Feasibility of Digital Memory Assessments in an Unsupervised and Remote Study Setting.

digital cognitive assessment episodic memory participant retention remote and unsupervised cognitive assessment smartphone-based cognitive assessments

Journal

Frontiers in digital health
ISSN: 2673-253X
Titre abrégé: Front Digit Health
Pays: Switzerland
ID NLM: 101771889

Informations de publication

Date de publication:
2022
Historique:
received: 09 03 2022
accepted: 12 04 2022
entrez: 20 6 2022
pubmed: 21 6 2022
medline: 21 6 2022
Statut: epublish

Résumé

Sensitive and frequent digital remote memory assessments via mobile devices hold the promise to facilitate the detection of cognitive impairment and decline. However, in order to be successful at scale, cognitive tests need to be applicable in unsupervised settings and confounding factors need to be understood. This study explored the feasibility of completely unsupervised digital cognitive assessments using three novel memory tasks in a Citizen Science project across Germany. To that end, the study aimed to identify factors associated with stronger participant retention, to examine test-retest reliability and the extent of practice effects, as well as to investigate the influence of uncontrolled settings such as time of day, delay between sessions or screen size on memory performance. A total of 1,407 adults (aged 18-89) participated in the study for up to 12 weeks, completing weekly memory tasks in addition to short questionnaires regarding sleep duration, subjective cognitive complaints as well as cold symptoms. Participation across memory tasks was pseudorandomized such that individuals were assigned to one of three memory paradigms resulting in three otherwise identical sub-studies. One hundred thirty-eight participants contributed to two of the three paradigms. Critically, for each memory task 12 independent parallel test sets were used to minimize effects of repeated testing. First, we observed a mean participant retention time of 44 days, or 4 active test sessions, and 77.5% compliance to the study protocol in an unsupervised setting with no contact between participants and study personnel, payment or feedback. We identified subject-level factors that contributed to higher retention times. Second, we found minor practice effects associated with repeated cognitive testing, and reveal evidence for acceptable-to-good retest reliability of mobile testing. Third, we show that memory performance assessed through repeated digital assessments was strongly associated with age in all paradigms, and individuals with subjectively reported cognitive decline presented lower mnemonic discrimination accuracy compared to non-complaining participants. Finally, we identified design-related factors that need to be incorporated in future studies such as the time delay between test sessions. Our results demonstrate the feasibility of fully unsupervised digital remote memory assessments and identify critical factors to account for in future studies.

Identifiants

pubmed: 35721797
doi: 10.3389/fdgth.2022.892997
pmc: PMC9199443
doi:

Types de publication

Journal Article

Langues

eng

Pagination

892997

Informations de copyright

Copyright © 2022 Berron, Ziegler, Vieweg, Billette, Güsten, Grande, Heneka, Schneider, Teipel, Jessen, Wagner and Düzel.

Déclaration de conflit d'intérêts

DB and ED are co-founders and hold shares of neotiv GmbH, OB is a full-time employee of neotiv GmbH. GZ has been a part time employee at neotiv GmbH from 05/2019 to 10/2019. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

David Berron (D)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
neotiv GmbH, Magdeburg, Germany.

Gabriel Ziegler (G)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
neotiv GmbH, Magdeburg, Germany.
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.

Paula Vieweg (P)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.

Ornella Billette (O)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
neotiv GmbH, Magdeburg, Germany.
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.

Jeremie Güsten (J)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.

Xenia Grande (X)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.

Michael T Heneka (MT)

German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurodegeneration and Geriatric Psychiatry, University of Bonn, Bonn, Germany.

Anja Schneider (A)

German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurodegeneration and Geriatric Psychiatry, University of Bonn, Bonn, Germany.

Stefan Teipel (S)

Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany.
German Center for Neurodegenerative Diseases, Rostock, Germany.

Frank Jessen (F)

German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Psychiatry, University Hospital Cologne, Cologne, Germany.

Michael Wagner (M)

German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurodegeneration and Geriatric Psychiatry, University of Bonn, Bonn, Germany.

Emrah Düzel (E)

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
neotiv GmbH, Magdeburg, Germany.
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.
Institute of Cognitive Neuroscience, University College London, London, United Kingdom.

Classifications MeSH