51
|
Abstract
PURPOSE OF REVIEW To describe examples of adapting apps for use in mental healthcare and to formulate recommendations for successful adaptation in mental healthcare settings. RECENT FINDINGS International examples are given to explore implementation procedures to address this multitude of challenges. There are only few published examples of adapting apps for use in mental healthcare. From these examples and from results of studies in implementation science in general clinical settings, it can be concluded that the process of adapting apps for mental healthcare needs to address clinician training and information needs, user needs which include cultural adaptation and go beyond mere translation, and organizational needs for blending app use into everyday clinical mental healthcare workflows.
Collapse
|
52
|
Sun J, Shen H, Ibn-Ul-Hassan S, Riaz A, Domil AE. The association between digitalization and mental health: The mediating role of wellbeing at work. Front Psychiatry 2022; 13:934357. [PMID: 35990046 PMCID: PMC9386346 DOI: 10.3389/fpsyt.2022.934357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
The study aims to measure the mediating relationship of wellbeing at work between digitalization (IT infrastructure, IT business spanning, and IT proactive stance) and their effect on mental health. The study uses primary data collection techniques to gather data and used purposive sampling to analyze the data. The sample size of the study is 349 respondents. The research uses Smart PLS software to measure the relationship through bootstrapping and algorithms. The study finds a significant positive mediating role of wellbeing between digitalization (IT infrastructure, IT business spanning, and IT proactive stance) and their effect on mental health. The study outcomes are helpful for managers and policymakers.
Collapse
Affiliation(s)
- Jianmin Sun
- School of Management, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Hongzhou Shen
- School of Management, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Syed Ibn-Ul-Hassan
- Department of Commerce and Business, Government College University Faisalabad, Layyah Campus, Layyah, Pakistan
| | - Amir Riaz
- Department of Management Sciences, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Aura Emanuela Domil
- Faculty of Economics and Business Administration, West University of Timisoara, Timisoara, Romania
| |
Collapse
|
53
|
Naslund JA, Aschbrenner KA. Technology use and interest in digital apps for mental health promotion and lifestyle intervention among young adults with serious mental illness. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2021. [DOI: 10.1016/j.jadr.2021.100227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
|
54
|
Nguyen B, Torres A, Sim W, Kenny D, Campbell DM, Beavers L, Lou W, Kapralos B, Peter E, Dubrowski A, Krishnan S, Bhat V. Digital Interventions to Reduce Distress Among Health Care Providers at the Frontline: Protocol for a Feasibility Trial. JMIR Res Protoc 2021; 11:e32240. [PMID: 34871178 PMCID: PMC8852627 DOI: 10.2196/32240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
Background Stress, anxiety, distress, and depression are high among health care workers during the COVID-19 pandemic, and they have reported acting in ways that are contrary to their moral values and professional commitments that degrade their integrity. This creates moral distress and injury due to constraints they have encountered, such as limited resources. Objective The purpose of this study is to develop and show the feasibility of digital platforms (a virtual reality and a mobile platform) to understand the causes and ultimately reduce the moral distress of health care providers during the COVID-19 pandemic. Methods This will be a prospective, single cohort, pre- and posttest study examining the effect of a brief informative video describing moral distress on perceptual, psychological, and physiological indicators of stress and decision-making during a scenario known to potentially elicit moral distress. To accomplish this, we have developed a virtual reality simulation that will be used before and after the digital intervention for monitoring short-term impacts. The simulation involves an intensive care unit setting during the COVID-19 pandemic, and participants will be placed in morally challenging situations. The participants will be engaged in an educational intervention at the individual, team, and organizational levels. During each test, data will be collected for (1) physiological measures of stress and after each test, data will be collected regarding (2) thoughts, feelings and behaviors during a morally challenging situation, and (3) perceptual estimates of psychological stress. In addition, participants will continue to be monitored for moral distress and other psychological stresses for 8 weeks through our Digital intervention/intelligence Group mobile platform. Finally, a comparison will be conducted using machine learning and biostatistical techniques to analyze the short- and long-term impacts of the virtual reality intervention. Results The study was funded in November 2020 and received research ethics board approval in March 2021. The study is ongoing. Conclusions This project is a proof-of-concept integration to demonstrate viability over 6 months and guide future studies to develop these state-of-the-art technologies to help frontline health care workers work in complex moral contexts. In addition, the project will develop innovations that can be used for future pandemics and in other contexts prone to producing moral distress and injury. This project aims to demonstrate the feasibility of using digital platforms to understand the continuum of moral distress that can lead to moral injury. Demonstration of feasibility will lead to future studies to examine the efficacy of digital platforms to reduce moral distress. Trial Registration ClinicalTrials.gov NCT05001542; https://clinicaltrials.gov/ct2/show/NCT05001542 International Registered Report Identifier (IRRID) DERR1-10.2196/32240
Collapse
Affiliation(s)
- Binh Nguyen
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, CA
| | - Andrei Torres
- maxSIMhealth Group, Ontario Tech University, 2000 Simcoe St N, Oshawa, CA
| | - Walter Sim
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, 193 Yonge Street, Suite 6-013, Toronto, CA
| | - Deborah Kenny
- Department of Nursing, University of Colorado, Colorado Springs, US
| | - Douglas M Campbell
- Neonatal Intensive Care Unit, St. Michael's Hospital, Unity Health Toronto, Toronto, CA.,Allan Waters Family Simulation Program, St. Michael's Hospital, Unity Health Toronto, Toronto, CA.,Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, CA.,Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, CA
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, St. Michael's Hospital, Unity Health Toronto, Toronto, CA.,Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, CA
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, CA
| | - Bill Kapralos
- maxSIMhealth Group, Ontario Tech University, 2000 Simcoe St N, Oshawa, CA
| | - Elizabeth Peter
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, CA
| | - Adam Dubrowski
- maxSIMhealth Group, Ontario Tech University, 2000 Simcoe St N, Oshawa, CA
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, CA
| | - Venkat Bhat
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, 193 Yonge Street, Suite 6-013, Toronto, CA.,Department of Psychiatry, University of Toronto, Toronto, CA
| |
Collapse
|
55
|
Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica KC, Kunta AR, Low CA. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Front Digit Health 2021; 3:769823. [PMID: 34870271 PMCID: PMC8636712 DOI: 10.3389/fdgth.2021.769823] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 01/14/2023] Open
Abstract
Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Carissa A. Low
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
56
|
Coghlan S, D'Alfonso S. Digital Phenotyping: an Epistemic and Methodological Analysis. PHILOSOPHY & TECHNOLOGY 2021; 34:1905-1928. [PMID: 34786325 PMCID: PMC8581123 DOI: 10.1007/s13347-021-00492-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 11/04/2021] [Indexed: 11/30/2022]
Abstract
Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human “digitypes” by digital phenotyping. These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience.
Collapse
Affiliation(s)
- Simon Coghlan
- School of Computing & Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, Australia
| | - Simon D'Alfonso
- School of Computing & Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, Australia
| |
Collapse
|
57
|
Abstract
BACKGROUND Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and actively (e.g., survey) collected data. Machine learning offers a possible predictive bridge between digital phenotyping and future clinical state. This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies. METHODS A systematic review of passive digital phenotyping literature was conducted using keywords related to severe mental illnesses, data-collection devices (e.g., smartphones, wearables, actigraphy devices), and streams of data collected. Searches of five databases initially yielded 3312 unique publications. Fifty-one studies were selected for inclusion, with 16 using machine-learning techniques. RESULTS All studies differed in features used, data pre-processing, analytical techniques, algorithms tested, and performance metrics reported. Across all studies, the data streams and other study factors reported also varied widely. Machine-learning studies focused on random forest, support vector, and neural net approaches, and almost exclusively on bipolar disorder. DISCUSSION Many machine-learning techniques have been applied to passively collected digital phenotyping data in schizophrenia and bipolar disorder. Larger studies, and with improved data quality, are needed, as is further research on the application of machine learning to passive digital phenotyping data in early diagnosis and treatment of psychosis. In order to achieve greater comparability of studies, common data elements are identified for inclusion in future studies.
Collapse
|
58
|
Bougeard A, Guay Hottin1 R, Houde V, Jean T, Piront T, Potvin S, Bernard P, Tourjman V, De Benedictis L, Orban P. Le phénotypage digital pour une pratique clinique en santé mentale mieux informée. SANTE MENTALE AU QUEBEC 2021. [DOI: 10.7202/1081513ar] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Objectifs Cette revue trouve sa motivation dans l’observation que la prise de décision clinique en santé mentale est limitée par la nature des mesures typiquement obtenues lors de l’entretien clinique et la difficulté des cliniciens à produire des prédictions justes sur les états mentaux futurs des patients. L’objectif est de présenter un survol représentatif du potentiel du phénotypage digital couplé à l’apprentissage automatique pour répondre à cette limitation, tout en en soulignant les faiblesses actuelles.
Méthode Au travers d’une revue narrative de la littérature non systématique, nous identifions les avancées technologiques qui permettent de quantifier, instant après instant et dans le milieu de vie naturel, le phénotype humain au moyen du téléphone intelligent dans diverses populations psychiatriques. Des travaux pertinents sont également sélectionnés afin de déterminer l’utilité et les limitations de l’apprentissage automatique pour guider les prédictions et la prise de décision clinique. Finalement, la littérature est explorée pour évaluer les barrières actuelles à l’adoption de tels outils.
Résultats Bien qu’émergeant d’un champ de recherche récent, de très nombreux travaux soulignent déjà la valeur des mesures extraites des senseurs du téléphone intelligent pour caractériser le phénotype humain dans les sphères comportementale, cognitive, émotionnelle et sociale, toutes étant affectées par les troubles mentaux. L’apprentissage automatique permet d’utiles et justes prédictions cliniques basées sur ces mesures, mais souffre d’un manque d’interprétabilité qui freinera son emploi prochain dans la pratique clinique. Du reste, plusieurs barrières identifiées tant du côté du patient que du clinicien freinent actuellement l’adoption de ce type d’outils de suivi et d’aide à la décision clinique.
Conclusion Le phénotypage digital couplé à l’apprentissage automatique apparaît fort prometteur pour améliorer la pratique clinique en santé mentale. La jeunesse de ces nouveaux outils technologiques requiert cependant un nécessaire processus de maturation qui devra être encadré par les différents acteurs concernés pour que ces promesses puissent être pleinement réalisées.
Collapse
Affiliation(s)
- Alan Bougeard
- Étudiant, Centre de recherche de l’Institut universitaire en santé mentale de Montréal
| | - Rose Guay Hottin1
- Étudiante, Centre de recherche de l’Institut universitaire en santé mentale de Montréal
| | - Valérie Houde
- M.D., étudiante, Centre de recherche de l’Institut universitaire en santé mentale de Montréal
| | - Thierry Jean
- Étudiant, Centre de recherche de l’Institut universitaire en santé mentale de Montréal
| | - Thibault Piront
- Professionnel de recherche, Centre de recherche de l’Institut universitaire en santé mentale de Montréal
| | - Stéphane Potvin
- Ph. D., chercheur, Centre de recherche de l’Institut universitaire en santé mentale de Montréal – professeur sous octroi titulaire, Département de psychiatrie et d’addictologie, Université de Montréal
| | - Paquito Bernard
- Ph. D., chercheur, Centre de recherche de l’Institut universitaire en santé mentale de Montréal – professeur régulier, Département des sciences de l’activité physique, Université du Québec à Montréal
| | - Valérie Tourjman
- M.D., psychiatre, Institut universitaire en santé mentale de Montréal – professeure agrégée de clinique, Département de psychiatrie et d’addictologie, Université de Montréal
| | - Luigi De Benedictis
- M.D., psychiatre, Institut universitaire en santé mentale de Montréal – professeur adjoint de clinique, Département de psychiatrie et d’addictologie, Université de Montréal
| | - Pierre Orban
- Ph. D., chercheur, Centre de recherche de l’Institut universitaire en santé mentale de Montréal – professeur sous octroi adjoint, Département de psychiatrie et d’addictologie, Université de Montréal
| |
Collapse
|
59
|
Rodriguez-Villa E, Rozatkar AR, Kumar M, Patel V, Bondre A, Naik SS, Dutt S, Mehta UM, Nagendra S, Tugnawat D, Shrivastava R, Raghuram H, Khan A, Naslund JA, Gupta S, Bhan A, Thirthall J, Chand PK, Lakhtakia T, Keshavan M, Torous J. Cross cultural and global uses of a digital mental health app: results of focus groups with clinicians, patients and family members in India and the United States. Glob Ment Health (Camb) 2021; 8:e30. [PMID: 34512999 PMCID: PMC8392688 DOI: 10.1017/gmh.2021.28] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/24/2021] [Accepted: 07/14/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Despite significant advancements in healthcare technology, digital health solutions - especially those for serious mental illnesses - continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. METHODS Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. RESULTS Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. CONCLUSION People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary - not substitutive - of therapeutic care from a clinician.
Collapse
Affiliation(s)
- Elena Rodriguez-Villa
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Abhijit R. Rozatkar
- All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India462020
| | - Mohit Kumar
- All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India462020
| | - Vikram Patel
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, USA
| | | | - Shalini S. Naik
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | - Siddharth Dutt
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | - Urvakhsh M. Mehta
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | - Srilakshmi Nagendra
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | | | | | | | - Azaz Khan
- Sangath, Bhopal, Madhya Pradesh, India462016
| | - John A. Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, USA
| | - Snehil Gupta
- All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India462020
| | - Anant Bhan
- Sangath, Bhopal, Madhya Pradesh, India462016
| | - Jagadisha Thirthall
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | - Prabhat K. Chand
- National Institute of Mental Health and NeuroSciences, Bangalore, India560029
| | - Tanvi Lakhtakia
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Matcheri Keshavan
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - John Torous
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| |
Collapse
|
60
|
Blom JMC, Colliva C, Benatti C, Tascedda F, Pani L. Digital Phenotyping and Dynamic Monitoring of Adolescents Treated for Cancer to Guide Intervention: Embracing a New Era. Front Oncol 2021; 11:673581. [PMID: 34262863 PMCID: PMC8273734 DOI: 10.3389/fonc.2021.673581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Johanna M. C. Blom
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Chiara Colliva
- Azienda Unità Sanitaria Locale di Modena, Distretto di Carpi, Carpi, Italy
| | - Cristina Benatti
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Fabio Tascedda
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Pani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, FL, United States
- VeraSci., Durham, NC, United States
| |
Collapse
|
61
|
Rauseo-Ricupero N, Henson P, Agate-Mays M, Torous J. Case studies from the digital clinic: integrating digital phenotyping and clinical practice into today's world. Int Rev Psychiatry 2021; 33:394-403. [PMID: 33792463 DOI: 10.1080/09540261.2020.1859465] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The following case series provides several examples from the Digital Clinic, an outpatient mental health program which uses smartphone technology to augment traditional mental health care. The themes highlighted in this piece, expanding emotional-awareness, symptom tracking, and medication management, provide real-clinical examples of how the Digital Clinic offered remote mental health care to a diverse group of people. Furthermore, the following piece demonstrates to practicing clinicians how digital technologies, like smartphone apps, can diversify methods of clinical engagement, assist with collecting health metrics in a safe and ethical manner, and promote person centred care. With the COVID-19 pandemic forcing re-evaluation of how mental health services are provided, it is critical to ensure that digitally infused systems of care, like the Digital Clinic, are effective, accessible, and scalable.
Collapse
Affiliation(s)
| | - Philip Henson
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mica Agate-Mays
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Smith College School for Social Work, Northampton, MA, USA
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
62
|
Kudina O, de Boer B. Co-designing diagnosis: Towards a responsible integration of Machine Learning decision-support systems in medical diagnostics. J Eval Clin Pract 2021; 27:529-536. [PMID: 33480150 PMCID: PMC8248235 DOI: 10.1111/jep.13535] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/15/2020] [Accepted: 12/28/2020] [Indexed: 12/02/2022]
Abstract
RATIONALE This paper aims to show how the focus on eradicating bias from Machine Learning decision-support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision-making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision-making. METHODS This paper presents a philosophical analysis, employing the conceptual frameworks of hermeneutics and technological mediation, while drawing on the case of Machine Learning algorithms assisting doctors in diagnosis. This paper unravels the non-neutral role of algorithms in the doctor's decision-making and points to the dialogical nature of interaction not only with the patients but also with the technologies that co-shape the diagnosis. FINDINGS Following the hermeneutical model of medical diagnosis, we review the notion of bias to show how it is an inalienable and productive part of diagnosis. We show how Machine Learning biases join the human ones to actively shape the diagnostic process, simultaneously expanding and narrowing medical attention, highlighting certain aspects, while disclosing others, thus mediating medical perceptions and actions. Based on that, we demonstrate how doctors can take Machine Learning systems on board for an enhanced medical diagnosis, while being aware of their non-neutral role. CONCLUSIONS We show that Machine Learning systems join doctors and patients in co-designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.
Collapse
Affiliation(s)
- Olya Kudina
- Department of Values, Technology & Innovation, Section on Ethics and Philosophy of TechnologyDelft University of TechnologyDelftthe Netherlands
| | - Bas de Boer
- Philosophy DepartmentUniversity of TwenteEnschedethe Netherlands
| |
Collapse
|
63
|
Connolly SL, Kuhn E, Possemato K, Torous J. Digital Clinics and Mobile Technology Implementation for Mental Health Care. Curr Psychiatry Rep 2021; 23:38. [PMID: 33961135 PMCID: PMC8103883 DOI: 10.1007/s11920-021-01254-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW Interest in digital mental health, especially smartphone apps, has expanded in light of limited access to mental health services and the need for remote care during COVID-19. Digital clinics, in which apps are blended into routine care, offer a potential solution to common implementation challenges including low user engagement and lack of clinical integration of apps. RECENT FINDINGS While the number of mental health apps available in commercial marketplaces continues to rise, there are few examples of successful implementation of these apps into care settings. We review one example of a digital clinic created within an academic medical center and another within the Department of Veterans Affairs. We then discuss how implementation science can inform new efforts to effectively integrate mental health technologies across diverse use cases. Integrating mental health apps into care settings is feasible but requires careful attention to multiple domains that will influence implementation success, including characteristics of the innovation (e.g., utility and complexity of the app), the recipients of the technology (e.g., patients and clinicians), and context (e.g., healthcare system buy-in, reimbursement, and regulatory policies). Examples of effective facilitation strategies that can be utilized to improve implementation efforts include co-production of technology involving all end users, specialized trainings for staff and patients, creation of new team members to aid in app usage (e.g., digital navigators), and re-design of clinical workflows.
Collapse
Affiliation(s)
- Samantha L Connolly
- Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Eric Kuhn
- National Center for PTSD, Dissemination and Training Division, VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyle Possemato
- VA Center for Integrated Healthcare, Syracuse Veterans Affairs Medical Center, Syracuse, NY, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02446, USA.
| |
Collapse
|
64
|
Jagesar RR, Vorstman JA, Kas MJ. Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study. J Med Internet Res 2021; 23:e20996. [PMID: 33825695 PMCID: PMC8060862 DOI: 10.2196/20996] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/31/2020] [Accepted: 01/25/2021] [Indexed: 12/27/2022] Open
Abstract
Background Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way. Objective This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform. Methods We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform. Results We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design. Conclusions Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping–driven research.
Collapse
Affiliation(s)
- Raj R Jagesar
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | - Jacob A Vorstman
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| |
Collapse
|
65
|
Li H, Yang S, Chi H, Xu L, Zhang T, Singleton G, Tang Y, Stone WS, Wang J. Enhancing attention and memory of individuals at clinical high risk for psychosis with mHealth technology. Asian J Psychiatr 2021; 58:102587. [PMID: 33618070 DOI: 10.1016/j.ajp.2021.102587] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Cognitive impairment has adverse impact on the social and role functions of those at clinical high risk for psychosis and it has become an important target for intervention. Mobile health applications are user-friendly, real-time, personalized and portable in administering cognitive training and have promising application prospects in the field of mental health. METHODS Eighty CHR subjects were randomized into an intervention group and a control group. CHR subjects of the intervention group performed attention and memory training via a Specific Memory Attention Resource and Training (SMART) application in their smart phones for 10 min per day, five days per week for three months. Both groups were followed up for three months. At baseline and follow-up phases, cognitive function was measured using the MATRICS Consensus Cognitive Battery (MCCB). In the follow-up, the intervention group completed the Mobile Application Rating Scale (MARS) to provide feedback to improve SMART. RESULTS There is a significant group by time interaction effect in the Attention/Vigilance domain, which is significantly better in the intervention group than in the control group at 3- month follow-up. The improvement in Attention/Vigilance in the intervention group is significantly related to the amount of cognitive training time. Global Assessment of Function (GAF) reduction rate at baseline could predict the improvement of Attention/Vigilance. MARS results indicate that CHR subjects were receptive of SMART. CONCLUSION Mobile technology can be applied to improve cognitive function of CHR individuals, especially in the Attention/Vigilance domain.
Collapse
Affiliation(s)
- Huijun Li
- Florida A&M University, 501 Orr Drive, Psychology Department, United States.
| | - Shuwen Yang
- Shanghai Mental Health Center, 600 Wanping Nan Road, Shanghai, China.
| | - Hongmei Chi
- Florida A&M University, 501 Orr Drive, Psychology Department, United States.
| | - Lihua Xu
- Shanghai Mental Health Center, 600 Wanping Nan Road, Shanghai, China.
| | - Tianghong Zhang
- Shanghai Mental Health Center, 600 Wanping Nan Road, Shanghai, China.
| | - Gwendolyn Singleton
- Florida A&M University, 501 Orr Drive, Psychology Department, United States.
| | - Yingying Tang
- Shanghai Mental Health Center, 600 Wanping Nan Road, Shanghai, China.
| | - William S Stone
- Beth Israel Deaconess Medical Center, 75 Fenwood Road, Boston, 02115, United States.
| | - Jijun Wang
- Shanghai Mental Health Center, 600 Wanping Nan Road, Shanghai, China.
| |
Collapse
|
66
|
Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Ment Health 2021; 8:e26811. [PMID: 33779570 PMCID: PMC8077937 DOI: 10.2196/26811] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/06/2021] [Accepted: 02/27/2021] [Indexed: 01/19/2023] Open
Abstract
The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence-based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).
Collapse
Affiliation(s)
- Luke Balcombe
- Australian Institute for Suicide Research and Prevention, Griffith University, Brisbane, Australia
| | - Diego De Leo
- Australian Institute for Suicide Research and Prevention, Griffith University, Brisbane, Australia
| |
Collapse
|
67
|
Henson P, Rodriguez-Villa E, Torous J. Investigating Associations Between Screen Time and Symptomatology in Individuals With Serious Mental Illness: Longitudinal Observational Study. J Med Internet Res 2021; 23:e23144. [PMID: 33688835 PMCID: PMC7991985 DOI: 10.2196/23144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 09/01/2020] [Accepted: 01/31/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness. OBJECTIVE To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis. METHODS In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising. Age-matched, smartphone-owning healthy controls were also part of the recruitment pool. A specification curve analysis was a priori designed to explore the relationship between every combination of independent variable and dependent variable in order to demonstrate to what degree screen time relates to symptoms in individuals with serious mental illness. RESULTS The sample consisted of 88 participants (54 with schizophrenia and 34 healthy controls) who completed both the initial and follow-up visits, completed at least one self-reported survey, and had a minimum passive data cutoff of 5 consecutive days. While we found an association between smartphone screen time metrics and cognition (adjusted R2=0.107, P<.001), specification curve analysis revealed a wide range of heterogenous associations with screen time from very negative to very positive. The effects differed based on diagnostic group, age bracket, type of regression model used, and the specific independent and dependent variables selected for analysis. CONCLUSIONS The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.
Collapse
Affiliation(s)
- Philip Henson
- Mt Sinai School of Medicine, New York, NY, United States
| | - Elena Rodriguez-Villa
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
68
|
Steinert T, Thoma S. [Digital Phenotyping - Blessing or Curse?]. PSYCHIATRISCHE PRAXIS 2021; 48:59-61. [PMID: 33652476 DOI: 10.1055/a-1347-3349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Tilman Steinert
- Klinik für Psychiatrie und Psychotherapie I der Universität Ulm (Weissenau), ZfP Südwürttemberg
| | - Samuel Thoma
- Immanuel Klinik Rüdersdorf, Hochschulklinik für Psychiatrie und Psychotherapie der Medizinischen Hochschule Brandenburg Theodor Fontane
| |
Collapse
|
69
|
Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:856-864. [PMID: 33571718 DOI: 10.1016/j.bpsc.2021.02.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.
Collapse
Affiliation(s)
- Ellen E Lee
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard University, Boston, Massachusetts
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - Sarah A Graham
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, California
| | | | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Neurosciences, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California.
| |
Collapse
|
70
|
Rauseo-Ricupero N, Torous J. Technology Enabled Clinical Care (TECC): Protocol for a Prospective Longitudinal Cohort Study of Smartphone-Augmented Mental Health Treatment. JMIR Res Protoc 2021; 10:e23771. [PMID: 33296869 PMCID: PMC7813563 DOI: 10.2196/23771] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/27/2020] [Accepted: 12/08/2020] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Even before COVID-19, there has been an urgent need to expand access to and quality of mental health care. This paper introduces an 8-week treatment protocol to realize that vision-Technology Enabled Clinical Care (TECC). TECC offers innovation in clinical assessment, monitoring, and interventions for mental health. TECC uses the mindLAMP app to enable digital phenotyping, clinical communication, and smartphone-based exercises that will augment in-person or telehealth virtual visits. TECC exposes participants to an array of evidence-based treatments (cognitive behavioral therapy, dialectical behavior therapy, acceptance and commitment therapy) introduced through clinical sessions and then practiced through interactive activities provided through a smartphone app called mindLAMP. OBJECTIVE TECC will test the feasibility of providing technology-enabled mental health care within an outpatient clinic; explore the practicality for providing this care to individuals with limited English proficiency; and track anxiety, depression, and mood symptoms for participants to measure the effectiveness of the TECC design. METHODS The TECC study will assess the acceptability and efficacy of this care model in 50 participants as compared to an age- and gender-matched cohort of patients presenting with similar clinical severity of depression, anxiety, or psychotic symptoms. Participants will be recruited from clinics in the Metro Boston area. Aspects of TECC will be conducted in both Spanish and English to ensure wide access to care for multiple populations. RESULTS The results of the TECC study will be used to support or adapt this model of care and create training resources to ensure its dissemination. The study results will be posted on ClinicalTrials.gov, with primary outcomes related to changes in mood, anxiety, and stress, and secondary outcomes related to engagement, alliance, and satisfaction. CONCLUSIONS TECC combines new digital mental health technology with updated clinical protocols and workflows designed to ensure patients can benefit from innovation in digital mental health. Supporting multiple languages, TECC is designed to ensure digital health equity and highlights how mobile health can bridge, not expand, gaps in care for underserved populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/23771.
Collapse
Affiliation(s)
- Natali Rauseo-Ricupero
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
71
|
Henson P, D’Mello R, Vaidyam A, Keshavan M, Torous J. Anomaly detection to predict relapse risk in schizophrenia. Transl Psychiatry 2021; 11:28. [PMID: 33431818 PMCID: PMC7798381 DOI: 10.1038/s41398-020-01123-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 11/10/2020] [Accepted: 11/23/2020] [Indexed: 01/18/2023] Open
Abstract
The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.
Collapse
Affiliation(s)
- Philip Henson
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Ryan D’Mello
- grid.38142.3c000000041936754XDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Aditya Vaidyam
- grid.38142.3c000000041936754XDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Matcheri Keshavan
- grid.38142.3c000000041936754XDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - John Torous
- Departments of Psychiatry and Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
72
|
Hays R, Keshavan M, Wisniewski H, Torous J. Deriving symptom networks from digital phenotyping data in serious mental illness. BJPsych Open 2020; 6:e135. [PMID: 33138889 PMCID: PMC7745255 DOI: 10.1192/bjo.2020.94] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms. AIMS In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis. METHOD Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains. RESULTS The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups. CONCLUSIONS Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.
Collapse
Affiliation(s)
- Ryan Hays
- Harvard Medical School, Department of Psychiatry, Beth Israel Deaconess Medical Center, USA
| | - Matcheri Keshavan
- Harvard Medical School, Department of Psychiatry, Beth Israel Deaconess Medical Center, USA
| | - Hannah Wisniewski
- Harvard Medical School, Department of Psychiatry, Beth Israel Deaconess Medical Center, USA
| | - John Torous
- Harvard Medical School, Department of Psychiatry, Beth Israel Deaconess Medical Center, USA
| |
Collapse
|
73
|
H Birk R, Samuel G. Can digital data diagnose mental health problems? A sociological exploration of 'digital phenotyping'. SOCIOLOGY OF HEALTH & ILLNESS 2020; 42:1873-1887. [PMID: 32914445 DOI: 10.1111/1467-9566.13175] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/09/2020] [Accepted: 07/16/2020] [Indexed: 05/11/2023]
Abstract
This paper critically explores the research and development of 'digital phenotyping', which broadly refers to the idea that digital data can measure and predict people's mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of 'mental health sensing'. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.
Collapse
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
| |
Collapse
|
74
|
Torous J, Choudhury T, Barnett I, Keshavan M, Kane J. Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care. World Psychiatry 2020; 19:308-309. [PMID: 32931109 PMCID: PMC7491614 DOI: 10.1002/wps.20805] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Ian Barnett
- Division of Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - John Kane
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Zucker Hillside Hospital, New York, NY, USA
| |
Collapse
|
75
|
Henson P, Wisniewski H, Stromeyer Iv C, Torous J. Digital Health Around Clinical High Risk and First-Episode Psychosis. Curr Psychiatry Rep 2020; 22:58. [PMID: 32880764 DOI: 10.1007/s11920-020-01184-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. RECENT FINDINGS There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.
Collapse
Affiliation(s)
- Philip Henson
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Hannah Wisniewski
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Charles Stromeyer Iv
- Consumer Advisory Board, Massachusetts Mental Health Center, Boston, MA, 02115, USA
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA.
| |
Collapse
|
76
|
Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. JOURNAL OF TECHNOLOGY IN BEHAVIORAL SCIENCE 2020; 5:245-257. [PMID: 33415185 PMCID: PMC7785056 DOI: 10.1007/s41347-020-00134-x] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 02/24/2020] [Accepted: 03/17/2020] [Indexed: 12/22/2022]
Abstract
Social media platforms are popular venues for sharing personal experiences, seeking information, and offering peer-to-peer support among individuals living with mental illness. With significant shortfalls in the availability, quality, and reach of evidence-based mental health services across the United States and globally, social media platforms may afford new opportunities to bridge this gap. However, caution is warranted, as numerous studies highlight risks of social media use for mental health. In this commentary, we consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services. Specifically, we summarize current research on the use of social media among mental health service users, and early efforts using social media for the delivery of evidence-based programs. We also review the risks, potential harms, and necessary safety precautions with using social media for mental health. To conclude, we explore opportunities using data science and machine learning, for example by leveraging social media for detecting mental disorders and developing predictive models aimed at characterizing the aetiology and progression of mental disorders. These various efforts using social media, as summarized in this commentary, hold promise for improving the lives of individuals living with mental disorders.
Collapse
Affiliation(s)
- John A. Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA
| | | | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA
| | | |
Collapse
|
77
|
Rodriguez-Villa E, Rauseo-Ricupero N, Camacho E, Wisniewski H, Keshavan M, Torous J. The digital clinic: Implementing technology and augmenting care for mental health. Gen Hosp Psychiatry 2020; 66:59-66. [PMID: 32688094 PMCID: PMC7324929 DOI: 10.1016/j.genhosppsych.2020.06.009] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/25/2020] [Accepted: 06/15/2020] [Indexed: 12/14/2022]
Abstract
Increasing both access to and quality of mental healthcare is a global priority. One solution is to integrate technologies such as smartphone apps and sensors directly into care. Acknowledging many prior attempts and barriers, we introduce the Digital Clinic which is an already functioning clinic using smartphone apps to augment and extend care today at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. In this piece, we outline the theoretical foundation of the Digital Clinic and its emphasis on the therapeutic alliance, measurement-based care, and shared decision making. We explore both workflow and engagement challenges as well as solutions including a new care team member, the Digital Navigator, and the customization of technology. Acknowledging that the Digital Clinic is an evolving program, we offer details on our implementation in order to allow others to replicate, expand on, and improve these initial efforts.
Collapse
Affiliation(s)
- Elena Rodriguez-Villa
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Natali Rauseo-Ricupero
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Erica Camacho
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Hannah Wisniewski
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Matcheri Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.
| |
Collapse
|
78
|
Vaidyam A, Roux S, Torous J. Patient Innovation in Investigating the Effects of Environmental Pollution in Schizophrenia: Case Report of Digital Phenotyping Beyond Apps. JMIR Ment Health 2020; 7:e19778. [PMID: 32559173 PMCID: PMC7432141 DOI: 10.2196/19778] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 01/22/2023] Open
Abstract
This patient perspective highlights the role of patients in the innovation and codesign of digital mental health technology. Though digital mental health apps have evolved and become highly functional, many still act as data collection silos without adequate support for patients to understand and investigate potentially meaningful inferences in their own data. Few digital health platforms respect the patient's agency and curiosity, allowing the individual to wear the hat of researcher and data scientist and share their experiences and insight with their clinicians. This case is cowritten with an individual with lived experiences of schizophrenia who has decided to openly share their name and experiences to share with others the methods and results of their curiosity and encourage and inspire others to follow their curiosity as well.
Collapse
Affiliation(s)
- Aditya Vaidyam
- Division of Digital Psychiatry, Beth Isreal Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Spencer Roux
- Division of Digital Psychiatry, Beth Isreal Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Division of Digital Psychiatry, Beth Isreal Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
79
|
Abstract
An estimated 792 million people live with mental health disorders worldwide-more than one in ten people-and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren't enough mental health professionals to treat all these people. Can artificial intelligence (AI) help? While many psychiatrists have different views on this question, recent developments suggest AI may change the practice of psychiatry for both clinicians and patients.
Collapse
|
80
|
Henson P, Torous J. Feasibility and correlations of smartphone meta-data toward dynamic understanding of depression and suicide risk in schizophrenia. Int J Methods Psychiatr Res 2020; 29:e1825. [PMID: 32333471 PMCID: PMC7301285 DOI: 10.1002/mpr.1825] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 03/17/2020] [Accepted: 03/29/2020] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES We investigate whether meta-data, specifically duration of responses to smartphone-delivered surveys, is correlated to elevated scores on the depression assessment PHQ-9 as well as the specific item around self-harm (item 9). METHODS In this observational study, we recruited 92 smartphone-owning adults (≥ 18) with schizophrenia (45) and healthy controls (43). We installed an open-source smartphone app called mindLAMP to collect survey results and latencies (response times) over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. A total of 1,218 PHQ-9 surveys were completed across all participants over 3 months. RESULTS A total of 75 participants (39 with schizophrenia and 36 healthy controls) completed both the initial visit and follow-up, as well as provided at least one self-reported PHQ-9 survey through the app. We found that depression symptom severity and response latencies were correlated for both individuals with schizophrenia (Spearman's ρ = .22, p = .037) and healthy controls (Spearman's ρ = .58, p < .001). Participants with schizophrenia scored higher (more severe) and took longer for every item of the PHQ-9 when compared to controls (p < .05 for each item). Item 9 response value and latency was slightly correlated for participants with schizophrenia (Spearman's ρ = .086, p = .035) but was not significant for controls (Spearman's ρ = .036, p = .37). CONCLUSIONS Meta-data revealed group differences between individuals with schizophrenia and healthy controls based on individual depression symptoms completed on a smartphone. Correlation between suicide specific question latency and severity for participants with schizophrenia but not for controls indicates the clinical potential and need for further research.
Collapse
Affiliation(s)
- Philip Henson
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
81
|
Lecomte T, Potvin S, Corbière M, Guay S, Samson C, Cloutier B, Francoeur A, Pennou A, Khazaal Y. Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses. JMIR Mhealth Uhealth 2020; 8:e17458. [PMID: 32348289 PMCID: PMC7293054 DOI: 10.2196/17458] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/11/2020] [Accepted: 02/26/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Mental health apps have great potential to help people needing support to cope with distress or specific symptoms. In fact, there is an exponential increase in the number of mental health apps available on the internet, with less than 5% being actually studied. OBJECTIVE This study aimed to assess the quality of the available evidence regarding the use of mental health apps and to summarize the results obtained so far. METHODS Systematic reviews and meta-analyses were searched, specifically for mobile apps on mental health issues or symptoms, and rated using the Grading of Recommendations Assessment, Development and Evaluation system. RESULTS A total of 7 meta-analyses were carefully reviewed and rated. Although some meta-analyses looked at any mental health issue and analyzed the data together, these studies were of poorer quality and did not offer strong empirical support for the apps. Studies focusing specifically on anxiety symptoms or depressive symptoms were of moderate to high quality and generally had small to medium effect sizes. Similarly, the effects of apps on stress and quality of life tended to offer small to medium effects and were of moderate to high quality. Studies looking at stand-alone apps had smaller effect sizes but better empirical quality than studies looking at apps with guidance. The studies that included follow-ups mostly found a sustained impact of the app at an 11-week follow-up. CONCLUSIONS This meta-review revealed that apps for anxiety and depression hold great promise with clear clinical advantages, either as stand-alone self-management or as adjunctive treatments. More meta-analyses and more quality studies are needed to recommend apps for other mental health issues or for specific populations.
Collapse
Affiliation(s)
- Tania Lecomte
- Department of Psychology, University of Montreal, Montreal, QC, Canada
- Centre de recherche, l'Institut Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada
| | - Stéphane Potvin
- Centre de recherche, l'Institut Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada
- Department of Psychiatry, University of Montreal, Montreal, QC, Canada
| | - Marc Corbière
- Centre de recherche, l'Institut Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada
- Department of Education, Career Counselling, University du Quebec a Montreal, Montreal, QC, Canada
| | - Stéphane Guay
- Centre de recherche, l'Institut Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada
- Department of Criminology, University of Montreal, Montreal, QC, Canada
| | - Crystal Samson
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Briana Cloutier
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Audrey Francoeur
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Antoine Pennou
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Yasser Khazaal
- Department of Psychiatry, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| |
Collapse
|
82
|
Towards clinically actionable digital phenotyping targets in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:13. [PMID: 32372059 PMCID: PMC7200667 DOI: 10.1038/s41537-020-0100-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/12/2020] [Indexed: 12/12/2022]
Abstract
Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from -0.23 to -0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.
Collapse
|
83
|
Affiliation(s)
| | - Matcheri Keshavan
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
| |
Collapse
|
84
|
Torous J, Jän Myrick K, Rauseo-Ricupero N, Firth J. Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow. JMIR Ment Health 2020; 7:e18848. [PMID: 32213476 PMCID: PMC7101061 DOI: 10.2196/18848] [Citation(s) in RCA: 437] [Impact Index Per Article: 109.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 02/06/2023] Open
Abstract
As interest in and use of telehealth during the COVID-19 global pandemic increase, the potential of digital health to increase access and quality of mental health is becoming clear. Although the world today must "flatten the curve" of spread of the virus, we argue that now is the time to "accelerate and bend the curve" on digital health. Increased investments in digital health today will yield unprecedented access to high-quality mental health care. Focusing on personal experiences and projects from our diverse authorship team, we share selected examples of digital health innovations while acknowledging that no single piece can discuss all the impressive global efforts past and present. Exploring the success of telehealth during the present crisis and how technologies like apps can soon play a larger role, we discuss the need for workforce training, high-quality evidence, and digital equity among other factors critical for bending the curve further.
Collapse
Affiliation(s)
- John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Keris Jän Myrick
- Los Angeles County Department of Mental Health, Los Angeles, CA, United States
| | - Natali Rauseo-Ricupero
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
85
|
Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness. J Psychiatr Pract 2020; 26:80-88. [PMID: 32134881 PMCID: PMC7135933 DOI: 10.1097/pra.0000000000000450] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance. This article introduces the need and theoretical foundation for and the experience of running the resulting Digital Opportunities for Outcomes in Recovery Services (DOORS) group in the setting of 2 programs: a first-episode psychosis program and a clubhouse for individuals with serious mental illness. The experience of running these 2 DOORS groups resulted in 2 publicly available, free training manuals to empower others to run such groups and adapt them for local needs. Future work on DOORS will expand the curriculum to best support digital health needs and increase equity of access to and knowledge and skills related to technology use in serious mental illness.
Collapse
|
86
|
Multiple uses of app instead of using multiple apps - a case for rethinking the digital health technology toolbox. Epidemiol Psychiatr Sci 2020; 29:e100. [PMID: 32000876 PMCID: PMC7216034 DOI: 10.1017/s2045796020000013] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
There are tens of thousands of mental health-related apps available today - representing extreme duplication in this digital age. Instead of a plethora of apps, there is a need for a few that meet the needs of many. Focusing on transparency and free sharing of software, we argue that a collaborative approach towards apps can advance care through creating customisable and future proofed digital tools that allow all stakeholders to engage in their design and use.
Collapse
|
87
|
Philip P, Dupuy L, Auriacombe M, Serre F, de Sevin E, Sauteraud A, Micoulaud-Franchi JA. Trust and acceptance of a virtual psychiatric interview between embodied conversational agents and outpatients. NPJ Digit Med 2020; 3:2. [PMID: 31934646 PMCID: PMC6946646 DOI: 10.1038/s41746-019-0213-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 12/02/2019] [Indexed: 11/16/2022] Open
Abstract
Virtual agents have demonstrated their ability to conduct clinical interviews. However, the factors influencing patients' engagement with these agents have not yet been assessed. The objective of this study is to assess in outpatients the trust and acceptance of virtual agents performing medical interviews and to explore their influence on outpatients' engagement. In all, 318 outpatients were enroled. The agent was perceived as trustworthy and well accepted by the patients, confirming the good engagement of patients in the interaction. Older and less-educated patients accepted the virtual medical agent (VMA) more than younger and well-educated ones. Credibility of the agent appeared to main dimension, enabling engaged and non-engaged outpatients to be classified. Our results show a high rate of engagement with the virtual agent that was mainly related to high trust and acceptance of the agent. These results open new paths for the future use of VMAs in medicine.
Collapse
Affiliation(s)
- Pierre Philip
- University of Bordeaux, Bordeaux, France
- Clinique du Sommeil, CHU de Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
| | - Lucile Dupuy
- University of Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
| | - Marc Auriacombe
- University of Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
- Pôle addictologie, CH Charles Perrens and Unité de Soins Complexes d’addictologie (USCA) CHU de Bordeaux, Bordeaux, France
| | - Fushia Serre
- University of Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
- Pôle addictologie, CH Charles Perrens and Unité de Soins Complexes d’addictologie (USCA) CHU de Bordeaux, Bordeaux, France
| | - Etienne de Sevin
- University of Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
| | - Alain Sauteraud
- Clinique du Sommeil, CHU de Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
| | - Jean-Arthur Micoulaud-Franchi
- University of Bordeaux, Bordeaux, France
- Clinique du Sommeil, CHU de Bordeaux, Bordeaux, France
- SANPSY, CNRS USR 3413, Bordeaux, France
| |
Collapse
|
88
|
Abstract
The development of effective intervention and prevention strategies among individuals with psychosis risk syndromes may help to reduce symptomatology and conversion to a psychotic disorder. Although strides have been made in this area, more work is needed, particularly given the setbacks that remain (such as heterogeneity among this group). There has been a shift with the introduction of clinical staging models toward expanding current intervention and prevention efforts to a more developmental and transdiagnostic approach. With this, this article seeks to review treatments both recently and currently discussed in the staging literature, introduce advances in psychosis risk syndrome treatments that may be beneficial to consider in clinical staging heuristics, and pinpoint other promising options.
Collapse
Affiliation(s)
- Tina Gupta
- Psychology, Northwestern University, 2029 Sheridan Road, Evanston, IL, 60208, USA
| | - Vijay A Mittal
- Psychology, Northwestern University, 2029 Sheridan Road, Evanston, IL, 60208, USA.,Department of Psychiatry, Northwestern University, 420 E. Superior Street, Chicago, IL, 60611, USA.,Institute for Policy Research, Northwestern University, 2040 Sheridan Road, Evanston, IL, 60208, USA.,Department of Medical Social Sciences, Northwestern University, 420 E. Superior Street, Chicago, IL, 60611, USA.,Institute for Innovations in Developmental Sciences, Northwestern University, 633 N. St. Claire Street, Chicago, IL, 60611, USA
| |
Collapse
|
89
|
Liu G, Henson P, Keshavan M, Pekka-Onnela J, Torous J. Assessing the potential of longitudinal smartphone based cognitive assessment in schizophrenia: A naturalistic pilot study. Schizophr Res Cogn 2019; 17:100144. [PMID: 31024801 PMCID: PMC6476810 DOI: 10.1016/j.scog.2019.100144] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/25/2019] [Accepted: 04/04/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Although cognition is a core symptom of schizophrenia and associated with functional impairment, the degree of training for and time associated with its assessment makes it difficult to routinely monitor in clinic care.Smartphone based cognitive assessments could serve as a tool to measure cognition in real time as well as being easily scalable for broad use.Combined with other data gathered from smartphone sensors such as steps, sleep, and self-reported symptoms - capturing 'cognition in context' could provide a powerful new tool for assessing the functional burden of disease in schizophrenia. METHODS 18 participants with schizophrenia and 17 healthy controls completed novel cognitive assessments on their personal smartphones over the course of 12 weeks while also capturing self-reported surveys and step count. No payment or incentives were offered for engaging with the smartphone app. Differing levels of difficulty in cognitive tasks were tested and the results were modeled using a modified Cox proportional hazard model. RESULTS On the smartphone cognitive assessments that involved on simple patterns, both controls and those with schizophrenia achieved similar scores. On the more complex assessment that added task switching in addition to pattern recognition, those with schizophrenia achieved scores lower than controls. Collecting other forms of data such as surveys and steps was also feasible using the same smartphone platform. DISCUSSION It is feasible for those with schizophrenia to use their own smartphones to complete cognitive assessments and other measures related to their mental health. While we did not investigate the correlations between these cognitive assessments and other smartphone captured metrics like step count or self-reported symptoms, the potential to longitudinally assess cognition in the context of patients' environments outside of the clinic presents unique opportunities for characterizing cognitive burden in schizophrenia.
Collapse
Affiliation(s)
- Gang Liu
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
| | - Philip Henson
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Matcheri Keshavan
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jukka Pekka-Onnela
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
| | - John Torous
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
90
|
Noel VA, Carpenter-Song E, Acquilano SC, Torous J, Drake RE. The technology specialist: a 21st century support role in clinical care. NPJ Digit Med 2019; 2:61. [PMID: 31388565 PMCID: PMC6594932 DOI: 10.1038/s41746-019-0137-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 06/05/2019] [Indexed: 11/17/2022] Open
Abstract
Mental health clinicians, clients, and researchers have shown keen interest in using technology to support mental health recovery. However, technology has not been routinely integrated into clinical care. Clients use a wide range of digital tools and apps to help manage their mental health, but clinicians rarely discuss this form of self-management in clinical interactions. This absence of communication is concerning because the safety and quality of the digital tools and apps people use may negatively affect their mental health outcomes. Mental health systems could benefit from someone to help identify technology-based supports that reflect current evidence and minimize privacy and security concerns. This technology specialist may also enhance the therapeutic bond between the client and the clinician. In working with a technology specialist, clients may begin to gain a sense of control over their mental health, and perhaps use fewer mental health services.
Collapse
Affiliation(s)
| | | | - Stephanie C Acquilano
- 3The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH USA
| | - John Torous
- 4Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Robert E Drake
- 1Westat, Inc., Lebanon, NH USA.,3The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH USA
| |
Collapse
|