A new smartphone app monitors subtle qualities of a person's voice during everyday phone conversations to detect early signs of mood changes in people with bipolar disorder, scientists, including one of Indian-origin, say.
Researchers from the University of Michigan said 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 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 programme analyses many characteristics of the sounds - and silences - of each conversation.
Only the patient's side of everyday phone calls is recorded Ė 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.
Standardised 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 said.
The U-M team was led by computer scientists Zahi Karam and Emily Mower Provost and psychiatrist Melvin McInnis. The study also included Satinder Singh, an artificial intelligence and machine learning expert.
"These pilot study results give us preliminary proof of the concept that we can detect mood states in regular phone calls by analysing broad features and properties of speech, without violating the privacy of those conversations," said 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," Karam said.