Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
132 participants
OBSERVATIONAL
2022-09-23
2023-07-05
Brief Summary
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This study seeks to evaluate the ability of the Device to aid clinical assessment for depression and anxiety by comparing its output with the established diagnostic standard consisting of a diagnosis made by a specialist clinician based on DSM-5 criteria.
The order of the assessments will be randomized. Audio captured during the SCID interview will be inputted into a machine learning model to determine the diagnostic accuracy of the Kintsugi Voice Device.
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Detailed Description
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The study is striving for a 50%/50% split of subjects who have depression and/or anxiety and subjects who do not have depression and/or anxiety.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Instruments Followed By SCID
Participants will complete the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 instruments virtually. Participants will then complete a video recorded virtual Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-5.
Kintsugi Voice Device
The Kintsugi Voice Device is an API with an underlying machine learning algorithm that drives the Device outputs. Depression and Anxiety are separate algorithms. The Device is designed to be adjunct to clinical assessment and estimate the presence of vocal characteristics consistent with a significant depressive episode and/or a clinically significant anxiety state, which are a necessary condition for the diagnosis of lifetime mood disorders, such as major depressive disorder and/or generalized anxiety disorder. Kintsugi Voice Device is not to be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.
SCID Followed By Instruments
Participants will complete a video recorded virtual Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-5. Participants will then complete the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 instruments virtually.
Kintsugi Voice Device
The Kintsugi Voice Device is an API with an underlying machine learning algorithm that drives the Device outputs. Depression and Anxiety are separate algorithms. The Device is designed to be adjunct to clinical assessment and estimate the presence of vocal characteristics consistent with a significant depressive episode and/or a clinically significant anxiety state, which are a necessary condition for the diagnosis of lifetime mood disorders, such as major depressive disorder and/or generalized anxiety disorder. Kintsugi Voice Device is not to be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.
Interventions
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Kintsugi Voice Device
The Kintsugi Voice Device is an API with an underlying machine learning algorithm that drives the Device outputs. Depression and Anxiety are separate algorithms. The Device is designed to be adjunct to clinical assessment and estimate the presence of vocal characteristics consistent with a significant depressive episode and/or a clinically significant anxiety state, which are a necessary condition for the diagnosis of lifetime mood disorders, such as major depressive disorder and/or generalized anxiety disorder. Kintsugi Voice Device is not to be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.
Eligibility Criteria
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Inclusion Criteria
* Participants must be able to read, understand, and sign the Informed Consent Form
* Access to a laptop, smartphone, tablet, or other Device with a functioning microphone
* Participants must be willing to be videotaped as part of the study
* Stated willingness to comply with all study procedures and availability for the duration of the study
* Fluency in English
* Availability for the duration of the study
* The participant must reside within the state of California
Exclusion Criteria
* Any known history of neurodegenerative or Central Nervous System disorders
* Any known history of schizophrenia, psychosis, or severe cognitive deficits
* Any known presence of disorders that may lead to false signal of depression or anxiety including Multiple Sclerosis, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Stroke, Traumatic Brain Injury
* Presence of voice disorders that may impact vocal cords such as acute or chronic laryngitis, vocal cord paresis or paralysis, or spasmodic dysphonia
* Any known history of vocal cord injury or cerebrovascular accident or head trauma with residual dysarthria in the past year
* Past or active heavy smokers if there is impact on the vocal cords
* Any known history of congenital deafness
* Subjects who do not speak English
* Subjects who do not live in the United States
* Subjects who have previously participated in any Kintsugi-sponsored study
22 Years
ALL
Yes
Sponsors
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Vituity
UNKNOWN
Kintsugi Mindful Wellness, Inc.
INDUSTRY
Responsible Party
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Principal Investigators
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Grace Chang, MBA
Role: PRINCIPAL_INVESTIGATOR
Kintsugi Mindful Wellness, Inc.
Locations
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Kintsugi Mindful Wellness, Inc.
Berkeley, California, United States
Countries
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References
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Williams SZ, Chung GS, Muennig PA. Undiagnosed depression: A community diagnosis. SSM Popul Health. 2017 Jul 28;3:633-638. doi: 10.1016/j.ssmph.2017.07.012. eCollection 2017 Dec.
Thomas JA, Burkhardt HA, Chaudhry S, Ngo AD, Sharma S, Zhang L, Au R, Hosseini Ghomi R. Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data. J Alzheimers Dis. 2020;76(3):905-922. doi: 10.3233/JAD-190783.
Evans-Lacko S, Aguilar-Gaxiola S, Al-Hamzawi A, Alonso J, Benjet C, Bruffaerts R, Chiu WT, Florescu S, de Girolamo G, Gureje O, Haro JM, He Y, Hu C, Karam EG, Kawakami N, Lee S, Lund C, Kovess-Masfety V, Levinson D, Navarro-Mateu F, Pennell BE, Sampson NA, Scott KM, Tachimori H, Ten Have M, Viana MC, Williams DR, Wojtyniak BJ, Zarkov Z, Kessler RC, Chatterji S, Thornicroft G. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018 Jul;48(9):1560-1571. doi: 10.1017/S0033291717003336. Epub 2017 Nov 27.
GBD Results Tool | GHDx. Accessed January 31, 2022. http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d780dffbe8a381b25e1416884959e88b
Major Depression. National Institute of Mental Health. Published January 2022. Accessed January 31, 2022. https://www.nimh.nih.gov/health/statistics/major-depression
Facts & Statistics | Anxiety and Depression Association of America, ADAA. Accessed January 31, 2022. https://adaa.org/understanding-anxiety/facts-statistics
Domogauer JD, Colangelo N, Aggarwal R. Study of Total and Undiagnosed Depression in a Cancer Patient Population at an Urban Cancer Center. International Journal of Radiation Oncology*Biology*Physics. 2017;99(2):S10. doi:10.1016/J.IJROBP.2017.06.040
Lewis K, Marrie RA, Bernstein CN, Graff LA, Patten SB, Sareen J, Fisk JD, Bolton JM; CIHR Team in Defining the Burden and Managing the Effects of Immune-Mediated Inflammatory Disease. The Prevalence and Risk Factors of Undiagnosed Depression and Anxiety Disorders Among Patients With Inflammatory Bowel Disease. Inflamm Bowel Dis. 2019 Sep 18;25(10):1674-1680. doi: 10.1093/ibd/izz045.
Sorkin DH, Ngo-Metzger Q, Billimek J, August KJ, Greenfield S, Kaplan SH. Underdiagnosed and undertreated depression among racially/ethnically diverse patients with type 2 diabetes. Diabetes Care. 2011 Mar;34(3):598-600. doi: 10.2337/dc10-1825. Epub 2011 Jan 27.
McDaid D, Park A la. Counting All the Costs: The Economic Impact of Comorbidity. Key Issues in Mental Health. 2015;179:23-32. doi:10.1159/000365941
Depression - Clinical Preventive Service Recommendation. American Academy of Family Physicians. Accessed February 1, 2022. https://www.aafp.org/family-physician/patient-care/clinical-recommendations/all-clinical-recommendations/depression.html
Siu AL; US Preventive Services Task Force (USPSTF); Bibbins-Domingo K, Grossman DC, Baumann LC, Davidson KW, Ebell M, Garcia FA, Gillman M, Herzstein J, Kemper AR, Krist AH, Kurth AE, Owens DK, Phillips WR, Phipps MG, Pignone MP. Screening for Depression in Adults: US Preventive Services Task Force Recommendation Statement. JAMA. 2016 Jan 26;315(4):380-7. doi: 10.1001/jama.2015.18392.
Olfson M, Kroenke K, Wang S, Blanco C. Trends in office-based mental health care provided by psychiatrists and primary care physicians. J Clin Psychiatry. 2014 Mar;75(3):247-53. doi: 10.4088/JCP.13m08834.
Aboraya A. The Reliability of Psychiatric Diagnoses: Point-Our psychiatric Diagnoses are Still Unreliable. Psychiatry (Edgmont). 2007 Jan;4(1):22-5. No abstract available.
Kraemer HC, Kupfer DJ, Clarke DE, Narrow WE, Regier DA. DSM-5: how reliable is reliable enough? Am J Psychiatry. 2012 Jan;169(1):13-5. doi: 10.1176/appi.ajp.2011.11010050. No abstract available.
Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet. 2009 Aug 22;374(9690):609-19. doi: 10.1016/S0140-6736(09)60879-5. Epub 2009 Jul 27.
Ozkanca Y, Ozturk MG, Ekmekci MN, Atkins DC, Demiroglu C, Ghomi RH. Depression Screening from Voice Samples of Patients Affected by Parkinson's Disease. Digit Biomark. 2019 May-Aug;3(2):72-82. doi: 10.1159/000500354. Epub 2019 Jun 12.
Di Y, Wang J, Liu X, Zhu T. Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders. Front Genet. 2021 Dec 20;12:761141. doi: 10.3389/fgene.2021.761141. eCollection 2021.
Fagherazzi G, Fischer A, Ismael M, Despotovic V. Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice. Digit Biomark. 2021 Apr 16;5(1):78-88. doi: 10.1159/000515346. eCollection 2021 Jan-Apr.
Lin H, Karjadi C, Ang TFA, Prajakta J, McManus C, Alhanai TW, Glass J, Au R. Identification of digital voice biomarkers for cognitive health. Explor Med. 2020;1:406-417. doi: 10.37349/emed.2020.00028. Epub 2020 Dec 31.
Tracy JM, Ozkanca Y, Atkins DC, Hosseini Ghomi R. Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease. J Biomed Inform. 2020 Apr;104:103362. doi: 10.1016/j.jbi.2019.103362. Epub 2019 Dec 19.
Zhang L, Duvvuri R, Chandra KKL, Nguyen T, Ghomi RH. Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative. Depress Anxiety. 2020 Jul;37(7):657-669. doi: 10.1002/da.23020. Epub 2020 May 7.
Deng K, Li Y, Zhang H, Wang J, Albin RL, Guan Y. Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease. Commun Biol. 2022 Jan 17;5(1):58. doi: 10.1038/s42003-022-03002-x.
Shin D, Cho WI, Park CHK, Rhee SJ, Kim MJ, Lee H, Kim NS, Ahn YM. Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning. J Clin Med. 2021 Jul 8;10(14):3046. doi: 10.3390/jcm10143046.
Kraepelin E. Manic Depressive Insanity and Paranoia. The Journal of Nervous and Mental Disease. 1921;53(4). https://journals.lww.com/jonmd/Fulltext/1921/04000/Manic_Depressive_Insanity_and_Paranoia.57.aspx
Szabadi E, Bradshaw CM, Besson JA. Elongation of pause-time in speech: a simple, objective measure of motor retardation in depression. Br J Psychiatry. 1976 Dec;129:592-7. doi: 10.1192/bjp.129.6.592.
Greden JF, Albala AA, Smokler IA, Gardner R, Carroll BJ. Speech pause time: a marker of psychomotor retardation among endogenous depressives. Biol Psychiatry. 1981 Sep;16(9):851-9.
Mundt JC, Vogel AP, Feltner DE, Lenderking WR. Vocal acoustic biomarkers of depression severity and treatment response. Biol Psychiatry. 2012 Oct 1;72(7):580-7. doi: 10.1016/j.biopsych.2012.03.015. Epub 2012 Apr 26.
Singh R, Baker JT, Pennant L, Morency LP. Deducing the severity of psychiatric symptoms from the human voice. ArXiv. 2017;abs/1703.05344.
Salekin A, Eberle JW, Glenn JJ, Teachman BA, Stankovic JA. A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018 Jun;2(2):81. doi: 10.1145/3214284.
Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol. 2020 Jan 31;5(1):96-116. doi: 10.1002/lio2.354. eCollection 2020 Feb.
Provided Documents
Download supplemental materials such as informed consent forms, study protocols, or participant manuals.
Document Type: Study Protocol
Document Type: Informed Consent Form
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