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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).
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.
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.
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.
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.
“The deeper you go, the more you know.” This headline captures the tantalizing promise of deeply probing digital health research in psychiatry [
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 [
Yet, the road to this innovative future is fraught with ethical dilemmas [
Supported by a National Institutes of Health (NIH) Bioethics Administrative Supplement award (NIH 1U01MH116925-01), we have been exploring 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 that data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue. As we use it in this article, the phrase “deep phenotyping” in psychiatric research is meant to describe research that—even if it does not encompass a large number of research subjects—goes deep into the lives of those subjects by collecting many digital and biological data streams (eg, digital data such as text messages, phone screen shots, and GPS location; health data such as heart rate and blood pressure; and clinical evaluations and biological data such as genetics and brain scans).
Consistent with recent ELSI analyses [
Until the field develops more robust consensus guidelines, however, the onus clearly falls on individual research teams to take the lead in shaping the applied ethics of digital health research in psychiatry.
To guide these ethics considerations, we 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 six 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. The questions included in the checklist are presented in
The goal of the Ethics Checklist is to promote the careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage of digital phenotyping research and without consensus guidance, there are a range of reasonable choices researchers may make. But 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.
Ethics checklist for digital health research.
Category | Category description | Checklist items | |
Informed consent | How can we meaningfully communicate and be transparent about research methods that involve deep, complex, often passive and continuous data collection, machine learning analysis, and interpretation? | 1. | Have we appropriately adapted our informed consent procedures to our specific study population, including possible use of surrogate consent? |
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2. | Will we provide background education on relevant technologies, such as explaining what social media companies may already be doing with the participant’s data? | |
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3. | Have we determined what a reasonable person would want to know, and explained in our institutional review board proposal the evidence on which we reached that determination? | |
Equity, diversity, and access | How will we address concerns that our research might replicate existing, or generate new, biased results or contribute to health inequities in access based on race, ethnicity, gender, sexual orientation, age, or another legally protected class? | 4. | Starting at the early conceptualization and research design stages, have we sought input from a diverse community of stakeholders to identify and address potential equity concerns and opportunities to advance justice with our proposed research? |
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5. | Has our research plan addressed potential inequities in access, for instance varying levels of access to mobile technology and to health care services? | |
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6. | Has every member of the research team completed our institution’s recommended trainings around diversity, inclusion, equity, and access? | |
Privacy and partnerships | How can we design our research to balance an interest in robust data collection, with a potentially competing interest in protecting participant privacy? | 7. | Have we consulted with information security experts about exactly where the data will flow, from start to finish? |
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8. | Do we have a written policy on data deidentification and participant privacy that is consistent with best practices in psychiatry and neuroscience? | |
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9. | Have we determined which, if any, third-party vendors will be required to be HIPAAa compliant and sign a Business Associate Agreement? | |
Regulation and law | Which state, federal, and international law and regulatory guidance must be adhered to in our research? | 10. | Have we examined the terms of service, end user license agreements, privacy statements, and HIPAA notices for each of the vendors and software applications involved in our research? |
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11. | Have we determined how laws in applicable jurisdictions will treat the data we collect, for instance considering the data to be “sensitive,” “special category,” or “personal health information”? | |
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12. | Have we ensured compliance with state, federal and international laws governing our research, HIPAA privacy requirements, state data privacy laws, and applicable international privacy laws? | |
Return of results | By which criteria will we determine if our data analytic models are sufficiently valid and reliable for us to share the individual research results and data with the research participant and the participant’s clinicians? | 13. | Have we considered whether our study will generate any “actionable” results, based on established guidelines and how we have defined actionability? |
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14. | Have we established with what frequency results will be returned? (eg, should participants have daily, weekly, and monthly access to some subset of their data?) | |
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15. | Have we clarified the protocols and mechanisms for returning different types of information, (eg, raw data, interpreted data, etc)? | |
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16. | Do we have a protocol in place for contacting a participant’s clinicians and nonclinical caregivers? | |
Duty to warn and duty to report | When might our research trigger a legal or ethical duty to report the potential for participant self-harm or harm to others, and what are our protocols for determining whether in individual instances we have such a duty? | 17. | Has everyone in our research lab received sufficient training to know when to flag data or results as requiring follow-up review by a supervisor? |
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18. | Will our analytic methods allow us to identify the precursors to dangerous or illegal behavior, to oneself or to others, and if so, at which point will we intervene to protect the research participant or a third party? | |
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19. | Have we updated our lab’s suicidality standard operating procedure to be consistent with the novel data acquisition and analysis techniques we are using in our study? | |
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20. | Do we have a protocol for responding to legally mandated reporting if our data uncover child pornography, restraining order violations, and so on? |
aHIPAA: Health Insurance Portability and Accountability Act.
Each of the 20 ethics checklist questions are phrased so that they can be answered with a “Yes,” “No,” or “Pending” response. In our view, deep phenotyping research in psychiatry should not proceed until a research team answers “Yes” or “Pending” to each checklist question. To arrive at “Yes” or “Pending” for each question will require research labs to carefully consider a complex interplay of ethical and legal considerations.
It is beyond the scope of this short paper to address all of these complexities, but we offer here several illustrative examples, from each of the 6 key domains, of how the checklist might be applied in practice.
The revised Common Rule requires researchers to present participants with information that is “most likely to assist … in understanding the reasons why one might or might not want to participate in the research,” and that is what “a reasonable person would want to have …” (Title 45 of the Code of Federal Regulations, part 46, effective July 2018) [
We offer here several of these many possibilities. One decision is whether to provide research participants a list of clear, concrete examples of the inferences that can likely be made from participants’ data. For example, the informed consent material could explicitly say, “You should know that, although we will not reveal this information outside the research team, we may be able to identify when you are going to the bathroom or having sex.” Another decision, especially for researchers who are collecting data on participants’ GPS data and social media content, is whether to provide basic background education to make participants more informed on the data collection and data sharing practices already being utilized by the mobile technology and apps they already use regularly. Third, ethics research has identified a need to make informed consent processes more meaningful and valid by improving communication [
In addition, researchers in psychiatry must address a further question that has long been challenging for the field, “how to ensure meaningful and valid informed consent with participants who have a mental illness?” [
It is well established that biomedical research generally [
The Ethics Checklist proposes that researchers answer the following question: Starting at the early conceptualization and research design stages, have we sought input from a diverse community of stakeholders to identify and address potential equity concerns and opportunities to advance justice with our proposed research? The question emphasizes that equity concerns extend beyond simply developing proportional samples, and that from the start, “[r]esearch relationships must become balanced, reciprocal, and community informed, without centering researcher and institutional priorities” [
In defining the stakeholders, the checklist encourages researchers to go beyond their own research team to seek guidance and build trust, even at the conceptual and research design stage. We agree with Wilkins [
In an analysis of smartphone digital phenotyping, Onnela and Rauch [
The regulation of mobile health apps is currently undergoing transformation [
For instance, the data collection may require interfacing with multiple third-party vendors, and it is the responsibility of the research team to examine the terms of service, end user license agreements, privacy statements, and Health Insurance Portability and Accountability Act (HIPAA) notices for each of these vendors and associated software applications (Checklist question 10). This may not be an easy task, as research on mobile health apps suggests that many vendors do not have a privacy policy publicly available [
In addition, when data collection follows the individual across state or international boundaries, and when data flow across those boundaries, the research will be exposed to multiple legal jurisdictions, including emerging state laws governing privacy and research [
The return of individual research results has garnered significant attention in the ethics literature [
For instance, if a research team is measuring step count data and a participant’s step count drops below average in a given week, alerting that participant of the data is actionable in the sense that the participant—informed by these data—may choose to walk substantially more steps next week. But what about more complex results, such as a machine learning algorithm that predicts that the participant has a 72% higher likelihood of experiencing a manic episode in the following year? When has the scientific knowledge base accumulated sufficiently to make such a prediction “actionable”? An even more fundamental question is implicated: for any measurement or prediction, what is the confidence in the measurement, sensitivity, or specificity of the interpretation or prediction, and how should that be shared? The effects of researcher mobile health interventions on participants within research studies are only now being studied [
The potential ethical responsibility and legal duty for reanalysis of data is also of concern. For instance, in genomics research, many genetic variations are classified as “variant of uncertain significance (VUS),” but as knowledge increases, those variations may be reclassified [
The duty to warn and the duty to report are well known to psychiatric researchers, but the advanced data collection and data analysis methods of deep phenotyping introduce unique concerns [
Deep phenotyping research offers a vision for vastly more effective care for people with or at risk of psychiatric disease. The potential perils en route to realizing this vision are significant; however, researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey.
The illustrative examples discussed above make clear that deep phenotyping researchers have few guideposts and little empirical data with which to address many pressing ethical and legal questions critical for their research. This lack of clarity regarding best practices is understandable for a field that has emerged rapidly, mainly in the past 5 years. But as the field continues to expand, there is a need to fill this gap by developing consensus guidance, informed by quantitative and qualitative bioethics research, as well as community and patient advocate input. This paper has raised more questions than answers, and it did not reach many other avenues of inquiry including considerations for international research and research with children.
To make progress toward consensus guidance, we identify 2 immediate action items. First, ethics should be integrated into the practice of deep phenotyping research (as is already being carried out at centers such as the McLean Institute for Technology in Psychiatry and the Connected and Open Research Ethics initiative at the University of California San Diego Research Center for Optimal Digital Ethics in Health).
Second, professional organizations such as the American Psychiatric Association and the Digital Medicine Society, along with institutions such as the NIH and National Academies, are well positioned to convene an interdisciplinary team to conduct in-depth analysis and produce foundational reports to guide the field.
The deeper you go in deep phenotyping research, the deeper the ethical and legal challenges. But with timely, concerted action, the research community can promote ethically sound and legally compliant digital health research in psychiatry.
Ethics checklist for digital health research in psychiatry and worksheet.
ethical, legal, and social implications
Health Insurance Portability and Accountability Act
institutional review board
National Institutes of Health
standard operating procedure
We thank our student research assistant Lois Yoo. We also thank the participants in a virtual workshop hosted by the McLean Institute for Technology in Psychiatry on May 8, 2020, to explore the ethical, legal, and social implications of return of results in deep phenotyping research. Research reported in this publication was supported by a Bioethics Supplement from the National Institute of Mental Health (NIMH) of the National Institutes of Health (NIH) under Award Number 1U01MH116925-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIMH or NIH.
FXS, BCS, and JTB conceived and designed the work. FS and BS substantially conducted the ethics research and drafted the manuscript. SLR and JTB substantially edited and critically revised the manuscript. PM and SK significantly contributed to the ethics research and revised the manuscript. All the authors gave final approval of the completed manuscript version and are accountable for all aspects of the work.
SLR is employed by McLean Hospital/Mass General Brigham, serves as the secretary of Society of Biological Psychiatry, and on the Boards of McLean Hospital, National Network of Depression Centers, National Association of Behavioral Healthcare, and Community Psychiatry/Mindpath Health. He receives royalties from Oxford University Press and APPI. None of these are known to represent conflicts of interest with this work. JTB has received consulting fees from Verily Life Sciences, as well as consulting fees and equity from Mindstrong Health, Inc, unrelated to the present work.