Efficacy of an Internet- and Mobile-Based Intervention for Subclinical Anxiety and Depression (ICare Prevent) with Two Guidance Formats: Results from a Three-Armed Randomized Controlled Trial.

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.

Who benefits from indirect prevention and treatment of depression using an online intervention for insomnia? Results from an individual-participant data meta-analysis.

BACKGROUND: Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. METHODS: Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. RESULTS: IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = -0.71 [95% CI-0.92 to -0.51]) and at follow-up (d = -0.84 [95% CI -1.11 to -0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = -0.48 and -0.87 (post) and d = - 0.66 to -0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. CONCLUSIONS: An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.

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.

How to promote usage of telehealth interventions for farmers' mental health? A qualitative study on supporting and hindering aspects for acceptance and satisfaction with a personalized telephone coaching for depression prevention.

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.

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.

Childhood adversities and mental disorders in first-year college students: results from the World Mental Health International College Student Initiative.

BACKGROUND: This study investigates associations of several dimensions of childhood adversities (CAs) with lifetime mental disorders, 12-month disorder persistence, and impairment among incoming college students. METHODS: Data come from the World Mental Health International College Student Initiative (WMH-ICS). Web-based surveys conducted in nine countries (n = 20 427) assessed lifetime and 12-month mental disorders, 12-month role impairment, and seven types of CAs occurring before the age of 18: parental psychopathology, emotional, physical, and sexual abuse, neglect, bullying victimization, and dating violence. Poisson regressions estimated associations using three dimensions of CA exposure: type, number, and frequency. RESULTS: Overall, 75.8% of students reported exposure to at least one CA. In multivariate regression models, lifetime onset and 12-month mood, anxiety, and substance use disorders were all associated with either the type, number, or frequency of CAs. In contrast, none of these associations was significant when predicting disorder persistence. Of the three CA dimensions examined, only frequency was associated with severe role impairment among students with 12-month disorders. Population-attributable risk simulations suggest that 18.7-57.5% of 12-month disorders and 16.3% of severe role impairment among those with disorders were associated with these CAs. CONCLUSION: CAs are associated with an elevated risk of onset and impairment among 12-month cases of diverse mental disorders but are not involved in disorder persistence. Future research on the associations of CAs with psychopathology should include fine-grained assessments of CA exposure and attempt to trace out modifiable intervention targets linked to mechanisms of associations with lifetime psychopathology and burden of 12-month mental disorders.

Mediators of digital depression prevention in patients with chronic back pain: Findings from a multicenter randomized clinical trial.

OBJECTIVE: The mechanisms of change in digital interventions for the prevention of depression are largely unknown. Here, we explored whether five theoretically derived intervening variables (i.e., pain intensity, pain-related disability, pain self-efficacy, quality of life [QoL], and work capacity) were mediating the effectiveness of a digital intervention specifically designed to prevent depression in patients with chronic back pain (CBP). METHOD: This study is a secondary analysis of a pragmatic, observer-masked randomized clinical trial conducted at 82 orthopedic clinics in Germany. A total of 295 adults with a diagnosis of CBP and subclinical depressive symptoms were randomized to either the intervention group (n = 149) or treatment-as-usual (n = 146). Longitudinal mediation analyses were conducted with structural equation modeling and depression symptom severity as primary outcome (Patient Health Questionnaire-9 [PHQ-9]; 6 months after randomization) on an intention-to-treat basis. RESULTS: Beside the effectiveness of the digital intervention in preventing depression, we found a significant causal mediation effect for QoL as measured with the complete scale of Assessment of Quality of Life (AQoL-6D; axb: -0.234), as well as for the QoL subscales mental health (axb: -0.282) and coping (axb: -0.249). All other potential intervening variables were not significant. CONCLUSION: Our findings suggest a relevant role of QoL, including active coping, as change mechanism in the prevention of depression. Yet, more research is needed to extend and specify our knowledge on empirically supported processes in digital depression prevention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Three-Armed Randomized Controlled Trial to Evaluate the Effectiveness, Acceptance, and Negative Effects of StudiCare Mindfulness, an Internet- and Mobile-Based Intervention for College Students with No and "On Demand" Guidance.

The college years can be accompanied by mental distress. Internet- and mobile-based interventions (IMIs) have the potential to improve mental health but adherence is problematic. Psychological guidance might promote adherence but is resource intensive. In this three-armed randomized controlled trial, "guidance on demand" (GoD) and unguided (UG) adherence-promoting versions of the seven-module IMI StudiCare Mindfulness were compared with a waitlist control group and each other. The GoD participants could ask for guidance as needed. A total of 387 students with moderate/low mindfulness were recruited. Follow-up assessments took place after 1 (t1), 2 (t2), and 6 (t3) months. Post-intervention (t2), both versions significantly improved the primary outcome of mindfulness (d = 0.91-1.06, 95% CI 0.66-1.32) and most other mental health outcomes (d = 0.25-0.69, 95% CI 0.00-0.94) compared with WL, with effects generally persisting after 6 months. Exploratory comparisons between UG and GoD were mostly non-significant. Adherence was low but significantly higher in GoD (39%) vs. UG (28%) at the 6-month follow-up. Across versions, 15% of participants experienced negative effects, which were mostly mild. Both versions effectively promoted mental health in college students. Overall, GoD was not associated with substantial gains in effectiveness or adherence compared with UG. Future studies should investigate persuasive design to improve adherence.

Non-suicidal self-injury among first-year college students and its association with mental disorders: results from the World Mental Health International College Student (WMH-ICS) initiative.

BACKGROUND: Although non-suicidal self-injury (NSSI) is an issue of major concern to colleges worldwide, we lack detailed information about the epidemiology of NSSI among college students. The objectives of this study were to present the first cross-national data on the prevalence of NSSI and NSSI disorder among first-year college students and its association with mental disorders. METHODS: Data come from a survey of the entering class in 24 colleges across nine countries participating in the World Mental Health International College Student (WMH-ICS) initiative assessed in web-based self-report surveys (20 842 first-year students). Using retrospective age-of-onset reports, we investigated time-ordered associations between NSSI and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-IV) mood (major depressive and bipolar disorder), anxiety (generalized anxiety and panic disorder), and substance use disorders (alcohol and drug use disorder). RESULTS: NSSI lifetime and 12-month prevalence were 17.7% and 8.4%. A positive screen of 12-month DSM-5 NSSI disorder was 2.3%. Of those with lifetime NSSI, 59.6% met the criteria for at least one mental disorder. Temporally primary lifetime mental disorders predicted subsequent onset of NSSI [median odds ratio (OR) 2.4], but these primary lifetime disorders did not consistently predict 12-month NSSI among respondents with lifetime NSSI. Conversely, even after controlling for pre-existing mental disorders, NSSI consistently predicted later onset of mental disorders (median OR 1.8) as well as 12-month persistence of mental disorders among students with a generalized anxiety disorder (OR 1.6) and bipolar disorder (OR 4.6). CONCLUSIONS: NSSI is common among first-year college students and is a behavioral marker of various common mental disorders.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment.

Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods: We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( N = 65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models' ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day. Results: In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ( MAE = 1.062 ). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12 % to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level. Discussion: Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.

Unveiling mechanisms of change in digital interventions for depression: Study protocol for a systematic review and individual participant data meta-analysis.

Introduction: The efficacy and effectiveness of digital interventions for depression are both well-established. However, precise effect size estimates for mediators transmitting the effects of digital interventions are not available; and integrative insights on the specific mechanisms of change in internet- and mobile-based interventions (IMIs)-as related to key features like delivery type, accompanying support and theoretical foundation-are largely pending. Objective: We will conduct a systematic review and individual participant data meta-analysis (IPD-MA) evaluating the mediators associated with therapeutic change in various IMIs for depression in adults. Methods: We will use three electronic databases (i.e., Embase, Medline/PubMed, PsycINFO) as well as an already established database of IPD to identify relevant published and unpublished studies. We will include (1) randomized controlled trials that examine (2) mediators of (3) guided and unguided (4) IMIs with (5) various theoretical orientations for (6) adults with (7) clinically relevant symptoms of depression (8) compared to an active or passive control condition (9) with depression symptom severity as primary outcome. Study selection, data extraction, as well as quality and risk of bias (RoB) assessment will be done independently by two reviewers. Corresponding authors of eligible primary studies will be invited to share their IPD for this meta-analytic study. In a 1-stage IPD-MA, mediation analyses (e.g., on potential mediators like self-efficacy, emotion regulation or problem solving) will be performed using a multilevel structural equation modeling approach within a random-effects framework. Indirect effects will be estimated, with multiple imputation for missing data; the overall model fit will be evaluated and statistical heterogeneity will be assessed. Furthermore, we will investigate if indirect effects are moderated by different variables on participant- (e.g., age, sex/gender, symptom severity), study- (e.g., quality, studies evaluating the temporal ordering of changes in mediators and outcomes), and intervention-level (e.g., theoretical foundation, delivery type, guidance). Discussion: This systematic review and IPD-MA will generate comprehensive information on the differential strength of mediators and associated therapeutic processes in digital interventions for depression. The findings might contribute to the empirically-informed advancement of psychotherapeutic interventions, leading to more effective interventions and improved treatment outcomes in digital mental health. Besides, with our novel approach to mediation analyses with IPD-MA, we might also add to a methodological progression of evidence-synthesis in psychotherapy process research. Study registration with Open Science Framework OSF: https://osf.io/md7pq/.

Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting.

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. Many of the currently available approaches to predict PHQ scores use passive data, e.g., from smartphones. However, there are several other scores and data besides PHQ, e.g., the Behavioral Activation for Depression Scale-Short Form (BADSSF), the Center for Epidemiologic Studies Depression Scale (CESD), or the Personality Dynamics Diary (PDD), all of which can be effortlessly collected on a daily basis. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.

Depression Diagnosis and Forecast based on Mobile Phone Sensor Data.

Previous studies have shown the correlation be-tween sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to access and less time-consuming. In particular, passive mobile phone data can be collected on a flexible time interval, thus detecting moment-by-moment psychological changes and helping achieve earlier interventions. Moreover, while previous studies mainly focused on depression diagnosis using mobile phone data, depression forecasting has not received sufficient attention. In this work, we extract four types of passive features from mobile phone data, including phone call, phone usage, user activity, and GPS features. We implement a long short-term memory (LSTM) network in a subject-independent 10-fold cross-validation setup to model both a diagnostic and a forecasting tasks. Experimental results show that the forecasting task achieves comparable results with the diagnostic task, which indicates the possibility of forecasting depression from mobile phone sensor data. Our model achieves an accuracy of 77.0 % for major depression forecasting (binary), an accuracy of 53.7 % for depression severity forecasting (5 classes), and a best RMSE score of 4.094 (PHQ-9, range from 0 to 27).

Examining differential responses to the Take Care of Me trial: A latent class and moderation analysis.

Given prevalent alcohol misuse-emotional comorbidities among young adults, we developed an internet-based integrated treatment called Take Care of Me. Although the treatment had an impact on several secondary outcomes, effects were not observed for the primary outcome. Therefore, the goal of the current study was to examine heterogeneity in treatment responses. The initial RCT randomized participants to either a treatment or psychoeducational control condition. We conducted an exploratory latent class analysis to distinguish individuals based on pre-treatment risk and then used moderated regressions to examine differential treatment responses based on class membership. We found evidence for three distinct groups. Most participants fell in the "low severity" group (n = 123), followed by the "moderate severity" group (n = 57) who had a higher likelihood of endorsing a previous mental health diagnosis and treatment and higher symptom severity than the low group. The "high severity" group (n = 42) endorsed a family history of alcoholism, and the highest symptom severity and executive dysfunction. Moderated regressions revealed significant class differences in treatment responses. In the treatment condition, high severity (relative to low) participants reported higher alcohol consumption and hazardous drinking and lower quality of life at follow-up, whereas moderate severity (relative to low) individuals had lower alcohol consumption at follow-up, and lower hazardous drinking at end-of-treatment. No class differences were found for participants in the control group. Higher risk individuals in the treatment condition had poorer responses to the program. Tailoring interventions to severity may be important to examine in future research.

Special issue on digital health interventions in chronic medical conditions: Editorial.

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.

Telephone coaching for the prevention of depression in farmers: Results from a pragmatic randomized controlled trial.

INTRODUCTION: Farmers have a high risk for depression (MDD). Preventive measures targeting this often remotely living population might reduce depression burden. The study aimed to evaluate the effectiveness of personalized telephone coaching in reducing depressive symptom severity and preventing MDD in farmers compared to enhanced treatment as usual (TAU + ). METHODS: In a two-armed, pragmatic randomized controlled trial (N = 314) with post-treatment at 6 months, farming entrepreneurs, collaborating family members and pensioners with elevated depressive symptoms (PHQ-9 ≥ 5) were randomized to personalized telephone coaching or TAU + . The coaching was provided by psychologists and consists on average of 13 (±7) sessions a 48 min (±15) over 6 months. The primary outcome was depressive symptom severity (QIDS-SR16). RESULTS: Coaching participants showed a significantly greater reduction in depressive symptom severity compared to TAU + (d = 0.39). Whereas reliable symptom deterioration was significantly lower in the intervention group compared to TAU + , no significant group differences were found for reliable improvement and in depression onset. Further significant effects in favor of the intervention group were found for stress (d = 0.34), anxiety (d = 0.30), somatic symptoms (d = 0.39), burnout risk (d = 0.24-0.40) and quality of life (d = 0.28). DISCUSSION: Limiting, we did not apply an upper cutoff score for depressive symptom severity or controlled for previous MDD episodes, leaving open whether the coaching was recurrence/relapse prevention or early treatment. Nevertheless, personalized telephone coaching can effectively improve mental health in farmers. It could play an important role in intervening at an early stage of mental health problems and reducing disease burden related to MDD. TRIAL REGISTRATION NUMBER AND TRIAL REGISTER: German Clinical Trial Registration: DRKS00015655.

Cannabis Use in Adults Who Screen Positive for Attention Deficit/Hyperactivity Disorder: CANreduce 2.0 Randomized Controlled Trial Subgroup Analysis.

BACKGROUND: Prevalence rates for lifetime cannabis use and cannabis use disorder are much higher in people with attention deficit/hyperactivity disorder than in those without. CANreduce 2.0 is an intervention that is generally effective at reducing cannabis use in cannabis misusers. This self-guided web-based intervention (6-week duration) consists of modules grounded in motivational interviewing and cognitive behavioral therapy. OBJECTIVE: We aimed to evaluate whether the CANreduce 2.0 intervention affects cannabis use patterns and symptom severity in adults who screen positive for attention deficit/hyperactivity disorder more than in those who do not. METHODS: We performed a secondary analysis of data from a previous study with the inclusion criterion of cannabis use at least once weekly over the last 30 days. Adults with and without attention deficit/hyperactivity disorder (based on the Adult Attention deficit/hyperactivity disorder Self-Report screener) who were enrolled to the active intervention arms of CANreduce 2.0 were compared regarding the number of days cannabis was used in the preceding 30 days, the cannabis use disorder identification test score (CUDIT) and the severity of dependence scale score (SDS) at baseline and the 3-month follow-up. Secondary outcomes were Generalized Anxiety Disorder score, Center for Epidemiological Studies Depression scale score, retention, intervention adherence, and safety. RESULTS: Both adults with (n=94) and without (n=273) positive attention-deficit/hyperactivity disorder screening reported significantly reduced frequency (reduction in consumption days: with: mean 11.53, SD 9.28, P<.001; without: mean 8.53, SD 9.4, P<.001) and severity of cannabis use (SDS: with: mean 3.57, SD 3.65, P<.001; without: mean 2.47, SD 3.39, P<.001; CUDIT: with: mean 6.38, SD 5.96, P<.001; without: mean 5.33, SD 6.05, P<.001), as well as anxiety (with: mean 4.31, SD 4.71, P<.001; without: mean 1.84, SD 4.22, P<.001) and depression (with: mean 10.25, SD 10.54; without: mean 4.39, SD 10.22, P<.001). Those who screened positive for attention deficit/hyperactivity disorder also reported significantly decreased attention deficit/hyperactivity disorder scores (mean 4.65, SD 4.44, P<.001). There were no significant differences in change in use (P=.08), dependence (P=.95), use disorder (P=.85), attention deficit/hyperactivity disorder status (P=.84), depression (P=.84), or anxiety (P=.26) between baseline and final follow-up, dependent on positive attention-deficit/hyperactivity disorder screening. Attention deficit/hyperactivity disorder symptom severity at baseline was not associated with reduced cannabis use frequency or severity but was linked to greater reductions in depression (Spearman ρ=.33) and anxiety (Spearman ρ=.28). Individuals with positive attention deficit/hyperactivity disorder screening were significantly less likely to fill out the consumption diary (P=.02), but the association between continuous attention deficit/hyperactivity disorder symptom severity and retention (Spearman ρ=-0.10, P=.13) was nonsignificant. There also was no significant intergroup difference in the number of completed modules (with: mean 2.10, SD 2.33; without: mean 2.36, SD 2.36, P=.34), and there was no association with attention deficit/hyperactivity disorder symptom severity (Spearman ρ=-0.09; P=.43). The same was true for the rate of adverse effects (P=.33). CONCLUSIONS: Cannabis users screening positive for attention deficit/hyperactivity disorder may benefit from CANreduce 2.0 to decrease the frequency and severity of cannabis dependence and attenuate symptoms of depression and attention deficit/hyperactivity disorder-related symptoms. This web-based program's advantages include its accessibility for remote users and a personalized counselling option that may contribute to increased adherence and motivation to change among program users. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 11086185; http://www.isrctn.com/ISRCTN11086185.

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood.

Background: Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. Methods: Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. Results: Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. Conclusions: The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Effects of self-guided stress management interventions in college students: A systematic review and meta-analysis.

BACKGROUND: College students face several sources of stress. Self-guided stress management interventions offer an excellent opportunity for scaling up evidence-based interventions for self-management of these stresses. However, little is known about the overall effects of these interventions. Increasing this understanding is essential because self-guided stress management interventions might be a cost-effective and acceptable way of providing help to this important segment of the population during a critical life course stage. METHODS: We carried out a systematic literature search of bibliographical databases (PubMed, PsycINFO, Embase, and Cochrane Library) for randomized controlled trials (RCTs) of self-guided stress management interventions published up through April 2020. We conducted two separate meta-analyses for perceived stress, depression, and anxiety. The first included interventions for general college student samples. The second included studies for students with high levels of perceived stress. RESULTS: The first meta-analysis included 26 studies with 29 intervention-control comparisons based on a total of 4468 students. The pooled effect size was small but statistically significant (g = 0.19; 95% CI [0.10, 0.29]; p < 0.001). Results showed moderate heterogeneity across studies [I 2 = 48%; 95% CI (19, 66%)]. The second meta-analysis, included four studies based on a total of 491 students with high levels of stress. The pooled effect size was small but statistically significant (g = 0.34; 95% CI [0.16, 0.52]; p < 0.001). Results showed no heterogeneity across studies (I 2 = 0%; 95% CI [0, 79%]), but risk of bias was substantial. DISCUSSION: Our results suggest that self-guided stress management programs may be effective when compared to control conditions, but with small average effects. These programs might be a useful element of a multi-component intervention system. Given the psychological barriers to treatment that exist among many college students, self-help interventions might be a good first step in facilitating subsequent help-seeking among students reluctant to engage in other types of treatment. More studies should be conducted to investigate these interventions, sample specifications, mediating effects, and individual-level heterogeneity of effects.

Guided internet-based transdiagnostic individually tailored Cognitive Behavioral Therapy for symptoms of depression and/or anxiety in college students: A randomized controlled trial.

Common mental disorders, such as depression and anxiety, often emerge in college students during the transition into early adulthood. Mental health problems can seriously impact students' functioning, interpersonal relationships, and academic achievement. Actively reaching out to college students with mental health problems and offering them internet-based interventions may be a promising way of providing low-threshold access to evidence-based treatment in colleges. This randomized controlled trial aimed to assess the effectiveness of a guided web-based transdiagnostic individually tailored Cognitive Behavioral Therapy (iCBT) in treating college students with depression and/or anxiety symptoms. Through an online survey that screened college students' mental health, we recruited 100 college students aged ≥18 years who reported mild to moderate depression and/or anxiety symptoms and were attending colleges in the Netherlands. Participants were randomly allocated to guided iCBT (n = 48) or treatment as usual (TAU) control (n = 52). Primary outcomes were symptoms of depression and anxiety measured at post-treatment (7 weeks post-randomization). We also measured all outcomes at 6- and 12-months post-randomization. All analyses were based on the intention-to-treat principle and were repeated using the complete-case sample. We found no evidence of a difference between the effects of guided iCBT and TAU in any of the examined outcomes (i.e., symptoms of depression and anxiety, quality of life, educational achievement, and college dropout) across all time points (p > .05). There was no evidence that effects of iCBT were associated with treatment satisfaction and adherence. More research into transdiagnostic individually tailored iCBT is necessary. Further, future studies should recruit larger samples to investigate possible smaller but clinically relevant effects of internet-based interventions for college students with depression and/or anxiety.