  Previous researchers have proposed intelligent systems for therapeutic
monitoring of cognitive impairments. However, most existing practical
approaches for this purpose are based on manual tests. This raises issues such
as excessive caretaking effort and the white-coat effect. To avoid these
issues, we present an intelligent conversational system for entertaining
elderly people with news of their interest that monitors cognitive impairment
transparently. Automatic chatbot dialogue stages allow assessing content
description skills and detecting cognitive impairment with Machine Learning
algorithms. We create these dialogue flows automatically from updated news
items using Natural Language Generation techniques. The system also infers the
gold standard of the answers to the questions, so it can assess cognitive
capabilities automatically by comparing these answers with the user responses.
It employs a similarity metric with values in [0, 1], in increasing level of
similarity. To evaluate the performance and usability of our approach, we have
conducted field tests with a test group of 30 elderly people in the earliest
stages of dementia, under the supervision of gerontologists. In the
experiments, we have analysed the effect of stress and concentration in these
users. Those without cognitive impairment performed up to five times better. In
particular, the similarity metric varied between 0.03, for stressed and
unfocused participants, and 0.36, for relaxed and focused users. Finally, we
developed a Machine Learning algorithm based on textual analysis features for
automatic cognitive impairment detection, which attained accuracy, F-measure
and recall levels above 80%. We have thus validated the automatic approach to
detect cognitive impairment in elderly people based on entertainment content.
