Remote monitoring technologies in Alzheimer's disease: design of the RADAR-AD study.

Alzheimer’s disease Remote monitoring technologies Wearable technologies

Journal

Alzheimer's research & therapy
ISSN: 1758-9193
Titre abrégé: Alzheimers Res Ther
Pays: England
ID NLM: 101511643

Informations de publication

Date de publication:
23 04 2021
Historique:
received: 28 07 2020
accepted: 11 04 2021
entrez: 24 4 2021
pubmed: 25 4 2021
medline: 25 6 2021
Statut: epublish

Résumé

Functional decline in Alzheimer's disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales. This study includes three tiers. For the main study, we will include participants (n = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants' phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure. First results are expected to be disseminated in 2022. Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection.

Sections du résumé

BACKGROUND
Functional decline in Alzheimer's disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales.
METHODS
This study includes three tiers. For the main study, we will include participants (n = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants' phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure.
RESULTS
First results are expected to be disseminated in 2022.
CONCLUSION
Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection.

Identifiants

pubmed: 33892789
doi: 10.1186/s13195-021-00825-4
pii: 10.1186/s13195-021-00825-4
pmc: PMC8063580
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

89

Investigateurs

Maximilian Buegler (M)
Richard Fischer (R)
Robbert Harms (R)
Irene B Meier (IB)
Ioannis Tarnanas (I)
Ana Diaz (A)
Jean Georges (J)
Dianne Gove (D)
Casper de Boer (C)
Marijn Muurling (M)
Pieter Jelle Visser (PJ)
Ioannis Kompatsiaris (I)
Ioulietta Lazarou (I)
Lampros Mpaltadoros (L)
Spiros Nikolopoulos (S)
Asterios Papastergiou (A)
Thanos Stavropoulos (T)
Dimitris Strantsalis (D)
Holger Froehlich (H)
Martin Hoffman-Apitius (M)
Meemansa Sood (M)
Nikolay Manyakov (N)
Vaibhav A Narayan (VA)
Jerry G Novak (JG)
Dorota Religa (D)
Emilia Schwertner (E)
Juraj Secnik (J)
Bengt Winblad (B)
Dag Aarsland (D)
Pauline Conde (P)
Amos Folarin (A)
Grace Lavelle (G)
Andrew P Owens (AP)
Andrew McCarthy (A)
Aidan Nickerson (A)
Janneke Boere (J)
Bruna Consiglio (B)
Yoanna Daskalova (Y)
Alexander Duyndam (A)
Irene Kanter-Schlifke (I)
Vera J M Nies (VJM)
Pieter Stolk (P)
Herman Verheij (H)
Neva Coello (N)
Jelena Curcic (J)
Gul Erdemli (G)
Tilo Hache (T)
Kristin Hannesdottir (K)
Alex Sverdlov (A)
Vanessa Vallejo (V)
Eric Yang (E)
Ariel Dowling (A)
Rouba Kozak (R)
Melissa Naylor (M)
Rodrigo Palma Dos Reis (RP)
Gene Shin (G)
Joris Borgdorff (J)
Elisa Cirillo (E)
Keyvan Hedayati (K)
Nivethika Mahasivam (N)
Aidan Doherty (A)
Chris Hinds (C)
Ivan Koychev (I)
Claire Lancaster (C)
Sebastien Libert (S)
Federica Lucivero (F)
Yuhao Wu (Y)
Andre Durudas (A)

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Auteurs

Marijn Muurling (M)

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands. m.muurling@amsterdamumc.nl.

Casper de Boer (C)

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.

Rouba Kozak (R)

Takeda Pharmaceuticals International Co., Cambridge, MA, USA.

Dorota Religa (D)

Department of Neurobiology, Care Sciences and Society, Karolinska Insitutet, Stockholm, Sweden.

Ivan Koychev (I)

Department of Psychiatry, University of Oxford, Oxford, UK.

Herman Verheij (H)

Lygature, Utrecht, The Netherlands.

Vera J M Nies (VJM)

Lygature, Utrecht, The Netherlands.

Alexander Duyndam (A)

Lygature, Utrecht, The Netherlands.

Meemansa Sood (M)

Fraunhofer Institute for Algorithms and Scientific Computing, University of Bonn, Bonn, Germany.

Holger Fröhlich (H)

Fraunhofer Institute for Algorithms and Scientific Computing, University of Bonn, Bonn, Germany.

Kristin Hannesdottir (K)

Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

Gul Erdemli (G)

Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

Federica Lucivero (F)

Ethox and Welcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK.

Claire Lancaster (C)

Big Data Institute, University of Oxford, Oxford, UK.

Chris Hinds (C)

Big Data Institute, University of Oxford, Oxford, UK.

Thanos G Stravopoulos (TG)

Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece.

Spiros Nikolopoulos (S)

Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece.

Ioannis Kompatsiaris (I)

Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece.

Nikolay V Manyakov (NV)

Data Science and Clinical Insights, Janssen Research & Development, Beerse, Belgium.

Andrew P Owens (AP)

Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

Vaibhav A Narayan (VA)

Janssen Neuroscience Research & Development, Titusville, NJ, USA.

Dag Aarsland (D)

Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.

Pieter Jelle Visser (PJ)

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.

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