JMIR Publications

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. Recent advances in online technology have enabled the use of web-based cognitive tests and questionnaires to identify cognitive impairment (CI) with greater convenience and scalability. While the effectiveness of the ECog has been demonstrated in clinical settings, its potential to detect CI in remote, unsupervised formats remains underexplored.

Objective:

This study aimed to compare the ability of the online ECog and the in-clinic ECog in distinguishing between individuals with CI and those who are cognitively unimpaired (CU), and to evaluate the effectiveness of the ECog12—the abbreviated version of the ECog—compared to the full-length 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. The ability of the self-rated ECog (Self-ECog), study partner-rated ECog (SP-ECog), Self-ECog12, and SP-ECog12 to discriminate CI from CU individuals was assessed 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:

Online Self-ECog and SP-ECog scores effectively discriminated CI from CU with AUCs of 0.722 and 0.818, respectively. Similarly the abbreviated online versions, Self-ECog12 and SP-ECog12, also demonstrated discriminative ability (AUC = 0.709 and 0.777, respectively). When compared to in-clinic ECog scores, there were no significant differences in the ability to distinguish CI from CU between online and in-clinic versions (BHR Self-ECog AUC = 0.722 vs. ADNI Self-ECog AUC = 0.769, DeLong’s P = .06; BHR SP-ECog AUC = 0.818 vs. ADNI SP-ECog AUC = 0.840, DeLong’s P = .50). Additionally, comparison between online ECog and ECog12 showed no significant difference in AUCs (BHR Self-ECog AUC = 0.722 vs. BHR Self-ECog12 AUC = 0.709, DeLong’s P = .18).

Conclusions:

Online ECog scores, both full-length and short-form, were as valid as in-clinic ECog scores for identifying clinically diagnosed CI. Also, Self-ECog12 was as effective as full-length Self-ECog to identify CI in an online setting, offering a cost-effective and accessible screening tool for large-scale online. These results highlight the value of the online ECog as a valid tool for identifying older adults with CI in an online clinical study, facilitating early detection and referral for comprehensive evaluations for identifying potential candidates for disease-modifying therapy.