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Yu X, Cheng P, Yang Z, Fan H, Wang Q, Xu J, Zhu H, Gao Q. A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study. J Psychiatr Res 2024; 177:420-428. [PMID: 39098285 DOI: 10.1016/j.jpsychires.2024.07.041] [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: 05/08/2024] [Revised: 07/22/2024] [Accepted: 07/27/2024] [Indexed: 08/06/2024]
Abstract
BACKGROUND Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. METHODS Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. RESULTS Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86-0.98), single (HR = 0.11, 95% CI: 0.02-0.68), having children (one child, HR = 0.07, 95%CI: 0.01-0.87; more than one child, HR = 0.33, 95%CI: 0.04-2.48), living in dormitory (HR = 0.25, 95%CI: 0.08-0.77), non-student (employee, HR = 0.24, 95% CI: 0.07-0.88; non-employee, HR = 0.09, 95% CI: 0.02-0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82-0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23-22.48), living alone (HR = 4.37, 95% CI: 1.60-11.94), having suicide history (HR = 2.51, 95% CI: 1.06-5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00-1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00-1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. CONCLUSIONS A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.
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Affiliation(s)
- Xinyi Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China
| | - Peixia Cheng
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, PR China; Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, PR China
| | - Zexi Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China
| | - Hua Fan
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Qian Wang
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Jiaying Xu
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Huiping Zhu
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, PR China; Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, PR China
| | - Qi Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China.
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Fu L, Cai M, Zhao Y, Zhang Z, Qian Q, Xue H, Chen Y, Sun Z, Zhao Q, Wang S, Wang C, Wang W, Jiang Y, Tian Y, Ma J, Guo W, Liu F. Twenty-five years of research on resting-state fMRI of major depressive disorder: A bibliometric analysis of hotspots, nodes, bursts, and trends. Heliyon 2024; 10:e33833. [PMID: 39050435 PMCID: PMC11266997 DOI: 10.1016/j.heliyon.2024.e33833] [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: 12/15/2023] [Revised: 06/15/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.
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Affiliation(s)
- Linhan Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qian Qian
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Chunyang Wang
- Department of Scientific Research, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenqin Wang
- School of Mathematical Sciences, Tianjin Polytechnic University, Tianjin, 300387, China
| | - Yifan Jiang
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Yuxuan Tian
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
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Lee JK, Kim MH, Hwang S, Lee KJ, Park JY, Shin T, Lim HS, Urtnasan E, Chung MK, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024; 14:e073290. [PMID: 38871664 DOI: 10.1136/bmjopen-2023-073290] [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] [Indexed: 06/15/2024] Open
Abstract
INTRODUCTION Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems. METHODS AND ANALYSIS The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning. ETHICS AND DISSEMINATION This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.
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Affiliation(s)
- Jin-Kyung Lee
- Yonsei University - Mirae Campus, Wonju, Gangwon-do, Republic of Korea
| | - Min-Hyuk Kim
- Yonsei University Wonju College of Medicine, Wonju, Gangwon, Republic of Korea
| | - Sangwon Hwang
- Yonsei University Wonju College of Medicine, Wonju, Gangwon, Republic of Korea
| | - Kyoung-Joung Lee
- Yonsei University - Mirae Campus, Wonju, Gangwon-do, Republic of Korea
| | - Ji Young Park
- Sangji University, Wonju, Gangwon-do, Republic of Korea
| | - Taeksoo Shin
- Yonsei University - Mirae Campus, Wonju, Gangwon-do, Republic of Korea
| | - Hyo-Sang Lim
- Yonsei University - Mirae Campus, Wonju, Gangwon-do, Republic of Korea
| | | | - Moo-Kwon Chung
- Yonsei University - Mirae Campus, Wonju, Gangwon-do, Republic of Korea
| | - Jinhee Lee
- Yonsei University Wonju College of Medicine, Wonju, Gangwon, Republic of Korea
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Leaning IE, Ikani N, Savage HS, Leow A, Beckmann C, Ruhé HG, Marquand AF. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neurosci Biobehav Rev 2024; 158:105541. [PMID: 38215802 DOI: 10.1016/j.neubiorev.2024.105541] [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: 09/20/2023] [Revised: 12/23/2023] [Accepted: 01/06/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. METHODS We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. RESULTS Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. DISCUSSION Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.
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Affiliation(s)
- Imogen E Leaning
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.
| | - Nessa Ikani
- Department of Developmental Psychology, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, the Netherlands.
| | - Hannah S Savage
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Alex Leow
- Department of Psychiatry, Department of Biomedical Engineering and Department of Computer Science, University of Illinois Chicago, Chicago, United States
| | - Christian Beckmann
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Henricus G Ruhé
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Khoo LS, Lim MK, Chong CY, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:348. [PMID: 38257440 PMCID: PMC10820860 DOI: 10.3390/s24020348] [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: 10/25/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.
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Affiliation(s)
- Lin Sze Khoo
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Mei Kuan Lim
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Chun Yong Chong
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Roisin McNaney
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
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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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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: 0] [Impact Index Per Article: 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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El Dahr Y, Perquier F, Moloney M, Woo G, Dobrin-De Grace R, Carvalho D, Addario N, Cameron EE, Roos LE, Szatmari P, Aitken M. Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study. JMIR Form Res 2023; 7:e42916. [PMID: 37943593 PMCID: PMC10667976 DOI: 10.2196/42916] [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: 09/23/2022] [Revised: 05/31/2023] [Accepted: 09/20/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases, collect more ecologically valid data, and increase our understanding of dynamic associations between variables. EMA is typically administered using an application that is downloaded on participants' devices, which presents cost and privacy concerns that may limit its use. Research Electronic Data Capture (REDCap), a web-based survey application freely available to nonprofit organizations, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap, especially for those who are new to using REDCap or lack advanced programming expertise. OBJECTIVE We provide an example of a simplified EMA setup in REDCap. This study aims to demonstrate the feasibility of this approach. We provide information on survey completion and user behavior in a sample of parents and children recruited across Canada. METHODS We recruited 66 parents and their children (aged 9-13 years old) from an existing longitudinal cohort study to participate in a study on risk and protective factors for children's mental health. Parents received survey prompts (morning and evening) by email or SMS text message for 14 days, twice daily. Each survey prompt contained 2 sections, one for parents and one for children to complete. RESULTS The completion rates were good (mean 82%, SD 8%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. Children were available to respond to their own survey questions most of the time (in 1134/1498, 75.7% of surveys submitted). The number of assessments submitted was significantly higher, and response times were significantly faster among participants who selected SMS text message survey notifications compared to email survey notifications. The average response time was 47.0 minutes after the initial survey notification, and the use of reminder messages increased survey completion. CONCLUSIONS Our results support the feasibility of using REDCap for EMA studies with parents and children. REDCap also has features that can accommodate EMA studies by recruiting participants across multiple time zones and providing different survey delivery methods. Offering the option of SMS text message survey notifications and reminders may be an important way to increase completion rates and the timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost or privacy are current barriers. Researchers should weigh these benefits with the potential limitations of REDCap and this design, including staff time to set up, monitor, and clean the data outputs of the project.
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Affiliation(s)
- Yola El Dahr
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Florence Perquier
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Madison Moloney
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Guyyunge Woo
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Faculty of Arts & Science, University of Toronto, Toronto, ON, Canada
| | - Roksana Dobrin-De Grace
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychology, Toronto Metropolitan University, Toronto, ON, Canada
| | - Daniela Carvalho
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Nicole Addario
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Emily E Cameron
- Department of Psychology, University of Manitoba, Winnipeg, MB, Canada
| | - Leslie E Roos
- Department of Psychology, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Institute of Manitoba, Winnipeg, MB, Canada
| | - Peter Szatmari
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Madison Aitken
- Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychology, York University, Toronto, ON, Canada
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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10
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Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. NPJ Digit Med 2023; 6:84. [PMID: 37147384 PMCID: PMC10163239 DOI: 10.1038/s41746-023-00828-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/19/2023] [Indexed: 05/07/2023] Open
Abstract
Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
- College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar
| | - Farag Shuweihdi
- School of Medicine, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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11
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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12
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Abd-Alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. J Med Internet Res 2023; 25:e42672. [PMID: 36656625 PMCID: PMC9896355 DOI: 10.2196/42672] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/18/2022] [Accepted: 12/11/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. OBJECTIVE This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. METHODS We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. RESULTS Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. CONCLUSIONS Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Kerstin Denecke
- Institute for Medical Informatics, Bern University of Applied Science, Bern, Switzerland
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute, Hamad bin Khalifa University, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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13
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Ates HC, Nguyen PQ, Gonzalez-Macia L, Morales-Narváez E, Güder F, Collins JJ, Dincer C. End-to-end design of wearable sensors. NATURE REVIEWS. MATERIALS 2022; 7:887-907. [PMID: 35910814 PMCID: PMC9306444 DOI: 10.1038/s41578-022-00460-x] [Citation(s) in RCA: 201] [Impact Index Per Article: 100.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 05/03/2023]
Abstract
Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.
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Affiliation(s)
- H. Ceren Ates
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | - Peter Q. Nguyen
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | | | - Eden Morales-Narváez
- Biophotonic Nanosensors Laboratory, Centro de Investigaciones en Óptica, León, Mexico
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, UK
| | - James J. Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
- Institute of Medical Engineering & Science, Department of Biological Engineering, MIT, Cambridge, MA USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Can Dincer
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
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14
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Dlima SD, Shevade S, Menezes SR, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e39618. [PMID: 38935947 PMCID: PMC11135220 DOI: 10.2196/39618] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 06/29/2024]
Abstract
BACKGROUND Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. OBJECTIVE The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. METHODS We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. RESULTS A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. CONCLUSIONS Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.
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15
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Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022; 22:421. [PMID: 35733121 PMCID: PMC9214685 DOI: 10.1186/s12888-022-04013-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 05/17/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively. AIMS Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology. RESULTS Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales. CONCLUSIONS Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.
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Affiliation(s)
- Daniel Zarate
- Institute for Health and Sport, Victoria University, Melbourne, Australia.
| | - Vasileios Stavropoulos
- grid.1019.90000 0001 0396 9544Institute for Health and Sport, Victoria University, Melbourne, Australia ,grid.5216.00000 0001 2155 0800Department of Psychology, University of Athens, Athens, Greece
| | - Michelle Ball
- grid.1019.90000 0001 0396 9544Institute for Health and Sport, Victoria University, Melbourne, Australia
| | - Gabriel de Sena Collier
- grid.1019.90000 0001 0396 9544Institute for Health and Sport, Victoria University, Melbourne, Australia
| | - Nicholas C. Jacobson
- grid.254880.30000 0001 2179 2404Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Hanover, USA ,grid.254880.30000 0001 2179 2404Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, USA ,grid.254880.30000 0001 2179 2404Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, USA ,grid.254880.30000 0001 2179 2404Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, USA
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16
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Abstract
BACKGROUND Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently. OBJECTIVE We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research. METHODS We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported. RESULTS Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1). CONCLUSION There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.
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Affiliation(s)
- Alex Z R Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore
| | - Melvyn W B Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore.,National Addictions Management Service, Institute of Mental Health, Singapore City, Singapore
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17
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Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Front Psychiatry 2022; 13:895860. [PMID: 35958638 PMCID: PMC9360315 DOI: 10.3389/fpsyt.2022.895860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. OBJECTIVE The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. METHODS We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. RESULTS Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). CONCLUSION The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
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Affiliation(s)
- Redwan Maatoug
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Antoine Oudin
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Vladimir Adrien
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Bertrand Saudreau
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Département de Psychiatrie de l'Enfant et de l'Adolescent, Assistance Publique des Hôpitaux de Paris (AP-HP), Sorbonne Université, Paris, France
| | - Olivier Bonnot
- CHU de Nantes, Department of Child and Adolescent Psychiatry, Nantes, France.,Pays de la Loire Psychology Laboratory, Nantes, France
| | - Bruno Millet
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Florian Ferreri
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Stephane Mouchabac
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Alexis Bourla
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,INICEA Korian, Paris, France
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18
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Mouchabac S, Maatoug R, Conejero I, Adrien V, Bonnot O, Millet B, Ferreri F, Bourla A. In Search of Digital Dopamine: How Apps Can Motivate Depressed Patients, a Review and Conceptual Analysis. Brain Sci 2021; 11:1454. [PMID: 34827453 PMCID: PMC8615613 DOI: 10.3390/brainsci11111454] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/26/2021] [Accepted: 10/28/2021] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION Depression is highly prevalent and causes considerable suffering and disease burden despite the existence of wide-ranging treatment options. Momentary assessment is a promising tool in the management of psychiatric disorders, and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. Treating the motivational and hedonic aspects of depression is a key target reported in the literature, but it is time-consuming in terms of human resources. Digital Applications offer a major opportunity to indirectly regulate impaired motivational circuits through dopaminergic pathways. OBJECTIVE The main objective of this review was twofold: (1) propose a conceptual and critical review of the literature regarding the theoretical and technical principles of digital applications focused on motivation in depression, activating dopamine, and (2) suggest recommendations on the relevance of using these tools and their potential place in the treatment of depression. MATERIAL AND METHODS A search for words related to "dopamine", "depression", "smartphone apps", "digital phenotype" has been conducted on PubMed. RESULTS Ecological momentary interventions (EMIs) differ from traditional treatments by providing relevant, useful intervention strategies in the context of people's daily lives. EMIs triggered by ecological momentary assessment (EMA) are called "Smart-EMI". Smart-EMIs can mimic the "dopamine reward system" if the intervention is tailored for motivation or hedonic enhancement, and it has been shown that a simple reward (such as a digital badge) can increase motivation. DISCUSSION The various studies presented support the potential interest of digital health in effectively motivating depressed patients to adopt therapeutic activation behaviors. Finding effective ways to integrate EMIs with human-provided therapeutic support may ultimately yield the most efficient and effective intervention method. This approach could be a helpful tool to increase adherence and motivation. CONCLUSION Smartphone apps can motivate depressed patients by enhancing dopamine, offering the opportunity to enhance motivation and behavioral changes, although longer term studies are still needed.
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Affiliation(s)
- Stephane Mouchabac
- Department of Psychiatry, Hôpital Saint-Antoine, AP-HP, Sorbonne Université, 75012 Paris, France
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Redwan Maatoug
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
- Sorbonne Université, AP-HP, Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau, ICM, 75013 Paris, France
| | - Ismael Conejero
- Department of Psychiatry, CHU Nîmes, University of Montpellier, 30090 Nîmes, France
- Inserm, Unit 1061 "Neuropsychiatry: Epidemiological and Clinical Research", 34000 Montpellier, France
| | - Vladimir Adrien
- Department of Psychiatry, Hôpital Saint-Antoine, AP-HP, Sorbonne Université, 75012 Paris, France
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Olivier Bonnot
- CHU de Nantes, Department of Child and Adolescent Psychiatry, 44093 Nantes, France
- Pays de la Loire Psychology Laboratory, EA 4638, 44000 Nantes, France
| | - Bruno Millet
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
- Sorbonne Université, AP-HP, Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau, ICM, 75013 Paris, France
| | - Florian Ferreri
- Department of Psychiatry, Hôpital Saint-Antoine, AP-HP, Sorbonne Université, 75012 Paris, France
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Alexis Bourla
- Department of Psychiatry, Hôpital Saint-Antoine, AP-HP, Sorbonne Université, 75012 Paris, France
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, 75013 Paris, France
- Jeanne d'Arc Hospital, INICEA Korian, 94160 Saint-Mandé, France
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Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatr Dis Treat 2021; 17:3415-3430. [PMID: 34848962 PMCID: PMC8612669 DOI: 10.2147/ndt.s339412] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/02/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. PATIENTS AND METHODS We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. RESULTS Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. CONCLUSION Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.
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Affiliation(s)
- Sunhae Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Korea
| | - Kounseok Lee
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Korea
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