Detecting functional impairment with the Digital Clock and Recall

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.

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

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.

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.

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