mHealth App May Help in Tracking, Treating Bipolar Disorder

mHealth App May Help in Tracking, Treating Bipolar Disorder   University of Michigan mHealth app bipolar disorder app Researchers at the University of Michigan have made groundbreaking progress on a new mHealth app designed to help individuals struggling with bipolar disorder better monitor and track their mood swings.

The smartphone app monitors subtle qualities of a person’s voice during everyday phone conversations. So far, it shows promise for detecting early signs of mood changes in people with bipolar disorder.

With bipolar disorder affecting about 5.7 million American adults, the app in question could bring guidance to a large segment of the population suffering from the ravages of bipolar disorder.

Bipolar disorder is a mental illness characterized by episodes of an elevated mood known as mania.

“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,” uofmhealth.org reports. “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.”

Those behind the project call it 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.”

Based on these “encouraging findings,” researchers say the technology and algorithms will be developed further 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 Zahi Karam, Ph.D., a 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.”

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