RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease
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 Technol...
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2025-01-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-025-01675-0 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Manuel Lentzen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-789e135650fb424787d398f5a39e039d |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-789e135650fb424787d398f5a39e039d2025-02-02T12:11:56ZengBMCAlzheimer’s Research & Therapy1758-91932025-01-0117111710.1186/s13195-025-01675-0RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s diseaseManuel Lentzen0Srinivasan Vairavan1Marijn Muurling2Vasilis Alepopoulos3Alankar Atreya4Merce Boada5Casper de Boer6Pauline Conde7Jelena Curcic8Giovanni Frisoni9Samantha Galluzzi10Martha Therese Gjestsen11Mara Gkioka12Margarita Grammatikopoulou13Lucrezia Hausner14Chris Hinds15Ioulietta Lazarou16Alexandre de Mendonça17Spiros Nikolopoulos18Dorota Religa19Gaetano Scebba20Pieter Jelle Visser21Gayle Wittenberg22Vaibhav A. Narayan23Neva Coello24Anna-Katharine Brem25Dag Aarsland26Holger Fröhlich27on behalf of RADAR-ADFraunhofer Institute for Algorithms and Scientific Computing SCAIJanssen Research and Development LLCAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcCentre for Research & Technology Hellas, Information Technologies InstituteUniversity of OxfordAce Alzheimer Center Barcelona, Universitat Internacional de CatalunyaAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcKing’s College LondonBiomedical Research, NovartisMemory Center, Geneva University and University HospitalLaboratory Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliCentre for Age-related Medicine, Stavanger University HospitalAlzheimer Hellas and Laboratory of Neurodegenerative Diseases, Aristotle University of ThessalonikiCentre for Research & Technology Hellas, Information Technologies InstituteCentral Institute of Mental Health, Medical Faculty Mannheim, University of HeidelbergUniversity of OxfordCentre for Research & Technology Hellas, Information Technologies InstituteFaculty of Medicine, University of LisbonCentre for Research & Technology Hellas, Information Technologies InstituteCenter for Alzheimer Research, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska InstitutetBiomedical Research, NovartisAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcJanssen Research and Development LLCDavos Alzheimer’s CollaborativeNovartis Pharma AGKing’s College LondonKing’s College LondonFraunhofer Institute for Algorithms and Scientific Computing SCAIAbstract 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.https://doi.org/10.1186/s13195-025-01675-0Alzheimer’s diseaseRemote monitoring technologiesWearablesMobile applicationsDiscriminative capacity |
| spellingShingle | 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 RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease Alzheimer’s Research & Therapy Alzheimer’s disease Remote monitoring technologies Wearables Mobile applications Discriminative capacity |
| title | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease |
| title_full | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease |
| title_fullStr | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease |
| title_full_unstemmed | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease |
| title_short | RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease |
| title_sort | radar ad assessment of multiple remote monitoring technologies for early detection of alzheimer s disease |
| topic | Alzheimer’s disease Remote monitoring technologies Wearables Mobile applications Discriminative capacity |
| url | https://doi.org/10.1186/s13195-025-01675-0 |
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