Ghosh’s work and my taps are part of the new but rapidly developing field called digital phenotyping. It aims to take the huge amounts of raw data that can be continuously collected from people’s use of smartphones, wearables and other digital devices and analyse them using artificial intelligence (AI) to infer behaviour related to health and disease.

If symptom-related digital signals – called digital biomarkers – can be properly teased out, it could provide a new route for diagnosing or monitoring a range of medical conditions, particularly those relating to mental or brain health. Early research suggests patterns in geolocation data may correlate with episodes of depression and relapses in schizophrenia; certain keystroke patterns could predict mania in bipolar disorder; and the way toddlers gaze at a smartphone screen could be used to detect autism.

Data streams include smartphone activity logs, measurements from any of a phone’s built-in sensors (such as the GPS, accelerometer or light sensor) as well as user-generated content, which can be mined for words or phrases. “It is classic big-data analytics… repurposing data for reasons other than it was primarily collected,” says Brit Davidson, an assistant professor of analytics at the University of Bath, UK who has been critically watching the field develop.