ArticleAlzheimer's & dementia (New York, N. Y.)2017
Predicting mild cognitive impairment from spontaneous spoken utterances.
Article in Alzheimer's & dementia (New York, N. Y.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01571427 (Conversational Engagement as a Means to Delay Alzheimer's Disease Onset), which is not on this map. Cited by 70 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Conversational Engagement as a Means to Delay Alzheimer's Disease Onset
Who cites it
70 citing papers in PubMed, 3 syntheses or guidelines pooled it, 179 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- From verbal fluency to naturalistic speech: a scoping review of language correlates of loneliness and social isolation in older adults.Frontiers in aging neuroscience · 2026Pooled it
- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
- A 12-week application-based group conversation intervention on cognitive health and psychosocial well-being among older adults during the COVID-19 pandemic: a randomized controlled trial.BMC geriatrics · 2025Trial
- A Comparative Single-Blind Randomized Controlled Trial With Language Training in People With Mild Cognitive Impairment.American journal of Alzheimer's disease and other dementias · 2019Trial
- Linguistic features from paragraph recall are markers of cognitive impairment.Journal of the International Neuropsychological Society : JINS · 2026Article
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- PROTOCOL: Barriers and Facilitators to the Adoption of Speech-Based Cognitive Screening Tools: A Protocol for Systematic Review of Qualitative Evidence.Campbell systematic reviews · 2026Review
- Assessment of Language Impairments Towards Identifying Markers for Early Diagnosis of Pathological Cognitive Decline.Behavioral sciences (Basel, Switzerland) · 2026Review
- Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.JMIR research protocols · 2026Article
- Listening for Alzheimer's clues: machine learning analysis of multidomain speech features for cognitive impairment screening.Frontiers in aging neuroscience · 2026Article
- Evaluating spoken language as a biomarker for automated screening of cognitive impairment.Communications medicine · 2025Article
- Bridging Language Markers and Pathology: Correlations Between Digital Speech Measures and Surrogate CSF Biomarkers in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- Everyday Language and Cognitive Functioning in Late Life.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Audio and linguistic prediction of objective and subjective cognition in older adults: what is the role of different prompts?Frontiers in psychiatry · 2025Article
- Protocol for detection and monitoring of post-stroke cognitive impairment through AI-powered speech analysis: a mixed methods pilot study.Frontiers in aging neuroscience · 2025Article
- LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data.Frontiers in artificial intelligence · 2025Article
- Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation.JMIR formative research · 2024Article
- Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.Expert systems with applications · 2024Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
introductionTrials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial.
methodsData came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants.
resultsMCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%. DISCUSSION: Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.