INTRODUCTION:Relapse rates in individuals with alcohol use disorder (AUD) are particularly high following inpatient treatment. Innovative strategies should specifically target the transitional gap between completion of inpatient treatment and uptake of standard continuing care. This study aimed to determine whether Appstinence, a digital approach that combines a smartphone app intervention with adjunct telephone coaching, more markedly reduces the risk of relapse for 6 months after inpatient AUD treatment in comparison to a control group with access to standard continuing care. METHODS:In this multicenter clinical trial, 356 participants were randomized to the intervention (n = 175) or control group (n = 181). Eligibility criteria included diagnosis of AUD, smartphone access, no acute suicidality, and no language or neurocognitive impairments. The primary outcome was risk of relapse within 6 months after randomization, as assessed with the Timeline-Follow-Back method. Secondary outcomes included uptake of standard continuing care, hazardous alcohol consumption, craving, depression and anxiety symptom severity, and well-being. RESULTS:The intervention reduced the risk of relapse within 6 months as indicated by a log-rank test (HR: 0.72, 95% CI: 0.53-0.98, p = 0.04) and Cox regression adjusted for baseline characteristics (HR: 0.67, 95% CI: 0.48-0.92, p = 0.01). This effect increased when participants fully adhered to the intervention protocol (log-rank test: HR: 0.61, 95% CI: 0.39-0.94, p = 0.02). No significant differences were observed in secondary outcomes. CONCLUSION:Our findings provide supportive evidence for digital AUD transition treatment. Specifically, we found that, in comparison with access standard continuing care, the novel intervention more effectively reduced risk of relapse within 6 months following inpatient treatment.

Preventing mental disorders is important to avoiding clinical conditions. This study evaluated the efficacy of internet-based indicated prevention for anxiety and depressive disorders. In a three-arm randomized controlled trial, 566 adults with subthreshold anxiety (GAD-7 ≥ 5) and/or depressive symptoms (CES-D ≥ 16), but no clinical diagnosis in the past six months (MINI 6.0), were assigned to either an individually (IG-IMI, n = 186) or automatically (AG-IMI, n = 189) guided digital intervention, or waitlist control (WLC, n = 191). The digital intervention comprised 8 transdiagnostic, self-tailored, CBT-based sessions. The primary outcome was time to onset of any anxiety or depressive disorder over 12 months, assessed via blinded diagnostic interviews (MINI). AD/DD onset was 19.4% in IG-IMI, 14.8% in AG-IMI, and 30.9% in WLC. Cumulative incidence was 23.1% (IG-IMI), 20.7% (AG-IMI), and 36.0% (WLC; p < 0.001). Hazard ratios were 0.59 and 0.47; NNTs were 7.76 and 5.79. Both individually guided and automated interventions effectively reduced AD/DD incidence. Trial Registration: The study was preregistered in the German Clinical Trial Registration (DRKS00011099; https://drks.de/search/de/trial/DRKS00011099 ).

BACKGROUND:To map out the potential benefits of widely available smartphone apps for mental health, especially in contexts where face-to-face services are limited or unavailable, it is crucial to examine their efficacy compared with inactive controls. Standalone smartphone apps might offer an accessible option for individuals waiting for treatment or living in under-resourced settings. Given the currently inconclusive evidence regarding these apps, this systematic review and meta-analysis aimed to assess the efficacy and study quality of randomised controlled trials (RCTs) evaluating standalone smartphone apps for mental health. METHODS:In this systematic review and meta-analysis, based on a previously published study, we conducted an updated systematic search of PubMed, PsycINFO, Web of Science, Cochrane Clinical Trial, and Scopus for RCTs published from database inception to Nov 10, 2023. We included RCTs that examined the efficacy of standalone smartphone apps for mental health in adults (age ≥18 years) with heightened symptom severity compared with an inactive control group (eg, waitlist, informational material, and control apps). We excluded control groups that received active treatment. Two independent researchers (AV and AD) extracted summary data, which were verified by a third researcher (JKK). The effect size Hedges' g, 95% CI, and p value were calculated for each target outcome. We applied a random-effects model to all analyses due to the expected heterogeneity between RCTs. We assessed quality using the Risk of Bias 2 tool (dated Aug 22, 2019) and assessed publication bias via the Egger's test, and the Duval and Tweedie trim-and-fill analysis. The study was registered with PROSPERO, CRD42022310762. FINDINGS:We retrieved 12 705 records from electronic databases and 74 records from other sources (ie, reviews and meta-analyses on digital interventions for mental health identified through database searches and their reference lists, reference lists of other studies, trial registrations in PROSPERO, and websites of researchers in the field). Of these, we included 72 RCTs (70 reports) with 21 702 participants (of the 21 048 participants with sex or gender data, 14 208 [67%] were female, 6744 [32%] were male, and 96 [<1%] were other). At post assessment (assessment after completion of intervention), we found significant effects of apps targeting depression (33 comparisons; Hedges' g 0·45 [95% CI 0·30 to 0·60], p≤0·0001, I2=81·30%), anxiety (23 comparisons; 0·35 [0·22 to 0·48], p≤0·0001, I2=74·91%), sleep problems (14 comparisons; 0·71 [0·51 to 0·92], p≤0·0001, I2=76·17%), post-traumatic stress disorder (nine comparisons; 0·15 [0·02 to 0·28], p=0·029, I2=28·65%), eating disorders (four comparisons; 0·50 [0·29 to 0·71], p≤0·0001, I2=50·49%), and body dysmorphic disorder (three comparisons; 0·86 [0·30 to 1·41], p=0·0025, I2=74·90%) compared with inactive control groups. No significant pooled effects were found for smoking (six comparisons), self-injury (six comparisons), suicidal ideation (five comparisons), and alcohol misuse (five comparisons). Effect sizes for obsessive-compulsive disorders (two comparisons) and schizophrenia (one comparison) ranged from 0·10 to 0·96 (-0·12 to 1·51). Risk of bias was moderate to high. Publication bias was found for RCTs targeting depression and anxiety, but not for sleep problems; adjustments reduced the effect sizes for depression from 0·45 to 0·18 (0·02 to 0·34) and anxiety from 0·35 to 0·18 (0·03 to 0·32), with no change for sleep problems. INTERPRETATION:Although some outcomes showed small to medium effect sizes, these results must be interpreted cautiously given the presence of uncertainty factors, including considerable heterogeneity and moderate study quality. Heterogeneity might result from sample characteristics, assessment methods and periods, dropout rates, intervention and app components, and control conditions, limiting the generalisability of findings. Standalone smartphone apps might be offered for symptoms of depression, anxiety, and sleep problems, if no evidence-based first-line intervention is available. FUNDING:None.

Introduction: Limited research exists on intervention efficacy for comorbid subclinical anxiety and depressive disorders, despite their common co-occurrence. Internet- and mobile-based interventions (IMIs) are promising to reach individuals facing subclinical symptoms. Objective: This study aimed to evaluate the efficacy of a transdiagnostic and self-tailored IMI in reducing subclinical anxiety and depressive symptom severity with either individualized (IG-IMI) or automated (AG-IMI) guidance compared to a waitlist control group with care-as-usual access (WLC). Methods: Participants included 566 adults with subclinical anxiety (GAD-7 ≥ 5) and/or depressive (CES-D ≥16) symptoms, who did not meet criteria for a full-syndrome depressive or anxiety disorder. In a three-arm randomized clinical trial, participants were randomized to a cognitive behavioral 7-session IMI plus booster session with IG-IMI (n = 186) or AG-IMI (n = 189) or WLC (n = 191). Primary outcomes included observer-rated anxiety (HAM-A) and depressive (QIDS) symptom severity 8 weeks after randomization assessed by blinded raters via telephone. Follow-up outcomes at 6 and 12 months are reported. Results: Symptom severity was significantly lower with small to medium effects in IG-IMI (anxiety: d = 0.45, depression: d = 0.43) and AG-IMI (anxiety: d = 0.31, depression: d = 0.32) compared to WLC. No significant differences emerged between guidance formats in primary outcomes. There was a significant effect in HAM-A after 6 months favoring AG-IMI. On average, participants completed 85.38% of IG-IMI and 77.38% of AG-IMI. Conclusions: A transdiagnostic, self-tailored IMI can reduce subclinical anxiety and depressive symptom severity, but 12-month long-term effects were absent. Automated guidance holds promise for enhancing the scalability of IMIs in broad prevention initiatives.

Abstract Background As the return to alcohol use in individuals with alcohol use disorder (AUD) is common during treatment and recovery, it is important that abstinence motivation is maintained after such critical incidences. Our study aims to explore how individuals with AUD participating in an app-based intervention with telephone coaching after inpatient treatment perceived their abstinence motivation after the return to alcohol use, whether their app use behavior was affected and to identify helpful factors to maintain abstinence motivation. Methods Using a mixed-methods approach, ten participants from the intervention group of the randomized controlled trial SmartAssistEntz who returned to alcohol use and recorded this in the app Appstinence, a smartphone application with telephone coaching designed for individuals with AUD, were interviewed about their experiences. The interviews were recorded, transcribed and coded using qualitative content analysis. App use behavior was additionally examined by using log data. Results Of the ten interviewees, seven reported their abstinence motivation increased after the return to alcohol use. Reasons included the reminder of negative consequences of drinking, the desire to regain control of their situation as well as the perceived support provided by the app. App data showed that app use remained stable after the return to alcohol use with an average of 58.70 days of active app use (SD = 25.96, Mdn = 58.50, range = 24–96, IQR = 44.25) after the return to alcohol use which was also indicated by the participants’ reported use behavior. Conclusions The findings of the study tentatively suggest that the app can provide support to individuals after the return to alcohol use to maintain and increase motivation after the incidence. Future research should (1) focus on specifically enhancing identification of high risk situations and reach during such critical incidences, (2) actively integrate the experience of the return to alcohol use into app-based interventions to better support individuals in achieving their personal AUD behavior change goals, and (3) investigate what type of support individuals might need who drop out of the study and intervention and discontinue app use altogether. Trial registration The primary evaluation study is registered in the German Clinical Trials Register (DRKS, registration number DRKS00017700) and received approval of the ethical committee of the Friedrich-Alexander University Erlangen-Nuremberg (193_19 B).

Zusammenfassung: Zielsetzung: Die vorliegende Studie thematisiert potenzielle Schnittstellenprobleme, indem sie die Nutzung von Anschlussmaßnahmen (AN) bei Patient_innen nach stationärem Alkoholentzug ohne Nahtlosverfahren untersuchte. Methodik: Analysiert wurde die Kontrollgruppe des SmartAssistEntz-Projekts. Dazu wurden 181 Personen drei und sechs Wochen sowie drei und sechs Monate nach ihrer Entzugsbehandlung telefonisch befragt und Informationen zur Nutzung und Bewertung verschiedener AN erfasst. Ergebnisse: Über den Studienzeitraum hinweg nahm mehr als die Hälfte der Teilnehmenden mindestens eine AN in Anspruch. Am häufigsten und kontinuierlich wurden Suchtberatungsangebote genutzt (20-35 % der Teilnehmenden). Entwöhnungen wurden drei Wochen nach der Entzugsbehandlung von 8-14 %, nach sechs Monaten von 27-32 % der Teilnehmenden berichtet. Die AN wurden mehrheitlich als „hilfreich“ bewertet, wobei Selbsthilfegruppen am schlechtesten abschnitten. Das Fehlen einer AN wurde am häufigsten (12-22 %) mit Wartezeit begründet. Das Aufsuchen einer Entwöhnungsbehandlung in den ersten drei Monaten nach Entzugsbehandlung hing mit einem geringeren Rückfallrisiko zusammen. Schlussfolgerungen: Insbesondere Suchtberatungsangebote scheinen eine niedrigschwellige und als hilfreich bewertete Maßnahme im Anschluss an eine stationäre Entzugsbehandlung zu sein. Gleichzeitig scheint bezogen auf Abstinenz das Aufsuchen einer Entwöhnung ausschlaggebend zu sein, sodass deren Nutzung stärker gefördert werden sollte.

The present study addresses potential problems with treatment transition by investigating the use of aftercare treatments by inpatients after alcohol withdrawal treatment without immediate transition to rehabilitation. Methods: We analyzed the control group of the SmartAssistEntz project. Therefor, 181 individuals were interviewed three and six weeks, as well as three and six months after their withdrawal treatment, assessing information regarding the use and evaluation of different aftercare treatments. Results: Over the course of the study, more than half of the participants reported using at least one aftercare treatment. Mostly and most continuously used were drug counseling offers (20-35 % of the participants). Rehabilitation treatments were reported by 8-14 % of the participants six weeks after withdrawal treatment, and by 27-33 % six months thereafter. The treatments were mostly considered to be "helpful", whereby self-help groups performed worst. The reason for the lack of aftercare was mostly waiting time (12-22 % of the cases). The use of rehabilitation treatment during the first three months after withdrawal treatment was associated with a reduced risk of relapse. Conclusions: Particularly drug counseling offers seem to be a low-threshold and subjectively helpful measure following inpatient withdrawal treatment. At the same time, regarding abstinence, the visit of rehabilitation treatments seems to be decisive. Thus, their use should be promoted more intensely.

Background: It is uncertain whether app-based interventions add value to existing mental health care. Objective: To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions. Methods: We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545. Results: We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power. Discussion: App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes.

Zusammenfassung: Konsumvariablen und Abstinenz scheinen unzureichend zur Erfassung der Genesung bei Personen mit einer Alkoholkonsumstörung. Stattdessen rücken patientenzentrierte Indikatoren wie die Lebensqualität zunehmend in den Vordergrund. Um den Forderungen adäquater Messinstrumente gerecht zu werden, wurde der Substance Use Recovery Evaluator von Neale et al. (2016 ) übersetzt und in verschiedenen Stichproben mit Alkoholkonsumstörung nach Entzugsbehandlung psychometrisch evaluiert. In der ersten Teilstichprobe ( n = 135) wurde explorativ die Faktorenstruktur identifiziert sowie Reliabilitäts- und Validitätsmaße (Zusammenhänge mit Alkoholkonsum, Craving und gesundheitsbezogener Lebensqualität) berechnet. In der zweiten Stichprobe ( n = 120) wurde die gefundene Struktur konfirmatorisch geprüft. Das Verfahren erwies sich als reliabel und valide. Die im Original vorgeschlagene fünffaktorielle Struktur zeigte einen guten Fit, wenn auch in der vorliegenden Studie eine dreifaktorielle Struktur etwas geeigneter erschien. Diese Ergebnisse wurden in einer dritten Stichprobe ( n = 224) größtenteils gestützt. Trotz Limitationen (z. B. kleine Stichprobe) erwies sich der deutsche SURE als psychometrisch abgesicherter Indikator der Genesung von einer Alkoholkonsumstörung.

Consumption patterns and abstinence are popular, albeit insufficient, indicators in the recovery process from an alcohol use disorder (AUD). To meet the requirement of suitable measurement tools for a patient-centered recovery, we developed a German version of the Substance Use Recovery Evaluator (SURE), which we then evaluated in individuals with AUD at 6 weeks (2 subsamples: n = 135 and 120) and 6 months (n = 224) after withdrawal treatment. In the first subsample, we identified the factorial structure and calculated reliability and validity indices. In the second subsample, we checked the factorial structure by a confirmative analysis. The tool was shown to be reliable and valid. The original 5-factor structure showed a good fit, although a 3-factor structure was more suitable. These results were largely confirmed in the third sample. Despite some study limitations, the German version is a psychometrically valid measurement tool to assess the recovery from an AUD.

Background Depression and anxiety are common mental health conditions in college and university student populations. Offering transdiagnostic, web-based prevention programs such as ICare Prevent to those with subclinical complaints has the potential to reduce some barriers to receiving help (eg, availability of services, privacy considerations, and students’ desire for autonomy). However, uptake of these interventions is often low, and accounts of recruitment challenges are needed to complement available effectiveness research in student populations. Objective The aims of this study were to describe recruitment challenges together with effective recruitment strategies for ICare Prevent and provide basic information on the intervention’s effectiveness. Methods A 3-arm randomized controlled trial was conducted in a student sample with subclinical symptoms of depression and anxiety on the effectiveness of an individually guided (human support and feedback on exercises provided after each session, tailored to each participant) and automatically guided (computer-generated messages provided after each session, geared toward motivation) version of ICare Prevent, a web-based intervention with transdiagnostic components for the indicated prevention of depression and anxiety. The intervention was compared with care as usual. Descriptive statistics were used to outline recruitment challenges and effective web-based and offline strategies as well as students’ use of the intervention. A basic analysis of intervention effects was conducted using a Bayesian linear mixed model, with Bayes factors reported as the effect size. Results Direct recruitment through students’ email addresses via the central student administration was the most effective strategy. Data from 35 participants were analyzed (individually guided: n=14, 40%; automatically guided: n=8, 23%; care as usual: n=13, 37%). Use of the intervention was low, with an average of 3 out of 7 sessions (SD 2.9) completed. The analyses did not suggest any intervention effects other than anecdotal evidence (all Bayes factors10≤2.7). Conclusions This report adds to the existing literature on recruitment challenges specific to the student population. Testing the feasibility of recruitment measures and the greater involvement of the target population in their design, as well as shifting from direct to indirect prevention, can potentially help future studies in the field. In addition, this report demonstrates an alternative basic analytical strategy for underpowered randomized controlled trials. Trial Registration International Clinical Trials Registry Platform NTR6562; https://tinyurl.com/4rbexzrk International Registered Report Identifier (IRRID) RR2-10.1186/s13063-018-2477-y

There is evidence that craving mediates the relationship between Impulsive Personality Traits (IPTs) and relapse during the treatment of an Alcohol Use Disorder (AUD). To provide tailored interventions, a deeper understanding of the relation between IPTs and craving, namely mediating processes, is important. Based on previous literature, we proposed that lower emotion regulation competencies mediate the relation between attentional as well as non-planning IPTs and craving. To investigate these interrelations, we used data from the baseline assessment (n = 320) of the SmartAssistEntz project (pre-registered in the German Clinical Trials Register [DRKS00017700]). Inpatients with a primary AUD diagnosis were interviewed using standardized self-report measures (IPTs: BIS-15, emotion regulation competencies: ERSQ, craving: OCDS-G short version) during their withdrawal treatment. Indirect effects were calculated using the SPSS macro PROCESS v3.5. Attentional as well as non-planning, but not motor, IPTs were associated with craving. Emotion regulation competencies mediated the relationship between attentional as well as non-planning IPTs and craving. Given their mediating role in the present study, it is interesting to investigate if addressing emotion regulation competencies can mitigate the negative influences of attentional and non-planning IPTs. The direct effect of attentional IPTs implicates alternate mediating processes, which should also be investigated in future research.

BACKGROUND:Alcohol use disorder, a prevalent and disabling mental health problem, is often characterized by a chronic disease course. While effective inpatient and aftercare treatment options exist, the transferal of treatment success into everyday life is challenging and many patients remain without further assistance. App-based interventions with human guidance have great potential to support individuals after inpatient treatment, yet evidence on their efficacy remains scarce. OBJECTIVES:To develop an app-based intervention with human guidance and evaluate its usability, efficacy, and cost-effectiveness. METHODS:Individuals with alcohol use disorder (DSM-5), aged 18 or higher, without history of schizophrenia, undergoing inpatient alcohol use disorder treatment (N = 356) were recruited in eight medical centres in Bavaria, Germany, between December 2019 and August 2021. Participants were randomized in a 1:1 ratio to either receive access to treatment as usual plus an app-based intervention with human guidance (intervention group) or access to treatment as usual plus app-based intervention after the active study phase (waitlist control/TAU group). Telephone-based assessments are conducted by diagnostic interviewers three and six weeks as well as three and six months after randomization. The primary outcome is the relapse risk during the six months after randomization assessed via the Timeline Follow-Back Interview. Secondary outcomes include intervention usage, uptake of aftercare treatments, AUD-related psychopathology, general psychopathology, and quality of life. DISCUSSION:This study will provide further insights into the use of app-based interventions with human guidance as maintenance treatment in individuals with AUD. If shown to be efficacious, the intervention may improve AUD treatment by assisting individuals in maintaining inpatient treatment success after returning into their home setting. Due to the ubiquitous use of smartphones, the intervention has the potential to become part of routine AUD care in Germany and countries with similar healthcare systems.

Introduction: As relapse in individuals with alcohol use disorder (AUD) is common during treatment and recovery, it is important that abstinence motivation is maintained after such critical incidences. Our study aims to explore how individuals with AUD participating in an app-based intervention with telephone coaching after inpatient treatment perceived their abstinence motivation after relapse, whether their app use behavior was affected and to identify helpful factors to maintain abstinence motivation.Methods: Using a qualitative approach, ten participants from the intervention group of the randomized controlled trial SmartAssistEntz who had relapsed and recorded this in the app Appstinence, a smartphone application with telephone coaching designed for individuals with AUD, were interviewed about their experiences. The interviews were recorded, transcribed and coded using qualitative content analysis. Data from clinical diagnostic interviews and web-based self-report assessments were assessed in a descriptive manner.Results: Of the ten interviewees, seven reported their abstinence motivation increased after relapse. Reasons included the reminder of negative consequences of drinking, the desire to regain control of their situation as well as the perceived support provided by the app. App data showed that app use remained stable after relapse which was also indicated by the participants’ reported use behavior. For the majority of the interviewees, a trend of successful health and behavior change was seen in terms of a decrease of AUD symptom severity and the utilization of aftercare.Conclusions: The findings of the study tentatively suggest that the app can provide support to individuals after relapse to maintain and increase motivation after the incidence. Future research should 1) focus on specifically enhancing identification of high risk situations and reach during such critical incidences, 2) actively integrate the relapse experience into app-based interventions to better support individuals in achieving their personal AUD behavior change goals, and 3) investigate what type of support individuals might need who dropout of the study and intervention and discontinue app use altogether.

OBJECTIVES:Impulsivity is related to a higher risk of relapse in alcohol use disorders. However, besides drinking behavior, other recovery outcomes like physical and mental health-related quality of life are at least as important. The present study aimed to fill a research gap regarding the association of different impulsivity facets with health-related quality of life and well-being in alcohol use disorder.METHODS:Individuals with a primary alcohol use disorder diagnosis (n = 167) were interviewed with standardized self-report measures at the progressed stage of their withdrawal treatment and 6 weeks thereafter. Multiple regression models were calculated to examine the association of impulsivity, craving, and drinking patterns with health-related quality of life and well-being 6 weeks after withdrawal treatment, as well as the predictive role of impulsivity assessed during withdrawal for these two outcomes.RESULTS:Craving was associated with health-related quality of life and well-being 6 weeks after withdrawal. Likewise, non-planning and attentional impulsivity were associated with well-being 6 weeks after withdrawal. Motor impulsivity during withdrawal treatment predicted health-related quality of life 6 weeks thereafter.CONCLUSION:Impulsivity seems to be negatively related to health-related quality of life and well-being in the first weeks after alcohol withdrawal treatment, probably to a higher extent than drinking patterns, but differentiating between its facets seems to be important. These findings emphasize the importance of treatment approaches aiming at reduced impulsivity in the early recovery process.

Background Online preventive interventions can help to reduce the incidence of mental disorders. Whereas knowledge on stakeholders’ attitudes and factors relevant for successfully integrating online treatment into existing healthcare systems is available, knowledge is scarce for online prevention. Methods Stakeholders from Germany, Switzerland, Austria and Spain were surveyed. Potential facilitators/delivery staff (e.g. psychologists, psychotherapists) completed an online questionnaire (n = 183), policy makers (i.e. from the governing sector or health insurance providers) participated in semi-structured interviews (n = 16) and target groups/potential users of mental illness prevention (n = 49) participated in ten focus groups. Thematic analysis was used to identify their experiences with and attitudes and needs regarding online programmes to prevent mental disorders. Additionally, it was examined which groups they consider underserved and which factors they consider as fostering and hindering for reach, adoption, implementation and maintenance (cf. RE-AIM model) when integrating online prevention into existing healthcare systems. Results Main advantages of online mental illness prevention are perceived in low structural and psychological barriers. Lack of personal contact, security, privacy and trust concerns were discussed as disadvantages. Relevant needs are high usability and target group appropriateness, evidence for effectiveness and the use of motivational tools. Conclusions Positive attitudes among stakeholders are the key for successful integration of online mental illness prevention into existing healthcare systems. Potential facilitators/delivery staff must receive training and support to implement these programmes; the programmes must be attractive and continuously evaluated, updated and promoted to ensure ongoing reach; and existing infrastructure and contextual factors must be considered.