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. (shrink)

In Italy, a judge reduced the sentence of a defendant by 1 year in response to evidence for a genetic predisposition to violence. The best characterized of these genetic differences, those in the monoamine oxidase A (MAOA), were cited as especially relevant. Several months previously in the USA, MAOA data contributed to a jury reducing charges from 1st degree murder (a capital offence) to voluntary manslaughter. Is there a rational basis for this type of use of MAOA evidence in criminal (...) court? This paper will review in context recent work on the MAOA gene–environment interaction in predisposing individuals to violence and address the relevance of such findings to murder trials. Interestingly, the MAOA genetic variants impact future violence and aggression only when combined with the adverse environmental stimuli of childhood maltreatment. Thus nature and nurture interact to determine the individual’s risk. Based on current evidence, I argue there is a weak case for mitigation. But should future experiments confirm the hypothesis that individual differences in impulse control and response to provocation found in MAOA-L men (without abuse) are significantly magnified when combined with childhood maltreatment, the case could turn into a stronger one. (shrink)

The American Journal of Bioethics, Volume 12, Issue 2, Page 22-24, February 2012.

Patients with psychiatric illness feel the brunt of the intersection of many of our society's and our health care system's disparities, and the vulnerability of this population during the Covid‐19 pandemic cannot be overstated. Patients with psychiatric illness often suffer from the stigma of mental illness and receive poor medical care. Many patients with severe and persistent mental illness face additional barriers, including poverty, marginal housing, and food insecurity. Patients who require psychiatric hospitalization now face the risk of transmission of (...) Covid‐19 due to the inherent difficulties of social distancing within a psychiatric hospital. Patients whose freedom and self‐determination have been temporarily overruled as they receive involuntary psychiatric treatment deserve a setting that maintains their health and safety. While tele‐mental health has been rapidly expanded to provide new ways to access psychiatric treatment, some patients may have limitations in technological literacy or access to devices. The social isolation, economic fallout, and potential traumatization related to the current pandemic will disproportionately affect this vulnerable population, and society's duties to them must be considered. (shrink)

The American Journal of Bioethics, Volume 11, Issue 12, Page 27-29, December 2011.

Substantial advancement in the diagnosis and treatment of psychiatric disorders may come from assembling diverse data streams from clinical notes, neuroimaging, genetics, and real-time digital footprints from smartphones and wearable devices. This is called “deep phenotyping” and often involves machine learning. We argue that incidental findings arising in deep phenotyping research have certain special, morally and legally salient features: They are specific, actionable, numerous, and probabilistic. We consider ethical and legal implications of these features and propose a practical ethics strategy (...) for managing them. (shrink)