Journal article
Vocal pattern detection of depression among older adults
International journal of mental health nursing, Vol.29(3), pp.440-449
06/2020
DOI: 10.1111/inm.12678
PMID: 31811697
Abstract
Depression is a serious problem for many older adults but is too often undetected by the person, family or providers. Although vocal patterns have been successfully used to detect and predict depression in adults aged 18 to 65 years, no studies to date have included older adults. The study purpose was to determine whether vocal patterns associated with clinical depression in younger people also signify depression in older adults. An observational, repeated measures design was used to enroll 46 volunteer older adults who completed a semi-structured interview composed the 9-item Patient Health Questionnaire or PHQ-9 depression scale and selected speech measures. Recorded interviews were analysed by machine learning algorithms to evaluate whether vocal patterns may predict presence of depression in older adults. In this study, using the PHQ-9 and a supervised machine learning algorithm accurately predicted high and low depression scores between 86% and 92% of the time. Change in raw PHQ-9 scores between interview cycles was predicted within 1.17 points. These results provide strong and promising evidence that vocal patterns can be used effectively to detect clinical depression in adults who are 65 years and older.
Details
- Title: Subtitle
- Vocal pattern detection of depression among older adults
- Creators
- Marianne Smith - University of IowaBryce Jensen Dietrich - University of IowaEr-Wei Bai - University of IowaHenry Jeremy Bockholt - Georgia State University
- Resource Type
- Journal article
- Publication Details
- International journal of mental health nursing, Vol.29(3), pp.440-449
- DOI
- 10.1111/inm.12678
- PMID
- 31811697
- ISSN
- 1445-8330
- eISSN
- 1447-0349
- Grant note
- The University of Iowa Strategic Research Leadership Program
- Language
- English
- Date published
- 06/2020
- Academic Unit
- Electrical and Computer Engineering; Nursing; Political Science; University College Courses
- Record Identifier
- 9984197079002771
Metrics
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