Smartphone data helps predict schizophrenia relapses

Passive data from smartphones—including movement, ambient sound and sleep patterns—can help predict episodes of schizophrenic relapse, according to new Cornell Tech research.

Two papers from the lab of Tanzeem Choudhury, professor of integrated health and technology at Cornell Tech, examined how smartphone data can predict patients’ own self-assessments of their condition, as well as changes in their behavior patterns in the 30 days leading to a relapse.

Early prediction of schizophrenic relapses—potentially dangerous episodes which may involve hallucinations, fears of harm, depression or withdrawal—could prevent hospitalizations, in addition to providing clinicians and patients with valuable information that could improve and personalize their care.

“The goal of this work was to predict digital indicators that are early warning signs of relapse, but these symptoms or changes can be very, very different from one individual to another,” said Dan Adler, doctoral student at Cornell Tech and first author of “Predicting Early Warning

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