Development and Evaluation of an Android-based Platform for Early MCI Detection in an Elderly Population

Abstract

Background: Mild cognitive impairment (MCI) is an intermediate stage of cognitive decline fitting in-between normal cognition and dementia. With the growing aging population, this study aimed to develop and psychometrically validate an android-based application for early MCI detection in elderly subjects. Method: This study was conducted in two phases, including 1- Initial design and prototyping of the application named M-Check, 2- psychometric evaluation. After the design and development of the M-Check app, it was evaluated by experts and elderly subjects. Face validity was determined by two checklists provided to the expert panel and the elderly subjects. Convergent validity of the M-Check app was assessed using the Montreal Cognitive Assessment (MoCA) battery through Pearson correlation. Test-retest and internal consistency and reliability were evaluated using Intra-Class Correlation (ICC) and Kuder-Richardson coefficients, respectively. In addition, the usability was assessed by a System Usability Scale (SUS) questionnaire. SPSS 16.0 was employed to analyze the data. Result: The app's usability assessment by elderlies and experts scored 77.11 and 82.5, respectively. Also, the correlation showed that the M-Check app was negatively correlated with the MoCA test (r = -0.71, p <0.005), and the ICC was more than 0.7. Moreover, the Richardson's Coder coefficient was 0.82, corresponding to an acceptable reliability. Conclusion: In this study, we validated the M-Check app for the detection of MCI based on the growing need for cognitive assessment tools that can identify early decline. Such screeners are expected to take much shorter time than typical neuropsychological batteries do. Additional work are yet to be underway to ensure that M-Check is ready to launch and used without the presence of a trained person.