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Technology for early detection of depression and anxiety in older people

Andrews, J., Astell, A. J., Brown, L. J. E., Harrison, R. F. and Hawley, M. (2017) Technology for early detection of depression and anxiety in older people. In: Cutt, P. and de Witt, L. (eds.) Harnessing the Power of Technology to Improve Lives. Studies in Health Technology and Informatics, 242. IOS Press, pp. 374-380. ISBN 9781614997979

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To link to this item DOI: 10.3233/978-1-61499-798-6-374

Abstract/Summary

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.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
ID Code:75412
Additional Information:Presented at AAATE Conference, 12-15 September 2017, Sheffield
Publisher:IOS Press

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