Cognitive abilities predict naturalistic speech length in older adults

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.