DOI: 10.1002/alz.076856 ISSN: 1552-5260

Assessment of RMTs for Discriminating Stages of Alzheimer’s Disease

Manuel Lentzen, Srinivasan Vairavan, Jelena Curcic, Alankar Atreya, Chris Hinds, Marijn Muurling, Pauline Conde, Margarita Grammatikopoulou, Ioulietta Lazarou, Spiros Nikolopoulos, Neva Coello, Casper de Boer, Dag Aarsland, Anna‐Katharine Brem, Holger Fröhlich,
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Geriatrics and Gerontology
  • Neurology (clinical)
  • Developmental Neuroscience
  • Health Policy
  • Epidemiology

Abstract

Background

RADAR‐AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer’s Disease (AD) via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices.

Method

We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer’s disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross‐validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient’s gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire‐based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests.

Result

Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality game slightly increased our model’s discriminative ability (Altoida ‐ Banking, Figure 2). The overall best performance was achieved when combining RMTs with the Amsterdam I‐ADL questionnaire.

Conclusion

Our results demonstrate the potential of RMTs and the Amsterdam I‐ADL questionnaire for identifying patients in prodromal stage in primary care settings.

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