Search

This systematic review aimed to synthesize evidence on predictors and moderators of treatment outcomes in internet- and mobile-based interventions (IMIs) for depression, informing personalized care. A systematic search across PubMed, PsycInfo, and Cochrane yielded 33,002 results. Two reviewers independently performed screening, data extraction, risk of bias assessment, and methodological quality evaluation. Fifty-eight single studies (m = 466 analyses) focusing on baseline-predictors (59.7 %, m = 278), process-predictors (16.5 %, m = 77), and moderators (21.9 %, m = 102), and six individual patient data meta-analyses (m = 93) were included. Only 24.0 % (m = 112/466) of analyses in single studies and 15.1 % (m = 14/93) in individual patient data meta-analyses were significant. Evidence from single studies was rated as insufficient for all variable categories with only 2 out of 40 categories showing >50 % significant results. Baseline depression severity had the strongest predictive value with higher scores linked to better outcomes followed by variables indicative for the course-of-change. Other frequently analyzed and potentially relevant variables with significant results were adherence, age, educational level, ethnicity, relationship status, treatment history, and behavioral variables. More high quality quantitative studies with sufficient power are essential to validate and expand findings, identifying predictors and moderators specifically relevant in IMIs to explain differential treatment effects.

Introduction: Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms. Methods: In an observational study of a German convenience sample, the Insomnia Severity Index (ISI) and smartphone usage data (e.g., time the screen was active, longest time the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlation analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to training, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neighbor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores ≥15. Results: 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SUF and insomnia symptoms were found. In the ML models, sensitivity was low, ranging from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes were the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in the testing subsample indicated a low discrimination capacity. Conclusions: Given the small magnitude of the correlations and low discrimination capacity of the ML models, SUFs, as measured in this study, do not appear to be sufficient for detecting insomnia symptoms. Further research is necessary to explore whether examining intra-individual variations and subpopulations or employing alternative smartphone sensors yields more promising outcomes.

Background: Despite severely burdened individuals, often being excluded from research studies on internet- and mobile-based interventions (IMIs), negative events (NEs) including suicidal thoughts and behaviors (STBs) can still occur during a trial. NEs require monitoring and adequate safety measures. However, study protocols frequently lack comprehensive descriptions of procedures for managing NEs. Aims: This study aimed to illustrate the assessment, monitoring, and procedures for addressing NEs in two studies on IMIs in adults and youth using case reports, to identify strengths and weaknesses of the NE management approaches, and to derive key learnings and recommendations. Methods: Two case reports were drawn from two distinct IMI studies. The first study, PSYCHOnlineTHERAPY, evaluates the combination of an IMI with on-site psychotherapy for anxiety and depressive disorders in adults (adult blended study). The second study evaluates a standalone, therapist-guided IMI for post-traumatic stress disorder (PTSD) in youth (youth standalone study). Potential NEs were predefined depending on the study sample. The case studies thoroughly document the systematic recording and ongoing monitoring of NEs through self-report and observer-based assessments during the interventions. The cases illustrate a variety of NE management strategies, including automated and personalized approaches, adapted to the specific nature and severity of the NEs. The NE management approaches are visualized using decision trees. Results: In the adult blended case study, online questionnaires detected STBs and triggered automated support information. As on-site therapy had already ended, a telephone consultation session allowed for the identification and discussion of the heightened intensity of suicidal thoughts, along with the development of specific additional help options. In the youth standalone case study, heightened tension in an adolescent with PTSD during trauma processing could be addressed in a telephone therapeutic session focusing on resource activation and emotion regulation. The referral to on-site treatment was supported. Overall, advantages of the NE management included automated procedures, multimodal assessment of a wide range of NEs, and standardized procedures tailored to different severity levels. Weaknesses included the use of single-item assessments for STBs and lack of procedures in case of deterioration or nonresponse to treatment. Conclusion: This study provides practical insights and derives key learnings and recommendations regarding the management of NEs in different IMI contexts for both adults and youth.

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.

Low-threshold and remotely delivered preventive interventions, like telephone coaching, are warranted for farmers who experience multiple risk factors for depression, live in underserved areas, and show low help-seeking behavior. Factors facilitating uptake and actual use of effective remote interventions are important to reduce depression disease burden. This study aimed at identifying factors that potentially can influence acceptance of and satisfaction with a telephone coaching in this occupational group.Semi-structured interviews were based on the ‘Unified Theory of Acceptance and Use of Technology’, the ‘Evaluation’, and ‘Discrepancy’ models for satisfaction. Interviews were conducted with 20 of 66 invited participants of a 6-months telephone coaching during an effectiveness or implementation study. Audio-recorded interviews were transcribed and analyzed (deductive-inductive qualitative content analysis). Independent coding by two persons resulted in good agreement (Κ = 0.80). Participants validated results via questionnaire.Overall, 32 supporting (SF) and 14 hindering factors (HF) for acceptance and satisfaction were identified and organized into five dimensions: Coaching result (SF = 9, HF = 3), coach (SF = 9, HF = 1), organization (SF = 5, HF = 2), the telephone as communication medium (SF = 4, HF = 5) and participant characteristics (SF = 5, HF = 3). Most named SFs were ‘Flexible appointment arrangement’ (n = 19/95 %) and ‘low effort’ (n = 17/85 %), while most reported HFs were ‘lack of visual cues’ (n = 12/60 %) and ‘social/professional involvement restricts change process’ (n = 10/50 %).The perceived changes initiated by coaching, a low effort through telephone conduct, and the indicated personalization were identified as important influencing factors on acceptance and satisfaction based on interviewees' statements. Both may be further enhanced by offering choice and advice for delivery formats (e.g., video-calls) and training of coaches in farm-related issues. Study registration: German Clinical Trial Registrations: DRKS00017078 and DRKS00015655.

Background: Depression is highly prevalent among individuals with chronic back pain. Internet-based interventions can be effective in treating and preventing depression in this patient group, but it is unclear who benefits most from this intervention format. Method: In an analysis of two randomized trials (N = 504), we explored ways to predict heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain. Univariate treatment-moderator interactions were explored in a first step. Multilevel model-based recursive partitioning was then applied to develop a decision tree model predicting individualized treatment benefits. Results: The average effect on depressive symptoms was d = −0.43 (95 % CI: −0.68 to –0.17; 9 weeks; PHQ-9). Using univariate models, only back pain medication intake was detected as an effect moderator, predicting higher effects. More complex interactions were found using recursive partitioning, resulting in a final decision tree with six terminal nodes. The model explained a large amount of variation (bootstrap-bias-corrected R2 = 45 %), with predicted subgroup-conditional effects ranging from di = 0.24 to −1.31. External validation in a pilot trial among patients on sick leave (N = 76; R2 = 33 %) pointed to the transportability of the model. Conclusions: The studied intervention is effective in reducing depressive symptoms, but not among all chronic back pain patients. Predictions of the multivariate tree learning model suggest a pattern in which patients with moderate depression and relatively low pain self-efficacy benefit most, while no benefits arise when patients' self-efficacy is already high. If corroborated in further studies, the developed tree algorithm could serve as a practical decision-making tool.

Introduction: Farmers, forest workers and gardeners have a higher risk of developing depression compared to other occupational populations. As part of the German pilot project “With us in balance”, the potential of six guided internet- and mobile-based interventions (IMIs) to prevent depression among their insurants is examined. The IMI program is tailored to various risk factors of depression, individual symptoms, and needs. Although IMIs have been shown to be effective in reducing depressive symptoms, there is little qualitative research about the acceptance of digital preventive IMIs. The aim of this qualitative study is to gain insights into participants' experiences with the guided IMIs by focusing on determinants for acceptance and satisfaction. Methods: Semi-structured interviews were conducted with 22/171 (13 %) intervention group (IG) participants of a randomized controlled trial. The interview guide was developed based on theoretical models of user acceptance (Unified Theory of Acceptance and Use of Technology) and patient satisfaction (evaluation model, discrepancy theory). The interviews were evaluated independently by two coders performing a deductive-inductive content analysis and attaining a substantial level of agreement (K = 0.73). Results: The qualitative analysis revealed 71 determinants for acceptance and satisfaction across ten dimensions: performance expectancy, organisation, e-coach, usability, training content and structure, training usage, training outcome, financing, social influence, and behavioural intention. The most frequently identified drivers for the IMI use include “location independence”, “positive relationship to the e-coach” (each n = 19, 86 %), “personal e-coach guidance”, “expertise of the e-coach”, “target group specific adaptation” (each n = 18, 82 %), “flexibility”, “high willingness for renewed participation” (each n = 17, 77 %), “fast and easy availability”, “training of health enhancing attitudes and behaviours” and “content with figurative expressions” (each n = 16, 73 %). Discussion: The qualitative findings predominantly suggest the acceptance of and satisfaction with the IMI program for the prevention of depression in famers and related lines of work. Many identified positive drivers are related to the e-coach guidance, which emphasizes its importance in the preventive setting from the perspective of the participants. Nevertheless, some negative aspects have been identified which help to understand potential weaknesses of the IMI program. Participants indicated different needs in terms of IMI content and usage, which points towards the potential benefit of individualisation. The possibility of being able to use IMIs anonymously, flexibly and independently of location might be highly relevant for this specific target group.

Background: There is evidence that extraversion and associated frequent personal and digital social contacts are associated with mental health, reflected in reduced risk for anxiety or depression. However, excessive social media use (SMU) has been related to a decrease of mental health. We test how extraversion moderates the effect of SMU on anxiety and depression in times of social distancing. Methods: Data were collected with an app-based survey combined with passive sensing of social media usage time. We analyzed SMU (objective average duration of communication app usage) and cross-sectional questionnaire data from 486 adults (mean age = 42.42). Using multiple regression models, we tested how SMU, extraversion and their interaction relate to individual depression and anxiety scores. Results: Depression scores were associated with a higher SMU and lower extraversion. There was a significant positive relationship between SMU and extraversion that predicted higher depression scores. Limitations: In the present sample, there is a recruitment bias since only data from smartphones running iOS were included. Future research should also take a closer look at the purpose behind SMU. Conclusions: We conclude that extraversion might be a protective factor for depression which can turn into a harmful one if it is related to higher SMU. Thus, the interplay between SMU and extraversion needs to be considered when predicting individual differences in mental health.

Chronic medical conditions are increasingly common and associated with a high burden for persons affected by them. Digital health interventions might be a viable way to support persons with a chronic illness in their coping and self-management. The present special issue's editorial on digital health interventions in chronic medical conditions summarizes core findings and discusses next steps needed to further the field while avoiding to reinvent the wheel, thereby elaborating on four topics extracted from the special issue's articles: 1) Needs assessment and digital intervention development, 2) Efficacy and (cost-)effectiveness, 3) Dissemination and implementation research: reach and engagement as well as 4) next generation of digital interventions.

Objective: Evidence of long-term stability for positive mental health effects of internet-based interventions (IBIs) for depression prevention is still scarce. We evaluate long-term effectiveness of a depression prevention program in green professions (i.e. agriculture, horticulture, forestry). Methods: This pragmatic RCT (n = 360) compares a tailored IBI program to enhanced treatment as usual (TAU+) in green professions with at least subthreshold depression (PHQ ≥ 5). Intervention group (IG) received one of six IBIs shown previously to efficaciously reduce depressive symptoms. We report 6- and 12-month follow-up measures for depression, mental health and intervention-related outcomes. Intention-to-treat and per-protocol regression analyses were conducted for each measurement point and complemented by latent growth modeling. Results: After 6 months, depression severity (β = −0.30, 95%-CI: −0.52; −0.07), insomnia (β = −0.22, 95%-CI: −0.41; −0.02), pain-associated disability (β = −0.26, 95%-CI: −0.48; −0.04) and quality of life (β = 0.29, 95%-CI: 0.13; 0.45) in IG were superior to TAU+. Onset of possible depression was not reduced. After 12 months, no intervention effects were found. Longitudinal modeling confirmed group effects attenuating over 12 months for most outcomes. After 12 months, 55.56% of IG had completed at least 80% of their IBI. Conclusions: Stability of intervention effects along with intervention adherence was restricted. Measures enhancing long-term effectiveness of IBIs for depression health promotion are indicated in green professions. Trial registration: German Clinical Trial Registration: DRKS00014000. Registered: 09 April 2018.

Internet- and mobile-based interventions (IMI) offer an effective way to complement health care. Acceptance of IMI, a key facilitator of their implementation in routine care, is often low. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study validates and adapts the UTAUT to digital health care.Following a systematic literature search, 10 UTAUT-grounded original studies (N = 1588) assessing patients' and health professionals' acceptance of IMI for different somatic and mental health conditions were included. All included studies assessed Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and acceptance as well as age, gender, internet experience, and internet anxiety via self-report questionnaires. For the model validation primary data was obtained and analyzed using structural equation modeling.The best fitting model (RMSEA = 0.035, SRMR = 0.029) replicated the basic structure of UTAUT's core predictors of acceptance. Performance Expectancy was the strongest predictor (γ = 0.68, p

Background: Software agents are computer-programs that conduct conversations with a human. The present study evaluates the feasibility of the software agent “SISU” aiming to uplift psychological wellbeing. Methods: Within a one-group pretest-posttest trial, N = 30 German-speaking participants were recruited. Assessments took place before (t1), during (t2) and after (t3) the intervention. The ability of SISU to guide participants through the intervention, acceptability, and negative effects were investigated. Data analyses are based on intention-to-treat principles. Linear mixed models will be used to investigate short-term changes over time in mood, depression, anxiety. Intervention: The intervention consists of two sessions. Each session comprises writing tasks on autobiographical negative life events and an Acceptance- and Commitment Therapy-based exercise respectively. Participants interact with the software agent on two consecutive days for about 30 min each. Results: All participants completed all sessions within two days. User experience was positive, with all subscales of the user experience questionnaire (UEQ) M > 0.8. Participants experienced their writings as highly self-relevant and personal. However, 57% of the participants reported at least one negative effect attributed to the intervention. Results on linear mixed models indicate an increase in anxiety over time (β = 1.33, p = .001). Qualitative User Feedback revealed that the best thing about SISU was its innovativeness (13%) and anonymity (13%). As worst thing about SISU participants indicated that the conversational style of SISU often felt unnatural (73%). Conclusion: SISU successfully guided participants through the two-day intervention. Moreover, SISU has the potential to enter the inner world of participants. However, intervention contents have the potential to evoke negative effects in individuals. Expectable short-term symptom deterioration due to writing about negative autobiographical life events could not be prevented by acceptance and commitment therapy-based exercises. Hence, results suggest a revision of intervention contents as well as of the conversational style of SISU. The good adherence rate indicates the useful and acceptable format of SISU as a mental health chatbot. Overall, little is known about the effectiveness of software agents in the context of psychological wellbeing. Results of the present trial underline that the innovative technology bears the potential of SISU to act as therapeutic agent but should not be used with its current intervention content. Trial-registration: The Trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Register (DRKS): DRKS00014933 (date of registration: 20.06.2018). Link: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00014933.

Background: Despite the high prevalence of comorbid depression in people living with coronary artery disease (CAD), uptake of psychological treatment is generally low. This study was designed to investigate the feasibility of an internet-based cognitive-behavioral (iCBT) depression intervention for people with CAD and depressive symptoms. Methods: People with CAD and depressive symptoms (PHQ-9 ≥ 5) were randomly assigned to the eight modules comprising iCBT (N = 18), or waitlist-control (N = 16). Measures were taken at baseline (t1) and at post-treatment (eight weeks after randomization, t2). Feasibility-related outcomes were recruitment strategy, study attrition, intervention dropout, satisfaction, negative effects as well as the potential of the intervention to affect likely outcomes in a future full-scale trial (depression, anxiety, quality of life, fear of progression). Data analyses were based on intention-to-treat principles. Linear regression models were used to detect between group differences. Linear Mixed Models were used to model potential changes over time. Results: This trial was terminated prior to a-priori defined sample size has been reached given low recruitment success as well as high intervention dropout (88%) and study attrition (23%). On average, participants in the intervention group completed M = 2.78 (SD = 3.23) modules. Participants in the waitlist control group barely started one module (M = 0.82, SD = 1.81). The satisfaction with the intervention was low (M = 20.6, SD = 0.88). Participants reported no negative effects attributed to the iCBT. Differences between groups with regard to depression, anxiety, fear of progression and quality of life remained non-significant (p > 0.05). Conclusion: This trial failed to recruit a sufficient number of participants. Future work should explore potential pitfalls with regards to the reach and persuasiveness of internet interventions for people living with CAD. The study gives important indications for future studies with regard to the need for new ideas to reach and treat people with CAD and depression.

Objective: A user-centered approach is critical for increasing the adherence to and effectiveness of an internet- and mobile-based intervention program. Therefore, potential future intervention users were involved in the development of an internet- and mobile-based cognitive behavioral therapy program (iCBT) for adolescents and young adults (AYA) with chronic medical conditions and comorbid symptoms of anxiety or depression. We aimed to identify challenges and coping strategies of the intended target group, as well as their needs and preferred intervention characteristics for an iCBT program. Methods: Twenty AYA (aged 14–20, 60% females) with either type 1 diabetes (55%), juvenile idiopathic arthritis (25%) or cystic fibrosis (20%) were interviewed in condition-specific focus groups (4–11 participants per group) either via videoconferencing or face-to-face. Transcript verbatim data was analyzed using content analysis. Results: Frequently reported disease-specific burdens were among others fear of disease progression, non-acceptance of disease and stressful incidents related to and aversions against medical therapy. Most frequently reported coping strategies included, seeking social support and accepting the disease. Recommendations for the content of an iCBT for comorbid symptoms of anxiety and depression comprised: dealing with disease-related fears and getting advice on health-promoting lifestyles. iCBT characteristics considered preferable by participants were: providing individual feedback by a real-person; implementation of a feature to monitor treatment progress; youthful and varied content presentation; time per session not exceeding 1 h; non-involvement of parents. A mobile-based reminder feature was considered useful, and individual tailoring and self-determination of iCBT content was considered desirable. Conclusions: The findings highlight important patient perspectives and age-specific recommendations which can help design more optimal iCBT interventions for AYA with chronic medical conditions.

Depression is highly prevalent among university students. Internet-based interventions have been found to be effective in addressing depressive symptoms, but it is open if this also applies to interventions directed at academic stress. It is also largely unclear if the techniques employed in such programs provide significant additional benefits when controlling for non-specific intervention effects.A sample of N = 200 students with elevated levels of depression (CES-D ≥ 16) of a large distance-learning university were randomly assigned to either an Internet- and App-based stress intervention group (IG; n = 100) or an active control group (CG; n = 100) receiving an Internet-based psychoeducational program of equal length. Self-report data was assessed at baseline, post-treatment (7 weeks) and three-month follow-up. The primary outcome was depression (CES-D) post-treatment. Secondary outcomes included mental health outcomes, modifiable risk factors, and academic outcomes.We found significant between-group effects on depressive symptom severity (d = 0.36; 95% CI: 0.08–0.64), as well as behavioral activation (d = 0.61; 95% CI: 0.30–0.91), perceived stress (d = 0.45; 95% CI: 0.18–0.73), anxiety (d = 0.35; 95% CI: 0.03–0.67) and other secondary outcomes post-treatment. Effects on depression were sustained at three-month follow-up. Response rates for depressive symptoms were significantly higher in the IG (26%) than the CG (14%) at post-test (χ2=4.5, p = 0.04), but not at three-month follow-up (p = 0.454). We also found significant effects on relevant academic outcomes, including work impairment (follow-up; d = 0.36), work output (post-treatment; d = 0.27) and work cutback (follow-up; d = 0.36).The intervention was more effective for depressive symptoms compared to the CG, and so controlling for unspecific intervention effects. This suggests that specific techniques of the intervention may provide significant additional benefits on depressive symptoms.Trial registration: German Clinical Trial Registration (DRKS): DRKS00011800 (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011800).

Background and objective: Pain spans a broad spectrum of diseases and types that are highly prevalent and cause substantial disease burden for individuals and society. Up to 40% of people affected by pain receive no or inadequate treatment. Providing a scalable, time-, and location-independent way for pain diagnostic, management, prevention and treatment mobile health applications (MHA) might be a promising approach to improve health care for pain. However, the commercial app market is rapidly growing and unregulated, resulting in an opaque market. Studies investigating the content, privacy and security features, quality and scientific evidence of the available apps are highly needed, to guide patients and clinicians to high quality MHA.Contributing to this challenge, the present study investigates the content, quality, and privacy features of pain apps available in the European app stores. Methods: An automated search engine was used to identify pain apps in the European Google Play and Apple App store. Pain apps were screened and checked for systematic criteria (pain-relatedness, functionality, availability, independent usability, English or German). Content, quality and privacy features were assessed by two independent reviewers using the German Mobile Application Rating Scale (MARS-G). The MARS-G assesses quality on four objectives (engagement, functionality, aesthetics, information quality) and two subjective scales (perceived impact, subjective quality). Results: Out of 1034 identified pain apps 218 were included. Pain apps covered eight different pain types. Content included basic information, advice, assessment and tracking, and stand-alone interventions. The overall quality of the pain apps was average M = 3.13 (SD = 0.56, min = 1, max = 4.69). The effectiveness of less than 1% of the included pain apps was evaluated in a randomized controlled trial. Major problems with data privacy were present: 59% provided no imprint, 70% had no visible privacy policy. Conclusion: A multitude of pain apps is available. Most MHA lack scientific evaluation and have serious privacy issues, posing a potential threat to users. Further research on evidence and improvements privacy and security are needed. Overall, the potential of pain apps is not exploited.

Background: Digital health interventions might extend service provisions for youth with chronic medical conditions (CC) and comorbid mental health symptoms. We aimed to comprehensively evaluate the efficacy of Internet- and mobile-based interventions (IMIs) for different psychological and disease-related outcomes in children and adolescents with CC. Method: Studies were identified by systematic searches in CENTRAL, Embase, MEDLINE/PubMed and PsycINFO, complemented by searches in reference lists of eligible studies and other reviews. We included studies, when they were randomized controlled trials (RCTs) comparing the efficacy of an IMI to control conditions in improving psychological and disease-related outcomes in youth (mean age ≤ 18 years) with CC. Study selection, data extraction and risk of bias assessment were conducted independently by two reviewers. Meta-analyses were performed within a random-effects model, and Hedges' g (with 95% confidence intervals) was calculated as effect size measure. Primary outcomes were comorbid mental health symptoms (i.e., depression, anxiety and stress), as well as quality of life and self-efficacy. Results: A total of 19 randomized controlled trials (2410 patients) were included in this meta-analysis. IMIs were associated with improvements in self-efficacy (g = 0.38; 95% CI, 0.15 to 0.61; I2 = 0) and combined disease-related outcomes (g = −0.13; 95% CI, −0.25 to −0.01; I2 = 21). Meta-analyses on other outcomes were non-significant, and some pre-planned analyses were not feasible because of a shortage of studies. Conclusion: The available evidence on IMIs for improving mental and health-related outcomes in youth with CC is limited. Our findings point to a rather small benefit and limited efficacy. Future research is needed, to comprehensively assess the potential of IMIs to extend collaborative care, and to identify factors contributing to improved user-centered interventions with better treatment outcomes.

Background: The estimated number of refugees worldwide resulting from persecution, conflict, violence, or human rights violations reached 25.4 million in 2017. An increased prevalence of mental disorders combined with language and socio-cultural barriers pose a challenge for healthcare systems. Internet-based interventions can help to meet this challenge. For the effective use of such interventions in refugees, cultural adaptations are necessary. The variety of their cultural backgrounds thereby is particularly challenging. Methods: We conducted this explorative qualitative study in order to identify elements of Internet-based interventions that need cultural adaptation to be suitable for refugees. Six refugees from Syria, Iran, Eritrea, Algeria, and Iraq, and six healthcare providers (two social workers, two psychologists, one physiotherapist, one physician) working with refugees went through an intervention for individuals with sleeping problems (eSano Sleep-e). Possible threats to user experience were identified using the Think Aloud method and semi-structured interviews. Statements were analysed based on the grounded theory method. Results: Results indicate the necessity to adapt the intervention to the specifics of refugees including aspects related to the flight (i.e., past and current stressors) and non-western characteristics (i.e., habits, disease and treatment concepts). Elements of adaptation should include pictures, role models, language, psychoeducational elements, structure of modules, and format of presentation. Conclusions: Cultural adaptation can be used to facilitate the identification with an intervention, which seems crucial to increase the acceptance among refugees. In spite of their diverse cultural backgrounds, it appears feasible to create interventions that allow identification by refugees from different home countries. Keywords: Low-threshold intervention, Stepped care, Mental health gap, eHealth, Cultural sensitive psychotherapy

Background: Procrastination, defined as irrational and voluntary delaying of necessary tasks, is widespread and clinically relevant. Its high prevalence among college students comes with serious consequences for mental health and well-being of those affected. Research for proper treatment is still relatively scarce and treatment of choice seems to be cognitive behavior therapy (CBT). The aim of this study is to evaluate the effectiveness and acceptability of an internet- and mobile-based intervention (IMI) for procrastination based on CBT for college students. Methods: A two-armed randomized controlled trial with a calculated sample size of N = 120 participants with problematic procrastination behavior will be conducted. Students will be recruited in Germany, Austria and Switzerland via circular emails at 15+ cooperating universities in the framework of StudiCare, a well-established project that provides IMIs to college students for different health related issues. The intervention group will receive the e-coach guided 5-week IMI StudiCare Procrastination. A waitlist-control group will get access to the unguided IMI 12 weeks after randomization. Assessments will take place before as well as 6 and 12 weeks after randomization. Primary outcome is procrastination, measured by the Irrational Procrastination Scale (IPS). Secondary outcomes include susceptibility to temptation, depression, anxiety, wellbeing and self-efficacy as well as acceptability aspects such as intervention satisfaction, adherence and potential side effects. Additionally, several potential moderators as well as the potential mediators self-efficacy and susceptibility to temptation will be examined exploratorily. Data-analysis will be performed on intention-to-treat basis. Discussion: This study will contribute to the evidence concerning effectiveness and acceptability of an intervention for procrastination delivered via the internet. If it shows to be effective, StudiCare Procrastination could provide a low-threshold, cost-efficient way to help the multitude of students suffering from problems caused by procrastination.Trial registration: The trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Trial Register (DRKS): DRKS00014321 (date of registration: 06.04.2018). In case of important protocol modifications, trial registration will be updated.Trial status: This is protocol version number 1, 11th December 2019. Recruitment started 9th of April 2018 and was completed 30th of November 2018. Assessment and intervention are still ongoing and will be completed by April 2019. Keywords: Procrastination, Internet-based intervention, Online intervention, Cognitive behavior therapy, Randomized controlled trial, College students

Background: Acceptance and Commitment Therapy (ACT) is an effective intervention for the treatment of chronic pain. Internet-based pain interventions might be an effective and cost-effective way to overcome treatment barriers of traditional face-to-face pain interventions such as the lack of availability and accessibility. However, little is known about the general (cost-)effectiveness of internet-based pain interventions and the specific (cost-) effectiveness of guided and unguided pain interventions. Therefore, the aim of this study is to investigate the effectiveness and cost-effectiveness of a guided and unguided ACT-based online intervention for persons with chronic pain (ACTonPain). Methods: ACTonPain is a pragmatic three-armed randomised controlled trial comparing ACTonPain with or without therapist guidance against a waitlist control group. Both active conditions differ only with regard to guidance provided by an eCoach, who sends feedback after each module. This study aims to include 300 participants. Randomisation and allocation will be performed using permuted block randomisation with variable block sizes. The intervention contains seven ACT-based modules with interactive exercises, and audio and video clips. Furthermore, the participants have the opportunity to receive daily text messages. Online self-assessments will take place at pre- and post-treatment, as well as at 6 month follow-up. The primary outcome is pain interference. Secondary outcomes include physical and emotional functioning, pain intensity, ACT-related variables as well as health-related quality of life. Moreover, a cost-effectiveness analysis will be conducted from a societal perspective. Demographic and medical variables will be assessed on the basis of self-reports in order to detect potential moderators or mediators of the effects. The data will be analysed on an intention-to-treat basis and also using per-protocol analyses. Discussion: This study will contribute to the evidence base of internet-based pain interventions and provide valuable information about the treatment success and cost-effectiveness regarding the intervention's level of guidance (self-help only vs. guided self-help). If ACTonPain is shown to be effective, investigations in different healthcare settings should follow, to examine possible ways of implementing ACTonPain into existing healthcare systems. The implementation of ACTonPain could help to shorten waiting times, expand access to pain treatment and, potentially, also reduce treatment costs.