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Chen K, Lane E, Burns J, Macrynikola N, Chang S, Torous J. The Digital Navigator: Standardizing Human Technology Support in App-Integrated Clinical Care. Telemed J E Health 2024. [PMID: 38574251 DOI: 10.1089/tmj.2024.0023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
Background: Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods: We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results: Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions: Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs.
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Affiliation(s)
- Kelly Chen
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Erlend Lane
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - James Burns
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Natalia Macrynikola
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah Chang
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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de Azevedo Cardoso T, Kochhar S, Torous J, Morton E. Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions. JMIR Ment Health 2024; 11:e58631. [PMID: 38557724 PMCID: PMC11019420 DOI: 10.2196/58631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 03/25/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.
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Affiliation(s)
- Taiane de Azevedo Cardoso
- The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Australia
- JMIR Publications, Toronto, ON, Canada
| | | | - John Torous
- Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Emma Morton
- School of Psychological Sciences, Monash University, Clayton, Australia
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Kilshaw RE, Boggins A, Everett O, Butner E, Leifker FR, Baucom BRW. Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e53857. [PMID: 38536220 PMCID: PMC11007613 DOI: 10.2196/53857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 01/27/2024] [Accepted: 02/22/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. OBJECTIVE Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. METHODS We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). RESULTS Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. CONCLUSIONS This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/53857.
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Affiliation(s)
- Robyn E Kilshaw
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Abigail Boggins
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Olivia Everett
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Emma Butner
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Feea R Leifker
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Brian R W Baucom
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
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Jacobucci R, Ammerman B, Ram N. Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis. JMIR Form Res 2024; 8:e55999. [PMID: 38506916 PMCID: PMC10993130 DOI: 10.2196/55999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/08/2024] [Accepted: 02/13/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots. OBJECTIVE This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research. METHODS To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots). RESULTS Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness. CONCLUSIONS Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.
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Affiliation(s)
- Ross Jacobucci
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Brooke Ammerman
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Nilam Ram
- Departments of Communication and Psychology, Stanford University, Stanford, CA, United States
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Schmitter-Edgecombe M, Luna C, Dai S, Cook DJ. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. Clin Neuropsychol 2024:1-25. [PMID: 38503715 DOI: 10.1080/13854046.2024.2330143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
OBJECTIVE Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA). METHOD Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors. RESULTS ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040. CONCLUSION Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.
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Affiliation(s)
| | - Catherine Luna
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Shenghai Dai
- College of Education, Washington State University, Pullman, WA, USA
| | - Diane J Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
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Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
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Chudzik A, Śledzianowski A, Przybyszewski AW. Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases. Sensors (Basel) 2024; 24:1572. [PMID: 38475108 DOI: 10.3390/s24051572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.
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Affiliation(s)
- Artur Chudzik
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Albert Śledzianowski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Andrzej W Przybyszewski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
- UMass Chan Medical School, Department of Neurology, 65 Lake Avenue, Worcester, MA 01655, USA
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Hurley ME, Sonig A, Herrington J, Storch EA, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Front Hum Neurosci 2024; 18:1332451. [PMID: 38435745 PMCID: PMC10904467 DOI: 10.3389/fnhum.2024.1332451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.
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Affiliation(s)
- Meghan E. Hurley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Anika Sonig
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - John Herrington
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry and Behavioral Sciences, Massachusetts General Hospital, Boston, MA, United States
| | | | - Kristin Kostick-Quenet
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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Ahn CY, Lee JS. Digital Phenotyping for Real-Time Monitoring of Nonsuicidal Self-Injury: Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e53597. [PMID: 38329791 PMCID: PMC10884894 DOI: 10.2196/53597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/29/2023] [Accepted: 01/18/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND Nonsuicidal self-injury (NSSI) is a major global health concern. The limitations of traditional clinical and laboratory-based methodologies are recognized, and there is a pressing need to use novel approaches for the early detection and prevention of NSSI. Unfortunately, there is still a lack of basic knowledge of a descriptive nature on NSSI, including when, how, and why self-injury occurs in everyday life. Digital phenotyping offers the potential to predict and prevent NSSI by assessing objective and ecological measurements at multiple points in time. OBJECTIVE This study aims to identify real-time predictors and explain an individual's dynamic course of NSSI. METHODS This study will use a hybrid approach, combining elements of prospective observational research with non-face-to-face study methods. This study aims to recruit a cohort of 150 adults aged 20 to 29 years who have self-reported engaging in NSSI on 5 or more days within the past year. Participants will be enrolled in a longitudinal study conducted at 3-month intervals, spanning 3 long-term follow-up phases. The ecological momentary assessment (EMA) technique will be used via a smartphone app. Participants will be prompted to complete a self-injury and suicidality questionnaire and a mood appraisal questionnaire 3 times a day for a duration of 14 days. A wrist-worn wearable device will be used to collect heart rate, step count, and sleep patterns from participants. Dynamic structural equation modeling and machine learning approaches will be used. RESULTS Participant recruitment and data collection started in October 2023. Data collection and analysis are expected to be completed by December 2024. The results will be published in a peer-reviewed journal and presented at scientific conferences. CONCLUSIONS The insights gained from this study will not only shed light on the underlying mechanisms of NSSI but also pave the way for the development of tailored and culturally sensitive treatment options that can effectively address this major mental health concern. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/53597.
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Affiliation(s)
- Chan-Young Ahn
- Department of Psychology, Kangwon National University, Chuncheon-si, Republic of Korea
| | - Jong-Sun Lee
- Department of Psychology, Kangwon National University, Chuncheon-si, Republic of Korea
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Fu M, Shen J, Gu C, Oliveira E, Shinchuk E, Isaac H, Isaac Z, Sarno DL, Kurz JL, Silbersweig DA, Onnela JP, Barron DS. The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management. Front Pain Res (Lausanne) 2024; 5:1327859. [PMID: 38371228 PMCID: PMC10869590 DOI: 10.3389/fpain.2024.1327859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year. Chronic pain is an instantiation of the perennial complexity of how to best assess and treat chronic diseases over time, especially in populations where age, medical comorbidities, and socioeconomic barriers may limit access to care. Chronic disease management poses a particular challenge for the healthcare system's transition from fee-for-service to value and risk-based reimbursement models. Remote, passive real-time data from smartphones could enable more timely interventions and simultaneously manage risk and promote better patient outcomes through predicting and preventing costly adverse outcomes; however, there is limited evidence whether remote monitoring is feasible, especially in the case of older patients with chronic pain. Here, we introduce the Pain Intervention and Digital Research (Pain-IDR) Program as a pilot initiative launched in 2022 that combines outpatient clinical care and digital health research. The Pain-IDR seeks to test whether functional status can be assessed passively, through a smartphone application, in older patients with chronic pain. We discuss two perspectives-a narrative approach that describes the clinical settings and rationale behind changes to the operational design, and a quantitative approach that measures patient recruitment, patient experience, and HERMES data characteristics. Since launch, we have had 77 participants with a mean age of 55.52, of which n = 38 have fully completed the 6 months of data collection necessitated to be considered in the study, with an active data collection rate of 51% and passive data rate of 78%. We further present preliminary operational strategies that we have adopted as we have learned to adapt the Pain-IDR to a productive clinical service. Overall, the Pain-IDR has successfully engaged older patients with chronic pain and presents useful insights for others seeking to implement digital phenotyping in other chronic disease settings.
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Affiliation(s)
- Melanie Fu
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- School of Medicine, University of Massachusetts, Wooster, MA, United States
| | - Joanna Shen
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Cheryl Gu
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ellina Oliveira
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Ellisha Shinchuk
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Hannah Isaac
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Zacharia Isaac
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Danielle L. Sarno
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Jennifer L. Kurz
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | | | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Daniel S. Barron
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
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Nestor BA, Chimoff J, Koike C, Weitzman ER, Riley BL, Uhl K, Kossowsky J. Adolescent and Parent Perspectives on Digital Phenotyping in Youths With Chronic Pain: Cross-Sectional Mixed Methods Survey Study. J Med Internet Res 2024; 26:e47781. [PMID: 38206665 PMCID: PMC10811597 DOI: 10.2196/47781] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/28/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Digital phenotyping is a promising methodology for capturing moment-to-moment data that can inform individually adapted and timely interventions for youths with chronic pain. OBJECTIVE This study aimed to investigate adolescent and parent endorsement, perceived utility, and concerns related to passive data stream collection through smartphones for digital phenotyping for clinical and research purposes in youths with chronic pain. METHODS Through multiple-choice and open-response survey questions, we assessed the perspectives of patient-parent dyads (103 adolescents receiving treatment for chronic pain at a pediatric hospital with an average age of 15.6, SD 1.6 years, and 99 parents with an average age of 47.8, SD 6.3 years) on passive data collection from the following 9 smartphone-embedded passive data streams: accelerometer, apps, Bluetooth, SMS text message and call logs, keyboard, microphone, light, screen, and GPS. RESULTS Quantitative and qualitative analyses indicated that adolescents and parent endorsement and perceived utility of digital phenotyping varied by stream, though participants generally endorsed the use of data collected by passive stream (35%-75.7% adolescent endorsement for clinical use and 37.9%-74.8% for research purposes; 53.5%-81.8% parent endorsement for clinical and 52.5%-82.8% for research purposes) if a certain level of utility could be provided. For adolescents and parents, adjusted logistic regression results indicated that the perceived utility of each stream significantly predicted the likelihood of endorsement of its use in both clinical practice and research (Ps<.05). Adolescents and parents alike identified accelerometer, light, screen, and GPS as the passive data streams with the highest utility (36.9%-47.5% identifying streams as useful). Similarly, adolescents and parents alike identified apps, Bluetooth, SMS text message and call logs, keyboard, and microphone as the passive data streams with the least utility (18.5%-34.3% identifying streams as useful). All participants reported primary concerns related to privacy, accuracy, and validity of the collected data. Passive data streams with the greatest number of total concerns were apps, Bluetooth, call and SMS text message logs, keyboard, and microphone. CONCLUSIONS Findings support the tailored use of digital phenotyping for this population and can help refine this methodology toward an acceptable, feasible, and ethical implementation of real-time symptom monitoring for assessment and intervention in youths with chronic pain.
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Affiliation(s)
- Bridget A Nestor
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
| | - Justin Chimoff
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Camila Koike
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Elissa R Weitzman
- Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Division of Addiction Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Bobbie L Riley
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
| | - Kristen Uhl
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, United States
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, United States
| | - Joe Kossowsky
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
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12
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Kasputytė G, Jenciūtė G, Šakinis N, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, Krikštolaitis R, Krilavičius T, Juozaitytė E, Bunevičius A. Smartphone sensors for evaluating COVID-19 fear in patients with cancer: a prospective study. Front Public Health 2024; 11:1308003. [PMID: 38249398 PMCID: PMC10797074 DOI: 10.3389/fpubh.2023.1308003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 12/04/2023] [Indexed: 01/23/2024] Open
Abstract
Objective This study aimed to analyze the association between the behavior of cancer patients, measured using passively and continuously generated data streams from smartphone sensors (as in digital phenotyping), and perceived fear of COVID-19 and COVID-19 vaccination status. Methods A total of 202 patients with different cancer types and undergoing various treatments completed the COVID-19 Fears Questionnaire for Chronic Medical Conditions, and their vaccination status was evaluated. Patients' behaviors were monitored using a smartphone application that passively and continuously captures high-resolution data from personal smartphone sensors. In all, 107 patients were monitored for at least 2 weeks. The study was conducted between August 2022 and August 2023. Distributions of clinical and demographical parameters between fully vaccinated, partially vaccinated, and unvaccinated patients were compared using the Chi-squared test. The fear of COVID-19 among the groups was compared using the Mann-Whitney and the Kruskal-Wallis criteria. Trajectories of passively generated data were compared as a function of fear of COVID-19 and COVID-19 vaccination status using local polynomial regression. Results In total, 202 patients were included in the study. Most patients were fully (71%) or partially (13%) vaccinated and 16% of the patients were unvaccinated for COVID-19. Fully vaccinated or unvaccinated patients reported greater fear of COVID-19 than partially vaccinated patients. Fear of COVID-19 was higher in patients being treated with biological therapy. Patients who reported a higher fear of COVID-19 spent more time at home, visited places at shorter distances from home, and visited fewer places of interest (POI). Fully or partially vaccinated patients visited more POI than unvaccinated patients. Local polynomial regression using passively generated smartphone sensor data showed that, although at the beginning of the study, all patients had a similar number of POI, after 1 week, partially vaccinated patients had an increased number of POI, which later remained, on average, around four POI per day. Meanwhile, fully vaccinated or unvaccinated patients had a similar trend of POI and it did not exceed three visits per day during the entire treatment period. Conclusion The COVID-19 pandemic continues to have an impact on the behavior of cancer patients even after the termination of the global pandemic. A higher perceived fear of COVID-19 was associated with less movement, more time spent at home, less time spent outside of home, and a lower number of visited places. Unvaccinated patients visited fewer places and were moving less overall during a 14-week follow-up as compared to vaccinated patients.
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Affiliation(s)
| | - Gabrielė Jenciūtė
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Nerijus Šakinis
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Inesa Bunevičienė
- Faculty of Political Science and Diplomacy, Vytautas Magnus University, Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Domas Vaitiekus
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arturas Inčiūra
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | | | | | - Tomas Krilavičius
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Adomas Bunevičius
- Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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13
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Glynn TR, Khanna SS, Hasdianda MA, Tom J, Ventakasubramanian K, Dumas A, O'Cleirigh C, Goldfine CE, Chai PR. Informing Acceptability and Feasibility of Digital Phenotyping for Personalized HIV Prevention among Marginalized Populations Presenting to the Emergency Department. Proc Annu Hawaii Int Conf Syst Sci 2024; 57:3192-3200. [PMID: 38196408 PMCID: PMC10774708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
For marginalized populations with ongoing HIV epidemics, alternative methods are needed for understanding the complexities of HIV risk and delivering prevention interventions. Due to lack of engagement in ambulatory care, such groups have high utilization of drop-in care. Therefore, emergency departments represent a location with those at highest risk for HIV and in highest need of novel prevention methods. Digital phenotyping via data collected from smartphones and other wearable sensors could provide the innovative vehicle for examining complex HIV risk and assist in delivering personalized prevention interventions. However, there is paucity in exploring if such methods are an option. This study aimed to fill this gap via a cross-sectional psychosocial assessment with a sample of N=85 emergency department patients with HIV risk. Findings demonstrate that although potentially feasible, acceptability of digital phenotyping is questionable. Technology-assisted HIV prevention needs to be designed with the target community and address key ethical considerations.
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Affiliation(s)
- Tiffany R Glynn
- Harvard Medical School, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, MA
| | | | | | | | | | | | | | | | - Peter R Chai
- Harvard Medical School, Brigham and Women's Hospital
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14
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Jurek M, Calder CA, Zigler C. Statistical inference for complete and incomplete mobility trajectories under the flight-pause model. J R Stat Soc Ser C Appl Stat 2024; 73:162-192. [PMID: 38222067 PMCID: PMC10782461 DOI: 10.1093/jrsssc/qlad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 07/05/2023] [Accepted: 09/07/2023] [Indexed: 01/16/2024]
Abstract
We formulate a statistical flight-pause model (FPM) for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the FPM, representing an understudied missing data phenomenon. We demonstrate the consequences of missing data and our proposed adjustments in both simulations and real data, outlining implications for MPT data collection and design.
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Affiliation(s)
- Marcin Jurek
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA
| | - Catherine A Calder
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA
| | - Corwin Zigler
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA
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15
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Chen HH, Lu HHS, Weng WH, Lin YH. Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study. J Med Internet Res 2023; 25:e48834. [PMID: 38157232 PMCID: PMC10787330 DOI: 10.2196/48834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/25/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.
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Affiliation(s)
- Hung-Hsun Chen
- Department of Mathematics, Fu Jen Catholic University, New Taipei City, Taiwan
- Program of Artificial Intelligence & Information Security, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States
| | - Wei-Hung Weng
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MN, United States
| | - Yu-Hsuan Lin
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
- Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan
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16
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Breitinger S, Gardea-Resendez M, Langholm C, Xiong A, Laivell J, Stoppel C, Harper L, Volety R, Walker A, D'Mello R, Byun AJS, Zandi P, Goes FS, Frye M, Torous J. Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study. J Med Internet Res 2023; 25:e47006. [PMID: 38157233 PMCID: PMC10787337 DOI: 10.2196/47006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 09/04/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. OBJECTIVE This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. METHODS We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. RESULTS We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. CONCLUSIONS Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.
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Affiliation(s)
- Scott Breitinger
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Ashley Xiong
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Joseph Laivell
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Stoppel
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Laura Harper
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Rama Volety
- Research Application Solutions Unit, Mayo Clinic, Rochester, MN, United States
| | - Alex Walker
- Johns Hopkins University, Baltimore, MD, United States
| | - Ryan D'Mello
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | | | - Peter Zandi
- Johns Hopkins University, Baltimore, MD, United States
| | | | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - John Torous
- Beth Israel Deaconess Medical Center, Boston, MA, United States
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17
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Kalman JL, Burkhardt G, Samochowiec J, Gebhard C, Dom G, John M, Kilic O, Kurimay T, Lien L, Schouler-Ocak M, Vidal DP, Wiser J, Gaebel W, Volpe U, Falkai P. Digitalising mental health care: Practical recommendations from the European Psychiatric Association. Eur Psychiatry 2023; 67:e4. [PMID: 38086744 PMCID: PMC10790232 DOI: 10.1192/j.eurpsy.2023.2466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/17/2023] [Accepted: 10/12/2023] [Indexed: 01/06/2024] Open
Abstract
The digitalisation of mental health care is expected to improve the accessibility and quality of specialised treatment services and introduce innovative methods to study, assess, and monitor mental health disorders. In this narrative review and practical recommendation of the European Psychiatric Association (EPA), we aim to help healthcare providers and policymakers to navigate this rapidly evolving field. We provide an overview of the current scientific and implementation status across two major domains of digitalisation: i) digital mental health interventions and ii) digital phenotyping, discuss the potential of each domain to improve the accessibility and outcomes of mental health services, and highlight current challenges faced by researchers, clinicians, and service users. Furthermore, we make several recommendations meant to foster the widespread adoption of evidence-based digital solutions for mental health care in the member states of the EPA. To realise the vision of a digitalised, patient-centred, and data-driven mental health ecosystem, a number of implementation challenges must be considered and addressed, spanning from human, technical, ethical-legal, and economic barriers. The list of priority areas and action points our expert panel has identified could serve as a playbook for this process.
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Affiliation(s)
- Janos L. Kalman
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Gerrit Burkhardt
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | | | - Geert Dom
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Miriam John
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Ozge Kilic
- Department of Psychiatry, Bezmialem Vakıf University Faculty of Medicine, Istanbul, Turkey
| | - Tamas Kurimay
- North-Buda Saint John Central Hospital, Buda Family Centred Mental Health Centre, Department of Psychiatry and Psychiatric Rehabilitation, Teaching Department of Semmelweis University, Budapest, Hungary
| | - Lars Lien
- National Advisory Unit on Concurrent Substance Abuse and Mental Health Disorders, Innlandet Hospital Trust, Hamar, Norway, Faculty of Social and Health Sciences, Inland Norway University of Applied Sciences, Elverum, Norway
| | - Meryam Schouler-Ocak
- Psychiatric University Clinic of Charité at St. Hedwig Hospital, Berlin, Germany
| | - Diego Palao Vidal
- Mental Health Service, Parc Taulí University Hospital, Unitat Mixta de Neurociència Traslacional I3PT-INc-UAB, Sabadell, Spain
- Department of Psychiatry and Forensic Medicine, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Jan Wiser
- CNWL NHS Foundation Trust, London, UK
| | - Wolfgang Gaebel
- WHO Collaborating Centre DEU-131, VR-Klinikum Düsseldorf, Department of Psychiatry and Psychotherapy, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Umberto Volpe
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, School of Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
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18
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Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res 2023; 25:e46778. [PMID: 38090800 PMCID: PMC10753422 DOI: 10.2196/46778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/29/2023] [Accepted: 07/31/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).
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Affiliation(s)
- Pasquale Bufano
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Sara Said
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | | | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
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19
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Kerz E, Zanwar S, Qiao Y, Wiechmann D. Toward explainable AI (XAI) for mental health detection based on language behavior. Front Psychiatry 2023; 14:1219479. [PMID: 38144474 PMCID: PMC10748510 DOI: 10.3389/fpsyt.2023.1219479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/31/2023] [Indexed: 12/26/2023] Open
Abstract
Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a "black-box" domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress).
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Affiliation(s)
- Elma Kerz
- Department of English and American Studies, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Sourabh Zanwar
- Department of English and American Studies, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Yu Qiao
- Department of English and American Studies, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Daniel Wiechmann
- Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands
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20
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Stern E, Micoulaud Franchi JA, Dumas G, Moreira J, Mouchabac S, Maruani J, Philip P, Lejoyeux M, Geoffroy PA. How Can Digital Mental Health Enhance Psychiatry? Neuroscientist 2023; 29:681-693. [PMID: 35658666 DOI: 10.1177/10738584221098603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The use of digital technologies is constantly growing around the world. The wider-spread adoption of digital technologies and solutions in the daily clinical practice in psychiatry seems to be a question of when, not if. We propose a synthesis of the scientific literature on digital technologies in psychiatry and discuss the main aspects of its possible uses and interests in psychiatry according to three domains of influence that appeared to us: 1) assist and improve current care: digital psychiatry allows for more people to have access to care by simply being more accessible but also by being less stigmatized and more convenient; 2) develop new treatments: digital psychiatry allows for new treatments to be distributed via apps, and practical guidelines can reduce ethical challenges and increase the efficacy of digital tools; and 3) produce scientific and medical knowledge: digital technologies offer larger and more objective data collection, allowing for more detection and prevention of symptoms. Finally, ethical and efficacy issues remain, and some guidelines have been put forth on how to safely use these solutions and prepare for the future.
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Affiliation(s)
- Emilie Stern
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
| | - Jean-Arthur Micoulaud Franchi
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Quebec, Canada
- Mila-Quebec Artificial Intelligence Institute, University of Montreal, Quebec, Canada
| | | | - Stephane Mouchabac
- Department of Psychiatry, Department of Psychiatry Hôpital Saint Antoine-APHP, Sorbonne University, Paris, France
- Infrastructure of Clinical Research in Neurosciences-Psychiatry, Brain and Spine Institute (ICM), Inserm, Sorbonne University, Paris, France
| | - Julia Maruani
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre Philip
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Michel Lejoyeux
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre A Geoffroy
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
- CNRS UPR 3212, Institute for Cellular and Integrative Neurosciences, Strasbourg, France
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Marengo D, Elhai JD, Montag C. Predicting Big Five personality traits from smartphone data: A meta-analysis on the potential of digital phenotyping. J Pers 2023; 91:1410-1424. [PMID: 36738137 DOI: 10.1111/jopy.12817] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 01/23/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Since the first study linking recorded smartphone variables to self-reported personality in 2011, many additional studies have been published investigating this association. In the present meta-analyses, we aimed to understand how strongly personality can be predicted via smartphone data. METHOD Meta-analytical calculations were used to assess the association between smartphone data and Big Five traits. Because of the lack of independence of many included studies, analyses were performed using a multilevel approach. RESULTS Based on data collected from 21 distinct studies, extraversion showed the largest association with the digital footprints derived from smartphone data (r = .35), while remaining traits showed smaller associations (ranging from 0.23 to 0.25). For all traits except neuroticism, moderator analyses showed that prediction performance was improved when multiple features were combined together in a single predictive model. Additionally, the strength of the prediction of extraversion was improved when call and text log data were used to perform the prediction, as opposed to other types of smartphone data CONCLUSIONS: Our synthesis reveals small-to-moderate associations between smartphone activity data and Big Five traits. The opportunities, but also dangers of the digital phenotyping of personality traits based on traces of users' activity on a smartphone data are discussed.
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Affiliation(s)
- Davide Marengo
- Department of Psychology, University of Turin, Turin, Italy
| | - Jon D Elhai
- Department of Psychology, The University of Toledo, Toledo, Ohio, USA
- Department of Psychiatry, The University of Toledo, Toledo, Ohio, USA
| | - Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
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Li LY, Trivedi E, Helgren F, Allison GO, Zhang E, Buchanan SN, Pagliaccio D, Durham K, Allen NB, Auerbach RP, Shankman SA. Capturing mood dynamics through adolescent smartphone social communication. J Psychopathol Clin Sci 2023; 132:1072-1084. [PMID: 37498714 PMCID: PMC10818010 DOI: 10.1037/abn0000855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Most adolescents with depression remain undiagnosed and untreated-missed opportunities that are costly from both personal and public health perspectives. A promising approach to detecting adolescent depression in real-time and at a large scale is through their social communication on the smartphone (e.g., text messages, social media posts). Past research has shown that language from online social communication reliably indicates interindividual differences in depression. To move toward detecting the emergence of depression symptoms intraindividually, the present study tested whether sentiment (i.e., words connoting positive and negative affect) from smartphone social communication prospectively predicted daily mood fluctuations in 83 adolescents (Mage = 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (i.e., more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (e.g., friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Lilian Y. Li
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Esha Trivedi
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Fiona Helgren
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | | | - Emily Zhang
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | - David Pagliaccio
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Katherine Durham
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | - Randy P. Auerbach
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
- Division of Clinical Developmental Neuroscience, Sackler Institute
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23
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Akbarova S, Im M, Kim S, Toshnazarov K, Chung KM, Chun J, Noh Y, Kim YA. Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach. Sensors (Basel) 2023; 23:8866. [PMID: 37960563 PMCID: PMC10649076 DOI: 10.3390/s23218866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/19/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
Depression is a significant mental health issue that profoundly impacts people's lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement. This study contributes to the detection of depression severity and depressive symptoms using mobile devices. Our proposed approach aims to distinguish between different patterns of depression and improve prediction accuracy. We conducted an experiment involving 381 participants over a period of at least three months, during which we collected comprehensive passive sensor data and Patient Health Questionnaire (PHQ-9) self-reports. To enhance the accuracy of predicting depression severity levels (classified as none/mild, moderate, or severe), we introduce a novel approach called symptom profiling. The symptom profile vector represents nine depressive symptoms and indicates both the probability of each symptom being present and its significance for an individual. We evaluated the effectiveness of the symptom-profiling method by comparing the F1 score achieved using sensor data features as inputs to machine learning models with the F1 score obtained using the symptom profile vectors as inputs. Our findings demonstrate that symptom profiling improves the F1 score by up to 0.09, with an average improvement of 0.05, resulting in a depression severity prediction with an F1 score as high as 0.86.
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Affiliation(s)
- Sabinakhon Akbarova
- Research and Development Department, Huno, Seoul 04146, Republic of Korea; (S.A.); (M.I.); (S.K.); (J.C.)
| | - Myeongji Im
- Research and Development Department, Huno, Seoul 04146, Republic of Korea; (S.A.); (M.I.); (S.K.); (J.C.)
| | - Suhyun Kim
- Research and Development Department, Huno, Seoul 04146, Republic of Korea; (S.A.); (M.I.); (S.K.); (J.C.)
| | | | - Kyong-Mee Chung
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea;
| | - Junghyun Chun
- Research and Development Department, Huno, Seoul 04146, Republic of Korea; (S.A.); (M.I.); (S.K.); (J.C.)
| | - Youngtae Noh
- Energy AI, KENTECH, Naju 58330, Republic of Korea;
| | - Young-Ah Kim
- Research and Development Department, Huno, Seoul 04146, Republic of Korea; (S.A.); (M.I.); (S.K.); (J.C.)
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Jenciūtė G, Kasputytė G, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, Krikštolaitis R, Krilavičius T, Juozaitytė E, Bunevičius A. Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study. JMIR Res Protoc 2023; 12:e49096. [PMID: 37815850 PMCID: PMC10599285 DOI: 10.2196/49096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. OBJECTIVE We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. METHODS We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. RESULTS As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. CONCLUSIONS This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/49096.
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Affiliation(s)
- Gabrielė Jenciūtė
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Inesa Bunevičienė
- Faculty of Political Science and Diplomacy, Vytautas Magnus University, Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Domas Vaitiekus
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arturas Inčiūra
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | | | | | - Tomas Krilavičius
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Adomas Bunevičius
- Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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25
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Marciano L, Vocaj E, Bekalu MA, La Tona A, Rocchi G, Viswanath K. The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. J Med Internet Res 2023; 25:e45540. [PMID: 37725422 PMCID: PMC10548333 DOI: 10.2196/45540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/12/2023] [Accepted: 07/06/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. OBJECTIVE This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. METHODS A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. RESULTS After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. CONCLUSIONS EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. TRIAL REGISTRATION PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.
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Affiliation(s)
- Laura Marciano
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Emanuela Vocaj
- Lombard School of Cognitive-Neuropsychological Psychotherapy, Pavia, Italy
| | - Mesfin A Bekalu
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Antonino La Tona
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
| | - Giulia Rocchi
- Department of Dynamic, Clinical Psychology and Health Studies, Sapienza University, Rome, Italy
| | - Kasisomayajula Viswanath
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
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Strauss GP, Raugh IM, Luther L, Walker EF, Mittal VA. Temporal Interactions Between Social Motivation and Behavior In Daily Life Among Individuals at Clinical High-Risk for Psychosis. Schizophr Bull 2023; 49:1150-1160. [PMID: 37467481 PMCID: PMC10483454 DOI: 10.1093/schbul/sbad096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
BACKGROUND AND HYPOTHESES Poor social functioning is common among individuals at clinical high-risk (CHR) for psychosis and is associated with greater likelihood of conversion. Unfortunately, processes contributing to social impairment are unclear, making social functioning difficult to improve via treatment. The current study examined whether abnormalities in social functioning result from aberrant temporal interactions between social motivation and behavior. STUDY DESIGN Participants included 105 individuals at CHR and 62 healthy controls (CN) who completed 6 days of ecological momentary assessment. Multilevel models examined time-lagged interactions between social behavior and motivation. STUDY RESULTS CHR and CN did not differ in social motivation; however, CHR were less likely to interact with family and coworkers and more likely to engage in interactions via phone and text/social media. Autocorrelations indicated that social behavior and motivation were generally consistent across time in CHR and CN groups. Time-lagged analyses indicated that both groups had an increase in social motivation across time when they were alone and a decrease in social motivation across time when they were with others. However, the relative decrease when with others and increase when alone were less robust in CHR than CN, particularly for in-person interactions. Social motivation at time t did not differentially impact social partner or modality at time t+1 in the groups. CONCLUSIONS Findings suggest that social behavior and motivation have different temporal interactions in CHR and CN. Psychosocial interventions may benefit from targeting the frequency of social behavior with specific partners and modalities to change social motivation.
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Affiliation(s)
| | - Ian M Raugh
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Lauren Luther
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
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Auerbach RP, Lan R, Galfalvy H, Alqueza KL, Cohn JF, Crowley RN, Durham K, Joyce KJ, Kahn LE, Kamath RA, Morency LP, Porta G, Srinivasan A, Zelazny J, Brent DA, Allen NB. Intensive Longitudinal Assessment of Adolescents to Predict Suicidal Thoughts and Behaviors. J Am Acad Child Adolesc Psychiatry 2023; 62:1010-1020. [PMID: 37182586 PMCID: PMC10524866 DOI: 10.1016/j.jaac.2023.03.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 03/24/2023] [Accepted: 05/05/2023] [Indexed: 05/16/2023]
Abstract
OBJECTIVE Suicide is a leading cause of death among adolescents. However, there are no clinical tools to detect proximal risk for suicide. METHOD Participants included 13- to 18-year-old adolescents (N = 103) reporting a current depressive, anxiety, and/or substance use disorder who owned a smartphone; 62% reported current suicidal ideation, with 25% indicating a past-year attempt. At baseline, participants were administered clinical interviews to assess lifetime disorders and suicidal thoughts and behaviors (STBs). Self-reports assessing symptoms and suicide risk factors also were obtained. In addition, the Effortless Assessment of Risk States (EARS) app was installed on adolescent smartphones to acquire daily mood and weekly suicidal ideation severity during the 6-month follow-up period. Adolescents completed STB and psychiatric service use interviews at the 1-, 3-, and 6-month follow-up assessments. RESULTS K-means clustering based on aggregates of weekly suicidal ideation scores resulted in a 3-group solution reflecting high-risk (n = 26), medium-risk (n = 47), and low-risk (n = 30) groups. Of the high-risk group, 58% reported suicidal events (ie, suicide attempts, psychiatric hospitalizations, emergency department visits, ideation severity requiring an intervention) during the 6-month follow-up period. For participants in the high-risk and medium-risk groups (n = 73), mood disturbances in the preceding 7 days predicted clinically significant ideation, with a 1-SD decrease in mood doubling participants' likelihood of reporting clinically significant ideation on a given week. CONCLUSION Intensive longitudinal assessment through use of personal smartphones offers a feasible method to assess variability in adolescents' emotional experiences and suicide risk. Translating these tools into clinical practice may help to reduce the needless loss of life among adolescents.
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Affiliation(s)
- Randy P Auerbach
- Columbia University, New York, and New York State Psychiatric Institute, New York; Sackler Institute, New York.
| | - Ranqing Lan
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | - Hanga Galfalvy
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | - Kira L Alqueza
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | | | | | - Katherine Durham
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | - Karla J Joyce
- University Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | | | - Rahil A Kamath
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | | | - Giovanna Porta
- University Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Apoorva Srinivasan
- Columbia University, New York, and New York State Psychiatric Institute, New York
| | - Jamie Zelazny
- University Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - David A Brent
- University Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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Kelkar RS, Currey D, Nagendra S, Mehta UM, Sreeraj VS, Torous J, Thirthalli J. Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study. JMIR Form Res 2023; 7:e40197. [PMID: 37656496 PMCID: PMC10504622 DOI: 10.2196/40197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/01/2023] [Accepted: 07/20/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Identifying biomarkers of response to transcranial magnetic stimulation (TMS) in treatment-resistant depression is a priority for personalizing care. Clinical and neurobiological determinants of treatment response to TMS, while promising, have limited scalability. Therefore, evaluating novel, technologically driven, and potentially scalable biomarkers, such as digital phenotyping, is necessary. OBJECTIVE This study aimed to examine the potential of smartphone-based digital phenotyping and its feasibility as a predictive biomarker of treatment response to TMS in depression. METHODS We assessed the feasibility of digital phenotyping by examining the adherence and retention rates. We used smartphone data from passive sensors as well as active symptom surveys to determine treatment response in a naturalistic course of TMS treatment for treatment-resistant depression. We applied a scikit-learn logistic regression model (l1 ratio=0.5; 2-fold cross-validation) using both active and passive data. We analyzed related variance metrics throughout the entire treatment duration and on a weekly basis to predict responders and nonresponders to TMS, defined as ≥50% reduction in clinician-rated symptom severity from baseline. RESULTS The adherence rate was 89.47%, and the retention rate was 73%. The area under the curve for correct classification of TMS response ranged from 0.59 (passive data alone) to 0.911 (both passive and active data) for data collected throughout the treatment course. Importantly, a model using the average of all features (passive and active) for the first week had an area under the curve of 0.7375 in predicting responder status at the end of the treatment. CONCLUSIONS The results of our study suggest that it is feasible to use digital phenotyping data to assess response to TMS in depression. Early changes in digital phenotyping biomarkers, such as predicting response from the first week of data, as shown in our results, may also help guide the treatment course.
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Affiliation(s)
| | - Danielle Currey
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | | | | | | | - John Torous
- Beth Israel Deaconess Medical Center, Boston, MA, United States
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Neethirajan S. Digital Phenotyping: A Game Changer for the Broiler Industry. Animals (Basel) 2023; 13:2585. [PMID: 37627376 PMCID: PMC10451972 DOI: 10.3390/ani13162585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/04/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. This paper critically evaluates this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.
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Affiliation(s)
- Suresh Neethirajan
- Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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30
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Washington P, Wall DP. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annu Rev Biomed Data Sci 2023; 6:211-228. [PMID: 37137169 DOI: 10.1146/annurev-biodatasci-020722-125454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
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Affiliation(s)
- Peter Washington
- Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Dennis P Wall
- Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA;
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Broulidakis MJ, Kiprijanovska I, Severs L, Stankoski S, Gjoreski M, Mavridou I, Gjoreski H, Cox S, Bradwell D, Stone JM, Nduka C. Optomyography-based sensing of facial expression derived arousal and valence in adults with depression. Front Psychiatry 2023; 14:1232433. [PMID: 37614653 PMCID: PMC10442807 DOI: 10.3389/fpsyt.2023.1232433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023] Open
Abstract
Background Continuous assessment of affective behaviors could improve the diagnosis, assessment and monitoring of chronic mental health and neurological conditions such as depression. However, there are no technologies well suited to this, limiting potential clinical applications. Aim To test if we could replicate previous evidence of hypo reactivity to emotional salient material using an entirely new sensing technique called optomyography which is well suited to remote monitoring. Methods Thirty-eight depressed and 37 controls (≥18, ≤40 years) who met a research diagnosis of depression and an age-matched non-depressed control group. Changes in facial muscle activity over the brow (corrugator supercilli) and cheek (zygomaticus major) were measured whilst volunteers watched videos varying in emotional salience. Results Across all participants, videos rated as subjectively positive were associated with activation of muscles in the cheek relative to videos rated as neutral or negative. Videos rated as subjectively negative were associated with brow activation relative to videos judged as neutral or positive. Self-reported arousal was associated with a step increase in facial muscle activation across the brow and cheek. Group differences were significantly reduced activation in facial muscles during videos considered subjectively negative or rated as high arousal in depressed volunteers compared with controls. Conclusion We demonstrate for the first time that it is possible to detect facial expression hypo-reactivity in adults with depression in response to emotional content using glasses-based optomyography sensing. It is hoped these results may encourage the use of optomyography-based sensing to track facial expressions in the real-world, outside of a specialized testing environment.
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Affiliation(s)
| | | | | | | | - Martin Gjoreski
- Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland
| | | | - Hristijan Gjoreski
- Ss. Cyril and Methodius University in Skopje (UKIM), Skopje, North Macedonia
| | | | | | - James M. Stone
- Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
| | - Charles Nduka
- Emteq Ltd., Brighton, United Kingdom
- Queen Victoria Hospital, East Grinstead, United Kingdom
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Kim SH, Subramanian P, Na YW, Hahn BS, Kim Y. RDA-Genebank and Digital Phenotyping for Next-Generation Research on Plant Genetic Resources. Plants (Basel) 2023; 12:2825. [PMID: 37570979 PMCID: PMC10421229 DOI: 10.3390/plants12152825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023]
Abstract
The National Agrobiodiversity Center under the Rural Development Administration (RDA) in Jeonju, Republic of Korea stands as the foremost national genebank in the country. Over the years, the National Agrobiodiversity Center has remained committed to enriching its collection with foreign genetic resources, elevating its status to a world-class plant genetic resources (PGR)- holding genebank. Currently, several steps are being undertaken to improve the accessibility of the collection to national as well as international researchers, improve the data available on the resources, and amend the passport information for the accessions. With the implementation of the Nagoya Protocol, the origin of genetic resources is being highlighted as an important input in the passport information. The RDA-Genebank actively responds to the Nagoya Protocol by supplementing passport data for resources lacking information on their origin. In addition, a large number of conserved resources are continuously multiplied, and agronomic traits are investigated concurrently. With the traditional methods of characterization of the germplasm requiring a significant amount of time and effort, we have initiated high-throughput phenotyping using digital techniques to improve our germplasm data. Primarily, we have started adding seed phenotype information followed by measuring root phenotypes which are stored under agronomic traits. This may be the initial step toward using largescale high-throughput techniques for a germplasm. In this study, we aim to provide an introduction to the RDA-Genebank, to adopted international standards, and to the establishment of high-throughput phenotyping techniques for the improvement of passport information.
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Affiliation(s)
- Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Parthiban Subramanian
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Young-Wang Na
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Bum-Soo Hahn
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Yoonha Kim
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
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Pryss R, Schlee W, Reichert M, Probst T, Langguth B, Spiliopoulou M. Editorial: Smart mobile data collection in the context of neuroscience, volume II. Front Neurosci 2023; 17:1259632. [PMID: 37588517 PMCID: PMC10425537 DOI: 10.3389/fnins.2023.1259632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 08/18/2023] Open
Affiliation(s)
- Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- Institute of Medical Data Science, University Hospital of Würzburg, Würzburg, Germany
| | - Winfried Schlee
- Institute for Information and Process Management, Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland
- Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Thomas Probst
- Division of Psychotherapy, Department of Psychology, Paris Lodron University Salzburg, Salzburg, Austria
| | - Berthold Langguth
- Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Myra Spiliopoulou
- Knowledge Management Discovery Lab, Otto-von-Guericke- University Magdeburg, Magdeburg, Germany
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Li A, Liu R, Liu X, Han J. Editorial: Improving the clinical value of digital phenotyping in mental health. Front Psychiatry 2023; 14:1251930. [PMID: 37533893 PMCID: PMC10392935 DOI: 10.3389/fpsyt.2023.1251930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 08/04/2023] Open
Affiliation(s)
- Ang Li
- Department of Psychology, Beijing Forestry University, Beijing, China
| | - Ronghui Liu
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoqian Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jin Han
- Black Dog Institute, University of New South Wales, Randwick, NSW, Australia
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Coffman M, Di Martino JM, Aiello R, Carpenter KL, Chang Z, Compton S, Eichner B, Espinosa S, Flowers J, Franz L, Perochon S, Krishnappa Babu PR, Sapiro G, Dawson G. Relationship between quantitative digital behavioral features and clinical profiles in young autistic children. Autism Res 2023; 16:1360-1374. [PMID: 37259909 PMCID: PMC10524806 DOI: 10.1002/aur.2955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/06/2023] [Indexed: 06/02/2023]
Abstract
Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.
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Affiliation(s)
- Marika Coffman
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steve Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Superieure Paris-Saclay, Gif-Sur-Yvette, France
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
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Pizzoli SFM, Monzani D, Conti L, Ferraris G, Grasso R, Pravettoni G. Issues and opportunities of digital phenotyping: ecological momentary assessment and behavioral sensing in protecting the young from suicide. Front Psychol 2023; 14:1103703. [PMID: 37441331 PMCID: PMC10333535 DOI: 10.3389/fpsyg.2023.1103703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 06/09/2023] [Indexed: 07/15/2023] Open
Abstract
Digital phenotyping refers to the collection of real-time biometric and personal data on digital tools, mainly smartphones, and wearables, to measure behaviors and variables that can be used as a proxy for complex psychophysiological conditions. Digital phenotyping might be used for diagnosis, clinical assessment, predicting changes and trajectories in psychological clinical conditions, and delivering tailored interventions according to individual real-time data. Recent works pointed out the possibility of using such an approach in the field of suicide risk in high-suicide-risk patients. Among the possible targets of such interventions, adolescence might be a population of interest, since they display higher odds of committing suicide and impulsive behaviors. The present work systematizes the available evidence of the data that might be used for digital phenotyping in the field of adolescent suicide and provides insight into possible personalized approaches for monitoring and treating suicidal risk or predicting risk trajectories. Specifically, the authors first define the field of digital phenotyping and its features, secondly, they organize the available literature to gather all the digital indexes (active and passive data) that can provide reliable information on the increase in the suicidal odds, lastly, they discuss the challenges and future directions of such an approach, together with its ethical implications.
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Affiliation(s)
- Silvia Francesca Maria Pizzoli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Psychology, Catholic University of the Sacred Heart,, Milan, Italy
| | - Dario Monzani
- Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy
| | - Lorenzo Conti
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Ferraris
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Roberto Grasso
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milan, Italy
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Barzilay S, Fine S, Akhavan S, Haruvi-Catalan L, Apter A, Brunstein-Klomek A, Carmi L, Zohar M, Kinarty I, Friedman T, Fennig S. Real-Time Real-World Digital Monitoring of Adolescent Suicide Risk During the Six Months Following Emergency Department Discharge: Protocol for an Intensive Longitudinal Study. JMIR Res Protoc 2023; 12:e46464. [PMID: 37358906 DOI: 10.2196/46464] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/28/2023] [Accepted: 05/29/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND Suicide is the second leading cause of death in adolescents, and self-harm is one of the strongest predictors of death by suicide. The rates of adolescents presenting to emergency departments (EDs) for suicidal thoughts and behaviors (STBs) have increased. Still, existing follow-up after ED discharge is inadequate, leaving a high-risk period for reattempts and suicide. There is a need for innovative evaluation of imminent suicide risk factors in these patients, focusing on continuous real-time evaluations with low assessment burden and minimal reliance on patient disclosure of suicidal intent. OBJECTIVE This study examines prospective longitudinal associations between observed real-time mobile passive sensing, including communication and activity patterns, and clinical and self-reported assessments of STB over 6 months. METHODS This study will include 90 adolescents recruited on their first outpatient clinic visit following their discharge from the ED due to a recent STB. Participants will complete brief weekly assessments and be monitored continuously for their mobile app usage, including mobility, activity, and communication patterns, over 6 months using the iFeel research app. Participants will complete 4 in-person visits for clinical assessment at baseline and at the 1-, 3-, and 6-month follow-ups. The digital data will be processed, involving feature extraction, scaling, selection, and dimensionality reduction. Passive monitoring data will be analyzed using both classical machine learning models and deep learning models to identify proximal associations between real-time observed communication, activity patterns, and STB. The data will be split into a training and validation data set, and predictions will be matched against the clinical evaluations and self-reported STB events (ie, labels). To use both labeled and unlabeled digital data (ie, passively collected), we will use semisupervised methods in conjunction with a novel method that is based on anomaly detection notions. RESULTS Participant recruitment and follow-up started in February 2021 and are expected to be completed by 2024. We expect to find prospective proximal associations between mobile sensor communication, activity data, and STB outcomes. We will test predictive models for suicidal behaviors among high-risk adolescents. CONCLUSIONS Developing digital markers of STB in a real-world sample of high-risk adolescents presenting to ED can inform different interventions and provide an objective means to assess the risk of suicidal behaviors. The results of this study will be the first step toward large-scale validation that may lead to suicide risk measures that aid psychiatric follow-up, decision-making, and targeted treatments. This novel assessment could facilitate timely identification and intervention to save young people's lives. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46464.
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Affiliation(s)
- Shira Barzilay
- Department of Community Mental Health, University of Haifa, Haifa, Israel
- Schneider Children's Medical Center of Israel, Petach Tikva, Israel
| | - Shai Fine
- Data Science Institute, Reichman University, Herzliya, Israel
| | - Shannel Akhavan
- Schneider Children's Medical Center of Israel, Petach Tikva, Israel
| | | | - Alan Apter
- Schneider Children's Medical Center of Israel, Petach Tikva, Israel
| | | | - Lior Carmi
- Data Science Institute, Reichman University, Herzliya, Israel
| | - Mishael Zohar
- Data Science Institute, Reichman University, Herzliya, Israel
| | - Inbar Kinarty
- Data Science Institute, Reichman University, Herzliya, Israel
| | - Talia Friedman
- Data Science Institute, Reichman University, Herzliya, Israel
| | - Silvana Fennig
- Schneider Children's Medical Center of Israel, Petach Tikva, Israel
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Campbell CI, Chen CH, Adams SR, Asyyed A, Athale NR, Does MB, Hassanpour S, Hichborn E, Jackson-Morris M, Jacobson NC, Jones HK, Kotz D, Lambert-Harris CA, Li Z, McLeman B, Mishra V, Stanger C, Subramaniam G, Wu W, Zegers C, Marsch LA. Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder. J Med Internet Res 2023; 25:e45556. [PMID: 37310787 DOI: 10.2196/45556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/29/2023] [Accepted: 04/10/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. OBJECTIVE The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. METHODS The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. RESULTS The participants' average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. CONCLUSIONS To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.3389/fpsyt.2022.871916.
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Affiliation(s)
- Cynthia I Campbell
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, United States
- Kaiser Permanente Bernard J Tyson School of Medicine, Pasadena, CA, United States
| | - Ching-Hua Chen
- Center for Computational Health, IBM Research, Yorktown Heights, NY, United States
| | - Sara R Adams
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
| | - Asma Asyyed
- Addiction Medicine and Recovery Services, The Permanente Medical Group Northern California, Oakland, CA, United States
| | - Ninad R Athale
- Addiction Medicine and Recovery Services, The Permanente Medical Group Northern California, Vallejo, CA, United States
| | - Monique B Does
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
| | - Saeed Hassanpour
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Emily Hichborn
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | | | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Heather K Jones
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
| | - David Kotz
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Chantal A Lambert-Harris
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Zhiguo Li
- Center for Computational Health, IBM Research, Yorktown Heights, NY, United States
- Profit Intelligence, Amazon.com, Seattle, WA, United States
| | - Bethany McLeman
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Varun Mishra
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
- Department of Health Sciences, Northeastern University, Boston, MA, United States
| | - Catherine Stanger
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Geetha Subramaniam
- Center for Clinical Trials Network, National Institute on Drug Abuse, Bethesda, MD, United States
| | - Weiyi Wu
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Christopher Zegers
- Addiction Medicine and Recovery Services, The Permanente Medical Group Northern California, Sacramento, CA, United States
| | - Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
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Galatzer-Levy IR, Onnela JP. Machine Learning and the Digital Measurement of Psychological Health. Annu Rev Clin Psychol 2023; 19:133-154. [PMID: 37159287 DOI: 10.1146/annurev-clinpsy-080921-073212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.
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Affiliation(s)
- Isaac R Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA;
- Current affiliation: Google LLC, Mountain View, California, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Zieschank V, Junker RR. Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field. Front Plant Sci 2023; 14:1141554. [PMID: 37229120 PMCID: PMC10203609 DOI: 10.3389/fpls.2023.1141554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Abstract
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
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Affiliation(s)
- Vincent Zieschank
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, Marburg, Germany
| | - Robert R. Junker
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, Marburg, Germany
- Department of Environment and Biodiversity, University of Salzburg, Salzburg, Austria
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Torous J, Benson NM, Myrick K, Eysenbach G. Focusing on Digital Research Priorities for Advancing the Access and Quality of Mental Health. JMIR Ment Health 2023; 10:e47898. [PMID: 37093624 PMCID: PMC10167575 DOI: 10.2196/47898] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023] Open
Abstract
Digital mental health solutions are now well recognized as critical to solving the global mental health crisis. As research accelerates, it is now clear that solutions ranging from computer-based therapy programs to virtual reality headsets and smartphone apps to large language model chatbots are of interest, feasible, and hold exciting potential to improve mental health. This research should now consider the next generation of scientific and clinical questions regarding if these new approaches are equitable, valid, effective, implementable, efficacious, and even cost-effective. This paper outlines several of the new frontiers for the next generation of research and introduces JMIR Publications' partnership with the Society of Digital Psychiatry to further advance these aims.
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Affiliation(s)
- John Torous
- Beth Israel Deaoness Medical Center, Boston, MA, United States
| | | | - Keris Myrick
- Beth Israel Deaoness Medical Center, Boston, MA, United States
- Inseperable, Washington, DC, United States
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Faurholt-Jepsen M, Busk J, Tønning ML, Bardram JE, Frost M, Vinberg M, Kessing LV. Irritability in bipolar disorder and unipolar disorder measured daily using smartphone-based data: An exploratory post hoc study. Acta Psychiatr Scand 2023; 147:593-602. [PMID: 37094823 DOI: 10.1111/acps.13558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/06/2023] [Accepted: 04/10/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVE To investigate (i) the proportions of time with irritability and (ii) the association between irritability and affective symptoms and functioning, stress, and quality of life in patients with bipolar disorder (BD) and unipolar depressive disorder (UD). METHODS A total of 316 patients with BD and 58 patients with UD provided self-reported once-a-day data on irritability and other affective symptoms using smartphones for a total of 64,129 days with observations. Questionnaires on perceived stress and quality of life and clinical evaluations of functioning were collected multiple times during the study. RESULTS During a depressive state, patients with UD spent a significantly higher proportion of time with presence of irritability (83.10%) as compared with patients with BD (70.27%) (p = 0.045). Irritability was associated with lower mood, activity level and sleep duration and with increased stress and anxiety level, in both patient groups (p-values<0.008). Increased irritability was associated with impaired functioning and increased perceived stress (p-values<0.024). In addition, in patients with UD, increased irritability was associated with decreased quality of life (p = 0.002). The results were not altered when adjusting for psychopharmacological treatments. CONCLUSIONS Irritability is an important part of the symptomatology in affective disorders. Clinicians could have focus on symptoms of irritability in both patients with BD and UD during their course of illness. Future studies investigating treatment effects on irritability would be interesting.
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Affiliation(s)
- Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonas Busk
- Department of Energy Conversion and Storage, Technical University of Denmark, Lyngby, Denmark
| | - Morten Lindberg Tønning
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg hospital, Frederiksberg, Denmark
| | - Jakob Eyvind Bardram
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | - Maj Vinberg
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Northern Zealand, Hilleroed, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Charron E, White A, Carlston K, Abdullah W, Baylis JD, Pierce S, Businelle MS, Gordon AJ, Krans EE, Smid MC, Cochran G. Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study. Front Psychiatry 2023; 14:1137071. [PMID: 37139320 PMCID: PMC10149825 DOI: 10.3389/fpsyt.2023.1137071] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/27/2023] [Indexed: 05/05/2023] Open
Abstract
Background While medications for opioid use disorder (MOUD) effectively treat OUD during pregnancy and the postpartum period, poor treatment retention is common. Digital phenotyping, or passive sensing data captured from personal mobile devices, namely smartphones, provides an opportunity to understand behaviors, psychological states, and social influences contributing to perinatal MOUD non-retention. Given this novel area of investigation, we conducted a qualitative study to determine the acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder (PPP-OUD). Methods This study was guided by the Theoretical Framework of Acceptability (TFA). Within a clinical trial testing a behavioral health intervention for PPP-OUD, we used purposeful criterion sampling to recruit 11 participants who delivered a child in the past 12 months and received OUD treatment during pregnancy or the postpartum period. Data were collected through phone interviews using a structured interview guide based on four TFA constructs (affective attitude, burden, ethicality, self-efficacy). We used framework analysis to code, chart, and identify key patterns within the data. Results Participants generally expressed positive attitudes about digital phenotyping and high self-efficacy and low anticipated burden to participate in studies that collect smartphone-based passive sensing data. Nonetheless, concerns were noted related to data privacy/security and sharing location information. Differences in participant assessments of burden were related to length of time required and level of remuneration to participate in a study. Interviewees voiced broad support for participating in a digital phenotyping study with known/trusted individuals but expressed concerns about third-party data sharing and government monitoring. Conclusion Digital phenotyping methods were acceptable to PPP-OUD. Enhancements in acceptability include allowing participants to maintain control over which data are shared, limiting frequency of research contacts, aligning compensation with participant burden, and outlining data privacy/security protections on study materials.
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Affiliation(s)
- Elizabeth Charron
- Department of Health Promotion Sciences, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Tulsa, OK, United States
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Ashley White
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Kristi Carlston
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Walitta Abdullah
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jacob D Baylis
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Stephanie Pierce
- Section of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Michael S Businelle
- TSET Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Adam J Gordon
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States
| | - Elizabeth E Krans
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, United States
- Center for Perinatal Addiction Research, Education and Evidence-based Solutions (Magee CARES), Magee-Womens Research Institute, Pittsburgh, PA, United States
| | - Marcela C Smid
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT, United States
| | - Gerald Cochran
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
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Parola A, Simonsen A, Lin JM, Zhou Y, Wang H, Ubukata S, Koelkebeck K, Bliksted V, Fusaroli R. Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation. Schizophr Bull 2023; 49:S125-S141. [PMID: 36946527 PMCID: PMC10031745 DOI: 10.1093/schbul/sbac128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND AND HYPOTHESIS Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages. STUDY DESIGN We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences. STUDY RESULTS We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages. CONCLUSIONS The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.
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Affiliation(s)
- Alberto Parola
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Department of Psychology, University of Turin, Turin, Italy
| | - Arndis Simonsen
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jessica Mary Lin
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
| | - Yuan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shiho Ubukata
- Department of Psychiatry, Kyoto University, Kyoto, Japan
| | - Katja Koelkebeck
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Hospital and Institute of the University of Duisburg-Essen, Essen, Germany
- Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Duisburg-Essen, Germany
| | - Vibeke Bliksted
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Riccardo Fusaroli
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Linguistic Data Consortium, University of Pennsylvania, Philadelphia, USA
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Langener AM, Stulp G, Kas MJ, Bringmann LF. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Ment Health 2023; 10:e42646. [PMID: 36930210 PMCID: PMC10132048 DOI: 10.2196/42646] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people's social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people's social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. OBJECTIVE Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. METHODS We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. CONCLUSIONS We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants' burden and form a holistic perspective on the social environment.
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Affiliation(s)
- Anna M Langener
- Groningen Institute for Evolutionary Life Sciences, Groningen, Netherlands.,Department of Sociology, Faculty of Behavioural and Social Sciences, University of Groningen & Inter-University Center for Social Science Theory and Methodology, Groningen, Netherlands.,Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands
| | - Gert Stulp
- Department of Sociology, Faculty of Behavioural and Social Sciences, University of Groningen & Inter-University Center for Social Science Theory and Methodology, Groningen, Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, Groningen, Netherlands
| | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands.,Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. J Med Internet Res 2023; 25:e39546. [PMID: 36917148 PMCID: PMC10132029 DOI: 10.2196/39546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology.
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Affiliation(s)
- Christopher De Boer
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Hassan Ghomrawi
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Department of Pediatrics, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Suhail Zeineddin
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Samuel Linton
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Soyang Kwon
- The Smith Child Health Research Program, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States
| | - Fizan Abdullah
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Form Res 2023; 7:e43296. [PMID: 36881444 PMCID: PMC10031448 DOI: 10.2196/43296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection. OBJECTIVE In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities. METHODS To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app's sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated. RESULTS Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate-the ratio between the actual and expected number of measurements-was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience. CONCLUSIONS To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.
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Affiliation(s)
- Koen Niemeijer
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Merijn Mestdagh
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Stijn Verdonck
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Kristof Meers
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Peter Kuppens
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
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Méline V, Caldwell DL, Kim BS, Khangura RS, Baireddy S, Yang C, Sparks EE, Dilkes B, Delp EJ, Iyer-Pascuzzi AS. Image-based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato. Plant J 2023; 113:887-903. [PMID: 36628472 DOI: 10.1111/tpj.16101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/12/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
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Affiliation(s)
- Valérian Méline
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Denise L Caldwell
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Bong-Suk Kim
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Rajdeep S Khangura
- Department of Biochemistry and Center for Plant Biology, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Sriram Baireddy
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Changye Yang
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences and the Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Brian Dilkes
- Department of Biochemistry and Center for Plant Biology, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Edward J Delp
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Anjali S Iyer-Pascuzzi
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
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Greenberg JK, Frumkin MR, Javeed S, Zhang JK, Dai R, Molina CA, Pennicooke BH, Agarwal N, Santiago P, Goodwin ML, Jain D, Pallotta N, Gupta MC, Buchowski JM, Leuthardt EC, Ghogawala Z, Kelly MP, Hall BL, Piccirillo JF, Lu C, Rodebaugh TL, Ray WZ. Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates. Neurosurgery 2023; 92:538-546. [PMID: 36700710 PMCID: PMC10158869 DOI: 10.1227/neu.0000000000002245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/19/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Rapid growth in smartphone use has expanded opportunities to use mobile health (mHealth) technology to collect real-time patient-reported and objective biometric data. These data may have important implication for personalized treatments of degenerative spine disease. However, no large-scale study has examined the feasibility and acceptability of these methods in spine surgery patients. OBJECTIVE To evaluate the feasibility and acceptability of a multimodal preoperative mHealth assessment in patients with degenerative spine disease. METHODS Adults undergoing elective spine surgery were provided with Fitbit trackers and sent preoperative ecological momentary assessments (EMAs) assessing pain, disability, mood, and catastrophizing 5 times daily for 3 weeks. Objective adherence rates and a subjective acceptability survey were used to evaluate feasibility of these methods. RESULTS The 77 included participants completed an average of 82 EMAs each, with an average completion rate of 86%. Younger age and chronic pulmonary disease were significantly associated with lower EMA adherence. Seventy-two (93%) participants completed Fitbit monitoring and wore the Fitbits for an average of 247 hours each. On average, participants wore the Fitbits for at least 12 hours per day for 15 days. Only worse mood scores were independently associated with lower Fitbit adherence. Most participants endorsed positive experiences with the study protocol, including 91% who said they would be willing to complete EMAs to improve their preoperative surgical guidance. CONCLUSION Spine fusion candidates successfully completed a preoperative multimodal mHealth assessment with high acceptability. The intensive longitudinal data collected may provide new insights that improve patient selection and treatment guidance.
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Affiliation(s)
- Jacob K. Greenberg
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Madelyn R. Frumkin
- Department of Psychology and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Saad Javeed
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Justin K. Zhang
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Ruixuan Dai
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Camilo A. Molina
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Brenton H. Pennicooke
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Nitin Agarwal
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Paul Santiago
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Matthew L. Goodwin
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Deeptee Jain
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Nicholas Pallotta
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Munish C. Gupta
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Jacob M. Buchowski
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Eric C. Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Zoher Ghogawala
- Department of Neurosurgery, Lahey Hospital and Medical Center, Burlington, Massachusetts, USA
| | - Michael P. Kelly
- Department of Orthopaedic Surgery, Rady Children's Hospital, San Diego, California, USA
| | - Bruce L. Hall
- Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Jay F. Piccirillo
- Department of Otolaryngology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Chenyang Lu
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Thomas L. Rodebaugh
- Department of Psychology and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Wilson Z. Ray
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Sariyanidi E, Zampella CJ, DeJardin E, Herrington JD, Schultz RT, Tunc B. Comparison of Human Experts and AI in Predicting Autism from Facial Behavior. CEUR Workshop Proc 2023; 3359:48-57. [PMID: 38037663 PMCID: PMC10687770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer. In this paper, we compare the performance of 19 human raters (8 autism experts and 11 non-experts) and that of an AI algorithm in terms of predicting autism diagnosis from short (3-minute) videos of N = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.
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Affiliation(s)
- Evangelos Sariyanidi
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
| | - Casey J Zampella
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
| | - Ellis DeJardin
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
| | - John D Herrington
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
- University of Pennsylvania, United States
| | - Robert T Schultz
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
- University of Pennsylvania, United States
| | - Birkan Tunc
- Center for Autism Research, The Children's Hospital of Philadelphia, United States
- University of Pennsylvania, United States
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