Accessibility navigation

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

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

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


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
Divisions: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

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation