TY  - JOUR
AU  - Yamada, Yasunori
AU  - Shinkawa, Kaoru
AU  - Shimmei, Keita
PY  - 2020
DA  - 2020/1/14
TI  - Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From a Regular Monitoring Service
JO  - JMIR Ment Health
SP  - e16790
VL  - 7
IS  - 1
KW  - dementia
KW  - Alzheimer disease
KW  - speech analysis
KW  - screening
KW  - monitoring
KW  - behavioral marker
KW  - daily conversation
AB  - Background: Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored. Objective: The objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations. Methods: We analyzed daily conversational data of seniors with and without AD obtained from longitudinal follow-up in a regular monitoring service (from n=15 individuals including 2 AD patients at an average follow-up period of 16.1 months; 1032 conversational data items obtained during phone calls and approximately 221 person-hours). In addition to the standard linguistic features used in previous studies on connected speech data during neuropsychological tests, we extracted novel features related to atypical repetition of words and topics reported by previous observational and descriptive studies as one of the prominent characteristics in everyday conversations of AD patients. Results: When we compared the discriminative power of AD, we found that atypical repetition in two conversations on different days outperformed other linguistic features used in previous studies on speech data during neuropsychological tests. It was also a better indicator than atypical repetition in single conversations as well as that in two conversations separated by a specific number of conversations. Conclusions: Our results show how linguistic features related to atypical repetition across days could be used for detecting AD from daily conversations in a passive manner by taking advantage of longitudinal data. 
SN  - 2368-7959
UR  - http://mental.jmir.org/2020/1/e16790/
UR  - https://doi.org/10.2196/16790
DO  - 10.2196/16790
ID  - info:doi/10.2196/16790
ER  -