Is the global prevalence rate of adult mental illness increasing? Systematic review and meta-analysis Richter D, Wall A, Bruen A, Whittington R. Is the global prevalence rate of adult mental illness increasing? Systematic review and metaanalysis Objectives: The question whether mental illness prevalence rates are increasing is a controversially debated topic. Epidemiological articles and review publications that look into this research issue are often compromised by methodological problems. The present study aimed at using a meta-analysis technique that is usually applied for the analysis of intervention studies to achieve more transparency and statistical precision. Methods: We searched PubMed, PsycINFO, CINAHL, Google Scholar and reference lists for repeated cross-sectional population studies on prevalence rates of adult mental illness based on ICD-or DSM-based diagnoses, symptom scales and distress scales that used the same methodological approach at least twice in the same geographical region. The study is registered with PROSPERO (CRD42018090959). Results: We included 44 samples from 42 publications, representing 1 035 697 primary observations for the first time point and 783 897 primary observations for the second and last time point. Studies were conducted between 1978 and 2015. Controlling for a hierarchical data structure, we found an overall global prevalence increase in odds ratio of 1.179 (95%-CI: 1.065-1.305). A multivariate meta-regression suggested relevant associations with methodological characteristics of included studies. Conclusions: We conclude that the prevalence increase in adult mental illness is small, and we assume that this increase is mainly related to demographic changes. Summations• The issue of potentially increasing prevalence rates of mental illness is controversial. • Using a meta-analysis, we found a small increase in prevalence rates over time.• The increase may be due to demographic changes in current societies. Limitations• There is a scarcity of data from non-Western regions.• The coverage of mental illness is unevenly distributed.• No data on prevalence changes of psychosis/schizophrenia were available.

Background Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. Objective This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. Methods We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. Results K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. Conclusions Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.

Background Suicide is a growing global public health problem that has resulted in an increase in the demand for psychological services to address mental health issues. It is expected that 1 in 6 people on a waiting list for mental health services will attempt suicide. Although suicidal ideation has been shown to be linked to a higher risk of death by suicide, not everybody openly discloses their suicidal thoughts or plans to friends and family or seeks professional help before suicide. Therefore, new methods are needed to track suicide risk in real time together with a better understanding of the ways in which people communicate or express their suicidality. Considering the dynamic nature and challenges in understanding suicide ideation and suicide risk, mobile apps could be better suited to prevent suicide as they have the ability to collect real-time data. Objective This study aims to report the practicalities and acceptability of setting up and trialing digital technologies within an inpatient mental health setting in the United Kingdom and highlight their implications for future studies. Methods Service users were recruited from 6 inpatient wards in the north west of England. Service users who were eligible to participate and provided consent were given an iPhone and Fitbit for 7 days and were asked to interact with a novel phone app, Strength Within Me (SWiM). Interaction with the app involved journaling (recording daily activities, how this made them feel, and rating their mood) and the option to create safety plans for emotions causing difficulties (identifying strategies that helped with these emotions). Participants also had the option to allow the study to access their personal Facebook account to monitor their social media use and activity. In addition, clinical data (ie, assessments conducted by trained researchers targeting suicidality, depression, and sleep) were also collected. Results Overall, 43.0% (80/186 response rate) of eligible participants were recruited for the study. Of the total sample, 67 participants engaged in journaling, with the average number of entries per user being 8.2 (SD 8.7). Overall, only 24 participants created safety plans and the most common difficult emotion to be selected was feeling sad (n=21). This study reports on the engagement with the SWiM app, the technical difficulties the research team faced, the importance of building key relationships, and the implications of using Facebook as a source to detect suicidality. Conclusions To develop interventions that can be delivered in a timely manner, prediction of suicidality must be given priority. This paper has raised important issues and highlighted lessons learned from implementing a novel mobile app to detect the risk of suicidality for service users in an inpatient setting.

A randomised controlled trial to evaluate the impact of a human rights based approach to dementia care in inpatient ward and care home settings This report should be referenced as follows:Kinderman P, Butchard S, Bruen AJ, Wall A, Goulden N, Hoare Z, et al. A randomised controlled trial to evaluate the impact of a human rights based approach to dementia care in inpatient ward and care home settings. Health Serv Deliv Res 2018;6(13). This journal is a member of and subscribes to the principles of the Committee on Publication Ethics (COPE) (www.publicationethics.org/). Health Services and Delivery ResearchEditorial contact: journals.library@nihr.ac.ukThe full HS&DR archive is freely available to view online at www.journalslibrary.nihr.ac.uk/hsdr. Print-on-demand copies can be purchased from the report pages of the NIHR Journals Library website: www.journalslibrary.nihr.ac.uk Criteria for inclusion in the Health Services and Delivery Research journalReports are published in Health Services and Delivery Research (HS&DR) if (1) they have resulted from work for the HS&DR programme or programmes which preceded the HS&DR programme, and (2) they are of a sufficiently high scientific quality as assessed by the reviewers and editors. HS&DR programmeThe Health Services and Delivery Research (HS&DR) programme, part of the National Institute for Health Research (NIHR), was established to fund a broad range of research. It combines the strengths and contributions of two previous NIHR research programmes: the Health Services Research (HSR) programme and the Service Delivery and Organisation (SDO) programme, which were merged in January 2012.The HS&DR programme aims to produce rigorous and relevant evidence on the quality, access and organisation of health services including costs and outcomes, as well as research on implementation. The programme will enhance the strategic focus on research that matters to the NHS and is keen to support ambitious evaluative research to improve health services.For more information about the HS&DR programme please visit the website: http://www.nets.nihr.ac.uk/programmes/hsdr This reportThe research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 12/209/53. The contractual start date was in May 2014. The final report began editorial review in October 2016 and was accepted for publication in August 2017. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HS&DR editors and production house have tried to ensure the accuracy of the authors' report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not...

The COVID-19 pandemic forced rapid innovative change to healthcare delivery. Understanding the unique challenges faced by staff may contribute to different approaches when managing future pandemics. Qualitative interviews were conducted with 21 staff from a Community Mental Health Team in the North West of England, UK, three months after the first wave of the pandemic. Thematic analysis was used to examine data reporting the challenges arising when working to deliver a service during the pandemic. Data is discussed under four headings; “senior trust managers trying to make it work”, “individuals making it work”, “making it work as a team”, and “making it work through working at home”. Clear communication was essential to ensure adherence to guidelines while providing safe care delivery. The initial response to the pandemic involved the imposition of boundaries on staff by senior leadership to ensure that vulnerable service users received a service while maintaining staff safety. The data raises questions about how boundaries were determined, the communication methods employed, and whether the same outcome could have been achieved through involving staff more in decision-making processes. Findings could be used to design interventions to support mental health staff working to deliver community services during future crises.