Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

Alzheimer disease brain amyloid clinical trial design cognition digital endpoints methodology study mobile phone

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
10 Aug 2022
Historique:
received: 03 12 2021
accepted: 13 06 2022
revised: 31 05 2022
entrez: 10 8 2022
pubmed: 11 8 2022
medline: 11 8 2022
Statut: epublish

Résumé

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. 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. 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. 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. 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. DERR1-10.2196/35442.

Sections du résumé

BACKGROUND 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 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 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 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 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.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/35442.

Identifiants

pubmed: 35947423
pii: v11i8e35442
doi: 10.2196/35442
pmc: PMC9403829
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e35442

Informations de copyright

©Jelena Curcic, Vanessa Vallejo, Jennifer Sorinas, Oleksandr Sverdlov, Jens Praestgaard, Mateusz Piksa, Mark Deurinck, Gul Erdemli, Maximilian Bügler, Ioannis Tarnanas, Nick Taptiklis, Francesca Cormack, Rebekka Anker, Fabien Massé, William Souillard-Mandar, Nathan Intrator, Lior Molcho, Erica Madero, Nicholas Bott, Mieko Chambers, Josef Tamory, Matias Shulz, Gerardo Fernandez, William Simpson, Jessica Robin, Jón G Snædal, Jang-Ho Cha, Kristin Hannesdottir. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 10.08.2022.

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Auteurs

Jelena Curcic (J)

Novartis Institutes for Biomedical Research, Basel, Switzerland.

Vanessa Vallejo (V)

Novartis Institutes for Biomedical Research, Basel, Switzerland.

Jennifer Sorinas (J)

Novartis Institutes for Biomedical Research, Basel, Switzerland.

Oleksandr Sverdlov (O)

Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States.

Jens Praestgaard (J)

Novartis Institutes for Biomedical Research, Cambridge, MA, United States.

Mateusz Piksa (M)

Novartis Institutes for Biomedical Research, Basel, Switzerland.

Mark Deurinck (M)

Novartis Institutes for Biomedical Research, Basel, Switzerland.

Gul Erdemli (G)

Novartis Institutes for Biomedical Research, Cambridge, MA, United States.

Maximilian Bügler (M)

Altoida Inc, Washington, DC, United States.

Ioannis Tarnanas (I)

Altoida Inc, Washington, DC, United States.
Global Brain Health Institute, Trinity College, Dublin, Ireland.

Nick Taptiklis (N)

Cambridge Cognition Ltd, Cambridge, United Kingdom.

Francesca Cormack (F)

Cambridge Cognition Ltd, Cambridge, United Kingdom.

Rebekka Anker (R)

MindMaze SA, Lausanne, Switzerland.

Fabien Massé (F)

MindMaze SA, Lausanne, Switzerland.

William Souillard-Mandar (W)

Linus Health, Boston, MA, United States.
Massachusetts Institute of Technology, Cambridge, MA, United States.

Nathan Intrator (N)

Neurosteer Inc, New York, NY, United States.

Lior Molcho (L)

Neurosteer Inc, New York, NY, United States.

Erica Madero (E)

Neurotrack Technologies Inc, Redwood City, CA, United States.

Nicholas Bott (N)

Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States.

Mieko Chambers (M)

Neurovision Imaging Inc, Sacramento, CA, United States.

Josef Tamory (J)

Neurovision Imaging Inc, Sacramento, CA, United States.

Matias Shulz (M)

ViewMind Inc, New York, NY, United States.

Gerardo Fernandez (G)

ViewMind Inc, New York, NY, United States.

William Simpson (W)

Winterlight Labs, Toronto, ON, Canada.

Jessica Robin (J)

Winterlight Labs, Toronto, ON, Canada.

Jón G Snædal (JG)

Memory Clinic, Landspitali, Reykjavik, Iceland.

Jang-Ho Cha (JH)

Novartis Institutes for Biomedical Research, Cambridge, MA, United States.

Kristin Hannesdottir (K)

Novartis Institutes for Biomedical Research, Cambridge, MA, United States.

Classifications MeSH