Data from Little et al. (2020) Deep learning-based automated speech detection as a marker of social functioning in late-life depression. Psychological Medicine, 1-10.

This is the dataset presented in the publication: Little, B., Alshabrawy, O., Stow, D., Ferrier, I. N., McNaney, R., Jackson, D. G., … O’Brien, J. T. (2019). Deep learning-based automated speech detection as a marker of social functioning in late-life depression. Psychological Medicine, 1-10. <br><br>These data were part of a project conducted at Newcastle University to investigate physical activity and social functioning in people with Late-Life Depression (DEMO-POD project). 30 patients with Late-Life Depression and 30 matched healthy controls participated in this study. Neuropsychological performance was measured using a standardised test battery and demographic information and clinical characteristics were collected form participants. Participants wore a wearable device that recorded movement data (accelerometer) and acoustic data. Deep learning was used to automatically classify speech from the acoustic data. <br>Please see the README.txt file for more information on the dataset attached. <br>Data from this project was also published in O’Brien, J. T., Gallagher, P., Stow, D., Hammerla, N., Ploetz, T., Firbank, M., … Olivier, P. (2017). A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression. Psychological Medicine, 47, 93–102. https://doi.org/10.1017/S0033291716002166