@inproceedings{0b89723c341245b5905e648e0968bb60,
title = "Technology for Early Detection of Depression and Anxiety in Older People",
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.",
keywords = "anxiety, depression, healthcare services, Machine learning, mental health, older adults",
author = "Andrews, {Jacob A.} and Astell, {Arlene J.} and Brown, {Laura J.E.} and Harrison, {Robert F.} and Hawley, {Mark S.}",
note = "Publisher Copyright: {\textcopyright} 2017 The authors and IOS Press. All rights reserved.",
year = "2017",
doi = "10.3233/978-1-61499-798-6-374",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "374--380",
editor = "Peter Cudd and {de Witte}, Luc",
booktitle = "Harnessing the Power of Technology to Improve Lives",
address = "Netherlands",
}