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Monaco F, Vignapiano A, Piacente M, Farina F, Pagano C, Marenna A, Leo S, Vecchi C, Mancuso C, Prisco V, Iodice D, Auricchio A, Cavaliere R, D'Agosto A, Fornaro M, Solmi M, Corrivetti G, Fasano A. Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder. Front Artif Intell 2024; 7:1366055. [PMID: 38774832 PMCID: PMC11106633 DOI: 10.3389/frai.2024.1366055] [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] [Received: 01/05/2024] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
Background Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.
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Affiliation(s)
- Francesco Monaco
- Department of Mental Health, ASL Salerno, Salerno, Italy
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Annarita Vignapiano
- Department of Mental Health, ASL Salerno, Salerno, Italy
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Martina Piacente
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Federica Farina
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Claudio Pagano
- Innovation Technology e Sviluppo (I.T.Svil), Salerno, Italy
| | - Alessandra Marenna
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Stefano Leo
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Corrado Vecchi
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Carlo Mancuso
- Innovation Technology e Sviluppo (I.T.Svil), Salerno, Italy
| | | | - Davide Iodice
- Department of Mental Health, ASL Salerno, Salerno, Italy
| | | | - Roberto Cavaliere
- Ufficio Trasferimento Tecnologico, Università degli Studi di Cassino e del Lazio Meridionale, Cassino, Italy
| | - Amelia D'Agosto
- Istituto Polidiagnostico D'Agosto & Marino, Nocera Inferiore, Italy
| | - Michele Fornaro
- Department of Neuroscience, Reproductive Sciences, and Odontostomatology, Clinical Section of Psychiatry and Psychology, University School of Medicine Federico II, Naples, Italy
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, The Ottawa Hospital, On Track: The Champlain First Episode Psychosis Program, Ottawa, ON, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Department of Child and Adolescent Psychiatry, Charité—Universitätsmedizin, Berlin, Germany
| | - Giulio Corrivetti
- Department of Mental Health, ASL Salerno, Salerno, Italy
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
| | - Alessio Fasano
- European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy
- Department of Pediatrics, Massachusetts General Hospital for Children, Harvard Medical School, Division of Pediatric Gastroenterology and Nutrition, Boston, MA, United States
- Mucosal Immunology and Biology Research Center, Massachusetts General Hospital for Children, Boston, MA, United States
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Larsen E, Murton O, Song X, Joachim D, Watts D, Kapczinski F, Venesky L, Hurowitz G. Validating the efficacy and value proposition of mental fitness vocal biomarkers in a psychiatric population: prospective cohort study. Front Psychiatry 2024; 15:1342835. [PMID: 38505797 PMCID: PMC10948552 DOI: 10.3389/fpsyt.2024.1342835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Background The utility of vocal biomarkers for mental health assessment has gained increasing attention. This study aims to further this line of research by introducing a novel vocal scoring system designed to provide mental fitness tracking insights to users in real-world settings. Methods A prospective cohort study with 104 outpatient psychiatric participants was conducted to validate the "Mental Fitness Vocal Biomarker" (MFVB) score. The MFVB score was derived from eight vocal features, selected based on literature review. Participants' mental health symptom severity was assessed using the M3 Checklist, which serves as a transdiagnostic tool for measuring depression, anxiety, post-traumatic stress disorder, and bipolar symptoms. Results The MFVB demonstrated an ability to stratify individuals by their risk of elevated mental health symptom severity. Continuous observation enhanced the MFVB's efficacy, with risk ratios improving from 1.53 (1.09-2.14, p=0.0138) for single 30-second voice samples to 2.00 (1.21-3.30, p=0.0068) for data aggregated over two weeks. A higher risk ratio of 8.50 (2.31-31.25, p=0.0013) was observed in participants who used the MFVB 5-6 times per week, underscoring the utility of frequent and continuous observation. Participant feedback confirmed the user-friendliness of the application and its perceived benefits. Conclusions The MFVB is a promising tool for objective mental health tracking in real-world conditions, with potential to be a cost-effective, scalable, and privacy-preserving adjunct to traditional psychiatric assessments. User feedback suggests that vocal biomarkers can offer personalized insights and support clinical therapy and other beneficial activities that are associated with improved mental health risks and outcomes.
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Affiliation(s)
| | | | | | | | - Devon Watts
- Neuroscience Graduate Program, Department of Health Sciences, McMaster University, Hamilton, ON, Canada
- St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
| | - Flavio Kapczinski
- Neuroscience Graduate Program, Department of Health Sciences, McMaster University, Hamilton, ON, Canada
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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Wang L, Liu R, Wang Y, Xu X, Zhang R, Wei Y, Zhu R, Zhang X, Wang F. Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial. Appl Psychophysiol Biofeedback 2024; 49:71-83. [PMID: 38165498 DOI: 10.1007/s10484-023-09612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual's affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.
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Affiliation(s)
- Lifei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
| | - Rongxun Liu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
- Henan Key Laboratory of Immunology and Targeted Drugs, School of Laboratory Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China
| | - Yang Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
- Psychology Institute, Inner Mongolia Normal University, Hohhot, Inner Mongolia, People's Republic of China
| | - Xiao Xu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ran Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
| | - Yange Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
| | - Rongxin Zhu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People's Republic of China.
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, People's Republic of China.
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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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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5
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McIntyre RS, Greenleaf W, Bulaj G, Taylor ST, Mitsi G, Saliu D, Czysz A, Silvesti G, Garcia M, Jain R. Digital health technologies and major depressive disorder. CNS Spectr 2023; 28:662-673. [PMID: 37042341 DOI: 10.1017/s1092852923002225] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
There is an urgent need to improve the clinical management of major depressive disorder (MDD), which has become increasingly prevalent over the past two decades. Several gaps and challenges in the awareness, detection, treatment, and monitoring of MDD remain to be addressed. Digital health technologies have demonstrated utility in relation to various health conditions, including MDD. Factors related to the COVID-19 pandemic have accelerated the development of telemedicine, mobile medical apps, and virtual reality apps and have continued to introduce new possibilities across mental health care. Growing access to and acceptance of digital health technologies present opportunities to expand the scope of care and to close gaps in the management of MDD. Digital health technology is rapidly evolving the options for nonclinical support and clinical care for patients with MDD. Iterative efforts to validate and optimize such digital health technologies, including digital therapeutics and digital biomarkers, continue to improve access to and quality of personalized detection, treatment, and monitoring of MDD. The aim of this review is to highlight the existing gaps and challenges in depression management and discuss the current and future landscape of digital health technology as it applies to the challenges faced by patients with MDD and their healthcare providers.
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Affiliation(s)
- Roger S McIntyre
- Department of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada
| | - Walter Greenleaf
- Virtual Human Interaction Lab, Stanford University, San Francisco, CA, USA
| | - Grzegorz Bulaj
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Steven T Taylor
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, McLean Hospital, Boston, MA, USA
| | | | | | - Andy Czysz
- Sage Therapeutics, Inc., Cambridge, MA, USA
| | | | | | - Rakesh Jain
- Department of Psychiatry, Texas Tech University School of Medicine, Lubbock, TX, USA
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Shin J, Bae SM. A Systematic Review of Location Data for Depression Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5984. [PMID: 37297588 PMCID: PMC10252667 DOI: 10.3390/ijerph20115984] [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: 04/14/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
Depression contributes to a wide range of maladjustment problems. With the development of technology, objective measurement for behavior and functional indicators of depression has become possible through the passive sensing technology of digital devices. Focusing on location data, we systematically reviewed the relationship between depression and location data. We searched Scopus, PubMed, and Web of Science databases by combining terms related to passive sensing and location data with depression. Thirty-one studies were included in this review. Location data demonstrated promising predictive power for depression. Studies examining the relationship between individual location data variables and depression, homestay, entropy, and the normalized entropy variable of entropy dimension showed the most consistent and significant correlations. Furthermore, variables of distance, irregularity, and location showed significant associations in some studies. However, semantic location showed inconsistent results. This suggests that the process of geographical movement is more related to mood changes than to semantic location. Future research must converge across studies on location-data measurement methods.
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Affiliation(s)
- Jaeeun Shin
- Department of psychology, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Sung Man Bae
- Department of Psychology and Psychotherapy, Dankook University, Cheonan 31116, Republic of Korea
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7
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The Effects of Tai Chi on the Executive Functions and Physical Fitness in Middle-Aged Adults with Depression: A Randomized Controlled Trial. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:1589106. [PMID: 36147650 PMCID: PMC9489347 DOI: 10.1155/2022/1589106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/22/2022] [Indexed: 12/02/2022]
Abstract
Objective The present study examined the effects of Tai Chi exercise on the executive functions (EFs) and physical fitness of middle-aged adults with depression. Methods A total of 39 middle-aged adults with depression (Mage = 50.59, SD = 7.38) were randomly assigned to the Tai Chi group (n = 20) or the waiting-list control group (n = 19). The Tai Chi group engaged in two 90 min sessions of Tai Chi exercise per week for 12 weeks; the waiting-list control group was asked to maintain their usual daily routines for 12 weeks. Depression symptoms, EFs (i.e., inhibitory control, planning, working memory, and cognitive flexibility), and physical fitness (i.e., cardiovascular fitness, muscular strength, muscular endurance, power, and flexibility) were evaluated at the baseline (pretest), 6-week (mid-test), and 12-week (post-test) marks. Results Both groups showed decreased depression symptoms over time. Compared with the control group, the Tai Chi group showed decreased reaction times for incongruent conditions in the Stroop test from pretest to mid- and post-test, and shorter reaction time for incongruent conditions in the Stroop test than the control group at post-test; the Tai Chi group performed significantly better than the control group in overall total move score of Tower of London (TOL). The Tai Chi group also showed increased total correct scores of TOL from pretest to mid- and post-test, and greater total correct scores of TOL than the control group at post-test. Additionally, results indicated that Tai Chi exercise comprehensively improved physical fitness from pretest to mid- and post-test. Greater performance in terms of cardiovascular fitness, muscular strength, and power was also found in the Tai Chi group at post-test than in the control group. Conclusions These findings suggest that the 12-week Tai Chi exercise improved inhibitory control, planning and working memory aspects of executive functions, and physical fitness in middle-aged adults with depression.
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Ratitch B, Rodriguez-Chavez IR, Dabral A, Fontanari A, Vega J, Onorati F, Vandendriessche B, Morton S, Damestani Y. Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies in Clinical Trials. Digit Biomark 2022; 6:83-97. [PMID: 36466953 PMCID: PMC9716191 DOI: 10.1159/000525897] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/31/2022] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The proliferation and increasing maturity of biometric monitoring technologies allow clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of traditional clinical settings. This includes capturing meaningful aspects of health in daily living and a more granular and objective manner compared to traditional tools in clinical settings. SUMMARY Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures. They are called upon to provide input into trial planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. KEY MESSAGES This paper discusses key considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that investigators may encounter while dealing with data from biometric monitoring technologies.
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Affiliation(s)
- Bohdana Ratitch
- Statistics and Data Insights, Bayer, Westmount, Québec, Canada
| | - Isaac R. Rodriguez-Chavez
- Strategy Center for Decentralized Clinical Trials and Digital Medicine, Drug Development Solutions, ICON plc, Blue Bell, Pennsylvania, USA
| | - Abhishek Dabral
- Global Development Operations, Amgen Inc., Thousand Oaks, California, USA
| | | | - Julio Vega
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Francesco Onorati
- Applied Data Science, Current Health, A Best Buy Health Company, Boston, Massachusetts, USA
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium & Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Stuart Morton
- Emerging Digital Medicines, Eli Lilly & Co., Indianapolis, Indiana, USA
| | - Yasaman Damestani
- Digital Medicine, Karyopharm Therapeutics, Newton, Massachusetts, USA
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9
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Curcic J, Vallejo V, Sorinas J, Sverdlov O, Praestgaard J, Piksa M, Deurinck M, Erdemli G, Bügler M, Tarnanas I, Taptiklis N, Cormack F, Anker R, Massé F, Souillard-Mandar W, Intrator N, Molcho L, Madero E, Bott N, Chambers M, Tamory J, Shulz M, Fernandez G, Simpson W, Robin J, Snædal JG, Cha JH, Hannesdottir K. Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study. JMIR Res Protoc 2022; 11:e35442. [PMID: 35947423 PMCID: PMC9403829 DOI: 10.2196/35442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/31/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID) DERR1-10.2196/35442
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Affiliation(s)
- Jelena Curcic
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Vanessa Vallejo
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | | | - Jens Praestgaard
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | - Mateusz Piksa
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Mark Deurinck
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Gul Erdemli
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | | | - Ioannis Tarnanas
- Altoida Inc, Washington, DC, United States.,Global Brain Health Institute, Trinity College, Dublin, Ireland
| | | | | | | | | | - William Souillard-Mandar
- Linus Health, Boston, MA, United States.,Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | - Erica Madero
- Neurotrack Technologies Inc, Redwood City, CA, United States
| | - Nicholas Bott
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States
| | | | - Josef Tamory
- Neurovision Imaging Inc, Sacramento, CA, United States
| | | | | | | | | | | | - Jang-Ho Cha
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
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Taliaz D, Serretti A. Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2022; 22:CNSNDDT-EPUB-124837. [PMID: 35762546 DOI: 10.2174/1871527321666220628103843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/17/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
In the last two decades the validity of clinical trials in psychiatry has been subject to discussion. The most accepted clinical study method in the medical area, randomized controlled trial (RCT), faces significant problems when applied to the psychiatric world. One of the causes for this scenario is the strict participant inclusion and exclusion criteria that may not represent the real world. The inconsistency of the different endpoint parameters that are used in the field is another cause. We think that psychiatric RCTs' challenges, together with the underlying complexity of psychiatry, lead to a problematic clinical practice. Today, psychoactive drugs are routinely tested not in an official clinical trial setting. Off-label psychoactive drugs are commonly prescribed, and other substances, such as herbal remedies, are also regularly consumed. Learning from those real-life experiments can teach us useful lessons. Real-world data (RWD) includes information about heterogeneous patient populations, and it can be measured with standardized parameters. Collecting RWD can also address the need for systematically documenting and sharing case reports' outcomes. We suggest using digital tools to capture objective and continuous behavioral data from patients passively. New conclusions will be constantly drawn, possibly allowing more personalized treatment outcomes. The relevant next-generation decision support tools are already available.
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Affiliation(s)
- Dekel Taliaz
- 2 Prof. Yehezkel Kaufmann Street, Textile Center (15th Floor), Tel Aviv 6801294, Israel
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy
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11
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Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. J Med Internet Res 2022; 24:e35951. [PMID: 35617003 PMCID: PMC9185357 DOI: 10.2196/35951] [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: 12/23/2021] [Revised: 02/14/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
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Affiliation(s)
- Ieuan Clay
- Digital Medicine Society, Boston, MA, United States
| | | | | | | | | | | | | | | | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Benjamin Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | | | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
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12
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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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13
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Makhmutova M, Kainkaryam R, Ferreira M, Min J, Jaggi M, Clay I. Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study. JMIR Mhealth Uhealth 2022; 10:e34148. [PMID: 35333186 PMCID: PMC8994145 DOI: 10.2196/34148] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/21/2021] [Accepted: 02/11/2022] [Indexed: 02/06/2023] Open
Abstract
Background
In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost.
Objective
This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes.
Methods
Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable.
Results
PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm–based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model.
Conclusions
These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual’s mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression.
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Affiliation(s)
| | | | | | - Jae Min
- Evidation Health Inc, San Mateo, CA, United States
| | - Martin Jaggi
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ieuan Clay
- Evidation Health Inc, San Mateo, CA, United States
- Digital Medicine Society, Boston, MA, United States
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14
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Serretti A. Precision medicine in mood disorders. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e1. [PMID: 38868801 PMCID: PMC11114272 DOI: 10.1002/pcn5.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/09/2021] [Accepted: 12/05/2021] [Indexed: 06/14/2024]
Abstract
The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.
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Affiliation(s)
- Alessandro Serretti
- Department of Biomedical and NeuroMotor SciencesUniversity of BolognaBolognaItaly
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15
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Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study. Schizophr Res 2022; 241:14-23. [PMID: 35074528 DOI: 10.1016/j.schres.2021.12.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/21/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Alterations in insular grey matter (GM) volume has been consistently reported for affective and non-affective psychoses both in chronic and first-episode patients, ultimately suggesting that the insula might represent a good region to study in order to assess the longitudinal course of psychotic disorders. Therefore, in this longitudinal Magnetic Resonance Imaging (MRI) study, we aimed at further investigating the key role of insular volumes in psychosis. MATERIAL AND METHODS 68 First-Episode Psychosis (FEP) patients, 68 patients with Schizophrenia (SCZ), 47 Bipolar Disorder (BD) patients, and 94 Healthy Controls (HC) were enrolled and underwent a 1.5 T MRI evaluation. A subsample of 99 subjects (10 HC, 23 BD, 29 SCZ, 37 FEP) was rescanned after 2,53 ± 1,68 years. The insular cortex was manually traced and then divided into an anterior and posterior portion. Group and correlation analyses were then performed both at baseline and at follow-up. RESULTS At baseline, greater anterior and lower posterior insular GM volumes were observed in chronic patients. At follow-up, we found that FEP patients had a significant GM volume increase from baseline to follow-up, especially in the posterior insula whereas chronic patients showed a relative stability. Finally, significant negative correlations between illness severity and pharmacological treatment and insular GM volumes were observed in the whole group of psychotic patients. CONCLUSIONS The longitudinal assessment of both chronic and first-episode patients allowed us to detect a complex pattern of GM abnormalities in selective sub-portions of insular volumes, ultimately suggesting that this structure could represent a key biological marker of psychotic disorders.
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Stancil SL, Tumberger J, Strawn JR. Target to Treatment: a charge to develop biomarkers of response and tolerability in child and adolescent psychiatry. Clin Transl Sci 2021; 15:816-823. [PMID: 34913258 PMCID: PMC9010264 DOI: 10.1111/cts.13216] [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] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/04/2021] [Accepted: 12/08/2021] [Indexed: 11/27/2022] Open
Abstract
The current pediatric mental health crisis is characterized by staggering rates of depression, anxiety, and suicide. Beyond this, first‐line pharmacologic interventions for depressive and anxiety disorders in children and adolescents produce variable responses with two in five youths failing to respond. Given the heterogeneity of treatment response in pediatric depressive and anxiety disorders, pharmacodynamic biomarkers are necessary to develop precision therapeutics by identifying clear targets to guide treatment. This mini‐review summarizes candidate biomarkers and their development in pediatric mental health conditions. A framework for how these biomarkers may relate to safety, efficacy (e.g., surrogates for clinical endpoints), tolerability or target engagement (i.e., drug action) in children and adolescents is also presented. Taken together, accumulating data suggest that, in children and adolescents with myriad psychiatric disorders, pharmacodynamic biomarkers could facilitate developing drugs with well‐defined targets in specific populations, could inform treatment decisions, and hasten patients’ recovery.
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Affiliation(s)
- Stephani L Stancil
- Division of Adolescent Medicine, Children's Mercy Kansas City.,Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation.,Department of Pediatrics, University of Missouri-Kansas City
| | - John Tumberger
- Division of Adolescent Medicine, Children's Mercy Kansas City.,Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation
| | - Jeffrey R Strawn
- Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati, Cincinnati, Ohio, USA.,Divisions of Clinical Pharmacology and Psychiatry, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati Ohio, USA
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