Subtle changes could act as early warning of need for care, U-M research suggests
A smartphone app that monitors subtle qualities of a person’s voice during everyday phone conversations shows promise for detecting early signs of mood changes in people with bipolar disorder, a University of Michigan team reports.
While the app still needs much testing before widespread use, early results from a small group of patients show its potential to monitor moods while protecting privacy.
The researchers hope the app will eventually give people with bipolar disorder and their health care teams an early warning of the changing moods that give the condition its name. The technology could also help people with other conditions.
More patients, all taking part in the study funded by the National Institute of Mental Health and facilitated by the Prechter Bipolar Research Fund at the U-M Depression Center, have already started to use the app on study-provided smartphones. As more patients volunteer, the team will continue to test and improve the technology.
The U-M team, led by computer scientists Zahi Karam, Ph.D. and Emily Mower Provost, Ph.D., and psychiatrist Melvin McInnis, M.D., presented its first findings today at the International Conference on Acoustics, Speech and Signal Processing in Italy, and published details simultaneously in the conference proceedings.
They call the project PRIORI, because they hope it will yield a biological marker to prioritize bipolar disorder care to those who need it most urgently to stabilize their moods – especially in regions of the world with scarce mental health services. Bipolar disorder affects tens of millions of people worldwide, and can have devastating effects including suicide.
But first, based on these encouraging findings, the technology and algorithms will be developed via research involving 60 American patients who receive treatment from U-M teams at the nation’s first center devoted to depression and related disorders.
“These pilot study results give us preliminary proof of the concept that we can detect mood states in regular phone calls by analyzing broad features and properties of speech, without violating the privacy of those conversations,” says Karam, a postdoctoral fellow and specialist in machine learning and speech analysis. “As we collect more data the model will become better, and our ultimate goal is to be able to anticipate swings, so that it may be possible to intervene early.”
Adds McInnis, a bipolar specialist, “This is tremendously exciting not only as a technical achievement, but also as an illustration of what the marriage of mental health research, engineering and innovative research funding can make possible.”
He adds, “The ability to predict mood changes with sufficient advance time to intervene would be an enormously valuable biomarker for bipolar disorder.”