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Zhang Y, Li D, Li X, Zhou X, Newman G. The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications. Soc Sci Med 2024; 354:117075. [PMID: 38959816 DOI: 10.1016/j.socscimed.2024.117075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 06/16/2024] [Accepted: 06/23/2024] [Indexed: 07/05/2024]
Abstract
With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.
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Affiliation(s)
- Yue Zhang
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA.
| | - Dongying Li
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA
| | - Xiaoyu Li
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA
| | - Xiaolu Zhou
- Department of Geography, Texas Christian University, Fort Worth, Texas, USA
| | - Galen Newman
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA
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2
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Langener AM, Bringmann LF, Kas MJ, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2024; 51:455-475. [PMID: 38200262 PMCID: PMC11196304 DOI: 10.1007/s10488-023-01328-0] [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] [Accepted: 11/22/2023] [Indexed: 01/12/2024]
Abstract
Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.
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Affiliation(s)
- Anna M Langener
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
- Faculty of Science and Engineering, Nijenborgh 7, 9747 AG, Groningen, The Netherlands.
| | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Interdisciplinary Center Psychopathology and Emotion Regulation, (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Gert Stulp
- Department of Sociology & Inter-University Center for Social Science Theory and Methodology, Grote Rozenstraat 31, 9712 TS, Groningen, The Netherlands
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3
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Kingsbury C, Buzzi M, Chaix B, Kanning M, Khezri S, Kiani B, Kirchner TR, Maurel A, Thierry B, Kestens Y. STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies. Arch Public Health 2024; 82:84. [PMID: 38867286 PMCID: PMC11170886 DOI: 10.1186/s13690-024-01310-8] [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: 01/09/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
CONTEXT While a growing body of research has been demonstrating how exposure to social and built environments relate to various health outcomes, specific pathways generally remain poorly understood. But recent technological advancements have enabled new study designs through continuous monitoring using mobile sensors and repeated questionnaires. Such geographically explicit momentary assessments (GEMA) make it possible to link momentary subjective states, behaviors, and physiological parameters to momentary environmental conditions, and can help uncover the pathways linking place to health. Despite its potential, there is currently no review of GEMA studies detailing how location data is used to measure environmental exposure, and how this in turn is linked to momentary outcomes of interest. Moreover, a lack of standard reporting of such studies hampers comparability and reproducibility. AIMS The objectives of this research were twofold: 1) conduct a systematic review of GEMA studies that link momentary measurement with environmental data obtained from geolocation data, and 2) develop a STROBE extension guideline for GEMA studies. METHOD The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria consisted of a combination of repeated momentary measurements of a health state or behavior with GPS coordinate collection, and use of these location data to derive momentary environmental exposures. To develop the guideline, the variables extracted for the systematic review were compared to elements of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CREMAS (CRedibility of Evidence from Multiple Analyses of the Same data) checklists, to provide a new guideline for GEMA studies. An international panel of experts participated in a consultation procedure to collectively develop the proposed checklist items. RESULTS AND DEVELOPED TOOLS: A total of 20 original GEMA studies were included in the review. Overall, several key pieces of information regarding the GEMA methods were either missing or reported heterogeneously. Our guideline provides a total of 27 categories (plus 4 subcategories), combining a total of 70 items. The 22 categories and 32 items from the original STROBE guideline have been integrated in our GEMA guideline. Eight categories and 6 items from the CREMAS guideline have been included to our guideline. We created one new category (namely "Consent") and added 32 new items specific to GEMA studies. CONCLUSIONS AND RECOMMENDATIONS This study offers a systematic review and a STROBE extension guideline for the reporting of GEMA studies. The latter will serve to standardize the reporting of GEMA studies, as well as facilitate the interpretation of results and their generalizability. In short, this work will help researchers and public health professionals to make the most of this method to advance our understanding of how environments influence health.
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Affiliation(s)
- Célia Kingsbury
- École de santé publique, Université de Montréal (ESPUM), 7101 Av. du Parc, Montréal, H3N 1X9, Québec, Canada.
- Centre de recherche de santé publique (CReSP), 7101, Av. du Parc, Montréal, H3N 1X9, Québec, Canada.
| | - Marie Buzzi
- Université de Lorraine, INSERM, INSPIIRE, Nancy, F-54000, France
| | - Basile Chaix
- Université de Sorbonne, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis Team, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, Paris, 75012, France
| | - Martina Kanning
- Department of Social and Health Sciences in Sport Science, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Baden-Wuerttemberg, Germany
| | - Sadun Khezri
- École de santé publique, Université de Montréal (ESPUM), 7101 Av. du Parc, Montréal, H3N 1X9, Québec, Canada
- Centre de recherche de santé publique (CReSP), 7101, Av. du Parc, Montréal, H3N 1X9, Québec, Canada
| | - Behzad Kiani
- Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane St Lucia, QLD, 4072, Australia
| | - Thomas R Kirchner
- Department of Social and Behavioral Sciences, New York University School of Global Public Health, 726 Broadway, New York, NY, 10012, USA
- Center for Urban Science and Progress, New York University Tandon School of Engineering, 6 MetroTech Center, Brooklyn, NY, 11201, USA
| | - Allison Maurel
- École de santé publique, Université de Montréal (ESPUM), 7101 Av. du Parc, Montréal, H3N 1X9, Québec, Canada
- Centre de recherche de santé publique (CReSP), 7101, Av. du Parc, Montréal, H3N 1X9, Québec, Canada
| | - Benoît Thierry
- École de santé publique, Université de Montréal (ESPUM), 7101 Av. du Parc, Montréal, H3N 1X9, Québec, Canada
- Centre de recherche de santé publique (CReSP), 7101, Av. du Parc, Montréal, H3N 1X9, Québec, Canada
| | - Yan Kestens
- École de santé publique, Université de Montréal (ESPUM), 7101 Av. du Parc, Montréal, H3N 1X9, Québec, Canada
- Centre de recherche de santé publique (CReSP), 7101, Av. du Parc, Montréal, H3N 1X9, Québec, Canada
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Rief W, Asmundson GJG, Bryant RA, Clark DM, Ehlers A, Holmes EA, McNally RJ, Neufeld CB, Wilhelm S, Jaroszewski AC, Berg M, Haberkamp A, Hofmann SG. The future of psychological treatments: The Marburg Declaration. Clin Psychol Rev 2024; 110:102417. [PMID: 38688158 DOI: 10.1016/j.cpr.2024.102417] [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: 09/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 05/02/2024]
Abstract
Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.
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Affiliation(s)
- Winfried Rief
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany.
| | | | - Richard A Bryant
- University of New South Wales, School of Psychology, Sydney, New South Wales, Australia
| | - David M Clark
- University of Oxford, Department of Experimental Psychology, Oxford, UK
| | - Anke Ehlers
- University of Oxford, Department of Experimental Psychology, Oxford, UK
| | - Emily A Holmes
- Uppsala University, Department of Women's and Children's Health, Uppsala, Sweden; Karolinska Institutet, Department of Clinical Neuroscience, Solna, Sweden
| | | | - Carmem B Neufeld
- University of São Paulo, Department of Psychology, Ribeirão Preto, SP, Brazil
| | - Sabine Wilhelm
- Massachusetts General Hospital and Harvard School of Medicine, Boston, USA
| | - Adam C Jaroszewski
- Massachusetts General Hospital and Harvard School of Medicine, Boston, USA
| | - Max Berg
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany
| | - Anke Haberkamp
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany
| | - Stefan G Hofmann
- Philipps-University of Marburg, Department of Psychology, Translational Clinical Psychology Group, Marburg, Germany
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Fernández-Álvarez J, Colombo D, Gómez Penedo JM, Pierantonelli M, Baños RM, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Ment Health 2024; 11:e46593. [PMID: 38574359 PMCID: PMC11027061 DOI: 10.2196/46593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 01/28/2024] [Accepted: 02/07/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet. OBJECTIVE This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science. RESULTS A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics. CONCLUSIONS AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.
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Affiliation(s)
- Javier Fernández-Álvarez
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
- Fundación Aiglé, Buenos Aires, Argentina
| | - Desirée Colombo
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
| | | | | | - Rosa María Baños
- Polibienestar Research Institute, University of Valencia, Valencia, Spain
- Department of Personality, Evaluation, and Psychological Treatments, University of Valencia, Valencia, Spain
- Ciber Fisiopatologia Obesidad y Nutricion (CB06/03 Instituto Salud Carlos III), Madrid, Spain
| | - Cristina Botella
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
- Ciber Fisiopatologia Obesidad y Nutricion (CB06/03 Instituto Salud Carlos III), Madrid, Spain
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Ralph-Nearman C, Sandoval-Araujo LE, Karem A, Cusack CE, Glatt S, Hooper MA, Rodriguez Pena C, Cohen D, Allen S, Cash ED, Welch K, Levinson CA. Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life. Psychol Med 2024; 54:1084-1090. [PMID: 37859600 PMCID: PMC10939805 DOI: 10.1017/s003329172300288x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
BACKGROUND Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings. METHODS From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature). RESULTS Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity. CONCLUSIONS This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.
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Affiliation(s)
- C. Ralph-Nearman
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - L. E. Sandoval-Araujo
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - A. Karem
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
| | - C. E. Cusack
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - S. Glatt
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - M. A. Hooper
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - C. Rodriguez Pena
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
| | - D. Cohen
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - S. Allen
- Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - E. D. Cash
- Department of Otolaryngology-HNS and Communicative Disorders, University of Louisville School of Medicine, Louisville, KY, USA
- University of Louisville Healthcare-Brown Cancer Center, Louisville, KY, USA
| | - K. Welch
- Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - C. A. Levinson
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
- Department of Pediatrics, Child and Adolescent Psychology and Psychiatry, University of Louisville, Louisville, KY, USA
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Knol L, Nagpal A, Leaning IE, Idda E, Hussain F, Ning E, Eisenlohr-Moul TA, Beckmann CF, Marquand AF, Leow A. Smartphone keyboard dynamics predict affect in suicidal ideation. NPJ Digit Med 2024; 7:54. [PMID: 38429434 PMCID: PMC10907683 DOI: 10.1038/s41746-024-01048-1] [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: 09/26/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024] Open
Abstract
While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (β = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.
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Affiliation(s)
- Loran Knol
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands.
| | - Anisha Nagpal
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Imogen E Leaning
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Elena Idda
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Emma Ning
- Department of Psychology, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Christian F Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
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Balliu B, Douglas C, Seok D, Shenhav L, Wu Y, Chatzopoulou D, Kaiser W, Chen V, Kim J, Deverasetty S, Arnaudova I, Gibbons R, Congdon E, Craske MG, Freimer N, Halperin E, Sankararaman S, Flint J. Personalized mood prediction from patterns of behavior collected with smartphones. NPJ Digit Med 2024; 7:49. [PMID: 38418551 PMCID: PMC10902386 DOI: 10.1038/s41746-024-01035-6] [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: 10/18/2022] [Accepted: 02/09/2024] [Indexed: 03/01/2024] Open
Abstract
Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.
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Affiliation(s)
- Brunilda Balliu
- Departments of Computational Medicine, University of California Los Angeles, Los Angeles, USA.
- Departments of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, USA.
- Department of Biostatistics, University of California Los Angeles, Los Angeles, USA.
| | - Chris Douglas
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles, Los Angeles, USA
| | - Darsol Seok
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - Liat Shenhav
- Department of Computer Science, University of California Los Angeles, Los Angeles, USA
| | - Yue Wu
- Department of Computer Science, University of California Los Angeles, Los Angeles, USA
| | - Doxa Chatzopoulou
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - William Kaiser
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, USA
| | - Victor Chen
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, USA
| | - Jennifer Kim
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - Sandeep Deverasetty
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - Inna Arnaudova
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - Robert Gibbons
- Departments of Medicine, Public Health Sciences and Comparative Human Development, University of Chicago, Chicago, USA
| | - Eliza Congdon
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles, Los Angeles, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - Michelle G Craske
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles, Los Angeles, USA
- Department of Psychology, University of California Los Angeles, Los Angeles, USA
| | - Nelson Freimer
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles, Los Angeles, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, USA
| | - Sriram Sankararaman
- Departments of Computational Medicine, University of California Los Angeles, Los Angeles, USA
- Department of Computer Science, University of California Los Angeles, Los Angeles, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles, Los Angeles, USA.
- Department of Human Genetics, University of California Los Angeles, Los Angeles, USA.
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Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips KT, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Res Protoc 2024; 13:e46493. [PMID: 38324375 PMCID: PMC10882478 DOI: 10.2196/46493] [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: 08/25/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-powered digital therapies that detect methamphetamine cravings via consumer devices have the potential to reduce health care disparities by providing remote and accessible care solutions to communities with limited care solutions, such as Native Hawaiian, Filipino, and Pacific Islander communities. However, Native Hawaiian, Filipino, and Pacific Islander communities are understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other racial groups. OBJECTIVE In this study, we aimed to understand the feasibility of continuous remote digital monitoring and ecological momentary assessments in Native Hawaiian, Filipino, and Pacific Islander communities in Hawaii by curating a novel data set of longitudinal Fitbit (Fitbit Inc) biosignals with the corresponding craving and substance use labels. We also aimed to develop personalized AI models that predict methamphetamine craving events in real time using wearable sensor data. METHODS We will develop personalized AI and machine learning models for methamphetamine use and craving prediction in 40 individuals from Native Hawaiian, Filipino, and Pacific Islander communities by curating a novel data set of real-time Fitbit biosensor readings and the corresponding participant annotations (ie, raw self-reported substance use data) of their methamphetamine use and cravings. In the process of collecting this data set, we will gain insights into cultural and other human factors that can challenge the proper acquisition of precise annotations. With the resulting data set, we will use self-supervised learning AI approaches, which are a new family of machine learning methods that allows a neural network to be trained without labels by being optimized to make predictions about the data. The inputs to the proposed AI models are Fitbit biosensor readings, and the outputs are predictions of methamphetamine use or craving. This paradigm is gaining increased attention in AI for health care. RESULTS To date, more than 40 individuals have expressed interest in participating in the study, and we have successfully recruited our first 5 participants with minimal logistical challenges and proper compliance. Several logistical challenges that the research team has encountered so far and the related implications are discussed. CONCLUSIONS We expect to develop models that significantly outperform traditional supervised methods by finetuning according to the data of a participant. Such methods will enable AI solutions that work with the limited data available from Native Hawaiian, Filipino, and Pacific Islander populations and that are inherently unbiased owing to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46493.
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Affiliation(s)
- Yinan Sun
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Ali Kargarandehkordi
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Christopher Slade
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Aditi Jaiswal
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Gerald Busch
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Anthony Guerrero
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Kristina T Phillips
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, HI, United States
| | - Peter Washington
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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10
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Choi H, Cho Y, Min C, Kim K, Kim E, Lee S, Kim JJ. Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning. Digit Health 2024; 10:20552076241256730. [PMID: 39114113 PMCID: PMC11303831 DOI: 10.1177/20552076241256730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/07/2024] [Indexed: 08/10/2024] Open
Abstract
Objective Social anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom severity. Method We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive features. Results Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of severity. Conclusions Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.
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Affiliation(s)
- Hyoungshin Choi
- AI Medtech R&D, Waycen Inc, Seoul, Republic of Korea
- Department of Electrical and Computer Engineering, Sungkyunkwan University and Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Yesol Cho
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Choongki Min
- AI Medtech R&D, Waycen Inc, Seoul, Republic of Korea
| | - Kyungnam Kim
- AI Medtech R&D, Waycen Inc, Seoul, Republic of Korea
| | - Eunji Kim
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seungmin Lee
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Jin Kim
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
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11
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Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res 2023; 25:e46778. [PMID: 38090800 PMCID: PMC10753422 DOI: 10.2196/46778] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/29/2023] [Accepted: 07/31/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).
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Affiliation(s)
- Pasquale Bufano
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Sara Said
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | | | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
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12
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Ramesh A, Nayak T, Beestrum M, Quer G, Pandit JA. Heart Rate Variability in Psychiatric Disorders: A Systematic Review. Neuropsychiatr Dis Treat 2023; 19:2217-2239. [PMID: 37881808 PMCID: PMC10596135 DOI: 10.2147/ndt.s429592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023] Open
Abstract
Introduction Heart rate variability (HRV) is a measure of the fluctuation in time interval between consecutive heart beats. Decreased heart rate variability has been shown to have associations with autonomic dysfunction in psychiatric conditions such as depression, substance abuse, anxiety, and schizophrenia, although its use as a prognostic tool remains highly debated. This study aims to review the current literature on heart rate variability as a diagnostic and prognostic tool in psychiatric populations. Methods A literature search was conducted using the MEDLINE, EMBASE, Cochrane, and PsycINFO libraries to identify full-text studies involving adult psychiatric populations that reported HRV measurements. From 1647 originally identified, 31 studies were narrowed down through an abstract and full-text screen. Studies were excluded if they enrolled adolescents or children, used animal models, enrolled patients with another primary diagnosis other than psychiatric as outlined by the diagnostic and statistical manual of mental disorders (DSM) V, or if they assessed HRV in the context of treatment rather than diagnosis. Study quality assessment was conducted using a modified Downs and Blacks quality assessment tool for observational rather than interventional studies. Data were reported in four tables: 1) summarizing study characteristics, 2) methods of HRV detection, 3) key findings and statistics, and 4) quality assessment. Results There is significant variability between studies in their methodology of recording as well as reporting HRV, which makes it difficult to meaningfully interpret data that is clinically applicable due to the presence of significant bias in existing studies. The presence of an association between HRV and the severity of various psychiatric disorders, however, remains promising. Conclusion Future studies should be done to further explore how HRV parameters may be used to enhance the diagnosis and prognosis of several psychiatric disorders.
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Affiliation(s)
- Ashvita Ramesh
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Tanvi Nayak
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Molly Beestrum
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Giorgio Quer
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Jay A Pandit
- Scripps Research Translational Institute, La Jolla, CA, USA
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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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14
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Timmons AC, Duong JB, Fiallo NS, Lee T, Vo HPQ, Ahle MW, Comer JS, Brewer LC, Frazier SL, Chaspari T. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023; 18:1062-1096. [PMID: 36490369 PMCID: PMC10250563 DOI: 10.1177/17456916221134490] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.
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Affiliation(s)
- Adela C. Timmons
- University of Texas at Austin Institute for Mental Health Research
- Colliga Apps Corporation
| | | | | | | | | | | | | | - LaPrincess C. Brewer
- Department of Cardiovascular Medicine, May Clinic College of Medicine, Rochester, Minnesota, United States
- Center for Health Equity and Community Engagement Research, Mayo Clinic, Rochester, Minnesota, United States
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15
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Washington P. Personalized Machine Learning using Passive Sensing and Ecological Momentary Assessments for Meth Users in Hawaii: A Research Protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.24.23294587. [PMID: 37662253 PMCID: PMC10473804 DOI: 10.1101/2023.08.24.23294587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Artificial intelligence (AI)-powered digital therapies which detect meth cravings delivered on consumer devices have the potential to reduce these disparities by providing remote and accessible care solutions to Native Hawaiians, Filipinos, and Pacific Islanders (NHFPI) communities with limited care solutions. However, NHFPI are fully understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other races. Objective We seek to fulfill two research aims: (1) Understand the feasibility of continuous remote digital monitoring and ecological momentary assessments (EMAs) in NHFPI in Hawaii by curating a novel dataset of longitudinal FitBit biosignals with corresponding craving and substance use labels. (2) Develop personalized AI models which predict meth craving events in real time using wearable sensor data. Methods We will develop personalized AI/ML (artificial intelligence/machine learning) models for meth use and craving prediction in 40 NHFPI individuals by curating a novel dataset of real-time FitBit biosensor readings and corresponding participant annotations (i.e., raw self-reported substance use data) of their meth use and cravings. In the process of collecting this dataset, we will glean insights about cultural and other human factors which can challenge the proper acquisition of precise annotations. With the resulting dataset, we will employ self-supervised learning (SSL) AI approaches, which are a new family of ML methods that allow a neural network to be trained without labels by being optimized to make predictions about the data itself. The inputs to the proposed AI models are FitBit biosensor readings and the outputs are predictions of meth use or craving. This paradigm is gaining increased attention in AI for healthcare. Conclusions We expect to develop models which significantly outperform traditional supervised methods by fine-tuning to an individual subject's data. Such methods will enable AI solutions which work with the limited data available from NHFPI populations and which are inherently unbiased due to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse.
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16
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Cohen ZD, Schueller SM. Expanding, improving, and understanding behaviour research and therapy through digital mental health. Behav Res Ther 2023; 167:104358. [PMID: 37418857 DOI: 10.1016/j.brat.2023.104358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Affiliation(s)
- Zachary D Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA.
| | - Stephen M Schueller
- Department of Psychological Science, University of California, Irvine, USA; Department of Informatics, University of California, Irvine, USA
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17
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Fancello G, Vallée J, Sueur C, van Lenthe FJ, Kestens Y, Montanari A, Chaix B. Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population. ENVIRONMENT INTERNATIONAL 2023; 178:108095. [PMID: 37487375 DOI: 10.1016/j.envint.2023.108095] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/29/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023]
Abstract
The urban environment plays an important role for the mental health of residents. Researchers mainly focus on residential neighbourhoods as exposure context, leaving aside the effects of non-residential environments. In order to consider the daily experience of urban spaces, a people-based approach focused on mobility paths is needed. Applying this approach, (1) this study investigated whether individuals' momentary mental well-being is related to the exposure to micro-urban spaces along the daily mobility paths within the two previous hours; (2) it explored whether these associations differ when environmental exposures are defined considering all location points or only outdoor location points; and (3) it examined the associations between the types of activity and mobility and momentary depressive symptomatology. Using a geographically-explicit ecological momentary assessment approach (GEMA), momentary depressive symptomatology of 216 older adults living in the Ile-de-France region was assessed using smartphone surveys, while participants were tracked with a GPS receiver and an accelerometer for seven days. Exposure to multiple elements of the streetscape was computed within a street network buffer of 25 m of each GPS point over the two hours prior to the questionnaire. Mobility and activity type were documented from a GPS-based mobility survey. We estimated Bayesian generalized mixed effect models with random effects at the individual and day levels and took into account time autocorrelation. We also estimated fixed effects. A better momentary mental wellbeing was observed when residents performed leisure activities or were involved in active mobility and when they were exposed to walkable areas (pedestrian dedicated paths, open spaces, parks and green areas), water elements, and commerce, leisure and cultural attractors over the previous two hours. These relationships were stronger when exposures were defined based only on outdoor location points rather than all location points, and when we considered within-individual differences compared to between-individual differences.
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Affiliation(s)
- Giovanna Fancello
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France.
| | - Julie Vallée
- UMR 8504 Géographie-cités (CNRS, Université Paris 1 Panthéon-Sorbonne, Université Paris Cité, EHESS), France
| | - Cédric Sueur
- UMR 7178 (CNRS, Unistra, Institut Pluridisciplinaire Hubert Curien), France; Anthropolab, ETHICS - EA 7446, Catholic University of Lille, Lille, France
| | - Frank J van Lenthe
- Department of Public Health, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, Netherlands
| | - Yan Kestens
- Montreal Université, École de santé publique - Département de médecine sociale et preventive, Canada
| | - Andrea Montanari
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France
| | - Basile Chaix
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France
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Persons JB, Marker CD, Bailey EN. Changes in affective and cognitive distortion symptoms of depression are reciprocally related during cognitive behavior therapy. Behav Res Ther 2023; 166:104338. [PMID: 37270956 DOI: 10.1016/j.brat.2023.104338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
We tested the predictions from Beck's cognitive theory that change in cognitive distortions precedes and predicts change in affective symptoms of depression, and his secondary prediction that change in affective symptoms precedes and predicts change in cognitive distortions during the course of cognitive behavior therapy (CBT; Beck, 1963). We used bivariate latent difference score modeling to examine change in affective and cognitive distortion symptoms of depression over time in a sample of 1402 outpatients who received naturalistic CBT in a private practice setting. Patients completed the Beck Depression Inventory (BDI) at each therapy session to monitor their progress in treatment. We selected items from the BDI to create measures of affective and cognitive distortion symptoms that allowed us to assess change in those phenomena over the course of treatment. We examined BDI data from up to 12 sessions of treatment for each patient. As predicted by Beck's theory, we found that change in cognitive distortion symptoms preceded and predicted change in affective symptoms of depression, and that change in affective symptoms preceded and predicted change in cognitive distortion symptoms. Both effects were small in size. These findings support the notion that change in affective and cognitive distortion symptoms of depression each precedes and predicts the other - that is, they are reciprocal in nature during cognitive behavior therapy. We discuss implications of our findings for the nature of the change process in CBT.
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Affiliation(s)
- Jacqueline B Persons
- Oakland Cognitive Behavior Therapy Center, USA; University of California, Berkeley, USA.
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Rosen FN, LaFreniere LS. Savoring, worry, and positive emotion duration in generalized anxiety disorder: Assessment and interventional experiment. J Anxiety Disord 2023; 97:102724. [PMID: 37207556 DOI: 10.1016/j.janxdis.2023.102724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 04/14/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND Intentional attempts to savor positive emotions may be infrequent in Generalized Anxiety Disorder (GAD) due to avoidance of emotional contrasts. Yet purposeful enjoyment may help reduce worry and increase wellbeing in GAD. We sought to explore 1) the frequency, intensity, and duration of positive emotions from savoring in GAD and 2) its effect on pre-existing worry. METHOD The same 139 participants participated in two studies. They first took baseline measures. After, they were explicitly taught about savoring practices. In study 1, all participants were instructed to savor a photograph and video, timing and rating their emotion. Then in study 2, participants underwent a worry induction followed by an interventional experiment. In a savoring condition, participants were instructed to savor a personally-chosen enjoyable video. In a control condition, participants watched an emotionally neutral video. RESULTS Participants who met DSM-5 criteria for GAD had significantly lower scores on naturalistic savoring via self-report than those without GAD. Yet when explicitly taught and directed to savor in study 1, there were no differences between those with and without GAD in positive emotion duration and intensity. In study 2, longitudinal linear mixed models demonstrated that savoring after a worry induction significantly decreased worry, decreased anxiety, and increased positive emotions to greater degrees than the control task. These changes did not differ between diagnostic groups. All analyses controlled for depression symptoms. CONCLUSION Although persons with GAD tend to savor less in daily life than those without GAD, intentional savoring may decrease worry and increase positive emotion for both groups.
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Affiliation(s)
- Felicia N Rosen
- Skidmore College, 151 Tisch Learning Center, 815 N. Broadway, Saratoga Springs, NY 12866, USA
| | - Lucas S LaFreniere
- Skidmore College, 151 Tisch Learning Center, 815 N. Broadway, Saratoga Springs, NY 12866, USA.
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Correll CU, Solmi M, Cortese S, Fava M, Højlund M, Kraemer HC, McIntyre RS, Pine DS, Schneider LS, Kane JM. The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials, and of some current trends aiming to de-risk trial programmes of novel agents. World Psychiatry 2023; 22:48-74. [PMID: 36640403 PMCID: PMC9840514 DOI: 10.1002/wps.21056] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/14/2022] [Indexed: 01/15/2023] Open
Abstract
Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.
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Affiliation(s)
- Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Marco Solmi
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, Ottawa Hospital, Ottawa, ON, Canada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program, University of Ottawa, Ottawa, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA
| | - Maurizio Fava
- Depression Clinical and Research Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mikkel Højlund
- Department of Public Health, Clinical Pharmacology, Pharmacy and Environmental Medicine, University of Southern Denmark, Odense, Denmark
- Mental Health Services in the Region of Southern Denmark, Department of Psychiatry Aabenraa, Aabenraa, Denmark
| | - Helena C Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Cupertino, CA, USA
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology, University of Toronto, Toronto, ON, Canada
- Brain and Cognition Discovery Foundation, Toronto, ON, Canada
| | - Daniel S Pine
- Section on Developmental Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA
| | - Lon S Schneider
- Department of Psychiatry and Behavioral Sciences, and Department of Neurology, Keck School of Medicine, and L. Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - John M Kane
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
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21
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Buck B, Wingerson M, Tauscher JS, Enkema M, Wang W, Campbell AT, Ben-Zeev D. Using Smartphones to Identify Momentary Characteristics of Persecutory Ideation Associated With Functional Disability. SCHIZOPHRENIA BULLETIN OPEN 2023; 4:sgad021. [PMID: 37601285 PMCID: PMC10439515 DOI: 10.1093/schizbullopen/sgad021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Objectives Though often a feature of schizophrenia-spectrum disorders, persecutory ideation (PI) is also common in other psychiatric disorders as well as among individuals who are otherwise healthy. Emerging technologies allow for a more thorough understanding of the momentary phenomenological characteristics that determine whether PI leads to significant distress and dysfunction. This study aims to identify the momentary phenomenological features of PI associated with distress, dysfunction, and need for clinical care. Methods A total of 231 individuals with at least moderate PI from 43 US states participated in a study involving 30 days of data collection using a smartphone data collection system combining ecological momentary assessment and passive sensors, wherein they reported on occurrence of PI as well as related appraisals, responses, and cooccurring states. Most (N = 120, 51.9%) participants reported never having received treatment for their PI, while 50 participants had received inpatient treatment (21.6%), and 60 (26.4%) had received outpatient care only. Results Individuals with greater functional disability did not differ in PI frequency but were more likely at the moment to describe threats as important to them, to ruminate about those threats, to experience distress related to them, and to change their behavior in response. Groups based on treatment-seeking patterns largely did not differ in baseline measures or momentary phenomenology of PI as assessed by self-report or passive sensors. Conclusions Smartphone data collection allows for granular assessment of PI-related phenomena. Functional disability is associated with differences in appraisals of and responses to PI at the moment.
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Affiliation(s)
- Benjamin Buck
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Mary Wingerson
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Justin S Tauscher
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Matthew Enkema
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Weichen Wang
- Department of Computer Science, Dartmouth College, Hanover, NH
| | | | - Dror Ben-Zeev
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
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22
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Holmgren JG, Morrow A, Coffee AK, Nahod PM, Santora SH, Schwartz B, Stiegmann RA, Zanetti CA. Utilizing digital predictive biomarkers to identify Veteran suicide risk. Front Digit Health 2022; 4:913590. [PMID: 36329831 PMCID: PMC9624222 DOI: 10.3389/fdgth.2022.913590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/12/2022] [Indexed: 12/02/2022] Open
Abstract
Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.
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Affiliation(s)
- Jackson G. Holmgren
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States,Correspondence: Jackson G. Holmgren
| | - Adelene Morrow
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Ali K. Coffee
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Paige M. Nahod
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Samantha H. Santora
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Brian Schwartz
- Department of Medical Humanities, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Regan A. Stiegmann
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Flight Medicine, US Air Force Academy, Colorado Springs, CO, United States
| | - Cole A. Zanetti
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Chief Health Informatics Officer, Ralph H Johnson VA Health System, Charleston, SC, United States
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23
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Alexander JD, Zhou Y, Freis SM, Friedman NP, Vrieze SI. Individual differences in adolescent and young adult daily mobility patterns and their relationships to big five personality traits: a behavioral genetic analysis. JOURNAL OF RESEARCH IN PERSONALITY 2022; 100:104277. [PMID: 35991708 PMCID: PMC9384572 DOI: 10.1016/j.jrp.2022.104277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Youth behavior changes and their relationships to personality have generally been investigated using self-report studies, which are subject to reporting biases and confounding variables. Supplementing these with objective measures, like GPS location data, and twin-based research designs, which help control for confounding genetic and environmental influences, may allow for more rigorous, causally informative research on adolescent behavior patterns. To investigate this possibility, this study aimed to (1) investigate whether behavior changes during the transition from adolescence to emerging adulthood are evident in changing mobility patterns, (2) estimate the influence of adolescent personality on mobility patterns, and (3) estimate genetic and environmental influences on mobility, personality, and the relationship between them. Twins aged Fourteen to twenty-two (N=709, 55% female) provided a baseline personality measure, the Big Five Inventory, and multiple years of smartphone GPS data from June 2016 - December 2019. Mobility, as measured by daily locations visited and distance travelled, was found via mixed effects models to increase during adolescence before declining slightly in emerging adulthood. Mobility was positively associated with Extraversion and Conscientiousness (r of 0.17 - 0.25, r of 0.10 - 0.16) and negatively with Openness (r of -0.11 - -0.13). ACE models found large genetic (A = 0.56 - 0.81) and small-moderate environmental (C of 0.12 - 0.28, E of 0.07 - 0.15) influences on mobility. A and E influences were highly shared across mobility measures (rg = 0.70, re= 0.58). Associations between mobility and personality were partially explained by mutual genetic influences (rg of -0.27 - 0.53). Results show that as autonomy increases during adolescence and emerging adulthood, we see corresponding increases in youth mobility. Furthermore, the heritability of mobility patterns and their relationship to personality demonstrate that mobility patterns are informative, psychologically meaningful behaviors worthy of continued interest in psychology.
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Affiliation(s)
| | - Yuan Zhou
- Department of Psychology, University of Minnesota
| | - Samantha M. Freis
- Institute for Behavioral Genetics, University of Colorado, Boulder
- Department of Psychology and Neuroscience, University of Colorado, Boulder
| | - Naomi P. Friedman
- Institute for Behavioral Genetics, University of Colorado, Boulder
- Department of Psychology and Neuroscience, University of Colorado, Boulder
| | - Scott I. Vrieze
- Department of Psychology, University of Minnesota
- Department of Psychology and Neuroscience, University of Colorado, Boulder
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24
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Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7842304. [PMID: 35845877 PMCID: PMC9279049 DOI: 10.1155/2022/7842304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/24/2022] [Accepted: 06/08/2022] [Indexed: 12/04/2022]
Abstract
The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students' MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.
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25
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Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Front Psychiatry 2022; 13:873392. [PMID: 35757212 PMCID: PMC9225201 DOI: 10.3389/fpsyt.2022.873392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.
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