Mental Health Care Technologies Research Articles

Integration of Sensor-Based Technology in Mental Healthcare: A Systematic Scoping Review

AbstractSensor-based technologies can collect objective and real-time data on physiological, behavioral, and contextual factors related to mental disorders. This not only holds potential for mental healthcare but also comes with challenges, such as handling large amounts of data and supporting the integration of sensors in clinical practice. This systematic scoping review aims to provide an overview of studies explicitly addressing the integration of sensor-based technology in mental healthcare by reporting on the way that therapists and patients work with sensors. In addition, we explore barriers and facilitators for the integration of sensor-based technology in clinical practice. Four databases were searched on April 5, 2023. Studies on sensor-based technology integrated in mental healthcare were included. A total of 14 studies were included. In these studies, a variety of sensor-based technologies were used. All studies were conducted between 2016 and 2022. Most studies showed that sensor-based technologies are accepted by patients and that their use is associated with symptom reduction. However, most studies did not systematically report on barriers and facilitators and mainly focused on the technology itself rather than on the broader context of its intended use. Also, sensor-based technologies are not yet embedded in clinical protocols. From the current review, we can conclude that sensor-based technologies are sufficiently accepted and feasible, and that sensors are promising for enhancing clinical outcomes. However, sensors are not properly integrated in treatment protocols yet. Therefore, we propose a next phase in research on sensor-based technology in mental healthcare treatment. This next phase asks for a multifaceted approach consisting of (1) embedding sensor-based technology in treatment protocols in co-creation with patients and clinicians, (2) examining the feasibility of these interventions together with small-scale evidence studies, and (3) systematically examining the implementation of sensor-based technology in clinical practice using existing frameworks for technology implementation. Open Science Framework: https://doi.org/10.17605/OSF.IO/XQHSY.

Application of big data and artificial intelligence inmental health prediction and intervention

In this article, we delve into applying Convolutional Neural Networks (CNNs) and big data in predicting and intervening in mental health issues, emphasizing the potential for early detection and personalized treatment. By analyzing patterns in social media data, CNNs can identify indicators of mental health conditions, offering insights for tailored interventions. The discussion highlights the importance of addressing privacy, data security, and algorithmic bias to ensure ethical implementation. Future directions include enhancing predictive accuracy, expanding AI applications in therapy, fostering interdisciplinary collaborations, developing ethical frameworks, and engaging the public. Embracing these technologies in mental health care promises significant advancements but necessitates careful consideration of ethical imperatives to maximize benefits while safeguarding patient welfare.

Therapeutic Chatbots as Cognitive-Affective Artifacts

Conversational Artificial Intelligence (CAI) systems (also known as AI “chatbots”) are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not “digital therapists” and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in mental health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects’ affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy.

Barriers and facilitators to the implementation of digital technologies in mental health systems: a qualitative systematic review to inform a policy framework

BackgroundDespite the potential for improved population mental health and wellbeing, the integration of mental health digital interventions has been difficult to achieve. In this qualitative systematic review, we aimed to identify barriers and facilitators to the implementation of digital technologies in mental healthcare systems, and map these to an implementation framework to inform policy development.MethodsWe searched Medline, Embase, Scopus, PsycInfo, Web of Science, and Google Scholar for primary research articles published between January 2010 and 2022. Studies were considered eligible if they reported barriers and/or facilitators to the integration of any digital mental healthcare technologies. Data were extracted using EPPI-Reviewer Web and analysed thematically via inductive and deductive cycles.ResultsOf 12,525 references identified initially, 81 studies were included in the final analysis. Barriers and facilitators were grouped within an implementation (evidence-practice gap) framework across six domains, organised by four levels of mental healthcare systems. Broadly, implementation was hindered by the perception of digital technologies as impersonal tools that add additional burden of care onto both providers and patients, and change relational power asymmetries; an absence of resources; and regulatory complexities that impede access to universal coverage. Facilitators included person-cantered approaches that consider patients’ intersectional features e.g., gender, class, disability, illness severity; evidence-based training for providers; collaboration among colleagues; appropriate investment in human and financial resources; and policy reforms that tackle universal access to digital health.ConclusionIt is important to consider the complex and interrelated nature of barriers across different domains and levels of the mental health system. To facilitate the equitable, sustainable, and long-term digital transition of mental health systems, policymakers should consider a systemic approach to collaboration between public and private sectors to inform evidence-based planning and strengthen mental health systems.Protocol registrationThe protocol is registered on PROSPERO, CRD42021276838.

The impact of smartphone use on childhood depression: A pediatric perspective

Introduction: the profound social and family changes experienced in recent decades have also played a decisive role in the rise of depression, especially changes in family models, competition as the axis of life (which is more important than other dimensions of existence) and the availability of mass media, which promote personal distancing. Objectives: to describe the scientific evidence on the impact of smartphone use on childhood depression from a pediatric perspective and what are the underlying factors that influence this relationship. Methods: a search was conducted in Pubmed, Scopus, Web of Science from January 2000 to October 2023. Results: this systematic review included several studies that evaluated different mental health interventions in various populations. Overall, we found that interventions based on smartphones, online cognitive behavioral therapy, and telemedicine had a positive impact on mental health. For example, postnatal mothers who received smartphone interventions experienced a significant reduction in depression. In addition, online therapy was shown to alleviate parental anxiety and depression and improve their quality of life. However, some studies had limitations, such as small samples or lack of effect on certain outcomes. More research would be needed to fully understand the scope and efficacy of these digital interventions. Conclusion: this review highlights the promising efficacy of smartphone-based interventions to improve mental health in different populations. Despite limitations and challenges, the use of mobile technology in mental health care is an evolving area that could offer significant benefits in the future, transforming the way we approach and treat childhood mental health disorders.

Digital interventions in mental health: challenges and perspectives

The high prevalence, burden and social impact of mental disorders, the lack of mental health care professionals and services around the world, the improvement and dissemination of digital technologies are the factors that have provided increased attention to the process of digital transformation in mental health care recently. The COVID-19 pandemic has increased the importance of remote interaction between different participants in the therapeutic process. Digital technologies in mental health care can bring significant advances in both clinical and scientific psychiatry. The article presents the most promising digital solutions for the diagnosis, therapeutic interventions and prevention of mental disorders. The technologies by which they are implemented are described, including computer programs, virtual and augmented reality, internet resources. The advantages and disadvantages of digital technologies usage in psychiatric practice are analyzed. The article described the main barriers that stand in the way of digital transformation in mental health, including insufficient scientific and practical support of the developed digital products, the lack of methodological foundations, the regulatory framework, and legislation.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

Technological Innovations in Mental Health: Enhancing Access and Affordability for Improved Well-Being

This research paper investigates the evolving landscape of mental health care, with a primary focus on the transformative impact of technology in improving accessibility and affordability. The current state of mental health services is characterized by significant challenges, including limited access, elevated costs, and persistent social stigma. Recognizing these barriers, our study systematically reviews the role of technology-driven interventions in reshaping mental health support structures. Through a meticulous exploration of telehealth platforms, mobile applications, and artificial intelligence, we assess the effectiveness of these innovations in overcoming geographical constraints, enhancing user engagement, and personalizing treatment plans. The methodology encompasses a comprehensive review of existing literature, case studies, and relevant datasets to provide an evidence-based analysis. The paper also scrutinizes ethical considerations tied to the integration of technology in mental health care, emphasizing privacy, confidentiality, and the promotion of equitable access. By addressing potential challenges and outlining future directions for research and development, we aim to guide policymakers, practitioners, and researchers toward optimizing the positive impact of technology in mental health care. The synthesis of our findings underscores the promising trajectory of technological innovations, paving the way for a more accessible, affordable, and inclusive mental health support ecosystem.

Asynchronous Technologies in Mental Health Care and Education.

Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings. This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga." Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first.

Digital Health Technologies in Mental Health Care: Changing Perspectives of Health Care Professionals from 2019 to 2021

AbstractIntroductionDemand for digital mental health tools has risen since the start of the COVID-19 pandemic; however, their evolving use in mental health care is not well understood. We surveyed mental health care professionals (HCPs) before and after the onset of the pandemic and assessed how use of and attitudes about digital technology changed.MethodsWe distributed a digital health survey to HCPs in the United States in 2019 (pre-pandemic; N = 141) and in 2021 (during the pandemic; N = 151). Both surveys recorded the respondents’ perceived barriers to integrating new digital health technologies and the tools they currently used in their practice.ResultsHCP use of telemedicine increased from 47% of respondents in 2019 to 81% in 2021, as did the use of mHealth sensors (2% vs 10%). Patient comfort with technology remained one of the biggest barriers to implementing new digital tools (40% vs 43%), while difficulty integrating digital tools into clinical practice became less common (40% vs 32%). Data management (19% vs 10%) and patient acceptability (19% vs 13%) were cited less often as barriers in 2021. Respondents’ thoughts on what can be most improved by digital technology shifted substantially, with increased access to care rising from 27% of responses in 2019 to 46% in 2021.ConclusionsThe pandemic has changed how HCPs perceive digital health technologies and how they implement these tools in clinical practice. A growing number of HCPs believe increased access to care is the outcome that technology can most improve.FundingOtsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ, USA

Development and functioning of the mobile app-based mh-GAP intervention guide in detection and treatment of people with mental health conditions in primary healthcare settings in Nepal.

This paper describes the development process of a mobile app-based version of the World Health Organization mental health Gap Action Programme Intervention Guide, testing of the app prototypes, and its functionality in the assessment and management of people with mental health conditions in Nepal. Health workers' perception of feasibility and acceptability of using mobile technology in mental health care was assessed during the inspiration phase (N=43); the ideation phase involved the creation of prototypes; and prototype testing was conducted over multiple rounds with 15 healthcare providers. The app provides provisional diagnoses and treatment options based on reported symptoms. Participants found the app prototype useful in reminding them of the process of assessment and management of mental disorders. Some challenges were noted, these included a slow app prototype with multiple technical problems, including difficulty in navigating 'yes'/'no' options, and there were challenges reviewing detailed symptoms of a particular disorder using a "more information" icon. The initial feasibility work suggests that if the technical issues are addressed, the e-mhGAP warrants further research to understand if it is a useful method in improving the detection of people with mental health conditions and initiation of evidence-based treatment in primary healthcare facilities.

E-mental health implementation in inpatient care: Exploring its potential and future challenges.

There is a great evidence base today for the effectiveness of e-mental health, or the use of technology in mental healthcare. However, large-scale implementation in mental healthcare organisations is lacking, especially in inpatient specialized mental healthcare settings. The current study aimed to gain insights into the factors that promote or hinder the implementation of e-mental health applications on organisational, professional and patient levels in Belgium. Four Belgian psychiatric hospitals and psychiatric departments of general hospitals invited their professionals and patients to use Moodbuster, which is a modular web-based platform with a connected smartphone application for monitoring. The platform was used in addition to treatment as usual for three to four months. The professionals and patients completed pre- and post-implementation questionnaires on their reasons to participate or to decline participation and experiences with the Moodbuster platform. Main reasons for the organisations to participate in the implementation study were a general interest in e-mental health and seeing it is a helpful add-on to regular treatment. The actual use of Moodbuster by professionals and patients proved to be challenging with only 10 professionals and 24 patients participating. Implementation was hindered by technical difficulties and inpatient care specific factors such as lack of structural facilities to use e-mental health and patient-specific factors. Professionals saw value in using e-mental health applications for bridging the transition from inpatient to outpatient care. Twenty-two professionals and 31 patients completed the questionnaire on reasons not to participate. For the patients, lack of motivation because of too severe depressive symptoms was the most important reason not to participate. For professionals, it was lack of time and high workload. The current implementation study reveals several important barriers to overcome in order to successfully implement e-mental health in inpatient psychiatric care.

Online consultations in mental healthcare: Modelling determinants of use and experience based on an international survey study at the onset of the pandemic

IntroductionWhile online consultations have shown promise to be a means for the effective delivery of high-quality mental healthcare and the first implementations of these digital therapeutic contacts go back nearly two decades, uptake has remained limited over the years. The onset of the COVID-19 pandemic dramatically altered this relative standstill and created a unique turning point, with a massive amount of both professionals and clients having first hands-on experiences with technology in mental healthcare. ObjectiveThe current study aimed to document the uptake of online consultations and explore if specific characteristics of mental health professionals across and beyond Europe could predict this. MethodsAn international survey was designed to assess mental health professionals' (initial) experiences with online consultations at the onset of the pandemic: their willingness to make use of them and their prior and current experiences, alongside several personal characteristics. Logistic mixed-effects models were used to identify predictors of the use of online consultations, personal experience with this modality, and the sense of telepresence. ResultsA total of 9115 healthcare professionals from 73 countries participated of which about two-thirds used online consultations during the initial COVID-19 outbreak. The current study identifies multiple determinants relating to the use and experience of online consultations, including the professionals' age, experience with the technology before the outbreak, the professional context, and training. ConclusionsDespite strong evidence supporting the relevance of training in digital mental health, this is clearly still lacking. Nevertheless, the COVID-19 pandemic presented a first, and potentially transformative, experience with online consultations for many healthcare professionals. The insights from this study can help support professionals and, importantly, (mental) healthcare organisations to create optimal circumstances for selective and high-quality continued use of online consultations.

R Care: Mental Healthcare Using Mobile Application

Abstract: There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a numberof years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medicalrecord (EMR) to assist both psychiatrists and patients hasnot been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, andevolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry and hence there is need of such applications that are ableto diagnose the user on the real time and give an alternative solution and in this case the application to be a mental healthcare companion for the user To help the user to cope up with his/her mental health To make a userfriendly environment for the user to interact and share Record the mood graph of the user and give him/her tips and suitable videos according to current mood of the userKeep the track of users mood in form of a journal and also analyses the current mood of the user and make a recordof it Gives a proper information and alternatives to the user

Design of a Game-Based Training Environment to Enhance Mental Health Care Professionals' Skills in Using e-Mental Health: Multiple Methods User Requirements Analysis.

BackgroundA major factor hampering the adoption of technology in mental health care is a lack of knowledge and skills. Serious gaming offers a potentially effective strategy to enhance the skills needed through experiencing and learning-by-doing in a playful way. However, serious gaming solutions are not widely available for mental health care. Therefore, the development of a game-based training environment in mental health care was pursued in a design project. The first step in such a design project is to identify user requirements that should be met.ObjectiveThis study aims to deliver user requirements that inform the design of a game-based training environment for mental health care professionals. This environment aims to support mental health care professionals’ knowledge and skill enhancement regarding the use of e–mental health (eMH); for example, video calling, mobile apps, web-based treatment modules, and techniques such as virtual or augmented reality.MethodsWe used an exploratory multiple methods design consisting of a web-based questionnaire, co-design sessions, and interviews. To ensure a good representation of the target user group, professionals from various disciplines within mental health care were included in the research. The multiple methods design facilitates a broad view of user needs and in-depth knowledge of specific design requirements. We describe the protocol for this research project in a protocol paper published in the JMIR Research Protocols in February 2021.ResultsThe user requirements analysis revealed three types of users for the envisioned game-based training environment: mental health care professionals who want to learn about the basic possibilities of eMH, mental health care professionals who want to develop their eMH skills to the next level, and mental health care professionals who want to experiment with new technologies. This reflects the diversity of needs that were identified, as well as the need to develop a diversity of suitable scenarios in the environment. User requirements analysis shows that the focus of a training environment should be on increasing knowledge about the possibilities of eMH, focusing on experiencing the benefits in particular situations, and building confidence in using eMH in a therapeutic setting. This requires careful consideration of the suitable game characteristics.ConclusionsImprovement of mental health care professionals’ skills in eMH requires an environment that is user driven and flexible, and simultaneously incorporates contextual factors that are relevant for its implementation in practice. This user requirements analysis contributes to the understanding of the issues that should be considered in the development of a game-based training environment. This shows that there are multiple and diverse learning needs among mental health care professionals. Various client populations, services, and situations demand various options for training.International Registered Report Identifier (IRRID)RR2-10.2196/18815

Digitale technologie in de geestelijke gezondheidszorg: een narratieve review en visietekst op weg naar gepersonaliseerde zorg

Digital technology in mental health care: a narrative review and vision on the road to personalised care Mental health problems are prevalent and are associated with a huge societal cost. Therefore, there is an urgent need for improvements in mental health care focusing on a person-centered approach. Digital technology offers a unique opportunity to contribute to a better understanding of the specific needs of an individual person by actively engaging this person in his or her treatment process. In this paper, the authors introduce the experience sampling method (ESM) as a clinical instrument fit for this purpose. ESM is a structured diary technique, provided on an app, that allows to assess individuals in their normal daily life. The authors demonstrate that a clinical application of ESM contributes to 1. more engagement and empowerment, 2. better self-management and recovery, 3. improved goal direction in clinical assessment and management of care, and 4. more shared decision making. They also discuss the implementation of ESM in routine mental health care and introduce the European project IMMERSE, which is specifically targeted on the clinical implementation of ESM in 4 European countries. This extensive international project will contribute to the implementation of ESM in clinical practice, resulting in a personalized, proactive, user-centered model of mental health care, transforming patients from passive care users into active managers of their own care.

A Tide of Technical Trends: Technology Competence Among Licensed Counselors

The inclusion of technology in mental health care can revolutionize the accessibility, affordability, and effectiveness of counseling services, while furnishing practical solutions to reduce mental health disparities and meet widening care demands. Steered by the Coalition for Technology in Behavioral Science (CTiBS) telebehavioral health (TBH) competencies, this study employed a descriptive survey design to investigate licensed counselors’ (LCs’) perceived technology competence in mental health care. The following research question steered the study’s exploration: What is the nature of perceived technology competence among LCs? The overarching hypothesis speculated that LCs’ exposure, familiarity, and current utilization of various mental health technologies would impact their perceived competence to integrate technology into their clinical work with clients. A total of 153 respondents completed all survey items. Through descriptive and chi-square analyses, the results illuminated LCs’ perceived technological proficiency. Implications and future study recommendations are detailed.

Deep learning for prediction of depressive symptoms in a large textual dataset

Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice.

BackgroundMachine‐learning algorithms and big data analytics, popularly known as ‘artificial intelligence’ (AI), are being developed and taken up globally. Patient and public involvement (PPI) in the transition to AI‐assisted health care is essential for design justice based on diverse patient needs.ObjectiveTo inform the future development of PPI in AI‐assisted health care by exploring public engagement in the conceptualization, design, development, testing, implementation, use and evaluation of AI technologies for mental health.MethodsSystematic scoping review drawing on design justice principles, and (i) structured searches of Web of Science (all databases) and Ovid (MEDLINE, PsycINFO, Global Health and Embase); (ii) handsearching (reference and citation tracking); (iii) grey literature; and (iv) inductive thematic analysis, tested at a workshop with health researchers.ResultsThe review identified 144 articles that met inclusion criteria. Three main themes reflect the challenges and opportunities associated with PPI in AI‐assisted mental health care: (a) applications of AI technologies in mental health care; (b) ethics of public engagement in AI‐assisted care; and (c) public engagement in the planning, development, implementation, evaluation and diffusion of AI technologies.ConclusionThe new data‐rich health landscape creates multiple ethical issues and opportunities for the development of PPI in relation to AI technologies. Further research is needed to understand effective modes of public engagement in the context of AI technologies, to examine pressing ethical and safety issues and to develop new methods of PPI at every stage, from concept design to the final review of technology in practice. Principles of design justice can guide this agenda.

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.

BackgroundUncertainty 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.ObjectiveThis 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.MethodsWe 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.ResultsWe 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.ConclusionsEthical 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.