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Franklin G, Stephens R, Piracha M, Tiosano S, Lehouillier F, Koppel R, Elkin PL. The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective. Life (Basel) 2024; 14:652. [PMID: 38929638 PMCID: PMC11204917 DOI: 10.3390/life14060652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 06/28/2024] Open
Abstract
Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.
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Affiliation(s)
- Gillian Franklin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Rachel Stephens
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Muhammad Piracha
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Shmuel Tiosano
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Frank Lehouillier
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Ross Koppel
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Institute for Biomedical Informatics, Perelman School of Medicine, and Sociology Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Peter L. Elkin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
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Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen MH. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Arch Clin Neuropsychol 2024; 39:290-304. [PMID: 38520381 DOI: 10.1093/arclin/acae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.
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Affiliation(s)
- Che Harris
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Yingfei Tang
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Eliana Birnbaum
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Christine Cherian
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Michelle H Chen
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
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3
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Pavarini G, Lyreskog DM, Newby D, Lorimer J, Bennett V, Jacobs E, Winchester L, Nevado-Holgado A, Singh I. Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology. BMJ MENTAL HEALTH 2024; 27:e300897. [PMID: 38508686 PMCID: PMC11021752 DOI: 10.1136/bmjment-2023-300897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 11/30/2023] [Indexed: 03/22/2024]
Abstract
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.
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Affiliation(s)
- Gabriela Pavarini
- Ethox Centre, Oxford Population Health, University of Oxford, Oxford, UK
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
| | - David M Lyreskog
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Jessica Lorimer
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Edward Jacobs
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | | | - Ilina Singh
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
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Fuhr DC, Wolf-Ostermann K, Hoel V, Zeeb H. [Digital technologies to improve mental health]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:332-338. [PMID: 38294700 DOI: 10.1007/s00103-024-03842-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/26/2024] [Indexed: 02/01/2024]
Abstract
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.
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Affiliation(s)
- Daniela C Fuhr
- Abteilung für Evaluation und Prävention, Leibniz Institut für Präventionsforschung und Epidemiologie, Achterstr. 30, 28359, Bremen, Deutschland.
- Gesundheitswissenschaften, Universität Bremen, Bremen, Deutschland.
| | - Karin Wolf-Ostermann
- Institut für Public Health und Pflegeforschung, Universität Bremen, Bremen, Deutschland
| | - Viktoria Hoel
- Institut für Public Health und Pflegeforschung, Universität Bremen, Bremen, Deutschland
| | - Hajo Zeeb
- Abteilung für Evaluation und Prävention, Leibniz Institut für Präventionsforschung und Epidemiologie, Achterstr. 30, 28359, Bremen, Deutschland
- Gesundheitswissenschaften, Universität Bremen, Bremen, Deutschland
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Worthington MA, Christie RH, Masino AJ, Kark SM. Identifying Unmet Needs in Major Depressive Disorder Using a Computer-Assisted Alternative to Conventional Thematic Analysis: Qualitative Interview Study With Psychiatrists. JMIR Form Res 2024; 8:e48894. [PMID: 38427407 PMCID: PMC10943432 DOI: 10.2196/48894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The development of digital health tools that are clinically relevant requires a deep understanding of the unmet needs of stakeholders, such as clinicians and patients. One way to reveal unforeseen stakeholder needs is through qualitative research, including stakeholder interviews. However, conventional qualitative data analytical approaches are time-consuming and resource-intensive, rendering them untenable in many industry settings where digital tools are conceived of and developed. Thus, a more time-efficient process for identifying clinically relevant target needs for digital tool development is needed. OBJECTIVE The objective of this study was to address the need for an accessible, simple, and time-efficient alternative to conventional thematic analysis of qualitative research data through text analysis of semistructured interview transcripts. In addition, we sought to identify important themes across expert psychiatrist advisor interview transcripts to efficiently reveal areas for the development of digital tools that target unmet clinical needs. METHODS We conducted 10 (1-hour-long) semistructured interviews with US-based psychiatrists treating major depressive disorder. The interviews were conducted using an interview guide that comprised open-ended questions predesigned to (1) understand the clinicians' experience of the care management process and (2) understand the clinicians' perceptions of the patients' experience of the care management process. We then implemented a hybrid analytical approach that combines computer-assisted text analyses with deductive analyses as an alternative to conventional qualitative thematic analysis to identify word combination frequencies, content categories, and broad themes characterizing unmet needs in the care management process. RESULTS Using this hybrid computer-assisted analytical approach, we were able to identify several key areas that are of interest to clinicians in the context of major depressive disorder and would be appropriate targets for digital tool development. CONCLUSIONS A hybrid approach to qualitative research combining computer-assisted techniques with deductive techniques provides a time-efficient approach to identifying unmet needs, targets, and relevant themes to inform digital tool development. This can increase the likelihood that useful and practical tools are built and implemented to ultimately improve health outcomes for patients.
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Affiliation(s)
- Michelle A Worthington
- AiCure, New York, NY, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | | | - Aaron J Masino
- AiCure, New York, NY, United States
- The School of Computing, Clemson University, Clemson, SC, United States
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Hurley ME, Sonig A, Herrington J, Storch EA, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Front Hum Neurosci 2024; 18:1332451. [PMID: 38435745 PMCID: PMC10904467 DOI: 10.3389/fnhum.2024.1332451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
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.
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Affiliation(s)
- Meghan E. Hurley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Anika Sonig
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - John Herrington
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry and Behavioral Sciences, Massachusetts General Hospital, Boston, MA, United States
| | | | - Kristin Kostick-Quenet
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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Shen FX, Baum ML, Martinez-Martin N, Miner AS, Abraham M, Brownstein CA, Cortez N, Evans BJ, Germine LT, Glahn DC, Grady C, Holm IA, Hurley EA, Kimble S, Lázaro-Muñoz G, Leary K, Marks M, Monette PJ, Jukka-Pekka O, O’Rourke PP, Rauch SL, Shachar C, Sen S, Vahia I, Vassy JL, Baker JT, Bierer BE, Silverman BC. Returning Individual Research Results from Digital Phenotyping in Psychiatry. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:69-90. [PMID: 37155651 PMCID: PMC10630534 DOI: 10.1080/15265161.2023.2180109] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
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.
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Affiliation(s)
- Francis X. Shen
- Harvard Medical School
- Massachusetts General Hospital
- Harvard Law School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Mason Marks
- Harvard Law School
- Florida State University College of Law
- Yale Law School
| | | | | | | | - Scott L. Rauch
- Harvard Medical School
- McLean Hospital
- Mass General Brigham
| | | | | | | | - Jason L. Vassy
- Harvard Medical School
- Brigham and Women’s Hospital
- VA Boston Healthcare System
| | | | - Barbara E. Bierer
- Harvard Medical School
- Brigham and Women’s Hospital
- Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard
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Bassil K. Balancing the Double-Edged Implications of AI in Psychiatric Digital Phenotyping. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:113-115. [PMID: 38295240 PMCID: PMC10841065 DOI: 10.1080/15265161.2023.2296437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
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Tani N, Fujihara H, Ishii K, Kamakura Y, Tsunemi M, Yamaguchi C, Eguchi H, Imamura K, Kanamori S, Kojimahara N, Ebara T. What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies. J Occup Health 2024; 66:uiad003. [PMID: 38258936 PMCID: PMC11020255 DOI: 10.1093/joccuh/uiad003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/21/2023] [Accepted: 10/10/2023] [Indexed: 01/24/2024] Open
Abstract
Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.
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Affiliation(s)
- Naomichi Tani
- Department of Ergonomics, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu 807-8555, Japan
| | - Hiroaki Fujihara
- Department of Ergonomics, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu 807-8555, Japan
| | - Kenji Ishii
- The Ohara Memorial Institute for Science of Labour, Tokyo 151-0051, Japan
| | - Yoshiyuki Kamakura
- Department of Information Systems, Faculty of Information Science and Technology, Osaka Institute of Technology, Osaka 573-0196, Japan
| | - Mafu Tsunemi
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences/Medical School, Nagoya 467-8601, Japan
| | - Chikae Yamaguchi
- Department of Nursing, Faculty of Nursing, Kinjo Gakuin University, Aichi 463-8521, Japan
| | - Hisashi Eguchi
- Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health,Kitakyushu 807-8555, Japan
| | - Kotaro Imamura
- Department of Digital Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Satoru Kanamori
- Graduate School of Public Health, Teikyo University, Tokyo 173-8605, Japan
| | - Noriko Kojimahara
- Section of Epidemiology, Shizuoka Graduate University of Public Health, Shizuoka 420-0881, Japan
| | - Takeshi Ebara
- Department of Ergonomics, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu 807-8555, Japan
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Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
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.
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Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
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11
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Marciano L, Vocaj E, Bekalu MA, La Tona A, Rocchi G, Viswanath K. The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. J Med Internet Res 2023; 25:e45540. [PMID: 37725422 PMCID: PMC10548333 DOI: 10.2196/45540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/12/2023] [Accepted: 07/06/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. OBJECTIVE This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. METHODS A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. RESULTS After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. CONCLUSIONS EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. TRIAL REGISTRATION PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.
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Affiliation(s)
- Laura Marciano
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Emanuela Vocaj
- Lombard School of Cognitive-Neuropsychological Psychotherapy, Pavia, Italy
| | - Mesfin A Bekalu
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Antonino La Tona
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
| | - Giulia Rocchi
- Department of Dynamic, Clinical Psychology and Health Studies, Sapienza University, Rome, Italy
| | - Kasisomayajula Viswanath
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
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Cho MK, Martinez-Martin N. Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:43-54. [PMID: 36507873 PMCID: PMC10258225 DOI: 10.1080/15265161.2022.2146785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.
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13
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Pizzoli SFM, Monzani D, Conti L, Ferraris G, Grasso R, Pravettoni G. Issues and opportunities of digital phenotyping: ecological momentary assessment and behavioral sensing in protecting the young from suicide. Front Psychol 2023; 14:1103703. [PMID: 37441331 PMCID: PMC10333535 DOI: 10.3389/fpsyg.2023.1103703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 06/09/2023] [Indexed: 07/15/2023] Open
Abstract
Digital phenotyping refers to the collection of real-time biometric and personal data on digital tools, mainly smartphones, and wearables, to measure behaviors and variables that can be used as a proxy for complex psychophysiological conditions. Digital phenotyping might be used for diagnosis, clinical assessment, predicting changes and trajectories in psychological clinical conditions, and delivering tailored interventions according to individual real-time data. Recent works pointed out the possibility of using such an approach in the field of suicide risk in high-suicide-risk patients. Among the possible targets of such interventions, adolescence might be a population of interest, since they display higher odds of committing suicide and impulsive behaviors. The present work systematizes the available evidence of the data that might be used for digital phenotyping in the field of adolescent suicide and provides insight into possible personalized approaches for monitoring and treating suicidal risk or predicting risk trajectories. Specifically, the authors first define the field of digital phenotyping and its features, secondly, they organize the available literature to gather all the digital indexes (active and passive data) that can provide reliable information on the increase in the suicidal odds, lastly, they discuss the challenges and future directions of such an approach, together with its ethical implications.
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Affiliation(s)
- Silvia Francesca Maria Pizzoli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Psychology, Catholic University of the Sacred Heart,, Milan, Italy
| | - Dario Monzani
- Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy
| | - Lorenzo Conti
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Ferraris
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Roberto Grasso
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
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Charron E, White A, Carlston K, Abdullah W, Baylis JD, Pierce S, Businelle MS, Gordon AJ, Krans EE, Smid MC, Cochran G. Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study. Front Psychiatry 2023; 14:1137071. [PMID: 37139320 PMCID: PMC10149825 DOI: 10.3389/fpsyt.2023.1137071] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/27/2023] [Indexed: 05/05/2023] Open
Abstract
Background While medications for opioid use disorder (MOUD) effectively treat OUD during pregnancy and the postpartum period, poor treatment retention is common. Digital phenotyping, or passive sensing data captured from personal mobile devices, namely smartphones, provides an opportunity to understand behaviors, psychological states, and social influences contributing to perinatal MOUD non-retention. Given this novel area of investigation, we conducted a qualitative study to determine the acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder (PPP-OUD). Methods This study was guided by the Theoretical Framework of Acceptability (TFA). Within a clinical trial testing a behavioral health intervention for PPP-OUD, we used purposeful criterion sampling to recruit 11 participants who delivered a child in the past 12 months and received OUD treatment during pregnancy or the postpartum period. Data were collected through phone interviews using a structured interview guide based on four TFA constructs (affective attitude, burden, ethicality, self-efficacy). We used framework analysis to code, chart, and identify key patterns within the data. Results Participants generally expressed positive attitudes about digital phenotyping and high self-efficacy and low anticipated burden to participate in studies that collect smartphone-based passive sensing data. Nonetheless, concerns were noted related to data privacy/security and sharing location information. Differences in participant assessments of burden were related to length of time required and level of remuneration to participate in a study. Interviewees voiced broad support for participating in a digital phenotyping study with known/trusted individuals but expressed concerns about third-party data sharing and government monitoring. Conclusion Digital phenotyping methods were acceptable to PPP-OUD. Enhancements in acceptability include allowing participants to maintain control over which data are shared, limiting frequency of research contacts, aligning compensation with participant burden, and outlining data privacy/security protections on study materials.
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Affiliation(s)
- Elizabeth Charron
- Department of Health Promotion Sciences, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Tulsa, OK, United States
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Ashley White
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Kristi Carlston
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Walitta Abdullah
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jacob D Baylis
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Stephanie Pierce
- Section of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Michael S Businelle
- TSET Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Adam J Gordon
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States
| | - Elizabeth E Krans
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, United States
- Center for Perinatal Addiction Research, Education and Evidence-based Solutions (Magee CARES), Magee-Womens Research Institute, Pittsburgh, PA, United States
| | - Marcela C Smid
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT, United States
| | - Gerald Cochran
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
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Smith KA, Blease C, Faurholt-Jepsen M, Firth J, Van Daele T, Moreno C, Carlbring P, Ebner-Priemer UW, Koutsouleris N, Riper H, Mouchabac S, Torous J, Cipriani A. Digital mental health: challenges and next steps. BMJ MENTAL HEALTH 2023; 26:e300670. [PMID: 37197797 PMCID: PMC10231442 DOI: 10.1136/bmjment-2023-300670] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023]
Abstract
Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.
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Affiliation(s)
- Katharine A Smith
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Charlotte Blease
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Tom Van Daele
- Expertise Unit Psychology, Technology and Society, Thomas More University of Applied Sciences, Mechelen, Belgium
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Universidad Complutense de Madrid Facultad de Medicina, Madrid, Spain
| | - Per Carlbring
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
- mHealth Methods in Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, München, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Heleen Riper
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Duivendrecht, Netherlands
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Stephane Mouchabac
- Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, Paris, France
- Infrastructure for Clinical Research in Neurosciences (iCRIN), Brain Institute (ICM), INSERM, CNRS, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Paris, France
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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16
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Winslow B, Mills E. Future of service member monitoring: the intersection of biology, wearables and artificial intelligence. BMJ Mil Health 2023:e002306. [PMID: 36702525 DOI: 10.1136/military-2022-002306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/15/2023] [Indexed: 01/28/2023]
Abstract
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed for early identification and treatment of these rising health issues. Considerable potential lies at the intersection of biology, wearables and artificial intelligence to provide high accuracy, objective monitoring of mental and behavioural health in training, operations and healthcare settings. While the current generation of wearable devices has predominantly targeted non-military use cases, military agencies have demonstrated successes in monitoring and diagnosis via off-label uses. Combined with context-aware and individualised algorithms, the integration of wearable data with artificial intelligence allows for a deeper understanding of individual-level and group-level mental and behavioural health at scale. Emerging digital phenotyping approaches which leverage ubiquitous sensing technology can provide monitoring at a greater scale, lower price point and lower individual burden by removing the need for additional body-worn technology. The intersection of this technology will enable individualised strategies to promote service member mental and physical health, reduce injury, and improve long-term well-being and deployability.
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Affiliation(s)
| | - E Mills
- Design Interactive Inc, Orlando, Florida, USA
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17
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Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung? PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.
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Affiliation(s)
- Harald Baumeister
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Patricia Garatva
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Rüdiger Pryss
- Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Deutschland
| | - Timo Ropinski
- Arbeitsgruppe Visual Computing, Institut für Medieninformatik, Universität Ulm, Deutschland
| | - Christian Montag
- Abteilung für Molekulare Psychologie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
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18
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Abstract
BACKGROUND Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently. OBJECTIVE We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research. METHODS We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported. RESULTS Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1). CONCLUSION There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.
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Affiliation(s)
- Alex Z R Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore
| | - Melvyn W B Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore.,National Addictions Management Service, Institute of Mental Health, Singapore City, Singapore
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Pavarini G, Yosifova A, Wang K, Wilcox B, Tomat N, Lorimer J, Kariyawasam L, George L, Alí S, Singh I. Data sharing in the age of predictive psychiatry: an adolescent perspective. EVIDENCE-BASED MENTAL HEALTH 2022; 25:69-76. [PMID: 35346984 PMCID: PMC9046833 DOI: 10.1136/ebmental-2021-300329] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 01/10/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Advances in genetics and digital phenotyping in psychiatry have given rise to testing services targeting young people, which claim to predict psychiatric outcomes before difficulties emerge. These services raise several ethical challenges surrounding data sharing and information privacy. OBJECTIVES This study aimed to investigate young people's interest in predictive testing for mental health challenges and their attitudes towards sharing biological, psychosocial and digital data for such purpose. METHODS Eighty UK adolescents aged 16-18 years took part in a digital role-play where they played the role of clients of a fictional predictive psychiatry company and chose what sources of personal data they wished to provide for a risk assessment. After the role-play, participants reflected on their choices during a peer-led interview. FINDINGS Participants saw multiple benefits in predictive testing services, but were highly selective with regard to the type of data they were willing to share. Largely due to privacy concerns, digital data sources such as social media or Google search history were less likely to be shared than psychosocial and biological data, including school grades and one's DNA. Participants were particularly reluctant to share social media data with schools (but less so with health systems). CONCLUSIONS Emerging predictive psychiatric services are valued by young people; however, these services must consider privacy versus utility trade-offs from the perspective of different stakeholders, including adolescents. CLINICAL IMPLICATIONS Respecting adolescents' need for transparency, privacy and choice in the age of digital phenotyping is critical to the responsible implementation of predictive psychiatric services.
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Affiliation(s)
- Gabriela Pavarini
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, Oxfordshire, UK
- Ethox Centre, Department of Population Health, University of Oxford, Oxford, UK
| | - Aleksandra Yosifova
- Department of Cognitive Science and Psychology, New Bulgarian University, Sofia, Bulgaria
| | - Keying Wang
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Benjamin Wilcox
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Nastja Tomat
- Department of Philosophy, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Jessica Lorimer
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Leya George
- Division of Psychology & Language Sciences, University College London, London, UK
| | - Sonia Alí
- Department of Psychology, University of Sussex, Brighton, UK
| | - Ilina Singh
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, Oxfordshire, UK
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Malhi GS, Bell E. Questions in Psychiatry (QuiP): Using your Apple all day, might keep the doctor away? Bipolar Disord 2021; 23:838-841. [PMID: 34816518 DOI: 10.1111/bdi.13157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gin S Malhi
- Academic Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, Kolling Institute, The University of Sydney, New South Wales, Australia.,Department of Psychiatry, CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, New South Wales, Australia
| | - Erica Bell
- Academic Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, Kolling Institute, The University of Sydney, New South Wales, Australia.,Department of Psychiatry, CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, New South Wales, Australia
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