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
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
e35442Informations 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.
Références
J Am Geriatr Soc. 1989 Jun;37(6):511-4
pubmed: 2715558
Neuropsychology. 2004 Jul;18(3):504-13
pubmed: 15291728
Arch Gerontol Geriatr. 2019 Jul - Aug;83:114-120
pubmed: 30999126
Alzheimers Res Ther. 2014 Jul 03;6(4):37
pubmed: 25024750
J Clin Exp Neuropsychol. 1998 Jun;20(3):310-9
pubmed: 9845158
Arch Clin Neuropsychol. 2013 Nov;28(7):665-71
pubmed: 23817438
Mach Learn. 2016 Mar;102(3):393-441
pubmed: 27057085
Health Qual Life Outcomes. 2003 Dec 16;1:79
pubmed: 14678568
Alzheimers Dement (N Y). 2018 May 24;4:234-242
pubmed: 29955666
J Neuropsychiatry Clin Neurosci. 2000 Spring;12(2):233-9
pubmed: 11001602
Alzheimers Dement. 2018 Apr;14(4):535-562
pubmed: 29653606
J Psychiatr Res. 1975 Nov;12(3):189-98
pubmed: 1202204
Neuropsychologia. 2011 Jun;49(7):1943-52
pubmed: 21435348
Alzheimers Dement (Amst). 2015 Nov 14;1(4):521-32
pubmed: 27239530
Neuropsychologia. 2012 Jul;50(8):1871-81
pubmed: 22525705
Neurol Sci. 2005 Oct;26(4):243-54
pubmed: 16193251
IEEE Trans Biomed Eng. 2012 Nov;59(11):3162-8
pubmed: 22955865
J Cogn Neurosci. 1996 Nov;8(6):588-602
pubmed: 23961986
Alzheimer Dis Assoc Disord. 1995 Spring;9(1):52-6
pubmed: 7605623
Neural Plast. 2016;2016:8032180
pubmed: 27200192
JMIR Serious Games. 2021 Jun 15;9(2):e26872
pubmed: 34128816
Eur Arch Psychiatry Clin Neurosci. 2012 Nov;262 Suppl 2:S71-7
pubmed: 22986448
Exp Mol Med. 2015 Mar 13;47:e148
pubmed: 25766617
JMIR Ment Health. 2019 Nov 18;6(11):e12814
pubmed: 31738172
Alzheimers Dement. 2011 May;7(3):300-8
pubmed: 21575871
BMJ Open. 2021 Jul 23;11(7):e049967
pubmed: 34301663
Front Psychiatry. 2021 May 06;12:640741
pubmed: 34025472
Front Digit Health. 2021 Oct 15;3:750661
pubmed: 34723243
Nepal J Epidemiol. 2020 Sep 30;10(3):878-887
pubmed: 33042591
Neuropsychology. 2008 Jul;22(4):531-44
pubmed: 18590364
JCI Insight. 2017 Aug 17;2(16):
pubmed: 28814675
Psychol Bull. 2010 May;136(3):375-89
pubmed: 20438143
KDD. 2019 Aug;2019:2547-2555
pubmed: 31799022
Neurology. 1997 May;48(5 Suppl 6):S10-6
pubmed: 9153155
Neurosci Lett. 2009 Jan 2;449(1):1-5
pubmed: 18977410
Lancet. 2005 Dec 17;366(9503):2112-7
pubmed: 16360788
IEEE Trans Biomed Eng. 2013 Jan;60(1):155-8
pubmed: 23268531
J Clin Exp Neuropsychol. 2018 Nov;40(9):917-939
pubmed: 29669461
J Alzheimers Dis. 2015;49(2):407-22
pubmed: 26484921
Neurology. 1993 Nov;43(11):2412-4
pubmed: 8232972
J Biomech. 2010 Nov 16;43(15):2999-3006
pubmed: 20656291
Neuroimage. 2011 Jan;54 Suppl 1:S204-17
pubmed: 20550967