This study explores the regulatory challenges posed by digital mental health applications in South Africa, particularly regarding the collection and protection of personal data. It aimed to assess whether South Africa's current legal framework sufficiently protects users' sensitive mental health data amidst the rise of digital mental health solutions, especially in the context of privacy concerns.

The research focused on the intersection of digital mental health applications, data protection laws, and user privacy in South Africa. It examined existing legal frameworks, including the Protection of Personal Information Act (POPIA), National Health Act (NHA), and Consumer Protection Act (CPA). The study reviewed relevant literature, legal texts, and case studies, focusing on mental health applications in both urban and rural contexts.

While South Africa has laws in place to protect personal information, these laws have significant gaps in addressing the unique risks associated with digital mental health technologies. Key findings include inadequate regulation of AI-driven mental health tools, insufficient guidelines for third-party data sharing, and challenges with cross-border data transfers.

The implications of these findings suggest that South Africa needs to modernize its legal framework to better regulate digital mental health tools and ensure user privacy. This includes improving AI regulation, strengthening consent mechanisms, and enhancing protections against third-party data misuse. Future research should focus on developing specific legal guidelines for mental health data and addressing the vulnerabilities faced by rural populations with low digital literacy. The study's conclusions align with global concerns over the ethical implications of mental health datacommodification and emphasize the need for robust, adaptable regulatory approaches.

Consumer mental health apps (MHAs) collect and generate mental health-related data on their users, which can be leveraged for research and product improvement studies. Such studies are associated with ethical issues that may be difficult for researchers and app developers to assess. To improve ethical study conduct, governance through rules, agreements and customs could be relied upon, but their translation into practice is subject to barriers. This qualitative interview study with 17 researchers and app developers looked into the role and impact of governance standards on consumer MHA studies. Interviewees experienced a significant number of rules, agreements and customs, although not all of the governance standards that can potentially be applicable. Standards did have an impact on the interests of researchers and app developers, app users and society, but this impact was mediated by several barriers related to their conceptualization and implementation. Conceptualization barriers impacted the development of a standard, the inclusion of relevant concepts and the coordination between standards. Implementation barriers concerned the resource cost of understanding a standard, as well as suboptimal enforcement. The framework developed in this study can support more effective efforts to improve the governance of future consumer MHA studies.

The Psychosocial Rehabilitation Project (PRP) is a tool designed to structure and organize mental health care, guided by the theoretical and practical principles of Psychosocial Rehabilitation (PR). This article aims to identify the initial requirements for the prototyping of a "Psychosocial Rehabilitation Project App". To achieve this, an integrative review was conducted with the research question: what initial requirements are important to compose the prototype of the "Psychosocial Rehabilitation Project App" in mental health? In the search process, 834 articles were identified and exported to the online systematic review application Rayyan QCRI, resulting in 36 eligible articles for this study, along with one app. The reading of this material allowed the elicitation of three themes: privacy and data protection policy; design; and software and programming. The prototyping of the "Psychosocial Rehabilitation Project App" should prioritize data security and protection, simplicity in design, and the integration of technological resources that facilitate the management, construction, monitoring, and evaluation of psychosocial rehabilitation projects by mental health professionals.

Modeling passively collected smartphone sensor data (called digital phenotyping) has the potential to detect distress among family caregivers and patients with advanced cancer and could lead to novel clinical models of cancer care. The purpose of this study was to assess the feasibility and acceptability of collecting passive smartphone data from family caregivers and their care recipients with advanced cancer over 24 weeks.

This was an observational feasibility study. Family caregivers and patients with advanced cancer were recruited through clinic or via social media and downloaded a digital phenotyping application (Beiwe) to their smartphones that passively collected sensor data over 24 weeks. Feasibility was evaluated by quantifying enrollment and retention and the quantity of acquired data. Acceptability was assessed through post-24 week qualitative interviews.

Of 178 caregiver and patient dyads approached, 22.5% of caregivers (n = 40) and 10.1% of patients (n = 18) both consented to the study and successfully downloaded the application, with most recruited through social media (93%). Of 24 weeks (168 days), the median number of days that data were received was 141 days. Interviews yielded three themes: (1) experiences with study procedures were generally positive despite some technical challenges; (2) security and privacy concerns were minimal, mitigated by clear explanations, trust in the health care system, and privacy norms; and (3) a clinical model that used passive smartphone monitoring to automatically trigger assistance could be beneficial but with concern about false alarms.

This pilot study of collecting passive smartphone data found mixed indicators of feasibility, with suboptimal enrollment rates, particularly via clinic, but positive retention and data collection rates for those who did enroll. Participants had generally positive views of passive monitoring.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.

This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs).

This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted.

The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback.

Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Digital mental health interventions are routinely integrated into mental health services internationally and can contribute to reducing the global mental health treatment gap identified by the World Health Organization. Research teams designing and delivering evaluations frequently invest substantial effort in deliberating on ethical and legal challenges around digital mental health interventions. In this article, we reflect on our own research experience with digital mental health intervention design and evaluation to identify 8 of the most critical challenges that we or others have faced, and that have ethical or legal consequences. These include: (1) harm caused by online recruitment work; (2) monitoring of intervention safety; (3) exclusion of specific demographic or clinical groups; (4) inadequate robustness of effectiveness and cost-effectiveness findings; (5) adequately conceptualizing and supporting engagement and adherence; (6) structural barriers to implementation; (7) data protection and intellectual property; and (8) regulatory ambiguity relating to digital mental health interventions that are medical devices. As we describe these challenges, we have highlighted serious consequences that can or have occurred, such as substantial delays to studies if regulations around Software as a Medical Device (SaMD) are not fully understood, or if regulations change substantially during the study lifecycle. Collectively, the challenges we have identified highlight a substantial body of required knowledge and expertise, either within the team or through access to external experts. Ensuring access to knowledge requires careful planning and adequate financial resources (for example, paying public contributors to engage in debate on critical ethical issues or paying for legal opinions on regulatory issues). Access to such resources can be planned for on a per-study basis and enabled through funding proposals. However, organizations regularly engaged in the development and evaluation of digital mental health interventions should consider creating or supporting structures such as advisory groups that can retain necessary competencies, such as in medical device regulation.

Depressive and anxiety disorders are common mental disorders ranking among the leading causes of global disease burden. Not all clients currently benefit from therapy and clients are looking for modern ways of therapy. Online psychotherapy is a promising option for better meeting clients' needs. Recently, a new psychotherapy concept has emerged that combines videoconferencing sessions with support through a mobile application. The latter allows for ecological momentary assessments and interventions, facilitates communication between patients and therapists in between sessions through chat, and allows for incorporating feedback-informed treatment principles.

The study was a retrospective observational matched cohort study, comparing online psychotherapy with Therapy As Usual (TAU) for clients with depressive or anxiety disorders. Data were obtained via questionnaires, which are part of standard clinical care. Primary outcomes included general mental functioning, and symptoms of depression and anxiety. Secondary outcomes were efficiency, client satisfaction, and therapy costs. Primary endpoints were analyzed using linear mixed models analysis, with an interaction term between time and group. Secondary outcomes were analyzed using linear regression.

Larger improvements were observed in the online compared to the TAU group for general mental functioning and depressive disorder (i.e., General mental functioning: B = -8.50, 95 CI: -15.01 - -1.97, p = 0.011; Depressive disorder: B = - 3.66, 95 % CI: -5.79 - -1.54p < 0.01). No significant differences in change over time between the two groups were observed for anxiety disorder (B = -3.64, 95 % CI: (-13.10 - 5.82) p = 0.447). The total number of sessions was significantly higher in the online psychotherapy group than in TAU (B = 3.71, p < 0.01), although clients were matched on treatment time in weeks. Treatment session duration in minutes was comparable across the groups.

Online psychotherapy with app support showed to be a promising alternative to TAU for depressive and anxiety disorders. More research is needed to evaluate the effectiveness, cost-effectiveness and client satisfaction of online psychotherapy compared to TAU, such as randomized controlled trials or studies multiple baseline series designs, and in-depth qualitative research.

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Use of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention.

We investigated the preferences and values of UK adolescents with regard to use of digital sensing information, including social media and internet searching behaviour. We also investigated the impact of risk information on adolescents' self-understanding.

Following a Design Bioethics approach, we created and disseminated a purpose-built digital game (www.tracingtomorrow.org) that immersed the player-character in a fictional scenario in which they received a risk assessment for depression Data were collected through game choices across relevant scenarios, with decision-making supported through clickable information points.

The game was played by 7337 UK adolescents aged 16-18 years. Most participants were willing to personally communicate mental health risk information to their parents or best friend. The acceptability of school involvement in risk predictions based on digital traces was mixed, due mainly to privacy concerns. Most participants indicated that risk information could negatively impact their academic self-understanding. Participants overwhelmingly preferred individual face-to-face over digital options for support.

The potential of digital phenotyping in supporting early intervention in mental health can only be fulfilled if data are collected, communicated and actioned in ways that are trustworthy, relevant and acceptable to young people.

To minimise the risk of ethical harms in real-world applications of preventive psychiatric technologies, it is essential to investigate young people's values and preferences as part of design and implementation processes.

In the last few years, online psychotherapy programs have burgeoned since they are a more accessible and scalable treatment option compared to in-person therapies. While these online programs are promising, understanding the user experience and perceptions of care is essential for program optimization.

This study investigated the experiences of end-users who had previously received online psychotherapy through a web-based platform. A 35-item multiple-choice survey was developed by the research team and distributed to past users to capture their perceptions of the program.

The survey yielded 163 responses, with a 90 % completion rate. Participants were predominantly white and female, with an average age of 42 years. While most participants preferred in-person therapy, they also reported the benefits of the online psychotherapy program. Participants had positive perceptions of the platform, the quality and interaction of their therapist, and the homework assignments and skills covered. Lack of motivation to complete weekly homework assignments was cited as a common struggle.

The findings support online psychotherapy as a beneficial digital mental health tool and highlight some areas for improvement. Scalability and accessibility are key benefits of the platform. At the same time, improvements in participant engagement, including those from equity-seeking and equity-deserving groups, may enhance the efficacy of the programs offered.

The worldwide prevalence of anxiety disorders among college students is high, which negatively affects countries, schools, families, and individual students to varying degrees. This paper reviews the relevant literature regarding risk factors and digital interventions for anxiety disorders among college students from the perspectives of different stakeholders. Risk factors at the national and societal levels include class differences and the coronavirus disease 2019 pandemic. College-level risk factors include the indoor environment design of the college environment, peer relationships, student satisfaction with college culture, and school functional levels. Family-level risk factors include parenting style, family relationship, and parental level of education. Individual-level risk factors include biological factors, lifestyle, and personality. Among the intervention options for college students' anxiety disorders, in addition to traditional cognitive behavioral therapy, mindfulness-based interventions, psychological counseling, and group counseling, digital mental health interventions are increasingly popular due to their low cost, positive effect, and convenient diagnostics and treatment. To better apply digital intervention to the prevention and treatment of college students' anxiety, this paper suggests that the different stakeholders form a synergy among themselves. The nation and society should provide necessary policy guarantees, financial support, and moral and ethical supervision for the prevention and treatment of college students' anxiety disorders. Colleges should actively participate in the screening and intervention of college students' anxiety disorders. Families should increase their awareness of college students' anxiety disorders and take the initiative to study and understand various digital intervention methods. College students with anxiety disorders should actively seek psychological assistance and actively accept and participate in digital intervention projects and services. We believe that in the future, the application of methods such as big data and artificial intelligence to improve digital interventions and provide individualized treatment plans will become the primary means of preventing and treating anxiety disorders among college students.

We describe relevant interfaces between law and psychiatry and current ethical and legal views and changes within the past decades. Ideas of patient autonomy and patients' rights have been major drivers of changes in legal frameworks. We describe developments in the areas of patient information and informed consent, involuntary placement and involuntary treatment, use of coercive measures, forensic psychiatry, digital mental health, data privacy, physician liability, suicide, assisted suicide, euthanasia, end of life decision-making, advance directives, legal and illegal drugs, and delegation and substitution of professional activities. There is no unidirectional pathway between law and ethics. Views, conflicts, and requirements differ between countries and within countries and will need to be balanced according to the societies' changing values also in the future.

The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided.

This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts.

We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions.

The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health-related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used.

We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.

Advances in genetics and digital phenotyping in psychiatry have given rise to testing services targeting young people, which claim to predict psychiatric outcomes before difficulties emerge. These services raise several ethical challenges surrounding data sharing and information privacy.

This study aimed to investigate young people's interest in predictive testing for mental health challenges and their attitudes towards sharing biological, psychosocial and digital data for such purpose.

Eighty UK adolescents aged 16-18 years took part in a digital role-play where they played the role of clients of a fictional predictive psychiatry company and chose what sources of personal data they wished to provide for a risk assessment. After the role-play, participants reflected on their choices during a peer-led interview.

Participants saw multiple benefits in predictive testing services, but were highly selective with regard to the type of data they were willing to share. Largely due to privacy concerns, digital data sources such as social media or Google search history were less likely to be shared than psychosocial and biological data, including school grades and one's DNA. Participants were particularly reluctant to share social media data with schools (but less so with health systems).

Emerging predictive psychiatric services are valued by young people; however, these services must consider privacy versus utility trade-offs from the perspective of different stakeholders, including adolescents.

Respecting adolescents' need for transparency, privacy and choice in the age of digital phenotyping is critical to the responsible implementation of predictive psychiatric services.

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health (DMH) ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as (1) a reduction in choice overload, (2) improvement to the digital therapeutic alliance, and (3) increased access to personal data & self-management. Ethical challenges identified are (1) lack of explainability, (2) complexities pertaining to the privacy/personalization trade-off and recommendation quality, and (3) the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems.

Digital technology has the potential to improve health outcomes and health system performance in fragmented and under-funded mental health systems. Despite this potential, the integration of digital technology tools into mental health systems has been relatively poor. This is a protocol for a synthesis of qualitative evidence that will aim to determine the barriers and facilitators to integrating digital technologies in mental health systems and classify them in contextual domains at individual, organisational and system levels.

The methodological framework for systematic review of qualitative evidence described in Lockwood et al. will be applied to this review. A draft search strategy was developed in collaboration with an experienced senior health research librarian. A systematic search of Medline, Embase, Scopus, PsycInfo, Web of Science and Google Scholar, as well as hand searching of reference lists and reviews will identify relevant studies for inclusion. Study selection will be carried out independently by two authors, with discrepancies resolved by consensus. The quality of selected studies will be assessed using JBI Critical Appraisal Checklist for Qualitative Research. Data will be charted using JBI QUARI Data Extraction Tool for Qualitative Research. Findings will be defined and classified both deductively in a priori conceptual framework and inductively by a thematic analysis. Results will be reported based on the Enhancing transparency in reporting the synthesis of qualitative research. The level of confidence of the findings will be assessed using GRADE-CERQual.

This study does not require ethics approval. The systematic review will inform policy and practices around improving the integration of digital technologies into mental health care systems.

Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human "digitypes" by digital phenotyping. These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience.

Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.

Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation.

This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised.

We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis.

We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance.

Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

E-mental health, or the use of technology in mental healthcare, has been the focus of research for over two decades. Over that period, the evidence base for the potential of technology to improve psychotherapeutic practice has grown steadily. This sharply contrasts with the actual use of e-mental health by psychotherapists, which has remained limited. In this article, we aim to illustrate how and when different technological tools and applications can play a role in psychotherapy. At the same time, we also highlight current limitations and discuss challenges for future research. A specific, yet hypothetical case, is used to guide this narrative review and make proposed applications tangible and concrete.

Rapid-acting interventions for the suicide crisis have the potential to transform treatment. In addition, recent innovations in suicide research methods may similarly expand our understanding of the psychological and neurobiological correlates of suicidal thoughts and behaviors. This review discusses the limitations and challenges associated with current methods of suicide risk assessment and presents new techniques currently being developed to measure rapid changes in suicidal thoughts and behavior. These novel assessment strategies include ecological momentary assessment, digital phenotyping, cognitive and implicit bias metrics, and neuroimaging paradigms and analysis methodologies to identify neural circuits associated with suicide risk. This review is intended to both describe the current state of our ability to assess rapid changes in suicide risk as well as to explore future directions for clinical, neurobiological, and computational markers research in suicide-focused clinical trials.

Objective: Where traditional approaches fall short, widely accessible and accepted, yet under leveraged, digital technologies such as text messaging present novel opportunities to solve a range of health care solutions. The following provides a preliminary analysis of the Text4Support program, a text-messaging intervention using the principles of cognitive behavioral therapy, which seeks to support the health and well-being of individuals seeking support for addiction or mental health concerns. The goal of this study was to assess whether the Text4Support program improved the perceived overall mental well-being of participants. Methods: The evaluation analyzes survey responses of individuals who were enrolled in the Text4Support program beginning in July 2019, who had completed the 6-months program by May 2020. Participants were asked to provide responses to three surveys during their time in the program-at baseline, 12-weeks and 6-months, which included questions documenting demographic information, general satisfaction with the program, and a participants' level of "global distress" through use of the Clinical Outcomes Routine Evaluation System (CORE-10)-a validated brief 10-item assessment and outcome measurement tool used to assess conditions including anxiety, depression, physical problems, and risk to self. Results and Conclusions: This data set did not include a large enough sample of participants to reach statistical significance. Nevertheless, the study provides some preliminary analysis, and identifies opportunities for the future analysis and research.

Digital psychiatry and e-mental health have proliferated and permeated vastly in the current landscape of mental health care provision. The COVID-19 crisis has accelerated this digital transformation, and changes that usually take many years to translate into clinical practice have been implemented in a matter of weeks. These have outpaced the checks and balances that would typically accompany such changes, which has brought into focus a need to have a proper approach for digital data handling. Health care data is sensitive, and is prone to hacking due to the lack of stringent protocols regarding its storage and access. Mental health care data need to be more secure due to the stigma associated with having a mental health condition. Thus, there is a need to emphasize proper data handling by mental health professionals, and policies to ensure safeguarding patient's privacy are required. The aim of useful, free, and fair use of mental health care data for clinical, business, and research purposes should be balanced with the need to ensure the data is accessible to only those who are authorized. Systems and policies should be in place to ensure that data storage, access, and disposal are systematic and conform to data safety norms.

The more people there are who use clinical information systems (CIS) beyond their traditional intramural confines, the more promising the benefits are, and the more daunting the risks will be. This review thus explores the areas of ethical debates prompted by CIS conceptualized as smart systems reaching out to patients and citizens. Furthermore, it investigates the ethical competencies and education needed to use these systems appropriately.

A literature review covering ethics topics in combination with clinical and health information systems, clinical decision support, health information exchange, and various mobile devices and media was performed searching the MEDLINE database for articles from 2016 to 2019 with a focus on 2018 and 2019. A second search combined these keywords with education.

By far, most of the discourses were dominated by privacy, confidentiality, and informed consent issues. Intertwined with confidentiality and clear boundaries, the provider-patient relationship has gained much attention. The opacity of algorithms and the lack of explicability of the results pose a further challenge. The necessity of sociotechnical ethics education was underpinned in many studies including advocating education for providers and patients alike. However, only a few publications expanded on ethical competencies. In the publications found, empirical research designs were employed to capture the stakeholders' attitudes, but not to evaluate specific implementations.

Despite the broad discourses, ethical values have not yet found their firm place in empirically rigorous health technology evaluation studies. Similarly, sociotechnical ethics competencies obviously need detailed specifications. These two gaps set the stage for further research at the junction of clinical information systems and ethics.