TY - JOUR
T1 - Returning Individual Research Results from Digital Phenotyping in Psychiatry
AU - Shen, Francis X.
AU - Baum, Matthew L.
AU - Martinez-Martin, Nicole
AU - Miner, Adam S.
AU - Abraham, Melissa
AU - Brownstein, Catherine A.
AU - Cortez, Nathan
AU - Evans, Barbara J.
AU - Germine, Laura T.
AU - Glahn, David C.
AU - Grady, Christine
AU - Holm, Ingrid A.
AU - Hurley, Elisa A.
AU - Kimble, Sara
AU - Lázaro-Muñoz, Gabriel
AU - Leary, Kimberlyn
AU - Marks, Mason
AU - Monette, Patrick J.
AU - Onnela, Jukka Pekka
AU - O’Rourke, P. Pearl
AU - Rauch, Scott L.
AU - Shachar, Carmel
AU - Sen, Srijan
AU - Vahia, Ipsit
AU - Vassy, Jason L.
AU - Baker, Justin T.
AU - Bierer, Barbara E.
AU - Silverman, Benjamin C.
N1 - Publisher Copyright:
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Research ethics
KW - human subjects research
KW - neuroethics
KW - psychiatry/psychology
UR - https://www.scopus.com/pages/publications/85158876178
UR - https://www.scopus.com/inward/citedby.url?scp=85158876178&partnerID=8YFLogxK
U2 - 10.1080/15265161.2023.2180109
DO - 10.1080/15265161.2023.2180109
M3 - Article
C2 - 37155651
AN - SCOPUS:85158876178
SN - 1526-5161
VL - 24
SP - 69
EP - 90
JO - American Journal of Bioethics
JF - American Journal of Bioethics
IS - 2
ER -