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dc.contributor.author
Curcic, Jelena
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Vallejo, Vanessa
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Sorinas, Jennifer
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Sverdlov, Oleksandr
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Praestgaard, Jens
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Piksa, Mateusz
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Deurinck, Mark
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Erdemli, Gul
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Bügler, Maximilian
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Tarnanas, Ioannis
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Taptiklis, Nick
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Cormack, Francesca
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Anker, Rebekka
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Massé, Fabien
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Mandar, William Souillard
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Intrator, Nathan
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Molcho, Lior
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Madero, Erica
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Bott, Nicholas
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Chambers, Mieko
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Tamory, Josef
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Shulz, Matias
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Fernández, Gerardo Abel
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Simpson, William
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Robin, Jessica
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Snædal, Jón G.
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Cha, Jang Ho
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Hannesdottir, Kristin
dc.date.available
2023-07-04T13:59:34Z
dc.date.issued
2022-08
dc.identifier.citation
Curcic, Jelena; Vallejo, Vanessa; Sorinas, Jennifer; Sverdlov, Oleksandr; Praestgaard, Jens; et al.; Description of the method for evaluating digital endpoints in alzheimer disease study: protocol for an exploratory, cross-sectional study; JMIR Publications Inc.; JMIR Research Protocols; 11; 8; 8-2022; 1-16
dc.identifier.issn
1929-0748
dc.identifier.uri
http://hdl.handle.net/11336/202238
dc.description.abstract
Background: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies' ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
JMIR Publications Inc.
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ALZHEIMER DISEASE
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BRAIN AMYLOID
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CLINICAL TRIAL DESIGN
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COGNITION
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DIGITAL ENDPOINTS
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METHODOLOGY STUDY
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MOBILE PHONE
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Biotecnología relacionada con la Salud
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Biotecnología de la Salud
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CIENCIAS MÉDICAS Y DE LA SALUD
dc.title
Description of the method for evaluating digital endpoints in alzheimer disease study: protocol for an exploratory, cross-sectional study
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-05-11T17:48:05Z
dc.journal.volume
11
dc.journal.number
8
dc.journal.pagination
1-16
dc.journal.pais
Canadá
dc.description.fil
Fil: Curcic, Jelena. Novartis Institutes for Biomedical Research; Suiza
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Fil: Vallejo, Vanessa. Novartis Institutes for Biomedical Research; Suiza
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Fil: Sorinas, Jennifer. Novartis Institutes for Biomedical Research; Suiza
dc.description.fil
Fil: Sverdlov, Oleksandr. Novartis Pharmaceuticals Corporation; Estados Unidos
dc.description.fil
Fil: Praestgaard, Jens. Novartis Institutes for Biomedical Research; Suiza
dc.description.fil
Fil: Piksa, Mateusz. Novartis Institutes for Biomedical Research; Suiza
dc.description.fil
Fil: Deurinck, Mark. Novartis Institutes for Biomedical Research; Suiza
dc.description.fil
Fil: Erdemli, Gul. Novartis Institutes for Biomedical Research; Estados Unidos
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Fil: Bügler, Maximilian. Altoida Inc; Estados Unidos
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Fil: Tarnanas, Ioannis. Altoida Inc; Estados Unidos
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Fil: Taptiklis, Nick. Cambridge Cognition Ltd; Reino Unido
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Fil: Cormack, Francesca. Cambridge Cognition Ltd; Reino Unido
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Fil: Anker, Rebekka. MindMaze SA; Suiza
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Fil: Massé, Fabien. MindMaze SA; Suiza
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Fil: Mandar, William Souillard. Massachusetts Institute of Technology; Estados Unidos
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Fil: Intrator, Nathan. Neurosteer Inc.; Estados Unidos
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Fil: Molcho, Lior. Neurosteer Inc.; Estados Unidos
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Fil: Madero, Erica. Neurotrack Technologies Inc; Estados Unidos
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Fil: Bott, Nicholas. University of Stanford; Estados Unidos
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Fil: Chambers, Mieko. Neurovision Imaging Inc; Estados Unidos
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Fil: Tamory, Josef. Neurovision Imaging Inc; Estados Unidos
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Fil: Shulz, Matias. ViewMind Inc; Estados Unidos
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Fil: Fernández, Gerardo Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
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Fil: Simpson, William. Winterlight Labs; Canadá
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Fil: Robin, Jessica. Winterlight Labs; Canadá
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Fil: Snædal, Jón G.. Memory Clinic; Islandia
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Fil: Cha, Jang Ho. Novartis Institutes for Biomedical Research; Estados Unidos
dc.description.fil
Fil: Hannesdottir, Kristin. Novartis Institutes for Biomedical Research; Estados Unidos
dc.journal.title
JMIR Research Protocols
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.2196/35442
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.researchprotocols.org/2022/8/e35442
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