RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease
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| Titel: | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease |
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| Autoren: | Manuel Lentzen, Srinivasan Vairavan, Marijn Muurling, Vasilis Alepopoulos, Alankar Atreya, Merce Boada, Casper de Boer, Pauline Conde, Jelena Curcic, Giovanni Frisoni, Samantha Galluzzi, Martha Therese Gjestsen, Mara Gkioka, Margarita Grammatikopoulou, Lucrezia Hausner, Chris Hinds, Ioulietta Lazarou, Alexandre de Mendonça, Spiros Nikolopoulos, Dorota Religa, Gaetano Scebba, Pieter Jelle Visser, Gayle Wittenberg, Vaibhav A. Narayan, Neva Coello, Anna-Katharine Brem, Dag Aarsland, Holger Fröhlich, on behalf of RADAR-AD |
| Quelle: | Alzheimers Res Ther Alzheimer’s Research & Therapy, Vol 17, Iss 1, Pp 1-17 (2025) |
| Verlagsinformationen: | Springer Science and Business Media LLC, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Alzheimer Disease/diagnosis, Male, Aged, 80 and over, Wearables, Research, Remote Sensing Technology/methods instrumentation, Neurosciences. Biological psychiatry. Neuropsychiatry, Remote monitoring technologies, Middle Aged, Neuropsychological Tests, Machine Learning, Early Diagnosis, Mobile applications, Alzheimer Disease, 80 and over, Humans, Female, Neurology. Diseases of the nervous system, Discriminative capacity, RC346-429, Alzheimer's disease, RC321-571, Aged |
| Beschreibung: | Background 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. Methods 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. Results 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. Conclusions 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. |
| Publikationsart: | Article Other literature type |
| Sprache: | English |
| ISSN: | 1758-9193 |
| DOI: | 10.1186/s13195-025-01675-0 |
| DOI: | 10.48620/85794 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/39865315 https://doaj.org/article/789e135650fb424787d398f5a39e039d https://cris.maastrichtuniversity.nl/en/publications/1859aac6-e194-408b-9692-de2c56d4d7ba https://doi.org/10.1186/s13195-025-01675-0 https://pure.amsterdamumc.nl/en/publications/4d72b938-9b0d-4871-8d81-6063b0f680d3 https://doi.org/10.1186/s13195-025-01675-0 https://ora.ox.ac.uk/objects/uuid:eb08b97b-b5b6-427a-8c10-1dc7f22546db https://doi.org/10.1186/s13195-025-01675-0 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....5a2cedbce2ddf96057a826124b0f0558 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Background 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. Methods 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. Results 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. Conclusions 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. |
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| ISSN: | 17589193 |
| DOI: | 10.1186/s13195-025-01675-0 |