Digital Cognitive Behavioral Therapy Research Articles

Implementing digital mental health interventions at scale: one-year evaluation of a national digital CBT service in Ireland

BackgroundIn recent years, exponential growth in digital innovations and internet access has provided opportunities to deliver health services at a much greater scale than previously possible. Evidence-based technology-enabled interventions can provide cost-effective, accessible, and resource-efficient solutions for addressing mental health issues. This study evaluated the first year of a supported digital cognitive behavioral therapy (CBT) service provided by the national health service in Ireland, which has been accessible to individuals who receive a referral from one of five referring groups: General Practitioners, Primary Care Psychology, Counselling Primary Care, Community Mental Health, and Jigsaw (a nationwide youth mental health service).MethodsA retrospective, observational study examining data from the service between April 2021 to April 2022 was conducted. Descriptive statistics on referrals, account activations, user demographics, program usage, and user satisfaction were extracted, and pre-to-post clinical outcomes for depression measured by the Patient Health Questionnaire-9 and for anxiety measured by the Generalised Anxiety Disorder-7 were analysed using linear mixed effect models.ResultsThere were 5,298 referrals and 3,236 (61%) account activations within the year. Most users were female (72.9%) and aged between 18 and 44 years (75.4%). The CBT programs were associated with significant reductions in both depression (β = 3.34, 95% CI [3.03, 3.65], p < 0.001) and anxiety (β = 3.64, 95% CI [3.36, 3.93], p < 0.001), with large effect sizes (Cohen’s d > 0.8). Time spent using the programs was also found to be a predictor of the variability in these clinical outcomes (p < 0.001), and accounting for this resulted in significantly better model fits (p < 0.001). User satisfaction ratings were also very high, exceeding 94%.ConclusionsEfforts to improve the representation of male and older adult users are warranted. However, overall, the results demonstrate how digital CBT can be provided at scale and lead to symptom reductions with large effect sizes for patients seeking help for depression and anxiety. The findings substantiate the continued use and expansion of this service in Ireland and the more widespread implementation of similar services in other international public healthcare settings.

The Sleepio After Cancer (SAC) study. Digital cognitive behavioural therapy for insomnia (dCBT-I) in women cancer patients – Trial protocol of a randomised controlled trial

AimsThis study will assess the efficacy of digital CBT for insomnia (dCBT-I) compared to sleep hygiene education (SHE) for the management of insomnia in women with cancer. Background30% of patients with cancer meet insomnia diagnostic criteria and this can be detrimental to health outcomes. Insomnia disorder comprises a dissatisfaction with sleep quantity or quality characterized by difficulty initiating sleep, frequent awakenings, or early morning wakening without the ability to return to sleep, at least 3 nights per week, for at least 3 months, causing significant impairment or distress in areas of functioning. MethodsWe will recruit 308 women with a current or prior cancer diagnosis who are currently experiencing insomnia; defined as a score of 16 or less on the Sleep Condition Indicator (SCI). Participants will be randomised to dCBT-I or SHE. dCBT-I will be delivered online via 6 sessions. SHE will be provided in an online format. Assessments of sleep and other related parameters, through validated questionnaires, will be taken at 12 and 24 weeks following intervention. Once 24 week assessments are completed, participants will crossover to the alternate arm (either SHE or dCBT-I) and undergo a final assessment at week 36. OutcomesThe primary outcome will be the mean continuous change in SCI score in the intervention arm compared to the control arm at 24 weeks. Additionally, the proportion of women with an SCI > 16 at 24 weeks will be assessed. Secondary outcomes include fatigue, sleep related quality of life, depression, anxiety, and hot flush interference. RegistrationThis study is registered on ClinicalTrials.gov with number NCT05816460

Digital cognitive behavioral therapy (D.CBT) for pregnancy-related depression and sleep disturbances: a systematic review and meta-analysis

Digital cognitive behavioral therapy (D.CBT) for pregnancy-related depression and sleep disturbances: a systematic review and meta-analysis

Disseminating cognitive behavioural therapy (CBT) for insomnia at scale: capitalising on the potential of digital CBT to deliver clinical guideline care.

Cognitive behavioural therapy (CBT) is the recommended first-line treatment for insomnia. However, guideline care is very seldom available and most patients receive no treatment, or less effective second-line pharmacotherapy or sleep hygiene, neither of which are evidence-based for chronic insomnia. The primary challenge for CBT has been supply. There are not enough therapists to meet the enormous demand. We must accelerate clinician training, but this approach can never be sufficient, even with abbreviated, efficient therapies. Fortunately, however, the treatment landscape has also changed dramatically. Fully-automated digital CBT (dCBT) has emerged as a safe, effective, and scalable treatment delivery format. dCBT is software only, so it can be disseminated as readily and widely as sleep medication. Moreover, dCBT can be integrated into services. Just as medications can be delivered through health professionals and health systems, approved dCBT programmes can be the same. However, an ecosystem of psychologically-based care should not necessitate a medical prescription model. Our proposed stepped care framework, comprises both population health and clinical health service initiatives, enabling universal access to guideline care for insomnia. The diverse ways in which CBT may be delivered (in-person, face-to-face, using telehealth, group therapy, digitally) can operate congruently and efficiently to optimise treatment for people at all levels of complexity and need. With safe and clinically effective dCBT products now set to become established as treatments, clearly differentiated from wellness apps, there is potential to rapidly transform insomnia services and, for the first time, to deliver clinical guideline care at international scale.

Patient perspectives on facilitators and barriers to equitable engagement with digital CBT-I

Study objectivesDigital cognitive behavioral therapy for insomnia has significant advantages for dissemination and scalability vs. in-person cognitive behavioral therapy for insomnia and is, therefore, well-positioned to be the first-line intervention for insomnia. However, only about half of patients remit following digital cognitive behavioral therapy for insomnia. Evidence suggests that treatment engagement is a critical driver of digital cognitive behavioral therapy for insomnia effectiveness, and barriers to engagement disproportionately impact people from under-resourced communities. For digital cognitive behavioral therapy for insomnia to be effective and scalable, we need to identify facilitators and barriers to digital cognitive behavioral therapy for insomnia engagement. MethodsResponses from an exit survey about participant experiences with digital cognitive behavioral therapy for insomnia were analyzed using mixed methods. The survey included quantitative measures of treatment engagement and a free-response item, which was coded and analyzed for themes using both inductive and deductive approaches. ResultsAnalyses revealed five themes that were relevant for engagement: (1) digital person-to-person components, (2) type and extent of information, (3) user’s sense of autonomy, (4) app functionality, and (5) importance of tailored content. Facilitators included enjoyment of digital cognitive behavioral therapy for insomnia elements, particularly those that enhanced a sense of connection (eg, a digital therapist avatar); content presented clearly and at an appropriate pace; and smooth app functionality. Barriers included desire for additional human support, perception that digital cognitive behavioral therapy for insomnia did not account for clinical complexities, and factors that interfered with implementation of key treatment recommendations. ConclusionMany barriers and facilitators are influenced by health literacy and technological literacy. Those with access to health and technological literacy are better equipped to engage with digital cognitive behavioral therapy for insomnia. Recommendations for adaptations and enhancements are discussed.

Effectiveness of digital cognitive-behavioral therapy for insomnia alone or combined with sleep medication: A real-world evidence study

Insomnia is a highly prevalent sleep disorder, but its first line of treatment (Cognitive Behavioral Therapy for Insomnia - CBTi) is not widely available and affordable. Digital therapy has been an alternative to these limitations and its combination with pharmacological treatment may improve patients' clinical outcomes and therapy adherence. This was a real-world evidence study aimed to evaluate the efficacy of a CBTi smartphone app combined or not with sleep medication, based on a sample of users of a CBTi smartphone app (SleepUp®) registered between June 2020 and June 2022. Individuals were categorized into two groups (“dCBTi alone” and “dCBTi combined”) according to their self-reported use of sleep medication on the first day of treatment. Assessments were made based on the self-report of sleep logs (sleep quality and efficiency) and on insomnia severity (Insomnia Severity Index). Comparisons were made considering the first entry (baseline) and the mean of the last three days in which sleep logs were filled out (final). The final samples comprised 143 individuals: 73 (51%) reported using sleep medication at baseline, while 70 (49%) did not. In both groups, there was an improvement in sleep quality, (p<0.001), sleep efficiency (p<0.001), and insomnia severity (p<0.001). The magnitude of effects on sleep efficiency and insomnia severity was greater in the dCTBi combined group. This result indicates that dCBTi can be useful as an adjuvant treatment for insomnia.

The clinical effects of digital cognitive behavioral therapy for insomnia in a heterogenous study sample: Results from a randomized controlled trial.

Numerous studies worldwide have reported beneficial effects of digital cognitive behavioral therapy for insomnia (dCBT-I). However, few focus on real-world study samples that reflect people in regular care. To test whether dCBT-I is suitable within German regular care, we designed a randomized controlled trial recruiting a heterogenous insomnia population. Participants aged 18+ who met the criteria for insomnia disorder were randomized to 8-weeks dCBT-I + care-as-usual (CAU) or they were set on a waitlist + CAU. The intervention group was followed up at six and 12-months. Primary outcome was self-reported insomnia severity, assessed with the Insomnia Severity Index (ISI) at eight-weeks post-randomization. A one-way ANCOVA with baseline score as covariate was fitted to determine group-differences. Secondary outcomes included measures on daytime functioning, quality of life, depression, anxiety, dreams, and nightmares. Of the N = 238 participants (67.6% female), age range 19 to 81 years, n = 118 were randomized to dCBT-I and n = 120 to the control group. At post-treatment, the use of dCBT-I was associated with a large reduction on the ISI (Diffadj = -7.60) in comparison to WLC (d = - 2.08). This clinical improvement was also reflected in responder and remission rates. Treatment effects were also observed for daytime functioning, quality of life, symptoms of depression and anxiety (ds = 0.26 - 1.02) and at long-term follow-up (intervention group only; ds = 0.18 - 1.65). No effects were found for dream and nightmare frequency. This study showed that dCBT-I reduces insomnia symptoms and improves daytime functioning in a heterogenous insomnia population in Germany with sustained long-term treatment effects in the intervention group. Our results underscore the potential of digital health applications, their suitability within regular care, and their role in facilitating widespread implementation of CBT-I as first-line treatment for insomnia.

Mechanisms of change in digital cognitive behavioral therapy for depression in patients with chronic back pain: A mediation analysis of a multicenter randomized clinical trial

BackgroundWhile there is evolving knowledge on change processes of digital cognitive behavioral therapy (CBT) in the treatment of depression, little is known about how these interventions produce therapeutic change in the comorbid constellation of chronic back pain (CBP). Here, we examined whether the effects of a digital intervention to treat depression in patients with CBP are mediated by three pain-related variables (i.e., pain self-efficacy, pain-related disability, pain intensity). MethodsThis study is a secondary analysis of a randomized clinical trial conducted in routine care at 82 orthopedic clinics across Germany. In total, 209 adults with CBP and diagnosed depression (SCID interview) were randomly assigned to the intervention (n = 104) or treatment-as-usual (n = 105). Cross-lagged mediation models were estimated to investigate longitudinal mediation effects of putative mediators with depression symptom severity (PHQ-9) as primary outcome at post-treatment. ResultsLongitudinal mediation effects were observed for pain self-efficacy (ß = −0.094, 95%-CI [−0.174, −0.014], p = 0.021) and pain-related disability (ß = −0.068, 95%-CI [−0.130, −0.001], p = 0.047). Furthermore, the hypothesized direction of the mediation effects was supported, reversed causation did not occur. Pain intensity did not reveal a mediation effect. ConclusionsThe results suggest a relevant role of pain self-efficacy and pain-related disability as change processes in the treatment of depression for patients with CBP in routine care. However, further research is needed to disclose potential reciprocal relationships of mediators, and to extend and specify our knowledge of the mechanisms of change in digital CBT for depression.

Identifying factors associated with user retention and outcomes of a digital intervention for substance use disorder: a retrospective analysis of real-world data.

Successful delivery of digital health interventions is affected by multiple real-world factors. These factors may be identified in routinely collected, ecologically valid data from these interventions. We propose ideas for exploring these data, focusing on interventions targeting complex, comorbid conditions. This study retrospectively explores pre-post data collected between 2016 and 2019 from users of digital cognitive behavioral therapy (CBT)-containing psychoeducation and practical exercises-for substance use disorder (SUD) at UK addiction services. To identify factors associated with heterogenous user responses to the technology, we employed multivariable and multivariate regressions and random forest models of user-reported questionnaire data. The dataset contained information from 14 078 individuals of which 12 529 reported complete data at baseline and 2925 did so again after engagement with the CBT. Ninety-three percent screened positive for dependence on 1 of 43 substances at baseline, and 73% screened positive for anxiety or depression. Despite pre-post improvements independent of user sociodemographics, women reported more frequent and persistent symptoms of SUD, anxiety, and depression. Retention-minimum 2 use events recorded-was associated more with deployment environment than user characteristics. Prediction accuracy of post-engagement outcomes was acceptable (Area Under Curve [AUC]: 0.74-0.79), depending non-trivially on user characteristics. Traditionally, performance of digital health interventions is determined in controlled trials. Our analysis showcases multivariate models with which real-world data from these interventions can be explored and sources of user heterogeneity in retention and symptom reduction uncovered. Real-world data from digital health interventions contain information on natural user-technology interactions which could enrich results from controlled trials.

The role of brain white matter in depression resilience and response to sleep interventions.

Insomnia poses a high risk for depression. Brain mechanisms of sleep and mood improvement following cognitive behavioural therapy for insomnia remain elusive. This longitudinal study evaluated whether (i) individual differences in baseline brain white matter microstructure predict improvements and (ii) intervention affects brain white matter microstructure. People meeting the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for Insomnia Disorder (n = 117) participated in a randomized controlled trial comparing 6 weeks of no treatment with therapist-guided digital cognitive behavioural therapy for insomnia, circadian rhythm support or their combination (cognitive behavioural therapy for insomnia + circadian rhythm support). Insomnia Severity Index and Inventory of Depressive Symptomatology-Self Report were assessed at baseline and followed up at Weeks 7, 26, 39 and 52. Diffusion-weighted magnetic resonance images were acquired at baseline and Week 7. Skeletonized white matter tracts, fractional anisotropy and mean diffusivity were quantified both tract-wise and voxel-wise using tract-based spatial statistics. Analyses used linear and mixed effect models while correcting for multiple testing using false discovery rate and Bonferroni for correlated endpoint measures. Our results show the following: (i) tract-wise lower fractional anisotropy in the left retrolenticular part of the internal capsule at baseline predicted both worse progression of depressive symptoms in untreated participants and more improvement in treated participants (fractional anisotropy × any intervention, PFDR = 0.053, Pcorr = 0.045). (ii) Only the cognitive behavioural therapy for insomnia + circadian rhythm support intervention induced a trend-level mean diffusivity decrease in the right superior corona radiata (PFDR = 0.128, Pcorr = 0.108), and individuals with a stronger mean diffusivity decrease showed a stronger alleviation of insomnia (R = 0.20, P = 0.035). In summary, individual differences in risk and treatment-supported resilience of depression involve white matter microstructure. Future studies could target the role of the left retrolenticular part of the internal capsule and right superior corona radiata and the brain areas they connect.

Does treating insomnia with digital cognitive behavioural therapy (Sleepio) mediate improvements in anxiety for those with insomnia and comorbid anxiety? An analysis using individual participant data from two large randomised controlled trials

BackgroundConsiderable comorbidity exists between insomnia and anxiety, and evidence shows that the benefits of CBT for insomnia extend to anxiety. Using data from two large trials of digital CBT (dCBT) for insomnia, we evaluated whether improving sleep is an effective treatment target to reduce both insomnia and anxiety symptoms in individuals with insomnia and clinically significant anxiety. MethodsThis was a controlled sub-analysis combining individual participant data from two previous randomised controlled trials of dCBT for insomnia (Sleepio). Participants (N = 2172) with insomnia disorder and clinically significant anxiety symptoms were included in this sub-analysis and received either dCBT or control (usual care or sleep hygiene education). Assessments were evaluated at baseline, post-intervention (week 8 or 10), and follow-up (week 22 or 24). Mediation was evaluated using structural equation models. ResultsdCBT for insomnia was superior to control at reducing both insomnia (Hedges' g range = 0.77–0.81; both p < 0.001) and anxiety symptoms (Hedges' g range = 0.39–0.44; both p < 0.001) at all time points. Baseline insomnia symptoms moderated the effects of dCBT on insomnia, however no variables moderated treatment effects on anxiety. Reductions in anxiety symptoms at follow-up were mediated by improvements in sleep at post-intervention (% mediated = 84 %), suggesting a causal pathway. LimitationsParticipants did not have a formal anxiety disorder diagnosis and so the effects of dCBT for insomnia on anxiety may differ by anxiety disorder. ConclusionsAddressing sleep using dCBT for insomnia may serve as a treatment target from which to improve anxiety in individuals with insomnia and clinically significant comorbid anxiety. Clinical trial registrationsDigital Insomnia therapy to Assist your Life as well as your Sleep (DIALS) - ISRCTN60530898 http://www.isrctn.com/ISRCTN60530898.Oxford Access for Students Improving Sleep (OASIS) - ISRCTN61272251 http://www.isrctn.com/ISRCTN61272251.

Layperson-Supported Internet-Delivered Cognitive Behavioral Therapy for Depression Among Older Adults

Purpose This study explores the feasibility, acceptability, and treatment outcomes of Empower@Home, a digital cognitive behavioral therapy intervention for geriatric depression. Method Participants with depressive symptoms (PHQ-9 ≥ 5) underwent a nine-session remote intervention supported by a lay coach ( N = 103). Results Most participants (91%) completed all nine sessions (mean = 8.5). According to the Treatment Evaluation Inventory, participant attitudes toward the program were largely positive. A medium effect in depression reduction was observed following the intervention (Cohen's d = 0.75) and at a 10-week follow-up (Cohen's d = 0.60). This reduction was large (Cohen's d = 1.31 at posttest and Cohen's d = 1.18 at 10-week follow-up) among those who presented with moderate depression at baseline (PHQ-9 ≥ 10). Significant improvements were also reported in anxiety, social isolation, loneliness, and behavioral activation. Discussion Empower@Home is a promising, acceptable digital mental health intervention for treating depression in older adults.

Effects of digital cognitive behavioral therapy for depression on suicidal thoughts and behavior: Protocol for a systematic review and meta-analysis of individual participant data.

Digital cognitive behavioral therapy (i-CBT) interventions for the treatment of depression have been extensively studied and shown to be effective in the reduction of depressive symptoms. However, little is known about their effects on suicidal thoughts and behaviors (STB). Information on the impact of digital interventions on STB are essential for patients' safety because most digital interventions are self-help interventions without direct support options in case of a suicidal crisis. Therefore, we aim to conduct a meta-analysis of individual participant data (IPDMA) to investigate the effects of i-CBT interventions for depression on STB and to explore potential effect moderators. Data will be retrieved from an established and annually updated IPD database of randomized controlled trials investigating the effectiveness of i-CBT interventions for depression in adults and adolescents. We will conduct a one-stage and a two-stage IPDMA on the effects of these interventions on STB. All types of control conditions are eligible. STB can be measured using specific scales (e.g., Beck scale suicide, BSS) or single items from depression scales (e.g., item 9 of the PHQ-9) or standardized clinical interviews. Multilevel linear regression will be used for specific scales, and multilevel logistic regression will be used for treatment response or deterioration, operationalized as a change in score by at least one quartile from baseline. Exploratory moderator analyses will be conducted at participant, study, and intervention level. Two independent reviewers will assess the risk of bias using the Cochrane Risk of Bias Tool 2. This IPDMA will harness the available data to assess the effects (response and deterioration) of i-CBT interventions for depression interventions on STB. Information about changes in STB is essential to estimate patients' safety when engaging in digital treatment formats. We will pre-register this study with the open science framework after article acceptance to ensure consistency between online registration and the published trial protocol.

The menopause after cancer study: A multimodal technology assisted intervention for the management of menopausal symptoms after cancer.

12024 Background: Vasomotor symptoms (VMS) of menopause represent a significant challenge for many patients after cancer treatment, particularly if menopausal hormone therapy (MHT) is contraindicated. Methods: The Menopause after Cancer (MAC) Study ( NCT04766229) was a single arm phase II trial examining the impact of a composite intervention on quality of life (QoL) in women with moderate to severe VMS after cancer. The intervention consisted of: (1) use of non-hormonal pharmacotherapy to manage VMS (citalopram for predominantly daytime VMS and/or gabapentin for predominantly nocturnal VMS) (2) digital cognitive behavioural therapy for insomnia using Sleepio (BigHealth), (3) self-management strategies for VMS delivered via a mobile application called myPatientSpace and (4) nomination of an additional support person/partner. The primary outcome was cancer specific QoL assessed by the EORTC QLQ C30 version 3 at 6 months. Secondary outcomes included frequency of VMS, bother/interference of VMS (hot flush rating scale [HFRS]) and prevalence of insomnia (sleep condition indicator [SCI] ≥16) at 6 months. Results: 205 women (82% history of breast cancer) with a median age of 49 years (range 28-66) were recruited. Date of diagnosis ranged from January 2004 until January 2022, with 65% of participants diagnosed from 2019 onwards. Participants had a median of 10 hot flashes in a day (SD 9.2) and 4 night sweats each night (SD 4.9). 17.1% were prescribed citalopram, 62.8% gabapentin alone and 20.1% were prescribed both medications. 120 women completed the protocol. In the intention to treat (ITT) cohort (n = 205) mean QoL scores increased from 62.2 (SEM 1.4) at baseline to 70.4 (SEM 1.7) at 6 months (p &lt; 0.001). In the per protocol (PP) cohort, (n = 120) mean QoL scores increased from 62 (SEM 1.7) to 70.4 (SEM 1.7) at 6 months (p &lt; 0.001). Clinically meaningful improvements were seen across multiple domains of the EORTC QLQ C30 including cognitive function, emotional function and social functioning. Frequency of VMS decreased by 50% at 6 months in the ITT and PP cohorts. Mean HFRS scores decreased from 7.6 (SEM 0.1) to 3.4 (SEM 0.2) at 6 months (p &lt; 0.0005) in the ITT cohort (7.5 (SEM 0.1) to 3.4 (SEM 0.2) (p &lt; 0.001) in the PP cohort) demonstrating reduction in bother/interference of VMS. The prevalence of insomnia reduced from 92% at baseline to 38.7% at 6 months (p = 0.006) in the ITT cohort (95.2% to 38.7% in the PP cohort (p = 0.006)). Mean SCI scores increased from 8.5 (SEM 0.4) at baseline to 17.3 (SEM 0.5) at 6 months (p &lt; 0.0005) in the ITT cohort (7.9 (SEM 0.4) to 17.3 (SEM 0.5) (p &lt; 0.001) in the PP cohort). Significant reductions in wakefulness after sleep onset and sleep onset latency were major contributors to this improvement in insomnia symptoms. Conclusions: The MAC study demonstrates that improvements in QoL can be achieved using this multimodal intervention to target VMS and insomnia after cancer. Clinical trial information: NCT04766229 .

Skill Enactment and Knowledge Acquisition in Digital Cognitive Behavioral Therapy for Depression and Anxiety: Systematic Review of Randomized Controlled Trials.

Digital cognitive behavioral therapy (CBT) interventions can effectively prevent and treat depression and anxiety, but engagement with these programs is often low. Although extensive research has evaluated program use as a proxy for engagement, the extent to which users acquire knowledge and enact skills from these programs has been largely overlooked. This study aimed to investigate how skill enactment and knowledge acquisition have been measured, evaluate postintervention changes in skill enactment and knowledge acquisition, examine whether mental health outcomes are associated with skill enactment or knowledge acquisition, and evaluate predictors of skill enactment and knowledge acquisition. PubMed, PsycINFO, and Cochrane CENTRAL were searched for randomized controlled trials (RCTs) published between January 2000 and July 2022. We included RCTs comparing digital CBT with any comparison group in adolescents or adults (aged ≥12 years) for anxiety or depression. Eligible studies reported quantitative measures of skill enactment or knowledge acquisition. The methodological quality of the studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for RCTs. Narrative synthesis was used to address the review questions. In total, 43 papers were included, of which 29 (67%) reported a skill enactment measure and 15 (35%) reported a knowledge acquisition measure. Skill enactment was typically operationalized as the frequency of enacting skills using the completion of in-program activities (ie, formal skill enactment; 13/29, 45%) and intervention-specific (9/29, 31%) or standardized (8/29, 28%) questionnaires. Knowledge measures included tests of CBT knowledge (6/15, 40%) or mental health literacy (5/15, 33%) and self-report questionnaires (6/15, 40%). In total, 17 studies evaluated postintervention changes in skill enactment or knowledge acquisition, and findings were mostly significant for skill enactment (6/8, 75% of the studies), CBT knowledge (6/6, 100%), and mental health literacy (4/5, 80%). Of the 12 studies that evaluated the association between skill enactment and postintervention mental health outcomes, most reported ≥1 significant positive finding on standardized questionnaires (4/4, 100%), formal skill enactment indicators (5/7, 71%), or intervention-specific questionnaires (1/1, 100%). None of the 4 studies that evaluated the association between knowledge acquisition and primary mental health outcomes reported significant results. A total of 13 studies investigated predictors of skill enactment; only type of guidance and improvements in psychological variables were associated with increased skill enactment in ≥2 analyses. Predictors of knowledge acquisition were evaluated in 2 studies. Digital CBT for depression and anxiety can improve skill enactment and knowledge acquisition. However, only skill enactment appears to be associated with mental health outcomes, which may depend on the type of measure examined. Additional research is needed to understand what types and levels of skill enactment and knowledge acquisition are most relevant for outcomes and identify predictors of these constructs. PROSPERO CRD42021275270; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=275270.

0352 Digital cognitive behavioral therapy in treating insomnia in older adulthood: A sub-analysis using individual participant data

Abstract Introduction Insomnia prevalence increases across the lifespan with rates reported as high as 40% in the over 65 yo population. Although cognitive behavioral therapy (CBT) for insomnia is the first-line recommended treatment, low accessibility leaves many older adults with less effective treatment options including medication and sleep hygiene. Medication is not recommended due to side effects, which may be more probable in older individuals. Digital CBT (dCBT) for insomnia permits access to first-line treatment; however, little is known about its effectiveness in older adults. We evaluated the effects of fully-automated dCBT on symptoms of insomnia, anxiety, and depression in adults aged 65 and older. Methods Individual participant data from prior randomized controlled trials of dCBT for insomnia (Sleepio) were combined for those aged 65+ (range=65-92yo) with insomnia disorder (N=486). Participants received access to dCBT or a control. As the insomnia, anxiety, and depression outcomes were assessed differently across studies, scores were standardized into z-scores. Mixed-effects models estimated the effect of treatment on the combined sleep, depression and anxiety outcomes at post-treatment and follow-up. For studies using the SCI-8, chi-squared tests evaluated insomnia remission rates between groups at post-treatment. Results Digital CBT led to significantly greater improvements at post-treatment and follow-up relative to control for the combined sleep (ds≤-1.34, ps&amp;lt;.001) and depression (ds≤-0.35, ps≤.001) outcomes. For the combined anxiety outcome, dCBT led to significantly greater improvements at post-treatment (d=-0.30, p=.004). Those randomized to dCBT were more likely to achieve remission of insomnia (60% vs. 16%, p&amp;lt;.001) at post-treatment. Conclusion Fully-automated dCBT for insomnia is effective at improving symptoms of insomnia, anxiety, and depression in adults aged 65+. Digital CBT may therefore serve as a first-line treatment option for older adults with insomnia to improve sleep and broader mental health. Importantly, dCBT may serve as an accessible means for older adults to receive guideline concordant treatment at scale. Support (if any) This work was supported in part by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR, or Department of Health and Social Care.

0365 The Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia, Fatigue, and Quality of Life in Cancer Patients

Abstract Introduction The emotional, psychological and financial challenges of a cancer diagnosis profoundly affect patients and contribute to sleep difficulties. Insomnia affects 31%-54% of those newly diagnosed or undergoing treatment, and persists up to 5 years after treatment for cancer. Cognitive behavioral therapy (CBT) for insomnia is effective for sleep and insomnia outcomes, with no difference in outcomes for in-person or self-help CBT. Trials of digital CBT (dCBT) have recruited breast cancer survivors from Denmark and past pilot work did not identify statistically significant effects for fatigue and quality of life in US cancer survivors with insomnia. This study examined the effectiveness of a fully automated digital therapeutic (Sleepio) for improving insomnia, fatigue, and quality of life in patients with cancer from the US, UK, and Australia. Methods This is a secondary analysis of participants (n=70) with insomnia who self-reported a cancer diagnosis before randomization in a large effectiveness trial of dCBT for insomnia. Participants were randomized to receive either dCBT or a sleep hygiene education control. Insomnia (SCI-8), fatigue (FFS), and global health-related quality of life (PROMIS-10) were evaluated at baseline, 8-weeks post treatment and 24-weeks follow-up. Effect estimates are mean group differences, adjusted for baseline scores estimated using regression models. Results Digital CBT was superior to control for reducing symptoms of insomnia and fatigue at both post-treatment and follow-up (all p&amp;lt; 0.05). For the dCBT group, global PROMIS-10 scores were improved, although not significantly, at post-treatment (p=0.076), but were significantly higher at follow-up (p&amp;lt; 0.05). Conclusion Digital CBT was effective for reducing symptoms of insomnia and fatigue in patients with cancer and results were maintained after five and a half months. Significant improvements in global health-related quality of life scores at follow-up suggest effects extend beyond improvements to sleep. Findings may benefit patients with cancer as dCBT may provide scalable access to an effective treatment without contraindications. Further research is needed to understand the mechanisms and value of improving sleep in those with cancer and sleep difficulty. Support (if any) Big Health Inc.

0418 Racial disparities in patient engagement and treatment outcomes in digital cognitive behavioral therapy for insomnia

Abstract Introduction Cognitive behavioral therapy for insomnia (CBTI) is gold standard for insomnia care. However, recent data suggest Black CBTI patients may complete fewer sessions and experience poorer outcomes relative to white CBTI patients. The present study examined whether racial disparities in CBTI engagement mediated group differences in treatment outcomes on insomnia symptoms. Methods We conducted a secondary analysis of pretreatment and posttreatment data from 569 Black and white patients with clinical insomnia symptoms (Insomnia Severity Index [ISI] ≥ 15) who were randomized to digital CBTI in a large-scale RCT. Study outcomes included number of sessions completed (≥4 sessions considered an adequate dose) and insomnia symptoms (ISI). Results Black CBTI patients completed fewer CBTI session than white patients (M±SD: 2.81±2.36 vs 3.79±2.45; t(561)=4.06, p&amp;lt;.001). Consequently, Black patients were 1.53 times more likely than white patients to prematurely discontinue CBTI before receiving an adequate dose (premature discontinuation rates: 62.5% vs 40.7%, Χ2=19.28, p&amp;lt;.001). A multivariate linear regression showed that Black patients reported higher ISI scores than white patients after CBTI (b=1.28, p=.009) while controlling for pretreatment ISI (p=&amp;lt;.001). However, race became non-significant in the multivariate model (b=.55, p=.248) when sessions completed was entered as a predictor (b=-.60, p&amp;lt;.001). The PRODLIN method estimated the indirect to be αβ=2.27 with a 95% Confident Interval of 1.59 to 2.96, thereby supporting a significant mediation. We estimated that 55.5% of racial disparities in posttreatment ISI outcomes were mediated through group differences in number of sessions completed. Notably, posttreatment insomnia remission (ISI≤9) rates did not differ between Black and white patients (36.9% vs 39.6% remission, p=.613), whereas insomnia remission rates were nearly twice as high among CBTI patients who received an adequate dose relative to those who discontinued prematurely (46.7% vs 24.9% remission, Χ2=23.46, p&amp;lt;.001). Conclusion Black patients reported greater insomnia symptoms after CBTI relative to white patients, but these racial disparities in insomnia outcomes were largely attributable to lower rates of CBTI engagement among them. No racial disparities in CBTI outcomes were observed among those who completed ≥4 sessions. Enhancing treatment engagement among Black patients may improve dCBTI efficacy in this group. Support (if any) This RCT (NCT03322774) was supported by NIMH R01-MH122636.

0393 Durability of treatment effects for a stepped-care model integrating digital therapy for insomnia

Abstract Introduction A stepped-care model going from digital cognitive behavioral therapy for insomnia (dCBT-I) to clinician-led CBT-I (fCBT-I) has been shown to be acutely effective. However, the sustainability of the effects over longer periods of time has not yet been examined. This study tested the durability of treatment effects of this stepped-care approach two years following the end of treatment. Methods 1018 individuals with insomnia (DSM-5 diagnostic criteria) were randomized into two conditions at step 1: dCBT-I (N=613), or an online sleep education control (N=624). Participants in the dCBT-I condition who did not remit (ISI&amp;gt;9) were further randomized to either face-to-face CBT-I (N=103) or sleep education (N=104). Insomnia remission and treatment gains at 2-year follow-up were compared across treatment conditions. Results Among step-1 non-remitters, odds of insomnia remission for those who stepped-up to fCBT-I was almost three times higher (60% in remission) than those who received the step-2 control (33% in remission), OR = 2.95, 95% CI [1.3, 6.7]. Odds of remission was not significantly different between those who remitted at step-1 (64% in remission at year 2) and those who received fCBT-I, OR = 3.53, 95% CI [1.7, 7.5]. The treatment gains were higher in the fCBT-I group (9.6 point improvement from baseline) compared to those who remitted at step-1 (7.7 point improvement from baseline) (Cohen’s d = 0.44). Conclusion Preliminary evidence indicate that the treatment effects of this stepped-care model are durable two years after treatment. Support (if any) Support for this study was provided from the National Institute of Mental Health R56MH115150 and R01MH122636 awarded to Dr. Christopher Drake.

0325 Reducing nocturnal cognitive arousal and rumination mediate treatment effects of digital cognitive behavioral therapy for insomnia

Abstract Introduction High cognitive arousal—i.e., heightened cognitive activity often in the form of perseverative thinking—is a central feature insomnia. Emerging evidence from randomized controlled trials (RCTs) support alleviating cognitive arousal as a mechanism by which cognitive behavioral therapy for insomnia (CBTI) reduces symptoms of insomnia and depression. However, few RCTs have published CBTI data on cognitive arousal outcomes. The present study tested whether CBTI alleviates cognitive arousal symptoms, and whether reducing cognitive arousal symptoms mediate CBTI effects on insomnia and depression. Methods 434 patients with clinical insomnia symptoms (Insomnia Severity Index [ISI] ≥ 15) were randomized to digital CBTI or digital sleep education control. Study outcomes included insomnia (ISI), depression (quick inventory of depressive symptomatology self-report 16-item survey, QIDS-SR16), and two indices of cognitive arousal (pre-sleep arousal scale’s cognitive factor [PSASC] and daytime insomnia symptoms response scale [DISRS]). Treatment effects and mediation analyses were conducted using multivariate linear regression. Results While controlling for baseline symptoms, CBTI patients, relative to controls, reported lower levels of insomnia (b=-2.45, p&amp;lt;.001), depression (b=-1.13, p&amp;lt;.001), nocturnal cognitive arousal (b=-2.37, p&amp;lt;.001) and insomnia-focused rumination (b=-3.43, p&amp;lt;.001) after treatment. Posthoc independent samples t-tests showed that CBTI produced small-medium effects on nocturnal cognitive arousal (Cohen’s d=.40) and insomnia-focused rumination (Cohen’s d=.31). Mediation analyses showed that reducing nocturnal cognitive arousal (PSASCT2-T1) mediated 29.0% of CBTI effects on insomnia (αβ=-.709, 95%CI=-1.09, -0.38), and 35.1% of CBTI effects on depression (αβ=-.397, 95%CI=-0.65, -0.19). Mediation analyses showed that reducing insomnia-focused rumination (DISRST2-T1) mediated 10.6% of CBTI on insomnia (αβ=-.261, 95%CI=-0.50, -0.08), but did not mediate antidepressant effects (αβ=-.300, 95%CI=-1.46, 0.72). Conclusion Digital CBTI effectively alleviated nocturnal cognitive arousal and insomnia-focused rumination. Moreover, alleviating nocturnal cognitive arousal was a mechanism by which CBTI reduced symptoms of insomnia and depression. By comparison, reducing insomnia-focused rumination mediated a small proportion of CBTI effects on insomnia and was uninvolved in depression effects. Although reducing cognitive arousal facilitated insomnia and depression outcomes, CBTI is likely limited by its modest effects on cognitive arousal symptoms. Improving insomnia therapy benefits for cognitive arousal may enhance patient outcomes. Support (if any) This RCT (NCT03322774) was supported by NIMH R01-MH122636.