Literature DB >> 28345076

Dyadic Behavior Analysis in Depression Severity Assessment Interviews.

Stefan Scherer1, Zakia Hammal2, Ying Yang3, Louis-Philippe Morency1, Jeffrey F Cohn4.   

Abstract

Previous literature suggests that depression impacts vocal timing of both participants and clinical interviewers but is mixed with respect to acoustic features. To investigate further, 57 middle-aged adults (men and women) with Major Depression Disorder and their clinical interviewers (all women) were studied. Participants were interviewed for depression severity on up to four occasions over a 21 week period using the Hamilton Rating Scale for Depression (HRSD), which is a criterion measure for depression severity in clinical trials. Acoustic features were extracted for both participants and interviewers using COVAREP Toolbox. Missing data occurred due to missed appointments, technical problems, or insufficient vocal samples. Data from 36 participants and their interviewers met criteria and were included for analysis to compare between high and low depression severity. Acoustic features for participants varied between men and women as expected, and failed to vary with depression severity for participants. For interviewers, acoustic characteristics strongly varied with severity of the interviewee's depression. Accommodation - the tendency of interactants to adapt their communicative behavior to each other - between interviewers and interviewees was inversely related to depression severity. These findings suggest that interviewers modify their acoustic features in response to depression severity, and depression severity strongly impacts interpersonal accommodation.

Entities:  

Keywords:  Accommodation; Algorithms; Depression; Dyadic Interaction; Experimentation; Human Factors; Voice Quality

Year:  2014        PMID: 28345076      PMCID: PMC5365085          DOI: 10.1145/2663204.2663238

Source DB:  PubMed          Journal:  Proc ACM Int Conf Multimodal Interact


  31 in total

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Journal:  JAMA       Date:  2010-01-06       Impact factor: 56.272

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  6 in total

1.  Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding.

Authors:  Hamdi Dibeklioglu; Zakia Hammal; Jeffrey F Cohn
Journal:  IEEE J Biomed Health Inform       Date:  2017-03-02       Impact factor: 5.772

2.  Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews.

Authors:  Shalini Bhatia; Roland Goecke; Zakia Hammal; Jeffrey F Cohn
Journal:  Proc Int Conf Autom Face Gesture Recognit       Date:  2019-07-11

Review 3.  Affective Computing for Late-Life Mood and Cognitive Disorders.

Authors:  Erin Smith; Eric A Storch; Ipsit Vahia; Stephen T C Wong; Helen Lavretsky; Jeffrey L Cummings; Harris A Eyre
Journal:  Front Psychiatry       Date:  2021-12-23       Impact factor: 4.157

Review 4.  Turning data into better mental health: Past, present, and future.

Authors:  Nidal Moukaddam; Akane Sano; Ramiro Salas; Zakia Hammal; Ashutosh Sabharwal
Journal:  Front Digit Health       Date:  2022-08-17

5.  Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech.

Authors:  Zhongde Pan; Chao Gui; Jing Zhang; Jie Zhu; Donghong Cui
Journal:  Psychiatry Investig       Date:  2018-07-04       Impact factor: 2.505

6.  LncRNA BDNF-AS inhibits proliferation, migration, invasion and EMT in oesophageal cancer cells by targeting miR-214.

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Journal:  J Cell Mol Med       Date:  2018-06-12       Impact factor: 5.295

  6 in total

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