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Background: Increasing interest has centered on the psychotherapeutic working alliance as a means of understanding clinical change in digital mental health interventions in recent years. However, little is understood about how and to what extent a digital mental health program can have an impact on the working alliance and clinical outcomes in a blended (therapist plus digital program) cognitive behavioral therapy (bCBT) intervention for depression. Objective: This study aimed to test the difference in working alliance scores between bCBT and treatment as usual (TAU), examine the association between working alliance and depression severity scores in both arms, and test for an interaction between system usability and working alliance with regard to the association between working alliance and depression scores in bCBT at 3 -month assessments. Methods: We conducted a secondary data analysis of the E -COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment versus Treatment -as -usual) trial, which compared bCBT with TAU across 9 European countries. Data were collected in primary care and specialized services between April 2015 and December 2017. Eligible participants aged 18 years or older and diagnosed with major depressive disorder were randomized to either bCBT (n=476) or TAU (n=467). bCBT consisted of 6-20 sessions of bCBT (involving face-to-face sessions with a therapist and an internet-based program). TAU consisted of usual care for depression. The main outcomes were scores of the working alliance (Working Alliance Inventory -Short Revised-Client [WAI-SR-C]) and depressive symptoms (Patient Health Questionnaire -9 [PHQ-9]) at 3 months after randomization. Other variables included system usability scores (System Usability Scale -Client [SUS -C]) at 3 months and baseline demographic information. Data from baseline and 3 -month assessments were analyzed using linear regression models that adjusted for a set of baseline variables. Result

Early access to evidence-based help is crucial for adolescents with anxiety disorders. Internet-delivered acceptance and commitment therapy (iACT) may offer adolescents increased access to care and more flexibility in engaging with treatment when and how they prefer. Process-based therapies, such as ACT, focus on the-oretically derived and empirically tested key mechanisms in treat-ment that enable change. This study aimed to investigate the effectiveness of iACT for adolescents with anxiety disorders. The study also assessed the relationship between psychological flexi-bility and treatment outcomes and the relationship between par-ticipating adolescents and therapists perceived alliance and treatment outcomes. This was a randomized controlled trial com-paring a 10-week intervention group with a wait-list control group. The 52 participants, aged 15 to 19, were recruited from all over Sweden. The treatment was effective in increasing quality of life and psychological flexibility, with moderate between-group effect sizes based on observed values. Changes in psychological flexibility was associated with changes in anxiety symptoms. The results further showed a statistically significant between-group difference in post-treatment diagnoses. No significant time per group interaction was found for anxiety symptoms, as both groups improved. Working alliance was rated as high by both participat-ing adolescents and therapists but showed no significant relation-ship with treatment outcomes. Participants found the treatment an acceptable intervention. This study shows promising results for iACT in treating adolescents with anxiety disorders. The results suggest the model of psychological flexibility as an important process of change in treatment outcomes. Future research should validate these findings in larger samples and clinical contexts., Funding Agencies|Ramp;D Primary Health Care, Vaestra Goetaland region

Importance: Personalized treatment choices would increase the effectiveness of internet-based cognitive behavioral therapy (iCBT) for depression to the extent that patients differ in interventions that better suit them. Objective: To provide personalized estimates of short-term and long-term relative efficacy of guided and unguided iCBT for depression using patient-level information. Data Sources: We searched PubMed, Embase, PsycInfo, and Cochrane Library to identify randomized clinical trials (RCTs) published up to January 1, 2019. Study Selection: Eligible RCTs were those comparing guided or unguided iCBT against each other or against any control intervention in individuals with depression. Available individual patient data (IPD) was collected from all eligible studies. Depression symptom severity was assessed after treatment, 6 months, and 12 months after randomization. Data Extraction and Synthesis: We conducted a systematic review and IPD network meta-analysis and estimated relative treatment effect sizes across different patient characteristics through IPD network meta-regression. Main Outcomes and Measures: Patient Health Questionnaire-9 (PHQ-9) scores. Results: Of 42 eligible RCTs, 39 studies comprising 9751 participants with depression contributed IPD to the IPD network meta-analysis, of which 8107 IPD were synthesized. Overall, both guided and unguided iCBT were associated with more effectiveness as measured by PHQ-9 scores than control treatments over the short term and the long term. Guided iCBT was associated with more effectiveness than unguided iCBT (mean difference [MD] in posttreatment PHQ-9 scores, -0.8; 95% CI, -1.4 to -0.2), but we found no evidence of a difference at 6 or 12 months following randomization. Baseline depression was found to be the most important modifier of the relative association for efficacy of guided vs unguided iCBT. Differences between unguided and guided iCBT in people with baseline symptoms of subthreshold depression (P

Background Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom. Methods We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683. Findings We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD −1·83 [95% credible interval (CrI) −2·90 to −0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and au

Blended Cognitive Behaviour Therapy (bCBT) is a new form of treatment, mixing internet-based modules and face-to-face therapist sessions. How participants rate the therapeutic alliance in bCBT has not yet been thoroughly explored, and neither is it clear whether therapist- and patient-rated alliances are predictors of change in depression during treatment. Depression and alliance ratings from 73 participants in a treatment study on bCBT (part of the E-COMPARED project) were analysed using growth curve models. Alliance, as rated by both patients and therapists, was high. The therapist-rated working alliance was predictive of subsequent changes in depression scores during treatment, whereas the patient-rated alliance was not. A therapeutic alliance can be established in bCBT. The role of the therapist-rated alliance seems to be of particular importance and should be carefully considered when collecting data in future studies on bCBT., We thank the E-COMPARED consortium forfinancialsupport.

Depression är en funktionsnedsättande problematik som påverkar en stor del av den vuxna populationen varje år. Trots ett omfattande behov av hjälp så råder det brist på tillgång till effektiv behandling. Kognitiv Beteendeterapi (KBT) är en evidensbaserad metod som har stöd vid behandling av depression och förmedlad via internet skulle metoden kunna tillgängliggöras för fler. Dock är det i dagsläget oklart vilka format och vilket innehåll som kan användas när behandlingen förmedlas via internet, samt vilken betydelse den terapeutiska alliansen har för en behandling som till största del sker på distans. Syftet med denna avhandling var att undersöka effekterna för olika format av internetbehandling (epostterapi, guidad självhjälp och blended treatment) vid depression, samt alliansens roll i dessa format. Studiernas resultat visar på att epostterapi och internetförmedlade självhjälpsprogram med behandlarstöd var effektiva metoder för att behandla depression. Alliansskattningar var höga, vilket visar att en positiv terapeutisk allians kan uppnås i internetbehandling. Patientskattningar av allians kunde inte predicera utfallet i någon av behandlingarna, men behandlarskattad allians predicerade förbättring på depressionsskattningar i blended treatment. Den här avhandlingen innehåller den första randomiserade kontrollerade studien på KBT-baserad epostterapi vid depression, samt det första internet-förmedlade självhjälpsprogrammet baserat på beteendeaktivering och ACT., Depression is a debilitating disorder that affects a large part of the adult population every year. Yet there is still a lack of access to effective care for people in need. Cognitive Behaviour therapy (CBT) is an evidence-based method for treating depression that together with the increased availability of Internet services provides an opportunity to increase access to effective treatment. Internet-based interventions can be effective in the treatment of depression, but there is a lack of knowledge concerning which formats of delivery that can be used and if therapeutic alliance is of equal importance when providing treatment over the Internet. The overall aim of this thesis was to examine the effects of different treatment formats (email therapy, guided self-help, and blended treatment) in internet-based CBT for depression and to further examine the role of alliance in these treatment modalities. Findings from this thesis show that email therapy and internetbased treatment programs were effective methods for treating depression. Alliance ratings were high, showing that a positive therapeutic alliance can be achieved in internet-based treatments. Patient-rated alliance could not predict outcome in any of the different treatment formats. However, therapist-rated alliance predicted change in depression during blended treatment. This thesis includes the first randomized controlled study on CBTbased email therapy, and the first internet-based behavioral activation program with ACT-components, for adult depression.

Introduction: Blended Cognitive Behaviour Therapy (bCBT) uses a combination of face-to-face sessions and online material, such as self-help modules, and can be used in the treatment of depression. The blended format has the potential of integrating internet-based components into treatment without completely removing the traditional face-to-face setting. This way of delivery can lower the threshold when it comes to using digital components in CBT. The role of therapeutic alliance in this format is yet to be explored, specifically the role of therapist rated alliance, as most studies only include patient rated measurements of alliance. Methods: Working alliance and depression ratings from 73 participants in a study on bCBT for depression (part of the E-COMPARED project) was analysed using growth curve models. Results: High ratings for both patient-and therapist rated working alliance were found. Therapist rated alliance predicted a positive change (decreased PHQ-9 scores) in depression during treatment. Discussion: Ratings of therapeutic alliance was similar to what have been seen in face-to-face and internet-based treatment. Patient rated alliance could not predict change rate in depression, which is a common finding in ICBT-studies. However, therapist rated alliance had a significant correlation with change rate and predicted a decrease in depression during treatment. These results sheds more light on the concept and role of therapeutic alliance in internet-based and blended treatments.

Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly.

Technology-assisted psychological treatments are becoming well-known in the scientific networks throughout the world and are being implemented into routine health care in a number of countries. The interest in evaluating the potential of different devices is growing. The main objective of the current study was to evaluate the effects of guided and unguided computerand smartphone-based Acceptance and Commitment Therapy (ACT) for social anxiety disorder (SAD) and panic disorder (PD). A total of 152 participants were randomized into a guided treatment group, an unguided treatment group and a waiting list control group. Both treatment groups got access to a computer-based ACT-treatment and a smartphone application (app) with corresponding content. The eight modules treatment program covered a number of topics such as the nature of anxiety, functional analyses, acceptance, mindfulness and valued actions. The purpose of the app was to make it easier for the participants to access the key points of the program and to do homework assignments in their everyday life. Automatic messages in the app aimed to give feedback to the participants on their work as well as to prompt them to continue with the program. In addition to that, the participants in the guided group got therapist support via the app. The therapists were encouraged to work with each of their patient 15 min/week during the 10 weeks treatment period and focus on motivating, validating and correcting mistakes. On the whole group level GAD-7 was used as the primary outcome measure. LSAS and PDSS-SR were used for subgroup analyses in SAD and PD participants respectively. The measurements were collected at pre-, mid- and post-treatment and at 12-months follow-up. There were no significant differences in adherence between the treatment groups except for significantly higher rates of smartphone usage in the guided group. No significant differences in treatment outcome were found between the treatment groups with moderat

Background: Almost nothing is known about the potential negative effects of Internet-based psychological treatments for depression. This study aims at investigating deterioration and its moderators within randomized trials on Internet-based guided self-help for adult depression, using an individual patient data meta-analyses (IPDMA) approach. Method: Studies were identified through systematic searches (PubMed, PsycINFO, EMBASE, Cochrane Library). Deterioration in participants was defined as a significant symptom increase according to the reliable change index (i.e. 7.68 points in the CES-D; 7.63 points in the BDI). Two-step IPDMA procedures, with a random-effects model were used to pool data. Results: A total of 18 studies (21 comparisons, 2079 participants) contributed data to the analysis. The risk for a reliable deterioration from baseline to post-treatment was significantly lower in the intervention v. control conditions (3.36 v. 7.60; relative risk 0.47, 95% confidence interval 0.29-0.75). Education moderated effects on deterioration, with patients with low education displaying a higher risk for deterioration than patients with higher education. Deterioration rates for patients with low education did not differ statistically significantly between intervention and control groups. The benefit-risk ratio for patients with low education indicated that 9.38 patients achieve a treatment response for each patient experiencing a symptom deterioration. Conclusions: Internet-based guided self-help is associated with a mean reduced risk for a symptom deterioration compared to controls. Treatment and symptom progress of patients with low education should be closely monitored, as some patients might face an increased risk for symptom deterioration. Future studies should examine predictors of deterioration in patients with low education., Funding Agencies|European Union [EU EFRE: ZW6-67280119999, CCI 2007DE161PR001, HEALTH.2013.3.1-1, 603098]

Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly., Funding Agencies|EU FP7 project E-COMPARED [603098]

Background: It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions. Method: A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined. Results: Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94). Conclusions: Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.

Background. It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions. Method. A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined. Results. Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94). Conclusions. Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.