Participants were psychiatric outpatients aged ≥15 years who presented with sleep complaints and met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for a sleep–wake disorder (e.g., insomnia disorder, circadian rhythm sleep–wake disorder, or nightmare disorder) [15]. Obstructive sleep apnea (OSA), when present, was recorded as a comorbid condition within usual care. Comorbid psychiatric diagnoses were determined by psychiatrists. Participants continued usual psychiatric care during the study. Exclusion criteria were as follows: 1) refusal to participate in the intervention program, and 2) presence of clinically severe physical or mental impairments.

A total of 72 participants were initially recruited from advertisements and outpatient visits at Seoul National University Hospital between September 2023 and February 2026. The initial sample consisted of 20 males (mean age 40.30±18.83 years, range 17–78 years) and 52 females (mean age 40.02±15.06 years, range 18–72 years). Of these, 16 participants were excluded from the final analysis: 5 did not start the intervention sessions and 11 discontinued participation during the intervention. As a result, 56 participants completed the study protocol and were included in the final analysis: 16 males (mean age 39.78±15.29 years, range 19–75 years) and 40 females (mean age 39.78±15.29 years, range 18–72 years).

This study was approved by the Institutional Review Board of Seoul National University Hospital (approval no. 2306-029-1437), and all procedures were performed after obtaining written informed consent from the participants. For minor participants, written informed consent was obtained from their legal guardians, and voluntary written assent was obtained from the minors after providing an age-appropriate explanation of the study procedures.

We delivered a five-session modular sleep intervention informed by CBT-I principles following the baseline assessment (T0). During Sessions 1 and 2, participants received standardized sleep hygiene education to establish fundamental healthy sleep habits and address comorbid psychiatric and sleep disturbances. From Session 3, participants received sleep-focused, disorder-tailored modules, including stimulus control and sleep restriction for insomnia disorder, circadian phase-shifting strategies for circadian rhythm sleep–wake disorders, and imagery rehearsal therapy for nightmare disorder. During the intervention, data from clinical questionnaires and sleep diaries were collected. Clinical assessments and self-report questionnaires were administered at three time points: baseline (T0), post-intervention (T1), and a 3-month follow-up (T2). All data were collected digitally using tablets at T0 and T1, and through either tablets or a secure online link at T2 to ensure consistent data management. In addition, participants were instructed on using a mobile app-based sleep diary developed by our research group and were encouraged to complete it daily throughout the entire intervention.

Clinical questionnaire outcomes were analyzed in R (version 4.5.2; R Foundation for Statistical Computing). Longitudinal changes across three time points (T0, T1, and T2) were examined using linear mixed-effects models fitted with the lme4 package (lmer), with p-values and denominator degrees of freedom obtained via the lmerTest package using Satterthwaite’s approximation. Separate models were fitted for each questionnaire outcome (9 models). Time was modeled as a categorical fixed effect, and age, sex, and body mass index (BMI) were included as covariates. Subject-specific variability and within-subject correlation were accounted for by including a random intercept for participants. Models were fitted using restricted maximum likelihood (REML) and all available data without listwise deletion (under the missing-at-random assumption). Fixed effects were evaluated using Type III F-tests. For significant main effects of time, post hoc comparisons between baseline and each subsequent time point (T0 vs. T1 and T0 vs. T2) were performed using estimated marginal means (emmeans) with Bonferroni adjustment. Effect sizes for the time effect were reported as partial eta squared (ηp2), computed using the effectsize package.

In addition, sleep diary parameters were analyzed using SPSS software (ver. 25.0; IBM Corp.). Owing to the fact that most participants completed the sleep diary only during the intervention period, sleep diary analyses were restricted to T0 and T1, and mean values and standard deviations were calculated over the T0 and T1 periods, respectively. For variability outcomes, within-participant standard deviations (SD) were computed across diary days within each window (T0 and T1) and compared using paired t-tests. Statistical significance was defined as a two-tailed p-value <0.05 for all analyses.

The demographic and baseline characteristics of the participants included in both the questionnaire and sleep diary analyses are summarized in Table 1.

A total of 56 participants (16 males and 40 females) were included in the questionnaire analysis, with their demographic characteristics as previously described. Regarding the primary sleep–wake disorder profiles (insomnia disorder, circadian rhythm sleep–wake disorder, nightmare disorder), the majority of participants received the insomnia disorder module (n=37, 66.07%), followed by circadian rhythm disorders (n=18, 32.14%) and nightmare disorder (n=1, 1.79%). The mean BMI for the total sample was 23.13±5.53 kg/m².

For the sleep diary analysis, a subset of 41 participants was analyzed, comprising 12 males (mean age 40.00±16.70 years, range 19–75) and 29 females (mean age 40.21±16.22 years, range 18–72). The average BMI of this subgroup was 22.90±4.96 kg/m². The distribution of specific intervention modules within this subgroup was as follows: 68.29% (n=28) for insomnia disorder, 29.27% (n=12) for circadian rhythm disorders, and 2.44% (n=1) for nightmare disorder.

Comorbid psychiatric diagnoses are summarized in Table 1. The intervention primarily targeted insomnia symptoms and related sleep disturbances; OSA, when present, was managed as a comorbid condition within usual care. Baseline demographic characteristics, including age, sex, and BMI, did not differ significantly between the questionnaire analysis group (n=56) and the sleep diary subgroup (n=41), indicating that the diary subgroup was representative of the overall sample.

Longitudinal analyses in the present study indicate that a sleep-focused intervention was associated with improvements across multiple functional domains in a diagnostically heterogeneous clinical sample. A randomized controlled trial of therapist-assisted, transdiagnostic, internet-delivered CBT for anxiety and depression reported that adding an insomnia-focused module resulted in greater short-term reductions in insomnia symptoms among individuals with comorbid insomnia [28]. Meta-analytic evidence further suggests that improvements in sleep are associated with improvements in broader mental health outcomes, including depression and anxiety, supporting the transdiagnostic relevance of sleep interventions [27]. Similarly, digital CBT-I has been shown to reduce both insomnia severity and depressive symptoms in adult samples, indicating that sleep-focused interventions may exert effects beyond sleep alone [29,30]. Together, these findings support the interpretation that prioritizing sleep within psychiatric care may be associated with improvement across multiple symptom domains; however, controlled comparisons are required to clarify the magnitude of any added benefit attributable to the sleep-focused component.

Sleep-related outcomes showed the most pronounced improvements following the intervention. Insomnia severity (ISI) declined from 17.39 at baseline to 10.43 post-intervention and was largely maintained at 11.80 at 3-month follow-up; ISI changed significantly across time points (p<0.001) (Table 2). Based on established ISI severity thresholds, this change reclassified the average participant from moderate insomnia to subthreshold severity, with a magnitude of reduction comparable to effects reported in meta-analytic evaluations of CBT-I [8,9] and recent trials incorporating insomnia-focused modules within broader CBT frameworks [28-30]. Subjective sleep quality (PSQI) also improved from 16.14 at baseline to 13.56 immediately post- intervention and remained better than baseline at 14.75 at 3-month follow-up; PSQI also showed a significant change across time points (p<0.001) (Table 2).

Although PSQI scores remained within the clinically impaired range, the sustained reduction suggests partial but meaningful improvement in perceived sleep quality, consistent with evidence that core CBT-I components such as sleep restriction and stimulus control are associated with improvements in subjective sleep outcomes [31]. Overall, these findings suggest durable attenuation of perceived sleep disturbance rather than complete remission.

In addition to improvements in sleep outcomes, the intervention was accompanied by significant reductions in anxiety and depressive symptoms. Anxiety (BAI) scores decreased from 17.46 at baseline to 11.63 post-intervention and 10.95 at follow-up, and depressive symptoms (PHQ-9) declined from 12.96 to 7.63 and 6.55, respectively (both p<0.001) (Table 2). Such cross-domain improvement is consistent with meta-analytic findings indicating that sleep interventions are associated with reductions in both anxiety and depressive symptoms [27,29]. Scores on the APPQ also decreased significantly (p<0.001) (Table 2), reflecting reduced phobic anxiety and avoidance. Meta-analytic evidence suggests that non-pharmacological sleep interventions produce modest but reliable reductions in anxiety symptoms across clinical samples, supporting the plausibility of the anxiety-related improvements observed in the present study [32]. Mechanistically, sleep disturbance has been linked to heightened emotional reactivity and dysregulated affective processing, providing a theoretical basis for concomitant improvements in fear- and arousal-related symptoms with improved sleep [26]. Taken together, these findings highlight the clinical importance of routinely assessing sleep problems in patients with depression and anxiety and integrating evidence-based sleep interventions (e.g., CBT-I) into care when indicated [26,27].

Differential changes were observed between daytime sleepiness and fatigue. Self-reported daytime sleepiness (ESS) did not change significantly (p=0.073) (Table 2), whereas subjective fatigue (FSS) decreased from 43.25 at baseline to 31.26 post-intervention and 35.15 at follow-up (p<0.001). This pattern may be influenced by the sedating effects of certain psychotropic medications, including some antidepressants, which can contribute to residual daytime somnolence despite subjective sleep improvement [33]. Nevertheless, the marked reduction in fatigue suggests that improved perceived restorative sleep may enhance daytime vitality even when medication-related sleepiness persists. These findings underscore the importance of distinguishing between sleepiness and fatigue when evaluating sleep interventions in medicated psychiatric populations.

The intervention was associated with significant improvements in health-related quality of life and clinician-rated severity. Health-related quality of life (SF-36) increased from 50.43 at baseline to 61.55 post-intervention and remained above baseline at 56.26 at 3-month follow-up (p<0.001). Such functional gains are consistent with evidence that sleep improvement was associated with broader benefits in mental health and daily functioning [27].

Clinician-rated illness severity (CGI-S) also improved significantly (p<0.001), supporting clinician-observed change beyond self-report measures. Clinically, this suggests that the effects of the sleep intervention extended beyond subjective symptom reporting and were reflected in overall illness severity as evaluated by the treating clinician. These findings highlight the practical value of prioritizing sleep as a treatment target, particularly within diagnostically heterogeneous psychiatric populations [26,27].

The prospective sleep diary data provide self-monitoring indices that complement retrospective questionnaire outcomes. Beyond the reduction in insomnia severity (ISI), diary-derived parameters indicated improvements in sleep continuity and regularity, characterized by a significant increase in SE (from 81.18% to 87.37%) and reductions in WASO and NT. These changes align with the primary behavioral targets of CBT-I: consolidating sleep and reducing time spent awake in bed [10,11]. Furthermore, the use of standardized diary metrics enabled the evaluation of night-to-night variability [13]. The observed decrease in SE variability suggests a more stable sleep pattern, which is particularly relevant for psychiatric outpatients given the well-established associations between sleep disturbances and psychiatric symptom burden [26,27]. These findings, however, should be interpreted within the context of a single-arm implementation.

A methodological feature of this study is the temporal distinction between questionnaire assessments and sleep diary recording windows. While questionnaires provided standardized symptom snapshots at T0 and T1, the sleep diary captured an early-treatment window (Sessions 1–2) and a late-treatment window (Sessions 4–5). Because the diary T0 period followed the initial instruction, the diary analysis reflects changes from early- to late-treatment phases rather than a pure pre-intervention baseline. Even within this conservative framing, significant reductions in snoozing frequency (p=0.03) and improvements in SE stability suggest increased consistency in sleep–wake behavior during the active intervention phase. A downward trend in the variability of post-awakening status may further reflect more stable morning functioning. The concurrent improvements in both diary-derived continuity (e.g., reduced WASO and NT) and global outcomes (CGI-S, SF-36) reinforce the feasibility of mobile-based prospective monitoring as a complementary measure in routine psychiatric care [13]. In clinical practice, such diaries can also facilitate the session-to-session adjustment of behavioral prescriptions, consistent with established CBT-I workflows [10,11].

This study has several limitations that should be acknowledged. First, the relatively small sample size (n=56) and the high clinical heterogeneity of participants, including multiple comorbid psychiatric conditions and medication regimens, may limit the generalizability of the findings. Although the transdiagnostic design is a strength, it makes it difficult to determine whether the intervention’s efficacy is consistent across specific diagnostic subgroups.

Second, the assessment of treatment effects relied primarily on subjective measures, including self-reported questionnaires and mobile sleep diaries. The absence of objective sleep measurements limits the ability to verify objective changes in sleep–wake patterns and sleep continuity (e.g., via actigraphy or polysomnography), and the self-reported nature of the diary may introduce recall bias and daytoday variability.

Third, the absence of a control condition (e.g., waitlist or active control) limits causal inference and does not rule out nonspecific effects (e.g., expectancy, regression to the mean, or repeated assessment effects).

Fourth, longitudinal follow-up data were limited by high attrition, and mobile sleep diary use declined markedly after the completion of face-to-face sessions. Follow-up completion at T2 was limited (n=20), which may introduce attrition bias. Furthermore, sleep diary data were limited to the early- and late-treatment periods and were not consistently collected during the 3-month follow-up, which may affect the interpretation of long-term behavioral changes. This suggests that digital self-monitoring may be most effective when integrated with ongoing clinical contact, and future studies should examine strategies to sustain long-term engagement with digital tools beyond the active treatment phase.

Finally, medication use and changes were not controlled or systematically recorded, which limits causal attribution of symptom changes to the intervention. Nevertheless, the consistent improvements across both retrospective and prospective measures within routine psychiatric care suggest the feasibility and potential clinical utility. Future controlled studies with standardized medication monitoring are warranted to further clarify these therapeutic effects.

The findings of the present study suggest that a structured, transdiagnostic sleep intervention was associated with significant improvements in insomnia severity and subjective sleep quality in a diagnostically heterogeneous psychiatric sample. These improvements were observed in the context of routine psychiatric care, suggesting that structured, nonpharmacological sleep care may be a feasible adjunctive approach alongside standard medical treatment. Improvements in sleep were accompanied by reductions in depressive and anxiety symptoms and better healthrelated quality of life, supporting the clinical relevance of prioritizing sleep problems within routine psychiatric care. However, subjective sleep quality remained clinically impaired on average, and some domains—most notably daytime sleepiness—did not significantly change, suggesting that residual symptoms may persist despite overall improvement. Overall, modular sleep programs incorporating core CBT-I components and disorderspecific modules may be feasible as an adjunct within comprehensive psychiatric care, although controlled studies are needed to clarify effectiveness. Future research using controlled designs, larger follow-up samples, and objective sleep measures is warranted to strengthen causal inference, evaluate durability, and clarify therapeutic mechanisms.