Health Professionals’ Experience Using an Azure Voice-Bot to Examine Cognitive Impairment (WAY2AGE)

A pilot screening for cognitive impairment through voice technology (WAY2AGE)

Voice technology has grown exponentially, offering an opportunity to different fields, such as the health area. Considering that language can be a sign of cognitive impairment and most screening tools are based on speech measures, these devices are of interest. The aim of this work was to examine a screening tool for Mild Cognitive Impairment (MCI) through voice technology. For this reason, the WAY2AGE voice Bot was tested across Mini-Mental (MMSE) scores. The main results depict a strong relationship between MMSE and WAY2AGE scores, as well as a good AUC value to discriminate between no cognitive impairment (NCI) and MCI groups. However, a relationship between age and WAY2AGE scores, but not between age and MMSE scores, was found. This would indicate that, even if WAY2AGE seems sensitive to detect MCI, the voice tool is age-sensitive and not as robust as the traditional MMSE scale. Future lines of research should look more deeply into parameters that distinguish developmental changes. As a screening tool, these results are of interest for the health area and for at-risk older adults.

Psychometric Properties of the Subjective Cognitive Decline Questionnaire (SCD-Q) and Its Invariance across Age Groups

Considering that a good sense of subjective cognitive decline seems to be crucial to prevent decline before clinical impairment, the interest in examining tools on this front were raised in the last decade. The aim of the present study is to examine the psychometric properties of the Subjective Cognitive Decline Questionnaire (SCD-Q) across age in its Spanish adaptation. It should be noted that two constructs were proposed in this context: mnestic processes and executive function factors. For this reason, a sample of 750 individuals aged from 18 to 82 years participated in the study. They were divided into three different groups: young, middle, and older adults. A confirmatory factor analysis (CFA) and invariance analysis were carried out. Moreover, a logistic regression was employed to address the role of age. The results support a good goodness of fit for both uni- and bifactorial models. The invariance analysis reached the structural covariances levels. Last, age did not predict the recognition of cognitive decline in the last two years, while the SCD-Q bifactorial model did. These results are of interest both on a theoretical level, to provide more information on models of cognitive impairment, and on a practical level, for screening.

Predicting Scores on the Mini-Mental State Examination (MMSE) from Spontaneous Speech

The aim of this study was to examine the relationship between language components, such as lexical density, length, and content in terms of “Time, Space and Action”, with MMSE scores. For this reason, a group of 33 older participants, without a diagnosis of dementia, was examined, providing information regarding recent and future events. Participants with higher MMSE scores showed higher lexical density, speech length, as well as number of tokens related to Time, Place and Action in their speech. However, these differences only reach the statistical level for lexical density when participants were divided into two groups (MCI and healthy controls). Word frequency was lower for participants with MCI but this difference was not statistically significant. Lastly, lexical density was positively correlated with MMSE scores and predicted MMSE scores. These results could be of interest at the applied level in the screening of MCI.