crossref Full Text | Google Scholar

50. Kroenke K, Spitzer RL, and Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. (2001) 16:606–13. doi: 10.1046/j.1525-1497.2001.016009606.x

PubMed Abstract | Crossref Full Text | Google Scholar

51. Furukawa Y, Nagaoka D, Sato S, Toyomoto R, Takashina HN, Kobayashi K, et al. Cognitive behavioral therapy for insomnia to treat major depressive disorder with comorbid insomnia: A systematic review and meta-analysis. J Affect Disord. (2024) 367:359–66. doi: 10.1016/j.jad.2024.09.017

PubMed Abstract | Crossref Full Text | Google Scholar

52. Riemann D, Krone LB, Wulff K, and Nissen C. Sleep, insomnia, and depression. Neuropsychopharmacology. (2020) 45:74–89. doi: 10.1038/s41386-019-0411-y

PubMed Abstract | Crossref Full Text | Google Scholar