Evaluating the Accuracy of Online and In-Clinic Subjective Cognitive Decline Assessments in Detecting Cognitive Impairment (Preprint)

Abstract

BACKGROUND Scalable tools to efficiently identify individuals likely to have cognitive impairment (CI) are critical in the Alzheimer’s disease and related dementias field. The Everyday Cognition scale (ECog) and its short form (ECog12) assess subjective cognitive and functional changes and are useful in predicting CI. OBJECTIVE This study aimed to compare the ability of the online ECog and the in-clinic ECog in distinguishing between CI and cognitively unimpaired (CU) individuals, and to evaluate the effectiveness of the ECog12 compared to the full ECog in an online setting. METHODS Participants were recruited from the Brain Health Registry (BHR; online) and Alzheimer’s Disease Neuroimaging Initiative (ADNI; in-clinic) with available clinical diagnoses. Ability of ECog and ECog12 (Self- and study partner [SP]-ECog) to discriminate CI from CU were calculated using Receiver Operating Characteristic (ROC) curves. Area under the ROC curves (AUCs) between BHR and ADNI were compared using the DeLong test, as were AUCs between ECog12 and ECog in BHR. RESULTS Both online and in-clinic ECog effectively discriminated CI from CU, with no significant differences in AUCs (BHR Self-ECog AUC = 0.722 vs. ADNI Self-ECog AUC = 0.769, DeLong P = .06; BHR SP-ECog AUC = 0.818 vs. ADNI SP-ECog AUC = 0.840, DeLong P = .50). Comparison between online ECog and ECog12 showed no significant differences in AUCs (Self-ECog AUC = 0.722 vs. Self-ECog12 AUC = 0.709, DeLong P = .18). CONCLUSIONS Online ECog, including the short-form ECog12, is as valid as in-clinic ECog for identifying clinically diagnosed CI, offering a cost-effective and accessible screening tool for large-scale online studies for identifying potential candidates for disease-modifying therapy.