ETRO VUB
About ETRO  |  News  |  Events  |  Vacancies  |  Contact  
Home Research Education Industry Publications About ETRO

ETRO Publications

Full Details

Other Publication

The use of mobile games to refine the diagnosis of dementia: Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council

This publication appears in: Frontiers in Neuroscience

Authors: B. Bonnechère, m. Van Vooren, S. De Breucker, O. Van Hove, B. Jansen, S. Van Sint Jan, V. Feipel and J. Bier

Publication Year: 2018


Abstract:

In the past years the number of applications developed to train memory and other cognitive functions has increased [1,2]. In addition to allowing training these applications record a series of parameters when subjects train. However, there are very few studies interested in the clinical relevance of the information collected using these mobile games (MG). The purpose of this study was to determine whether MG could be used to refine the diagnosis of dementia in comparison with conventional clinical tests. A set of 7 short MG were used in this study. Twenty-seven aged control (MMSE > 28) and 29 patients with a MMSE score between 20 and 24 were included. An Addenbrooke’s Cognitive Evaluation-Revised (ACE-R) was performed by trained clinicians before MG evaluations. Logistic regression was applied using MMSE alone and a combination of MMSE with the different MG to classify subjects with and without Cognitive Impairment (CI) (based on ACE-R score with a cut-off of 88 points [3]). Using MMSE only the percentage of correct classification was 87.5% (area under ROC = 0.93). We then applied stepwise regression to select the best combination of MG. The best model was found using the MMSE and only one MG: the percentage of classification was then 92.9% (area under ROC = 0.97). Because the MMSE is included in the ACE-R we did the same regression using the ACE-R score without the MMSE to be sure that the variables are independent. The score is then out of 70 and we used a cut-off of 88% to define subjects with and without CI. Using the MMSE the percentage of correct classification is 82.1% (area under ROC = 0.89). The best model fitted with the stepwise regression is a combination of MMSE and two MG: using this combination the percentage of classification is 94.6% (area under ROC = 0.97). Each of the MG last for one minute and could therefore easily be integrated in the evaluation of elderly with and without CI. This study suggests that MG are clinically-meaningful and could be used to develop scores related to the cognitive status of patients with MMSE superior to 20.

Other Reference Styles
Current ETRO Authors

Mr. Bruno Bonnechère

+32 (0)02 629 168

bbonnech@etrovub.be

more info

Prof. Dr. Bart Jansen

+32 (0)02 629 103

bjansen@etrovub.be

more info

Other Publications

• Journal publications

IRIS • LAMI • AVSP

• Conference publications

IRIS • LAMI • AVSP

• Book publications

IRIS • LAMI • AVSP

• Reports

IRIS • LAMI • AVSP

• Laymen publications

IRIS • LAMI • AVSP

• PhD Theses

Search ETRO Publications

Author:

Keyword:  

Type:








- Contact person

- IRIS

- AVSP

- LAMI

- Contact person

- Thesis proposals

- ETRO Courses

- Contact person

- Spin-offs

- Know How

- Journals

- Conferences

- Books

- Vacancies

- News

- Events

- Press

Contact

ETRO Department

info@etro.vub.ac.be

Tel: +32 2 629 29 30

©2019 • Vrije Universiteit Brussel • ETRO Dept. • Pleinlaan 2 • 1050 Brussels • Tel: +32 2 629 2930 (secretariat) • Fax: +32 2 629 2883 • WebmasterDisclaimer