Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression: A Systematic Review and Meta-Analysis.

Importance: While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary. Objective: To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness. Data Sources: A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials. Study Selection: Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted. Main Outcomes and Measures: Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs. Results: The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (ß̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (ß̂ = -0.24, 95% CrI, -0.03 to -0.46). Conclusions and Relevance: In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial.

Background: Mental health problems are prevalent among people with diabetes, yet often under-diagnosed. Smart sensing, utilizing passively collected digital markers through digital devices, is an innovative diagnostic approach that can support mental health screening and intervention. However, the acceptance of this technology remains unclear. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aimed to investigate (1) the acceptance of smart sensing in a diabetes sample, (2) the determinants of acceptance, and (3) the effectiveness of an acceptance facilitating intervention (AFI). Methods: A total of N = 132 participants with diabetes were randomized to an intervention group (IG) or a control group (CG). The IG received a video-based AFI on smart sensing and the CG received an educational video on mindfulness. Acceptance and its potential determinants were assessed through an online questionnaire as a single post-measurement. The self-reported behavioral intention, interest in using a smart sensing application and installation of a smart sensing application were assessed as outcomes. The data were analyzed using latent structural equation modeling and t-tests. Results: The acceptance of smart sensing at baseline was average (M = 12.64, SD = 4.24) with 27.8% showing low, 40.3% moderate, and 31.9% high acceptance. Performance expectancy (γ = 0.64, p < 0.001), social influence (γ = 0.23, p = .032) and trust (γ = 0.27, p = .040) were identified as potential determinants of acceptance, explaining 84% of the variance. SEM model fit was acceptable (RMSEA = 0.073, SRMR = 0.059). The intervention did not significantly impact acceptance (γ = 0.25, 95%-CI: -0.16-0.65, p = .233), interest (OR = 0.76, 95% CI: 0.38-1.52, p = .445) or app installation rates (OR = 1.13, 95% CI: 0.47-2.73, p = .777). Discussion: The high variance in acceptance supports a need for acceptance facilitating procedures. The analyzed model supported performance expectancy, social influence, and trust as potential determinants of smart sensing acceptance; perceived benefit was the most influential factor towards acceptance. The AFI was not significant. Future research should further explore factors contributing to smart sensing acceptance and address implementation barriers.

Treatment effect heterogeneity of cognitive behavioral therapy for insomnia - A meta-analysis.

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

Motivational interviewing from the paediatricians' perspective: assessments after a 2-day training for physicians caring for adolescents with chronic medical conditions (CMCs).

BACKGROUND: Counselling adolescents with chronic medical conditions (CMCs) can be challenging regarding suitable interviewing skills and clinicians' attitudes toward the patient. Successful communication can be a key element of treatment. Motivational Interviewing (MI) is broadly applicable in managing behavioural problems and diseases by increasing patient motivation for lifestyle changes. However, data concerning the applicability, feasibility and implementation of MI sessions in everyday practice are missing from the physicians' point of view. METHOD: The present study was conducted as a mixed methods design. Twenty paediatricians were randomized to a 2-day MI course followed by MI consultations. Data were collected through a questionnaire one year after MI training. Factors for effective training and possible barriers to successful use of MI were examined. RESULTS: Completed questionnaires were returned by 19 of 20 paediatricians. The paediatricians' experiences with MI demonstrate that MI is regarded as a valuable tool when working with adolescents with CMCs. 95% of all respondents reported that they found MI education necessary for their clinical work and were using it also outside the COACH-MI study context. 73.7% percent saw potential to strengthen the connection to their patients by using MI. The doctors were already using more MI conversation techniques after a 2-day MI course. Obstacles were seen in the short training, the lack of time and missing undisturbed environment (interruptions by telephone, staff, etc.) during clinical flow. CONCLUSIONS: MI techniques are not yet a regular part of medical training. However, a 2-day MI course was rated effective and provided a lasting impact by physicians caring for children and adolescents with chronic medical conditions (CMCs), although booster sessions should be offered regularly. TRIAL REGISTRATION: The study was registered in the German Clinical Trials Register (DRKS00014043) on 26/04/2018.

Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial.

Objective: Smart sensing has the potential to make psychotherapeutic treatments more effective. It involves the passive analysis and collection of data generated by digital devices. However, acceptance of smart sensing among psychotherapy patients remains unclear. Based on the unified theory of acceptance and use of technology (UTAUT), this study investigated (1) the acceptance toward smart sensing in a sample of psychotherapy patients (2) the effectiveness of an acceptance facilitating intervention (AFI) and (3) the determinants of acceptance. Methods: Patients (N = 116) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a control video. An online questionnaire was used to assess acceptance of smart sensing, performance expectancy, effort expectancy, facilitating conditions and social influence. The intervention effects of the AFI on acceptance were investigated. The determinants of acceptance were analyzed with structural equation modeling (SEM). Results: The IG showed a moderate level of acceptance (M = 3.16, SD = 0.97), while the CG showed a low level (M = 2.76, SD = 1.0). The increase in acceptance showed a moderate effect in the intervention group (p < .05, d = 0.4). For the IG, performance expectancy (M = 3.92, SD = 0.7), effort expectancy (M = 3.90, SD = 0.98) as well as facilitating conditions (M = 3.91, SD = 0.93) achieved high levels. Performance expectancy (γ = 0.63, p < .001) and effort expectancy (γ = 0.36, p < .001) were identified as the core determinants of acceptance explaining 71.1% of its variance. The fit indices supported the model's validity (CFI = .95, TLI = .93, RMSEA = .08). Discussion: The low acceptance in the CG suggests that enhancing the acceptance should be considered, potentially increasing the use and adherence to the technology. The current AFI was effective in doing so and is thus a promising approach. The IG also showed significantly higher performance expectancy and social influence and, in general, a strong expression of the UTAUT factors. The results support the applicability of the UTAUT in the context of smart sensing in a clinical sample, as the included predictors were able to explain a great amount of the variance of acceptance.

Persuasive technologies design for mental and behavioral health platforms: A scoping literature review.

This review investigates persuasive design frameworks within eHealth, concentrating on methodologies, their prevalence in mental and behavioral health applications, and identifying current research gaps. An extensive search was conducted across 8 databases, focusing on English publications with full text available. The search prioritized primary research articles, post-2011 applications, and eHealth platforms emphasizing treatment or support. The inclusion process was iterative, involving multiple authors, and relied on detailed criteria to ensure the relevance and contemporaneity of selected works. The final review set comprised 161 articles, providing an overview of persuasive design frameworks in eHealth. The review highlights the state of the art in the domain, emphasizing the utilization and effectiveness of these frameworks in eHealth platforms. This review details the restricted adoption of persuasive design frameworks within the field of eHealth, particularly in the mental and behavioral sectors. Predominant gaps include the scarcity of comparative evaluations, the underrepresentation of tailored interventions, and the unclear influence of persuasive components on user experience. There is a notable requirement for further scrutiny and refinement of persuasive design frameworks. Addressing these concerns promises a more substantial foundation for persuasive design in eHealth, potentially enhancing user commitment and platform efficiency.

The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior.

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.

Anxiety and depression symptoms in adolescents and young adults with juvenile idiopathic arthritis: results of an outpatient screening.

BACKGROUND: Previous studies have shown that growing up with rheumatic conditions can fuel dissatisfaction and psychological distress, which in turn affects disease self-management and treatment adherence. Primary objective of this study was to estimate the prevalence of anxiety and depression symptoms in adolescents and young adults (AYA) with juvenile idiopathic arthritis (JIA) and to identify correlates of conspicuous screening results. METHODS: Initiated as part of the COACH multicenter observational study, outpatients aged 12 to 21 years participating in the National Pediatric Rheumatological Database (NPRD) were prospectively screened for mental health using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder Scale-7 (GAD-7). RESULTS: Data from 1,150 adolescents with JIA (mean age 15.6 ± 2.2 years; mean disease duration 7.2 ± 4.9 years, 69% female, 43% oligoarthritis, 26% polyarthritis) were analysed. Overall, 32.7% (n = 316) of AYA showed conspicuous screening results, of whom 30.4% reported clinically relevant suicidal or self-harm thoughts. About 19% of screened patients showed moderate to severe depressive or anxious symptoms. AYA with conspicuous screening results were older (15.8 vs. 15.2 years; p < 0.0001), more often female (81% vs. 64%; p < 0.0001) and more often overweight (25% vs. 17%; p = 0.006). They had higher disease activity (physician global assessment on NRS 0-10; 1.7 vs. 1.2; p < 0.0001), more functional limitations (CHAQ; 0.44 vs. 0.14; <0.0001) and rated their health status worse (NRS 0-10; 3.5 vs. 1.8; p < 0.0001) than AYA with inconspicuous screening results. Females (OR 2.33 [CI 1.53-3.56]; p < 0.0001), older age (OR 1.09 [CI 1.01-1.18]; p = 0.026), patients with more functional limitations (OR 3.36 [CI 1.98-5.72]; p < 0.0001), and patients with worse subjective health status (OR 1.17 [CI 1.07-1.27]; p < 0.0001) were more likely to have a conspicuous screening result. Regular sports participation was associated with a lower likelihood of conspicuous screening result (OR 0.69 [CI 0.49-0.98]; p = 0.039). CONCLUSIONS: A large-scale outpatient screening of AYA with JIA in Germany shows a high prevalence of anxiety and depression symptoms. The need for routine screening for early detection of mental health problems became apparent.

The Impact of Video-Based Microinterventions on Attitudes Toward Mental Health and Help Seeking in Youth: Web-Based Randomized Controlled Trial.

BACKGROUND: Mental health (MH) problems in youth are prevalent, burdening, and frequently persistent. Despite the existence of effective treatment, the uptake of professional help is low, particularly due to attitudinal barriers. OBJECTIVE: This study evaluated the effectiveness and acceptability of 2 video-based microinterventions aimed at reducing barriers to MH treatment and increasing the likelihood of seeking professional help in young people. METHODS: This study was entirely web based and open access. The interventions addressed 5 MH problems: generalized anxiety disorder, depression, bulimia, nonsuicidal self-injury, and problematic alcohol use. Intervention 1 aimed to destigmatize and improve MH literacy, whereas intervention 2 aimed to induce positive outcome expectancies regarding professional help seeking. Of the 2435 participants who commenced the study, a final sample of 1394 (57.25%) participants aged 14 to 29 years with complete data and sufficient durations of stay on the video pages were randomized in a fully automated manner to 1 of the 5 MH problems and 1 of 3 conditions (control, intervention 1, and intervention 2) in a permuted block design. After the presentation of a video vignette, no further videos were shown to the control group, whereas a second, short intervention video was presented to the intervention 1 and 2 groups. Intervention effects on self-reported potential professional help seeking (primary outcome), stigma, and attitudes toward help seeking were examined using analyses of covariance across and within the 5 MH problems. Furthermore, we assessed video acceptability. RESULTS: No significant group effects on potential professional help seeking were found in the total sample (F2,1385=0.99; P=.37). However, the groups differed significantly with regard to stigma outcomes and the likelihood of seeking informal help (F2,1385=3.75; P=.02). Furthermore, separate analyses indicated substantial differences in intervention effects among the 5 MH problems. CONCLUSIONS: Interventions to promote help seeking for MH problems may require disorder-specific approaches. The study results can inform future research and public health campaigns addressing adolescents and young adults. TRIAL REGISTRATION: German Clinical Trials Register DRKS00023110; https://drks.de/search/de/trial/DRKS00023110.

Mental health services for German university students: acceptance of intervention targets and preference for delivery modes.

Introduction: Most university students with mental disorders remain untreated. Evaluating the acceptance of intervention targets in mental health treatment, promotion, and prevention, as well as mental health service delivery modes is crucial for reducing potential barriers, increasing healthcare utilization, and efficiently allocating resources in healthcare services. Aim: The study aimed to evaluate the acceptance of various intervention targets and delivery modes of mental health care services in German first-year university students. Methods: In total, 1,376 first-year students from two German universities from the 2017-2018 multi-center cross-sectional cohort of the StudiCare project, the German arm of the World Mental Health International College Student Survey initiative, completed a web-based survey assessing their mental health. Mental disorder status was based on self-reported data fulfilling the DSM-IV criteria. We report frequencies of accepted delivery modes [categories: group or in-person therapy with on or off campus services, self-help internet- or mobile-based intervention (IMI) with or without coaching, or a combination of a in-person and IMI (blended)]. In a multinomial logistic regression, we estimate correlates of the preference for in-person vs. IMI vs. a combination of both modes (blended) modalities. Additionally, we report frequencies of intervention targets (disorder specific: e.g., social phobia, depressive mood; study-related: test anxiety, procrastination; general well-being: sleep quality, resilience) their association with mental disorders and sex, and optimal combinations of treatment targets for each mental illness. Results: German university students' acceptance is high for in-person (71%-76%), moderate for internet- and mobile-based (45%-55%), and low for group delivery modes (31%-36%). In-person treatment (72%) was preferred over IMI (19%) and blended modalities (9%). Having a mental disorder [odds ratio (OR): 1.56], believing that digital treatments are effective (OR: 3.2), and showing no intention to use services (OR: 2.8) were associated with a preference for IMI compared to in-person modes. Students with prior treatment experience preferred in-person modes (OR: 0.46). In general, treatment targets acceptance was higher among female students and students with mental disorders. However, this was not true for targets with the highest (i.e., procrastination) and the lowest (i.e., substance-use disorder) acceptance. If only two intervention targets were offered, a combination of study-related targets (i.e., procrastination, stress, time management) would reach 85%-88% of the students. Conclusion: In-person services are preferred, yet half of the students consider using IMI, preferably aiming for a combination of at least two study-related intervention targets. Student mental health care services should offer a combination of accepted targets in different delivery modes to maximize service utilization.

Internet- and mobile-based anxiety and depression interventions for children and adolescents: efficacy and negative effects - a systematic review and meta-analysis.

Mental disorders, most commonly anxiety disorders and fourth most common depression, are prevalent in children and adolescents. Internet- and mobile-based interventions might represent a scalable approach to improve mental health care, however, evidence so far is inconclusive and systematic reports on negative effects are missing. Four data-bases were searched for randomized controlled trials evaluating internet- and mobile-based interventions (IMIs) targeting anxiety disorders or depression in children and adolescents up to 18 years exhibiting clinically relevant symptoms. Meta-analytic evaluations were conducted in comparison to active and passive control groups, furthermore, pre-defined sub-groups were explored and reported negative effects examined. Pooled estimates showed a moderate positive effect for IMIs targeting anxiety disorders compared to passive control groups (g = -0.69; CI -0.94 to -0.45; k = 8; n = 559; p ≤ 0,001), but not for depression. Pooled estimates compared to active control groups remained non-significant. Subgroup analyses were largely omitted due to an insufficient number of trials or were non-significant. Negative effects were mainly reported as drop-out rates and (non)-response rates, while additional negative effects, such as deterioration rates or the development of additional symptoms, were reported by only one third of included studies. The focus on children and adolescents with clinically relevant symptoms allowed the present findings to complement previous work, however, the limited amount of trials hindered many planned comparisons. The overview of reported negative effects highlighted that negative effects are being neglected in the majority of RCTs. Hence, in the future RCTs should include more information about potential negative effects, at best a combination of quantitative and qualitative information. Open Science Framework (osf.io/ch5nj).

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials.

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.

Testing a model of benefit-finding and growth in youths with chronic health conditions.

BACKGROUND: The experience of benefit-finding and growth (BFG), defined as perceiving positive life changes resulting from adversity, is increasingly studied among youths with chronic health conditions (CCs). However, empirical evidence is scarce for explaining individual differences in BFG. The study aimed to test a model of BFG, including an interplay of personal and environmental factors and coping processes. METHODS: A sample of N = 498 youths (12-21 years) recruited from three German patient registries for CCs (type 1 diabetes: n = 388, juvenile idiopathic arthritis: n = 82, cystic fibrosis: n = 28) completed a questionnaire including self-reported optimism, social support from parents and peers, coping strategies, and BFG. The model was created to reflect the theoretical assumptions of the Life Crisis and Personal Growth model and current empirical evidence. Structural equation modeling was conducted to evaluate the incremental explanatory power of optimism, peer group integration, parental support, acceptance, cognitive reappraisal, and seeking social support over and above sociodemographic and disease-related characteristics. RESULTS: The model (CFI = 0.93; RMSEA = 0.04; SRMR = 0.05) explained 32% of the variance in BFG. Controlling for sociodemographic and disease-related characteristics, acceptance, cognitive reappraisal, and seeking social support were directly and positively linked to BFG. All tested coping strategies significantly mediated the association between optimism and BFG, whereas seeking social support significantly mediated the relation between peer group integration and BFG. DISCUSSION: The study stresses the prominent role of emotion-focused coping strategies and peer group integration in enhancing BFG in youths with CCs. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), no. DRKS00025125. Registered on May 17, 2021.

The Impact of COVID-19 Pandemic on Mental Health of Adolescents With Chronic Medical Conditions: Findings From a German Pediatric Outpatient Clinic.

PURPOSE: The impact of the COVID-19 pandemic on the mental health of adolescents is of great concern, especially in the vulnerable group of adolescents with chronic medical conditions. The aim of this study was to examine this impact on the mental health of adolescents with chronic medical conditions treated in a German pediatric outpatient clinic. METHODS: Changes in the mental health status of adolescents with chronic medical conditions treated in a German pediatric outpatient clinic during the COVID-19 pandemic were explored via validated screening tools for anxiety and depression. RESULTS: The relative risk for adolescents with chronic medical conditions to develop clinically relevant symptoms of anxiety or depression was significantly higher (odds ratio 1,78 [confidence interval 1.06-3.04]) during the pandemic. DISCUSSION: This study identifies the COVID-19 pandemic as a potential additional risk for adolescents with chronic medical conditions to develop clinically relevant signs of anxiety or depression.

What to do when the unwanted happens? Negative event management in studies on internet- and mobile-based interventions for youths and adults with two case reports.

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.

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

OBJECTIVE: Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. METHOD: Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal-external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. RESULTS: Performances ranged from R² = 21.0%-23.4%, decreasing only slightly after model optimism correction (R² = 17.0%-19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R2 = 32.6%-35.6%). Using a minimally important difference of d = -0.24 as assignment cut point, approximately 84.6%-93.3% of patients are helped by this type of intervention, while the remaining 6.7%-15.4% would experience clinically negligible benefits (δ^ = -0.02 to -0.19). Using reliable change as cut point, a smaller subset (39.3%-46.2%) with substantial expected benefits (δ^ = -0.68) receives a treatment recommendation. CONCLUSIONS: Meta-analytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Outpatient screening for anxiety and depression symptoms in adolescents with type 1 diabetes - a cross-sectional survey.

BACKGROUND: The daily demands of type 1 diabetes management may jeopardize adolescents' mental health. We aimed to assess anxiety and depression symptoms by broad-scale, tablet-based outpatient screening in adolescents with type 1 diabetes in Germany. METHODS: Adolescent patients with type 1 diabetes mellitus (n = 2,394; mean age 15.4 y [SD 2.0]; 50.7% male) were screened for anxiety (GAD-7) and depression symptoms (PHQ-9) by self-report questionnaires and linked to clinical data from the DPV patient registry. Logistic regression was used to estimate the contribution of clinical parameters to positive screening results. RESULTS: Altogether, 30.2% showed a positive screening (score ≥ 7 in either test), and 11.3% reported suicidal ideations or self-harm. Patients with anxiety and depression symptoms were older (15.7 y [CI 15.5-15.8] vs. 15.3 y [CI 15.2-15.4]; p < 0.0001), had higher HbA1c levels (7.9% [CI 7.8-8.0] (63 mmol/mol) vs. 7.5% [CI 7.4-7.5] (58 mmol/mol); p < 0.0001), and had higher hospitalization rates. Females (adjusted odds ratio (aOR) 2.66 [CI 2.21-3.19]; p < 0.0001), patients > 15 years (aOR 1.40 [1.16-1.68]; p < 0.001), who were overweight (aOR 1.40 [CI 1.14-1.71]; p = 0.001), with HbA1c > 9% (> 75 mmol/mol; aOR 2.58 [1.83-3.64]; each p < 0.0001), with a migration background (aOR 1.46 [CI 1.17-1.81]; p < 0.001), or smoking (aOR 2.72 [CI 1.41-5.23]; p = 0.003) had a higher risk. Regular exercise was a significant protective factor (aOR 0.65 [CI 0.51-0.82]; p < 0.001). Advanced diabetes technologies did not influence screening outcomes. CONCLUSIONS: Electronic mental health screening was implemented in 42 centers in parallel, and outcomes showed an association with clinical parameters from sociodemographic, lifestyle, and diabetes-related data. It should be integrated into holistic patient counseling, enabling early recognition of mild mental health symptoms for preventive measures. Females were disproportionally adversely affected. The use of advanced diabetes technologies did not yet reduce the odds of anxiety and depression symptoms in this cross-sectional assessment.

Effects of a Web-Based Behavioral Activation Intervention on Depressive Symptoms, Activation, Motivation, and Volition: Results of a Randomized Controlled Trial.

INTRODUCTION: Behavioral activation (BA) is effective for the treatment of depression. The Health Action Process Approach (HAPA), which is derived from health psychology, can provide a motivational-volitional framework of BA. OBJECTIVE: This study investigated the efficacy of a HAPA-based internet-delivered BA intervention (iBA; called InterAKTIV) in individuals with depression, also assessing HAPA-based motivational and volitional outcomes. METHODS: In a two-arm randomized controlled efficacy trial with a parallel design, 128 participants with a major depressive episode were randomly allocated to the intervention group (IG; TAU + immediate access to iBA) or control group (CG; TAU + access to iBA after follow-up). The primary outcome of clinician-rated depressive symptoms and secondary outcomes were assessed at baseline (T1), 8 weeks (T2), 6-month after randomization (T3). Data were analyzed on an intention-to-treat basis. RESULTS: Linear mixed model analyses revealed a significant group*time interaction effect on clinician-rated depressive symptoms favoring the IG (F2, 156.0 = 7.40; p < 0.001, d = 0.79 at T2, d = 0.25 at T3). The IG was also superior regarding self-rated depressive symptoms, BA, most motivational, and all volitional outcomes. CONCLUSION: This study shows that HAPA-based iBA can significantly improve clinician-rated depressive symptoms, as well as outcomes used in the HAPA model in people with depression. Building on these efficacy results, in the next step, the relationship between BA interventions and activity levels should be investigated, taking into account motivation and volition as potential mediators.

Optimizing the predictive power of depression screenings using machine learning.

Objective: Mental health self-report and clinician-rating scales with diagnoses defined by sum-score cut-offs are often used for depression screening. This study investigates whether machine learning (ML) can detect major depressive episodes (MDE) based on screening scales with higher accuracy than best-practice clinical sum-score approaches. Methods: Primary data was obtained from two RCTs on the treatment of depression. Ground truth were DSM 5 MDE diagnoses based on structured clinical interviews (SCID) and PHQ-9 self-report, clinician-rated QIDS-16, and HAM-D-17 were predictors. ML models were trained using 10-fold cross-validation. Performance was compared against best-practice sum-score cut-offs. Primary outcome was the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve. DeLong's test with bootstrapping was used to test for differences in AUC. Secondary outcomes were balanced accuracy, precision, recall, F1-score, and number needed to diagnose (NND). Results: A total of k = 1030 diagnoses (no diagnosis: k = 775; MDE: k = 255) were included. ML models achieved an AUCQIDS-16 = 0.94, AUCHAM-D-17 = 0.88, and AUCPHQ-9 = 0.83 in the testing set. ML AUC was significantly higher than sum-score cut-offs for QIDS-16 and PHQ-9 (ps ≤ 0.01; HAM_D-17: p = 0.847). Applying optimal prediction thresholds, QIDS-16 classifier achieved clinically relevant improvements (Δbalanced accuracy = 8%, ΔF1-score = 14%, ΔNND = 21%). Differences for PHQ_9 and HAM-D-17 were marginal. Conclusions: ML augmented depression screenings could potentially make a major contribution to improving MDE diagnosis depending on questionnaire (e.g., QIDS-16). Confirmatory studies are needed before ML enhanced screening can be implemented into routine care practice.

An Online Mindfulness Intervention for International Students: A Randomized Controlled Feasibility Trial.

Background: Student mobility across borders poses challenges to health systems at the university and country levels. International students suffer from stress more than their local peers, however, do not seek help or underutilize existing help offers. Some barriers to help-seeking among international students are insufficient information regarding the health offers, stigma, and language, which might be overcome via culturally adapted internet and mobile-based interventions (IMI). Method: A randomized controlled feasibility trial with a parallel design assessed the feasibility and potential efficacy of an online mindfulness intervention adapted for international university students. Participants were randomized into either an adapted online mindfulness intervention (StudiCareM-E) (IG, n = 20) or a waitlist control group (WL, n = 20). Participants were assessed at baseline (t0) and eight-week post-randomization (t1). The feasibility of StudiCareM-E was evaluated regarding intervention adherence, client satisfaction, and potential negative effects. The potential efficacy of StudiCareM-E was measured by means of the level of mindfulness, perceived stress, depression, anxiety, presenteeism, and wellbeing. Efficacy outcomes were evaluated with regression models on the intention-to-treat (ITT) sample (n = 40), adjusting for the baseline values. Results: Participants' formative feedback suggested improvements in the content of the IMI. There were no crucial negative effects compared to WL. Assessment dropout was 35% (IG: 50%: WL: 20%), and intervention dropout was 60%. StudiCareM-E yielded significant improvements in mindfulness (ß = .34), well-being (ß = .37), and anxiety (ß = -.42) compared to WL. Conclusion: StudiCareM-E might be used among culturally diverse international student populations to improve their well-being. Future studies might carefully inspect the extent of the adaptation needs of their target group and design their interventions accordingly.