An online evidence-based dictionary of common adverse events of antidepressants: a new tool to empower patients and clinicians in their shared decision-making process.

BACKGROUND: Adverse events (AEs) are commonly reported in clinical studies using the Medical Dictionary for Regulatory Activities (MedDRA), an international standard for drug safety monitoring. However, the technical language of MedDRA makes it challenging for patients and clinicians to share understanding and therefore to make shared decisions about medical interventions. In this project, people with lived experience of depression and antidepressant treatment worked with clinicians and researchers to co-design an online dictionary of AEs associated with antidepressants, taking into account its ease of use and applicability to real-world settings. METHODS: Through a pre-defined literature search, we identified MedDRA-coded AEs from randomised controlled trials of antidepressants used in the treatment of depression. In collaboration with the McPin Foundation, four co-design workshops with a lived experience advisory panel (LEAP) and one independent focus group (FG) were conducted to produce user-friendly translations of AE terms. Guiding principles for translation were co-designed with McPin/LEAP members and defined before the finalisation of Clinical Codes (CCs, or non-technical terms to represent specific AE concepts). FG results were thematically analysed using the Framework Method. RESULTS: Starting from 522 trials identified by the search, 736 MedDRA-coded AE terms were translated into 187 CCs, which balanced key factors identified as important to the LEAP and FG (namely, breadth, specificity, generalisability, patient-understandability and acceptability). Work with the LEAP showed that a user-friendly language of AEs should aim to mitigate stigma, acknowledge the multiple levels of comprehension in 'lay' language and balance the need for semantic accuracy with user-friendliness. Guided by these principles, an online dictionary of AEs was co-designed and made freely available ( https://thesymptomglossary.com ). The digital tool was perceived by the LEAP and FG as a resource which could feasibly improve antidepressant treatment by facilitating the accurate, meaningful expression of preferences about potential harms through a shared decision-making process. CONCLUSIONS: This dictionary was developed in English around AEs from antidepressants in depression but it can be adapted to different languages and cultural contexts, and can also become a model for other interventions and disorders (i.e., antipsychotics in schizophrenia). Co-designed digital resources may improve the patient experience by helping to deliver personalised information on potential benefits and harms in an evidence-based, preference-sensitive way.

Using clinician and patient input to assess utility, accuracy, efficiency, and therapeutic implementation of a new data-driven digital therapeutic for personalized clinical eating disorder treatment: Awaken digital guide.

OBJECTIVE: Eating disorders (EDs) take a life every 52 minutes and treatments are ineffective for ∼50% of individuals. Though EDs are heterogeneous illnesses, current evidence-based treatments take a "one-size-fits-all" approach. Network-Informed Personalized Treatment is a new promising treatment for EDs, but clinician-patient-friendly software tools are needed to integrate this guidance system into routine treatment. Adoption is key for impact, necessitating the inclusion of clinicians in the software development. The current pilot assessed a new data-driven clinician-guidance therapeutic. METHOD: A two-part pilot was analyzed for quantitative (0-not at all to 10-extremely) and qualitative input on user perception through quantitative and open-ended prompted questions evaluating using personalizing ED treatment with the Awaken Digital Guide therapeutic. RESULTS: Results demonstrated that clinicians in a focus group (N = 9) and clinician/patient dyads within implementation (N = 10) endorsed improved efficiency, effectiveness, self-awareness, and accuracy using Awaken Digital Guide compared to current treatment as suggested by quantitative and qualitative results. Both clinicians and patients rated the tool positively (6.8-9.6/5.8-8.6, respectively) with an average rating of good and excellent. CONCLUSION: Findings suggest that ED-specialized clinicians desire data-driven guidance on personalizing ED treatment. Users perceive Awaken Digital Guide therapeutic with potential to increase collaboration, motivation, efficiency, and effectiveness of ED personalized treatment.

Digital psychiatry in Nigeria: A scoping review.

Background: Mental healthcare workforce shortage in Nigeria poses a major obstacle to mental health services scale-up. Digital psychiatry may provide a veritable platform to bridge treatment gaps. Aim: To provide an overview of quantity and range of peer-reviewed publications on digital psychiatry in Nigeria. Setting: A comprehensive literature search encompassed all original, peer-reviewed research articles on digital psychiatry in Nigeria. PubMed, Google Scholar, and a direct exploration of relevant journal article reference lists were utilised. Inclusion criteria covered peer-reviewed original articles conducted in Nigeria between January 2013 and January 2023, regardless of quality. Exclusions comprised case reports, reviews, dissertations, and abstracts. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines were adhered to, while methodological framework of Arksey and O'Malley was used to describe the review. Results: Fourteen studies meeting inclusion criteria exhibited two primary research areas: implementation and intervention. Most studies focused on intervention strategies, showcasing efficacy of digital devices in enhancing outcomes in depression and clinic appointments. Implementation studies indicated favorable acceptance by both clients and healthcare practitioners. Conclusion: Digital technology seems acceptable to Nigerian patients and clinicians. Policies to operationalise provision of digital healthcare services will have positive impact in addressing unmet mental health needs. Finally, the quality of the evidence from majority of studies has to be enhanced, and additional studies are required to uncover gaps in some regions of the country. Contribution: This research demonstrates that, despite some drawbacks, digital methods of providing mental healthcare are practical in Nigeria.

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems.

BACKGROUND: Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events ("flags") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic. METHODS: The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI2). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories' relationships with client and medication-subtype characteristics were tested. RESULTS: The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ2 = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up0 and Not-followed up1 flags (M0 = 31.78; M1 = 45.55; U = 12,119; p < 0.001; η2 = .05). CONCLUSION: These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

[The application of virtual reality in the treatment of mental disorders]. / Die Anwendung der Virtuellen Realität in der Behandlung psychischer Störungen.

BACKGROUND: Virtual reality (VR) enables immersion in an interactive digital world with realistic experiences, that can be applied for controlled and personalized interventions. This review summarizes the current research on VR in the treatment of mental disorders. METHODS: Selective literature search in PubMed and Google Scholar. RESULTS: An increasing number of publications report the therapeutic application of VR for the treatment of mental disorders. Most VR applications are based on established therapy approaches, such as exposure therapy. According to meta-analytic data, virtual exposure therapy (VRET) for specific phobia and agoraphobia with panic disorder is as effective as traditional in vivo exposure therapy. VRET for the treatment of social phobia is significantly more effective than waitlist and placebo control groups with, however, currently inconsistent metanalytic results when compared to in vivo exposure therapy. VRET for the treatment of posttraumatic stress disorder (PTSD) is similar in effectiveness compared to active psychotherapy. For psychosis, positive results have been reported for the VR-based treatment of auditory verbal hallucinations. For patients with a substance use disorder, VR can induce craving, with still unverified diagnostic and therapeutic relevance. CONCLUSIONS: VRET can broaden the psychotherapy options for anxiety disorders. Encouraging results of VR-based treatments for psychosis and PTSD indicate the need for further research concerning its effectiveness and safety. In the field of substance use disorders, evaluation of clinical-orientated VR applications is needed.

The role of digital tools in providing youth mental health: results from an international multi-center study.

Since the traditional mental health system showed significant limitations in the early identification, diagnosis and treatment of the current new youth psychopathological trajectories, by substantially failing in targeting the needs of the current young generation, there is the demand to redesign and digitally adapt youth mental health care and systems. Indeed, the level of digital literacy and the level of digital competency and knowledge in the field of digital psychiatry is still under-investigated among mental health professionals, particularly in youth mental health. Therefore, we aimed at: (a) carrying out a post-hoc analysis of an international multi-centre study, to investigate the opinions of mental health professionals regarding the feasibility, efficacy and clinical experience in delivering digital mental health interventions (DMHIs) in youths; (b) providing a comprehensive overview on the integrated digitally-based youth mental health care models and innovations. Mental health professionals declared the lack of a formal training in digital psychiatry, particularly in youth mental health. Subjects who received a formal theoretical/practical training on DMHIs displayed a statistical trend towards a positive feasibility of digital psychiatry in youth mental health (p = 0.053) and a perceived increased efficacy of digital psychiatry in youths (p = 0.051). Respondents with higher Digital Psychiatry Opinion (DPO) scores reported a positive perceived feasibility of DMHIs in youths (p < 0.041) and are more prone to deliver DMHIs to young people (p < 0.001). Respondents with higher knowledge scores (KS) declared that DMHIs are more effective in youth mental health (p < 0.001). Overall, the digitalisation indeed allowed young people to keep in touch with a mental health professional, facilitating a more dynamic and fluid mental health care access and monitoring, generally preferred and considered more feasible by post-Millennial youngsters. Accordingly, our findings demonstrated that mental health professionals are more prone to offer DMHIs in youth mental health, particularly whether previously trained and knowledgeable on the topic.

Artificial Intelligence and Chatbots in Psychiatry.

The utilization of artificial intelligence (AI) in psychiatry has risen over the past several years to meet the growing need for improved access to mental health solutions. Additionally, shortages of mental health providers during the COVID-19 pandemic have continued to exacerbate the burden of mental illness worldwide. AI applications already in existence include those enabled to assist with psychiatric diagnoses, symptom tracking, disease course prediction, and psychoeducation. Modalities of AI mental health care delivery include availability through the internet, smartphone applications, and digital gaming. Here we review emerging AI-based interventions in the form of chat and therapy bots, specifically conversational applications that teach the user emotional coping mechanisms and provide support for people with communication difficulties, computer generated images of faces that form the basis of avatar therapy, and intelligent animal-like robots with new advances in digital psychiatry. We discuss the implications of incorporating AI chatbots into clinical practice and offer perspectives on how these AI-based interventions will further impact the field of psychiatry.

Sample size, sample size planning, and the impact of study context: systematic review and recommendations by the example of psychological depression treatment.

BACKGROUND: Sample size planning (SSP) is vital for efficient studies that yield reliable outcomes. Hence, guidelines, emphasize the importance of SSP. The present study investigates the practice of SSP in current trials for depression. METHODS: Seventy-eight randomized controlled trials published between 2013 and 2017 were examined. Impact of study design (e.g. number of randomized conditions) and study context (e.g. funding) on sample size was analyzed using multiple regression. RESULTS: Overall, sample size during pre-registration, during SSP, and in published articles was highly correlated (r's ≥ 0.887). Simultaneously, only 7-18% of explained variance related to study design (p = 0.055-0.155). This proportion increased to 30-42% by adding study context (p = 0.002-0.005). The median sample size was N = 106, with higher numbers for internet interventions (N = 181; p = 0.021) compared to face-to-face therapy. In total, 59% of studies included SSP, with 28% providing basic determinants and 8-10% providing information for comprehensible SSP. Expected effect sizes exhibited a sharp peak at d = 0.5. Depending on the definition, 10.2-20.4% implemented intense assessment to improve statistical power. CONCLUSIONS: Findings suggest that investigators achieve their determined sample size and pre-registration rates are increasing. During study planning, however, study context appears more important than study design. Study context, therefore, needs to be emphasized in the present discussion, as it can help understand the relatively stable trial numbers of the past decades. Acknowledging this situation, indications exist that digital psychiatry (e.g. Internet interventions or intense assessment) can help to mitigate the challenge of underpowered studies. The article includes a short guide for efficient study planning.

Lessons Learned or Forgotten? Impacts of COVID-19 on the Future Direction of Global (e-)Mental Health Care.

PURPOSE OF REVIEW: The COVID-19 pandemic has impacted lives globally, posing unique challenges to mental health services exposing vulnerability and limitations within these systems. During the course of the pandemic, telecommunications technologies (e-mental health care) have served a critical role in psychiatric care. It is important to understand current lessons learned in e-mental health care and implications for global mental health systems for both emerging from the pandemic and after the pandemic has ended. RECENT FINDINGS: There are significant regulatory, policy, and evaluation challenges for global e-mental health impacting patients, clinicians, health systems, and decision-makers. These include complex regulatory issues, difficulties of providing care across boundaries, and keeping pace with the implementation of new technologies in behavioral health. The collaborative development of global standards along with policies, appropriate regulations, and developing new models of research and development opens the possibility of improved access to care across national boundaries.

Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations.

BACKGROUND: Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months. OBJECTIVE: Our paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase. METHODS: The experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context. RESULTS: For task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient's state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match. CONCLUSIONS: BN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines. TRIAL REGISTRATION: ClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881.

Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators.

Digital mental health technologies such as mobile health (mHealth) tools can offer innovative ways to help develop and facilitate mental health care provision, with the COVID-19 pandemic acting as a pivot point for digital health implementation. This viewpoint offers an overview of the opportunities and challenges mHealth innovators must navigate to create an integrated digital ecosystem for mental health care moving forward. Opportunities exist for innovators to develop tools that can collect a vast range of active and passive patient and transdiagnostic symptom data. Moving away from a symptom-count approach to a transdiagnostic view of psychopathology has the potential to facilitate early and accurate diagnosis, and can further enable personalized treatment strategies. However, the uptake of these technologies critically depends on the perceived relevance and engagement of end users. To this end, behavior theories and codesigning approaches offer opportunities to identify behavioral drivers and address barriers to uptake, while ensuring that products meet users' needs and preferences. The agenda for innovators should also include building strong evidence-based cases for digital mental health, moving away from a one-size-fits-all well-being approach to embrace the development of comprehensive digital diagnostics and validated digital tools. In particular, innovators have the opportunity to make their clinical evaluations more insightful by assessing effectiveness and feasibility in the intended context of use. Finally, innovators should adhere to standardized evaluation frameworks introduced by regulators and health care providers, as this can facilitate transparency and guide health care professionals toward clinically safe and effective technologies. By laying these foundations, digital services can become integrated into clinical practice, thus facilitating deeper technology-enabled changes.

Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study.

BACKGROUND: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. OBJECTIVE: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. METHODS: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform's configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. RESULTS: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users' data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser-based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. CONCLUSIONS: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health-related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.

The Future of Digital Psychiatry.

PURPOSE OF REVIEW: Treatments in psychiatry have been rapidly changing over the last century, following the development of psychopharmacology and new research achievements. However, with advances in technology, the practice of psychiatry in the future will likely be influenced by new trends based on computerized approaches and digital communication. We examined four major areas that will probably impact on the clinical practice in the next few years: telepsychiatry; social media; mobile applications and internet of things; artificial intelligence; and machine learning. RECENT FINDINGS: Developments in these four areas will benefit patients throughout the journey of the illness, encompassing early diagnosis, even before the patients present to a clinician; personalized treatment on demand at anytime and anywhere; better prediction on patient outcomes; and even how mental illnesses are diagnosed in the future. Though the evidence for many technology-based interventions or mobile applications is still insufficient, it is likely that such advances in technology will play a larger role in the way that patient receives mental health interventions in the future, leading to easier access to them and improved outcomes.

Methodological Challenges in Randomized Controlled Trials on Smartphone-Based Treatment in Psychiatry: Systematic Review.

BACKGROUND: Smartphone-based technology is developing at high speed, and many apps offer potential new ways of monitoring and treating a range of psychiatric disorders and symptoms. However, the effects of most available apps have not been scientifically investigated. Within medicine, randomized controlled trials (RCTs) are the standard method for providing the evidence of effects. However, their rigidity and long time frame may contrast with the field of information technology research. Therefore, a systematic review of methodological challenges in designing and conducting RCTs within mobile health is needed. OBJECTIVE: This systematic review aimed to (1) identify and describe RCTs investigating the effect of smartphone-based treatment in adult patients with a psychiatric diagnosis, (2) discuss methodological challenges in designing and conducting individual trials, and (3) suggest recommendations for future trials. METHODS: A systematic search in English was conducted in PubMed, PsycINFO, and EMBASE up to August 12, 2019. The search terms were (1) psychiatric disorders in broad term and for specific disorders AND (2) smartphone or app AND (3) RCT. The Consolidated Standards of Reporting Trials electronic health guidelines were used as a template for data extraction. The focus was on trial design, method, and reporting. Only trials having sufficient information on diagnosis and acceptable diagnostic procedures, having a smartphone as a central part of treatment, and using an RCT design were included. RESULTS: A total of 27 trials comprising 3312 patients within a range of psychiatric diagnoses were included. Among them, 2 trials were concerning drug or alcohol abuse, 3 psychosis, 10 affective disorders, 9 anxiety and posttraumatic stress disorder, 1 eating disorder, and 1 attention-deficit/hyperactivity disorder. In addition, 1 trial used a cross-diagnostic design, 7 trials included patients with a clinical diagnosis that was subsequently assessed and validated by the researchers, and 11 trials had a sample size above 100. Generally, large between-trial heterogeneity and multiple approaches to patient recruitment, diagnostic procedures, trial design, comparator, outcome measures, and analyses were identified. Only 5 trials published a trial protocol. Furthermore, 1 trial provided information regarding technological updates, and only 18 trials reported on the conflicts of interest. No trial addressed the ethical aspects of using smartphones in treatment. CONCLUSIONS: This first systematic review of the methodological challenges in designing and conducting RCTs investigating smartphone-based treatment in psychiatric patients suggests an increasing number of trials but with a lower quality compared with classic medical RCTs. Heterogeneity and methodological issues in individual trials limit the evidence. Methodological recommendations are presented.

Choosing the right treatment - combining clinicians' expert knowledge with data-driven predictions.

Context: This study proposes a Bayesian network model to aid mental health specialists making data-driven decisions on suitable treatments. The aim is to create a probabilistic machine learning model to assist psychologists in selecting the most suitable treatment for individuals for four potential mental disorders: Depression, Panic Disorder, Social Phobia, or Specific Phobia. Methods: This study utilized a dataset from 1,094 individuals in Denmark containing socio-demographic details and mental health information. A Bayesian network was initially employed in a purely data-driven approach and was later refined with expert knowledge, referred to as a hybrid model. The model outputted probabilities for each disorder, with the highest probability indicating the most suitable disorder for treatment. Results: By incorporating expert knowledge, the model demonstrated enhanced performance compared to a strictly data-driven approach. Specifically, it achieved an AUC score of 0.85 vs 0.80 on the test data. Furthermore, we evaluated some cases where the predictions of the model did not match the actual treatment. The symptom questionnaires indicated that these participants likely had comorbid disorders, with the actual treatment being proposed by the model with the second highest probability. Conclusions: In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which treatment to prioritize.

Digital psychiatry in low-and-middle-income countries: New developments and the way forward.

Low- and middle-income countries (LMICs) bear the greater share of the global mental health burden but are ill-equipped to deal with it because of severe resource constraints leading to a large treatment gap. The remote provision of mental health services by digital means can effectively augment conventional services in LMICs to reduce the treatment gap. Digital psychiatry in LMICs has always lagged behind high-income countries, but there have been encouraging developments in the last decade. There is increasing research on the efficacy of digital psychiatric interventions. However, the evidence is not adequate to conclude that digital psychiatric interventions are invariably effective in LMICs. A striking development has been the rise in mobile and smartphone ownership in LMICs, which has driven the increasing use of mobile technologies to deliver mental health services. An innovative use of mobile technologies has been to optimize task-shifting, which involves delivering mental healthcare services in community settings using non-specialist health professionals. Emerging evidence from LMICs shows that it is possible to use digital tools to train non-specialist workers effectively and ensure that the psychosocial interventions they deliver are efficacious. Despite these promising developments, many barriers such as service costs, underdeveloped infrastructure, lack of trained professionals, and significant disparities in access to digital services impede the progress of digital psychiatry in LMICs. To overcome these barriers, digital psychiatric services in LMICs should address contextual factors influencing the delivery of digital services, ensure collaboration between different stakeholders, and focus on reducing the digital divide.

A Curriculum on Digital Psychiatry for a US-Based Psychiatry Residency Training Program: Pilot Implementation Study.

BACKGROUND: Digital psychiatry, defined as the application of health technologies to the prevention, assessment, and treatment of mental health illnesses, is a growing field. Interest in the clinical use of these technologies continues to grow. However, psychiatric trainees receive limited or no formal education on the topic. OBJECTIVE: This study aims to pilot a curriculum on digital psychiatry for a US-based psychiatry residency training program and examine the change in learner confidence regarding appraisal and clinical recommendation of digital mental health apps. METHODS: Two 60-minute sessions were presented through a web-based platform to postgraduate year 2-4 residents training in psychiatry at a US-based adult psychiatry residency program. Learner confidence was assessed using pre- and postsession surveys. RESULTS: Matched pre- and postsession quizzes showed improved confidence in multiple domains aligning with the course objectives. This included the structured appraisal of digital mental health apps (P=.03), assessment of a patient's digital health literacy (P=.01), formal recommendation of digital health tools (P=.03), and prescription of digital therapeutics to patients (P=.03). Though an improvement from baseline, mean ratings for confidence did not exceed "somewhat comfortable" on any of the above measures. CONCLUSIONS: Our study shows the feasibility of implementing a digital psychiatry curriculum for residents in multiple levels of training. We also identified an opportunity to increase learner confidence in the appraisal and clinical use of digital mental health apps through the use of a formal curriculum.

Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study.

BACKGROUND: Limited awareness, social stigma, and access to mental health professionals hinder early detection and intervention of internet gaming disorder (IGD), which has emerged as a significant concern among young individuals. Prevalence estimates vary between 0.7% and 15.6%, and its recognition in the International Classification of Diseases, 11th Revision and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition underscores its impact on academic functioning, social isolation, and mental health challenges. OBJECTIVE: This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases. METHODS: A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes. RESULTS: A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=-0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (ß=.33; P<.001), time interval between keys (ß=-0.26; P=.01), word spacing (ß=-0.25; P<.001), deletion (ß=-0.24; P<.001), and horizontal length of strokes (ß=0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD. CONCLUSIONS: The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.

From palm to practice: prescription digital therapeutics for mental and brain health at the National Institutes of Health.

Prescription Digital Therapeutics (PDTs) are emerging as promising tools for treating and managing mental and brain health conditions within the context of daily life. This commentary distinguishes PDTs from other Software as Medical Devices (SaMD) and explores their integration into mental and brain health treatments. We focus on research programs and support from the National Institutes of Health (NIH), discussing PDT research supported by the NIH's National Institute on Child Health and Development (NICHD), National Institute of Mental Health (NIMH), and National Institute on Aging (NIA). We present a hierarchical natural language processing topic analysis of NIH-funded digital therapeutics research projects. We delineate the PDT landscape across different mental and brain health disorders while highlighting opportunities and challenges. Additionally, we discuss the research foundation for PDTs, the unique therapeutic approaches they employ, and potential strategies to improve their validity, reliability, safety, and effectiveness. Finally, we address the research and collaborations necessary to propel the field forward, ultimately enhancing patient care through innovative digital health solutions.