A risk algorithm that predicts alcohol use disorders among college students.

The first year of college may carry especially high risk for onset of alcohol use disorders. We assessed the one-year incidence of alcohol use disorders (AUD) among incoming first-year students, predictors of AUD-incidence, prediction accuracy and population impact. A prospective cohort study of first-year college students (baseline: N = 5843; response rate = 51.8%; 1-year follow-up: n = 1959; conditional response rate = 41.6%) at a large university in Belgium was conducted. AUD were evaluated with the AUDIT and baseline predictors with the Composite International Diagnostic Interview Screening Scales (CIDI-SC). The one-year incidence of AUD was 3.9% (SE = 0.4). The most important individual-level baseline predictors of AUD incidence were being male (OR = 1.53; 95% CI = 1.12-2.10), a break-up with a romantic partner (OR = 1.67; 95% CI = 1.08-2.59), hazardous drinking (OR = 3.36; 95% CI = 1.31-8.63), and alcohol use characteristics at baseline (ORs between 1.29 and 1.38). Multivariate cross-validated prediction (cross-validated AUC = 0.887) shows that 55.5% of incident AUD cases occurred among the 10% of students at highest predicted risk (20.1% predicted incidence in this highest-risk subgroup). Four out of five students with incident AUD would hypothetically be preventable if baseline hazardous drinking was to be eliminated along with a reduction of one standard deviation in alcohol use characteristics scores, and another 15.0% would potentially be preventable if all 12-month stressful events were eliminated. Screening at college entrance is a promising strategy to identify students at risk of transitioning to more problematic drinking and AUD, thus improving the development and deployment of targeted preventive interventions.

[Major depressive episode in college freshmen: prevalence, academic functioning and receipt of treatment]. / Depressieve stoornis bij Belgische eerstejaarsstudenten: voorkomen, academische impact en zorggebruik.

Background Little is known about the epidemiology of Major Depressive Episode (mde) in university students. Aim To investigate the prevalence of mde, psychiatric comorbidity, and the association with academic performance among first-year university students, and to investigate to what extent these students use professional mental health services. Method All first-year students at the ku Leuven (Leuven, Belgium) were invited to complete a computer-assisted survey with a weighted cross-sectional design (N=5,460; response rate corrected for drop-out=51.8%). mde was assessed using the Composite International Diagnostic Interview - Screening Scales (cidi-sc) with dsm-iv criteria. Results We found that 13.6% of first-year students met criteria for a cidi-sc mde in the past year. mde was associated with a wide range of other comorbid disorders (such as generalized anxiety disorder or hypo[mania]) and suicidal thoughts and behaviors. mde was associated with significantly lower academic year percentage (-3.6 to -6.4%) and elevated odds of academic year failure (ors=1.5-2.0). Professional service use was estimated at 21.5%. Conclusion mde is common among first-year university students and is associated with a high degree of psychiatric comorbidity and poor academic performance. It is therefore surprising that so few students actually receive treatment for their psychiatric and emotional problems. Tijdschrift voor Psychiatrie 63(2021)1, 24-31.

The effect of e-mental health interventions on academic performance in university and college students: A meta-analysis of randomized controlled trials.

BACKGROUND: Mental health symptoms are common among college and university students and these can affect their academic performance. E-mental health interventions have proven effective in addressing mental health complaints but their effect on academic performance has not been synthesized yet. OBJECTIVES: To synthesize the evidence from randomized controlled trials for the effectiveness of e-mental health interventions on academic performance in college and university students compared to inactive controls. DATA SOURCES AND ELIGIBILITY CRITERIA: We searched six databases (PubMed, Cochrane library, CINAHL, ERIC, PsycINFO, Web of Science) during the period January 2000 until September 2019 for randomized controlled trials that reported on e-mental health interventions (guided or unguided) for college and university students and measured academic performance (e.g. grade point average). STUDY APPRAISAL AND SYNTHESIS METHODS: Study and participant characteristics and the academic performance measures at post-intervention were extracted. The latter were pooled and Hedges' g was calculated as the effect size. Heterogeneity and publication bias were investigated. RESULTS: Six studies containing 2428 participants were included in the meta-analysis. These focussed on either mood and anxiety or alcohol and tobacco use. The pooling of data resulted in a small but non-significant effect of g = 0.26 (95% CI, -0.00, 0.52; p = .05) on academic performance, favouring e-mental health interventions over inactive controls. Interventions had positive effects on depression (g = -0.24) and anxiety (g = -0.2). Heterogeneity was high. DISCUSSION: Despite the small and non-significant effect, our meta-analysis points to a promising direction for the effectiveness of e-mental health interventions on academic performance. Yet, these results must be interpreted with caution, as heterogeneity was high and few studies on the effectiveness of e-mental health interventions for students reported academic performance measures.

Was Eysenck right after all? A reassessment of the effects of psychotherapy for adult depression.

AIMS: In the 1950s, Eysenck suggested that psychotherapies may not be effective at all. Twenty-five years later, the first meta-analysis of randomised controlled trials showed that the effects of psychotherapies were considerable and that Eysenck was wrong. However, since that time methods have become available to assess biases in meta-analyses. METHODS: We examined the influence of these biases on the effects of psychotherapies for adult depression, including risk of bias, publication bias and the exclusion of waiting list control groups. RESULTS: The unadjusted effect size of psychotherapies compared with control groups was g = 0.70 (limited to Western countries: g = 0.63), which corresponds to a number-needed-to-treat of 4.18. Only 23% of the studies could be considered as a low risk of bias. When adjusting for several sources of bias, the effect size across all types of therapies dropped to g = 0.31. CONCLUSIONS: These results suggest that the effects of psychotherapy for depression are small, above the threshold that has been suggested as the minimal important difference in the treatment of depression, and Eysenck was probably wrong. However, this is still not certain because we could not adjust for all types of bias. Unadjusted meta-analyses of psychotherapies overestimate the effects considerably, and for several types of psychotherapy for adult depression, insufficient evidence is available that they are effective because too few low-risk studies were available, including problem-solving therapy, interpersonal psychotherapy and behavioural activation.

[Internet-based self-management interventions : Quality criteria for their use in prevention and treatment of mental disorders]. / Internetbasierte Selbstmanagementinterventionen : Qualitätskriterien für ihren Einsatz in Prävention und Behandlung psychischer Störungen.

BACKGROUND: Internet-based self-management interventions are effective in the prevention and treatment of mental disorders; however, for those affected as well as treating clinicians and decision makers in the healthcare sector, it is difficult to identify safe and effective interventions. AIM: Development of quality criteria for self-management interventions. METHODS: Based on a non-specific assessment matrix, a task force from two scientific societies formulated specific quality criteria for self-management interventions for mental disorders. Patients and other relevant stakeholders were involved in the process. RESULTS: A total of 8 key criteria with 17 subordinate points were developed. These must be met for the certification of an intervention. The criteria focus on therapeutic quality requirements, patient safety, data protection and security as well as proof of efficacy in at least one randomized study. A further five criteria are only descriptive and are not required for certification. DISCUSSION: These quality criteria serve as a starting point for the establishment of a certification process. This could help to make internet-based self-management interventions for mental disorders part of routine care in the German healthcare system.

Patients' experiences in a guided Internet- and App-based stress intervention for college students: A qualitative study.

INTRODUCTION: Academic education is often associated with increased stress and adverse effects on mental health. Internet-based interventions have shown to be effective in reducing stress-related symptoms. However, college students as target group so far have not been reached appropriately with psychological interventions and little is known about college students' perception of Internet-based stress management interventions. The objective of this study was to explore the experiences of students participating in an Internet- and App-based stress management intervention originally developed for stressed employees and subsequently adapted and tailored to college students. METHOD: Semi-structured interviews were conducted with ten participants selected from a randomized controlled trial that evaluated the effectiveness of an Internet- and App-based stress training. The selection of participants aimed to include students with different levels of treatment success. In order to enable an in-depth examination of intervention elements causing dissatisfaction, the interviews were systematically adapted regarding participants' statements in a precedent questionnaire. The interview material was analyzed based on the grounded theory method and thematic analysis. RESULTS: Results suggest students perceive a necessity to adapt Internet-based interventions to their particular needs. Students' statements indicate that a scientific perspective on the intervention and instable life circumstances could be student-specific factors affecting treatment experience. General themes emerging from the data were attitudes towards individualization and authenticity as well as demands towards different functions of feedback. DISCUSSION: Participants' experiences hint at certain intellectual and lifestyle-related characteristics of this population. Future studies should explore whether adaptions to these characteristics lead to a higher acceptance, adherence and effectiveness in the target population.

Effectiveness and cost-effectiveness of a guided Internet- and mobile-based intervention for the indicated prevention of major depression in patients with chronic back pain-study protocol of the PROD-BP multicenter pragmatic RCT.

BACKGROUND: Reducing the disease burden of major depressive disorder (MDD) is of major public health relevance. The prevention of depression is regarded as one possible approach to reach this goal. People with multiple risk factors for MDD such as chronic back pain and subthreshold depressive symptoms may benefit most from preventive measures. The Internet as intervention setting allows for scaling up preventive interventions on a public mental health level. METHODS: This study is a multicenter pragmatic randomized controlled trial (RCT) of parallel design aiming to investigate the (cost-) effectiveness of an Internet- and mobile-based intervention (IMI) for the prevention of depression in chronic back pain patients (PROD-BP) with subthreshold depressive symptoms. eSano BackCare-DP is a guided, chronic back pain-specific depression prevention intervention based on cognitive behavioral therapy (CBT) principles comprising six weekly plus three optional modules and two booster sessions after completion of the intervention. Trained psychologists provide guidance by sending feedback messages after each module. A total of 406 patients with chronic back pain and without a depressive disorder at baseline will be recruited following orthopedic rehabilitation care and allocated to either intervention or treatment-as-usual (TAU). Primary patient-relevant endpoint of the trial is the time to onset of MDD measured by the telephone-administered Structured Clinical Interview for DSM (SCID) at baseline and 1-year post-randomization. Key secondary outcomes are health-related quality of life, depression severity, pain intensity, pain-related disability, ability to work, intervention satisfaction and adherence as well as side effects of the intervention. Online assessments take place at baseline and 9 weeks as well as 6 and 12 months post-randomization. Cox regression survival analysis will be conducted to estimate hazard ratio at 12-month follow-up. Moreover, an economic analysis will be conducted from a societal and public health perspective. DISCUSSION: This is the first study examining an IMI for depression prevention in a sample of chronic pain patients. If this implementation of a depression prevention IMI into orthopedic aftercare proves effective, the intervention could be integrated into routine care with minimal costs and extended for use with other chronic diseases. Results will have implications for researchers, health care providers and public health policy makers. TRIAL REGISTRATION: The trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Trial Register (DRKS): DRKS00007960 . Registered 12 August 2015.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

The 6-month effectiveness of Internet-based guided self-help for depression in adults with Type 1 and 2 diabetes mellitus.

AIM: The aim of this research is to examine the 6-month effects of an Internet-based guided self-help intervention for comorbid depressive symptoms in people with diabetes. METHODS: Participants (n = 260) with Type 1 or 2 diabetes and elevated depressive symptoms [Center for Epidemiological Studies Depression Scale (CES-D) ≥ 23] were randomly assigned to a guided Internet-based self-help intervention or a control condition (treatment as usual + online psychoeducation about depression). The primary outcome was a change in depressive symptom severity (CES-D) from baseline to 6-month follow-up. The secondary outcomes included numbers of people achieving treatment response (reliable change of depressive symptoms) and remission (CES-D ≤ 16), as well as the effects on glycaemic control, diabetes-related emotional distress and diabetes acceptance. Repeated measures analysis of variance examined between-group differences using intent-to-treat principles. RESULTS: Both conditions showed improvements in depression severity: intervention condition, d = 1.48 [95% confidence interval (95% CI): 1.21 to 1.76]; control condition d = 0.55 (95% CI: 0.30 to 0.80). Changes were significantly greater in the intervention condition with a large between-group effect size (d = 0.83, 95% CI: 0.57 to 1.08). Accordingly, effects on response [relative risk (RR) = 2.60 (95% CI: 2.01 to 3.36), P < 0.001] and remission [RR = 3.36 (95% CI: 2.98 to 5.44), P < 0.001] were in favour of the intervention group, as were differences in change in diabetes emotional distress (d = 0.50, 95% CI: 0.04 to 0.54), and physical and mental functioning [Short Form Health Survey (SF-12) Physical d = 0.27 (95% CI: 0.01 to 0.51) and SF-12 Mental d = 0.68 (95% CI: 0.11 to 0.40)]. The intervention group was not superior with regard to glycaemic control, diabetes self-management and diabetes acceptance. CONCLUSIONS: The trial indicates that Internet-based guided self-help treatments for depression in people with diabetes can have sustained effects on depressive symptoms, well-being and emotional distress associated with diabetes.

Does Internet-based guided-self-help for depression cause harm? An individual participant data meta-analysis on deterioration rates and its moderators in randomized controlled trials.

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.

Mental disorders among college students in the World Health Organization World Mental Health Surveys.

BACKGROUND: Although mental disorders are significant predictors of educational attainment throughout the entire educational career, most research on mental disorders among students has focused on the primary and secondary school years. METHOD: The World Health Organization World Mental Health Surveys were used to examine the associations of mental disorders with college entry and attrition by comparing college students (n = 1572) and non-students in the same age range (18-22 years; n = 4178), including non-students who recently left college without graduating (n = 702) based on surveys in 21 countries (four low/lower-middle income, five upper-middle-income, one lower-middle or upper-middle at the times of two different surveys, and 11 high income). Lifetime and 12-month prevalence and age-of-onset of DSM-IV anxiety, mood, behavioral and substance disorders were assessed with the Composite International Diagnostic Interview (CIDI). RESULTS: One-fifth (20.3%) of college students had 12-month DSM-IV/CIDI disorders; 83.1% of these cases had pre-matriculation onsets. Disorders with pre-matriculation onsets were more important than those with post-matriculation onsets in predicting subsequent college attrition, with substance disorders and, among women, major depression the most important such disorders. Only 16.4% of students with 12-month disorders received any 12-month healthcare treatment for their mental disorders. CONCLUSIONS: Mental disorders are common among college students, have onsets that mostly occur prior to college entry, in the case of pre-matriculation disorders are associated with college attrition, and are typically untreated. Detection and effective treatment of these disorders early in the college career might reduce attrition and improve educational and psychosocial functioning.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Increasing the acceptance of internet-based mental health interventions in primary care patients with depressive symptoms. A randomized controlled trial.

BACKGROUND: Internet-based interventions (IBI) are effective in treating depression. However, uptake rates in routine care are still limited. Hence, this study aimed to (1) assess the acceptance of IBIs in primary care patients with depressive symptoms and to (2) examine the effects of a brief acceptance facilitating intervention in the form of an informational video on patients' acceptance of IBIs. METHODS: Primary care patients (N=128) with Minor or Major Depression were randomly assigned to an intervention (IG) or control group (CG). Patients in the IG were shown a brief informational video about IBIs before receiving a questionnaire that assessed their acceptance of IBIs and other secondary outcomes. Patients of the CG filled out the questionnaire immediately. RESULTS: Baseline acceptance of IBIs in the CG was high for 6.3%, moderate for 53.1% and low for 40.6% of patients. Acceptance of IBIs was significantly higher in the IG when compared to the CG (d=.71, 95%-CI:.09-2.91). Except for social influence and the general attitude towards psychological treatment, all secondary outcomes were also significantly improved (e.g. effort- (d=.40) and performance-expectancy: d=.65; knowledge about Internet interventions d=.35). LIMITATIONS: Depression of the participants was only assessed using a self-report measure (PHQ-9). CONCLUSION: Primary care patients' acceptance of IBIs for depressive symptoms was low but could be increased significantly using a brief acceptance facilitating intervention on the basis of an informational video. Future studies should further examine the potential of acceptance facilitating interventions for patients and health care providers to exploit the public health impact of IBIs.

Impact of an acceptance facilitating intervention on diabetes patients' acceptance of Internet-based interventions for depression: a randomized controlled trial.

AIMS: To (1) determine diabetes patients' acceptance of Internet-based interventions (IBIs) for depression, to (2) examine the effectiveness of an acceptance facilitating intervention (AFI) and to (3) explore subgroup specific effects. METHODS: 141 diabetes patients from two inpatient rehabilitation units and one outpatient clinic in Germany were randomly allocated to an intervention (IG) and a no-intervention control group (CG). The IG received an AFI consisting of a personal information session before filling-out a questionnaire on patients' acceptance of IBIs, predictors of acceptance (performance expectancy, effort expectancy, social influence, facilitating conditions, and Internet anxiety) as well as sociodemographic, depression-related and diabetes-related variables. The CG filled out the questionnaire immediately. Patients' acceptance of IBIs was measured with a four-item scale (sum-score ranging from 4 to 20). RESULTS: The CG showed a low (50.7%) to medium (40.8%) acceptance with only 8.5% of all diabetes patients reporting a high acceptance of IBIs for depression. The AFI had no significant effect on acceptance (IG: M=10.55, SD=4.69, n=70; KG: M=9.65, SD=4.27, n=71; d=0.20 [95%-CI: -0.13;0.53]) and the predictors of acceptance. Yet, subgroup analyses yielded a trend for depressed, diabetes-related distressed, female and younger (<59) participants and for those who do not frequently use the Internet to profit from the AFI. CONCLUSION: Diabetes patients show a rather low acceptance toward IBIs for depression. Findings indicate that the AFI is likely to be effective in the subgroup of depressed, diabetes-related distressed, female or younger diabetes patients, but not in the whole target population. Hence, AFIs might need to be tailored to the specific needs of subpopulations.

[Internet based cognitive behavioral interventions: state of the art and implementation possibilities in rehabilitation]. / Internetbasierte kognitiv-behaviorale Behandlungs-ansätze: State of the Art und Einsatzmöglichkeiten in der Rehabilitation.

Internet-based health interventions (IHIs) provide a way to treat people via the Internet. The spectrum is wide, comprising interventions for mental disorders and somatic diseases as well as health and risk behavior change interventions. Numerous international studies have demonstrated the efficacy of IHIs for the aforementioned areas. Through the resource-saving applications IHIs are cost-efficient, not least suggesting their usefulness for medical rehabilitation. The present overview starts with a description of IHIs with a differentiation of technical-formal and thematic aspects. In doing so, the focus lies primarily on cognitive-behavioral treatment approaches, as the best scientifically investigated IHIs to date. An overview of the empirical evidence of IHIs is given, followed by a discussion of the implementation possibilities in medical rehabilitation.

[Web-based rehabilitation aftercare following inpatient psychosomatic treatment]. / Web-basierte Rehabilitationsnachsorge nach stationärer psychosomatischer Therapie (W-RENA).

BACKGROUND: High relapse rates following treatment for mental health disorders are a challenge for psychosomatic rehabilitation treatments. The goal of the present study is to evaluate the feasibility, acceptance and process-quality of a 12-week transdiagnostic Internet-based maintenance treatment (W-RENA) following psychosomatic rehabilitation treatment. Findings regarding effectiveness and moderators of treatment outcome that were already reported elsewhere are briefly summarized.In a preliminary study we first assessed whether rehab patients have the technical requirements and abilities to successfully participate in Internet-based treatments. Patients expressing interest for participation in W-RENA (N=400) were compared with non-participants (N=1789) with regard to sociodemographic and clinical characteristics. METHOD: In a 2-arm randomized controlled trial (N=400) we subsequently compared participants of W-RENA with participants of a treatment as usual group (TAU). Self-report measures were assessed at the beginning of inpatient treatment (t1), at discharge from inpatient treatment/start of W-RENA (t2), and at 3- (t3) and 12-months follow-ups (t4). RESULTS: The majority of assessed rehab-patients had the technical prerequisites (78.79%) and necessary skills (79.9%) to successfully participate in an Internet-based intervention. A third of the patients (32%) which were invited to take part in the intervention (and the study) expressed interest to participate. Study participants and non-participants differed only slightly. Most participants (80.6%) reported to have gained benefit from participating. Treatment achievements as well as quality of therapist alliance were rated high from both patients and therapists. Moreover, participants of the W-RENA group could stabilize their inpatient treatment outcomes up to 3- and 12-months follow-up better than controls could do (differences in symptom change from discharge to 3-months follow-up: d=0.38; to 12-months follow-up: d=0.55). Clinical significant symptom deterioration from discharge to 1-year follow-up could be reduced by 2/3 (29.45% vs. 11.45%). We could not identify any subgroup not profiting from study participation. Patients with low education benefited particularly. CONCLUSION: Internet-based aftercare interventions are a feasible, accepted and effective approach to successfully sustain treatment outcomes achieved in inpatient psychosomatic rehabilitation.