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Big data and public health
Published in Sridhar Venkatapuram, Alex Broadbent, The Routledge Handbook of Philosophy of Public Health, 2023
A burgeoning strategy whose effect on health disparities is contested is the use of web apps, and especially mobile apps, for public health research, surveillance, and intervention. Mobile apps are used to monitor various activities of patients, including online activity, behaviors like eating and sleeping, and adherence to medical treatment. Data from these apps can be shared with patients’ healthcare providers. These data can also be harnessed for digital phenotyping. Digital phenotypes are constructed by identifying associations between a phenotype of interest and online activity, such as social media use and web searches. Digital phenotypes for health conditions, especially mental disorders, are created to support public health surveillance and predict health outcomes (Torous and Baker 2016). Data for digital phenotypes may also be gathered from chatbots—online algorithms that can converse with individuals—trained to provide therapy or improve mental health (Tekin 2021).
Black sites in the matrix
Published in Lester D. Friedman, Therese Jones, Routledge Handbook of Health and Media, 2022
Psychiatrists have critiqued the RDoC as divorced from clinical needs (Pickersgill). Certainly the RDoC “domain” categories seem foreign to psychiatry’s more familiar language of emotions, affects, and behaviors. Understood, however, as an initiative with the goal of “precision psychiatry” – that is, the development of tailored psychiatric medications or, perhaps, simply the capacity to spur additional pharmaceutical research – the RDoC clarifies itself as very much divorced from the needs of the clinic. As Dorte Bemme, Natassia Brenman, and Beth Semel put it, “[T]he emergent knowledge practices of digital phenotyping are part of a burgeoning digital psychiatry that crafts an alternate future no longer reliant on shared linguistic constructs, symptom recall, interpersonal rapport, or the clinic as a separate sphere and infrastructure” (Bemme, Brenman, and Semel). Indeed, the envisioned future is one that realizes the dream of biopsychiatry, where treatment of people in distress could perhaps be accomplished without any professional consultation at all. Instead, data gathered from wearables, DNA testing, and fMRIs will be enough to match a patient to a pharmaceutical salve.
The role of digital tools in providing youth mental health: results from an international multi-center study
Published in International Review of Psychiatry, 2022
Laura Orsolini, Cristina Appignanesi, Simone Pompili, Umberto Volpe
Another challenging dimension of mood disorders is early detection, namely the detection of prodromal features before the symptoms reach a full-blown expression and match diagnostic criteria. Within this context, the ecological momentary assessment (EMA), consisting in repeated real-time observations in real-world settings, allows a timely monitoring of BD and depressive (MDD) symptomatology (Hall et al., 2021; Shiffman et al., 2008). EMA allows a real-time monitoring of symptoms which is a key component of the self-management of DD and BD (Beames et al., 2021). EMA-based programs, using Personal Digital Assistants (PDA), have been developed for smartphones, to target psychoeducation and Cognitive Behavioural Therapy (CBT) interventions, integrated with face-to-face treatments (Beames et al., 2021). Within this framework, digital phenotyping has been largely investigated to be used in mood, activity and sleep monitoring, early identification and prevention (Kamath et al., 2022; Orsolini et al., 2021).
Beyond non-inferior: how telepsychiatry technologies can lead to superior care
Published in International Review of Psychiatry, 2021
John Zulueta, Olusola A. Ajilore
Regarding the use of video conferencing, Sabin and Skimming (2015) describe a framework for how to apply existing ethical principles to this new modality. Among the issues they raise are the need to address differences in Internet access to address equity. In addition to addressing access, it is also critical to implement these technologies in ways that recognize existing and historical disparities. A recent study by Anthony et al. (2018) on Patient Portal usage is informative on this issue. Among the study’s findings were that non-Whites were less likely to be offered access to patient portals and that among patients who did have access but chose not use the portal, a common reason cited by patients was a preference to speak directly with their providers. This highlights the importance of making sure that these technologies are used to augment – not diminish – patient–physician relationships and to ensure that patients are informed partners in their care. To this end, on the topic of digital phenotyping, it has been proposed that even the use of the term ‘phenotype’ is problematically opaque to patients whose data are at risk (Mohr et al., 2020). Discussions of the ethical application of digital phenotypes have focussed on the rights of individual patients to determine how their data can be used (Dagum & Montag, 2019), however, such frameworks may be inadequate when considering the impact that insights gained by digital phenotyping may have on groups, necessitating the development of new paradigms (Loi, 2019).
Returning Individual Research Results from Digital Phenotyping in Psychiatry
Published in The American Journal of Bioethics, 2023
Francis X. Shen, Matthew L. Baum, Nicole Martinez-Martin, Adam S. Miner, Melissa Abraham, Catherine A. Brownstein, Nathan Cortez, Barbara J. Evans, Laura T. Germine, David C. Glahn, Christine Grady, Ingrid A. Holm, Elisa A. Hurley, Sara Kimble, Gabriel Lázaro-Muñoz, Kimberlyn Leary, Mason Marks, Patrick J. Monette, Jukka-Pekka Onnela, P. Pearl O’Rourke, Scott L. Rauch, Carmel Shachar, Srijan Sen, Ipsit Vahia, Jason L. Vassy, Justin T. Baker, Barbara E. Bierer, Benjamin C. Silverman
Digital phenotyping has been defined by Torous et al. (2016) as the “moment-by-moment quantification of the individual-level human phenotype in-situ using data from smartphones and other personal digital devices” and more broadly by Martinez-Martin et al. (2018) as “approaches in which personal data gathered from mobile devices and sensors … [are] analyzed to provide health information.” In this article we use the term “digital phenotyping” broadly to refer to research that might combine some or all of (1) smart phone and social media data; (2) data from wearables and implantables, (3) data from ambient sensors such as smart home sensors; (4) brain data, including magnetic resonance imaging (MRI), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) and other modalities; and (5) clinical assessments. The term “digital phenotyping” has become a term of art in multiple fields, and as this list shows, includes non-digital assessments such as biological measures and clinical assessments as potential components of digital phenotyping. We include clinical assessments because current research is already utilizing data from wearables alongside, and integrated with, more traditional clinical assessments (Torrado et al. 2022). As Melcher, Hays, and Torous (2020) have pointed out in the context of college mental health, “Data from smartphones paired with clinical assessment data can reveal what behaviors or combination of behaviors are correlated with mental health problems …” [emphasis added]. The specific clinical assessments to be included in a digital phenotype will vary across research contexts. Our broad definition of “digital phenotyping” can be read to overlap with alternative terms such as deep phenotyping, computational phenotyping, deep biobehavioral-typing, and deep geno/phenotyping (Baker 2019; Shen et al. 2022). We also use the term phenotyping in this article interchangeably with “phenotypic assays” and “phenotype”.