Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

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Older adults are often stereotyped as having less technological ability than younger age groups. As a result, older individuals may avoid using technology due to stereotype threat, the fear of confirming negative stereotypes about their social group. The present research examined the role of stereotype threat within the Technology Acceptance Model (TAM). Across two studies, experiencing stereotype threat in the technological domain was indirectly associated with lower levels of technology use among older adults. This was found for subjective (Study 1) and objective measures (Study 2) of use behaviour, and for technology use in general (Study 1) and computer use in particular (Study 2). In line with the predictions of the Technology Acceptance Model, this relationship was mediated by anxiety, perceived ease of use, perceived usefulness, and behavioural intention. Specifically, stereotype threat was negatively associated with perceived ease of use (Studies 1 and 2) and anxiety mediated this relationship (Study 2). These findings suggest that older adults underuse technology due to the threat of confirming ageist stereotypes targeting their age group. Stereotype threat may thus be an important barrier to technology acceptance and usage in late adulthood.