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Langener AM, Bringmann LF, Kas MJ, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2024; 51:455-475. [PMID: 38200262 PMCID: PMC11196304 DOI: 10.1007/s10488-023-01328-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2023] [Indexed: 01/12/2024]
Abstract
Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.
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Affiliation(s)
- Anna M Langener
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
- Faculty of Science and Engineering, Nijenborgh 7, 9747 AG, Groningen, The Netherlands.
| | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Interdisciplinary Center Psychopathology and Emotion Regulation, (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Gert Stulp
- Department of Sociology & Inter-University Center for Social Science Theory and Methodology, Grote Rozenstraat 31, 9712 TS, Groningen, The Netherlands
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Deng D, Ostrem JL, Nguyen V, Cummins DD, Sun J, Pathak A, Little S, Abbasi-Asl R. Interpretable video-based tracking and quantification of parkinsonism clinical motor states. NPJ Parkinsons Dis 2024; 10:122. [PMID: 38918385 PMCID: PMC11199701 DOI: 10.1038/s41531-024-00742-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Quantification of motor symptom progression in Parkinson's disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical "black-box" ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.
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Affiliation(s)
- Daniel Deng
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Vy Nguyen
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel D Cummins
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Julia Sun
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Reza Abbasi-Asl
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Weill Institute for Neurosciences, San Francisco, CA, USA.
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3
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Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. NPJ Digit Med 2024; 7:161. [PMID: 38890529 PMCID: PMC11189504 DOI: 10.1038/s41746-024-01151-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Department of Behavioral and Social Sciences, Brown University, Providence, USA
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jürgen Bernard
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computer Science, University of Zurich, Zurich, Switzerland
| | | | - Viktor von Wyl
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
- Digital Society Initiative, University of Zurich, Zurich, Switzerland.
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
- Swiss School of Public Health (SSPH+), Zurich, Switzerland.
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van Heerden A, Poudyal A, Hagaman A, Maharjan SM, Byanjankar P, Bemme D, Thapa A, Kohrt BA. Integration of passive sensing technology to enhance delivery of psychological interventions for mothers with depression: the StandStrong study. Sci Rep 2024; 14:13535. [PMID: 38866839 PMCID: PMC11169515 DOI: 10.1038/s41598-024-63232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 05/27/2024] [Indexed: 06/14/2024] Open
Abstract
Psychological interventions delivered by non-specialist providers have shown mixed results for treating maternal depression. mHealth solutions hold the possibility for unobtrusive behavioural data collection to identify challenges and reinforce change in psychological interventions. We conducted a proof-of-concept study using passive sensing integrated into a depression intervention delivered by non-specialists to twenty-four adolescents and young mothers (30% 15-17 years old; 70% 18-25 years old) with infants (< 12 months old) in rural Nepal. All mothers showed a reduction in depression symptoms as measured with the Beck Depression Inventory. There were trends toward increased movement away from the house (greater distance measured through GPS data) and more time spent away from the infant (less time in proximity measured with the Bluetooth beacon) as the depression symptoms improved. There was considerable heterogeneity in these changes and other passively collected data (speech, physical activity) throughout the intervention. This proof-of-concept demonstrated that passive sensing can be feasibly used in low-resource settings and can personalize psychological interventions. Care must be taken when implementing such an approach to ensure confidentiality, data protection, and meaningful interpretation of data to enhance psychological interventions.
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Affiliation(s)
- Alastair van Heerden
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa.
- South African Medical Research Council/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Anubhuti Poudyal
- Department of Sociomedical Sciences, Columbia Mailman School of Public Health, New York, NY, USA
- Department of Psychiatry and Behavioral Sciences, Center for Global Mental Health Equity, George Washington School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley Hagaman
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, USA
| | | | | | - Dörte Bemme
- Department for Global Health and Social Medicine, Kings College London, London, UK
| | - Ada Thapa
- Division of Global Health Equity, Brigham and Women's Hospital Boston, Boston, MA, USA
| | - Brandon A Kohrt
- Department of Psychiatry and Behavioral Sciences, Center for Global Mental Health Equity, George Washington School of Medicine and Health Sciences, Washington, DC, USA
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Ciharova M, Amarti K, van Breda W, Peng X, Lorente-Català R, Funk B, Hoogendoorn M, Koutsouleris N, Fusar-Poli P, Karyotaki E, Cuijpers P, Riper H. Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review. Biol Psychiatry 2024:S0006-3223(24)01362-3. [PMID: 38866173 DOI: 10.1016/j.biopsych.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 05/24/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved.
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Affiliation(s)
- Marketa Ciharova
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Black Dog Institute, University of New South Wales, Sydney, New South Wales, Australia.
| | - Khadicha Amarti
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ward van Breda
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Xianhua Peng
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Methodology and Statistics, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, the Netherlands
| | - Rosa Lorente-Català
- Department of Basic and Clinical Psychology and Psychobiology, Universitat Jaume I, Castellon, Spain
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nikolaos Koutsouleris
- Artificial Intelligence in Mental Health Group, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Precision Psychiatry Group, Max Planck Institute, Munich, Germany; Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Paolo Fusar-Poli
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center, Ludwig-Maximilians-University Munich, Munich, Germany; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; OASIS Service, South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Eirini Karyotaki
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; WHO Collaborating Center for Research and Dissemination of Psychological Interventions, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pim Cuijpers
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; WHO Collaborating Center for Research and Dissemination of Psychological Interventions, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Babeș-Bolyai University, International Institute for Psychotherapy, Cluj-Napoca, Romania
| | - Heleen Riper
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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6
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Choi A, Ooi A, Lottridge D. Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review. JMIR Mhealth Uhealth 2024; 12:e40689. [PMID: 38780995 PMCID: PMC11157179 DOI: 10.2196/40689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/03/2022] [Accepted: 09/27/2023] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious. OBJECTIVE This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges? METHODS We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded. RESULTS We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression. CONCLUSIONS This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression.
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Affiliation(s)
- Adrien Choi
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
| | - Aysel Ooi
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
| | - Danielle Lottridge
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
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Benrimoh D, Dlugunovych V, Wright AC, Phalen P, Funaro MC, Ferrara M, Powers AR, Woods SW, Guloksuz S, Yung AR, Srihari V, Shah J. On the proportion of patients who experience a prodrome prior to psychosis onset: A systematic review and meta-analysis. Mol Psychiatry 2024; 29:1361-1381. [PMID: 38302562 DOI: 10.1038/s41380-024-02415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/20/2023] [Accepted: 01/04/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Preventing or delaying the onset of psychosis requires identification of those at risk for developing psychosis. For predictive purposes, the prodrome - a constellation of symptoms which may occur before the onset of psychosis - has been increasingly recognized as having utility. However, it is unclear what proportion of patients experience a prodrome or how this varies based on the multiple definitions used. METHODS We conducted a systematic review and meta-analysis of studies of patients with psychosis with the objective of determining the proportion of patients who experienced a prodrome prior to psychosis onset. Inclusion criteria included a consistent prodrome definition and reporting the proportion of patients who experienced a prodrome. We excluded studies of only patients with a prodrome or solely substance-induced psychosis, qualitative studies without prevalence data, conference abstracts, and case reports/case series. We searched Ovid MEDLINE, Embase (Ovid), APA PsycInfo (Ovid), Web of Science Core Collection (Clarivate), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, APA PsycBooks (Ovid), ProQuest Dissertation & Thesis, on March 3, 2021. Studies were assessed for quality using the Critical Appraisal Checklist for Prevalence Studies. Narrative synthesis and proportion meta-analysis were used to estimate prodrome prevalence. I2 and predictive interval were used to assess heterogeneity. Subgroup analyses were used to probe sources of heterogeneity. (PROSPERO ID: CRD42021239797). RESULTS Seventy-one articles were included, representing 13,774 patients. Studies varied significantly in terms of methodology and prodrome definition used. The random effects proportion meta-analysis estimate for prodrome prevalence was 78.3% (95% CI = 72.8-83.2); heterogeneity was high (I2 97.98% [95% CI = 97.71-98.22]); and the prediction interval was wide (95% PI = 0.411-0.936). There were no meaningful differences in prevalence between grouped prodrome definitions, and subgroup analyses failed to reveal a consistent source of heterogeneity. CONCLUSIONS This is the first meta-analysis on the prevalence of a prodrome prior to the onset of first episode psychosis. The majority of patients (78.3%) were found to have experienced a prodrome prior to psychosis onset. However, findings are highly heterogenous across study and no definitive source of heterogeneity was found despite extensive subgroup analyses. As most studies were retrospective in nature, recall bias likely affects these results. While the large majority of patients with psychosis experience a prodrome in some form, it is unclear if the remainder of patients experience no prodrome, or if ascertainment methods employed in the studies were not sensitive to their experiences. Given widespread investment in indicated prevention of psychosis through prospective identification and intervention during the prodrome, a resolution of this question as well as a consensus definition of the prodrome is much needed in order to effectively direct and organize services, and may be accomplished through novel, densely sampled and phenotyped prospective cohort studies that aim for representative sampling across multiple settings.
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Affiliation(s)
- David Benrimoh
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, Stanford University, Stanford, CA, USA.
| | | | - Abigail C Wright
- Center of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Peter Phalen
- Division of Psychiatric Services Research, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Melissa C Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Maria Ferrara
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Scott W Woods
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Sinan Guloksuz
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Neuropsychology Maastricht University Medical Center, Maastricht, Netherlands
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Melbourne, Australia
| | - Vinod Srihari
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jai Shah
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada
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Cho K, Kim M, Cho Y, Hur JW, Kim DH, Park S, Park S, Jang M, Lee CG, Kwon JS. Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study. JMIR Ment Health 2024; 11:e50259. [PMID: 38683658 DOI: 10.2196/50259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Limited awareness, social stigma, and access to mental health professionals hinder early detection and intervention of internet gaming disorder (IGD), which has emerged as a significant concern among young individuals. Prevalence estimates vary between 0.7% and 15.6%, and its recognition in the International Classification of Diseases, 11th Revision and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition underscores its impact on academic functioning, social isolation, and mental health challenges. OBJECTIVE This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases. METHODS A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes. RESULTS A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=-0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (β=.33; P<.001), time interval between keys (β=-0.26; P=.01), word spacing (β=-0.25; P<.001), deletion (β=-0.24; P<.001), and horizontal length of strokes (β=-0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD. CONCLUSIONS The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.
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Affiliation(s)
- Kwangsu Cho
- 3R Innovation Research Center, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Youngeun Cho
- Department of Artificial Intelligence, Hanyang University, Ansan, Republic of Korea
| | - Ji-Won Hur
- School of Psychology, Korea University, Seoul, Republic of Korea
| | - Do Hyung Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | | | - Sunghyun Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Moonyoung Jang
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chang-Gun Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
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9
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Emish M, Young SD. Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review. Biomimetics (Basel) 2024; 9:237. [PMID: 38667247 PMCID: PMC11048695 DOI: 10.3390/biomimetics9040237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.
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Affiliation(s)
- Mohamed Emish
- Department of Informatics, University of California, Irvine, CA 92697-3100, USA;
| | - Sean D. Young
- Department of Informatics, University of California, Irvine, CA 92697-3100, USA;
- Department of Emergency Medicine, University of California, Irvine, CA 92697-3100, USA
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10
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Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. J Med Internet Res 2024; 26:e51138. [PMID: 38602750 PMCID: PMC11046386 DOI: 10.2196/51138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 11/15/2023] [Accepted: 01/30/2024] [Indexed: 04/12/2024] Open
Abstract
Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.
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Affiliation(s)
- Peter Washington
- Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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11
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Fuller-Tyszkiewicz M, Messer M, Krug I, Linardon J. Digital health applications for eating disorders treatment. Trends Mol Med 2024; 30:324-326. [PMID: 37996311 DOI: 10.1016/j.molmed.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023]
Abstract
Eating disorders (EDs) are characterized by multifaceted etiologies, difficulties in accessing care (especially in regional locations), and variable responsiveness to treatments. Digital technologies are viewed as an important innovation in the assessment and treatment of EDs. We discuss current implementation of these innovations as well as important future directions for the field.
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Affiliation(s)
| | - Mariel Messer
- Deakin University, SEED-Lifespan, School of Psychology, Faculty of Health, Geelong, VIC, Australia
| | - Isabel Krug
- University of Melbourne, School of Psychological Sciences, Melbourne, VIC, Australia
| | - Jake Linardon
- Deakin University, SEED-Lifespan, School of Psychology, Faculty of Health, Geelong, VIC, Australia
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12
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Ratnaparkhi A, Beckett J. Digital Phenotyping, Wearables, and Outcomes. Neurosurg Clin N Am 2024; 35:235-241. [PMID: 38423739 DOI: 10.1016/j.nec.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
There is a significant need for robust and objective outcome assessments in spine surgery. Constant monitoring via smartphones and wearable devices has the potential to fill this role by providing an in-depth picture of human well-being, creating an unprecedented amount of objective data to augment clinical decision-making. The metrics obtained from continuous patient monitoring increase the amount and ecological validity of data relevant to spine surgery. This can provide physicians with patient and disease-specific medical information, facilitating personalized patient care.
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Affiliation(s)
- Anshul Ratnaparkhi
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles
| | - Joel Beckett
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles; David Geffen School of Medicine, University of California Los Angeles.
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13
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Hsu JH, Wu CH, Lin ECL, Chen PS. MoodSensing: A smartphone app for digital phenotyping and assessment of bipolar disorder. Psychiatry Res 2024; 334:115790. [PMID: 38401488 DOI: 10.1016/j.psychres.2024.115790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/29/2024] [Accepted: 02/11/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Daily life tracking has proven to be of great help in the assessment of patients with bipolar disorder. Although there are many smartphone apps for tracking bipolar disorder, most of them lack academic verification, privacy policy and long-term maintenance. METHODS Our developed app, MoodSensing, aims to collect users' digital phenotyping for assessment of bipolar disorder. The data collection was approved by the Institutional Review Board. This study collaborated with professional clinicians to ensure that the app meets both clinical needs and user experience requirements. Based on the collected digital phenotyping, deep learning techniques were applied to forecast participants' weekly HAM-D and YMRS scale scores. RESULTS In experiments, the data collected by our app can effectively predict the scale scores, reaching the mean absolute error of 0.84 and 0.22 on the scales. The statistical data also demonstrate the increase in user engagement. CONCLUSIONS Our analysis reveals that the developed MoodSensing app can not only provide a good user experience, but also the recorded data have certain discriminability for clinical assessment. Our app also provides relevant policies to protect user privacy, and has been launched in the Apple Store and Google Play Store.
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Affiliation(s)
- Jia-Hao Hsu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan
| | - Chung-Hsien Wu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan.
| | | | - Po-See Chen
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Taiwan
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14
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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] [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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15
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Chen J, Chan NY, Li CT, Chan JWY, Liu Y, Li SX, Chau SWH, Leung KS, Heng PA, Lee TMC, Li TMH, Wing YK. Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese. Transl Psychiatry 2024; 14:150. [PMID: 38499546 PMCID: PMC10948748 DOI: 10.1038/s41398-024-02873-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 03/20/2024] Open
Abstract
There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.
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Affiliation(s)
- Jie Chen
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Psychiatry, Fujian Medical University Affiliated Fuzhou Neuropsychiatric Hospital, Fuzhou, China
| | - Ngan Yin Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Chun-Tung Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Joey W Y Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Yaping Liu
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shirley Xin Li
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
- Sleep Research Clinic and Laboratory, Department of Psychology, The University of Hong Kong, Hong Kong SAR, China
| | - Steven W H Chau
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kwong Sak Leung
- Department of Applied Data Science, Hong Kong Shue Yan University, Hong Kong SAR, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
- Department of Psychology, The University of Hong Kong, Hong Kong SAR, China
| | - Tim M H Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
| | - Yun-Kwok Wing
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
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16
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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] [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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17
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Wang P, Leong QY, Lau NY, Ng WY, Kwek SP, Tan L, Song SW, You K, Chong LM, Zhuang I, Ong YH, Foo N, Tadeo X, Kumar KS, Vijayakumar S, Sapanel Y, Raczkowska MN, Remus A, Blasiak A, Ho D. N-of-1 medicine. Singapore Med J 2024; 65:167-175. [PMID: 38527301 PMCID: PMC11060644 DOI: 10.4103/singaporemedj.smj-2023-243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/19/2024] [Indexed: 03/27/2024]
Abstract
ABSTRACT The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.
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Affiliation(s)
- Peter Wang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Qiao Ying Leong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Ni Yin Lau
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Wei Ying Ng
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Siong Peng Kwek
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Lester Tan
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Shang-Wei Song
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Kui You
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Li Ming Chong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Isaiah Zhuang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoong Hun Ong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Nigel Foo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Xavier Tadeo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Kirthika Senthil Kumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Smrithi Vijayakumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoann Sapanel
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Marlena Natalia Raczkowska
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Alexandria Remus
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Heat Resilience Performance Centre (HRPC), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Agata Blasiak
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Dean Ho
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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18
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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] [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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19
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Shen FX, Baum ML, Martinez-Martin N, Miner AS, Abraham M, Brownstein CA, Cortez N, Evans BJ, Germine LT, Glahn DC, Grady C, Holm IA, Hurley EA, Kimble S, Lázaro-Muñoz G, Leary K, Marks M, Monette PJ, Jukka-Pekka O, O’Rourke PP, Rauch SL, Shachar C, Sen S, Vahia I, Vassy JL, Baker JT, Bierer BE, Silverman BC. Returning Individual Research Results from Digital Phenotyping in Psychiatry. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:69-90. [PMID: 37155651 PMCID: PMC10630534 DOI: 10.1080/15265161.2023.2180109] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.
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Affiliation(s)
- Francis X. Shen
- Harvard Medical School
- Massachusetts General Hospital
- Harvard Law School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Mason Marks
- Harvard Law School
- Florida State University College of Law
- Yale Law School
| | | | | | | | - Scott L. Rauch
- Harvard Medical School
- McLean Hospital
- Mass General Brigham
| | | | | | | | - Jason L. Vassy
- Harvard Medical School
- Brigham and Women’s Hospital
- VA Boston Healthcare System
| | | | - Barbara E. Bierer
- Harvard Medical School
- Brigham and Women’s Hospital
- Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard
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20
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Leuenberger M. Track Thyself? The Value and Ethics of Self-knowledge Through Technology. PHILOSOPHY & TECHNOLOGY 2024; 37:13. [PMID: 38288051 PMCID: PMC10821817 DOI: 10.1007/s13347-024-00704-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Novel technological devices, applications, and algorithms can provide us with a vast amount of personal information about ourselves. Given that we have ethical and practical reasons to pursue self-knowledge, should we use technology to increase our self-knowledge? And which ethical issues arise from the pursuit of technologically sourced self-knowledge? In this paper, I explore these questions in relation to bioinformation technologies (health and activity trackers, DTC genetic testing, and DTC neurotechnologies) and algorithmic profiling used for recommender systems, targeted advertising, and technologically supported decision-making. First, I distinguish between impersonal, critical, and relational self-knowledge. Relational self-knowledge is a so far neglected dimension of self-knowledge which is introduced in this paper. Next, I investigate the contribution of these technologies to the three types of self-knowledge and uncover the connected ethical concerns. Technology can provide a lot of impersonal self-knowledge, but we should focus on the quality of the information which tends to be particularly insufficient for marginalized groups. In terms of critical self-knowledge, the nature of technologically sourced personal information typically impedes critical engagement. The value of relational self-knowledge speaks in favour of transparency of information technology, notably for algorithms that are involved in decision-making about individuals. Moreover, bioinformation technologies and digital profiling shape the concepts and norms that define us. We should ensure they not only serve commercial interests but our identity and self-knowledge interests.
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Affiliation(s)
- Muriel Leuenberger
- Center for Ethics, University of Zurich, Zollikerstrasse 117, 8008 Zurich, Switzerland
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21
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Rykov YG, Patterson MD, Gangwar BA, Jabar SB, Leonardo J, Ng KP, Kandiah N. Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment. BMC Med 2024; 22:36. [PMID: 38273340 PMCID: PMC10809621 DOI: 10.1186/s12916-024-03252-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. METHODS We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation. RESULTS The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. CONCLUSIONS Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.
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Affiliation(s)
| | | | | | | | - Jacklyn Leonardo
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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22
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Pai A, Santiago R, Glantz N, Bevier W, Barua S, Sabharwal A, Kerr D. Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes. NPJ Digit Med 2024; 7:7. [PMID: 38212415 PMCID: PMC10784546 DOI: 10.1038/s41746-023-00985-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
Digital phenotyping refers to characterizing human bio-behavior through wearables, personal devices, and digital health technologies. Digital phenotyping in populations facing a disproportionate burden of type 2 diabetes (T2D) and health disparities continues to lag compared to other populations. Here, we report our study demonstrating the application of multimodal digital phenotyping, i.e., the simultaneous use of CGM, physical activity monitors, and meal tracking in Hispanic/Latino individuals with or at risk of T2D. For 14 days, 36 Hispanic/Latino adults (28 female, 14 with non-insulin treated T2D) wore a continuous glucose monitor (CGM) and a physical activity monitor (Actigraph) while simultaneously logging meals using the MyFitnessPal app. We model meal events and daily digital biomarkers representing diet, physical activity choices, and corresponding glycemic response. We develop a digital biomarker for meal events that differentiates meal events into normal and elevated categories. We examine the contribution of daily digital biomarkers of elevated meal event count and step count on daily time-in-range 54-140 mg/dL (TIR54-140) and average glucose. After adjusting for step count, a change in elevated meal event count from zero to two decreases TIR54-140 by 4.0% (p = 0.003). An increase in 1000 steps in post-meal step count also reduces the meal event glucose response by 641 min mg/dL (p = 0.0006) and reduces the odds of an elevated meal event by 55% (p < 0.0001). The proposed meal event digital biomarkers may provide an opportunity for non-pharmacologic interventions for Hispanic/Latino adults facing a disproportionate burden of T2D.
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Affiliation(s)
- Amruta Pai
- Electrical and Computer Engineering, Rice University, Houston, TX, USA.
| | - Rony Santiago
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Namino Glantz
- Santa Barbara County Education Office, Children & Family Resource Services, Santa Barbara, CA, USA
| | - Wendy Bevier
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Souptik Barua
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | | | - David Kerr
- Sutter Center for Health Systems Research, Santa Barbara, CA, USA
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23
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Nourse R, Dingler T, Kelly J, Kwasnicka D, Maddison R. The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions. J Med Internet Res 2023; 25:e44265. [PMID: 38109188 PMCID: PMC10758944 DOI: 10.2196/44265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 06/07/2023] [Accepted: 06/29/2023] [Indexed: 12/19/2023] Open
Abstract
The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.
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Affiliation(s)
- Rebecca Nourse
- School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
| | - Tilman Dingler
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Jaimon Kelly
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Dominika Kwasnicka
- NHMRC CRE in Digital Technology to Transform Chronic Disease Outcomes, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Wroclaw, Poland
| | - Ralph Maddison
- School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
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24
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Hong CS, Smith TR. Aerobic exercise interventions to address impaired quality of life in patients with pituitary tumors. PLoS One 2023; 18:e0295907. [PMID: 38100429 PMCID: PMC10723697 DOI: 10.1371/journal.pone.0295907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Patients with pituitary tumors may experience persistent fatigue and reduced physical activity, based on subjective measures after treatment. These symptoms may persist despite gross total resection of their tumors and biochemical normalization of pituitary function. While reduced quality of life has been commonly acknowledged in pituitary tumor patients, there is a lack of studies on what interventions may be best implemented to ameliorate these issues, particularly when hormonal levels have otherwise normalized. Aerobic exercise programs have been previously described to ameliorate symptoms of chronic fatigue and reduced physical capacity across a variety of pathologies in the literature. As such, a prescribed aerobic exercise program may be an underrecognized but potentially impactful intervention to address quality of life in pituitary tumor patients. This review seeks to summarize the existing literature on aerobic exercise interventions in patients with pituitary tumors. In addition, future areas of study are discussed, including tailoring exercise programs to the hormonal status of the patient and incorporating more objective measures in monitoring response to interventions.
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Affiliation(s)
- Christopher S. Hong
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Computational Neuroscience Outcomes Center (CNOC), Boston, Masachusettts, United States of America
| | - Timothy R. Smith
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Computational Neuroscience Outcomes Center (CNOC), Boston, Masachusettts, United States of America
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25
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Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
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Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
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26
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Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000347. [PMID: 37819910 PMCID: PMC10566734 DOI: 10.1371/journal.pdig.0000347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/14/2023] [Indexed: 10/13/2023]
Abstract
Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.
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Affiliation(s)
- Jana Sedlakova
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
| | - Andrea Horn Wintsch
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center for Gerontology, University of Zurich, Zurich, Switzerland
- CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Markus Wolf
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Mina Stanikic
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chloé Sieber
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Gerold Schneider
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Kaspar Staub
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Dominik Alois Ettlin
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Oliver Grübner
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Fondazione Bruno Kessler, Trento, Italy
- Swiss Institute of Bioinformatics, Switzerland
| | - Viktor von Wyl
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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27
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Alsaqqa HH, Alwawi A. Digital intervention for public health: searching for implementing characteristics, concepts and recommendations: scoping review. Front Public Health 2023; 11:1142443. [PMID: 37790710 PMCID: PMC10544338 DOI: 10.3389/fpubh.2023.1142443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 09/04/2023] [Indexed: 10/05/2023] Open
Abstract
Studying the impact of digital interventions on public health can help ensure that the offered services produce the desired results. In order to address these factors, the subsequent study uses a scope review to evaluate the state of the field while concentrating on ideas and suggestions that represent factors that have been crucial in the management of digital intervention for public health. To shed light on the traits, ideas and suggestions related to public health digital intervention, a scoping review was carried out. Five electronic databases were used to locate pertinent research that were published before February 2022. All texts were examined, and study abstracts were scrutinized to determine their eligibility. The last analysis of this study included fifteen publications; five reviews, four qualitative studies, two quantitative studies, one viewpoint study, one mixed-method study, one perspective study, and one interventional study. The key ideas for digital interventions in population management and health studies are presented in this overview. Many concepts, implementation characteristics and recommendations have been raised which highlight the future role of these interventions to enhance public engagement and health equity.
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Affiliation(s)
- Hatem H. Alsaqqa
- Deanship of Scientific Research, Al-Quds University, Jerusalem, Palestine
- Ministry of Health, Gaza Strip, Palestine
| | - Abdallah Alwawi
- Anesthesia and Resuscitation Technology, Health Professions Faculty, Al Quds University, Jerusalem, Palestine
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28
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Ribba B, Peck R, Hutchinson L, Bousnina I, Motti D. Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology. Clin Pharmacol Ther 2023; 114:578-590. [PMID: 37392464 DOI: 10.1002/cpt.2989] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/24/2023] [Indexed: 07/03/2023]
Abstract
The promise of transforming digital technologies into treatments is what drives the development of digital therapeutics (DTx), generally known as software applications embedded within accessible technologies-such as smartphones-to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve the life of patients in multiple therapeutic areas, there is a general consensus that generating therapeutic evidence for DTx presents challenges and open questions. We believe there are three main areas where the application of clinical pharmacology principles from the drug development field could benefit DTx development: the characterization of the mechanism of action, the optimization of the intervention, and, finally, its dosing. We reviewed DTx studies to explore how the field is approaching these topics and to better characterize the challenges associated with them. This leads us to emphasize the role that the application of clinical pharmacology principles could play in the development of DTx and to advocate for a development approach that merges such principles from development of traditional therapeutics with important considerations from the highly attractive and fast-paced world of digital solutions.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Richard Peck
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Lucy Hutchinson
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Imein Bousnina
- Genentech, A Member of the Roche Group, Washington, DC, USA
| | - Dario Motti
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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29
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Wyant K, Moshontz H, Ward SB, Fronk GE, Curtin JJ. Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study. JMIR Mhealth Uhealth 2023; 11:e41833. [PMID: 37639300 PMCID: PMC10495858 DOI: 10.2196/41833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 03/14/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications. OBJECTIVE We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses. METHODS Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. RESULTS Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04). CONCLUSIONS These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.
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Affiliation(s)
- Kendra Wyant
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Hannah Moshontz
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Stephanie B Ward
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Gaylen E Fronk
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - John J Curtin
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
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Starke G, D’Imperio A, Ienca M. Out of their minds? Externalist challenges for using AI in forensic psychiatry. Front Psychiatry 2023; 14:1209862. [PMID: 37692304 PMCID: PMC10483237 DOI: 10.3389/fpsyt.2023.1209862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions about future violent behaviour and criminal recidivism pose ethical challenges that require careful deliberation due to their social and legal significance. In this paper, we shed light on these challenges by considering externalist accounts of psychiatric disorders which stress that the presentation and development of psychiatric disorders is intricately entangled with their outward environment and social circumstances. We argue that any use of predictive AI in forensic psychiatry should not be limited to neurobiology alone but must also consider social and environmental factors. This thesis has practical implications for the design of predictive AI systems, especially regarding the collection and processing of training data, the selection of ML methods, and the determination of their explainability requirements.
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Affiliation(s)
- Georg Starke
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
- Munich School of Philosophy, Munich, Germany
| | - Ambra D’Imperio
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry, Hôpitaux Universitaires de Genève, Geneva, Switzerland
- Service of Forensic Psychiatry CURML, Geneva University Hospitals, Geneva, Switzerland
| | - Marcello Ienca
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
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Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Form Res 2023; 7:e47380. [PMID: 37561561 PMCID: PMC10450536 DOI: 10.2196/47380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
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Affiliation(s)
- Jodie Nghiem
- Medical College, Weill Cornell Medicine, New York, NY, United States
| | - Daniel A Adler
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Deborah Estrin
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Cecilia Livesey
- Optum Labs, UnitedHealth Group, Minnetonka, MN, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Tanzeem Choudhury
- College of Computing and Information Science, Cornell Tech, New York, NY, 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] [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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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: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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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Lozano Hernández CM, Medina-García R, de Hoyos-Alonso MC, Garrido-Barral A, Minué Lorenzo C, Sanz-Cuesta T, Serrano J, Del Rio Ponce A, Gómez-Gascón T, Del Cura-González I. Improvement in Quality of Life With the Use of a Technological System Among Patients With Chronic Disease Followed Up in Primary Care (TeNDER Project): Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e47331. [PMID: 37399054 PMCID: PMC10365573 DOI: 10.2196/47331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 07/04/2023] Open
Abstract
BACKGROUND Among chronic diseases, cognitive, neurological, and cardiovascular impairments are becoming increasingly prevalent, generating a shift in health and social needs. Technology can create an ecosystem of care integrated with microtools based on biosensors for motion, location, voice, and expression detection that can help people with chronic diseases. A technological system capable of identifying symptoms, signs, or behavioral patterns could provide notification of the development of complications of disease. This would help the self-care of patients with chronic disease and save health care costs, promoting the autonomy and empowerment of patients and their caregivers, improving their quality of life (QoL), and providing health professionals with monitoring tools. OBJECTIVE The main objective of this study is to evaluate the effectiveness of a technological system (the TeNDER system) to improve quality of life in patients with chronic diseases: Alzheimer disease, Parkinson disease, and cardiovascular disease. METHODS A multicenter, randomized, parallel-group clinical trial will be conducted with a follow-up of 2 months. The scope of the study will be the primary care health centers of the Community of Madrid belonging to the Spanish public health system. The study population will be patients diagnosed with Parkinson disease, Alzheimer disease, and cardiovascular disease; their caregivers; and health professionals. The sample size will be 534 patients (380 in the intervention group). The intervention will consist of the use of the TeNDER system. The system will monitor the patients by means of biosensors, and their data will be integrated into the TeNDER app. With the information provided, the TeNDER system will generate health reports that can be consulted by patients, caregivers, and health professionals. Sociodemographic variables and technological affinity will be measured, as will views on the usability of and satisfaction with the TeNDER system. The dependent variable will be the mean difference in QoL score between the intervention and control groups at 2 months. To study the effectiveness of the TeNDER system in improving QoL in patients, an explanatory linear regression model will be constructed. All analyses will be performed with the 95% CI and robust estimators. RESULTS Ethics approval for this project was received on September 11, 2019. The trial was registered on August 14, 2020. Recruitment commenced in April 2021, and the expected results will be available during 2023 or 2024. CONCLUSIONS This clinical trial among patients with highly prevalent chronic illnesses and the people most involved in their care will provide a more realistic view of the situation experienced by people with long-term illness and their support networks. The TeNDER system is in continuous development based on a study of the needs of the target population and on feedback during its use from the users: patients, caregivers, and primary care health professionals. TRIAL REGISTRATION ClinicalTrials.gov NCT05681065; https://clinicaltrials.gov/ct2/show/NCT05681065. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/47331.
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Affiliation(s)
- Cristina María Lozano Hernández
- Research Unit, Primary Health Care Management, Madrid, Spain
- Biosanitary Research and Innovation Foundation of Primary Care, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
| | - Rodrigo Medina-García
- Research Unit, Primary Health Care Management, Madrid, Spain
- Biosanitary Research and Innovation Foundation of Primary Care, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
- Primary Health Care Management, General Ricardos Primary Health Care Centre, Madrid, Spain
| | - Mª Canto de Hoyos-Alonso
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
- Primary Health Care Management, Lain Entralgo Primary Health Care Centre, Madrid, Spain
| | - Araceli Garrido-Barral
- Primary Health Care Management, Barrio del Pilar Primary Health Care Centre, Madrid, Spain
| | - César Minué Lorenzo
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
- Primary Health Care Management, Perales del Río Primary Health Care Centre, Madrid, Spain
| | - Teresa Sanz-Cuesta
- Research Unit, Primary Health Care Management, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
| | - Javier Serrano
- Visual Telecommunications Application Research Group, Signals, Systems and Radiocommunications Department, Universidad Politécnica, Madrid, Spain
| | - Alberto Del Rio Ponce
- Visual Telecommunications Application Research Group, Signals, Systems and Radiocommunications Department, Universidad Politécnica, Madrid, Spain
| | - Tomas Gómez-Gascón
- Biosanitary Research and Innovation Foundation of Primary Care, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
| | - Isabel Del Cura-González
- Research Unit, Primary Health Care Management, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
- Department of Medical Specialties and Public Health, Faculty of Health Sciences, Rey Juan Carlos University, Madrid, Spain
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Kleiman EM, Glenn CR, Liu RT. The use of advanced technology and statistical methods to predict and prevent suicide. NATURE REVIEWS PSYCHOLOGY 2023; 2:347-359. [PMID: 37588775 PMCID: PMC10426769 DOI: 10.1038/s44159-023-00175-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 08/18/2023]
Abstract
In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.
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Affiliation(s)
- Evan M. Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Richard T. Liu
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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Braund TA, O'Dea B, Bal D, Maston K, Larsen M, Werner-Seidler A, Tillman G, Christensen H. Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study. JMIR Ment Health 2023; 10:e44986. [PMID: 37184904 DOI: 10.2196/44986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/24/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. OBJECTIVE In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. METHODS A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. RESULTS Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. CONCLUSIONS Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. TRIAL REGISTRATION Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true.
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Affiliation(s)
- Taylor A Braund
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Bridianne O'Dea
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Debopriyo Bal
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Kate Maston
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Mark Larsen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Aliza Werner-Seidler
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Gabriel Tillman
- Institute of Health and Wellbeing, Federation University, Ballarat, Australia
| | - Helen Christensen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
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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] [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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Rost N, Dwyer DB, Gaffron S, Rechberger S, Maier D, Binder EB, Brückl TM. Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians. J Affect Disord 2023; 327:330-339. [PMID: 36750160 DOI: 10.1016/j.jad.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/23/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions. METHODS We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis. RESULTS Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians. LIMITATIONS Missing data limited the power to identify biological predictors of treatment outcome from certain modalities. CONCLUSIONS So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | | | | | | | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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Exploring interpretable representations for heart sound abnormality detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Marciano L, Saboor S. Reinventing mental health care in youth through mobile approaches: Current status and future steps. Front Psychol 2023; 14:1126015. [PMID: 36968730 PMCID: PMC10033533 DOI: 10.3389/fpsyg.2023.1126015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/17/2023] [Indexed: 03/12/2023] Open
Abstract
In this perspective, we aim to bring together research on mobile assessments and interventions in the context of mental health care in youth. After the COVID-19 pandemic, one out of five young people is experiencing mental health problems worldwide. New ways to face this burden are now needed. Young people search for low-burden services in terms of costs and time, paired with high flexibility and easy accessibility. Mobile applications meet these principles by providing new ways to inform, monitor, educate, and enable self-help, thus reinventing mental health care in youth. In this perspective, we explore the existing literature reviews on mobile assessments and interventions in youth through data collected passively (e.g., digital phenotyping) and actively (e.g., using Ecological Momentary Assessments—EMAs). The richness of such approaches relies on assessing mental health dynamically by extending beyond the confines of traditional methods and diagnostic criteria, and the integration of sensor data from multiple channels, thus allowing the cross-validation of symptoms through multiple information. However, we also acknowledge the promises and pitfalls of such approaches, including the problem of interpreting small effects combined with different data sources and the real benefits in terms of outcome prediction when compared to gold-standard methods. We also explore a new promising and complementary approach, using chatbots and conversational agents, that encourages interaction while tracing health and providing interventions. Finally, we suggest that it is important to continue to move beyond the ill-being framework by giving more importance to intervention fostering well-being, e.g., using positive psychology.
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Affiliation(s)
- Laura Marciano
- Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Lee Kum Sheung Center for Health and Happiness and Dana Farber Cancer Institute, Boston, MA, United States
- *Correspondence: Laura Marciano,
| | - Sundas Saboor
- Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Form Res 2023; 7:e42935. [PMID: 36811951 PMCID: PMC9996420 DOI: 10.2196/42935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/13/2022] [Accepted: 01/26/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Various behavioral sensing research studies have found that depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity in unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against the total score of depressive symptoms, and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected. OBJECTIVE We aimed to understand depression as a multidimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions. We also aimed to highlight the nonergodicity in psychological processes and the importance of disaggregating within- and between-person effects in the analysis. METHODS Data used in this study were collected by Mindstrong Health, a telehealth provider that focuses on individuals with serious mental illness. Depressive symptoms were measured by the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult Survey every 60 days for a year. Participants' interactions with their smartphones were passively recorded, and 5 behavioral measures were developed and were expected to be associated with depressive symptoms according to either theoretical proposition or previous empirical evidence. Multilevel modeling was used to explore the longitudinal relations between the severity of depressive symptoms and these behavioral measures. Furthermore, within- and between-person effects were disaggregated to accommodate the nonergodicity commonly found in psychological processes. RESULTS This study included 982 records of DSM Level 1 depressive symptom measurements and corresponding human-smartphone interaction data from 142 participants (age range 29-77 years; mean age 55.1 years, SD 10.8 years; 96 female participants). Loss of interest in pleasurable activities was associated with app count (γ10=-0.14; P=.01; within-person effect). Depressed mood was associated with typing time interval (γ05=0.88; P=.047; within-person effect) and session duration (γ05=-0.37; P=.03; between-person effect). CONCLUSIONS This study contributes new evidence for associations between human-smartphone interaction behaviors and the severity of depressive symptoms from a dimensional perspective, and it highlights the importance of considering the nonergodicity of psychological processes and analyzing the within- and between-person effects separately.
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Affiliation(s)
- Xiao Yang
- Mindstrong Health, Menlo Park, CA, United States
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Smith KA, Blease C, Faurholt-Jepsen M, Firth J, Van Daele T, Moreno C, Carlbring P, Ebner-Priemer UW, Koutsouleris N, Riper H, Mouchabac S, Torous J, Cipriani A. Digital mental health: challenges and next steps. BMJ MENTAL HEALTH 2023; 26:e300670. [PMID: 37197797 PMCID: PMC10231442 DOI: 10.1136/bmjment-2023-300670] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023]
Abstract
Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.
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Affiliation(s)
- Katharine A Smith
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Charlotte Blease
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Tom Van Daele
- Expertise Unit Psychology, Technology and Society, Thomas More University of Applied Sciences, Mechelen, Belgium
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Universidad Complutense de Madrid Facultad de Medicina, Madrid, Spain
| | - Per Carlbring
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
- mHealth Methods in Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, München, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Heleen Riper
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Duivendrecht, Netherlands
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Stephane Mouchabac
- Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, Paris, France
- Infrastructure for Clinical Research in Neurosciences (iCRIN), Brain Institute (ICM), INSERM, CNRS, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Paris, France
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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Mood and implicit confidence independently fluctuate at different time scales. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:142-161. [PMID: 36289181 DOI: 10.3758/s13415-022-01038-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 02/15/2023]
Abstract
Mood is an important ingredient of decision-making. Human beings are immersed into a sea of emotions where episodes of high mood alternate with episodes of low mood. While changes in mood are well characterized, little is known about how these fluctuations interact with metacognition, and in particular with confidence about our decisions. We evaluated how implicit measurements of confidence are related with mood states of human participants through two online longitudinal experiments involving mood self-reports and visual discrimination decision-making tasks. Implicit confidence was assessed on each session by monitoring the proportion of opt-out trials when an opt-out option was available, as well as the median reaction time on standard correct trials as a secondary proxy of confidence. We first report a strong coupling between mood, stress, food enjoyment, and quality of sleep reported by participants in the same session. Second, we confirmed that the proportion of opt-out responses as well as reaction times in non-opt-out trials provided reliable indices of confidence in each session. We introduce a normative measure of overconfidence based on the pattern of opt-out selection and the signal-detection-theory framework. Finally and crucially, we found that mood, sleep quality, food enjoyment, and stress level are not consistently coupled with these implicit confidence markers, but rather they fluctuate at different time scales: mood-related states display faster fluctuations (over one day or half-a-day) than confidence level (two-and-a-half days). Therefore, our findings suggest that spontaneous fluctuations of mood and confidence in decision making are independent in the healthy adult population.
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Swist T, Collin P, Lewis J, Medlow S, Williams I, Davies C, Steinbeck K. A digital innovation typology: Navigating the complexity of emerging technologies to negotiate health systems research with young people. Digit Health 2023; 9:20552076231212286. [PMID: 38025097 PMCID: PMC10631344 DOI: 10.1177/20552076231212286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This study aims to explore young people's perspectives of emerging technologies and health systems research in an adolescent health community of practice. Methods The context of this integrated knowledge translation study is the Wellbeing Health & Youth Centre of Research Excellence in Adolescent Health. A theory-building, non-systematic review was conducted to examine the concepts and interrelationships of emerging technologies associated with digital innovation and health systems. This typology informed the design of an online workshop with young people to explore their views, concerns, and ideas about health systems research. Results A digital innovation typology was identified to differentiate and explain emerging technology concepts and interrelationships that can be applied to the health systems context. Aligned with this typology, youth perspectives about digital health challenges and opportunities were identified to support future research, policy, and practice. Conclusion The integrated findings from this study can assist the navigation of complex emerging technologies, and the negotiation of equitable health systems research, between youth and adult stakeholders. Further, with these typology-related resources, mutual learning and the public involvement of young people in health systems research and priority setting agendas can be supported.
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Affiliation(s)
- Teresa Swist
- Institute for Culture and Society, Young and Resilient Research Centre, Western Sydney University, Penrith, NSW, Australia
- Education Futures Studio, Sydney School of Education and Social Work, University of Sydney, Camperdown, NSW, Australia
| | - Philippa Collin
- Institute for Culture and Society, Young and Resilient Research Centre, Western Sydney University, Penrith, NSW, Australia
| | - John Lewis
- Wellbeing Health & Youth Commission, Sydney, NSW, Australia
| | - Sharon Medlow
- Speciality of Child and Adolescent Health, Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Academic Department of Adolescent Medicine, The Children's Hospital Westmead, Westmead, NSW, Australia
| | - Ian Williams
- Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Cristyn Davies
- Specialty of Child and Adolescent Health, Faculty of Medicine and Health, University of Sydney, Children's Hospital Westmead Clinical School, Westmead, NSW, Australia
| | - Katharine Steinbeck
- Speciality of Child and Adolescent Health, Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Academic Department of Adolescent Medicine, The Children's Hospital Westmead, Westmead, NSW, Australia
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Ford T, Buchanan DM, Azeez A, Benrimoh DA, Kaloiani I, Bandeira ID, Hunegnaw S, Lan L, Gholmieh M, Buch V, Williams NR. Taking modern psychiatry into the metaverse: Integrating augmented, virtual, and mixed reality technologies into psychiatric care. Front Digit Health 2023; 5:1146806. [PMID: 37035477 PMCID: PMC10080019 DOI: 10.3389/fdgth.2023.1146806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
The landscape of psychiatry is ever evolving and has recently begun to be influenced more heavily by new technologies. One novel technology which may have particular application to psychiatry is the metaverse, a three-dimensional digital social platform accessed via augmented, virtual, and mixed reality (AR/VR/MR). The metaverse allows the interaction of users in a virtual world which can be measured and manipulated, posing at once exciting new possibilities and significant potential challenges and risks. While the final form of the nascent metaverse is not yet clear, the immersive simulation and holographic mixed reality-based worlds made possible by the metaverse have the potential to redefine neuropsychiatric care for both patients and their providers. While a number of applications for this technology can be envisioned, this article will focus on leveraging the metaverse in three specific domains: medical education, brain stimulation, and biofeedback. Within medical education, the metaverse could allow for more precise feedback to students performing patient interviews as well as the ability to more easily disseminate highly specialized technical skills, such as those used in advanced neurostimulation paradigms. Examples of potential applications in brain stimulation and biofeedback range from using AR to improve precision targeting of non-invasive neuromodulation modalities to more innovative practices, such as using physiological and behavioral measures derived from interactions in VR environments to directly inform and personalize treatment parameters for patients. Along with promising future applications, we also discuss ethical implications and data security concerns that arise when considering the introduction of the metaverse and related AR/VR technologies to psychiatric research and care.
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Affiliation(s)
- T.J. Ford
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Derrick M. Buchanan
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Correspondence: Derrick M. Buchanan
| | - Azeezat Azeez
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - David A. Benrimoh
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Irakli Kaloiani
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Igor D. Bandeira
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Saron Hunegnaw
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Lucy Lan
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Mia Gholmieh
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Vivek Buch
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Neurosurgery, Stanford University, Palo Alto, CA, United States
| | - Nolan R. Williams
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
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Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2023; 273:113-127. [PMID: 35587279 PMCID: PMC9957888 DOI: 10.1007/s00406-022-01418-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/11/2022] [Indexed: 12/19/2022]
Abstract
Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Elisabeth B. Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
| | - Tanja M. Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
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König A, Tröger J, Mallick E, Mina M, Linz N, Wagnon C, Karbach J, Kuhn C, Peter J. Detecting subtle signs of depression with automated speech analysis in a non-clinical sample. BMC Psychiatry 2022; 22:830. [PMID: 36575442 PMCID: PMC9793349 DOI: 10.1186/s12888-022-04475-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. METHODS We included n = 118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0-60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. RESULTS In our sample, n = 93 individuals scored below and n = 25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. CONCLUSIONS Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.
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Affiliation(s)
- Alexandra König
- grid.457356.6Institut National de Recherche en Informatique Et en Automatique (INRIA), Sophia Antipolis, Stars Team, Valbonne, France
| | | | | | | | | | - Carole Wagnon
- grid.5734.50000 0001 0726 5157University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bolligenstrasse 111, CH-3000 Bern 60, Switzerland
| | - Julia Karbach
- grid.5892.60000 0001 0087 7257Department of Psychology, University of Koblenz-Landau, Koblenz, Germany
| | - Caroline Kuhn
- grid.11749.3a0000 0001 2167 7588Department of Psychology, Clinical Neuropsychology, University of Saarland, Saarbrücken, Germany
| | - Jessica Peter
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bolligenstrasse 111, CH-3000, Bern 60, Switzerland.
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Miner AS, Fleming SL, Haque A, Fries JA, Althoff T, Wilfley DE, Agras WS, Milstein A, Hancock J, Asch SM, Stirman SW, Arnow BA, Shah NH. A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency. NPJ MENTAL HEALTH RESEARCH 2022; 1:19. [PMID: 38609510 PMCID: PMC10956022 DOI: 10.1038/s44184-022-00020-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 10/18/2022] [Indexed: 04/14/2024]
Abstract
Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.
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Affiliation(s)
- Adam S Miner
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
| | - Scott L Fleming
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Albert Haque
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jason A Fries
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Tim Althoff
- Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Denise E Wilfley
- Departments of Psychiatry, Medicine, Pediatrics, and Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - W Stewart Agras
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Arnold Milstein
- Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
| | - Jeff Hancock
- Department of Communication, Stanford University, Stanford, CA, USA
| | - Steven M Asch
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Shannon Wiltsey Stirman
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- National Center for Posttraumatic Stress Disorders, Dissemination and Training Division, VA Palo Alto Healthcare System, Menlo Park, CA, USA
| | - Bruce A Arnow
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Nigam H Shah
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
- Technology and Digital Solutions, Stanford Healthcare, Stanford, CA, USA
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49
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Hallinan CM, Gunn JM, Bonomo YA. Use of electronic medical records to monitor the safe and effective prescribing of medicinal cannabis: is it feasible? Aust J Prim Health 2022; 28:564-572. [PMID: 35927928 DOI: 10.1071/py22054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/17/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND General practitioners are well positioned to contribute to the pharmacovigilance of medical cannabis via the general practice electronic medical record (EMR). The aim of this research is to interrogate de-identified patient data from the Patron primary care data repository for reports of medicinal cannabis to ascertain the feasibility of using EMRs to monitor medicinal cannabis prescribing in Australia. METHODS EMR rule-based digital phenotyping of 1 164 846 active patients from 109 practices was undertaken to investigate reports of medicinal cannabis use from September 2017 to September 2020. RESULTS Eighty patients with 170 prescriptions of medicinal cannabis were identified in the Patron repository. Reasons for prescription included anxiety, multiple sclerosis, cancer, nausea, and Crohn's disease. Nine patients showed symptoms of a possible adverse event, including depression, motor vehicle accident, gastrointestinal symptoms, and anxiety. CONCLUSIONS The recording of medicinal cannabis effects in the patient EMR provides potential for medicinal cannabis monitoring in the community. This is especially feasible if monitoring were to be embedded into general practitioner workflow.
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Affiliation(s)
- Christine M Hallinan
- Department of General Practice, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences of the University of Melbourne, Level 2, 780 Elizabeth Street, Melbourne, Vic. 3004, Australia; and Faculty of Medicine, Dentistry and Health Sciences of the University of Melbourne, Level 2, Alan Gilbert Building, Grattan Street, Parkville, Vic. 3010, Australia; and Health and Biomedical Research Information Technology Unit (HaBIC R2), Faculty of Medicine, Dentistry and Health Sciences of the University of Melbourne, Level 2, 780 Elizabeth Street, Melbourne, Vic. 3004, Australia
| | - Jane M Gunn
- Faculty of Medicine, Dentistry and Health Sciences of the University of Melbourne, Level 2, Alan Gilbert Building, Grattan Street, Parkville, Vic. 3010, Australia
| | - Yvonne A Bonomo
- Faculty of Medicine, Dentistry and Health Sciences of the University of Melbourne, Level 2, Alan Gilbert Building, Grattan Street, Parkville, Vic. 3010, Australia; and Department of Addiction Medicine, St Vincent's Hospital, Fitzroy, Vic. 3065, Australia
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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