Digital cognitive health screening in the primary care setting using working memory and voice tasks

Music City Center 

Introduction: Primary care providers (PCPs) are the first line of medical care, and as such, they often are the first to hear of concerns regarding cognitive decline. Yet, under-diagnosis of Alzheimer's disease and related disorders (ADRD) in the primary care setting is widely recognized, and PCPs are uncertain about which patients to assess, which tools to use, and how to use them. This project aims to develop a brief digital cognitive screening tool that analyzes cognitive and voice data using a machine learning framework to offer a practical and efficient solution for cognitive health screening to the PCP.

Methods: Our study group comprised 53 cognitively normal and 51 cognitively impaired older adults. Each completed a risk assessment task, a symbol matching task, and four speech/language tasks, followed by a second administration of working memory to investigate the added utility of practice effects. The speech/language tasks were processed to extract acoustic and linguistic features. Bayesian adaptive regression trees were used to test 11 models. Traditional clinical cognitive evaluations for mild cognitive impairment were used as the gold standard.

Results: The top three models included the results of the symbol matching tasks alone (classification accuracy of c = 0.91) or in combination with either the personal narrative task (c = 0.94) or the counting backward task (c = 0.90). These results were similar to or slightly better than the traditional Quick Mild Cognitive Impairment (QMCI) screen.

Conclusion: The combination of features from the working memory task and voice tasks can accurately classify cognitively normal versus cognitively impaired older adults. Future work is being conducted to understand this tool's usability in the real-world clinical setting, and we are looking to develop a Spanish version of this tool and evaluate its effectiveness in a Spanish cohort.

cognitive screening

machine learning

cognitive impairment

primary care

voice analysis