Evidence map›Paper›PMID 40499520›Full record

ArticleDementia and geriatric cognitive disorders2026

Tablet-Based Assessment of Picture Naming in Prodromal Alzheimer's Disease: An Accessible and Effective Tool for Distinguishing Mild Cognitive Impairment from Normal Aging.

Lauren Seidman, Sara Hyman, Rachel Kenney, Avivit Nsiri, Steven Galetta, Arjun V Masurkar, Laura Balcer

Abstract read
In one paragraph

Article in Dementia and geriatric cognitive disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Lauren SeidmanNew York Medical College, Valhalla, New York, USA.
Sara HymanDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA.
Rachel KenneyDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA.
Avivit NsiriDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA.
Steven GalettaDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA.
Arjun V MasurkarDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA.
Laura BalcerDepartment of Neurology, NYU Grossman School of Medicine, New York, New York, USA, laura.balcer@nyulangone.org.

Funding

Research Education ComponentP30AG066512 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI REBECCA A. BETENSKY · 2020 to 2026
$28.4M
Achieving specificity in imaging neurodegeneration with visible light Optical Coherence TomographyOT2OD038130 · OD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BALCER, LAURA, SRINIVASAN, VIVEK JAY · 2024 to 2025
$4.8M
NIA NIH HHS P30 AG066512NIH HHS OT2 OD038130
6 · The paper itself

Abstract

<p>Introduction: Effective mild cognitive impairment (MCI) screening requires accessible testing. This study compared two tests for distinguishing MCI patients from controls: rapid automatized naming (RAN) for naming speed and low-contrast letter acuity (LCLA) for sensitivity to low-contrast letters.

methodsTwo RAN tasks were used: the Mobile Universal Lexicon Evaluation System (MULES, picture naming) and the Staggered Uneven Number test (SUN, number naming). Both RAN tasks were administered on a tablet and in a paper/pencil format. The tablet format was administered using the Mobile Integrated Cognitive Kit application. LCLA was tested at 2.5% and 1.25% contrast.

resultsSixty-four participants (31 MCI, 34 controls; mean age 73.2 ± 6.8 years) were included. MCI patients were slower than controls for paper/pencil (75.0 vs. 53.6 s, p < 0.001), and tablet MULES (69.0 s vs. 50.2 s, p = 0.01). The paper/pencil SUN showed no significant difference (MCI: 59.5 s vs. controls: 59.9 s, p = 0.07) nor did the tablet SUN (MCI: 59.3 s vs. controls: 55.7 s, p = 0.36). MCI patients had worse performance on LCLA testing at 2.5% contrast (33 letters vs. 36, p = 0.04*) and 1.25% (0 letters vs. 14 letters, p < 0.001). Receiver operating characteristic (ROC) analysis showed similar performance of paper/pencil and tablet MULES in distinguishing MCI from controls (area under the ROC curve [AUC] = 0.77), outperforming both SUN (AUC = 0.63 paper, 0.59 tablet) and LCLA (2.5% contrast: AUC = 0.65, 1.25% contrast: AUC = 0.72).

conclusionThe MULES, in both formats, may be a valuable screening tool for MCI. </p>.

Indexed as

AgingAlzheimer DiseaseCognitive DysfunctionComputers, HandheldNeuropsychological TestsAgedAged, 80 and overFemaleHumansMaleProdromal SymptomsROC CurveAging and cognitionAlzheimer’s dementiaAssessment measuresCognitive agingCognitive impairmentCognitive screening testContrast sensitivityDigital assessmentMild cognitive impairmentRapid automatized naming taskVisual acuity

Identifiers

PMID40499520
PMCPMC12240569

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.