University of Michigan researchers have created a smartphone app that monitors subtle qualities of a person’s voice during everyday phone conversations, showing promise for detecting early signs of mood changes in people with bipolar disorder.
The researchers hope the app will eventually give people with bipolar disorder and their healthcare teams an early warning of the changing moods that give the condition its name. The researchers presented their findings at the International Conference on Acoustics, Speech and Signal Processing in Italy, and published details simultaneously in the conference proceedings.
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. 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.
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.
The app runs in the background on an ordinary smartphone, and automatically monitors the patients’ voice patterns during any 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.
Only the patient’s side of everyday phone calls is recorded— and the recordings themselves are encrypted and kept off-limits to the research team. They can see only the results of computer analysis of the recordings, which are stored in secure servers that comply with patient privacy laws.
Standardized weekly mood assessments with a trained clinician provide a benchmark for the patient’s mood, and are used to correlate the acoustic features of speech with their mood state. 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, the researchers say.
The first six patients all have a rapid-cycling form of Type 1 bipolar disorder and a history of being prone to frequent depressive and manic episodes. The researchers showed that their analysis of voice characteristics from everyday conversations could detect elevated and depressed moods. The detection of mood states will improve over time as the software gets trained based on more conversations and data from more patients.
“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,” Zahi Karam, Ph.D., a postdoctoral fellow and specialist in machine learning and speech analysis, said in a news release. “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.”