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A smartphone-based tool that tracks mental health status by detecting changes in voice may complement traditional psychiatric assessments and improve an individual’s ability to self-monitor depressive and other mental health symptoms, new research suggested.
Investigators used the Mental Fitness Vocal Biomarker (MFVB) scoring algorithm, which is incorporated into a smartphone voice journaling application, to detect increased or decreased risk for elevated mental health symptom severity by analyzing 30-second free speech voice recordings for specific vocal patterns previously linked to mental health.
“While the MFVB tool is not intended to diagnose or treat mental health conditions, these findings provide a robust initial foundation upon which to further explore its potential in personalized wellness tracking, which has so far not yet been able to extend measurement of physical health to mental wellbeing,” reported the researchers, led by Erik Larsen, PhD, with Boston-based Sonde Health, which developed the tool.
The study was published online in Frontiers in Psychiatry.
Eight Vocal Features
The potential value of vocal biomarkers for mental health assessment has gained increasing attention.
“Somebody that is depressed often sounds more monotone; they may have less inflection in their voice and speak slower with less energy, which can be recognized in voice recordings,” Dr. Larsen told this news organization.
“This is an area which has received quite a bit of research in the last few decades to find out what specific aspects of acoustics and rhythm of speech could point to conditions like depression,” Dr. Larsen said.
In the current study, the researchers set out to validate the ability of the MFVB platform to detect mental health symptoms.
With the tool, users record their thoughts and feelings as a 30-second voice journal. The tool analyzes the recordings for eight vocal features previously shown to be relevant to mental health. These include jitter, shimmer, pitch variability, energy variability, vowel space, phonation duration, speech rate, and pause duration.
The tool calculates a real-time MFVB score ranging from 0 to 100. A score of 80-100 is defined as “excellent” and 70-79 as “good,” while a score of 0-69 is categorized as “pay attention.” It was trained on more than 1 million voice samples to optimize performance across a diverse range of cultures, languages, and socioeconomic groups.
The current study included 104 outpatient psychiatric patients (73% women) with anxiety-related diagnoses, trauma, and stress-related disorders or depressive disorders. The cohort was mostly made up of White, non-Hispanic young adults. Patients with a history of substance abuse or who were taking psychiatric medications that may affect voice and speech were excluded.
During the 4-week study period, participants conducted 1336 app sessions with voice recordings, resulting in an average of 12.8 sessions per participant, or 3.2 per week.
MFVB scores were cross-referenced against the results of participants’ M3 Checklist, a clinically validated mental health assessment tool.
Over a period of 2 weeks, participants were twice as likely to report elevated mental health symptoms if their MFVB scores remained in the “pay attention” range vs in the “excellent” range, the researchers found.
The effect was more pronounced in those who used the app more frequently, with frequent users 8.5 times more likely to show elevated symptoms.
The correlation between MFVB scores and established mental health assessments was “not only statistically significant but also meaningful for participants,” researchers wrote. Subgroup analyses suggest the app works best for depression and stress- and trauma-related disorders.
The tool provides psychiatric outpatients with “immediate quantitative feedback on their mental health symptom severity,” the researchers noted.
In their paper, the investigators caution that the results highlight the “general ability” of MFVB score categories to differentiate mental health symptom severity levels but do not distinguish what type of symptoms these may be, such as depression, anxiety, or posttraumatic stress disorder.
In a statement, study investigator Lindsey Venesky, PhD, psychologist and clinical director at the Cognitive Behavior Institute in Pittsburgh, noted that the ability to collect mental health data from patients between clinic visits “could transform how we monitor symptoms and optimize treatment plans.”
“Voice-based health tracking technology can provide accurate insights into a client’s mental health status over time and can do so seamlessly and unobtrusively, with little added effort for clients,” Dr. Venesky said.
Need for Replication, Validation
Commenting on the findings, John Torous, MD, director of the division of digital psychiatry at Beth Israel Deaconess Medical Center, Boston, noted that “over the last 20 years, there has been a lot of interest in voice biomarkers, yet somehow that research has never been translated into mainstream clinical care.”
Voice biomarkers are “relevant and have potential” in mental health, he noted. The findings in this study are “interesting, but they need to be thoroughly externally replicated and validated to show that these biomarkers are valid and reliable,” Dr. Torous added.
Changes in voice are part of the mental status exam, Dr. Torous said, “but it’s only one piece of information that we collect in a clinical assessment of many pieces of information.”
Dr. Torous also cautioned that “as a practicing psychiatrist, it can be tricky to be given new data if you don’t know how to interpret it or what it means. An important step would be education, outreach, and resources for physicians to learn about potential voice biomarkers.”
The authors received internal financial support for the research, authorship, and/or publication of this article. The pilot phase of the study at St. Joseph’s Healthcare Hamilton was partially supported through Mitacs Accelerate International, Canada. Dr. Larsen and three coauthors are employed by Sonde Health. Dr. Torous had no relevant conflicts of interest.
A version of this article appeared on Medscape.com.
A smartphone-based tool that tracks mental health status by detecting changes in voice may complement traditional psychiatric assessments and improve an individual’s ability to self-monitor depressive and other mental health symptoms, new research suggested.
Investigators used the Mental Fitness Vocal Biomarker (MFVB) scoring algorithm, which is incorporated into a smartphone voice journaling application, to detect increased or decreased risk for elevated mental health symptom severity by analyzing 30-second free speech voice recordings for specific vocal patterns previously linked to mental health.
“While the MFVB tool is not intended to diagnose or treat mental health conditions, these findings provide a robust initial foundation upon which to further explore its potential in personalized wellness tracking, which has so far not yet been able to extend measurement of physical health to mental wellbeing,” reported the researchers, led by Erik Larsen, PhD, with Boston-based Sonde Health, which developed the tool.
The study was published online in Frontiers in Psychiatry.
Eight Vocal Features
The potential value of vocal biomarkers for mental health assessment has gained increasing attention.
“Somebody that is depressed often sounds more monotone; they may have less inflection in their voice and speak slower with less energy, which can be recognized in voice recordings,” Dr. Larsen told this news organization.
“This is an area which has received quite a bit of research in the last few decades to find out what specific aspects of acoustics and rhythm of speech could point to conditions like depression,” Dr. Larsen said.
In the current study, the researchers set out to validate the ability of the MFVB platform to detect mental health symptoms.
With the tool, users record their thoughts and feelings as a 30-second voice journal. The tool analyzes the recordings for eight vocal features previously shown to be relevant to mental health. These include jitter, shimmer, pitch variability, energy variability, vowel space, phonation duration, speech rate, and pause duration.
The tool calculates a real-time MFVB score ranging from 0 to 100. A score of 80-100 is defined as “excellent” and 70-79 as “good,” while a score of 0-69 is categorized as “pay attention.” It was trained on more than 1 million voice samples to optimize performance across a diverse range of cultures, languages, and socioeconomic groups.
The current study included 104 outpatient psychiatric patients (73% women) with anxiety-related diagnoses, trauma, and stress-related disorders or depressive disorders. The cohort was mostly made up of White, non-Hispanic young adults. Patients with a history of substance abuse or who were taking psychiatric medications that may affect voice and speech were excluded.
During the 4-week study period, participants conducted 1336 app sessions with voice recordings, resulting in an average of 12.8 sessions per participant, or 3.2 per week.
MFVB scores were cross-referenced against the results of participants’ M3 Checklist, a clinically validated mental health assessment tool.
Over a period of 2 weeks, participants were twice as likely to report elevated mental health symptoms if their MFVB scores remained in the “pay attention” range vs in the “excellent” range, the researchers found.
The effect was more pronounced in those who used the app more frequently, with frequent users 8.5 times more likely to show elevated symptoms.
The correlation between MFVB scores and established mental health assessments was “not only statistically significant but also meaningful for participants,” researchers wrote. Subgroup analyses suggest the app works best for depression and stress- and trauma-related disorders.
The tool provides psychiatric outpatients with “immediate quantitative feedback on their mental health symptom severity,” the researchers noted.
In their paper, the investigators caution that the results highlight the “general ability” of MFVB score categories to differentiate mental health symptom severity levels but do not distinguish what type of symptoms these may be, such as depression, anxiety, or posttraumatic stress disorder.
In a statement, study investigator Lindsey Venesky, PhD, psychologist and clinical director at the Cognitive Behavior Institute in Pittsburgh, noted that the ability to collect mental health data from patients between clinic visits “could transform how we monitor symptoms and optimize treatment plans.”
“Voice-based health tracking technology can provide accurate insights into a client’s mental health status over time and can do so seamlessly and unobtrusively, with little added effort for clients,” Dr. Venesky said.
Need for Replication, Validation
Commenting on the findings, John Torous, MD, director of the division of digital psychiatry at Beth Israel Deaconess Medical Center, Boston, noted that “over the last 20 years, there has been a lot of interest in voice biomarkers, yet somehow that research has never been translated into mainstream clinical care.”
Voice biomarkers are “relevant and have potential” in mental health, he noted. The findings in this study are “interesting, but they need to be thoroughly externally replicated and validated to show that these biomarkers are valid and reliable,” Dr. Torous added.
Changes in voice are part of the mental status exam, Dr. Torous said, “but it’s only one piece of information that we collect in a clinical assessment of many pieces of information.”
Dr. Torous also cautioned that “as a practicing psychiatrist, it can be tricky to be given new data if you don’t know how to interpret it or what it means. An important step would be education, outreach, and resources for physicians to learn about potential voice biomarkers.”
The authors received internal financial support for the research, authorship, and/or publication of this article. The pilot phase of the study at St. Joseph’s Healthcare Hamilton was partially supported through Mitacs Accelerate International, Canada. Dr. Larsen and three coauthors are employed by Sonde Health. Dr. Torous had no relevant conflicts of interest.
A version of this article appeared on Medscape.com.
A smartphone-based tool that tracks mental health status by detecting changes in voice may complement traditional psychiatric assessments and improve an individual’s ability to self-monitor depressive and other mental health symptoms, new research suggested.
Investigators used the Mental Fitness Vocal Biomarker (MFVB) scoring algorithm, which is incorporated into a smartphone voice journaling application, to detect increased or decreased risk for elevated mental health symptom severity by analyzing 30-second free speech voice recordings for specific vocal patterns previously linked to mental health.
“While the MFVB tool is not intended to diagnose or treat mental health conditions, these findings provide a robust initial foundation upon which to further explore its potential in personalized wellness tracking, which has so far not yet been able to extend measurement of physical health to mental wellbeing,” reported the researchers, led by Erik Larsen, PhD, with Boston-based Sonde Health, which developed the tool.
The study was published online in Frontiers in Psychiatry.
Eight Vocal Features
The potential value of vocal biomarkers for mental health assessment has gained increasing attention.
“Somebody that is depressed often sounds more monotone; they may have less inflection in their voice and speak slower with less energy, which can be recognized in voice recordings,” Dr. Larsen told this news organization.
“This is an area which has received quite a bit of research in the last few decades to find out what specific aspects of acoustics and rhythm of speech could point to conditions like depression,” Dr. Larsen said.
In the current study, the researchers set out to validate the ability of the MFVB platform to detect mental health symptoms.
With the tool, users record their thoughts and feelings as a 30-second voice journal. The tool analyzes the recordings for eight vocal features previously shown to be relevant to mental health. These include jitter, shimmer, pitch variability, energy variability, vowel space, phonation duration, speech rate, and pause duration.
The tool calculates a real-time MFVB score ranging from 0 to 100. A score of 80-100 is defined as “excellent” and 70-79 as “good,” while a score of 0-69 is categorized as “pay attention.” It was trained on more than 1 million voice samples to optimize performance across a diverse range of cultures, languages, and socioeconomic groups.
The current study included 104 outpatient psychiatric patients (73% women) with anxiety-related diagnoses, trauma, and stress-related disorders or depressive disorders. The cohort was mostly made up of White, non-Hispanic young adults. Patients with a history of substance abuse or who were taking psychiatric medications that may affect voice and speech were excluded.
During the 4-week study period, participants conducted 1336 app sessions with voice recordings, resulting in an average of 12.8 sessions per participant, or 3.2 per week.
MFVB scores were cross-referenced against the results of participants’ M3 Checklist, a clinically validated mental health assessment tool.
Over a period of 2 weeks, participants were twice as likely to report elevated mental health symptoms if their MFVB scores remained in the “pay attention” range vs in the “excellent” range, the researchers found.
The effect was more pronounced in those who used the app more frequently, with frequent users 8.5 times more likely to show elevated symptoms.
The correlation between MFVB scores and established mental health assessments was “not only statistically significant but also meaningful for participants,” researchers wrote. Subgroup analyses suggest the app works best for depression and stress- and trauma-related disorders.
The tool provides psychiatric outpatients with “immediate quantitative feedback on their mental health symptom severity,” the researchers noted.
In their paper, the investigators caution that the results highlight the “general ability” of MFVB score categories to differentiate mental health symptom severity levels but do not distinguish what type of symptoms these may be, such as depression, anxiety, or posttraumatic stress disorder.
In a statement, study investigator Lindsey Venesky, PhD, psychologist and clinical director at the Cognitive Behavior Institute in Pittsburgh, noted that the ability to collect mental health data from patients between clinic visits “could transform how we monitor symptoms and optimize treatment plans.”
“Voice-based health tracking technology can provide accurate insights into a client’s mental health status over time and can do so seamlessly and unobtrusively, with little added effort for clients,” Dr. Venesky said.
Need for Replication, Validation
Commenting on the findings, John Torous, MD, director of the division of digital psychiatry at Beth Israel Deaconess Medical Center, Boston, noted that “over the last 20 years, there has been a lot of interest in voice biomarkers, yet somehow that research has never been translated into mainstream clinical care.”
Voice biomarkers are “relevant and have potential” in mental health, he noted. The findings in this study are “interesting, but they need to be thoroughly externally replicated and validated to show that these biomarkers are valid and reliable,” Dr. Torous added.
Changes in voice are part of the mental status exam, Dr. Torous said, “but it’s only one piece of information that we collect in a clinical assessment of many pieces of information.”
Dr. Torous also cautioned that “as a practicing psychiatrist, it can be tricky to be given new data if you don’t know how to interpret it or what it means. An important step would be education, outreach, and resources for physicians to learn about potential voice biomarkers.”
The authors received internal financial support for the research, authorship, and/or publication of this article. The pilot phase of the study at St. Joseph’s Healthcare Hamilton was partially supported through Mitacs Accelerate International, Canada. Dr. Larsen and three coauthors are employed by Sonde Health. Dr. Torous had no relevant conflicts of interest.
A version of this article appeared on Medscape.com.