Data mining for health: staking out the ethical territory of digital phenotyping

Nanopsychiatry: Advancing psychiatric diagnosis and monitoring through nanotechnology-based detection

Nanopsychiatry, operating at the nanoscale, leverages engineered nanomaterials and nanodevices to revolutionize psychiatric diagnostics and therapeutics. This review systematically analyzes the implementation of advanced nanomaterials, including quantum dots, carbon nanotubes (CNTs), and metal nanoparticles, in neural interface systems for neurotransmitter detection and drug monitoring. We evaluate the integration of nanoscale architectures in developing high-specificity biosensors for key neurotransmitters such as dopamine, serotonin, and glutamate. The review critically examines recent advances in nanomaterial-based electrochemical and optical sensing platforms, incorporating modified electrodes with conducting polymers, metallic nanocomposites, and functionalized graphene derivatives. These systems demonstrate enhanced sensitivity and selective multi-analyte detection capabilities in complex biological matrices. We analyze how these nanosensors complement conventional neuroimaging techniques, enabling monitoring of neurochemical dynamics in psychiatric conditions with improved spatial and temporal resolution. Furthermore, we assess the development of flexible, nanomaterial-enhanced wearable biosensors incorporating screen-printed electrodes and microfluidic systems. These devices achieve continuous monitoring of neurological biomarkers, facilitating quantitative assessment of psychiatric symptoms and treatment responses. The integration of machine learning algorithms with these nanoscale sensing platforms enables data processing and pattern recognition for personalized psychiatric interventions.

Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis

BACKGROUND AND HYPOTHESIS

Digital remote monitoring (DRM) captures service users' health-related data remotely using devices such as smartphones and wearables. Data can be analyzed using advanced statistical methods (eg, machine learning) and shared with clinicians to aid assessment of people with psychosis' mental health, enabling timely intervention. Such methods show promise in detecting early signs of psychosis relapse. However, little is known about clinicians' views on the use of DRM for psychosis. This study explores multi-disciplinary staff perspectives on using DRM in practice.

STUDY DESIGN

Fifty-nine mental health professionals were interviewed about their views on DRM in psychosis care. Interviews were analyzed using reflexive thematic analysis. Study Results: Five overarching themes were developed, each with subthemes: (1) the perceived value of digital remote monitoring; (2) clinicians' trust in digital remote monitoring (3 subthemes); (3) service user factors (2 subthemes); (4) the technology-service user-clinician interface (2 subthemes); and (5) organizational context (2 subthemes).

CONCLUSIONS

Participants saw the value of using DRM to detect early signs of relapse and to encourage service user self-reflection on symptoms. However, the accuracy of data collected, the impact of remote monitoring on therapeutic relationships, data privacy, and workload, responsibility and resource implications were key concerns. Policies and guidelines outlining clinicians' roles in relation to DRM and comprehensive training on its use are essential to support its implementation in practice. Further evaluation regarding the impact of digital remote monitoring on service user outcomes, therapeutic relationships, clinical workflows, and service costs is needed.

Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior

BACKGROUND

People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understand communication in psychosis and suicide risk prediction, but not to predict future suicidal behavior in people with psychosis. We utilized NLP-derived linguistic features from a dyadic task among people with psychotic disorders to predict current suicide ideation, past suicide attempts, and future suicidal behavior.

METHODS

N = 112 adults with psychotic disorders completed the Columbia-Suicide Severity Rating Scale at baseline and one-year follow-up to capture lifetime suicide attempts, current suicidal ideation, and suicidal behavior during the follow-up period. At baseline, participants completed a dyadic role-play task called the Social Skills Performance Assessment. Lexical features, lexical diversity, and sentiment features were extracted from task transcripts. Models for each outcome were generated using a 70 %-30 % train-test split with MLPRegressor. SHapley Additive exPlanations (SHAP) was utilized for feature analysis.

RESULTS

A total of 42.9 % of participants had baseline suicidal ideation, 67.9 % had at least one past suicide attempt, and 13.3 % had at least one reported new suicidal behavior at one-year follow-up. The models had strong predictive performance for past attempts (F1 = 0.75) and current ideation (F1 = 0.74-0.79), with future suicide behavior models showing the strongest predictive performance (F1 = 0.86-0.93). The top features varied across suicide ideation, past attempts, and future behavior.

DISCUSSION

NLP-derived features from a dyadic task were associated with high predictive accuracy for future suicidal behavior. Pending replication, these findings suggest that NLP-derived linguistic features from dyadic tasks could contribute to understanding suicide risk among people with psychosis.

Developing Digital Mental Health Tools With Culturally Diverse Parents and Young People: Qualitative User-Centered Design Study

BACKGROUND

Approximately 39% of young people (aged 16-24 y) experience mental ill health, but only 23% seek professional help. Early intervention is essential for reducing the impacts of mental illness, but young people, particularly those from culturally diverse communities, report experiencing shame and stigma, which can deter them from engaging with face-to-face services. Digital mental health (DMH) tools promise to increase access, but there is a lack of literature exploring the suitability of DMH tools for culturally diverse populations.

OBJECTIVE

The project was conducted in partnership with a large-scale national DMH organization that promotes evidence-based early intervention, treatment, and support of mental health in young people and their families. The organization wanted to develop a self-directed web-based platform for parents and young people that integrates psychological assessments and intervention pathways via a web-based "check-in" tool. Our project explored the views of culturally diverse parents and young people on the opportunities and barriers to engagement with a web-based DMH screening tool.

METHODS

We conducted a 2-phase qualitative study aiming to identify potential issues faced by culturally diverse communities when engaging with DMH tools designed for the Australian public. We worked with 18 culturally diverse participants (parents: n=8, 44%; young people: n=10, 56%) in a series of design-led workshops drawing on methods from speculative design and user experience to understand the opportunities and barriers that organizations might face when implementing population-level DMH tools with culturally diverse communities. NVivo was used to conduct thematic analyses of the audio-recorded and transcribed workshop data.

RESULTS

Five themes were constructed from the workshops: (1) trust in the use and application of a DMH tool, (2) data management and sharing, (3) sociocultural influences on mental health, (4) generational differences in mental health and digital literacy, and (5) stigma and culturally based discrimination in mental health support.

CONCLUSIONS

The emergent themes have important considerations for researchers wishing to develop more inclusive DMH tools. The study found that healthy parent-child relationships will increase engagement in mental health support for young persons from culturally diverse backgrounds. Barriers to engagement with DMH tools included culturally based discrimination, the influence of culture on mental health support, and the potential impact of a diagnostic label on help seeking. The study's findings suggest a need for culturally safe psychoeducation for culturally diverse end users that fosters self-determination with tailored resources. They also highlight important key challenges when working with culturally diverse populations.

The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact

Digital phenotyping collects health data digitally, supporting early disease diagnosis and health management. This paper systematically reviews the diversity of research methods in digital phenotyping and its clinical benefits, while also focusing on its importance within the P4 medicine paradigm and its core role in advancing its application in biobanks. Furthermore, the paper envisions the continued clinical benefits of digital phenotyping, driven by technological innovation, global collaboration, and policy support.

Current limitations in technology-based cognitive assessment for severe mental illnesses: a focus on feasibility, reliability, and ecological validity

Cognitive impairments are frequently observed in subjects with severe mental illnesses (SMI), leading to a remarkable impact in their real-world functioning. Well-validated and gold standard instruments are available for the assessment of cognitive deficits, but different limitations should be considered, such as the need for specific training, lengthy administration times, practice effects, or reliance on subjective reports. Recent advances in digital technologies, such as ecological momentary assessments (EMA), virtual reality (VR), and passive digital phenotyping (DP), offer promising complementary approaches for capturing real-world cognitive functioning. In the current mini-review, we examine current research gaps that limit the application of these technologies, with a specific focus on feasibility, reliability and ecological validity. EMA may capture real-world functioning by increasing the number of evaluations throughout the day, but its use might be hindered by high participant burden and missing data. Furthermore, to achieve an accurate interpretation of EMA, studies should account for sampling and moment selection biases and the presence of several confounding factors. DP faces significant ethical and logistical challenges, including privacy and informed consent concerns, as well as challenges in data interpretation. VR could serve as a platform for both more ecologically valid cognitive assessments and rehabilitation interventions, but current barriers include technological and psychometric limitations, underdeveloped theoretical frameworks, and ethical considerations. Addressing these issues is crucial for ensuring that these novel technologies can effectively serve as valuable complements to traditional neuropsychological cognitive batteries.

Using Active and Passive Smartphone Data to Enhance Adolescents' Emotional Awareness in Forensic Outpatient Setting: A Qualitative Feasibility and Usability Study

BACKGROUND

Delinquent behavior in adolescence is a prevalent issue, often associated with difficulties across multiple life domains, which in turn perpetuates negative life outcomes. While current treatment programs show partial success in improving behavioral changes and reducing recidivism, comprehensive conclusions regarding the overall efficacy of these interventions have yet to be established. In forensic outpatient settings, the discrepancy between adolescents' limited emotional awareness and the predominant emphasis on cognitive reflection, combined with low treatment adherence, may be factors that undermine treatment efficacy. New technologies, such as smartphone apps, may offer a solution by integrating real-life data into treatment to improve emotional and behavioral patterns. The low-threshold use of smartphone data can be useful in addressing these treatment challenges.

OBJECTIVE

This study aimed to explore the feasibility and usability of Feelee (Garage2020), a smartphone app that integrates active emoji and passive behavioral data, as a potential addition to treatment for adolescents in a forensic outpatient setting.

METHODS

We conducted a prepilot study with adolescents (n=4) who used the Feelee app over a 2-week period. App usage included completing a brief emoji survey 3 times a day (active data) and allowing Feelee to track the call logs, Bluetooth devices in proximity, cell tower IDs, app usage, and phone status (passive data). During treatment sessions, both adolescents and clinicians reviewed and discussed the active and passive data. Semistructured interviews were conducted with adolescents and clinicians (n=7) to gather experiences and feedback on the feasibility and usability of incorporating smartphone data into treatment.

RESULTS

The study showed that adolescents (n=3) succeeded in using Feelee for the full 2 weeks, and data were available for discussion in at least 1 session per participant. Both adolescents and clinicians (n=7) stated that Feelee was valuable for viewing, discussing, and gaining insight into their emotions, which facilitated targeted actions based on the Feelee data. However, neither adolescents nor clinicians reported increased engagement in treatment as a result of using Feelee. Despite technical issues, overall feedback on the Feelee app, in addition to treatment, was positive (n=7). However, further improvements are needed to address the high battery consumption and the inaccuracies in the accelerometer.

CONCLUSIONS

This qualitative study provides an in-depth understanding of the potential benefits of integrating active and passive smartphone data for adolescents in a forensic outpatient setting. Feelee appears to contribute to a better understanding of emotions and behaviors, suggesting its potential value in enhancing emotional awareness in treatment. Further research is needed to assess Feelee's clinical effectiveness and explore how it enhances emotional awareness. Recommendations from adolescents and clinicians emphasize the need for prepilot studies to address user issues, guiding technical improvements and future research in forensic outpatient settings.

Behind the good of digital tools for occupational safety and health: a scoping review of ethical issues surrounding the use of the internet of things

Introduction

The internet of things (IoT) is increasingly used for occupational safety and health (OSH) purposes in private and public organisations. Current practices and regulations are unclear, and some stakeholders raised concerns about deploying this technology at work.

Methods

Following the PRISMA-ScR checklist, we reviewed the main opportunities and ethical issues raised by using IoT devices for OSH purposes, as discussed in the academic literature. We searched peer-reviewed papers from 2008 to September 2023, written in English and available in "Web of Science," "PhilPapers," and "Google Scholar." We found 1,495 articles, of which 61 fulfilled the selection criteria. We classified ethical topics discussed in the papers in a coherent description frame.

Results

We obtained 6 overarching categories: "ethical opportunities," "surveillance and problematic data re-purposing," "difficulty to inform, consult, and obtain consent from employees," "unintended and unpredictable adverse effects," "suboptimal data management," and "external factors that are conducive to ethical issues." The resulting list of problematic issues is unexpectedly furnished and substantial. Such a list provides information and guidance for those who wish to develop evaluation frameworks in line with a preventive regulatory approach. It also informs policymakers and practitioners about the governance of such tools for ensuring more OSH.

Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.

From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease

Speech impairment is a common and disabling symptom in Parkinson's disease (PD), affecting communication and quality of life. Advances in digital speech processing and artificial intelligence have revolutionized objective speech analysis. Given the complex nature of speech impairment, acoustic speech analysis offers unique biomarkers for neuroprotective treatments from the prodromal stages of PD. Digital speech biomarkers can monitor levodopa-induced motor complications, detect the effects of deep brain stimulation, and provide feedback for behavioral speech therapy. This review updates the mechanisms underlying speech impairment, the impact of speech phenotypes, and the effects of interventions on speech. We evaluate the strengths, potential weaknesses, and suitability of promising digital speech biomarkers in PD for capturing disease progression and treatment efficacy. Additionally, we explore the translational potential of PD speech biomarkers to other neuropsychiatric diseases, offering insights into motion, cognition, and emotion. Finally, we highlight knowledge gaps and suggest directions for future research to enhance the use of quantitative speech measures in disease-modifying clinical trials. The findings demonstrate that one year is sufficient to detect disease progression in early PD through speech biomarkers. Voice quality, pitch, loudness, and articulation measures appear to capture the efficacy of treatment interventions most effectively. Certain speech features, such as loudness and articulation rate, behave oppositely in different neurological diseases, offering valuable insights for differential diagnosis. In conclusion, this review highlights speech as a biomarker in tracking disease progression, especially in the prodromal stages of PD, and calls for further longitudinal studies to establish its efficacy across diverse populations.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Ethical challenges in contemporary psychiatry: an overview and an appraisal of possible strategies and research needs

Psychiatry shares most ethical issues with other branches of medicine, but also faces special challenges. The Code of Ethics of the World Psychiatric Association offers guidance, but many mental health care professionals are unaware of it and the principles it supports. Furthermore, following codes of ethics is not always sufficient to address ethical dilemmas arising from possible clashes among their principles, and from continuing changes in knowledge, culture, attitudes, and socio-economic context. In this paper, we identify topics that pose difficult ethical challenges in contemporary psychiatry; that may have a significant impact on clinical practice, education and research activities; and that may require revision of the profession's codes of ethics. These include: the relationships between human rights and mental health care, research and training; human rights and mental health legislation; digital psychiatry; early intervention in psychiatry; end-of-life decisions by people with mental health conditions; conflicts of interests in clinical practice, training and research; and the role of people with lived experience and family/informal supporters in shaping the agenda of mental health care, policy, research and training. For each topic, we highlight the ethical concerns, suggest strategies to address them, call attention to the risks that these strategies entail, and highlight the gaps to be narrowed by further research. We conclude that, in order to effectively address current ethical challenges in psychiatry, we need to rethink policies, services, training, attitudes, research methods and codes of ethics, with the concurrent input of a range of stakeholders, open minded discussions, new models of care, and an adequate organizational capacity to roll-out the implementation across routine clinical care contexts, training and research.

Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder

BACKGROUND

Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone.

OBJECTIVE

The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario.

METHODS

This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic.

RESULTS

A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening.

CONCLUSION

We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Talking about diseases; developing a model of patient and public-prioritised disease phenotypes

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.

With advancement in health technology comes great responsibility - Ethical and safety considerations for using digital health technology: A narrative review

The accelerated adoption of digital health technologies in the last decades has raised important ethical and safety concerns. Despite the potency and usefulness of digital health technologies, addressing safety, and ethical considerations needs to take greater prominence. This review paper focuses on ethical and safety facets, including health technology-related risks, users' safety and well-being risks, security and privacy concerns, and risks to transparency and diminished accountability associated with the utilization of digital health technologies. In order to maximize the potential of health technology benefits, awareness of safety risks, and ethical concerns should be increased, and the use of appropriate strategies and measures should be considered.

Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study

BACKGROUND

Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement.

OBJECTIVE

This qualitative study explores the experiences and attitudes of users of the Mom2B mobile health (mHealth) research app (Uppsala University) during the perinatal period. In particular, we aimed to determine the acceptability of the app and any concerns about providing data through a mobile app.

METHODS

Semistructured focus group interviews were conducted digitally in Swedish with 13 groups and a total of 41 participants. Participants had been active users of the Mom2B app for at least 6 weeks and included pregnant and postpartum women, both with and without depression symptomatology apparent in their last screening test. Interviews were recorded, transcribed verbatim, translated to English, and evaluated using inductive thematic analysis.

RESULTS

Four themes were elicited: acceptability of sharing data, motivators and incentives, barriers to task completion, and user experience. Participants also gave suggestions for the improvement of features and user experience.

CONCLUSIONS

The study findings suggest that app-based digital phenotyping is a feasible and acceptable method of conducting research and health care delivery among perinatal women. The Mom2B app was perceived as an efficient and practical tool that facilitates engagement in research as well as allows users to monitor their well-being and receive general and personalized information related to the perinatal period. However, this study also highlights the importance of trustworthiness, accessibility, and prompt technical issue resolution in the development of future research apps in cooperation with end users. The study contributes to the growing body of literature on the usability and acceptability of mobile apps for research and ecological momentary assessment and underscores the need for continued research in this area.

Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry.

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology

BACKGROUND

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

OBJECTIVES

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.

METHODS

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.

FINDINGS

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.

CONCLUSIONS

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.

CLINICAL IMPLICATIONS

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.

[Digital technologies to improve mental health]

The burden of mental diseases is enormous and constantly growing worldwide. The resulting increase in demand for psychosocial help is also having a negative impact on waiting times for psychotherapy in Germany. Digital interventions for mental health, such as interventions delivered through or with the help of a website (e.g. "telehealth"), smartphone, or tablet app-based interventions and interventions that use text messages or virtual reality, can help. This article begins with an overview of the functions and range of applications of digital technologies for mental health. The evidence for individual digital forms of interventions is addressed. Overall, it is shown that digital interventions for mental health are likely to be cost-effective compared to no therapy or a non-therapeutic control group. Newer approaches such as "digital phenotyping" are explained in the article. Finally, individual papers from the "Leibniz ScienceCampus Digital Public Health" are presented, and limitations and challenges of technologies for mental health are discussed.

Ethical considerations for integrating multimodal computer perception and neurotechnology

Background

Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures.

Methods

We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis.

Results

Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data.

Discussion

Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.

Ethical issues in direct-to-consumer healthcare: A scoping review

An increasing number of health products and services are being offered on a direct-to-consumer (DTC) basis. To date, however, scholarship on DTC healthcare products and services has largely proceeded in a domain-specific fashion, with discussions of relevant ethical challenges occurring within specific medical specialties. The present study therefore aimed to provide a scoping review of ethical issues raised in the academic literature across types of DTC healthcare products and services. A systematic search for relevant publications between 2011-2021 was conducted on PubMed and Google Scholar using iteratively developed search terms. The final sample included 86 publications that discussed ethical issues related to DTC healthcare products and services. All publications were coded for ethical issues mentioned, primary DTC product or service discussed, type of study, year of publication, and geographical context. We found that the types of DTC healthcare products and services mentioned in our sample spanned six categories: neurotechnology (34%), testing (20%), in-person services (17%), digital health tools (14%), telemedicine (13%), and physical interventions (2%). Ethical arguments in favor of DTC healthcare included improved access (e.g., financial, geographical; 31%), increased autonomy (29%), and enhanced convenience (16%). Commonly raised ethical concerns included insufficient regulation (72%), questionable efficacy and quality (70%), safety and physical harms (66%), misleading advertising claims (56%), and privacy (34%). Other frequently occurring ethical concerns pertained to financial costs, targeting vulnerable groups, informed consent, and potential burdens on healthcare providers, the healthcare system, and society. Our findings offer insights into the cross-cutting ethical issues associated with DTC healthcare and underscore the need for increased interdisciplinary communication to address the challenges they raise.

Returning Individual Research Results from Digital Phenotyping in Psychiatry

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Alzheimer's in the modern age: Ethical challenges in the use of digital monitoring to identify cognitive changes

Pushes toward earlier detection of Alzheimer's disease (AD)-related cognitive changes are creating interest in leveraging technologies, like cellphones, that are already widespread and well-equipped for data collection to facilitate digital monitoring for AD. Studies are ongoing to identify and validate potential "digital biomarkers" that might indicate someone has or is at risk of developing AD dementia. Digital biomarkers for AD have potential as a tool in aiding more timely diagnosis, though more robust research is needed to support their validity and utility. While there are grounds for optimism, leveraging digital monitoring and informatics for cognitive changes also poses ethical challenges, related to topics such as algorithmic bias, consent, and data privacy and security. As we confront the modern era of Alzheimer's disease, individuals, companies, regulators and policymakers alike must prepare for a future in which our day-to-day interactions with technology in our daily life may identify AD-related cognitive changes.

The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions

The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.

First-person disavowals of digital phenotyping and epistemic injustice in psychiatry

Digital phenotyping will potentially enable earlier detection and prediction of mental illness by monitoring human interaction with and through digital devices. Notwithstanding its promises, it is certain that a person's digital phenotype will at times be at odds with their first-person testimony of their psychological states. In this paper, we argue that there are features of digital phenotyping in the context of psychiatry which have the potential to exacerbate the tendency to dismiss patients' testimony and treatment preferences, which can be instances of epistemic injustice. We first explain what epistemic injustice is, and why it is argued to be an extensive problem in health and disability settings. We then explain why epistemic injustice is more likely to apply with even greater force in psychiatric contexts, and especially where digital phenotyping may be involved. Finally, we offer some tentative suggestions of how epistemic injustice can be minimised in digital psychiatry.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BACKGROUND

Targeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.

OBJECTIVE

This scoping review provides a synthesis of key concepts, definitions and the language used in the context of these terms in an effort to delineate their use for future research.

STUDY SELECTION AND ANALYSIS

Our search on PubMed, EBSCO and Cochrane provided 2835 relevant titles. A total of 176 titles were found eligible for extracting data. A thematic analysis was conducted to synthesise the underlying aspects of individualisation, personalisation and person-centredness. Network visualisations of co-occurring words in 2625 abstracts were performed using VOSViewer.

FINDINGS

Overall, 106 out of 176 (60.2%) articles provided concepts for individualisation, personalisation and person-centredness. Studies using person-centredness provided a conceptualisation more often than the others. A thematic analysis revealed medical, psychological, sociocultural, biological, behavioural, economic and environmental dimensions of the concepts. Practical frameworks were mostly found related to person-centredness, while theoretical frameworks emerged in studies on personalisation. Word co-occurrences showed common psychiatric words in all three network visualisations, but differences in further contexts.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The use of individualisation, personalisation and person-centredness in mental healthcare is multifaceted. While individualisation was the most generic term, personalisation was often used in biomedical or technological studies. Person-centredness emerged as the most well-defined concept, with many frameworks often related to dementia care. We recommend that the use of these terms follows a clear definition within the context of their respective disorders, treatments or medical settings.

SCOPING REVIEW REGISTRATION

Open Science Framework: osf.io/uatsc.

Autonomic complexity dynamically indexes affect regulation in everyday life

Affect regulation often is disrupted in depression. Understanding biomarkers of affect regulation in ecologically valid contexts is critical for identifying moments when interventions can be delivered to improve regulation and may have utility for identifying which individuals are vulnerable to psychopathology. Autonomic complexity, which includes linear and nonlinear indices of heart rate variability, has been proposed as a novel marker of neurovisceral integration. However, it is not clear how autonomic complexity tracks with regulation in everyday life, and whether low complexity serves as a marker of related psychopathology. To measure regulation phenotypes with diminished influence of current symptoms, 37 young adults with remitted major depressive disorder (rMDD) and 28 healthy comparisons (HCs) completed ambulatory assessments of autonomic complexity and affect regulation across one week in everyday life. Multilevel models indicated that in HCs, but not rMDD, autonomic complexity fluctuated in response to regulation cues, increasing in response to reappraisal and distraction and decreasing in response to negative affect. Higher complexity across the week predicted greater everyday regulation success, whereas greater variability of complexity predicted lower (and less variable) negative affect, rumination, and mind-wandering. Results suggest that ambulatory assessment of autonomic complexity can passively index dynamic aspects of real-world affect and regulation, and that dynamic physiological reactivity to regulation is restricted in rMDD. These results demonstrate how intensive sampling of dynamic, nonlinear regulatory processes can advance our understanding of potential mechanisms underlying psychopathology. Such measurements might inform how to test interventions to enhance neurovisceral complexity and affect regulation success in real time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention

OBJECTIVE

Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes.

METHODS

We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics.

RESULTS

Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals.

CONCLUSIONS

Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Ethical Issues in the Use of Smartphone Apps for HIV Prevention in Malaysia: Focus Group Study With Men Who Have Sex With Men

BACKGROUND

The use of smartphone apps can improve the HIV prevention cascade for key populations such as men who have sex with men (MSM). In Malaysia, where stigma and discrimination toward MSM are high, mobile health app-based strategies have the potential to open new frontiers for HIV prevention. However, little guidance is available to inform researchers about the ethical concerns that are unique to the development and implementation of app-based HIV prevention programs.

OBJECTIVE

This study aimed to fill this gap by characterizing the attitudes and concerns of Malaysian MSM regarding HIV prevention mobile apps, particularly regarding the ethical aspects surrounding their use.

METHODS

We conducted web-based focus group discussions with 23 MSM between August and September 2021. Using in-depth semistructured interviews, participants were asked about the risks and ethical issues they perceived to be associated with using mobile apps for HIV prevention. Each session was digitally recorded and transcribed. Transcripts were inductively coded using the Dedoose software (SocioCultural Research Consultants) and analyzed to identify and interpret emerging themes.

RESULTS

Although participants were highly willing to use app-based strategies for HIV prevention, they raised several ethical concerns related to their use. Prominent concerns raised by participants included privacy and confidentiality concerns, including fear of third-party access to personal health information (eg, friends or family and government agencies), issues around personal health data storage and management, equity and equitable access, informed consent, and regulation.

CONCLUSIONS

The study's findings highlight the role of ethical concerns related to the use of app-based HIV prevention programs. Given the ever-growing nature of such technological platforms that are intermixed with a complex ethical-legal landscape, mobile health platforms must be safe and secure to minimize unintended harm, safeguard user privacy and confidentiality, and obtain public trust and uptake.

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

BACKGROUND

Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured.

OBJECTIVE

The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications.

METHODS

We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations.

RESULTS

A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common.

CONCLUSIONS

Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint

BACKGROUND

Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data-on location, movement, email and text communications, and social media-with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI).

OBJECTIVE

The goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols.

METHODS

We explored the ELSI of digital health research in psychiatry, with a particular focus on what we label "deep phenotyping" psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue.

RESULTS

Consistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry ("the Ethics Checklist"). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report.

CONCLUSIONS

Deep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey.

Understanding 'passivity' in digital health through imaginaries and experiences of coronavirus disease 2019 contact tracing apps

Growing interest is being directed to the health applications of so-called 'passive data' collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing 'passive data' suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.

Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations

The application of artificial intelligence (AI) not only yields in advantages for healthcare but raises several ethical questions. Extant research on ethical considerations of AI in digital health is quite sparse and a holistic overview is lacking. A systematic literature review searching across 853 peer-reviewed journals and conferences yielded in 50 relevant articles categorized in five major ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. The ethical landscape of AI in digital health is portrayed including a snapshot guiding future development. The status quo highlights potential areas with little empirical but required research. Less explored areas with remaining ethical questions are validated and guide scholars' efforts by outlining an overview of addressed ethical principles and intensity of studies including correlations. Practitioners understand novel questions AI raises eventually leading to properly regulated implementations and further comprehend that society is on its way from supporting technologies to autonomous decision-making systems.

Play the Pain: A Digital Strategy for Play-Oriented Research and Action

The value of understanding patients' illness experience and social contexts for advancing medicine and clinical care is widely acknowledged. However, methodologies for rigorous and inclusive data gathering and integrative analysis of biomedical, cultural, and social factors are limited. In this paper, we propose a digital strategy for large-scale qualitative health research, using play (as a state of being, a communication mode or context, and a set of imaginative, expressive, and game-like activities) as a research method for recursive learning and action planning. Our proposal builds on Gregory Bateson's cybernetic approach to knowledge production. Using chronic pain as an example, we show how pragmatic, structural and cultural constraints that define the relationship of patients to the healthcare system can give rise to conflicted messaging that impedes inclusive health research. We then review existing literature to illustrate how different types of play including games, chatbots, virtual worlds, and creative art making can contribute to research in chronic pain. Inspired by Frederick Steier's application of Bateson's theory to designing a science museum, we propose DiSPORA (Digital Strategy for Play-Oriented Research and Action), a virtual citizen science laboratory which provides a framework for delivering health information, tools for play-based experimentation, and data collection capacity, but is flexible in allowing participants to choose the mode and the extent of their interaction. Combined with other data management platforms used in epidemiological studies of neuropsychiatric illness, DiSPORA offers a tool for large-scale qualitative research, digital phenotyping, and advancing personalized medicine.

Key considerations for the implementation of clinically focused Prescription Drug Monitoring Programs to avoid unintended consequences

Prescription Drug Monitoring Programs (PDMP) are electronic databases that are used to track and monitor the prescribing and dispensing of controlled substances, including opioid analgesics and benzodiazepines. PDMP have been widely implemented throughout North America and are currently being introduced in Australia and some parts of Europe. PDMP were originally developed by and for law enforcement, however many jurisdictions have now shifted use toward clinical care and harm reduction through early identification of prescription dependence and extra-medical use, and to ensure appropriate supply of controlled substances to the community through monitoring health care provider prescribing and dispensing patterns (Deloitte, 2018; Picco et al., 2021a; CDC, 2021a, U.S Department of Justice, 2018). Clinically-motivated PDMP highlight medication-related risk, based on the patient's prescribing and dispensing history. Health care professionals can use this information to aid or inform clinical decision-making and provide opportunities for intervention and treatment (Deloitte, 2018) . However, a number of harms have been associated with the use of PDMP, including increased stigma and discrimination, untreated pain and mental illness, and denial of appropriate health care for those identified as 'high risk'. In this article we examine these harms and potential mitigating factors. We conclude with some suggestions and future directions for research to address some of the current uncertainties regarding PDMP use. We highlight the need for mixed methods research to better understand the personal impacts of PDMP policy on the populations they were designed to aid.

Digital Phenotyping: an Epistemic and Methodological Analysis

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.

Le phénotypage digital pour une pratique clinique en santé mentale mieux informée

Objectifs Cette revue trouve sa motivation dans l’observation que la prise de décision clinique en santé mentale est limitée par la nature des mesures typiquement obtenues lors de l’entretien clinique et la difficulté des cliniciens à produire des prédictions justes sur les états mentaux futurs des patients. L’objectif est de présenter un survol représentatif du potentiel du phénotypage digital couplé à l’apprentissage automatique pour répondre à cette limitation, tout en en soulignant les faiblesses actuelles.

Méthode Au travers d’une revue narrative de la littérature non systématique, nous identifions les avancées technologiques qui permettent de quantifier, instant après instant et dans le milieu de vie naturel, le phénotype humain au moyen du téléphone intelligent dans diverses populations psychiatriques. Des travaux pertinents sont également sélectionnés afin de déterminer l’utilité et les limitations de l’apprentissage automatique pour guider les prédictions et la prise de décision clinique. Finalement, la littérature est explorée pour évaluer les barrières actuelles à l’adoption de tels outils.

Résultats Bien qu’émergeant d’un champ de recherche récent, de très nombreux travaux soulignent déjà la valeur des mesures extraites des senseurs du téléphone intelligent pour caractériser le phénotype humain dans les sphères comportementale, cognitive, émotionnelle et sociale, toutes étant affectées par les troubles mentaux. L’apprentissage automatique permet d’utiles et justes prédictions cliniques basées sur ces mesures, mais souffre d’un manque d’interprétabilité qui freinera son emploi prochain dans la pratique clinique. Du reste, plusieurs barrières identifiées tant du côté du patient que du clinicien freinent actuellement l’adoption de ce type d’outils de suivi et d’aide à la décision clinique.

Conclusion Le phénotypage digital couplé à l’apprentissage automatique apparaît fort prometteur pour améliorer la pratique clinique en santé mentale. La jeunesse de ces nouveaux outils technologiques requiert cependant un nécessaire processus de maturation qui devra être encadré par les différents acteurs concernés pour que ces promesses puissent être pleinement réalisées.

Perceptions of Alerts Issued by Social Media Platforms in Response to Self-injury Posts Among Latinx Adolescents: Qualitative Analysis

BACKGROUND

There is growing interest in using social media data to detect and address nonsuicidal self-injury (NSSI) among adolescents. Adolescents often do not seek clinical help for NSSI and may adopt strategies to obscure detection; therefore, social media platforms may be able to facilitate early detection and treatment by using machine learning models to screen posts for harmful content and subsequently alert adults. However, such efforts have raised privacy and ethical concerns among health researchers. Little is currently known about how adolescents perceive these efforts.

OBJECTIVE

The aim of this study is to examine perceptions of automated alerts for NSSI posts on social media among Latinx adolescents, who are at risk for NSSI yet are underrepresented in both NSSI and health informatics research. In addition, we considered their perspectives on preferred recipients of automated alerts.

METHODS

We conducted semistructured, qualitative interviews with 42 Latinx adolescents between the ages of 13 and 17 years who were recruited from a nonprofit organization serving the Latinx community in Milwaukee, Wisconsin. The Latinx population in Milwaukee is largely of Mexican descent. All interviews were conducted between June and July 2019. Transcripts were analyzed using framework analysis to discern their perceptions of automated alerts sent by social media platforms and potential alert recipients.

RESULTS

Participants felt that automated alerts would make adolescents safer and expedite aid before the situation escalated. However, some worried that hyperbolic statements would generate false alerts and instigate conflicts. Interviews revealed strong opinions about ideal alert recipients. Parents were most commonly endorsed, but support was conditional on perceptions that the parent would respond appropriately. Emergency services were judged as safer but inappropriate for situations considered lower risk. Alerts sent to school staff generated the strongest privacy concerns. Altogether, the preferred alert recipients varied by individual adolescents and perceived risks in the situation. None raised ethical concerns about the collection, analysis, or storage of personal information regarding their mental health status.

CONCLUSIONS

Overall, Latinx adolescents expressed broad support for automated alerts for NSSI on social media, which indicates opportunities to address NSSI. However, these efforts should be co-constructed with adolescents to ensure that preferences and needs are met, as well as embedded within broader approaches for addressing structural and cultural barriers to care.