Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care

Abstract

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c = 0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.