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

Area of Science:

• Digital Health
• Computational Psychiatry
• Bioethics

Background:

• Digital phenotyping collects personal data from devices to monitor mental health.
• Applications range from consumer use to clinical care, promising improved diagnoses and treatment.
• Ethical concerns include privacy, data protection, consent, bias, and accountability.

Purpose of the Study:

• To develop consensus statements on ethical guidance for digital phenotyping in US mental health applications.
• To identify and formulate guidance for emerging ethical challenges in this field.

Main Methods:

• Modified Delphi technique involving experts in digital phenotyping, data science, mental health, law, and ethics.
• Iterative process of interviews and surveys to reach consensus.
• Focus on clinical applications, with recommendations on transparency and data protection.

Main Results:

• Strong agreement on prioritizing privacy, transparency, consent, accountability, and fairness.
• Broadly stated guidance facilitated consensus across diverse applications.
• Critical privacy concerns stem from inadequate regulations and potential data misuse outside healthcare.

Conclusions:

• Consensus statements provide ethical principles for digital phenotyping in mental health.
• Further empirical research and stakeholder consultation are needed for implementation.
• Addressing ethical issues is crucial as digital phenotyping integrates into clinical care.