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. 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. Please see the README.txt file for more information on the dataset attached. 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