scholarly journals Digital phenotyping in psychiatry: When mental health goes binary

2021 ◽  
Vol 30 (2) ◽  
pp. 191
Author(s):  
Jyoti Prakash ◽  
Suprakash Chaudhury ◽  
Kaushik Chatterjee
Keyword(s):  
Mental Health ◽  
Digital Phenotyping

scholarly journals Digital phenotyping for mental health of college students: a clinical review

2020 ◽  
Vol 23 (4) ◽  
pp. 161-166
Author(s):  
Jennifer Melcher ◽  
Ryan Hays ◽  
John Torous
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Interactions ◽  
Mental Health Problems ◽  
Average Duration ◽  
Mental Illnesses ◽  
Digital Phenotyping ◽  
Health Centres ◽  
Practical Tool ◽  
Larger Sample

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help—the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

scholarly journals Psychology and Surveillance Capitalism: The Risk of Pushing Mental Health Apps During the COVID-19 Pandemic

2020 ◽  
Vol 60 (5) ◽  
pp. 611-625
Author(s):  
Lisa Cosgrove ◽  
Justin M. Karter ◽  
Zenobia Morrill ◽  
Mallaigh McGinley
Keyword(s):  
Mental Health ◽  
Mobile Apps ◽  
Negative Mood ◽  
Unintended Consequences ◽  
Humanistic Psychology ◽  
The Public ◽  
Health Apps ◽  
Digital Phenotyping ◽  
The Rich

During the COVID-19 pandemic, telehealth technologies and mental health apps have been promoted to manage distress in the public and to augment existing mental health services. From a humanistic perspective, the promotion and use of mobile apps raises ethical concerns regarding the autonomy of the person using the app. However, there are other dangers that arise when technological fixes are embraced at a time of crisis. Naomi Klein and Shoshanna Zuboff have recently warned about disaster and surveillance capitalism—using crises to pass legislation that will benefit the rich and deepen inequality, and using anonymized behavioral data for commercial purposes. This analysis reveals that mental health apps may take individuals at their most vulnerable and make them part of a hidden supply chain for the marketplace. We provide a case study of a mental health app that uses digital phenotyping to predict negative mood states. We describe the logic of digital phenotyping and assess the efficacy data on which claims of its validity are based. Drawing from the frameworks of disaster and surveillance capitalism, we also use a humanistic psychology lens to identify the ethical entanglements and the unintended consequences of promoting and using this technology during the COVID-19 pandemic.

scholarly journals Digital phenotyping of complex psychological responses to the COVID-19 pandemic (Preprint)

2020 ◽  
Author(s):  
Thomas D. Hull ◽  
Jacob Levine ◽  
Niels Bantilan ◽  
Angel N. Desai ◽  
Maimuna S. Majumder
Keyword(s):  
Mental Health ◽  
Language Processing ◽  
Anxiety Symptoms ◽  
Depression Symptoms ◽  
The Novel ◽  
Depression And Anxiety ◽  
Clinical Assessments ◽  
Digital Phenotyping ◽  
Novel Coronavirus ◽  
Therapy Service

BACKGROUND The novel coronavirus disease 2019 (COVID-19) has negatively impacted mortality, economic conditions, and mental health and these impacts are likely to continue after the pandemic comes to an end. OBJECTIVE At present, no method has characterized the mental health burden of the pandemic distinct from pre-COVID-19 levels. Accurate detection of illness is critical to facilitate pandemic-related treatment to prevent worsening symptoms. METHODS An algorithm for the isolation of pandemic-related concerns on a large digital mental health service is reported that utilized natural language processing (NLP) on unstructured therapy transcript data, in parallel with brief clinical assessments of depression and anxiety symptoms. RESULTS Results demonstrate a significant increase in COVID-related intake anxiety symptoms, but no detectable difference in intake depression symptoms. Transcript analyses identified terms classifiable into 24 symptoms in excess of those included in the diagnostic criteria for anxiety and depression. CONCLUSIONS Findings for this large digital therapy service suggest that treatment seekers are presenting with more severe intake anxiety levels than before the COVID-19 outbreak. Importantly, monitoring additional symptoms as part of a new COVID-19 Syndrome category could be advised to fully capture the effects of COVID019 on mental health.

scholarly journals On the Need for Digital Phenotyping to Obtain Insights into Mental States in the COVID-19 Pandemic

2020 ◽  
Vol 1 (2) ◽  
pp. 40-42
Author(s):  
Christian Montag ◽  
Paul Dagum ◽  
Jon D. Elhai
Keyword(s):  
Mental Health ◽  
Population Level ◽  
Mental States ◽  
User Privacy ◽  
Policy Makers ◽  
Building Trust ◽  
Digital Phenotyping ◽  
Level Information ◽  
Population Mental Health ◽  
Insight Into

Highlights Digital phenotyping provides real-time insight into population mental health in a crisis such as COVID-19. Digital phenotyping empowers policy makers with population level information to help fight a pandemic like COVID-19. User privacy and informed consent is paramount in building trust with digital phenotyping.

scholarly journals For what it's worth. Unearthing the values embedded in digital phenotyping for mental health

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110473
Author(s):  
Rasmus Birk ◽  
Anna Lavis ◽  
Federica Lucivero ◽  
Gabrielle Samuel
Keyword(s):  
Mental Health ◽  
User Experience ◽  
Rapid Growth ◽  
Mobile Applications ◽  
The Other ◽  
Digital Data ◽  
Wearable Technologies ◽  
Digital Phenotyping ◽  
The One

Digital phenotyping for mental health is an emerging trend which uses digital data, derived from mobile applications, wearable technologies and digital sensors, to measure, track and predict the mental health of an individual. Digital phenotyping for mental health is a growing, but as yet underexamined, field. As we will show, the rapid growth of digital phenotyping for mental health raises crucial questions about the values that underpin and are reinforced by this technology, as well as regarding to whom it may become valuable. In this commentary, we explore these questions by focusing on the construction of value across two interrelated domains: user experience and epistemologies on the one hand, and issues of data and ownership on the other. In doing so, we demonstrate the need for a deeper ethical and epistemological engagement with the value assumptions that underpin the promise of digital phenotyping for mental health.

scholarly journals Sensing Applications and Public Datasets for Digital Phenotyping of Mental Health (Preprint)

2021 ◽  
Author(s):  
Jean P. M. Marques ◽  
Ivan R. Moura ◽  
Pepijn Van de Ven ◽  
Davi V. Santos ◽  
Francisco J. S. Silva ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Digital Libraries ◽  
Scientific Literature ◽  
Data Extraction ◽  
Exclusion Criteria ◽  
Sensing Applications ◽  
Digital Phenotyping ◽  
Depth Analysis ◽  
Public Datasets

BACKGROUND Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as Digital Phenotyping of Mental Health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. OBJECTIVE This article aims to identify and characterize technically the sensing applications and public datasets for DPMH. METHODS We performed a systematic review of scientific literature and datasets. We searched digital libraries and dataset repositories to find results that met the selection criteria. RESULTS After applying inclusion and exclusion criteria, 31 articles and 8 datasets were selected for data extraction, in which we summarized their characteristics and identified trends and research opportunities. CONCLUSIONS Results evidenced growth in proposals for DPMH sensing applications in recent years as opposed to a scarcity of public datasets. This systematic review provides in-depth analysis regarding solutions for DPMH.

scholarly journals Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study (Preprint)

2021 ◽  
Author(s):  
Nicole Martinez-Martin ◽  
Henry T Greely ◽  
Mildred K Cho
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Data Protection ◽  
Delphi Study ◽  
Ethical Issues ◽  
Clinical Care ◽  
Personal Data ◽  
Digital Phenotyping ◽  
Consensus Statements ◽  
Health Applications

BACKGROUND Digital phenotyping (also known as <i>personal sensing</i>, <i>intelligent sensing</i>, or <i>body computing</i>) involves the collection of biometric and personal data <i>in situ</i> from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. OBJECTIVE This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. METHODS We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. RESULTS The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. CONCLUSIONS The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

Reading the Minds of Young People for Their Own Good – The Ethics of Digital Phenotyping for Mental Health in Schools

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 121-121
Author(s):  
David M. Lyreskog ◽  
◽  
Gabriela Pavarini ◽  
Edward Jacobs ◽  
Vanessa Bennett ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Health Care Providers ◽  
Ethical Issues ◽  
Digital Data ◽  
Educational Systems ◽  
Care Providers ◽  
Ethical Implications ◽  
Digital Phenotyping ◽  
The Uk

"Across the globe the phenomenon of digital phenotyping – the collection and analysis of digital data for mental health – is growing increasingly popular within the education sector. Schools enter collaborations with health care providers, often with the aim to support young people and to reduce the risk for severe mental health challenges, self-harm, and suicide. In developing technologies for these purposes, algorithms and artificial intelligence (broadly construed) could be utilized to provide as rich and accurate data as possible. The data can then be used to flag up at-risk individuals within the system. Despite the increasing interest in digital mental health tools in many educational systems, there has been remarkably little written about the ethical issues that accompany the emergence of digital phenotyping. Arguably more alarming is that almost no research has been conducted on the acceptability and ethics of these technologies in stakeholder populations: we have not asked young people about their values in this context. In this paper, we present results from a large quantitative study from the UK, showing what young people value and choose in scenarios involving digital phenotyping in schools. We highlight clear discrepancies between what young people value – and how they conceptualize those values – and how the literature describes the ethical implications of related technologies in schools. We argue that policymakers and ethicists urgently need to learn to recognize and respect the moral boundaries of young people. "

Sign in / Sign up

Export Citation Format

Share Document