Technology for Early Detection of Depression and Anxiety in Older People

Jacob A. Andrews, Arlene J. Astell, Laura J.E. Brown, Robert F. Harrison, Mark S. Hawley

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Under-diagnosis of depression and anxiety is common in older adults. This project took a mixed methods approach to explore the application of machine learning and technology for early detection of these conditions. Mood measures collected with digital technologies were used to predict depression and anxiety status according to the Geriatric Depression Scale (GDS) and the Hospital Anxiety and Depression Scale (HADS). Interactive group activities and interviews were used to explore views of older adults and healthcare professionals on this approach respectively. The results show good potential for using a machine learning approach with mood data to predict later depression, though prospective results are preliminary. Qualitative findings highlight motivators and barriers to use of mental health technologies, as well as usability issues. If consideration is given to these issues, this approach could allow alerts to be provided to healthcare staff to draw attention to service users who may go on to experience depression.

Original languageEnglish
Title of host publicationHarnessing the Power of Technology to Improve Lives
EditorsPeter Cudd, Luc de Witte
PublisherIOS Press
Pages374-380
Number of pages7
ISBN (Electronic)9781614997979
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameStudies in Health Technology and Informatics
Volume242
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Cite this