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

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, 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

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.

International registered report identifier (irrid): DERR1-10.2196/35442.

Keywords: Alzheimer disease; brain amyloid; clinical trial design; cognition; digital endpoints; methodology study; mobile phone.

Conflict of interest statement

Conflicts of Interest: JC, VV, JS, OS, JP, MP, MD, GE, J-HC, and KH are employees of Novartis. MB and IT are employees of Altoida Inc, NT and FC are employees of Cambridge Cognition, RA and FM are employees of MindMaze SA, WS-M is an advisor for Linus Health Inc, NI and LM are employees of Neurosteer Inc, EM is an employee of Neurotrack Technologies Inc, NB was an employee of Neurotrack Technologies Inc, MC and JT are employees of NeuroVision Imaging Inc, MS and GF are employees of ViewMind Inc, and BS and JR are employees of Winterlight Labs.

©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.

Figures

Figure 1
Figure 1
A road map to advance digital end points within the drug development process.
Figure 2
Figure 2
Method for Evaluating Digital Endpoints in Alzheimer Disease study design.
Figure 3
Figure 3
Order of assessments at each visit of the MEDIA study. CDR: Clinical Dementia Rating scale; ECog: Everyday Cognition scale; FACIT: Functional Assessment of Chronic Illness Therapy; KSS: Karolinska Sleepiness Scale; MEDIA: Method for Evaluating Digital Endpoints in Alzheimer Disease; MMSE: Mini-Mental State Examination; NPI-Q: Neuropsychiatric Inventory–Questionnaire; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status.
Figure 4
Figure 4
All technologies included in the MEDIA study. EEG: electroencephalogram; MEDIA: Method for Evaluating Digital Endpoints in Alzheimer Disease; VR: virtual reality.
Figure 5
Figure 5
Task flow during dual tasking paradigm testing.
Figure 6
Figure 6
Study progress in Iceland during the worldwide COVID-19 pandemic.

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