Cybersecurity: a critical priority for digital mental health.

There has been a surge in the supply and demand of digital mental health support services in recent times. There have also been high profile cyberattacks specifically targeting mental health and behavioral services, along with a shift toward targeting vulnerable people directly. Cyberattacks involving personal health data, especially sensitive mental health data, could have devastating consequences to vulnerable people, those close to them, and many other stakeholders. This article calls for the immediate examination of the current state of cybersecurity in the digital mental healthcare industry to collectively identify risks and to protect user and provider vulnerabilities. This article points to the need to build a global cybersecurity culture within digital mental health while also working closely with other industries. The article concludes by making some preliminary recommendations to help support the creation of standards that will enhance the collective preparedness for future responses to cybersecurity threats and attacks.

Understanding the impact of an AI-enabled conversational agent mobile app on users' mental health and wellbeing with a self-reported maternal event: a mixed method real-world data mHealth study.

Background: Maternal mental health care is variable and with limited accessibility. Artificial intelligence (AI) conversational agents (CAs) could potentially play an important role in supporting maternal mental health and wellbeing. Our study examined data from real-world users who self-reported a maternal event while engaging with a digital mental health and wellbeing AI-enabled CA app (Wysa) for emotional support. The study evaluated app effectiveness by comparing changes in self-reported depressive symptoms between a higher engaged group of users and a lower engaged group of users and derived qualitative insights into the behaviors exhibited among higher engaged maternal event users based on their conversations with the AI CA. Methods: Real-world anonymised data from users who reported going through a maternal event during their conversation with the app was analyzed. For the first objective, users who completed two PHQ-9 self-reported assessments (n = 51) were grouped as either higher engaged users (n = 28) or lower engaged users (n = 23) based on their number of active session-days with the CA between two screenings. A non-parametric Mann-Whitney test (M-W) and non-parametric Common Language effect size was used to evaluate group differences in self-reported depressive symptoms. For the second objective, a Braun and Clarke thematic analysis was used to identify engagement behavior with the CA for the top quartile of higher engaged users (n = 10 of 51). Feedback on the app and demographic information was also explored. Results: Results revealed a significant reduction in self-reported depressive symptoms among the higher engaged user group compared to lower engaged user group (M-W p = .004) with a high effect size (CL = 0.736). Furthermore, the top themes that emerged from the qualitative analysis revealed users expressed concerns, hopes, need for support, reframing their thoughts and expressing their victories and gratitude. Conclusion: These findings provide preliminary evidence of the effectiveness and engagement and comfort of using this AI-based emotionally intelligent mobile app to support mental health and wellbeing across a range of maternal events and experiences.

Artificial intelligence and machine learning in mobile apps for mental health: A scoping review.

Mental health conditions can have significant negative impacts on wellbeing and healthcare systems. Despite their high prevalence worldwide, there is still insufficient recognition and accessible treatments. Many mobile apps are available to the general population that aim to support mental health needs; however, there is limited evidence of their effectiveness. Mobile apps for mental health are beginning to incorporate artificial intelligence and there is a need for an overview of the state of the literature on these apps. The purpose of this scoping review is to provide an overview of the current research landscape and knowledge gaps regarding the use of artificial intelligence in mobile health apps for mental health. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Population, Intervention, Comparator, Outcome, and Study types (PICOS) frameworks were used to structure the review and the search. PubMed was systematically searched for randomised controlled trials and cohort studies published in English since 2014 that evaluate artificial intelligence- or machine learning-enabled mobile apps for mental health support. Two reviewers collaboratively screened references (MMI and EM), selected studies for inclusion based on the eligibility criteria and extracted the data (MMI and CL), which were synthesised in a descriptive analysis. 1,022 studies were identified in the initial search and 4 were included in the final review. The mobile apps investigated incorporated different artificial intelligence and machine learning techniques for a variety of purposes (risk prediction, classification, and personalisation) and aimed to address a wide range of mental health needs (depression, stress, and suicide risk). The studies' characteristics also varied in terms of methods, sample size, and study duration. Overall, the studies demonstrated the feasibility of using artificial intelligence to support mental health apps, but the early stages of the research and weaknesses in the study designs highlight the need for more research into artificial intelligence- and machine learning-enabled mental health apps and stronger evidence of their effectiveness. This research is essential and urgent, considering the easy availability of these apps to a large population.

Opportunities and Challenges for Digital Social Prescribing in Mental Health: Questionnaire Study.

BACKGROUND: The concept of digital social prescription usually refers to social prescriptions that are facilitated by using technology. Tools that enable such digital social prescriptions may be beneficial in recommending nonmedical activities to people with mental illness. As these tools are still somewhat novel and emerging, little is known about their potential advantages and disadvantages. OBJECTIVE: The objective of this study is to identify the potential opportunities and challenges that may arise from digital social prescriptions. METHODS: We developed a qualitative questionnaire that was disseminated through social media (Facebook and Twitter). A purposive sample targeting digital mental health experts and nonexperts was approached. The questionnaire asked participants' views about digital social prescription; the core elements linked with a definition of digital social prescription; and the strengths, weaknesses, opportunities, and threats associated with digital social prescription. RESULTS: Four core elements were recommended to define the concept of digital social prescription: digital, facilitate, user, and social. The main strength identified was the possibility to rapidly start using digital social prescription tools, which were perceived as cost-effective. The main weaknesses were their poor adherence and difficulties with using such tools. The main opportunities were an increased access to social prescription services and the prevention of serious mental illness. The main threats were certain groups being disadvantaged, patients being subject to unintended negative consequences, and issues relating to confidentiality and data protection. CONCLUSIONS: Although digital social prescriptions may be able to effectively augment the social prescriptions, a careful consideration of practical challenges and data ethics is imperative in the design and implementation of such technologies.

Correction: Opportunities and Challenges for Digital Social Prescribing in Mental Health: Questionnaire Study.

[This corrects the article DOI: .].

Digital Health Management During and Beyond the COVID-19 Pandemic: Opportunities, Barriers, and Recommendations.

During the coronavirus disease (COVID-19) crisis, digital technologies have become a major route for accessing remote care. Therefore, the need to ensure that these tools are safe and effective has never been greater. We raise five calls to action to ensure the safety, availability, and long-term sustainability of these technologies: (1) due diligence: remove harmful health apps from app stores; (2) data insights: use relevant health data insights from high-quality digital tools to inform the greater response to COVID-19; (3) freely available resources: make high-quality digital health tools available without charge, where possible, and for as long as possible, especially to those who are most vulnerable; (4) digital transitioning: transform conventional offline mental health services to make them digitally available; and (5) population self-management: encourage governments and insurers to work with developers to look at how digital health management could be subsidized or funded. We believe this should be carried out at the population level, rather than at a prescription level.

Early Warning Signs of a Mental Health Tsunami: A Coordinated Response to Gather Initial Data Insights From Multiple Digital Services Providers.

Introduction: The immediate impact of coronavirus 2019 (COVID-19) on morbidity and mortality has raised the need for accurate and real-time data monitoring and communication. The aim of this study is to document the initial observations from multiple digital services providers during the COVID-19 crisis, especially those related to mental health and well-being. Methods: We used email and social media to announce an urgent call for support. Digital mental health services providers (N = 46), financial services providers (N = 4), and other relevant digital data source providers (N = 3) responded with quantitative and/or qualitative data insights. People with lived experience of distress, as service users/consumers, and carers are included as co-authors. Results: This study provides proof-of-concept of the viability for researchers and private companies to work collaboratively toward a common good. Digital services providers reported a diverse range of mental health concerns. A recurring observation is that demand for digital mental health support has risen, and that the nature of this demand has also changed since COVID-19, with an apparent increased presentation of anxiety and loneliness. Conclusion: Following this study, we will continue to work with providers in more in-depth ways to capture follow-up insights at regular time points. We will also onboard new providers to address data representativeness. Looking ahead, we anticipate the need for a rigorous process to interpret insights from an even wider variety of sources in order to monitor and respond to mental health needs.

How data science can advance mental health research.

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study.

BACKGROUND: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. OBJECTIVE: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. METHODS: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. RESULTS: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. CONCLUSIONS: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.

GSK3ß: a plausible mechanism of cognitive and hippocampal changes induced by erythropoietin treatment in mood disorders?

Mood disorders are associated with significant psychosocial and occupational disability. It is estimated that major depressive disorder (MDD) will become the second leading cause of disability worldwide by 2020. Existing pharmacological and psychological treatments are limited for targeting cognitive dysfunctions in mood disorders. However, growing evidence from human and animal studies has shown that treatment with erythropoietin (EPO) can improve cognitive function. A recent study involving EPO-treated patients with mood disorders showed that the neural basis for their cognitive improvements appeared to involve an increase in hippocampal volume. Molecular mechanisms underlying hippocampal changes have been proposed, including the activation of anti-apoptotic, antioxidant, pro-survival and anti-inflammatory signalling pathways. The aim of this review is to describe the potential importance of glycogen synthase kinase 3-beta (GSK3ß) as a multi-potent molecular mechanism of EPO-induced hippocampal volume change in mood disorder patients. We first examine published associations between EPO administration, mood disorders, cognition and hippocampal volume. We then highlight evidence suggesting that GSK3ß influences hippocampal volume in MDD patients, and how this could assist with targeting more precise treatments particularly for cognitive deficits in patients with mood disorders. We conclude by suggesting how this developing area of research can be further advanced, such as using pharmacogenetic studies of EPO treatment in patients with mood disorders.

Unravelling the GSK3ß-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder.

OBJECTIVE: Glycogen synthase kinase 3ß (GSK3ß) has been implicated in mood disorders. We previously reported associations between a GSK3ß polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3ß-regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3ß to identify a genotypic network influencing hippocampal volume in MDD. PARTICIPANTS AND METHODS: We used discovery (N=141) and replication (N=77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models. RESULTS: The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P=0.0088, replication sample; P=0.017, discovery sample; Stouffer's combined P=0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications. CONCLUSION: Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.

Proceedings from the Digital Innovation in Mental Health Conference, London, 2017.

Aims and Scope: The conference aims were two-fold: (1) to explore how digital technology is implemented into personalized and/or group mental health interventions and (2) to promote digital equality through developing culturally sensitive ways of bringing technological innovation to disadvantaged groups. A broad scope of perspectives were welcomed and encouraged, from lived experience, academic, clinical, media, the arts, policy-making, tech innovation, and other perspectives.

Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome.

How does human brain structure mature during adolescence? We used MRI to measure cortical thickness and intracortical myelination in 297 population volunteers aged 14-24 y old. We found and replicated that association cortical areas were thicker and less myelinated than primary cortical areas at 14 y. However, association cortex had faster rates of shrinkage and myelination over the course of adolescence. Age-related increases in cortical myelination were maximized approximately at the internal layer of projection neurons. Adolescent cortical myelination and shrinkage were coupled and specifically associated with a dorsoventrally patterned gene expression profile enriched for synaptic, oligodendroglial- and schizophrenia-related genes. Topologically efficient and biologically expensive hubs of the brain anatomical network had greater rates of shrinkage/myelination and were associated with overexpression of the same transcriptional profile as cortical consolidation. We conclude that normative human brain maturation involves a genetically patterned process of consolidating anatomical network hubs. We argue that developmental variation of this consolidation process may be relevant both to normal cognitive and behavioral changes and the high incidence of schizophrenia during human brain adolescence.

Effects of Erythropoietin on Hippocampal Volume and Memory in Mood Disorders.

BACKGROUND: Persistent cognitive dysfunction in depression and bipolar disorder (BD) impedes patients' functional recovery. Erythropoietin (EPO) increases neuroplasticity and reduces cognitive difficulties in treatment-resistant depression (TRD) and remitted BD. This magnetic resonance imaging study assessed the neuroanatomical basis for these effects. METHODS: Patients with TRD who were moderately depressed or BD in partial remission were randomized to 8 weekly EPO (40,000 IU) or saline infusions in a double-blind, parallel-group design. Patients underwent magnetic resonance imaging, memory assessment with the Rey Auditory Verbal Learning Test, and mood ratings with the Beck Depression Inventory, Hamilton Depression Rating Scale, and Young Mania Rating Scale at baseline and week 14. Hippocampus segmentation and analysis of hippocampal volume, shape, and gray matter density were conducted with FMRIB Software Library tools. Memory change was analyzed with repeated-measures analysis of covariance adjusted for depression symptoms, diagnosis, age, and gender. RESULTS: Eighty-four patients were randomized; 1 patient withdrew and data collection was incomplete for 14 patients; data were thus analyzed for 69 patients (EPO: n = 35, saline: n = 34). Compared with saline, EPO was associated with mood-independent memory improvement and reversal of brain matter loss in the left hippocampal cornu ammonis 1 to cornu ammonis 3 and subiculum. Using the entire sample, memory improvement was associated with subfield hippocampal volume increase independent of mood change. CONCLUSIONS: EPO-associated memory improvement in TRD and BD may be mediated by reversal of brain matter loss in a subfield of the left hippocampus. EPO may provide a therapeutic option for patients with mood disorders who have impaired neuroplasticity and cognition.