Related Experiment Video
Updated: Oct 16, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive impairment screening using m-health: an android implementation of the mini-mental state examination (MMSE)
Paul Devos1, Jari Debeer2, Jelle Ophals2
1Department of Information Technology, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium. p.devos@ugent.be.
Purpose:
This paper describes an implementation of an Android application that allows cognitive functions to be tested.
Methods:
The application consists of a slightly modified Mini Mental state Examination (MMSE) test that provides examiner assistance in diagnosing cognitive impairments. The application deploys speech recognition techniques to allow easy and automated scoring of the test. The test results and test-related information are stored in a database providing easy access to data for follow-up and analysis, resulting in overall benefits to the examiner workflow. A small-scale pilot study of 5 months duration was conducted in a nursing home where 15 residents were tested with the MMSE app test and with the (paper) MMSE test with the aim of determining the agreement between the two test methods.
Results:
The final MMSE test scores, with a maximum score of 30, agree; the differences have a mean of 0.1, a standard deviation of 2.1 and fall in a [- 4, + 4] range as is illustrated in a Bland-Altman analysis. From examiner reflections, the motoric skills of the participant are indicated to contribute strongly to the time benefit of the assessment itself.
Conclusions:
The findings of this study suggest that the mobile digital version of the slightly modified MMSE test has the potential to be used as an attractive alternative for the conventional paper version of the test.
