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Laferrière-Langlois P, Imrie F, Geraldo MA, Wingert T, Lahrichi N, van der Schaar M, Cannesson M. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach. Anesth Analg 2024; 139:174-185. [PMID: 38051671 PMCID: PMC11150330 DOI: 10.1213/ane.0000000000006753] [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] [Indexed: 12/07/2023]
Abstract
BACKGROUND Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS). METHODS We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score. RESULTS A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71). CONCLUSIONS For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.
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Affiliation(s)
- Pascal Laferrière-Langlois
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Maisonneuve-Rosemont Hospital Research Center, Montréal, Québec, Canada
- Department of Anesthesiology and Pain Medicine, Maisonneuve-Rosemont Hospital, CIUSSS de l’Est de L’Ile de Montréal, Montréal, Québec, Canada
| | - Fergus Imrie
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, USA
| | - Marc-Andre Geraldo
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Maisonneuve-Rosemont Hospital Research Center, Montréal, Québec, Canada
| | - Theodora Wingert
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
| | - Nadia Lahrichi
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
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Cohen A, Naslund J, Lane E, Bhan A, Rozatkar A, Mehta UM, Vaidyam A, Byun AJS, Barnett I, Torous J. Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample. Acta Psychiatr Scand 2024. [PMID: 38807465 DOI: 10.1111/acps.13712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/14/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
INTRODUCTION Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.
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Affiliation(s)
- Asher Cohen
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Erlend Lane
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Abhijit Rozatkar
- Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
- National Institute of Advanced Studies, Bangalore, India
| | - Aditya Vaidyam
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew Jin Soo Byun
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ian Barnett
- Department of Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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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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Yoo DW, Woo H, Nguyen VC, Birnbaum ML, Kruzan KP, Kim JG, Abowd GD, De Choudhury M. Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:702. [PMID: 38894725 PMCID: PMC11184595 DOI: 10.1145/3613904.3642369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.
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Affiliation(s)
| | - Hayoung Woo
- Georgia Institute of Technology, Atlanta, Georgia, USA
| | | | | | | | | | - Gregory D Abowd
- Northeastern University, Boston, Massachusetts, USA, Georgia Institute of Technology, Atlanta, Georgia, USA
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Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen MH. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Arch Clin Neuropsychol 2024; 39:290-304. [PMID: 38520381 DOI: 10.1093/arclin/acae016] [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: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.
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Affiliation(s)
- Che Harris
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Yingfei Tang
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Eliana Birnbaum
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Christine Cherian
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Michelle H Chen
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
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Zaher F, Diallo M, Achim AM, Joober R, Roy MA, Demers MF, Subramanian P, Lavigne KM, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophr Res 2024; 266:205-215. [PMID: 38428118 DOI: 10.1016/j.schres.2024.02.036] [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: 10/15/2023] [Revised: 02/18/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.
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Affiliation(s)
- Farida Zaher
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Mariama Diallo
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Amélie M Achim
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Vitam - Centre de Recherche en Santé Durable, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Ridha Joober
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Marc-André Roy
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Marie-France Demers
- Centre de Recherche CERVO, Québec City, QC, Canada; Faculté de Pharmacie, Université Laval, Québec City, QC, Canada
| | - Priya Subramanian
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Katie M Lavigne
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Daniela Gonzalez
- Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Irnes Zeljkovic
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Kristin Davis
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Michael Mackinley
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Priyadharshini Sabesan
- Lakeshore General Hospital and Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Shalini Lal
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; School of Rehabilitation, Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Alban Voppel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Robarts Research Institute, Western University, London, ON, Canada.
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Lee JS, Browning E, Hokayem J, Albrechta H, Goodman GR, Venkatasubramanian K, Dumas A, Carreiro SP, O'Cleirigh C, Chai PR. Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics. J Med Toxicol 2024; 20:205-214. [PMID: 38436819 PMCID: PMC10959908 DOI: 10.1007/s13181-024-01000-5] [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: 12/19/2023] [Revised: 02/08/2024] [Accepted: 02/20/2024] [Indexed: 03/05/2024] Open
Abstract
Digital phenotyping is a process that allows researchers to leverage smartphone and wearable data to explore how technology use relates to behavioral health outcomes. In this Research Concepts article, we provide background on prior research that has employed digital phenotyping; the fundamentals of how digital phenotyping works, using examples from participant data; the application of digital phenotyping in the context of substance use and its syndemics; and the ethical, legal and social implications of digital phenotyping. We discuss applications for digital phenotyping in medical toxicology, as well as potential uses for digital phenotyping in future research. We also highlight the importance of obtaining ground truth annotation in order to identify and establish digital phenotypes of key behaviors of interest. Finally, there are many potential roles for medical toxicologists to leverage digital phenotyping both in research and in the future as a clinical tool to better understand the contextual features associated with drug poisoning and overdose. This article demonstrates how medical toxicologists and researchers can progress through phases of a research trajectory using digital phenotyping to better understand behavior and its association with smartphone usage.
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Affiliation(s)
- Jasper S Lee
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, USA
- The Fenway Institute, Fenway Health, Boston, USA
| | - Emma Browning
- The Fenway Institute, Fenway Health, Boston, USA
- Department of Community Health, Tufts University, Boston, USA
| | | | | | - Georgia R Goodman
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, USA
- The Fenway Institute, Fenway Health, Boston, USA
| | | | - Arlen Dumas
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, USA
| | - Stephanie P Carreiro
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Boston, USA
| | - Conall O'Cleirigh
- Department of Psychiatry, Massachusetts General Hospital, Boston, USA
- The Fenway Institute, Fenway Health, Boston, USA
| | - Peter R Chai
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA.
- The Fenway Institute, Fenway Health, Boston, USA.
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, USA.
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Boston, USA.
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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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Hurley ME, Sonig A, Herrington J, Storch EA, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Front Hum Neurosci 2024; 18:1332451. [PMID: 38435745 PMCID: PMC10904467 DOI: 10.3389/fnhum.2024.1332451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.
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Affiliation(s)
- Meghan E. Hurley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Anika Sonig
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - John Herrington
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry and Behavioral Sciences, Massachusetts General Hospital, Boston, MA, United States
| | | | - Kristin Kostick-Quenet
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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Fu M, Shen J, Gu C, Oliveira E, Shinchuk E, Isaac H, Isaac Z, Sarno DL, Kurz JL, Silbersweig DA, Onnela JP, Barron DS. The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management. FRONTIERS IN PAIN RESEARCH 2024; 5:1327859. [PMID: 38371228 PMCID: PMC10869590 DOI: 10.3389/fpain.2024.1327859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year. Chronic pain is an instantiation of the perennial complexity of how to best assess and treat chronic diseases over time, especially in populations where age, medical comorbidities, and socioeconomic barriers may limit access to care. Chronic disease management poses a particular challenge for the healthcare system's transition from fee-for-service to value and risk-based reimbursement models. Remote, passive real-time data from smartphones could enable more timely interventions and simultaneously manage risk and promote better patient outcomes through predicting and preventing costly adverse outcomes; however, there is limited evidence whether remote monitoring is feasible, especially in the case of older patients with chronic pain. Here, we introduce the Pain Intervention and Digital Research (Pain-IDR) Program as a pilot initiative launched in 2022 that combines outpatient clinical care and digital health research. The Pain-IDR seeks to test whether functional status can be assessed passively, through a smartphone application, in older patients with chronic pain. We discuss two perspectives-a narrative approach that describes the clinical settings and rationale behind changes to the operational design, and a quantitative approach that measures patient recruitment, patient experience, and HERMES data characteristics. Since launch, we have had 77 participants with a mean age of 55.52, of which n = 38 have fully completed the 6 months of data collection necessitated to be considered in the study, with an active data collection rate of 51% and passive data rate of 78%. We further present preliminary operational strategies that we have adopted as we have learned to adapt the Pain-IDR to a productive clinical service. Overall, the Pain-IDR has successfully engaged older patients with chronic pain and presents useful insights for others seeking to implement digital phenotyping in other chronic disease settings.
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Affiliation(s)
- Melanie Fu
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- School of Medicine, University of Massachusetts, Wooster, MA, United States
| | - Joanna Shen
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Cheryl Gu
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ellina Oliveira
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Ellisha Shinchuk
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Hannah Isaac
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Zacharia Isaac
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Danielle L. Sarno
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Jennifer L. Kurz
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | | | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Daniel S. Barron
- Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States
- Department of Physiatry, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
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11
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Gleeson JF, McGuckian TB, Fernandez DK, Fraser MI, Pepe A, Taskis R, Alvarez-Jimenez M, Farhall JF, Gumley A. Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders. Clin Psychol Rev 2024; 107:102357. [PMID: 38065010 DOI: 10.1016/j.cpr.2023.102357] [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: 03/08/2023] [Revised: 11/03/2023] [Accepted: 11/14/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Identification of the early warning signs (EWS) of relapse is key to relapse prevention in schizophrenia spectrum disorders, however, limitations to their precision have been reported. Substantial methodological innovations have recently been applied to the prediction of psychotic relapse and to individual psychotic symptoms. However, there has been no systematic review that has integrated findings across these two related outcomes and no systematic review of EWS of relapse for a decade. METHOD We conducted a systematic review of EWS of psychotic relapse and the behavioural antecedents of worsening psychotic symptoms. Traditional EWS and ecological momentary assessment/intervention studies were included. We completed meta-analyses of the pooled sensitivity and specificity of EWS in predicting relapse, and for the prediction of relapse from individual symptoms. RESULTS Seventy two studies were identified including 6903 participants. Sleep, mood, and suspiciousness, emerged as predictors of worsening symptoms. Pooled sensitivity and specificity of EWS in predicting psychotic relapse was 71% and 64% (AUC value = 0.72). There was a large pooled-effect size for the model predicting relapse from individual symptom which did not reach statistical significance (d = 0.81, 95%CIs = -0.01, 1.63). CONCLUSIONS Important methodological advancements in the prediction of psychotic relapse in schizophrenia spectrum disorders are evident with improvements in the precision of prediction. Further efforts are required to translate these advances into effective clinical innovations.
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Affiliation(s)
- J F Gleeson
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia.
| | - T B McGuckian
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - D K Fernandez
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - M I Fraser
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - A Pepe
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - R Taskis
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - M Alvarez-Jimenez
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - J F Farhall
- Department of Psychology and Counselling, La Trobe University, Melbourne, VIC, Australia
| | - A Gumley
- Glasgow Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
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12
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Albrechta H, Goodman GR, Oginni E, Mohamed Y, Venkatasubramanian K, Dumas A, Carreiro S, Lee JS, Glynn TR, O'Cleirigh C, Mayer KH, Fisher CB, Chai PR. Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use. PLOS DIGITAL HEALTH 2024; 3:e0000457. [PMID: 38386618 PMCID: PMC10883553 DOI: 10.1371/journal.pdig.0000457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/01/2024] [Indexed: 02/24/2024]
Abstract
Once-daily oral HIV pre-exposure prophylaxis (PrEP) is an effective strategy to prevent HIV, but is highly dependent on adherence. Men who have sex with men (MSM) who use substances face unique challenges maintaining PrEP adherence. Digital pill systems (DPS) allow for real-time adherence measurement through ingestible sensors. Integration of DPS technology with other digital health tools, such as digital phenotyping, may improve understanding of nonadherence triggers and development of personalized adherence interventions based on ingestion behavior. This study explored the willingness of MSM with substance use to share digital phenotypic data and interact with ancillary systems in the context of DPS-measured PrEP adherence. Adult MSM on PrEP with substance use were recruited through a social networking app. Participants were introduced to DPS technology and completed an assessment to measure willingness to participate in DPS-based PrEP adherence research, contribute digital phenotyping data, and interact with ancillary systems in the context of DPS-based research. Medical mistrust, daily worry about PrEP adherence, and substance use were also assessed. Participants who identified as cisgender male and were willing to participate in DPS-based research (N = 131) were included in this subsample analysis. Most were White (76.3%) and non-Hispanic (77.9%). Participants who reported daily PrEP adherence worry had 3.7 times greater odds (95% CI: 1.03, 13.4) of willingness to share biometric data via a wearable device paired to the DPS. Participants with daily PrEP adherence worry were more likely to be willing to share smartphone data (p = 0.006) and receive text messages surrounding their daily activities (p = 0.003), compared to those with less worry. MSM with substance use disorder, who worried about PrEP adherence, were willing to use DPS technology and share data required for digital phenotyping in the context of PrEP adherence measurement. Efforts to address medical mistrust can increase advantages of this technology for HIV prevention.
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Affiliation(s)
- Hannah Albrechta
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Georgia R Goodman
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Elizabeth Oginni
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Yassir Mohamed
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Krishna Venkatasubramanian
- Department of Computer Science and Statistics, The University of Rhode Island, Kingston, Rhode Island, United States of America
| | - Arlen Dumas
- Department of Computer Science and Statistics, The University of Rhode Island, Kingston, Rhode Island, United States of America
| | - Stephanie Carreiro
- Department of Emergency Medicine, University of Massachusetts Chan Medical School
| | - Jasper S Lee
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Tiffany R Glynn
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Conall O'Cleirigh
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kenneth H Mayer
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Celia B Fisher
- Center for Ethics Education, Fordham University, New York City, New York, United States of America
| | - Peter R Chai
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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13
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Rogan J, Bucci S, Firth J. Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis. JMIR Ment Health 2024; 11:e49577. [PMID: 38261403 PMCID: PMC10848143 DOI: 10.2196/49577] [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: 06/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. OBJECTIVE This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. METHODS A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. RESULTS Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. CONCLUSIONS Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.
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Affiliation(s)
- Jessica Rogan
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Joseph Firth
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
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14
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Nestor BA, Chimoff J, Koike C, Weitzman ER, Riley BL, Uhl K, Kossowsky J. Adolescent and Parent Perspectives on Digital Phenotyping in Youths With Chronic Pain: Cross-Sectional Mixed Methods Survey Study. J Med Internet Res 2024; 26:e47781. [PMID: 38206665 PMCID: PMC10811597 DOI: 10.2196/47781] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/28/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Digital phenotyping is a promising methodology for capturing moment-to-moment data that can inform individually adapted and timely interventions for youths with chronic pain. OBJECTIVE This study aimed to investigate adolescent and parent endorsement, perceived utility, and concerns related to passive data stream collection through smartphones for digital phenotyping for clinical and research purposes in youths with chronic pain. METHODS Through multiple-choice and open-response survey questions, we assessed the perspectives of patient-parent dyads (103 adolescents receiving treatment for chronic pain at a pediatric hospital with an average age of 15.6, SD 1.6 years, and 99 parents with an average age of 47.8, SD 6.3 years) on passive data collection from the following 9 smartphone-embedded passive data streams: accelerometer, apps, Bluetooth, SMS text message and call logs, keyboard, microphone, light, screen, and GPS. RESULTS Quantitative and qualitative analyses indicated that adolescents and parent endorsement and perceived utility of digital phenotyping varied by stream, though participants generally endorsed the use of data collected by passive stream (35%-75.7% adolescent endorsement for clinical use and 37.9%-74.8% for research purposes; 53.5%-81.8% parent endorsement for clinical and 52.5%-82.8% for research purposes) if a certain level of utility could be provided. For adolescents and parents, adjusted logistic regression results indicated that the perceived utility of each stream significantly predicted the likelihood of endorsement of its use in both clinical practice and research (Ps<.05). Adolescents and parents alike identified accelerometer, light, screen, and GPS as the passive data streams with the highest utility (36.9%-47.5% identifying streams as useful). Similarly, adolescents and parents alike identified apps, Bluetooth, SMS text message and call logs, keyboard, and microphone as the passive data streams with the least utility (18.5%-34.3% identifying streams as useful). All participants reported primary concerns related to privacy, accuracy, and validity of the collected data. Passive data streams with the greatest number of total concerns were apps, Bluetooth, call and SMS text message logs, keyboard, and microphone. CONCLUSIONS Findings support the tailored use of digital phenotyping for this population and can help refine this methodology toward an acceptable, feasible, and ethical implementation of real-time symptom monitoring for assessment and intervention in youths with chronic pain.
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Affiliation(s)
- Bridget A Nestor
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
| | - Justin Chimoff
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Camila Koike
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Elissa R Weitzman
- Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Division of Addiction Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Bobbie L Riley
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
| | - Kristen Uhl
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, United States
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, United States
| | - Joe Kossowsky
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesia, Harvard Medical School, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
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15
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Siafis S, Brandt L, McCutcheon RA, Gutwinski S, Schneider-Thoma J, Bighelli I, Kane JM, Arango C, Kahn RS, Fleischhacker WW, McGorry P, Carpenter WT, Falkai P, Hasan A, Marder SR, Schooler N, Engel RR, Honer WG, Buchanan RW, Davidson M, Weiser M, Priller J, Davis JM, Howes OD, Correll CU, Leucht S. Relapse in clinically stable adult patients with schizophrenia or schizoaffective disorder: evidence-based criteria derived by equipercentile linking and diagnostic test accuracy meta-analysis. Lancet Psychiatry 2024; 11:36-46. [PMID: 38043562 DOI: 10.1016/s2215-0366(23)00364-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/30/2023] [Accepted: 10/19/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND There is no consensus on defining relapse in schizophrenia, and scale-derived criteria with unclear clinical relevance are widely used. We aimed to develop an evidence-based scale-derived set of criteria to define relapse in patients with schizophrenia or schizoaffective disorder. METHODS We searched the Yale University Open Data Access (YODA) for randomised controlled trials (RCTs) in clinically stable adults with schizophrenia or schizoaffective disorder, and obtained individual participant data on Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression Severity (CGI-S), Personal and Social Performance (PSP), and Social and Occupational Functioning Assessment Scale (SOFAS). Our main outcomes were PANSS-derived criteria based on worsening in PANSS total score. We examined their relevance using equipercentile linking with CGI-S and functioning scales, and their test-performance in defining relapse with diagnostic test accuracy meta-analysis against CGI-S worsening (≥1-point increase together with a score ≥4 points) and psychiatric hospitalisation. FINDINGS Based on data from seven RCTs (2354 participants; 1348 men [57·3%] and 1006 women [42·7%], mean age of 39·5 years [SD 12·0, range 17-89]; 303 Asian [12.9%], 255 Black [10.8%], 1665 White [70.7%], and other or unspecified 131 [5.6%]), an increase of 12 points or more in PANSS total (range 30-210 points) corresponded to clinically important deterioration in global severity of illness (≥1 point increase in CGI-S, range 1-7) and functioning (≥10 points decline in PSP or SOFAS, range 1-100). The interpretation of percentage changes varied importantly across different baseline scores. An increase of 12 points or more in PANSS total had good sensitivity and specificity using CGI-S as reference standard (sensitivity 82·1% [95% CI 77·1-86·4], specificity 86·9% [82·9-90·3]), as well as good sensitivity but lower specificity compared to hospitalisation (sensitivity 81·7% [74·1-87·7], specificity 69·2% [60·5-76·9]). Requiring either an increase in PANSS total or in specific items for positive and disorganization symptoms further improved test-performance. Cutoffs for situations where high sensitivity or specificity is needed are presented. INTERPRETATION An increase of either 12 points or more in the PANSS total score, or worsening of specific positive and disorganisation symptom items could be a reasonable evidence-based definition of relapse in schizophrenia, potentially linking symptoms used to define remission and relapse. Percentage changes should not be used to define relapse because their interpretation depends on baseline scores. FUNDING German Research Foundation (grant number: 428509362).
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Affiliation(s)
- Spyridon Siafis
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany; German Center for Mental Health (DZPG), Germany.
| | - Lasse Brandt
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert A McCutcheon
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Stefan Gutwinski
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Johannes Schneider-Thoma
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany; German Center for Mental Health (DZPG), Germany
| | - Irene Bighelli
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany; German Center for Mental Health (DZPG), Germany
| | - John M Kane
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks NY, USA; The Donald and Barbara Zucker School of Medicine, Department of Psychiatry and Molecular Medicine, Hempstead NY, USA
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York NY, USA
| | | | - Patrick McGorry
- Orygen, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - William T Carpenter
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore MD, USA
| | - Peter Falkai
- German Center for Mental Health (DZPG), Germany; Department of Psychiatry and Psychotherapy, School of Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Alkomiet Hasan
- German Center for Mental Health (DZPG), Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, University of Augsburg, Medical Faculty, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Stephen R Marder
- Semel Institute for Neuroscience at UCLA, VA Desert Pacific Mental Illness Research, Education and Clinical Center, Los Angeles CA, USA
| | - Nina Schooler
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn NY, USA
| | - Rolf R Engel
- Department of Psychiatry and Psychotherapy, School of Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - William G Honer
- University of British Columbia, Department of Psychiatry, Faculty of Medicine, Vancouver BC, Canada; BC Mental Health and Substance Use Services Research Institute, Vancouver BC, Canada
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore MD, USA
| | - Michael Davidson
- Minerva Neurosciences, Waltham MA, USA; Department of Basic and Clinical Sciences, Psychiatry, University of Nicosia Medical School, Nicosia, Cyprus
| | - Mark Weiser
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany; German Center for Mental Health (DZPG), Germany; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Neuropsychiatry, Charité-Universitätsmedizin Berlin and DZNE, Berlin, Germany; University of Edinburgh and UK DRI, Edinburgh, UK
| | - John M Davis
- Psychiatric Institute, University of Illinois, Chicago IL, USA
| | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Medical Sciences, Medical Research Council London, London, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Christoph U Correll
- German Center for Mental Health (DZPG), Germany; Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks NY, USA; The Donald and Barbara Zucker School of Medicine, Department of Psychiatry and Molecular Medicine, Hempstead NY, USA; Department of Child and Adolescent Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany; German Center for Mental Health (DZPG), Germany
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16
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Horan WP, Sachs G, Velligan DI, Davis M, Keefe RS, Khin NA, Butlen-Ducuing F, Harvey PD. Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward. INNOVATIONS IN CLINICAL NEUROSCIENCE 2024; 21:19-30. [PMID: 38495609 PMCID: PMC10941857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.
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Affiliation(s)
- William P. Horan
- Dr. Horan is with Karuna Therapeutics in Boston, Massachusetts, and University of California in Los Angeles, California
| | - Gary Sachs
- Dr. Sachs is with Signant Health in Boston, Massachusetts, and Harvard Medical School in Boston, Massachusetts
| | - Dawn I. Velligan
- Dr. Velligan is with University of Texas Health Science Center at San Antonio in San Antonio, Texas
| | - Michael Davis
- Dr. Davis is with Usona Institute in Madison, Wisconsin
| | - Richard S.E. Keefe
- Dr. Keefe is with Duke University Medical Center in Durham, North Carolina
| | - Ni A. Khin
- Dr. Khin is with Neurocrine Biosciences, Inc. in San Diego, California
| | - Florence Butlen-Ducuing
- Dr. Butlen-Ducuing is with Office for Neurological and Psychiatric Disorders, European Medicines Agency in Amsterdam, The Netherlands
| | - Philip D. Harvey
- Dr. Harvey is with University of Miami Miller School of Medicine in Miami, Florida
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Widge AS. Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity. Neuropsychopharmacology 2024; 49:138-149. [PMID: 37415081 PMCID: PMC10700701 DOI: 10.1038/s41386-023-01643-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: 04/02/2023] [Revised: 05/28/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
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18
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Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res 2023; 25:e46778. [PMID: 38090800 PMCID: PMC10753422 DOI: 10.2196/46778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/29/2023] [Accepted: 07/31/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).
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Affiliation(s)
- Pasquale Bufano
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Sara Said
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | | | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
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Wu T, Sherman G, Giorgi S, Thanneeru P, Ungar LH, Kamath PS, Simonetto DA, Curtis BL, Shah VH. Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder. Hepatol Commun 2023; 7:e0329. [PMID: 38055637 PMCID: PMC10984664 DOI: 10.1097/hc9.0000000000000329] [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: 08/27/2023] [Accepted: 09/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes. METHODS A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving. RESULTS Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude. CONCLUSIONS Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease.
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Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Garrick Sherman
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Salvatore Giorgi
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Priya Thanneeru
- Department of Medicine and Pediatrics, The Brooklyn Hospital Center, Brooklyn, New York, USA
| | - Lyle H. Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Patrick S. Kamath
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Brenda L. Curtis
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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20
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Stern E, Micoulaud Franchi JA, Dumas G, Moreira J, Mouchabac S, Maruani J, Philip P, Lejoyeux M, Geoffroy PA. How Can Digital Mental Health Enhance Psychiatry? Neuroscientist 2023; 29:681-693. [PMID: 35658666 DOI: 10.1177/10738584221098603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The use of digital technologies is constantly growing around the world. The wider-spread adoption of digital technologies and solutions in the daily clinical practice in psychiatry seems to be a question of when, not if. We propose a synthesis of the scientific literature on digital technologies in psychiatry and discuss the main aspects of its possible uses and interests in psychiatry according to three domains of influence that appeared to us: 1) assist and improve current care: digital psychiatry allows for more people to have access to care by simply being more accessible but also by being less stigmatized and more convenient; 2) develop new treatments: digital psychiatry allows for new treatments to be distributed via apps, and practical guidelines can reduce ethical challenges and increase the efficacy of digital tools; and 3) produce scientific and medical knowledge: digital technologies offer larger and more objective data collection, allowing for more detection and prevention of symptoms. Finally, ethical and efficacy issues remain, and some guidelines have been put forth on how to safely use these solutions and prepare for the future.
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Affiliation(s)
- Emilie Stern
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
| | - Jean-Arthur Micoulaud Franchi
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Quebec, Canada
- Mila-Quebec Artificial Intelligence Institute, University of Montreal, Quebec, Canada
| | | | - Stephane Mouchabac
- Department of Psychiatry, Department of Psychiatry Hôpital Saint Antoine-APHP, Sorbonne University, Paris, France
- Infrastructure of Clinical Research in Neurosciences-Psychiatry, Brain and Spine Institute (ICM), Inserm, Sorbonne University, Paris, France
| | - Julia Maruani
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre Philip
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Michel Lejoyeux
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre A Geoffroy
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
- CNRS UPR 3212, Institute for Cellular and Integrative Neurosciences, Strasbourg, France
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21
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Wu J, Zhou T, Guo Y, Tian Y, Lou Y, Feng J, li J. Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation. Health Inf Sci Syst 2023; 11:39. [PMID: 37649855 PMCID: PMC10462598 DOI: 10.1007/s13755-023-00240-z] [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: 04/26/2023] [Accepted: 08/12/2023] [Indexed: 09/01/2023] Open
Abstract
Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.
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Affiliation(s)
- Junya Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Yufan Guo
- Department of Pediatrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Yu Tian
- Key Laboratory for Biomedical Engineering of Ministry of Education, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 China
| | - Yuting Lou
- Department of Pediatrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Jianhua Feng
- Department of Pediatrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Jingsong li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 China
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, 311100 China
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22
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Tully LM, Nye KE, Ereshefsky S, Tryon VL, Hakusui CK, Savill M, Niendam TA. Incorporating Community Partner Perspectives on eHealth Technology Data Sharing Practices for the California Early Psychosis Intervention Network: Qualitative Focus Group Study With a User-Centered Design Approach. JMIR Hum Factors 2023; 10:e44194. [PMID: 37962921 PMCID: PMC10685281 DOI: 10.2196/44194] [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: 11/10/2022] [Revised: 07/26/2023] [Accepted: 09/23/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Increased use of eHealth technology and user data to drive early identification and intervention algorithms in early psychosis (EP) necessitates the implementation of ethical data use practices to increase user acceptability and trust. OBJECTIVE First, the study explored EP community partner perspectives on data sharing best practices, including beliefs, attitudes, and preferences for ethical data sharing and how best to present end-user license agreements (EULAs). Second, we present a test case of adopting a user-centered design approach to develop a EULA protocol consistent with community partner perspectives and priorities. METHODS We conducted an exploratory, qualitative, and focus group-based study exploring mental health data sharing and privacy preferences among individuals involved in delivering or receiving EP care within the California Early Psychosis Intervention Network. Key themes were identified through a content analysis of focus group transcripts. Additionally, we conducted workshops using a user-centered design approach to develop a EULA that addresses participant priorities. RESULTS In total, 24 participants took part in the study (14 EP providers, 6 clients, and 4 family members). Participants reported being receptive to data sharing despite being acutely aware of widespread third-party sharing across digital domains, the risk of breaches, and motives hidden in the legal language of EULAs. Consequently, they reported feeling a loss of control and a lack of protection over their data. Participants indicated these concerns could be mitigated through user-level control for data sharing with third parties and an understandable, transparent EULA, including multiple presentation modalities, text at no more than an eighth-grade reading level, and a clear definition of key terms. These findings were successfully integrated into the development of a EULA and data opt-in process that resulted in 88.1% (421/478) of clients who reviewed the video agreeing to share data. CONCLUSIONS Many of the factors considered pertinent to informing data sharing practices in a mental health setting are consistent among clients, family members, and providers delivering or receiving EP care. These community partners' priorities can be successfully incorporated into developing EULA practices that can lead to high voluntary data sharing rates.
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Affiliation(s)
- Laura M Tully
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
| | - Kathleen E Nye
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
| | - Sabrina Ereshefsky
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
| | - Valerie L Tryon
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
| | - Christopher Komei Hakusui
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
| | - Mark Savill
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Tara A Niendam
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, United States
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23
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Caballero N, Machiraju S, Diomino A, Kennedy L, Kadivar A, Cadenhead KS. Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis. Curr Psychiatry Rep 2023; 25:683-698. [PMID: 37755654 PMCID: PMC10654175 DOI: 10.1007/s11920-023-01456-2] [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] [Accepted: 09/05/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE OF REVIEW This review highlights recent advances in the prediction and treatment of psychotic conversion. Over the past 25 years, research into the prodromal phase of psychotic illness has expanded with the promise of early identification of individuals at clinical high risk (CHR) for psychosis who are likely to convert to psychosis. RECENT FINDINGS Meta-analyses highlight conversion rates between 20 and 30% within 2-3 years using existing clinical criteria while research into more specific risk factors, biomarkers, and refinement of psychosis risk calculators has exploded, improving our ability to predict psychotic conversion with greater accuracy. Recent studies highlight risk factors and biomarkers likely to contribute to earlier identification and provide insight into neurodevelopmental abnormalities, CHR subtypes, and interventions that can target specific risk profiles linked to neural mechanisms. Ongoing initiatives that assess longer-term (> 5-10 years) outcome of CHR participants can provide valuable information about predictors of later conversion and diagnostic outcomes while large-scale international biomarker studies provide hope for precision intervention that will alter the course of early psychosis globally.
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Affiliation(s)
- Noe Caballero
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Siddharth Machiraju
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Anthony Diomino
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Leda Kennedy
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Armita Kadivar
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA.
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24
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Medich M, Cannedy SL, Hoffmann LC, Chinchilla MY, Pila JM, Chassman SA, Calderon RA, Young AS. Clinician and Patient Perspectives on the Use of Passive Mobile Monitoring and Self-Tracking for Patients With Serious Mental Illness: User-Centered Approach. JMIR Hum Factors 2023; 10:e46909. [PMID: 37874639 PMCID: PMC10630855 DOI: 10.2196/46909] [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: 03/03/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Early intervention in mental health crises can prevent negative outcomes. A promising new direction is remote mental health monitoring using smartphone technology to passively collect data from individuals to rapidly detect the worsening of serious mental illness (SMI). This technology may benefit patients with SMI, but little is known about health IT acceptability among this population or their mental health clinicians. OBJECTIVE We used the Health Information Technology Acceptability Model to analyze the acceptability and usability of passive mobile monitoring and self-tracking among patients with serious mental illness and their mental health clinicians. METHODS Data collection took place between December 2020 and June 2021 in 1 Veterans Administration health care system. Interviews with mental health clinicians (n=16) assessed the acceptability of mobile sensing, its usefulness as a tool to improve clinical assessment and care, and recommendations for program refinements. Focus groups with patients with SMI (n=3 groups) and individual usability tests (n=8) elucidated patient attitudes about engaging in health IT and perceptions of its usefulness as a tool for self-tracking and improving mental health assessments. RESULTS Clinicians discussed the utility of web-based data dashboards to monitor patients with SMI health behaviors and receiving alerts about their worsening health. Potential benefits included improving clinical care, capturing behaviors patients do not self-report, watching trends, and receiving alerts. Clinicians' concerns included increased workloads tied to dashboard data review, lack of experience using health IT in clinical care, and how SMI patients' associated paranoia and financial instability would impact patient uptake. Despite concerns, all mental health clinicians stated that they would recommend it. Almost all patients with SMI were receptive to using smartphone dashboards for self-monitoring and having behavioral change alerts sent to their mental health clinicians. They found the mobile app easy to navigate and dashboards easy to find and understand. Patient concerns centered on privacy and "government tracking," and their phone's battery life and data plans. Despite concerns, most reported that they would use it. CONCLUSIONS Many people with SMI would like to have mobile informatics tools that can support their illness and recovery. Similar to other populations (eg, older adults, people experiencing homelessness) this population presents challenges to adoption and implementation. Health care organizations will need to provide resources to address these and support successful illness management. Clinicians are supportive of technological approaches, with adapting informatics data into their workflow as the primary challenge. Despite clear challenges, technological developments are increasingly designed to be acceptable to patients. The research development-clinical deployment gap must be addressed by health care systems, similar to computerized cognitive training. It will ensure clinicians operate at the top of their skill set and are not overwhelmed by administrative tasks, data summarization, or reviewing data that do not indicate a need for intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/39010.
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Affiliation(s)
- Melissa Medich
- Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, North Hills, CA, United States
- The Lundquist Institute for Biomedical Research, Torrance, CA, United States
| | - Shay L Cannedy
- Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, North Hills, CA, United States
| | - Lauren C Hoffmann
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Melissa Y Chinchilla
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Jose M Pila
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Stephanie A Chassman
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Ronald A Calderon
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Alexander S Young
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles Geffen School of Medicine, Los Angeles, CA, United States
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25
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Jenciūtė G, Kasputytė G, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, Krikštolaitis R, Krilavičius T, Juozaitytė E, Bunevičius A. Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study. JMIR Res Protoc 2023; 12:e49096. [PMID: 37815850 PMCID: PMC10599285 DOI: 10.2196/49096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. OBJECTIVE We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. METHODS We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. RESULTS As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. CONCLUSIONS This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/49096.
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Affiliation(s)
- Gabrielė Jenciūtė
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Inesa Bunevičienė
- Faculty of Political Science and Diplomacy, Vytautas Magnus University, Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Domas Vaitiekus
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arturas Inčiūra
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | | | | | - Tomas Krilavičius
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Adomas Bunevičius
- Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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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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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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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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Maechling C, Yrondi A, Cambon A. Mobile health in the specific management of first-episode psychosis: a systematic literature review. Front Psychiatry 2023; 14:1137644. [PMID: 37377474 PMCID: PMC10291100 DOI: 10.3389/fpsyt.2023.1137644] [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: 01/04/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Purpose The purpose of this systematic literature review is to assess the therapeutic efficacy of mobile health methods in the management of patients with first-episode psychosis (FEP). Method The participants are patients with FEP. The interventions are smartphone applications. The studies assess the preliminary efficacy of various types of application. Results One study found that monitoring symptoms minimized relapses, visits to A&E and hospital admissions, while one study showed a decrease in positive psychotic symptoms. One study found an improvement in anxiety symptoms and two studies noted an improvement in psychotic symptoms. One study demonstrated its efficacy in helping participants return to studying and employment and one study reported improved motivation. Conclusion The studies suggest that mobile applications have potential value in the management of young patients with FEP through the use of various assessment and intervention tools. This systematic review has several limitations due to the lack of randomized controlled studies available in the literature.
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Affiliation(s)
- Claire Maechling
- Pôle de Psychiatrie, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Antoine Yrondi
- Service de Psychiatrie et de Psychologie Médicale, Centre Expert Dépression Résistante Fonda Mental, CHU de Toulouse, Hôpital Purpan, ToNIC Toulouse NeuroImaging Centre, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Amandine Cambon
- Programme d'intervention précoce RePeps, réseau Transition, Clinique Aufrery, Toulouse, France
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Radhakrishnan K, Julien C, O'Hair M, Tunis R, Lee G, Rangel A, Custer J, Baranowski T, Rathouz PJ, Kim MT. Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e45801. [PMID: 37163342 PMCID: PMC10209796 DOI: 10.2196/45801] [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: 01/27/2023] [Revised: 03/14/2023] [Accepted: 03/21/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes. OBJECTIVE This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors. METHODS A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups. RESULTS Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026. CONCLUSIONS This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF. TRIAL REGISTRATION ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/45801.
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Affiliation(s)
| | - Christine Julien
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States
| | | | - Rachel Tunis
- School of Information, University of Texas at Austin, Austin, TX, United States
| | - Grace Lee
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States
| | - Angelica Rangel
- School of Nursing, The University of Texas at Austin, Austin, TX, United States
| | - James Custer
- Department of Population Health, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - Tom Baranowski
- Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Paul J Rathouz
- Department of Population Health, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - Miyong T Kim
- School of Nursing, The University of Texas at Austin, Austin, TX, United States
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Langener AM, Stulp G, Kas MJ, Bringmann LF. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Ment Health 2023; 10:e42646. [PMID: 36930210 PMCID: PMC10132048 DOI: 10.2196/42646] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people's social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people's social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. OBJECTIVE Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. METHODS We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. CONCLUSIONS We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants' burden and form a holistic perspective on the social environment.
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Affiliation(s)
- Anna M Langener
- Groningen Institute for Evolutionary Life Sciences, Groningen, Netherlands.,Department of Sociology, Faculty of Behavioural and Social Sciences, University of Groningen & Inter-University Center for Social Science Theory and Methodology, Groningen, Netherlands.,Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands
| | - Gert Stulp
- Department of Sociology, Faculty of Behavioural and Social Sciences, University of Groningen & Inter-University Center for Social Science Theory and Methodology, Groningen, Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, Groningen, Netherlands
| | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands.,Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. J Med Internet Res 2023; 25:e39546. [PMID: 36917148 PMCID: PMC10132029 DOI: 10.2196/39546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology.
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Affiliation(s)
- Christopher De Boer
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Hassan Ghomrawi
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Department of Pediatrics, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Suhail Zeineddin
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Samuel Linton
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Soyang Kwon
- The Smith Child Health Research Program, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States
| | - Fizan Abdullah
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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Kalisperakis E, Karantinos T, Lazaridi M, Garyfalli V, Filntisis PP, Zlatintsi A, Efthymiou N, Mantas A, Mantonakis L, Mougiakos T, Maglogiannis I, Tsanakas P, Maragos P, Smyrnis N. Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders. Front Psychiatry 2023; 14:1024965. [PMID: 36993926 PMCID: PMC10040533 DOI: 10.3389/fpsyt.2023.1024965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 02/20/2023] [Indexed: 03/16/2023] Open
Abstract
IntroductionMonitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.MethodsWe continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly.ResultsOur results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose.DiscussionOur findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.
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Affiliation(s)
- Emmanouil Kalisperakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Thomas Karantinos
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
| | - Marina Lazaridi
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasiliki Garyfalli
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Panagiotis P. Filntisis
- School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece
| | - Athanasia Zlatintsi
- School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece
| | - Niki Efthymiou
- School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece
| | - Asimakis Mantas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
| | - Leonidas Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | | | | | - Panayotis Tsanakas
- School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece
| | - Petros Maragos
- School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece
| | - Nikolaos Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, Athens, Greece
- 2nd Department of Psychiatry, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, Athens, Greece
- *Correspondence: Nikolaos Smyrnis,
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Schmidt S, D'Alfonso S. Clinician perspectives on how digital phenotyping can inform client treatment. Acta Psychol (Amst) 2023; 235:103886. [PMID: 36921359 DOI: 10.1016/j.actpsy.2023.103886] [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: 06/10/2022] [Revised: 02/05/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023] Open
Abstract
This qualitative study explores mental health clinician perspectives on how information extracted from client interactions with digital devices such as smartphones and the Internet (their digital footprint data) can inform client treatment. The process of learning about an individual's behaviours and psychology from their digital footprint, what has been termed 'digital phenotyping', has emerged in recent years as a field of research with potential to offer insights of clinical value that could be used to predict/detect mental ill-health and inform treatment. This research agenda has largely consisted of quantitative studies exploring statistical associations between smartphone data and psychometric outcomes among relatively small participant cohorts. We on the other hand focus on how the data gathered from smartphones and other digital sources could be converted to pieces of meaningful information that clinicians could directly access and interpret to augment their practice and inform their treatment of clients. Through a reflexive thematic analysis of interviews involving clinical psychologists, this study presents ideas and a framework for understanding how digital phenotyping can inform, augment, and innovate client treatment. In total, five themes concerning the ethics, praxis, and value of digital phenotyping for client treatment are generated.
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Affiliation(s)
- Simone Schmidt
- School of Computing and Information Systems, The University of Melbourne, Australia
| | - Simon D'Alfonso
- School of Computing and Information Systems, The University of Melbourne, Australia.
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The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2022.107603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Lee K, Cheongho Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas MA, Wright AA, Demiris G, Ritchie CS, Pickering CE, Nicholas Dionne-Odom J. Using Digital phenotyping to understand health-related outcomes: A scoping review. Int J Med Inform 2023; 174:105061. [PMID: 37030145 DOI: 10.1016/j.ijmedinf.2023.105061] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/10/2023] [Accepted: 03/24/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations. OBJECTIVES The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes. METHODS A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. RESULTS A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes. CONCLUSION This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.
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Zabihi Poursaadati M, Maarefvand M, Bolhari J, Hosseinzadeh S, Songhori N, Derakhshan L, Khubchandani J. Caregivers' experiences and perspectives of factors associated with relapse in Iranian people living with schizophrenia: A qualitative study. Int J Soc Psychiatry 2023; 69:86-100. [PMID: 34971526 DOI: 10.1177/00207640211068977] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Relapse in People Living with Schizophrenia (PLS) has several reasons and recognizing these can increase the effectiveness of treatment interventions. Formal and informal caregivers are an informed source to reduce relapse in PLS. AIM This study explores the caregivers' perspective in Iran on the factors affecting relapse in PLS. METHOD A total of 28 caregivers (16 formal caregivers and 12 informal caregivers) of PLS were enrolled in our qualitative study. A content analysis was conducted using individual and group, semi-structured in-depth interviews with informal and formal caregivers of PLS. This study was conducted in a hospital, three universities, and a non-governmental organization in Tehran, Iran. RESULTS The majority (69%) of the participants were females. About half of the informal caregivers were over 60 years old and about 40% of the formal caregivers were in the age range of 30 to 40 years. The average number of years of work for informal caregivers was 17.6 years and the average of work experience among the formal caregivers was 14.1 years. Seven key dual themes were identified from data: 'awareness-stigma', 'social support-social exclusion', 'treatment adherence-treatment discontinuation', 'holistic approach - one-dimensional approach', 'supported employment-social dysfunction', 'emotional management in family - family with high emotional expression', and 'access to treatment-treatment gap'. CONCLUSION The results of this research can help practitioners and policymakers to enable evidence-based practices to reduce relapse in PLS by emphasizing and acting on factors identified in our analyses.
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Affiliation(s)
| | - Masoomeh Maarefvand
- Department of Social Work, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Jafar Bolhari
- Spiritual Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Samaneh Hosseinzadeh
- Biostatistics department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Nahid Songhori
- Department of Social Work, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Leili Derakhshan
- Department of Social Work, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Jagdish Khubchandani
- Department of Public Health Sciences, New Mexico University, Las Cruces, NM, USA
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Currey D, Torous J. Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies. BMJ MENTAL HEALTH 2023; 26:e300718. [PMID: 37197799 PMCID: PMC10231441 DOI: 10.1136/bmjment-2023-300718] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 04/26/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Digital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements. OBJECTIVE To assess the impact of population, clinical, research and technological factors on the digital phenotyping data quality as measured by rates of missing digital phenotyping data. METHODS This study analyses retrospective cohorts of mindLAMP smartphone application digital phenotyping studies run at Beth Israel Deaconess Medical Center between May 2019 and March 2022 involving 1178 participants (studies of college students, people with schizophrenia and people with depression/anxiety). With this large combined data set, we report on the impact of sampling frequency, active engagement with the application, phone type (Android vs Apple), gender and study protocol features on missingness/data quality. FINDINGS Missingness from sensors in digital phenotyping is related to active user engagement with the application. After 3 days of no engagement, there was a 19% decrease in average data coverage for both Global Positioning System and accelerometer. Data sets with high degrees of missingness can generate incorrect behavioural features that may lead to faulty clinical interpretations. CONCLUSIONS Digital phenotyping data quality requires ongoing technical and protocol efforts to minimise missingness. Adding run-in periods, education with hands-on support and tools to easily monitor data coverage are all productive strategies studies can use today. CLINICAL IMPLICATIONS While it is feasible to capture digital phenotyping data from diverse populations, clinicians should consider the degree of missingness in the data before using them for clinical decision-making.
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Affiliation(s)
- Danielle Currey
- Harvard Medical School, Boston, Massachusetts, USA
- Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - John Torous
- Harvard Medical School, Boston, Massachusetts, USA
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Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform 2023; 138:104278. [PMID: 36586498 DOI: 10.1016/j.jbi.2022.104278] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.
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Affiliation(s)
- Ivan Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil.
| | - Ariel Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil; Federal Institute of Maranhão, Brazil
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Jean Marques
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
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Cohen A, Naslund JA, Chang S, Nagendra S, Bhan A, Rozatkar A, Thirthalli J, Bondre A, Tugnawat D, Reddy PV, Dutt S, Choudhary S, Chand PK, Patel V, Keshavan M, Joshi D, Mehta UM, Torous J. Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:6. [PMID: 36707524 PMCID: PMC9880926 DOI: 10.1038/s41537-023-00332-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/13/2023] [Indexed: 01/28/2023]
Abstract
Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.
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Affiliation(s)
- Asher Cohen
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - John A. Naslund
- grid.38142.3c000000041936754XDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA USA
| | - Sarah Chang
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Srilakshmi Nagendra
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | | | - Abhijit Rozatkar
- grid.464753.70000 0004 4660 3923Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Jagadisha Thirthalli
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | | | | | - Preethi V. Reddy
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Siddharth Dutt
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Soumya Choudhary
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Prabhat Kumar Chand
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Vikram Patel
- grid.38142.3c000000041936754XDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA USA
| | - Matcheri Keshavan
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Devayani Joshi
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Urvakhsh Meherwan Mehta
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - John Torous
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
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Koinis L, Mobbs RJ, Fonseka RD, Natarajan P. A commentary on the potential of smartphones and other wearable devices to be used in the identification and monitoring of mental illness. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1420. [PMID: 36660675 PMCID: PMC9843326 DOI: 10.21037/atm-21-6016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 10/22/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Lianne Koinis
- Department of Psychology, University of New South Wales, Sydney, Australia
| | - Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
| | - R. Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
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Lal S, Czesak A, Tibbo P, Joober R, Williams R, Chandrasena R, Otter N, Malla A. Young Adults' Perspectives on Factors Related to Relapse After First-Episode Psychosis: Qualitative Focus Group Study. Psychiatr Serv 2022; 73:1380-1388. [PMID: 35770426 DOI: 10.1176/appi.ps.202000641] [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] [Indexed: 12/01/2022]
Abstract
Relapse after first-episode psychosis (FEP) is a major clinical challenge for specialized early intervention services. Understanding patient perspectives on factors contributing to relapse can inform the development of risk assessments and preventive interventions. The objective of this study was to identify factors that may contribute to and prevent relapse from the perspectives of patients receiving services for FEP. Data from 25 participants across four focus groups in Canada were analyzed with a descriptive content analysis approach. Twelve factors were identified, of which four (social environment, technology use, medication, and lifestyle behaviors) had both contributory and preventive roles. In descending order of frequency, risk factors for relapse included substance use; unsupportive social environment; technology use; taking and not taking medication; lack of sleep; work, career, or school stress; significant life events; symptoms of depression or mania; generalized worry; and financial stress. Preventive factors consisted of having a supportive social environment, using technology, taking medication, using coping strategies, and engaging in healthy lifestyle behaviors and meaningful activities. These findings extend the literature on relapse vulnerability and protective factors. Importantly, the factors identified in this study are modifiable, and thereby provide insights for the development and optimization of relapse risk assessments and preventive interventions.
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Affiliation(s)
- Shalini Lal
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Anna Czesak
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Philip Tibbo
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Ridha Joober
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Richard Williams
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Ranjith Chandrasena
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Nicola Otter
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
| | - Ashok Malla
- School of Rehabilitation, University of Montreal, Montreal (Lal, Czesak); Youth Mental Health and Technology Lab, Innovation and Evaluation Hub, University of Montreal Hospital Research Centre, Montreal (Lal); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Tibbo); Douglas Mental Health University Institute, Montreal, and Department of Psychiatry, McGill University, Montreal (Joober, Malla); Victoria Mental Health Centre, Victoria, British Columbia, Canada (Williams); Schulich School of Medicine, University of Western Ontario, London, and Mental Health and Addictions Program, Chatham-Kent Health Alliance, Chatham, Ontario (Chandrasena); Canadian Consortium for Early Intervention in Psychosis (CCEIP), Hamilton, Ontario (Otter)
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Mow JL, Gard DE, Mueser KT, Mote J, Gill K, Leung L, Kangarloo T, Fulford D. Smartphone-based mobility metrics capture daily social motivation and behavior in schizophrenia. Schizophr Res 2022; 250:13-21. [PMID: 36242786 PMCID: PMC10372850 DOI: 10.1016/j.schres.2022.09.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/22/2022] [Accepted: 09/24/2022] [Indexed: 12/12/2022]
Abstract
Impaired social functioning contributes to reduced quality of life and is associated with poor physical and psychological well-being in schizophrenia, and thus is a key psychosocial treatment target. Low social motivation contributes to impaired social functioning, but is typically examined using self-report or clinical ratings, which are prone to recall biases and do not adequately capture the dynamic nature of social motivation in daily life. In the current study, we examined the utility of global positioning system (GPS)-based mobility data for capturing social motivation and behavior in people with schizophrenia. Thirty-one participants with schizophrenia engaged in a 60-day mobile intervention designed to increase social motivation and functioning. We examined associations between twice daily self-reports of social motivation and behavior (e.g., number of social interactions) collected via Ecological Momentary Assessment (EMA) and passively collected daily GPS mobility metrics (e.g., number of hours spent at home) in 26 of these participants. Findings suggested that greater mobility on a given day was associated with more EMA-reported social interactions on that day for four out of five examined mobility metrics: number of hours spent at home, number of locations visited, probability of being stationary, and likelihood of following one's typical routine. In addition, greater baseline social functioning was associated with less daily time spent at home and lower probability of following a daily routine during the intervention. GPS-based mobility thus corresponds with social behavior in daily life, suggesting that more social interactions may occur at times of greater mobility in people with schizophrenia, while subjective reports of social interest and motivation are less associated with mobility for this population.
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Affiliation(s)
- Jessica L Mow
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA.
| | - David E Gard
- Psychology Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA
| | - Kim T Mueser
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Jasmine Mote
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Kathryn Gill
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Lawrence Leung
- Psychology Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA
| | - Tairmae Kangarloo
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Daniel Fulford
- Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Boston, MA, 02215, USA
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Buck B, Munson J, Chander A, Wang W, Brenner CJ, Campbell AT, Ben-Zeev D. The relationship between appraisals of auditory verbal hallucinations and real-time affect and social functioning. Schizophr Res 2022; 250:112-119. [PMID: 36399900 PMCID: PMC9750498 DOI: 10.1016/j.schres.2022.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 09/03/2022] [Accepted: 10/30/2022] [Indexed: 11/18/2022]
Abstract
In addition to being a hallmark symptom of schizophrenia-spectrum disorders, auditory verbal hallucinations (AVH) are present in a range of psychiatric disorders as well as among individuals who are otherwise healthy. People who experience AVH are heterogeneous, and research has aimed to better understand what characteristics distinguish, among those who experience AVH, those who experience significant disruption and distress from those who do not. The cognitive model of AVH suggests that appraisals of voices determine the extent to which voices cause distress and social dysfunction. Previous work has relied largely on comparisons of "clinical" and "non-clinical" voice hearers, and few studies have been able to provide insight into the moment-to-moment relationships between appraisals and outcomes. The current study examines longitudinal data provided through ecological momentary assessment and passive sensors of 465 individuals who experience cross-diagnostic AVH. Results demonstrated associations of AVH appraisals to negative affect and social functioning. Above and beyond within-individual averages, when a participant reported increased appraisals of their voices as powerful and difficult to control, they were more likely to feel increased negative affect and reduced feelings of safety. AVH power appraisals were also associated with next-day number and duration of phone calls placed, and AVH controllability appraisals were associated with increased time near speech and reduced next-day time away from primary location. These results suggest that appraisals are state-like characteristics linked with day-to-day and moment-to-moment changes in impactful affective and behavioral outcomes; intervention approaches should aim to address these domains in real-time.
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Affiliation(s)
- Benjamin Buck
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States of America.
| | - Jeffrey Munson
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States of America
| | - Ayesha Chander
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States of America
| | - Weichen Wang
- Department of Computer Science, Dartmouth College, Hanover, NH, United States of America
| | - Carolyn J Brenner
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States of America
| | - Andrew T Campbell
- Department of Computer Science, Dartmouth College, Hanover, NH, United States of America
| | - Dror Ben-Zeev
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States of America
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Social-Ecological Measurement of Daily Life: How Relationally Focused Ambulatory Assessment can Advance Clinical Intervention Science. REVIEW OF GENERAL PSYCHOLOGY 2022. [DOI: 10.1177/10892680221142802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Individuals’ daily behaviors and social interactions play a central role in the diagnosis and treatment of psychological disorders. Despite this, observational ambulatory assessment methods—research methods that allow for direct and passive assessment of individuals’ momentary activities and interactions—have a remarkably scant history in the clinical science field. Prior discussions of ambulatory assessment methods in clinical science have focused on subjective methods (e.g., ecological momentary assessment) and physiological methods (e.g., wearable heart rate monitoring). Comparatively less attention has been dedicated to ambulatory assessment methods that collect objective, relational data about individuals’ social behaviors and their interactions with their momentary environmental contexts. Drawing on extant social-ecological measurement frameworks, this article first provides a conceptual and psychometric rationale for the integration of daily relational data into clinical science research. Next, the nascent research applying such methods to clinical science is reviewed, and priorities for further research organized by the NIH Stage Model for Clinical Science Research are recommended. These data can provide unique information about the social contexts of diverse patient populations; identify social-ecological targets for transdiagnostic, precision, and culturally responsive interventions; and contribute novel data about the effectiveness of established interventions at creating behavioral and relational change.
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Kwon S, Firth J, Joshi D, Torous J. Accessibility and availability of smartphone apps for schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:98. [PMID: 36385116 PMCID: PMC9668219 DOI: 10.1038/s41537-022-00313-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/05/2022] [Indexed: 05/25/2023]
Abstract
App-based interventions have the potential to enhance access to and quality of care for patients with schizophrenia. However, less is known about the current state of schizophrenia apps in research and how those translate to publicly available apps. This study, therefore, aimed to review schizophrenia apps offered on marketplaces and research literature with a focus on accessibility and availability. A search of recent reviews, gray literature, PubMed, and Google Scholar was conducted in August 2022. A search of the U.S. Apple App Store and Google Play App Store was conducted in July 2022. All eligible studies and apps were systematically screened/reviewed. The academic research search produced 264 results; 60 eligible studies were identified. 51.7% of research apps were built on psychosis-specific platforms and 48.3% of research apps were built on non-specific platforms. 83.3% of research apps offered monitoring functionalities. Only nine apps, two designed on psychosis-specific platforms and seven on non-specific platforms were easily accessible. The search of app marketplaces uncovered 537 apps; only six eligible marketplace apps were identified. 83.3% of marketplace apps only offered psychoeducation. All marketplace apps lacked frequent updates with the average time since last update 1121 days. There are few clinically relevant apps accessible to patients on the commercial marketplaces. While research efforts are expanding, many research apps are unavailable today. Better translation of apps from research to the marketplace and a focus on sustainable interventions are important targets for the field.
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Affiliation(s)
- Sam Kwon
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Devayani Joshi
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'ÉVOLUTION PSYCHIATRIQUE 2022. [DOI: 10.1016/j.evopsy.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Zlatintsi A, Filntisis PP, Garoufis C, Efthymiou N, Maragos P, Menychtas A, Maglogiannis I, Tsanakas P, Sounapoglou T, Kalisperakis E, Karantinos T, Lazaridi M, Garyfalli V, Mantas A, Mantonakis L, Smyrnis N. E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures. SENSORS (BASEL, SWITZERLAND) 2022; 22:7544. [PMID: 36236643 PMCID: PMC9572170 DOI: 10.3390/s22197544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.
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Affiliation(s)
- Athanasia Zlatintsi
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | | | - Christos Garoufis
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Niki Efthymiou
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Petros Maragos
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | - Andreas Menychtas
- Department of Digital Systems, University of Piraeus, 185 34 Pireas, Greece
| | - Ilias Maglogiannis
- Department of Digital Systems, University of Piraeus, 185 34 Pireas, Greece
| | - Panayiotis Tsanakas
- School of ECE, National Technical University of Athens, 157 73 Athens, Greece
| | | | - Emmanouil Kalisperakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Thomas Karantinos
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
| | - Marina Lazaridi
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Vasiliki Garyfalli
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Asimakis Mantas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
| | - Leonidas Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Nikolaos Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, 115 27 Athens, Greece
- 2nd Department of Psychiatry, University General Hospital “ATTIKON”, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece
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Herpertz J, Richter MF, Barkhau C, Storck M, Blitz R, Steinmann LA, Goltermann J, Dannlowski U, Baune BT, Varghese J, Dugas M, Lencer R, Opel N. Symptom monitoring based on digital data collection during inpatient treatment of schizophrenia spectrum disorders - A feasibility study. Psychiatry Res 2022; 316:114773. [PMID: 35994863 DOI: 10.1016/j.psychres.2022.114773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 10/15/2022]
Abstract
Digital acquisition of patients' self-reports on individual risk factors and symptom severity represents a promising, cost-efficient, and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. Yet, studies investigating digital data collection in patients with a schizophrenia spectrum disorder (PSSDs) are scarce. The objective of this study was to explore the feasibility of digitally acquired self-report assessments of risk and symptom profiles at the time of admission into inpatient treatment in an age-representative sample of hospitalized PSSDs. We investigated the required support, the data entry pace, and the subjective user experience. Findings were compared with those of patients with an affective disorder (PADs). Of 82 PSSDs who were eligible for inclusion, 59.8% (n=49) agreed to participate in the study, of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates, and subjective experience ratings did not differ between PSSDs and PADs. Patients reported high satisfaction with the assessment. PSSDs required more support and time for the data entry than PADs. Our results indicate that digital data collection is a feasible and well-received method in PSSDs. Future clinical and research efforts on digitized assessments in psychiatry should include PSSDs and offer support to reduce digital exclusion.
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Affiliation(s)
- Julian Herpertz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Maike Frederike Richter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Rogério Blitz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute of Medical Informatics, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Lavinia A Steinmann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne Parkville, Melbourne, Australia; Department of Psychiatry, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Interdisciplinary Centre for Clinical Research Münster, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany.
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Birk RH, Samuel G. Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems. Curr Psychiatry Rep 2022; 24:523-528. [PMID: 36001220 DOI: 10.1007/s11920-022-01358-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW We review recent developments within digital phenotyping for mental health, a field dedicated to using digital data for diagnosing, predicting, and monitoring mental health problems. We especially focus on recent critiques and challenges to digital phenotyping from within the social sciences. RECENT FINDINGS Three significant strands of criticism against digital phenotyping for mental health have been developed within the social sciences. This literature problematizes the idea that digital data can be objective, that it can be unbiased, and argues that it has multiple ethical and practical challenges. Digital phenotyping for mental health is a rapidly growing and developing field, but with considerable challenges that are not easily solvable. This includes when, and if, data from digital phenotyping is actionable in practice; the involvement of user and patient perspectives in digital phenotyping research; the possibility of biased data; and challenges to the idea that digital phenotyping can be more objective than other forms of psychiatric assessment.
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Affiliation(s)
- Rasmus H Birk
- Department of Communication & Psychology, Aalborg University, Aalborg, Denmark.
| | - Gabrielle Samuel
- Department of Global Health & Social Medicine, King's College London, London, UK
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