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Zafar F, Fakhare Alam L, Vivas RR, Wang J, Whei SJ, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024; 16:e56472. [PMID: 38638735 PMCID: PMC11025697 DOI: 10.7759/cureus.56472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.
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Affiliation(s)
- Fabeha Zafar
- Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK
| | | | - Rafael R Vivas
- Nutrition, Food and Exercise Sciences, Florida State University College of Human Sciences, Tallahassee, USA
| | - Jada Wang
- Medicine, St. George's University, Brooklyn, USA
| | - See Jia Whei
- Internal Medicine, Sriwijaya University, Palembang, IDN
| | | | | | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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2
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Chow PI, Roller DG, Boukhechba M, Shaffer KM, Ritterband LM, Reilley MJ, Le TM, Kunk PR, Bauer TW, Gioeli DG. Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework. Internet Interv 2023; 34:100644. [PMID: 38099095 PMCID: PMC10719510 DOI: 10.1016/j.invent.2023.100644] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/28/2023] [Accepted: 07/07/2023] [Indexed: 12/17/2023] Open
Abstract
As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.
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Affiliation(s)
- Philip I. Chow
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | - Devin G. Roller
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, USA
| | - Mehdi Boukhechba
- Department of Engineering Systems and Environment, University of Virginia, USA
- Janssen Pharmaceutical Companies of Johnson & Johnson, USA
| | - Kelly M. Shaffer
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
| | - Lee M. Ritterband
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | | | - Tri M. Le
- Department of Medicine, University of Virginia, USA
| | - Paul R. Kunk
- Department of Medicine, University of Virginia, USA
| | - Todd W. Bauer
- Department of Surgery, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | - Daniel G. Gioeli
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
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Hornstein S, Zantvoort K, Lueken U, Funk B, Hilbert K. Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms. Front Digit Health 2023; 5:1170002. [PMID: 37283721 PMCID: PMC10239832 DOI: 10.3389/fdgth.2023.1170002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
Introduction Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has. Methods We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals. Results Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention. Discussion We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed. Systematic Review Registration Identifier: CRD42022357408.
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Affiliation(s)
- Silvan Hornstein
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kirsten Zantvoort
- Institute of Information Systems, Leuphana University, Lueneburg, Germany
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lueneburg, Germany
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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Zlatintsi A, Filntisis PP, Garoufis C, Efthymiou N, Maragos P, Menychtas A, Maglogiannis I, Tsanakas P, Sounapoglou T, Kalisperakis E, Karantinos T, Lazaridi M, Garyfalli V, Mantas A, Mantonakis L, Smyrnis N. E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures. SENSORS (BASEL, SWITZERLAND) 2022; 22:7544. [PMID: 36236643 PMCID: PMC9572170 DOI: 10.3390/s22197544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.
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Affiliation(s)
- Athanasia Zlatintsi
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | | | - Christos Garoufis
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Niki Efthymiou
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Petros Maragos
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Andreas Menychtas
- Department of Digital Systems, University of Piraeus, 185 34 Pireas, Greece
| | - Ilias Maglogiannis
- Department of Digital Systems, University of Piraeus, 185 34 Pireas, Greece
| | - Panayiotis Tsanakas
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | | | - Emmanouil Kalisperakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Thomas Karantinos
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
| | - Marina Lazaridi
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Vasiliki Garyfalli
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Asimakis Mantas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
| | - Leonidas Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Nikolaos Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 2nd Department of Psychiatry, University General Hospital “ATTIKON”, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece
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Together they shall not fade away: Opportunities and challenges of self-tracking for dementia care. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.103024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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COOK DIANEJ, STRICKLAND MIRANDA, SCHMITTER-EDGECOMBE MAUREEN. Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2022; 3:33. [PMID: 35815157 PMCID: PMC9268550 DOI: 10.1145/3508020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 12/01/2021] [Indexed: 06/15/2023]
Abstract
In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.
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Affiliation(s)
- DIANE J. COOK
- School of Electrical Engineering and Computer Science.
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Ji M, Xie W, Huang R, Qian X. Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph181910048. [PMID: 34639348 PMCID: PMC8507671 DOI: 10.3390/ijerph181910048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/10/2021] [Accepted: 09/16/2021] [Indexed: 11/16/2022]
Abstract
Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.
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Affiliation(s)
- Meng Ji
- School of Languages and Cultures, University of Sydney, Sydney 2006, Australia;
- Correspondence:
| | - Wenxiu Xie
- Department of Computer Science, City University of Hong Kong, Hong Kong 518057, China;
| | - Riliu Huang
- School of Languages and Cultures, University of Sydney, Sydney 2006, Australia;
| | - Xiaobo Qian
- School of Computer Science, South China Normal University, Guangzhou 510631, China;
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8
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Lai J, Rahmani A, Yunusova A, Rivera AP, Labbaf S, Hu S, Dutt N, Jain R, Borelli JL. Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study. JMIR Form Res 2021; 5:e26186. [PMID: 33882022 PMCID: PMC8115397 DOI: 10.2196/26186] [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] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/01/2021] [Accepted: 04/13/2021] [Indexed: 01/27/2023] Open
Abstract
Background The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things. Objective To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September. Methods The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual’s psychological well-being across the pandemic. Results Over the course of the pandemic, Lee’s depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee’s positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=–0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=–0.58, P=.03) and diurnal beats per minute (r=–0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors. Conclusions We observed more pandemic concerns in April and noticed other contextual events relating to this individual’s well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health.
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Affiliation(s)
- Jocelyn Lai
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Amir Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,School of Nursing, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Asal Yunusova
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Alexander P Rivera
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Sirui Hu
- Department of Statistics, University of California, Irvine, Irvine, CA, United States.,Department of Economics, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States.,Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.,Department of Cognitive Science, Irvine, CA, United States
| | - Ramesh Jain
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Jessica L Borelli
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
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Escobar-Viera CG, Cernuzzi LC, Miller RS, Rodríguez-Marín HJ, Vieta E, González Toñánez M, Marsch LA, Hidalgo-Mazzei D. Feasibility of mHealth interventions for depressive symptoms in Latin America: a systematic review. Int Rev Psychiatry 2021; 33:300-311. [PMID: 34102945 PMCID: PMC8318676 DOI: 10.1080/09540261.2021.1887822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Depression is a prevalent disorder and leading cause of disability in Latin America, where the mental health treatment gap is still above 50%. We sought to synthesise and assess the quality of the evidence on the feasibility of mHealth-based interventions for depression in Latin America. We conducted a literature search of studies published in 2007 and after using four electronic databases. We included peer-reviewed articles, in English, Spanish or Portuguese, that evaluated interventions for depressive symptoms. Two authors independently extracted data using forms developed a priori. We assessed appropriateness of reporting utilising the CONSORT checklist for feasibility trials. Eight manuscripts were included for full data extraction. Appropriate reporting varied greatly. Most (n = 6, 75%) of studies were conducted in primary care settings and sought to deliver psychoeducation or behaviour change interventions for depressive symptoms. We found great heterogeneity in the assessment of feasibility. Two studies used comparator conditions. mHealth research for depression in Latin America is scarce. Included studies showed some feasibility despite methodological inconsistencies. Given the dire need for evidence-based mental health interventions in this region, governments and stakeholders must continue promoting and funding research tailored to cultural and population characteristics with subsequent pragmatic clinical trials.
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Affiliation(s)
- César G. Escobar-Viera
- Center for Research on Behavioral Health, Media, and Technology, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Luca C. Cernuzzi
- Facultad de Ciencias y Tecnología, Universidad Católica Nuestra Señora de la Asunción, Asunción, Paraguay
| | - Rebekah S. Miller
- Health Sciences Library System, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hugo J. Rodríguez-Marín
- Dirección de Salud Mental, Ministerio de Salud Pública y Bienestar Social, Asunción, Paraguay;,Facultad de Ciencias de la Salud, Universidad Católica Nuestra Señora de la Asunción, Asunción, Paraguay
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Magalí González Toñánez
- Facultad de Ciencias y Tecnología, Universidad Católica Nuestra Señora de la Asunción, Asunción, Paraguay
| | - Lisa A. Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
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Wu C, Barczyk AN, Craddock RC, Harari GM, Thomaz E, Shumake JD, Beevers CG, Gosling SD, Schnyer DM. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. ACTA ACUST UNITED AC 2021. [DOI: 10.1016/j.smhl.2021.100180] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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11
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Yunusova A, Lai J, Rivera AP, Hu S, Labbaf S, Rahmani AM, Dutt N, Jain RC, Borelli JL. Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study. JMIR Res Protoc 2021; 10:e25775. [PMID: 33513124 PMCID: PMC7927950 DOI: 10.2196/25775] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/26/2020] [Accepted: 01/06/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation. OBJECTIVE In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health. METHODS We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months. RESULTS Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments. CONCLUSIONS Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/25775.
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Affiliation(s)
- Asal Yunusova
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Jocelyn Lai
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Alexander P Rivera
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Sirui Hu
- Department of Economics, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Amir M Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- School of Nursing, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ramesh C Jain
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Jessica L Borelli
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
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12
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Deif R, Salama M. Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments. Front Psychiatry 2021; 12:650318. [PMID: 34045980 PMCID: PMC8144285 DOI: 10.3389/fpsyt.2021.650318] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/13/2021] [Indexed: 11/13/2022] Open
Abstract
Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.
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Affiliation(s)
- Reem Deif
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt
| | - Mohamed Salama
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt.,Faculty of Medicine, Mansoura University, Mansoura, Egypt.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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13
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Lee J, Solomonov N, Banerjee S, Alexopoulos GS, Sirey JA. Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges. Front Psychiatry 2021; 12:732773. [PMID: 34777042 PMCID: PMC8580874 DOI: 10.3389/fpsyt.2021.732773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
Late-life depression is heterogenous and patients vary in disease course over time. Most psychotherapy studies measure activity levels and symptoms solely using self-report scales, administered periodically. These scales may not capture granular changes during treatment. We introduce the potential utility of passive sensing data collected with smartphone to assess fluctuations in daily functioning in real time during psychotherapy for late life depression in elder abuse victims. To our knowledge, this is the first investigation of passive sensing among depressed elder abuse victims. We present data from three victims who received a 9-week intervention as part of a pilot randomized controlled trial and showed a significant decrease in depressive symptoms (50% reduction). Using a smartphone, we tracked participants' daily number of smartphone unlocks, time spent at home, time spent in conversation, and step count over treatment. Independent assessment of depressive symptoms and behavioral activation were collected at intake, Weeks 6 and 9. Data revealed patient-level fluctuations in activity level over treatment, corresponding with self-reported behavioral activation. We demonstrate how passive sensing data could expand our understanding of heterogenous presentations of late-life depression among elder abuse. We illustrate how trajectories of change in activity levels as measured with passive sensing and subjective measures can be tracked concurrently over time. We outline challenges and potential solutions for application of passive sensing data collection in future studies with larger samples using novel advanced statistical modeling, such as artificial intelligence algorithms.
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Affiliation(s)
- Jihui Lee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Nili Solomonov
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
| | - Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - George S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
| | - Jo Anne Sirey
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
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14
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Bakker D, Rickard N. Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission. AUSTRALIAN PSYCHOLOGIST 2020. [DOI: 10.1111/ap.12383] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- David Bakker
- Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Nikki Rickard
- Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Centre for Positive Psychology, University of Melbourne, Melbourne, Victoria, Australia
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15
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Rajula HSR, Manchia M, Carpiniello B, Fanos V. Big data in severe mental illness: the role of electronic monitoring tools and metabolomics. Per Med 2020; 18:75-90. [PMID: 33124507 DOI: 10.2217/pme-2020-0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.
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Affiliation(s)
- Hema Sekhar Reddy Rajula
- Department of Surgical Sciences, Neonatal Intensive Care Unit, Neonatal Pathology & Neonatal Section, University of Cagliari, Cagliari, Italy
| | - Mirko Manchia
- Department of Medical Science & Public Health, Section of Psychiatry, University of Cagliari, Cagliari, Italy.,Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia B3H4R2, Canada.,Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Bernardo Carpiniello
- Department of Medical Science & Public Health, Section of Psychiatry, University of Cagliari, Cagliari, Italy.,Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Vassilios Fanos
- Department of Surgical Sciences, Neonatal Intensive Care Unit, Neonatal Pathology & Neonatal Section, University of Cagliari, Cagliari, Italy
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16
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Berkout OV, Cathey AJ, Berkout DV. Inflexitext: A program assessing psychological inflexibility in unstructured verbal data. JOURNAL OF CONTEXTUAL BEHAVIORAL SCIENCE 2020. [DOI: 10.1016/j.jcbs.2020.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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17
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Going beyond (electronic) patient-reported outcomes: harnessing the benefits of smart technology and ecological momentary assessment in cancer survivorship research. Support Care Cancer 2020; 29:7-10. [PMID: 32844316 PMCID: PMC7686201 DOI: 10.1007/s00520-020-05648-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/22/2020] [Indexed: 12/12/2022]
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18
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Inan OT, Tenaerts P, Prindiville SA, Reynolds HR, Dizon DS, Cooper-Arnold K, Turakhia M, Pletcher MJ, Preston KL, Krumholz HM, Marlin BM, Mandl KD, Klasnja P, Spring B, Iturriaga E, Campo R, Desvigne-Nickens P, Rosenberg Y, Steinhubl SR, Califf RM. Digitizing clinical trials. NPJ Digit Med 2020; 3:101. [PMID: 32821856 PMCID: PMC7395804 DOI: 10.1038/s41746-020-0302-y] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/19/2020] [Indexed: 01/31/2023] Open
Abstract
Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.
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Affiliation(s)
- O. T. Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - P. Tenaerts
- Clinical Trials Transformation Initiative, Duke University, Durham, NC 27708 USA
| | - S. A. Prindiville
- Coordinating Center for Clinical Trials, Office of the Director, National Cancer Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - H. R. Reynolds
- School of Medicine, New York University, New York, NY 10003 USA
| | - D. S. Dizon
- The Lifespan Cancer Institute, Brown University, Providence, RI 02912 USA
| | - K. Cooper-Arnold
- National, Heart, Lung and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
- Present Address: Fortira at AstraZeneca, Gaithersburg, MD 20877 USA
| | - M. Turakhia
- VA Palo Alto Health Care System and the Center for Digital Health, Stanford University, Stanford, CA 94305 USA
| | - M. J. Pletcher
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143 USA
| | - K. L. Preston
- Intramural Research Program of the National Institute on Drug Abuse at the National Institutes of Health, Baltimore, MD 21224 USA
| | - H. M. Krumholz
- The Center for Outcomes Research, Yale New Haven Hospital, Yale University, New Haven, CT 06510 USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510 USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut 06510 USA
| | - B. M. Marlin
- College of Information and Computer Sciences, University of Massachusetts at Amherst, Amherst, MA 01003 USA
| | - K. D. Mandl
- Computational Health Informatics Program at Boston Children’s Hospital, Departments of Biomedical Informatics and Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - P. Klasnja
- School of Information, University of Michigan, Ann Arbor, MI 48109 USA
| | - B. Spring
- Northwestern University Feinberg School of Medicine, Chicago, IL 60611 USA
| | - E. Iturriaga
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - R. Campo
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - P. Desvigne-Nickens
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - Y. Rosenberg
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - S. R. Steinhubl
- Scripps Research Translational Institute, La Jolla, CA 92037 USA
| | - R. M. Califf
- School of Medicine, Duke University, Durham, NC 27710 USA
- Verily Life Sciences and Google Health, South San Francisco, CA 94080 USA
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19
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Becker D, Bremer V, Funk B, Hoogendoorn M, Rocha A, Riper H. Evaluation of a temporal causal model for predicting the mood of clients in an online therapy. EVIDENCE-BASED MENTAL HEALTH 2020; 23:27-33. [PMID: 32046990 PMCID: PMC10231483 DOI: 10.1136/ebmental-2019-300135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 12/15/2022]
Abstract
Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.
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Affiliation(s)
- Dennis Becker
- Institute of Information Systems, Leuphana University of Lüneburg, Luneburg, Germany
| | - Vincent Bremer
- Institute of Information Systems, Leuphana University of Lüneburg, Luneburg, Germany
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University of Lüneburg, Luneburg, Germany
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Artur Rocha
- Centre for Information Systems and Computer Graphics, INESC TEC, Porto, Portugal
| | - Heleen Riper
- Department of Clinical, Neuro- & Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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20
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Maglogiannis I, Zlatintsi A, Menychtas A, Papadimatos D, Filntisis PP, Efthymiou N, Retsinas G, Tsanakas P, Maragos P. An Intelligent Cloud-Based Platform for Effective Monitoring of Patients with Psychotic Disorders. IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY 2020. [PMCID: PMC7256582 DOI: 10.1007/978-3-030-49186-4_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The therapy of patients with psychotic disorders (i.e., bipolar disorder and schizophrenia) could benefit from the constant monitoring of their physiological and motor parameters. In this paper, we present an innovative and advanced cloud based platform that facilitates the effective monitoring of such patients. A commodity smartwatch is used for biosignal and motion data collection at a 24/7 basis. The paper describes the technical details of the implemented application both on the smartwatch and the cloud server side. Technical challenges regarding the upload, the storage and the battery constraints of the smartwatch are also discussed, along with the initial results regarding data visualization and processing.
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21
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Nagano H, Sarashina E, Sparrow W, Mizukami K, Begg R. General Mental Health Is Associated with Gait Asymmetry. SENSORS 2019; 19:s19224908. [PMID: 31717634 PMCID: PMC6891551 DOI: 10.3390/s19224908] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/05/2019] [Accepted: 11/07/2019] [Indexed: 11/16/2022]
Abstract
Wearable sensors are being applied to real-world motion monitoring and the focus of this work is assessing health status and wellbeing. An extensive literature has documented the effects on gait control of impaired physical health, but in this project, the aim was to determine whether emotional states associated with older people's mental health are also associated with walking mechanics. If confirmed, wearable sensors could be used to monitor affective responses. Lower limb gait mechanics of 126 healthy individuals (mean age 66.2 ± 8.38 years) were recorded using a high-speed 3D motion sensing system and they also completed a 12-item mental health status questionnaire (GHQ-12). Mean step width and minimum foot-ground clearance (MFC), indicative of tripping risk, were moderately correlated with GHQ-12. Ageing and variability (SD) of gait parameters were not significantly correlated with GHQ-12. GHQ-12 scores were, however, highly correlated with left-right gait control, indicating that greater gait symmetry was associated with better mental health. Maintaining good mental health with ageing may promote safer gait and wearable sensor technologies could be applied to gait asymmetry monitoring, possibly using a single inertial measurement unit attached to each shoe.
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Affiliation(s)
- Hanatsu Nagano
- Institute for Health and Sport (IHeS), Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia; (H.N.); (W.S.)
| | - Eri Sarashina
- Graduate School of Comprehensive Human Sciences, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8574, Japan; (E.S.); (K.M.)
| | - William Sparrow
- Institute for Health and Sport (IHeS), Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia; (H.N.); (W.S.)
| | - Katsuyoshi Mizukami
- Graduate School of Comprehensive Human Sciences, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8574, Japan; (E.S.); (K.M.)
| | - Rezaul Begg
- Institute for Health and Sport (IHeS), Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia; (H.N.); (W.S.)
- Correspondence: ; Tel.: +61-3-9919-1116
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22
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Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry 2019; 24:1583-1598. [PMID: 30770893 DOI: 10.1038/s41380-019-0365-9] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 01/02/2019] [Accepted: 01/24/2019] [Indexed: 01/03/2023]
Abstract
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them. We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential in this area. We will also comment on an emerging area that so far has been much less well explored: by embedding semantically interpretable computational models of brain dynamics or behavior into a statistical machine learning context, insights into dysfunction beyond mere prediction and classification may be gained. Especially this marriage of computational models with statistical inference may offer insights into neural and behavioral mechanisms that could open completely novel avenues for psychiatric treatment.
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Affiliation(s)
- Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159, Mannheim, Germany.
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159, Mannheim, Germany.,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159, Mannheim, Germany
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23
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Levinson CA, Christian C, Shankar-Ram S, Brosof LC, Williams B. Sensor technology implementation for research, treatment, and assessment of eating disorders. Int J Eat Disord 2019; 52:1176-1180. [PMID: 31190438 DOI: 10.1002/eat.23120] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/23/2019] [Accepted: 05/28/2019] [Indexed: 01/02/2023]
Abstract
Sensor technology has made huge technological advances in the past decade. Many sensor technologies (e.g., wearable wristbands) have been integrated into health research with the ability to substantially improve health outcomes and reduce health care costs. Despite the rapid technological developments in sensor technology, little research has examined sensor technology in eating disorders (EDs). The overarching aim of the current article is to briefly review the literature on sensor technology and health outcomes, including EDs, and discuss several potential ideas for the application of sensor technology in the treatment, assessment, and diagnosis of EDs. We will also present data from a feasibility case study with an ED participant and healthy control providing a brief example of how wearable sensor technology might be implemented in ED research. Overall, we will discuss how sensor technology could be used to improve treatment and assessment of EDs and represents an idea in need of more research in the ED field.
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Affiliation(s)
- Cheri A Levinson
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Caroline Christian
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Shruti Shankar-Ram
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Leigh C Brosof
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Brenna Williams
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky
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24
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Huang Q, Cohen D, Komarzynski S, Li XM, Innominato P, Lévi F, Finkenstädt B. Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data. J R Soc Interface 2019; 15:rsif.2017.0885. [PMID: 29436510 PMCID: PMC5832732 DOI: 10.1098/rsif.2017.0885] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 01/11/2018] [Indexed: 12/22/2022] Open
Abstract
Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates, based on probabilities of transitions between rest and activity, that are interpretable and of interest to circadian research.
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Affiliation(s)
- Qi Huang
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
| | - Dwayne Cohen
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
| | | | - Xiao-Mei Li
- INSERM U935, Hospital Paul Brousse and University Paris-Saclay, Villejuif, 94800, France
| | - Pasquale Innominato
- Medical School, University of Warwick, Coventry, CV4 7AL, UK.,Department of Oncology, North Wales Cancer Treatment Centre, Bodelwyddan, LL18 5UJ, UK
| | - Francis Lévi
- Medical School, University of Warwick, Coventry, CV4 7AL, UK.,INSERM U935, Hospital Paul Brousse and University Paris-Saclay, Villejuif, 94800, France
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25
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Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Ment Health 2019; 6:e9819. [PMID: 30785404 PMCID: PMC6401668 DOI: 10.2196/mental.9819] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 06/30/2018] [Accepted: 12/15/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. OBJECTIVE To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. METHODS A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. RESULTS Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. CONCLUSIONS Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.
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Affiliation(s)
- Jussi Seppälä
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland.,Department of Mental and Substance Use Services, Eksote, Lappeenranta, Finland
| | | | - Timo Jämsä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Jouko Miettunen
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Matti Isohanni
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland
| | - Katya Rubinstein
- The Gertner Institute for Epidemiology and Health Policy Research, Tel Aviv, Israel
| | - Yoram Feldman
- The Gertner Institute for Epidemiology and Health Policy Research, Tel Aviv, Israel
| | - Eva Grasa
- Department of Psychiatry, Biomedical Research Institute Sant Pau (IIB-SANT PAU), Hospital Sant Pau, Barcelona, Spain.,Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.,CIBERSAM, Madrid, Spain
| | - Iluminada Corripio
- Department of Psychiatry, Biomedical Research Institute Sant Pau (IIB-SANT PAU), Hospital Sant Pau, Barcelona, Spain.,Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.,CIBERSAM, Madrid, Spain
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- m-RESIST, Barcelona, Spain
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Reinertsen E, Clifford GD. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas 2018; 39:05TR01. [PMID: 29671754 PMCID: PMC5995114 DOI: 10.1088/1361-6579/aabf64] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.
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Affiliation(s)
- Erik Reinertsen
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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