Broadly speaking, digital phenotyping refers to approaches in which personal data gathered from mobile devices and sensors is analyzed to provide health information. There is some variation in how the term has been defined. “Digital phenotyping” is sometimes more narrowly applied to the use of smartphones and wearables to collect data on physiological functions, such as pulse, or behavioral indicators, such as the user’s mobility, tapping and keyboard interactions, or features of voice or speech.2,3,4 Some approaches to digital phenotyping include the study of “digital exhaust,” such as social media posts and internet searches, as an indicator of health risks.5,6,7,8 In most current models of digital phenotyping, data collection is passive—once the wearable or app is downloaded, it collects information while the users otherwise go about their daily activities. Some forms of digital phenotyping, such as keyboard interactions, are “content-free,” meaning that only reaction times for tapping or scrolling are measured but the content of text or speech is not collected. Other forms of digital phenotyping which collect geolocation, search history, or social media posts can be described as “content-rich”.

Acquiring data is the first part of digital phenotyping. Analyzing these data to create insights about the user is the second part. Usually the algorithms developed to analyze this complex, multi-dimensional data are derived from some form of machine learning. These results are predictors of risk or probabilities, although they may be used for binary decisions (hospitalize vs discharge, alter medication vs continue status quo, etc.). Thus far, most published reports of digital phenotyping have focused on measures of mental health, such as relapse from depression or risk for psychotic episodes.9,10,11 Eventually, features associated with cognition such as executive function or verbal memory could be used to identify early signs of dementia,12 reduced alertness, risk of violent behavior, or predict severity of Parkinson’s disease.13

Digital phenotyping promises significant benefits when applied to medical uses. For psychiatry, which has heretofore relied exclusively on episodic reports of mood, digital phenotyping offers a powerful approach for the systematic detection of behavioral states,14 subtyping current heterogeneous diagnostic categories, and measuring outcomes. For neurology, which has required expensive, clinic-based assessments of cognitive performance, digital phenotyping offers an inexpensive, ecological assessment of function under real-world conditions. As digital phenotyping delivers rich data to both patients and providers, it may reconfigure the roles of both in the delivery of healthcare. The data analysis may also result in new insights that generate new categories for understanding mental disorder and risk.14

The ethical, legal, and social landscape will vary, depending on whether those with control over the data collection and the resulting data and analyses are medical researchers, clinicians, employers, educators, governments, consumers, or others (see Box 1). Some of the ethical concerns raised here, such as informed consent of patients who are children or have mental illness, are extensions of issues that arise with other digital health technologies, as well as in behavioral health as a whole. The novel ethical challenges posed by digital phenotyping arise from the way that the technology can transform seemingly mundane data into powerful indicators of mental function, with implications not only for healthcare but potentially in a range of areas where a measure of a change in cognitive performance or mood might have broad implications. For instance, within healthcare, digital phenotyping has the potential to gather and generate health-related information, such as a psychiatric diagnosis, outside the setting of a clinical encounter (i.e., through a direct-to-consumer app). Such use would be subject to regulations on informed consent and the Health Insurance Portability and Accountability Act (HIPAA). However, outside of healthcare, the regulatory frameworks are less clear.

Recent scandals involving Facebook and Cambridge Analytica are unfortunate reminders of the vulnerability of individuals to, and the relative ease of, the large-scale misuse of personally identifiable data that were detailed enough to create psychographic profiles of individuals.15 The military, employers, insurance organizations, and the criminal justice system could have interests in the prediction of behavioral states and disorders, as well as surveillance of individuals. The ability to collect and analyze data surreptitiously or to transform material that is voluntarily made available by individuals for their own purposes into data about those individuals’ psychological status raises novel issues of accountability and privacy. This technology will need to be designed and implemented so that it delivers benefits, while minimizing risks to individual users.