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Burns J, Chen K, Flathers M, Currey D, Macrynikola N, Vaidyam A, Langholm C, Barnett I, Byun AJS, Lane E, Torous J. Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial. J Med Internet Res 2024; 26:e58502. [PMID: 39178032 PMCID: PMC11380059 DOI: 10.2196/58502] [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: 03/17/2024] [Revised: 04/22/2024] [Accepted: 06/19/2024] [Indexed: 08/24/2024] Open
Abstract
As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.
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Affiliation(s)
- James Burns
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Kelly Chen
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Matthew Flathers
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Danielle Currey
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Case Western Reserve University School of Medicine,, Cleveland, OH, United States
| | - Natalia Macrynikola
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Aditya Vaidyam
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Carle Illinois College of Medicine, Urbana, IL, United States
| | - Carsten Langholm
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Ian Barnett
- Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine at the University of Pennsylvania, Philadephia, PA, United States
| | - Andrew Jin Soo Byun
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Erlend Lane
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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Kepper MM, Fowler LA, Kusters IS, Davis JW, Baqer M, Sagui-Henson S, Xiao Y, Tarfa A, Yi JC, Gibson B, Heron KE, Alberts NM, Burgermaster M, Njie-Carr VP, Klesges LM. Expanding a Behavioral View on Digital Health Access: Drivers and Strategies to Promote Equity. J Med Internet Res 2024; 26:e51355. [PMID: 39088246 PMCID: PMC11327633 DOI: 10.2196/51355] [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: 07/28/2023] [Revised: 05/02/2024] [Accepted: 05/27/2024] [Indexed: 08/02/2024] Open
Abstract
The potential and threat of digital tools to achieve health equity has been highlighted for over a decade, but the success of achieving equitable access to health technologies remains challenging. Our paper addresses renewed concerns regarding equity in digital health access that were deepened during the COVID-19 pandemic. Our viewpoint is that (1) digital health tools have the potential to improve health equity if equitable access is achieved, and (2) improving access and equity in digital health can be strengthened by considering behavioral science-based strategies embedded in all phases of tool development. Using behavioral, equity, and access frameworks allowed for a unique and comprehensive exploration of current drivers of digital health inequities. This paper aims to present a compilation of strategies that can potentially have an actionable impact on digital health equity. Multilevel factors drive unequal access, so strategies require action from tool developers, individual delivery agents, organizations, and systems to effect change. Strategies were shaped with a behavioral medicine focus as the field has a unique role in improving digital health access; arguably, all digital tools require the user (individual, provider, and health system) to change behavior by engaging with the technology to generate impact. This paper presents a model that emphasizes using multilevel strategies across design, delivery, dissemination, and sustainment stages to advance digital health access and foster health equity.
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Affiliation(s)
- Maura M Kepper
- Prevention Research Center, Washington University in St. Louis, St. Louis, MO, United States
| | - Lauren A Fowler
- Sexuality, Health, and Gender Center, Washington University in St. Louis School of Medicine, Saint Louis, MO, United States
| | - Isabelle S Kusters
- Department of Health, Human, and Biomedical Sciences, University of Houston-Clear Lake, Houston, TX, United States
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Jean W Davis
- College of Nursing, University of Central Florida, Orlando, FL, United States
| | - Manal Baqer
- Neamah Health Consulting, Boston, MA, United States
| | - Sara Sagui-Henson
- Clinical Strategy and Research Team, Modern Health, San Francisco, CA, United States
| | - Yunyu Xiao
- Department of Population Health Science, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Adati Tarfa
- School of Medicine, Yale University, New Haven, CT, United States
| | - Jean C Yi
- Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Bryan Gibson
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Kristin E Heron
- Psychology Department, Old Dominion University, Norfolk, VA, United States
- Virginia Consortium Program in Clinical Psychology, Norfolk, VA, United States
| | - Nicole M Alberts
- Department of Psychology, Concordia University, Montreal, QC, Canada
| | - Marissa Burgermaster
- Department of Nutritional Sciences, University of Texas at Austin, Austin, TX, United States
- Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, United States
| | - Veronica Ps Njie-Carr
- Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD, United States
| | - Lisa M Klesges
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
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Kolbinger FR, Veldhuizen GP, Zhu J, Truhn D, Kather JN. Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2024; 4:71. [PMID: 38605106 PMCID: PMC11009315 DOI: 10.1038/s43856-024-00492-0] [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: 08/18/2023] [Accepted: 03/27/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND The field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field. METHODS Based on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items. RESULTS AI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages. CONCLUSIONS Our analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.
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Grants
- UM1 TR004402 NCATS NIH HHS
- JNK is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max-Eder-Programme of the German Cancer Aid (grant #70113864), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (Transplant.KI, 01VSF21048) the European Union (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre.
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Affiliation(s)
- Fiona R Kolbinger
- Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA
- Department of Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
- Indiana University Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gregory P Veldhuizen
- Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
| | - Jiefu Zhu
- Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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Abondio P, Bruno F, Passarino G, Montesanto A, Luiselli D. Pangenomics: A new era in the field of neurodegenerative diseases. Ageing Res Rev 2024; 94:102180. [PMID: 38163518 DOI: 10.1016/j.arr.2023.102180] [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: 09/07/2023] [Revised: 12/14/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024]
Abstract
A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.
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Affiliation(s)
- Paolo Abondio
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy.
| | - Francesco Bruno
- Academy of Cognitive Behavioral Sciences of Calabria (ASCoC), Lamezia Terme, Italy; Regional Neurogenetic Centre (CRN), Department of Primary Care, Azienda Sanitaria Provinciale Di Catanzaro, Viale A. Perugini, 88046 Lamezia Terme, CZ, Italy; Association for Neurogenetic Research (ARN), Lamezia Terme, CZ, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Donata Luiselli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
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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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6
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Coghlan S, Gyngell C, Vears DF. Ethics of artificial intelligence in prenatal and pediatric genomic medicine. J Community Genet 2024; 15:13-24. [PMID: 37796364 PMCID: PMC10857992 DOI: 10.1007/s12687-023-00678-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/27/2023] [Indexed: 10/06/2023] Open
Abstract
This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.
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Affiliation(s)
- Simon Coghlan
- School of Computing and Information Systems (CIS), Centre for AI and Digital Ethics (CAIDE), The University of Melbourne, Grattan St, Melbourne, Victoria, 3010, Australia.
- Australian Research Council Centre of Excellence for Automated Decision Making and Society (ADM+S), Melbourne, Victoria, Australia.
| | - Christopher Gyngell
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Danya F Vears
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
- Centre for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
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7
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Roberts W, McKee S, Miranda R, Barnett N. Navigating ethical challenges in psychological research involving digital remote technologies and people who use alcohol or drugs. AMERICAN PSYCHOLOGIST 2024; 79:24-38. [PMID: 38236213 PMCID: PMC10798215 DOI: 10.1037/amp0001193] [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] [Indexed: 01/19/2024]
Abstract
Digital and remote technologies (DRT) are increasingly being used in scientific investigations to objectively measure human behavior during day-to-day activities. Using these devices, psychologists and other behavioral scientists can investigate health risk behaviors, such as drug and alcohol use, by closely examining the causes and consequences of monitored behaviors as they occur naturalistically. There are, however, complex ethical issues that emerge when using DRT methodologies in research with people who use substances. These issues must be identified and addressed so DRT devices can be incorporated into psychological research with this population in a manner that comports the ethical standards of the American Psychological Association. In this article, we discuss the ethical ramifications of using DRT in behavioral studies with people who use substances. Drawing on allied fields with similar ethical issues, we make recommendations to researchers who wish to incorporate DRT into their own research. Major topics include (a) threats to and methods for protecting participant and nonparticipant privacy, (b) shortcomings of traditional informed consent in DRT research, (c) researcher liabilities introduced by real-time continuous data collection, (d) threats to distributive justice arising from computational tools often used to manage and analyze DRT data, and (e) ethical implications of the "digital divide." We conclude with a more optimistic discussion of how DRT may provide safer alternatives to gold standard paradigms in substance use research, allowing researchers to test hypotheses that were previously prohibited on ethical grounds. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Walter Roberts
- Department of Psychiatry, Yale University School of Medicine
| | - Sherry McKee
- Department of Psychiatry, Yale University School of Medicine
| | - Robert Miranda
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University
- Department of Behavioral and Social Sciences, Center for Alcohol and Addiction Studies, Brown University School of Public Health
| | - Nancy Barnett
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University
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8
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Zhang X, Gu Y, Yin J, Zhang Y, Jin C, Wang W, Li AM, Wang Y, Su L, Xu H, Ge X, Ye C, Tang L, Shen B, Fang J, Wang D, Feng R. Development, Reliability, and Structural Validity of the Scale for Knowledge, Attitude, and Practice in Ethics Implementation Among AI Researchers: Cross-Sectional Study. JMIR Form Res 2023; 7:e42202. [PMID: 37883175 PMCID: PMC10636617 DOI: 10.2196/42202] [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: 08/26/2022] [Revised: 01/31/2023] [Accepted: 09/24/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Medical artificial intelligence (AI) has significantly contributed to decision support for disease screening, diagnosis, and management. With the growing number of medical AI developments and applications, incorporating ethics is considered essential to avoiding harm and ensuring broad benefits in the lifecycle of medical AI. One of the premises for effectively implementing ethics in Medical AI research necessitates researchers' comprehensive knowledge, enthusiastic attitude, and practical experience. However, there is currently a lack of an available instrument to measure these aspects. OBJECTIVE The aim of this study was to develop a comprehensive scale for measuring the knowledge, attitude, and practice of ethics implementation among medical AI researchers, and to evaluate its measurement properties. METHODS The construct of the Knowledge-Attitude-Practice in Ethics Implementation (KAP-EI) scale was based on the Knowledge-Attitude-Practice (KAP) model, and the evaluation of its measurement properties was in compliance with the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) reporting guidelines for studies on measurement instruments. The study was conducted in 2 phases. The first phase involved scale development through a systematic literature review, qualitative interviews, and item analysis based on a cross-sectional survey. The second phase involved evaluation of structural validity and reliability through another cross-sectional study. RESULTS The KAP-EI scale had 3 dimensions including knowledge (10 items), attitude (6 items), and practice (7 items). The Cronbach α for the whole scale reached .934. Confirmatory factor analysis showed that the goodness-of-fit indices of the scale were satisfactory (χ2/df ratio:=2.338, comparative fit index=0.949, Tucker Lewis index=0.941, root-mean-square error of approximation=0.064, and standardized root-mean-square residual=0.052). CONCLUSIONS The results show that the scale has good reliability and structural validity; hence, it could be considered an effective instrument. This is the first instrument developed for this purpose.
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Affiliation(s)
- Xiaobo Zhang
- Children's Hospital of Fudan University, Shanghai, China
| | - Ying Gu
- Children's Hospital of Fudan University, Shanghai, China
| | - Jie Yin
- School of Philosophy Fudan University, Shanghai, China
| | - Yuejie Zhang
- School of Computer Science Fudan University, Shanghai, China
| | - Cheng Jin
- School of Computer Science Fudan University, Shanghai, China
| | - Weibing Wang
- School of Public Health Fudan University, Shanghai, China
| | - Albert Martin Li
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yingwen Wang
- Children's Hospital of Fudan University, Shanghai, China
| | - Ling Su
- Children's Hospital of Fudan University, Shanghai, China
| | - Hong Xu
- Children's Hospital of Fudan University, Shanghai, China
| | - Xiaoling Ge
- Children's Hospital of Fudan University, Shanghai, China
| | - Chengjie Ye
- Children's Hospital of Fudan University, Shanghai, China
| | - Liangfeng Tang
- Children's Hospital of Fudan University, Shanghai, China
| | - Bing Shen
- Shanghai Hospital Development Center, Shanghai, China
| | - Jinwu Fang
- School of Public Health Fudan University, Shanghai, China
| | - Daoyang Wang
- School of Public Health Fudan University, Shanghai, China
| | - Rui Feng
- School of Computer Science Fudan University, Shanghai, China
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9
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Goldberg SB, Sun S, Carlbring P, Torous J. Selecting and describing control conditions in mobile health randomized controlled trials: a proposed typology. NPJ Digit Med 2023; 6:181. [PMID: 37775522 PMCID: PMC10541862 DOI: 10.1038/s41746-023-00923-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 09/20/2023] [Indexed: 10/01/2023] Open
Abstract
Hundreds of randomized controlled trials (RCTs) have tested the efficacy of mobile health (mHealth) tools for a wide range of mental and behavioral health outcomes. These RCTs have used a variety of control condition types which dramatically influence the scientific inferences that can be drawn from a given study. Unfortunately, nomenclature across mHealth RCTs is inconsistent and meta-analyses commonly combine control conditions that differ in potentially important ways. We propose a typology of control condition types in mHealth RCTs. We define 11 control condition types, discuss key dimensions on which they differ, provide a decision tree for selecting and identifying types, and describe the scientific inferences each comparison allows. We propose a five-tier comparison strength gradation along with four simplified categorization schemes. Lastly, we discuss unresolved definitional, ethical, and meta-analytic issues related to the categorization of control conditions in mHealth RCTs.
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Affiliation(s)
- Simon B Goldberg
- Department of Counseling Psychology, University of Wisconsin-Madison, Madison, WI, USA.
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA.
| | - Shufang Sun
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA
- International Health Institute, Brown University School of Public Health, Providence, RI, USA
- Mindfulness Center, Brown University School of Public Health, Providence, RI, USA
| | - Per Carlbring
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Harvard Business School, Boston, MA, USA
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10
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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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Eder J, Dom G, Gorwood P, Kärkkäinen H, Decraene A, Kumpf U, Beezhold J, Samochowiec J, Kurimay T, Gaebel W, De Picker L, Falkai P. Improving mental health care in depression: A call for action. Eur Psychiatry 2023; 66:e65. [PMID: 37534402 PMCID: PMC10486253 DOI: 10.1192/j.eurpsy.2023.2434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/20/2023] [Accepted: 06/29/2023] [Indexed: 08/04/2023] Open
Abstract
Depressive disorders have one of the highest disability-adjusted life years (DALYs) of all medical conditions, which led the European Psychiatric Association to propose a policy paper, pinpointing their unmet health care and research needs. The first part focuses on what can be currently done to improve the care of patients with depression, and then discuss future trends for research and healthcare. Through the narration of clinical cases, the different points are illustrated. The necessary political framework is formulated, to implement such changes to fundamentally improve psychiatric care. The group of European Psychiatrist Association (EPA) experts insist on the need for (1) increased awareness of mental illness in primary care settings, (2) the development of novel (biological) markers, (3) the rapid implementation of machine learning (supporting diagnostics, prognostics, and therapeutics), (4) the generalized use of electronic devices and apps into everyday treatment, (5) the development of the new generation of treatment options, such as plasticity-promoting agents, and (6) the importance of comprehensive recovery approach. At a political level, the group also proposed four priorities, the need to (1) increase the use of open science, (2) implement reasonable data protection laws, (3) establish ethical electronic health records, and (4) enable better healthcare research and saving resources.
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Affiliation(s)
- Julia Eder
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
| | - Geert Dom
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Philip Gorwood
- Université Paris Cité, GHU Paris (Sainte Anne hospital, CMME) & INSERM UMR1266, Paris, France
| | - Hikka Kärkkäinen
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Andre Decraene
- EUFAMI, the European Organisation representing Families of persons affected by severe Mental Ill Health, Leuven, Belgium
| | - Ulrike Kumpf
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julian Beezhold
- Norfolk and Suffolk NHS Foundation Trust, Norwich, UK, University of East Anglia, Norwich, UK
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Tamas Kurimay
- North-Central Buda Center, New Saint John Hospital and Outpatient Clinic, Buda Family Centered Mental Health Centre, Department of Psychiatry and Psychiatric Rehabilitation, Teaching Department of Semmelweis University, Budapest, Hungary
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, WHO Collaborating Centre DEU-131, Germany
| | - Livia De Picker
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
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Scangos KW, State MW, Miller AH, Baker JT, Williams LM. New and emerging approaches to treat psychiatric disorders. Nat Med 2023; 29:317-333. [PMID: 36797480 PMCID: PMC11219030 DOI: 10.1038/s41591-022-02197-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 02/18/2023]
Abstract
Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.
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Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Justin T Baker
- McLean Hospital Institute for Technology in Psychiatry, Belmont, MA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Mental Illness Research Education and Clinical Center (MIRECC), VA Palo Alto Health Care System, Palo Alto, CA, USA
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Birk RH, Samuel G. Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems. Curr Psychiatry Rep 2022; 24:523-528. [PMID: 36001220 DOI: 10.1007/s11920-022-01358-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW We review recent developments within digital phenotyping for mental health, a field dedicated to using digital data for diagnosing, predicting, and monitoring mental health problems. We especially focus on recent critiques and challenges to digital phenotyping from within the social sciences. RECENT FINDINGS Three significant strands of criticism against digital phenotyping for mental health have been developed within the social sciences. This literature problematizes the idea that digital data can be objective, that it can be unbiased, and argues that it has multiple ethical and practical challenges. Digital phenotyping for mental health is a rapidly growing and developing field, but with considerable challenges that are not easily solvable. This includes when, and if, data from digital phenotyping is actionable in practice; the involvement of user and patient perspectives in digital phenotyping research; the possibility of biased data; and challenges to the idea that digital phenotyping can be more objective than other forms of psychiatric assessment.
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Affiliation(s)
- Rasmus H Birk
- Department of Communication & Psychology, Aalborg University, Aalborg, Denmark.
| | - Gabrielle Samuel
- Department of Global Health & Social Medicine, King's College London, London, UK
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