Detecting functional impairment with the Digital Clock and Recall.

Abstract

Background: Distinguishing between mild cognitive impairment (MCI) and early dementia requires both neuropsychological and functional assessment that often relies on caregivers' insights. Contacting a patient's caregiver can be time-consuming in a physician's already-filled workday.

Objective: To assess the utility of a brief, machine learning (ML)-enabled digital cognitive assessment, the Digital Clock and Recall (DCR), for detecting functional dependence.

Methods: We evaluated whether the DCR can help identify individuals at risk of functional deficits as measured by the informant-rated Functional Activities Questionnaire (FAQ) in older individuals including cognitively unimpaired, MCI, and dementia likely due to Alzheimer's disease.

Results: The DCR scaled well with FAQ scores, and ML classifiers trained on multimodal DCR features demonstrated strong performance in predicting functional impairment on a held-out test set. Differences in FAQ scores between DCR-predicted classes were comparable across key demographic groups.

Conclusions: The DCR can streamline the clinical decision-making, triage, and intervention planning associated with functional impairment in primary care.