BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

BackgroundParticipant retention strategies that minimise attrition in longitudinal cohort studies have evolved considerably in recent years. This study aimed to assess, via systematic review and meta-analysis, the effectiveness of both traditional strategies and contemporary innovations for retention adopted by longitudinal cohort studies in the past decade.MethodsHealth research databases were searched for retention strategies used within longitudinal cohort studies published in the 10-years prior, with 143 eligible longitudinal cohort studies identified (141 articles; sample size range: 30 to 61,895). Details on retention strategies and rates, research designs, and participant demographics were extracted. Meta-analyses of retained proportions were performed to examine the association between cohort retention rate and individual and thematically grouped retention strategies.ResultsResults identified 95 retention strategies, broadly classed as either: barrier-reduction, community-building, follow-up/reminder, or tracing strategies. Forty-four of these strategies had not been identified in previous reviews. Meta-regressions indicated that studies using barrier-reduction strategies retained 10% more of their sample (95%CI [0.13 to 1.08]; p = .01); however, studies using follow-up/reminder strategies lost an additional 10% of their sample (95%CI [− 1.19 to − 0.21]; p = .02). The overall number of strategies employed was not associated with retention.ConclusionsEmploying a larger number of retention strategies may not be associated with improved retention in longitudinal cohort studies, contrary to earlier narrative reviews. Results suggest that strategies that aim to reduce participant burden (e.g., flexibility in data collection methods) might be most effective in maximising cohort retention.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0586-7) contains supplementary material, which is available to authorized users.

Objectives: E-mental health (digital) interventions can help overcome existing barriers that stand in the way of people receiving help for an eating disorder (ED). Although e-mental health interventions for treating and preventing EDs have been met with enthusiasm, earlier reviews brought attention to poor quality of evidence, and offered solutions to enhance their evidence base. To assess developments in the field, we conducted an updated meta-analysis on the efficacy of e-mental health interventions for treating and preventing EDs, paying attention to whether trial quality and outcomes have improved in recent trials. We also assessed whether user-centered design principles have been implemented in existing digital interventions. Method: Four databases were searched for RCTs of digital interventions for treating and preventing EDs. Thirty-six RCTs (28 prevention- and 8 treatment-focused) were included. Results: Some evidence that study quality improved in recent prevention-focused trials was found. Few trials involved the end-user in the design or development stage of the intervention. Issues with intervention engagement were noted, and 1 in 4 participants dropped out from prevention- and treatment-focused trials. Digital interventions were more effective than control conditions in reducing established risk factors and symptoms in prevention- (g’s = 0.19 to 0.43) and treatment-focused trials (g’s = 0.29 to 0.69), respectively. Effect sizes have not increased in recent trials. Few trials compared a digital intervention with a face-to-face intervention. Whether digital interventions can prevent ED onset is unclear. Conclusion: Digital interventions are a promising approach to ED treatment and prevention, but improvements are still needed. Three key recommendations are provided.

BackgroundDespite the growing number of mental health apps available for smartphones, the perceived usability of these apps from the perspectives of end users or health care experts has rarely been reported. This information is vital, particularly for self-guided mHealth interventions, as perceptions of navigability and quality of content are likely to impact participant engagement and treatment compliance.ObjectiveThe aim of this study was to conduct a usability evaluation of a personalized, self-guided, app-based intervention for depression.MethodsParticipants were administered the System Usability Scale and open-ended questions as part of a semistructured interview. There were 15 participants equally divided into 3 groups: (1) individuals with clinical depression who were the target audience for the app, (2) mental health professionals, and (3) researchers who specialize in the area of eHealth interventions and/or depression research.ResultsThe end-user group rated the app highly, both in quantitative and qualitative assessments. The 2 expert groups highlighted the self-monitoring features and range of established psychological treatment options (such as behavioral activation and cognitive restructuring) but had concerns that the amount and layout of content may be difficult for end users to navigate in a self-directed fashion. The end-user data did not confirm these concerns.ConclusionsEncouraging participant engagement via self-monitoring and feedback, as well as personalized messaging, may be a viable way to maintain participation in self-guided interventions. Further evaluation is necessary to determine whether levels of engagement with these features enhance treatment effects.