RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease

• Published in: Alzheimer's research & therapy Vol. 17; no. 1; pp. 29 - 17
• Main Authors: Lentzen, Manuel, Vairavan, Srinivasan, Muurling, Marijn, Alepopoulos, Vasilis, Atreya, Alankar, Boada, Merce, de Boer, Casper, Conde, Pauline, Curcic, Jelena, Frisoni, Giovanni, Galluzzi, Samantha, Gjestsen, Martha Therese, Gkioka, Mara, Grammatikopoulou, Margarita, Hausner, Lucrezia, Hinds, Chris, Lazarou, Ioulietta, de Mendonça, Alexandre, Nikolopoulos, Spiros, Religa, Dorota, Scebba, Gaetano, Jelle Visser, Pieter, Wittenberg, Gayle, Narayan, Vaibhav A., Coello, Neva, Brem, Anna-Katharine, Aarsland, Dag, Fröhlich, Holger
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.01.2025; BioMed Central; BMC
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer Disease - psychology; Alzheimer's disease; Diagnosis; Discriminative capacity; Early Diagnosis; Female; Humans; Machine Learning; Male; Methods; Middle Aged; Mobile applications; Neuropsychological Tests; Remote monitoring technologies; Remote sensing; Wearables; Germany; Discriminative capacity; Wearables; Mobile applications; Alzheimer’s disease; Remote monitoring technologies
• DOI: 10.1186/s13195-025-01675-0
• ISSN: 1758-9193

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care. The RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs. The ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities. RMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.