Detection of Age-Related Cognitive Decline Over Short Time Frames Using Digital Multiday Learning Assessments: Longitudinal Study.
4 affiliations
- 1German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany.
- 2Department of Neurology, Mass General Brigham, Harvard Medical School, 60 Fenwood Road, Boston, MA, United States, 1 617-732-8060.
- 3Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia.
- 4Department of Radiology, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
Abstract
Background
High-resolution digital cognitive tools administered at frequent intervals enable the study of age-related cognitive change at a previously unattainable level of granularity. Computational modeling further enables the simultaneous examination of multiple aspects of cognitive performance.Objective
The objective of this study was to examine whether repeated digital multiday learning assessments can detect age-related cognitive decline over short time frames in cognitively unimpaired older adults and to quantify age-related differences in process-based cognitive markers derived from computational modeling.Methods
A total of 209 cognitively unimpaired older adults (mean age 73.6, SD 8.2 years; n=136, 65.1% female) from the Harvard Aging Brain Study and affiliated studies were administered the Boston Remote Assessment for Neurocognitive Health, which includes the face-name task, a paired associative learning task. Daily median response times for correct responses were used as the primary outcome. A new version of the face-name task was administered across up to 4 testing intervals over a mean of 2.8 (SD 0.4) years. Using computational modeling, we estimated longitudinal changes in peak performance, a practice-adjusted marker of an individual's best expected response time, as an indicator of age-related cognitive decline. We further examined age-related differences in peak performance, stability (within-person variability), learning within and across testing intervals, and between-interval forgetting, with embedded linear regressions for age, sex, years of education, and device type.Results
Peak performance slowed by 0.04 seconds per year (95% credible interval 0.02-0.06; approximately 2% per year), indicating age-related cognitive decline. Higher age (+1-SD) was associated with slower peak performance (+0.19 seconds, 95% credible interval 0.12-0.26), steeper annual slowing in peak performance (+0.036 seconds per year), greater within-person variability (+0.043 seconds), reduced learning across testing intervals, and more forgetting.Conclusions
Our findings indicate that a digital multiday learning paradigm combined with computational modeling of response times sensitively detects age-related cognitive decline over short timescales (<3 years), which are difficult to capture in conventional study designs relying on infrequent assessments. In addition, this approach identifies specific process-based cognitive markers associated with aging, including slower performance, reduced stability, and diminished learning across testing intervals.
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BibTeX
@article{blotenberg2026detection,
title = {{Detection of Age-Related Cognitive Decline Over Short Time Frames Using Digital Multiday Learning Assessments: Longitudinal Study.}},
author = {Iris Blotenberg and Hairin Kim and Daniel Soberanes and Mark A Dubbelman and Lex R Minto and Onyinye J Udeogu and Vivienne Lubbat and Rachel F Buckley and Gad A Marshall and Keith A Johnson and Dorene M Rentz and Reisa A Sperling and Kathryn V Papp and Rebecca E Amariglio},
journal = {JMIR aging},
year = {2026},
month = oct,
doi = {10.2196/100917},
url = {https://europepmc.org/article/MED/42954140},
note = {CC BY},
}
RIS
TY - JOUR
TI - Detection of Age-Related Cognitive Decline Over Short Time Frames Using Digital Multiday Learning Assessments: Longitudinal Study.
AU - Iris Blotenberg
AU - Hairin Kim
AU - Daniel Soberanes
AU - Mark A Dubbelman
AU - Lex R Minto
AU - Onyinye J Udeogu
AU - Vivienne Lubbat
AU - Rachel F Buckley
AU - Gad A Marshall
AU - Keith A Johnson
AU - Dorene M Rentz
AU - Reisa A Sperling
AU - Kathryn V Papp
AU - Rebecca E Amariglio
PY - 2026
DA - 2026/10/27
JO - JMIR aging
DO - 10.2196/100917
UR - https://europepmc.org/article/MED/42954140
AB - Background High-resolution digital cognitive tools administered at frequent intervals enable the study of age-related cognitive change at a previously unattainable level of granularity. Computational modeling further enables the simultaneous examination of multiple aspects of cognitive performance.Objective The objective of this study was to examine whether repeated digital multiday learning assessments can detect age-related cognitive decline over short time frames in cognitively unimpaired older adults and to quantify age-related differences in process-based cognitive markers derived from computational modeling.Methods A total of 209 cognitively unimpaired older adults (mean age 73.6, SD 8.2 years; n=136, 65.1% female) from the Harvard Aging Brain Study and affiliated studies were administered the Boston Remote Assessment for Neurocognitive Health, which includes the face-name task, a paired associative learning task. Daily median response times for correct responses were used as the primary outcome. A new version of the face-name task was administered across up to 4 testing intervals over a mean of 2.8 (SD 0.4) years. Using computational modeling, we estimated longitudinal changes in peak performance, a practice-adjusted marker of an individual's best expected response time, as an indicator of age-related cognitive decline. We further examined age-related differences in peak performance, stability (within-person variability), learning within and across testing intervals, and between-interval forgetting, with embedded linear regressions for age, sex, years of education, and device type.Results Peak performance slowed by 0.04 seconds per year (95% credible interval 0.02-0.06; approximately 2% per year), indicating age-related cognitive decline. Higher age (+1-SD) was associated with slower peak performance (+0.19 seconds, 95% credible interval 0.12-0.26), steeper annual slowing in peak performance (+0.036 seconds per year), greater within-person variability (+0.043 seconds), reduced learning across testing intervals, and more forgetting.Conclusions Our findings indicate that a digital multiday learning paradigm combined with computational modeling of response times sensitively detects age-related cognitive decline over short timescales (<3 years), which are difficult to capture in conventional study designs relying on infrequent assessments. In addition, this approach identifies specific process-based cognitive markers associated with aging, including slower performance, reduced stability, and diminished learning across testing intervals.
SN - 2561-7605
ER -