Literature DB >> 21253440

Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology.

James C Mundt1, Peter J Snyder, Michael S Cannizzaro, Kara Chappie, Dayna S Geralts.   

Abstract

Efforts to develop more effective depression treatments are limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Depression also affects speech. Research suggests several objective voice acoustic measures affected by depression can be obtained reliably over the telephone. Thirty-five physician-referred patients beginning treatment for depression were assessed weekly, using standard depression severity measures, during a six-week observational study. Speech samples were also obtained over the telephone each week using an IVR system to automate data collection. Several voice acoustic measures correlated significantly with depression severity. Patients responding to treatment had significantly greater pitch variability, paused less while speaking, and spoke faster than at baseline. Patients not responding to treatment did not show similar changes. Telephone standardization for obtaining voice data was identified as a critical factor influencing the reliability and quality of speech data. This study replicates and extends previous research with a larger sample of patients assessing clinical change associated with treatment. The feasibility of obtaining voice acoustic measures reflecting depression severity and response to treatment using computer-automated telephone data collection techniques is also established. Insight and guidance for future research needs are also identified.

Entities:  

Year:  2007        PMID: 21253440      PMCID: PMC3022333          DOI: 10.1016/j.jneuroling.2006.04.001

Source DB:  PubMed          Journal:  J Neurolinguistics        ISSN: 0911-6044            Impact factor:   1.710


  53 in total

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  31 in total

1.  Effects of Parkinson's Disease on Fundamental Frequency Variability in Running Speech.

Authors:  Leah K Bowen; Gabrielle L Hands; Sujata Pradhan; Cara E Stepp
Journal:  J Med Speech Lang Pathol       Date:  2013-09

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3.  Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote Digital Health Studies.

Authors:  Samantha Kolovson; Abhishek Pratap; Jaden Duffy; Ryan Allred; Sean A Munson; Patricia A Areán
Journal:  Int Conf Pervasive Comput Technol Healthc       Date:  2020-05

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Authors:  Yasin Ozkanca; Miraç Göksu Öztürk; Merve Nur Ekmekci; David C Atkins; Cenk Demiroglu; Reza Hosseini Ghomi
Journal:  Digit Biomark       Date:  2019-06-12

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Authors:  Ying Yang; Catherine Fairbairn; Jeffrey F Cohn
Journal:  IEEE Trans Affect Comput       Date:  2013-07-11       Impact factor: 10.506

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Review 8.  Towards automated detection of depression from brain structural magnetic resonance images.

Authors:  Kuryati Kipli; Abbas Z Kouzani; Lana J Williams
Journal:  Neuroradiology       Date:  2013-01-22       Impact factor: 2.804

9.  Phonetic measures of reduced tongue movement correlate with negative symptom severity in hospitalized patients with first-episode schizophrenia-spectrum disorders.

Authors:  Michael A Covington; S L Anya Lunden; Sarah L Cristofaro; Claire Ramsay Wan; C Thomas Bailey; Beth Broussard; Robert Fogarty; Stephanie Johnson; Shayi Zhang; Michael T Compton
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Authors:  Adam P Vogel; Paul Maruff; Peter J Snyder; James C Mundt
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