ArticleJMIR mental health2020
Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From Regular Monitoring Service.
Article in JMIR mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 32 citations in OpenAlex.
- Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal Framework.JMIR medical informatics · 2026Article
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- Audio and linguistic prediction of objective and subjective cognition in older adults: what is the role of different prompts?Frontiers in psychiatry · 2025Article
- Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study.JMIR aging · 2024Article
- Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research.Journal of the American Heart Association · 2024Review
- Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study.JMIR formative research · 2023Article
- Developments in scalable strategies for detecting early markers of cognitive decline.Translational psychiatry · 2022Review
- Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.JMIR formative research · 2022Article
- The Role of lncRNA TUG1 in the Parkinson Disease and Its Effect on Microglial Inflammatory Response.Neuromolecular medicine · 2021Article
- Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study.Journal of medical Internet research · 2021Article
- Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.Journal of medical Internet research · 2021Article
- Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment.IEEE open journal of engineering in medicine and biology · 2021Article
- Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions.Frontiers in psychiatry · 2021Article
- Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.Frontiers in psychiatry · 2021Article
- Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions.Frontiers in digital health · 2021Article
- Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.Journal of Alzheimer's disease : JAD · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying 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.
objectiveThe objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations.
methodsWe 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.
resultsWhen we compared the discriminative power for 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.
conclusionsOur 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.
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