Investigating the efficacy of digital cognitive behavioural therapy in comparison to a sleep-monitoring application via integrated diary and actigraphy: A randomised-controlled trial.

Dissemination of digital cognitive behavioural therapy is a promising approach for treating insomnia in the broad population. Current evidence supports the effectiveness of the digital format, but clinical findings are often limited by the choice of control group and lack of in-depth therapeutic measures. This study was designed to investigate the specific effects of digital cognitive behavioural therapy in comparison to a self-monitoring application. Participants meeting criteria for insomnia were randomly allocated (1:1) to 8 weeks of digital cognitive behavioural therapy or 8 weeks of digital sleep monitoring (control application). The primary outcome, insomnia severity, was assessed at baseline, 8- and 16-weeks post-randomisation. Secondary outcomes included the assessment of sleep via application-integrated sleep diaries and actigraphy. Linear-mixed models were fitted to assess between-group differences. Fifty-six participants (48 females, mean age: M = 45.55 ± 13.70 years) were randomised to either digital cognitive behavioural therapy (n = 29) or digital sleep monitoring (n = 27). At 8- and 16-weeks post-randomisation, large treatment effects (d = 0.87-1.08) indicated robust reductions (-3.70 and -2.97, respectively; p ≤ 0.003) in insomnia severity in the digital cognitive behavioural therapy arm, relative to digital sleep monitoring. Treatment effects in favour of digital cognitive behavioural therapy were also found for self-reported and actigraphy-derived sleep continuity variables, indicating that sleep improved throughout the 8-week intervention period. Our study reinforces the role of digital cognitive behavioural therapy in achieving clinical improvements for patients with insomnia, affirming previous findings and supporting the specific effects of cognitive behavioural therapy.

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.

Insomnia is a highly prevalent mental disorder, and is often co-occurring with depression and anxiety disorders. Cognitive behavioural therapy for insomnia as treatment of choice for insomnia can also be applied digitally (digital cognitive behavioural therapy for insomnia), making it more accessible. This is a secondary data analysis of a two-armed parallel randomized-controlled trial. In the primary publication, N = 238 participants meeting criteria for the 5th edition of Diagnostic and Statistical Manual of Mental Disorders chronic insomnia disorder were randomly assigned to either 8 weeks of digital cognitive behavioural therapy for insomnia + treatment-as-usual, or waitlist + treatment-as-usual. To determine the clinical effects of digital cognitive behavioural therapy for insomnia in populations with comorbid anxiety and depression symptoms, this secondary analysis focused on two subgroups: (1) participants with high initial depressive symptoms; and (2) participants with high initial anxiety symptoms. Symptoms of insomnia, depression and anxiety as primary outcome measures were obtained at baseline, 8 weeks post-randomization and, in the intervention group only, at 6- and 12-months follow-up. At 8 weeks post-randomization, the use of digital cognitive behavioural therapy for insomnia in both subgroups was associated with large reductions in insomnia severity in comparison to control (depression subgroup: d = 2.37; anxiety subgroup: d = 2.13). Between-group treatment effects were also observed for symptoms of depression in the depression subgroup (d = 1.59), and for symptoms of anxiety in the anxiety subgroup (d = 1.28). Within-group effects were stable over time (d = 0.64-1.63). This secondary analysis shows that digital cognitive behavioural therapy for insomnia reduces insomnia and comorbid symptoms in participants with high initial symptoms of either depression or anxiety with sustained long-term effects.

[Experiences of digital health care applications (DIGA) manufacturers with the BfArM Fast-Track procedure]. / Erfahrungen von Herstellern digitaler Gesundheitsanwendungen (DiGA) mit dem Fast-Track-Verfahren des BfArM.

Since October 2020, the first digital health applications (DiGA) have been listed with the Federal Institute for Drugs and Medical Devices (BfArM) and can be prescribed by physicians and psychotherapists in the statutory health insurance system. In this article, four of the first BfArM-listed DiGA manufacturers report on their experiences with the BfArM Fast-Track procedure for DiGA, which enables listing from the time of application in just three months.Regarding the DiGA Fast-Track procedure and the cooperation with the BfArM, overall positive experiences are reported. However, applicants need to consider some points before and during the application process as well as after the positive decision. Before submitting the application, it is advisable to conduct audits to ensure that all requirements are met. BfArM consultations should be used, especially to clarify questions regarding proof of positive supply effect. Manufacturers should be prepared for numerous queries and follow-up requests from BfArM and be able to respond quickly. In the case of ambiguities, direct contact should be made with the BfArM. Manufacturers should note that sales of their product will not automatically follow a positive decision, as the medical profession still needs to be informed more fundamentally about DiGA and the path from the prescription to the activation of DiGA is currently still cumbersome. Manufacturers should also be prepared for continued interaction with BfArM after listing.Overall, manufacturers are recommended to cooperate early, proactively, openly, and closely with the BfArM because they themselves are liable for the information they provide in the application and the BfArM does not check all of it in detail.

Randomized Controlled Trial to Test the Efficacy of an Unguided Online Intervention with Automated Feedback for the Treatment of Insomnia.

BACKGROUND: Insomnia has become a major public health concern. AIMS: The study examined the efficacy of a web-based unguided self-help programme with automated feedback. The programme was based on cognitive behaviour therapy for insomnia (CBT-I). The investigation particularly focused on factors that contribute to the maintenance of insomnia and tested whether treatment effects were stable over a period of 12 months. METHOD: Fifty-six participants were randomly assigned either to web-based CBT-I or to the waiting-list control group. Included measures assessed insomnia severity, sleep-related cognitions, safety behaviours, depression, anxiety and somatization. In the intervention group, a sleep diary was used to assess sleep continuity parameters, sleep quality and daytime performance. RESULTS: Large between- and within-group effect sizes (d = 1.79, d = 1.59) for insomnia severity were found. The treatment group effect remained stable over the period of 12 months. Further, sleep-related cognitions, safety behaviours, depression and somatization significantly decreased in the treatment group compared with the control group. On all sleep diary parameters, medium to large effects were revealed within the treatment group. Anxiety did not decrease significantly from pre- to post-assessment. For all measures except somatization and anxiety significant within-group effects were found at 12-month follow-up assessment indicating long-lasting effects. CONCLUSIONS: This study adds evidence to the literature on unguided online interventions for insomnia, and indicates that online CBT-I can have substantial long-term effects on relevant sleep-related outcome parameters. Moreover, the results indicate that sleep-related cognitions and safety behaviour can be successfully altered with an unguided CBT-I intervention.

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

STUDY OBJECTIVES: Numerous studies worldwide have reported the 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. METHODS: 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 6- and 12-months. The primary outcome was self-reported insomnia severity, assessed with the Insomnia Severity Index (ISI) at 8-weeks post-randomization. A one-way ANCOVA with baseline score as a covariate was fitted to determine group differences. Secondary outcomes included measures of daytime functioning, quality of life, depression, anxiety, dreams, and nightmares. RESULTS: Of the N = 238 participants (67.6% female), age range 19-81 years, n = 118 were randomized to dCBT-I and n = 120 to the control group. At posttreatment, the use of dCBT-I was associated with a large reduction in 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. CONCLUSIONS: 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 a first-line treatment for insomnia.

Temporal Associations of Daily Changes in Sleep and Depression Core Symptoms in Patients Suffering From Major Depressive Disorder: Idiographic Time-Series Analysis.

BACKGROUND: There is a strong link between sleep and major depression; however, the causal relationship remains unclear. In particular, it is unknown whether changes in depression core symptoms precede or follow changes in sleep, and whether a longer or shorter sleep duration is related to improvements of depression core symptoms. OBJECTIVE: The aim of this study was to investigate temporal associations between sleep and depression in patients suffering from major depressive disorder using an idiographic research approach. METHODS: Time-series data of daily sleep assessments (time in bed and total sleep time) and self-rated depression core symptoms for an average of 173 days per patient were analyzed in 22 patients diagnosed with recurrent major depressive disorder using a vector autoregression model. Granger causality tests were conducted to test for possible causality. Impulse response analysis and forecast error variance decomposition were performed to quantify the temporal mutual impact of sleep and depression. RESULTS: Overall, 11 positive and 5 negative associations were identified between time in bed/total sleep time and depression core symptoms. Granger analysis showed that time in bed/total sleep time caused depression core symptoms in 9 associations, whereas this temporal order was reversed for the other 7 associations. Most of the variance (10%) concerning depression core symptoms could be explained by time in bed. Changes in sleep or depressive symptoms of 1 SD had the greatest impact on the other variable in the following 2 to 4 days. CONCLUSIONS: Longer rather than shorter bedtimes were associated with more depression core symptoms. However, the temporal orders of the associations were heterogeneous.

Utilization of Patient-Generated Data Collected Through Mobile Devices: Insights From a Survey on Attitudes Toward Mobile Self-Monitoring and Self-Management Apps for Depression.

BACKGROUND: Depression is a severe psychiatric disease with high prevalence and an elevated risk for recurrence and chronicity. A substantial proportion of individuals with a diagnosis of unipolar depressive disorder do not receive treatment as advised by national guidelines. Consequently, self-monitoring and self-management become increasingly important. New mobile technologies create unique opportunities to obtain and utilize patient-generated data. As common adherence rates to mobile technologies are scarce, a profound knowledge of user behavior and attitudes and preferences is important throughout any developmental process of mobile technologies and apps. OBJECTIVE: The aim of this survey was to provide descriptive data upon usage and anticipated usage of self-monitoring and self-management of depression and preferences of potential users in terms of documented parameters and data-sharing options. METHODS: A Web-based survey comprising 55 questions was conducted to obtain data on the usage of mobile devices, app usage, and participant's attitudes and preferences toward mobile health apps for the self-monitoring and self-management of depression. RESULTS: A total of 825 participants provided information. Moreover, two-thirds of the sample self-reported to be affected by depressive symptoms, but only 12.1% (81/668) of those affected by depression have ever used any mobile self-monitoring or self-management app. Analysis showed that people want personally relevant information and feedback but also focus on handling sensitive data. CONCLUSIONS: New mobile technologies and smartphone apps, especially in combination with mobile sensor systems, offer unique opportunities to overcome challenges in the treatment of depression by utilizing the potential of patient-generated data. Focus on patient-relevant information, security and safe handling of sensitive personal data, as well as options to share data with self-selected third parties should be considered mandatory throughout any development process.

Circadian skin temperature rhythms, circadian activity rhythms and sleep in individuals with self-reported depressive symptoms.

BACKGROUND: Disturbed circadian rhythms have been associated with depression. New body-worn devices allow the objective recording of circadian parameters such as physical activity, skin temperature and sleep. The objective of this study was to investigate whether circadian skin temperature and circadian activity rhythms are altered in depressed individuals. METHODS: Data on skin temperature, physical activity and sleep were available for 1610 subjects from a population-based cohort study. In a matching process two groups were formed for analysis: 121 participants with pronounced depression symptoms (CES-D Score > 21) and n = 121 matched non-depressed controls (CES-D Score < 15). Circadian rhythms were investigated by analyzing non-parametric rhythm indicators of 24-h skin temperature and physical activity data. Sleep timing, continuity and quantity were calculated from actigraphy. RESULTS: No differences between the groups were found when all participants were considered. After excluding antidepressant medicated participants, the depression group was found to have a lower skin temperature amplitude t(208) = 2.45, p = .015 and a less stable skin temperature rhythm t(208) = 2.40, p = .017. The amplitude predicted the group status (beta = -5.529, p = .016). No effects were found for activity or sleep. CONCLUSION: The results indicate that skin-temperature rhythms are blunted in unmedicated depressed individuals. This could be a promising non-invasive marker for further analysis.

Working alliance with an avatar: How far can we go with internet interventions?

OBJECTIVE: To examine the working alliance between users and an avatar and users' treatment expectations in an unguided Internet intervention for the treatment of insomnia. METHODS: The sample included participants from the treatment condition (N = 29) of a randomised controlled trial. The task and goal subscales of the Working Alliance Inventory Short Revised (WAI-SR) were applied in week three. Five items of the Bern Post-Session Report and one question about the extent to which users had missed a human therapist were administered after each session. Treatment expectations were measured with the Credibility Expectancy Questionnaire (CEQ), and the Insomnia Severity Index (ISI) was used as the primary outcome measure. RESULTS: The mean scores for the WAI-SR task and goal subscales were relatively high (M = 3.24, SD = 0.79; M = 3.16, SD = 0.91, respectively). The mean score of the five Bern Post-Session Report items remained stable over time, but some users increasingly indicated that they missed a real therapist over the course of the intervention, with a strong linear effect (t(87) = 3.16, p < 0.01). ISI chance score was predicted by the mean score of the Bern Post-Session Report (b = -0.3.83, t(21.80) = -2.97, p < 0.01), missing a human therapist (b = -0.0.13, t(20.47) = -2.72, p = 0.01) and the CEQ (b = 0.18, t(19.03) = -2.69, p = 0.01), but not by WAI-SR task and goal subscales. CONCLUSIONS: Results indicate that users established a working alliance with the avatar. The affective bond remained stable over time, but towards the end of the intervention some users indicated that they missed having a human therapist. Affective bond and missing a real therapist predicted symptom change.