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Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.
Curcic, Jelena; Vallejo, Vanessa; Sorinas, Jennifer; Sverdlov, Oleksandr; Praestgaard, Jens; Piksa, Mateusz; Deurinck, Mark; Erdemli, Gul; Bügler, Maximilian; Tarnanas, Ioannis; Taptiklis, Nick; Cormack, Francesca; Anker, Rebekka; Massé, Fabien; Souillard-Mandar, William; Intrator, Nathan; Molcho, Lior; Madero, Erica; Bott, Nicholas; Chambers, Mieko; Tamory, Josef; Shulz, Matias; Fernandez, Gerardo; Simpson, William; Robin, Jessica; Snædal, Jón G; Cha, Jang-Ho; Hannesdottir, Kristin.
  • Curcic J; Novartis Institutes for Biomedical Research, Basel, Switzerland.
  • Vallejo V; Novartis Institutes for Biomedical Research, Basel, Switzerland.
  • Sorinas J; Novartis Institutes for Biomedical Research, Basel, Switzerland.
  • Sverdlov O; Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States.
  • Praestgaard J; Novartis Institutes for Biomedical Research, Cambridge, MA, United States.
  • Piksa M; Novartis Institutes for Biomedical Research, Basel, Switzerland.
  • Deurinck M; Novartis Institutes for Biomedical Research, Basel, Switzerland.
  • Erdemli G; Novartis Institutes for Biomedical Research, Cambridge, MA, United States.
  • Bügler M; Altoida Inc, Washington, DC, United States.
  • Tarnanas I; Altoida Inc, Washington, DC, United States.
  • Taptiklis N; Global Brain Health Institute, Trinity College, Dublin, Ireland.
  • Cormack F; Cambridge Cognition Ltd, Cambridge, United Kingdom.
  • Anker R; Cambridge Cognition Ltd, Cambridge, United Kingdom.
  • Massé F; MindMaze SA, Lausanne, Switzerland.
  • Souillard-Mandar W; MindMaze SA, Lausanne, Switzerland.
  • Intrator N; Linus Health, Boston, MA, United States.
  • Molcho L; Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Madero E; Neurosteer Inc, New York, NY, United States.
  • Bott N; Neurosteer Inc, New York, NY, United States.
  • Chambers M; Neurotrack Technologies Inc, Redwood City, CA, United States.
  • Tamory J; Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States.
  • Shulz M; Neurovision Imaging Inc, Sacramento, CA, United States.
  • Fernandez G; Neurovision Imaging Inc, Sacramento, CA, United States.
  • Simpson W; ViewMind Inc, New York, NY, United States.
  • Robin J; ViewMind Inc, New York, NY, United States.
  • Snædal JG; Winterlight Labs, Toronto, ON, Canada.
  • Cha JH; Winterlight Labs, Toronto, ON, Canada.
  • Hannesdottir K; Memory Clinic, Landspitali, Reykjavik, Iceland.
JMIR Res Protoc ; 11(8): e35442, 2022 Aug 10.
Article in English | MEDLINE | ID: covidwho-1987322
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

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: JMIR Res Protoc Year: 2022 Document Type: Article Affiliation country: 35442

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: JMIR Res Protoc Year: 2022 Document Type: Article Affiliation country: 35442