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.