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Abstract Background Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention “works” and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation. Methods In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software. Results Drawing on Rubin’s potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset. Discussion Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable “weak spots” of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Abstract Background Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention. Methods A secondary data analysis was conducted using data from two randomised‐controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close‐to‐symptom‐free status postintervention (6–7 weeks) and at follow‐up (3–6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression. Results Small effects were observed at follow‐up for depressive symptoms (β = −.39, 95% confidence interval [CI]: [−0.75, −0.03], p = .032, padjusted = .130) and close‐to‐symptom‐free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity. Conclusion More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention. Patient or Public Contribution This secondary analysis did not involve patients, service users, care‐givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults. Clinical Trial Registration Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Abstract Background Occupational stress is a major public health challenge that requires a variety of evidence-based preventative approaches to increase their reach within the working population. Behavioral stress management interventions are considered an established approach for occupational stress prevention. Both in-person group-based stress management training (gSMT) and individual Internet-based training (iSMT) have been shown to be effective at reducing stress in employees. However, there remains a lack of evidence on the comparative efficacy of the newer digital format compared to well-established, in-person, group-based training. This study aims (1) to directly compare an evidence-based iSMT with an established gSMT on stress in employees, (2) to analyze the two conditions from a cost perspective, and (3) to explore moderators of the comparative efficacy. Methods In a randomized, controlled, non-inferiority trial employees from the general working population will be allocated to iSMT or gSMT. The primary outcome will be perceived stress, assessed using the Perceived Stress Scale, three months after randomization. The non-inferiority margin for the primary outcome measure will be set at 2 points (Cohen’s d = 0.29). This trial will also compare the two interventions from a health economics perspective, and conduct explorative analyses to identify potential effect moderators. Discussion To reach a larger proportion of the working population, well-established gSMT should be complemented with interventions that fit today’s society’s increasingly digital lifestyle. The current trial will provide evidence supporting the responsible implementation of Internet-based stress management training if the digital format proves to at least be non-inferior to established group-based training. Additional explorative moderator analyses may guide future practices to aid in matching select programs with select users. Trial registration German Register of Clinical Studies (DRKS): DRKS00024892, date of registration: 2021-04-09. Protocol version: 02, 16-10-2021.

The internet network continues to be a major source of health-related information. Our research provides insights into the online health-seeking behaviors of the general population, and evaluates the potential relationship between the COVID-19 pandemic and public interest and awareness of general sleep health, mental health and wellbeing.Google Trends’ weekly relative search volumes (RSVs) were examined during 2020 for searches specifically related to COVID-19 symptoms, and for searches related to general health, sleep and wellbeing, in the United Kingdom, the United States of America, France, Italy and Japan. To obtain insight into the association between the initiation of public restrictions and online search trends, we assessed a six-week period; the ‘early pandemic period’ (EPP) (01 March 2020 – 11 April 2020). To provide a meaningful pre-pandemic comparison, a similar period during 2019 (03 March – 13 April 2019) was compared for RSV and median difference analysis.The EPP was associated with increased online searches related to COVID-19 symptoms, as compared with those related to more general sleep health, mental health and wellbeing. The latter search terms frequently showed a decrease or minimal change in RSV during the EPP compared with the equivalent period in 2019. This finding illustrates the potential link between the COVID-19 pandemic and online search behavior and corroborates existing findings regarding internet searches during this period.Proactive communication by healthcare professionals during future pandemics and as an ongoing measure could help prevent public neglect of general health and wellbeing symptoms, and encourage reporting and early intervention.

Introduction Only a minority of people living with mental health problems are getting professional help. As digitalisation moves on, the possibility of providing internet/mobile-based interventions (IMIs) arises. One type of IMIs are fully automated conversational software agents (chatbots). Software agents are computer programs that can hold conversations with a human by mimicking a human conversational style. Software agents could deliver low-threshold and cost-effective interventions aiming at promoting psychological well-being in a large number of individuals. The aim of this trial is to evaluate the clinical effectiveness and acceptance of the brief software agent-based IMI SISU in comparison with a waitlist control group.Methods and analysis Within a two-group randomised controlled trial, a total of 120 adult participants living with low well-being (Well-being Scale/WHO-5) will be recruited in Germany, Austria and Switzerland. SISU is based on therapeutic writing and acceptance and commitment therapy-based principles. The brief intervention consists of three modules. Participants work through the intervention on 3 consecutive days. Assessment takes place before (t1), during (t2) and after (t3) the interaction with SISU, as well as 4 weeks after randomisation (t4). Primary outcome is psychological well-being (WHO-5). Secondary outcomes are emotional well-being (Flourishing Scale), psychological flexibility (Acceptance and Action Questionnaire-II), quality of life (Assessment of Quality of Life -8D), satisfaction with the intervention (Client Satisfaction Questionnaire-8) and side effects (Inventory for the assessment of negative effectsof psychotherapy). Examined mediators and moderators are sociodemographic variables, personality (Big Five Inventory-10), emotion regulation (Emotion Regulation Questionnaire), alexithymia (Toronto Alexithymia Scale-20), centrality of events (Centrality of Events Scale), treatment expectancies (Credibility Expectancy Questionnaire) and technology alliance (Inventory of Technology Alliance–Online Therapy). Data analysis will be based on intention-to-treat principles. SISU guides participants through a 3-day intervention.Ethics and dissemination This trial has been approved by the ethics committee of the Ulm University (No. 448/18, 18.02.2019). Results will be submitted for publication in a peer-reviewed journal and presented at conferences.Trial registration The trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Trials Register (DRKS): DRKS00016799 (date of registration: 25 April 2019). In case of important protocol modifications, trial registration will be updated. This is protocol version number 1.

Introduction Chronic pain is highly prevalent, associated with substantial personal and economic burdens, and increased risk for mental disorders. Individuals in green professions (agriculturists, horticulturists, foresters) show increased prevalence of chronic pain and other risk factors for mental disorders. Available healthcare services in rural areas are limited. Acceptance towards face-to-face therapy is low. Internet and mobile-based interventions (IMIs) based on Acceptance and Commitment Therapy (ACT) might be a promising alternative for this population and may enable effective treatment of chronic pain. The present study aims to evaluate the clinical and cost-effectiveness of an ACT-based IMI for chronic pain in green professions in comparison with enhanced treatment as usual (TAU+).Methods and analysis A two-armed pragmatic randomised controlled trial will be conducted. Two hundred eighty-six participants will be randomised and allocated to either an intervention or TAU+ group. Entrepreneurs in green professions, collaborating spouses, family members and pensioners with chronic pain are eligible for inclusion. The intervention group receives an internet-based intervention based on ACT (7 modules, over 7 weeks) guided by a trained e-coach to support adherence (eg, by positive reinforcement). Primary outcome is pain interference (Multidimensional Pain Interference scale; MPI) at 9 weeks post-randomisation. Secondary outcomes are depression severity (Quick Inventory Depressive Symptomology; QIDS-SR16), incidence of major depressive disorder, quality of life (Assessment of Quality of Life; AQoL-8D) and possible side effects associated with the treatment (Inventory for the Assessment of Negative Effects of Psychotherapy; INEP). Psychological flexibility (Chronic Pain Acceptance Questionnaire, Committed Action Questionnaire, Cognitive Fusion Questionnaire) will be evaluated as a potential mediator of the treatment effect. Furthermore, mediation, moderation and health-economic analyses from a societal perspective will be performed. Outcomes will be measured using online self-report questionnaires at baseline, 9-week, 6-month, 12-month, 24-month and 36-month follow-ups.Ethics and dissemination This study was approved by the Ethics Committee of the University of Ulm, Germany (file no. 453/17—FSt/Sta; 22 February 2018). Results will be submitted for publication in peer-reviewed journals and presented at conferences.Trial registration number German Clinical Trial Registration: DRKS00014619. Registered on 16 April 2018.

Abstract Background Due to limited resources, waiting periods for psychotherapy are often long and burdening for those in need of treatment and the health care system. In order to bridge the gap between initial contact and the beginning of psychotherapy, web-based interventions can be applied. The implementation of a web-based depression intervention during waiting periods has the potential to reduce depressive symptoms and enhance well-being in depressive individuals waiting for psychotherapy. Methods In a two-arm randomized controlled trial, effectiveness and acceptance of a guided web-based intervention for depressive individuals on a waitlist for psychotherapy are evaluated. Participants are recruited in several German outpatient clinics. All those contacting the outpatient clinics with the wish to enter psychotherapy receive study information and a depression screening. Those adults (age ≥ 18) with depressive symptoms above cut-off (CES-D scale > 22) and internet access are randomized to either intervention condition (treatment as usual and immediate access to the web-based intervention) or waiting control condition (treatment as usual and delayed access to the web-based intervention). At three points of assessment (baseline, post-treatment, 3-months-follow-up) depressive symptoms and secondary outcomes, such as quality of life, attitudes towards psychotherapy and web-based interventions and adverse events are assessed. Additionally, participants’ acceptance of the web-based intervention is evaluated, using measures of intervention adherence and satisfaction. Discussion This study investigates a relevant setting for the implementation of web-based interventions, potentially improving the provision of psychological health care. The results of this study contribute to the evaluation of innovative and resource-preserving health care models for outpatient psychological treatment. Trial registration This trial has been registered on 13 February 2017 in the German clinical trials register (DRKS); registration number DRKS00010282.

Background: Computerized versions of well-established measurements such as the PHQ-9 are widely used, but data on the comparability of psychometric properties are scarce. Objective: Our objective was to compare the interformat reliability of the paper-and-pen version with a computerized version of the PHQ-9 in a clinical sample. Methods: 130 participants with mental health disorders were recruited during psychotherapeutic treatment in a mental health clinic. In a crossover design, they all completed the PHQ-9 in both the computerized and paper-and-pen versions in randomized order. Results: The internal consistency was comparable for the computer (α = 0.88) and paper versions (α = 0.89), and highly significant correlations were found between the formats (r = 0.92). PHQ-9 total scores were not significantly different between the paper and the computer delivered versions. There was a significant interaction effect between format and order of administration for the PHQ-9, indicating that the first administration delivered slightly higher scores. Limitations: In order to reduce the required effort for the participants, we did not ask them to fill out anything but the PHQ-9 once in paper and once in computer version. Conclusions: Our findings suggest that the PHQ-9 can be transferred to computerized use without affecting psychometric properties in a clinically meaningful way.

BACKGROUND: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). METHODS AND FINDINGS: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. CONCLUSION: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.

BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS: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.

Background: Depressive disorder is a major societal challenge. Despite the availability of clinically and cost-effective treatments including Internet interventions, the number of patients receiving treatment is limited. Evidence-based Internet interventions promise wide availability and high efficiency of treatments. However, these interventions often do not enter routine mental healthcare delivery at a large scale. The MasterMind project aims to provide insight into the factors that promote or hinder the uptake and implementation of evidence-based Internet interventions by mental healthcare practice. Internet-based Cognitive Behaviour Therapy (iCBT) and videoconferencing facilitating collaborative care (ccVC) will be implemented in routine mental healthcare. The services will be offered to 5230 depressed adults in 15 European regions. The current paper describes the evaluation protocol for this large-scale implementation project. Design: Current summative evaluation study follows a naturalistic one-group pretest–posttest design and assesses three distinct stakeholders: patients, mental healthcare professionals, and mental healthcare organisations. The Model for Assessment of Telemedicine applications (MAST) will be employed to structure the implementation and evaluation study. The primary focal points of interest are reach, clinical effect, acceptability, appropriateness, implementation costs, and sustainability of the interventions in practice. Mixed-methods are used to provide an understanding of what (quantitative) the implementation projects have achieved and their meaning to various stakeholders (qualitative). Discussion: The use of Internet interventions in routine practice is limited. MasterMind attempts to bridge the gap between routine practice and effectiveness research by evaluating the implementation of evidence-based Internet interventions for depressive disorders in routine mental healthcare settings in Europe.

Following publication of the original article [1], the author reported the errors in the figure and in text reference in the published article. These errors are detailed below. The original article has been corrected.

In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as "digital epidemiology"), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.

BackgroundAlcohol misuse ranks within the top ten health conditions with the highest global burden of disease. Low-intensity, Internet interventions for curbing adult alcohol misuse have been shown effective. Few meta-analyses have been carried out, however, and they have involved small numbers of studies, lacked indicators of drinking within low risk guidelines, and examined the effectiveness of unguided self-help only. We therefore conducted a more thorough meta-analysis that included both guided and unguided interventions.MethodsSystematic literature searches were performed up to September 2013. Primary outcome was the mean level of alcohol consumption and drinking within low risk guidelines for alcohol consumption at post-treatment.FindingsWe selected 16 randomised controlled trials (with 23 comparisons and 5,612 participants) for inclusion. Results, showed a small but significant overall effect size in favour of Internet interventions (g = 0.20, 95% CI: 0.13-0.27, pConclusionsInternet interventions are effective in reducing adult alcohol consumption and inducing alcohol users to adhere to guidelines for low-risk drinking. This effect is small but from a public health point of view this may warrant large scale implementation at low cost of Internet interventions for adult alcohol misuse. Moderator analyses with sufficient power are, however, needed in order to assess the robustness of these overall results and to assess whether these interventions may impact on subgroups with different levels of success.

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created are then compared to a traditional predictive model.