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Tiribelli S, Calvaresi D. Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis. SCIENCE AND ENGINEERING ETHICS 2024; 30:22. [PMID: 38801621 PMCID: PMC11129984 DOI: 10.1007/s11948-024-00479-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 04/02/2024] [Indexed: 05/29/2024]
Abstract
Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS' technical aspects allows us to shed light on the ethical risks and challenges they might raise on individuals' well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA.
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Affiliation(s)
- Simona Tiribelli
- Department of Political Sciences, Communication, and International Relations, University of Macerata, 62100, Macerata, Italy.
- Institute for Technology and Global Health, PathCheck Foundation, 955 Massachusetts Ave, Cambridge, MA, 02139, USA.
| | - Davide Calvaresi
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Rue de l'Industrie 23, 1950, Sion, Switzerland
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Abdillahi I. Algorithmic surveillance in the era of the mental health appsphere. AMERICAN JOURNAL OF COMMUNITY PSYCHOLOGY 2024; 73:17-26. [PMID: 38497439 DOI: 10.1002/ajcp.12743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 02/10/2024] [Accepted: 02/10/2024] [Indexed: 03/19/2024]
Abstract
Digital mental health applications, also known as mHealth apps, are designed to help users manage their mental health using technology such as mobile devices and smartphones. However, there has been little critical engagement surrounding their impact on marginalized communities, and Black people in Canada. This article aims to explore how state and private actors conceptualize the digitalization of mental health access, leading to the creation and implementation of these apps. It is important to consider the links and interconnections between the platform and the app, and to be cautious and curious before adopting yet another app that claims to manage and mitigate moods and address mental health needs. We should be considering the implications of what comes with this access and apparent ease of use.
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Affiliation(s)
- Idil Abdillahi
- School of Disability Studies, Toronto Metropolitan University, Toronto, Ontario, Canada
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Lee E, Lee D, Baek JH, Kim SY, Park WY. Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort. Sci Rep 2024; 14:4500. [PMID: 38402308 PMCID: PMC10894302 DOI: 10.1038/s41598-023-49490-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/08/2023] [Indexed: 02/26/2024] Open
Abstract
Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers.
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Affiliation(s)
- Eunjin Lee
- Samsung Genome Institute, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dongbin Lee
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Ji Hyun Baek
- Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - So Yeon Kim
- Department of Artificial Intelligence, Ajou University, Suwon, Republic of Korea
- Department of Software and Computer Engineering, Ajou University, Suwon, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Health Science and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea.
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Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin Exp Res 2023; 35:2363-2397. [PMID: 37682491 PMCID: PMC10627901 DOI: 10.1007/s40520-023-02552-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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Affiliation(s)
- Richard J Woodman
- Centre of Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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Laure T, Engels RCME, Remmerswaal D, Spruijt-Metz D, Konigorski S, Boffo M. Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e46603. [PMID: 37889525 PMCID: PMC10638637 DOI: 10.2196/46603] [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: 02/18/2023] [Revised: 07/20/2023] [Accepted: 08/22/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Many university students experience mental health problems such as anxiety and depression. To support their mental health, a transdiagnostic mobile app intervention has been developed. The intervention provides short exercises rooted in various approaches (eg, positive psychology, mindfulness, self-compassion, and acceptance and commitment therapy) that aim to facilitate adaptive emotion regulation (ER) to help students cope with the various stressors they encounter during their time at university. OBJECTIVE The goals of this study are to investigate whether the intervention and its components function as intended and how participants engage with them. In addition, this study aims to monitor changes in distress symptoms and ER skills and identify relevant contextual factors that may moderate the intervention's impact. METHODS A sequential explanatory mixed methods design combining a microrandomized trial and semistructured interviews will be used. During the microrandomized trial, students (N=200) will be prompted via the mobile app twice a day for 3 weeks to evaluate their emotional states and complete a randomly assigned intervention (ie, an exercise supporting ER) or a control intervention (ie, a health information snippet). A subsample of participants (21/200, 10.5%) will participate in interviews exploring their user experience with the app and the completed exercises. The primary outcomes will be changes in emotional states and engagement with the intervention (ie, objective and subjective engagement). Objective engagement will be evaluated through log data (eg, exercise completion time). Subjective engagement will be evaluated through exercise likability and helpfulness ratings as well as user experience interviews. The secondary outcomes will include the distal outcomes of the intervention (ie, ER skills and distress symptoms). Finally, the contextual moderators of intervention effectiveness will be explored (eg, the time of day and momentary emotional states). RESULTS The study commenced on February 9, 2023, and the data collection was concluded on June 13, 2023. Of the 172 eligible participants, 161 (93.6%) decided to participate. Of these 161 participants, 137 (85.1%) completed the first phase of the study. A subsample of participants (18/172, 10.5%) participated in the user experience interviews. Currently, the data processing and analyses are being conducted. CONCLUSIONS This study will provide insight into the functioning of the intervention and identify areas for improvement. Furthermore, the findings will shed light on potential changes in the distal outcomes of the intervention (ie, ER skills and distress symptoms), which will be considered when designing a follow-up randomized controlled trial evaluating the full-scale effectiveness of this intervention. Finally, the results and data gathered will be used to design and train a recommendation algorithm that will be integrated into the app linking students to relevant content. TRIAL REGISTRATION ClinicalTrials.gov NCT05576883; https://www.clinicaltrials.gov/study/NCT05576883. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46603.
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Affiliation(s)
- Tajda Laure
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Rutger C M E Engels
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Danielle Remmerswaal
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Donna Spruijt-Metz
- Dornsife Center for Economic & Social Research, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Psychology, University of Southern California, Los Angeles, CA, United States
| | - Stefan Konigorski
- Department of Statistics, Harvard University, Boston, MA, United States
- Digital Health Center, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Icahn School of Medicine at Mount Sinai, Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
| | - Marilisa Boffo
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
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Schafer M, Lachman JM, Gardner F, Zinser P, Calderon F, Han Q, Facciola C, Clements L. Integrating intimate partner violence prevention content into a digital parenting chatbot intervention during COVID-19: Intervention development and remote data collection. BMC Public Health 2023; 23:1708. [PMID: 37667352 PMCID: PMC10476288 DOI: 10.1186/s12889-023-16649-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/29/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Intimate partner violence (IPV) is a serious public health issue which experienced a sharp incline during the onset of COVID-19. Increases in other forms of violence, such as violence against children (VAC), have also been linked to the pandemic, and there have been calls for greater prevention efforts that tackle both forms of violence concurrently. The COVID-19 crisis has highlighted the urgent need for evidence-based and scalable violence prevention interventions that target multiple forms of family violence. Parenting programmes have shown promising results in preventing various forms of family violence, including IPV and VAC, and have recently experienced an expansion in delivery, with digital intervention formats growing. This paper describes the development and evaluation of the IPV prevention content designed and integrated into ParentText, a chatbot parenting intervention adapted from Parenting for Lifelong Health programmes. METHODS The ParentText IPV prevention content was developed using the Six Steps in Quality Intervention Development (6SQuID) framework. This involved targeted literature searches for key studies to identify causal factors associated with IPV and determining those with greatest scope for change. Findings were used to develop the intervention content and theory of change. Consultations were held with academic researchers (n = 5), practitioners (n = 5), and local community organisations (n = 7), who reviewed the content. A formative evaluation was conducted with parents in relationships (n = 96) in Jamaica to better understand patterns in user engagement with the intervention and identify strategies to further improve engagement. RESULTS Using the 6SQuID model, five topics on IPV prevention were integrated into the ParentText chatbot. Text-messages covering each topic, including additional materials such as cartoons and videos, were also developed. The formative evaluation revealed an average user-engagement length of 14 days, 0.50 chatbot interactions per day, and over half of participants selected to view additional relationship content. CONCLUSIONS This article provides a unique contribution as the first to integrate IPV prevention content into a remotely delivered, digital parenting intervention for low-resource settings. The findings from this research and formative evaluation shed light on the promising potential of chatbots as scalable and accessible forms of violence prevention, targeting multiple types of family violence.
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Affiliation(s)
- Moa Schafer
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom.
| | - Jamie M Lachman
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
- Social and Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland
- Centre for Social Science Research, University of Cape Town, Cape Town, South Africa
| | - Frances Gardner
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
| | - Paula Zinser
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
| | - Francisco Calderon
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
| | - Qing Han
- Centre for Evidence Based Intervention, Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
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Kruzan KP, Reddy M, Washburn JJ, Mohr DC. Developing a Mobile App for Young Adults with Nonsuicidal Self-Injury: A Prototype Feedback Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16163. [PMID: 36498234 PMCID: PMC9739032 DOI: 10.3390/ijerph192316163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/23/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Nonsuicidal self-injury (NSSI) affects approximately 13% of young adults. Though evidence-based treatments for NSSI exist, most young adults do not receive treatment. Digital interventions can provide access to evidence-based treatments for NSSI at scale. Further, preliminary research suggests the acceptability, feasibility, and potential efficacy of digital interventions for NSSI. To date, however, there are few publicly available digital interventions developed specifically for young adults who engage in NSSI. The aim of this study was to solicit young adults' impressions of early app prototypes to identify ways of improving interactive features and content needs. Building on a prior interview study which explored young adults' self-management of NSSI and their use of technology in self-management, this study involved three waves of iterative app prototype feedback sessions with 10 young adults with past month NSSI. In general, participants responded favorably and provided feedback to augment the app to better meet their needs, including adding new features and functionality as well as increasing opportunities for personalization. We discuss two key design challenges related to the roles of tracking and temporality in digital interventions for NSSI, and then frame design considerations related to these challenges within the lived informatics model.
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Affiliation(s)
- Kaylee Payne Kruzan
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Madhu Reddy
- Department of Informatics, Donald Bren School of Information & Computer Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Jason J. Washburn
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - David C. Mohr
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Gaysynsky A, Heley K, Chou WYS. An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15073. [PMID: 36429796 PMCID: PMC9690360 DOI: 10.3390/ijerph192215073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/04/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
Innovative approaches are needed to make health communication research and practice more timely, responsive, and effective in a rapidly changing information ecosystem. In this paper we provide an overview of strategies that can enhance the delivery and effectiveness of health communication campaigns and interventions, as well as research approaches that can generate useful data and insights for decisionmakers and campaign designers, thereby reducing the research-to-practice gap. The discussion focuses on the following approaches: digital segmentation and microtargeting, social media influencer campaigns, recommender systems, adaptive interventions, A/B testing, efficient message testing protocols, rapid cycle iterative message testing, megastudies, and agent-based modeling. For each method highlighted, we also outline important practical and ethical considerations for utilizing the approach in the context of health communication research and practice, including issues related to transparency, privacy, equity, and potential for harm.
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Affiliation(s)
- Anna Gaysynsky
- Health Communication and Informatics Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA
- ICF Next, ICF, Rockville, MD 20850, USA
| | - Kathryn Heley
- Health Communication and Informatics Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA
| | - Wen-Ying Sylvia Chou
- Health Communication and Informatics Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA
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