ArticleScientific reports2024
Cognitive abilities predict naturalistic speech length in older adults.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Mapping cognitive diversity in older adults through community-based digital screening via mobile devices: a cross-sectional latent class analysis.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Past research has demonstrated the association between social engagement and the maintenance of cognitive abilities. However, inconsistent definitions of social engagement have posed challenges to systematically investigate this association. This paper addresses the role of social relationships in cognitive functioning among older adults, focusing on the real-life communication indicator-length of own speech-as a measure of social activity. Utilizing advanced technology to unobtrusively measure older adults' real-life speech, this study investigates its association with various cognitive abilities and sociodemographic factors. Differential cognitive measures, and sociodemographic data including factors like age, sex, education, income, persons living in the same household, loneliness, and subjective hearing status were included. Audio data of 83 participants are analyzed with a machine learning speaker identification algorithm. Using Elastic Net regularized regression, results indicate that higher levels of working memory, cognitive speed, and semantic fluency predict own speech in everyday life. While having no partner negatively predicted own speech length, we unexpectedly found that higher hearing status was related to lower speech frequency. Age was neither a relevant predictor in the regression nor correlated with any other variables. We discuss implications and future research applications based on the findings from our novel approach.
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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 Socratic graph.