Researchers at the University of Michigan have developed a mobile app that monitors subtle qualities of a person’s voice during everyday phone conversations that shows promise for detecting early signs of mood changes in people with bipolar disorder.

The app runs on a smartphone and automatically monitors a patient’s voice patterns during calls made as well as during weekly conversations with a member of the patient’s care team. The computer program analyzes many characteristics of the sounds--and silences--of each conversation. The detection of mood states will improve over time as the software is tweaked based on more conversations and data from patients.

The technology and algorithms will be developed via research involving 60 patients who receive treatment from University of Michigan teams at the nation’s first center devoted to depression and related disorders. Researchers hope the project 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.

Because other mental health conditions also cause changes in a person’s voice, the same technology framework developed for bipolar disorder could prove useful in everything from schizophrenia and post-traumatic stress disorder to Parkinson’s disease, researchers say.

The team developing the mood monitoring app, led by computer scientists Zahi Karam and Emily Mower Provost, and psychiatrist Melvin McInnis, M.D., presented its first findings at the recent International Conference on Acoustics, Speech and Signal Processing in Italy, and published details simultaneously in the conference proceedings.

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