Evidence map›Paper›PMID 40273338›Full record

ArticleJMIR aging2025

Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study.

Andrew Hooyman, Matt J Huentelman, Matt De Both, Lee Ryan, Kevin Duff, Sydney Y Schaefer

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Andrew HooymanDepartment of Physical Therapy, Chapman University, 9401 Jeronimo Rd, Irvine, CA, 92618, United States, 1 7146287208.ORCID http://orcid.org/0000-0002-8705-3759
Matt J HuentelmanDivision of Early Detection and Prevention, The Translational Genomics Research Institute, Phoenix, AZ, United States.ORCID http://orcid.org/0000-0001-7390-9918
Matt De BothDivision of Early Detection and Prevention, The Translational Genomics Research Institute, Phoenix, AZ, United States.ORCID http://orcid.org/0009-0001-2992-347X
Lee RyanDepartment of Psychology, The University of Arizona, Tucson, AZ, United States.ORCID http://orcid.org/0000-0002-4009-801X
Kevin DuffDepartment of Neurology, Oregon Health and Science University, Portland, OR, United States.ORCID http://orcid.org/0000-0002-9336-2400
Sydney Y SchaeferSchool of Biological Health Systems Engineering, Arizona State University, Tempe, AZ, United States.ORCID http://orcid.org/0000-0002-6976-8419

Funding

Regulatory and Human Study Operations (RHSO) Core CU19AG065169 · NIA · UNIVERSITY OF ARIZONA · PI SCHORK, NICHOLAS JOSEPH · 2021 to 2025
$59.8M
Research Education ComponentP30AG066518 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI Randall L Woltjer · 2020 to 2026
$28.6M
Using an Online Video Game to Predict Functional and Cognitive Decline within the MindCrowd Electronic CohortF32AG071110 · NIA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HOOYMAN, ANDREW · 2021 to 2024
$189k
NIA NIH HHS F32 AG071110NIA NIH HHS P30 AG066518NIA NIH HHS U19 AG065169
6 · The paper itself

Abstract

Background: Previous research has shown that in-lab motor skill acquisition (supervised by an experimenter) is sensitive to biomarkers of Alzheimer disease (AD). However, remote unsupervised screening of AD risk through a skill-based task via the web has the potential to sample a wider and more diverse pool of individuals at scale. Objective: The purpose of this study was to examine a web-based motor skill game ("Super G") and its sensitivity to risk factors of AD (eg, age, sex, APOE ε4 carrier status, and verbal learning deficits). Methods: Emails were sent to 662 previous MindCrowd participants who had agreed to be contacted for future research and have their APOE ε4 carrier status recorded and those who were at least 45 years of age or older. Participants who chose to participate were redirected to the Super G site where they completed the Super G task using their personal computer remotely and unsupervised. Once completed, different Super G variables were derived. Linear and logistic multivariable regression was used to examine the relationship between available AD risk factors (age, sex, APOE ε4 carrier status, and verbal learning) and distinct Super G performance metrics. Results: Fifty-four participants (~8% response rate) from the MindCrowd web-based cohort (mean age of 62.39 years; 39 females; and 23 APOE ε4 carriers) completed 75 trials of Super G. Results show that Super G performance was significantly associated with each of the targeted risk factors. Specifically, slower Super G response time was associated with being an APOE ε4 carrier (odds ratio 0.12, 95% CI 0.02-0.44; P=.006), greater Super G time in target (TinT) was associated with being male (odds ratio 32.03, 95% CI 3.74-1192,61; P=.01), and lower Super G TinT was associated with greater age (β -3.97, 95% CI -6.64 to -1.30; P=.005). Furthermore, a sex-by-TinT interaction demonstrated a differential relationship between Super G TinT and verbal learning depending on sex (βmale:TinT 6.77, 95% CI 0.34-13.19; P=.04). Conclusions: This experiment demonstrated that this web-based game, Super G, has the potential to be a skill-based digital biomarker for screening of AD risk on a large scale with relatively limited resources.

Indexed as

Alzheimer DiseaseMotor SkillsAgedApolipoprotein E4Cohort StudiesCross-Sectional StudiesFemaleHumansMaleMiddle AgedRisk FactorsApolipoprotein E4adultsagingAPOEdigital health technologymotor skillsolder adultsrisk factorssensitivityweb-based assessment

Identifiers

PMID40273338
PMCPMC12045524

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.