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Anxiety and depression in children and adolescents are undertreated. Computer- and Internet-based cognitive behavioral treatments (cCBT) may be an attractive treatment alternative to regular face-to-face treatment.This meta-analysis aims to evaluate whether cCBT is effective for treating symptoms of anxiety and depression in youth. We conducted systematic searches in bibliographical databases (Pubmed, Cochrane controlled trial register, PsychInfo) up to December 4, 2013. Only randomized controlled trials in which a computer-, Internet- or mobile-based cognitive behavioral intervention targeting either depression, anxiety or both in children or adolescents up to the age of 25 were compared to a control condition were selected. We employed a random-effects pooling model in overall effect analyses and a mixed effect model for sub-group analyses. Searches resulted in identifying 13 randomized trials, including 796 children and adolescents that met inclusion criteria. Seven studies were directed at treating anxiety, four studies at depression, and two were of a transdiagnostic nature, targeting both anxiety and depression. The overall mean effect size (Hedges' g) of cCBT on symptoms of anxiety or depression at post-test was g=0.72 (95% CI:0.55-0.90, numbers needed to be treated (NNT)=2.56). Heterogeneity was low (I²=20.14%, 95% CI: 0-58%). The superiority of cCBT over controls was evident for interventions targeting anxiety (g=0.68; 95% CI: 0.45-0.92; p < .001; NNT=2.70) and for interventions targeting depression (g=0.76; 95% CI: 0.41-0.12; p < .001; NNT=2.44) as well as for transdiagnostic interventions (g=0.94; 95% CI: 0.23-2.66; p < .001; NNT=2.60). Results provide evidence for the efficacy of cCBT in the treatment of anxiety and depressive symptoms in youth. Hence, such interventions may be a promising treatment alternative when evidence based face-to-face treatment is not feasible. Future studies should examine long-term effects of treatments and should focus on obtaining patient-level data from existing studies, to perform an individual patient data meta-analysis.

Face-to-face brief interventions for problem drinking are effective, but they have found limited implementation in routine care and the community. Internet-based interventions could overcome this treatment gap. We investigated effectiveness and moderators of treatment outcomes in internet-based interventions for adult problem drinking (iAIs). Systematic searches were performed in medical and psychological databases to 31 December 2016. A one-stage individual patient data meta-analysis (IPDMA) was conducted with a linear mixed model complete-case approach, using baseline and first follow-up data. The primary outcome measure was mean weekly alcohol consumption in standard units (SUs, 10 grams of ethanol). Secondary outcome was treatment response (TR), defined as less than 14/21 SUs for women/men weekly. Putative participant, intervention, and study moderators were included. Robustness was verified in three sensitivity analyses: a two-stage IPDMA, a one-stage IPDMA using multiple imputation, and a missing-not-at-random (MNAR) analysis. We obtained baseline data for 14,198 adult participants (19 randomised controlled trials [RCTs], mean age 40.7 [SD = 13.2], 47.6% women). Their baseline mean weekly alcohol consumption was 38.1 SUs (SD = 26.9). Most were regular problem drinkers (80.1%, SUs 44.7, SD = 26.4) and 19.9% (SUs 11.9, SD = 4.1) were binge-only drinkers. About one third were heavy drinkers, meaning that women/men consumed, respectively, more than 35/50 SUs of alcohol at baseline (34.2%, SUs 65.9, SD = 27.1). Post-intervention data were available for 8,095 participants. Compared with controls, iAI participants showed a greater mean weekly decrease at follow-up of 5.02 SUs (95% CI -7.57 to -2.48, p < 0.001) and a higher rate of TR (odds ratio [OR] 2.20, 95% CI 1.63-2.95, p < 0.001, number needed to treat [NNT] = 4.15, 95% CI 3.06-6.62). Persons above age 55 showed higher TR than their younger counterparts (OR = 1.66, 95% CI 1.21-2.27, p = 0.002). Drinking profiles were not significantly associated with treatment outcomes. Human-supported interventions were superior to fully automated ones on both outcome measures (comparative reduction: -6.78 SUs, 95% CI -12.11 to -1.45, p = 0.013; TR: OR = 2.23, 95% CI 1.22-4.08, p = 0.009). Participants treated in iAIs based on personalised normative feedback (PNF) alone were significantly less likely to sustain low-risk drinking at follow-up than those in iAIs based on integrated therapeutic principles (OR = 0.52, 95% CI 0.29-0.93, p = 0.029). The use of waitlist control in RCTs was associated with significantly better treatment outcomes than the use of other types of control (comparative reduction: -9.27 SUs, 95% CI -13.97 to -4.57, p < 0.001; TR: OR = 3.74, 95% CI 2.13-6.53, p < 0.001). The overall quality of the RCTs was high; a major limitation included high study dropout (43%). Sensitivity analyses confirmed the robustness of our primary analyses. To our knowledge, this is the first IPDMA on internet-based interventions that has shown them to be effective in curbing various patterns of adult problem drinking in both community and healthcare settings. Waitlist control may be conducive to inflation of treatment outcomes.

In the past 10 years an increasing number of randomised trials have examined the effects of transdiagnostic treatments of patients with depression or anxiety. We conducted the first comprehensive meta-analysis of the outcomes of this emerging field. We used the searches in PubMed, PsychINFO, Embase and the Cochrane library of an existing database of randomised trials of psychological interventions for depression to identify studies comparing a transdiagnostic treatment of patients with depression or anxiety with a control group (deadline 1 January 2022). We conducted random-effects meta-analyses and examined the effects on depression and anxiety at the short and longer term. We included 45 randomised controlled trials with 51 comparisons between a psychotherapy and a control group and 5530 participants. Thirty-five (78%) studies were conducted in the last 10 years. The overall effect size was g = 0.54 (95% CI 0.40-0.69; NNT = 5.87), with high heterogeneity ( = 78; 95% CI 71-83), and a broad PI (-0.31-1.39). The effects remained significant in a series of sensitivity analyses, including exclusion of outliers, adjustment for publication bias, for studies with low risk of bias, and in multilevel analyses. The results were comparable for depression and anxiety separately. At 6 months after randomisation the main effects were still significant, but not at 12 months, although the number of studies was small. Transdiagnostic treatments of patients with depression or anxiety are increasingly examined and are probably effective at the short term.

Introduction: Evidence on effects of Internet-based interventions to treat subthreshold depression (sD) and prevent the onset of major depression (MDD) is inconsistent. Objective: We conducted an individual participant data meta-analysis to determine differences between intervention and control groups (IG, CG) in depressive symptom severity (DSS), treatment response, close to symptom-free status, symptom deterioration and MDD onset as well as moderators of intervention outcomes. Methods: Randomized controlled trials were identified through systematic searches via PubMed, PsycINFO, Embase and Cochrane Library. Multilevel regression analyses were used to examine efficacy and moderators. Results: Seven trials (2,186 participants) were included. The IG was superior in DSS at all measurement points (posttreatment: 6–12 weeks; Hedges’ g = 0.39 [95% CI: 0.25–0.53]; follow-up 1: 3–6 months; g = 0.30 [95% CI: 0.15–0.45]; follow-up 2: 12 months, g = 0.27 [95% CI: 0.07–0.47], compared with the CG. Significantly more participants in the IG than in the CG reached response and close to symptom-free status at all measurement points. A significant difference in symptom deterioration between the groups was found at the posttreatment assessment and follow-up 2. Incidence rates for MDD onset within 12 months were lower in the IG (19%) than in the CG (26%). Higher initial DSS and older age were identified as moderators of intervention effect on DSS. Conclusions: Our findings provide evidence for Internet-based interventions to be a suitable low-threshold intervention to treat individuals with sD and to reduce the incidence of MDD. This might be particularly true for older people with a substantial symptom burden.

Here, we summarise the extinction risk of the sharks and rays endemic to coastal, shelf, and slope waters of the southwest Indian Ocean and adjacent waters (SWIO+, Namibia to Kenya, including SWIO islands). This region is a hotspot of endemic and evolutionarily distinct sharks and rays. Nearly one-fifth ( n = 13 of 70, 18.6%) of endemic sharks and rays are threatened, of these: one is Critically Endangered, five are Endangered, and seven are Vulnerable. A further seven (10.0%) are Near Threatened, 33 (47.1%) are Least Concern, and 17 (24.3%) are Data Deficient. While the primary threat is overfishing, there are the first signs that climate change is contributing to elevated extinction risk through habitat reduction and inshore distributional shifts. By backcasting their status, few endemic species were threatened in 1980, but this changed soon after the emergence of targeted shark and ray fisheries. South Africa has the highest national conservation responsibility, followed by Mozambique and Madagascar. Yet, while fisheries management and enforcement have improved in South Africa over recent decades, substantial improvements are urgently needed elsewhere. To avoid extinction and ensure robust populations of the region’s endemic sharks and rays and maintain ecosystem functionality, there is an urgent need for the strict protection of Critically Endangered and Endangered species and sustainable management of Vulnerable, Near Threatened, and Least Concern species, underpinned by species-level data collection and reduction of incidental catch.

Blended therapy is a new approach combining advantages of face-to-face psychotherapy and Internet- and mobile-based interventions. Acceptance is a fundamental precondition for its implementation. The aim of this study was to assess 1) the acceptance of psychotherapists towards blended therapy, 2) the effectiveness of an acceptance facilitating intervention (AFI) on psychotherapists' acceptance towards blended therapy and 3) to identify potential effect moderators. Psychotherapists (N = 284) were randomly assigned to a control (CG) or an intervention group (IG). The IG received a short video showing an example of blended therapy, the CG an attention placebo video. Both groups received a reliable online questionnaire assessing acceptance, effort expectancy, performance expectancy, facilitating conditions, social influence and internet anxiety. Between group differences were examined using t-tests and Mann-Whitney tests. Exploratory analysis was conducted to identify moderators. Psychotherapists in CG showed mixed baseline acceptance towards blended therapy (low = 40%, moderate = 33%, high = 27%). IG showed significantly higher acceptance compared to CG (d = .27, p.sub.one-sided = .029; low = 24%, moderate = 47%, high = 30%). Bootstrapped confidence intervals were overlapping. Performance expectancy (d = .35), effort expectancy (d = .44) and facilitating conditions (d = .28) were significantly increased (p .05). Exploratory analysis indicated psychodynamic oriented psychotherapists profiting particularly from the AFI. Blended therapy is a promising approach to improve healthcare. Psychotherapists show mixed acceptance, which might be improvable by AFIs, particularly in subpopulations of initially rather skeptical psychotherapists. Forthcoming studies should extend the present study by shifting focus from attitudes to the impact of different forms of AFIs on uptake.

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. 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). 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). NSSI is common among first-year college students and is a behavioral marker of various common mental disorders.

Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime.

While smartphone usage is ubiquitous, and the app market for smartphone apps targeted at mental health is growing rapidly, the evidence of standalone apps for treating mental health symptoms is still unclear. This meta-analysis investigated the efficacy of standalone smartphone apps for mental health. A comprehensive literature search was conducted in February 2018 on randomized controlled trials investigating the effects of standalone apps for mental health in adults with heightened symptom severity, compared to a control group. A random-effects model was employed. When insufficient comparisons were available, data was presented in a narrative synthesis. Outcomes included assessments of mental health disorder symptom severity specifically targeted at by the app. In total, 5945 records were identified and 165 full-text articles were screened for inclusion by two independent researchers. Nineteen trials with 3681 participants were included in the analysis: depression ( k  = 6), anxiety ( k  = 4), substance use ( k  = 5), self-injurious thoughts and behaviors ( k  = 4), PTSD ( k  = 2), and sleep problems ( k  = 2). Effects on depression (Hedges’ g  = 0.33, 95%CI 0.10–0.57, P  = 0.005, NNT = 5.43, I 2  = 59%) and on smoking behavior ( g  = 0.39, 95%CI 0.21–0.57, NNT = 4.59, P  ≤ 0.001, I 2  = 0%) were significant. No significant pooled effects were found for anxiety, suicidal ideation, self-injury, or alcohol use ( g  = −0.14 to 0.18). Effect sizes for single trials ranged from g  = −0.05 to 0.14 for PTSD and g  = 0.72 to 0.84 for insomnia. Although some trials showed potential of apps targeting mental health symptoms, using smartphone apps as standalone psychological interventions cannot be recommended based on the current level of evidence.