Abstract
Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer’s disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUC clinic_visit = 0.84 [0.75–0.93], AUC at_home = 0.77 [0.61–0.93]) was as good as the cognitive score (AUC = 0.85 [0.78–0.93]), while for classifying the preAD group, the digital score (AUC clinic_visit = 0.66 [0.53–0.78], AUC at_home = 0.76 [0.61–0.91]) was superior to the cognitive score (AUC = 0.55 [0.42–0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.
Original language | English |
---|---|
Article number | 234 |
Journal | NPJ digital medicine |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2023 |
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In: NPJ digital medicine, Vol. 6, No. 1, 234, 01.12.2023.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Augmented reality versus standard tests to assess cognition and function in early Alzheimer’s disease
AU - Muurling, Marijn
AU - de Boer, Casper
AU - Vairavan, Srinivasan
AU - Harms, Robbert L.
AU - Chadha, Antonella Santuccione
AU - Tarnanas, Ioannis
AU - Luis, Estefania Vilarino
AU - Religa, Dorota
AU - Gjestsen, Martha Therese
AU - Galluzzi, Samantha
AU - Ibarria Sala, Marta
AU - Koychev, Ivan
AU - Hausner, Lucrezia
AU - Gkioka, Mara
AU - Aarsland, Dag
AU - Visser, Pieter Jelle
AU - Brem, Anna-Katharine
N1 - Funding Information: The authors thank all RADAR-AD participants and clinical sites for their contribution. The RADAR-AD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806999. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Software AG. See www.imi.europa.eu for more details. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Research of Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. Ivan Koychev declares support for this work through the National Institute of Health Research (personal award and Oxford Health Biomedical Research Centre) and the Medical Research Council (Dementias Platform UK grant). Funding Information: The authors thank all RADAR-AD participants and clinical sites for their contribution. The RADAR-AD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806999. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Software AG. See www.imi.europa.eu for more details. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Research of Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. Ivan Koychev declares support for this work through the National Institute of Health Research (personal award and Oxford Health Biomedical Research Centre) and the Medical Research Council (Dementias Platform UK grant). We thank all past and present RADAR-AD consortium members for their contribution to the project (in alphabetical order): Dag Aarsland, Halil Agin, Vasilis Alepopoulos, Alankar Atreya, Sudipta Bhattacharya, Virginie Biou, Joris Borgdorff, Anna-Katharine Brem, Neva Coello, Pauline Conde, Nick Cummins, Jelena Curcic, Casper de Boer, Yoanna de Geus, Paul de Vries, Ana Diaz, Richard Dobson, Aidan Doherty, Andre Durudas, Gul Erdemli, Amos Folarin, Suzanne Foy, Holger Froehlich, Jean Georges, Dianne Gove, Margarita Grammatikopoulou, Kristin Hannesdottir, Robbert Harms, Mohammad Hattab, Keyvan Hedayati, Chris Hinds, Adam Huffman, Dzmitry Kaliukhovich, Irene Kanter-Schlifke, Ivan Koychev, Rouba Kozak, Julia Kurps, Sajini Kuruppu, Claire Lancaster, Robert Latzman, Ioulietta Lazarou, Manuel Lentzen, Federica Lucivero, Florencia Lulita, Nivethika Mahasivam, Nikolay Manyakov, Emilio Merlo Pich, Peyman Mohtashami, Marijn Muurling, Vaibhav Narayan, Vera Nies, Spiros Nikolopoulos, Andrew Owens, Marjon Pasmooij, Dorota Religa, Gaetano Scebba, Emilia Schwertner, Rohini Sen, Niraj Shanbhag, Laura Smith, Meemansa Sood, Thanos Stavropoulos, Pieter Stolk, Ioannis Tarnanas, Srinivasan Vairavan, Nick van Damme, Natasja van Velthogen, Herman Verheij, Pieter Jelle Visser, Bert Wagner, Gayle Wittenberg, and Yuhao Wu. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer’s disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUC clinic_visit = 0.84 [0.75–0.93], AUC at_home = 0.77 [0.61–0.93]) was as good as the cognitive score (AUC = 0.85 [0.78–0.93]), while for classifying the preAD group, the digital score (AUC clinic_visit = 0.66 [0.53–0.78], AUC at_home = 0.76 [0.61–0.91]) was superior to the cognitive score (AUC = 0.55 [0.42–0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.
AB - Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer’s disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUC clinic_visit = 0.84 [0.75–0.93], AUC at_home = 0.77 [0.61–0.93]) was as good as the cognitive score (AUC = 0.85 [0.78–0.93]), while for classifying the preAD group, the digital score (AUC clinic_visit = 0.66 [0.53–0.78], AUC at_home = 0.76 [0.61–0.91]) was superior to the cognitive score (AUC = 0.55 [0.42–0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.
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UR - http://www.scopus.com/inward/record.url?scp=85180123160&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41746-023-00978-6
DO - https://doi.org/10.1038/s41746-023-00978-6
M3 - Article
C2 - 38110486
SN - 2398-6352
VL - 6
JO - NPJ digital medicine
JF - NPJ digital medicine
IS - 1
M1 - 234
ER -