ArticleAlzheimer's research & therapy2025
RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review.Journal of medical Internet research · 2026Pooled it
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.The journal of prevention of Alzheimer's disease · 2026Article
- Which activity tracker features matter to you? Older Black participants living with memory challenges and care partner preferences.Innovation in aging · 2026Article
- Multimodal passive smartphone sensing in older adults: a guide for clinical scientists based upon an ongoing cohort study.Innovation in aging · 2026Article
- Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease.The journal of prevention of Alzheimer's disease · 2025Article
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Authors and funding
29 authors.
Funding
Abstract
backgroundAlzheimer'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.
methodsThe 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.
resultsThe 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.
conclusionsRMTs 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.