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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Jan 22, 2021
Date Accepted: May 21, 2021

The final, peer-reviewed published version of this preprint can be found here:

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Martinez-Martin N, Greely HT, Cho MK

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

JMIR Mhealth Uhealth 2021;9(7):e27343

DOI: 10.2196/27343

PMID: 34319252

PMCID: 8367187

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Delphi Study Regarding Ethical Development of Digital Phenotyping Tools for Mental Health Applications

  • Nicole Martinez-Martin; 
  • Henry T. Greely; 
  • Mildred K. Cho

ABSTRACT

Background:

Digital phenotyping refers to new approaches to measuring behavior, physiological states, and cognitive functioning by applying algorithms, often generated by machine learning, to gather moment-by-moment physiological and biometric data using smartphones or other sensors, such as pulse, keyboard interactions, or voice features. For example, collecting smartphone data regarding screen taps in order to assess an individual’s risk of having a psychotic episode. Digital phenotyping applications to assess behavioral states and mental disorder show promise for medical uses, and also have potential uses in education, the military, insurance, and the criminal justice system. At the same time, digital phenotyping raises novel ethical questions regarding the risks and benefits of collecting massive amounts of highly granular personal data, as well as the collection of sensitive personal information outside of traditional ethical and regulatory frameworks for protecting patients and health data.

Objective:

Use a modified Delphi technique to arrive at consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping.

Methods:

A modified Delphi technique was used in order to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law and ethics participated in the study. Through an iterative process of 3 rounds involving interviews and surveys, the panel arrived at consensus recommendations. The experts focused primarily on clinical applications for digital phenotyping of mental health, but addressed items relevant to other domains.

Results:

The consensus statements address key ethical areas: privacy and data protections, validation and utility, bias and fairness, and transparency.

Conclusions:

The Delphi study found agreement on a number of areas regarding the priority ethical issues in the development of digital phenotyping for mental health applications. Standards and guidelines for key areas of digital phenotyping, such as privacy standards outside of health institutions and development of algorithms, are still evolving and thus consensus statements identified general principles regarding ethical digital phenotyping of mental health. Clinical Trial: not applicable


 Citation

Please cite as:

Martinez-Martin N, Greely HT, Cho MK

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

JMIR Mhealth Uhealth 2021;9(7):e27343

DOI: 10.2196/27343

PMID: 34319252

PMCID: 8367187

Per the author's request the PDF is not available.