Evidence map›Paper›PMID 40359928›Full record

ArticleGerontology2025

A 20-s Video-Based Assessment of Cognitive Frailty: Results from a Cohort Study within the Precision Aging Network.

Bijan Najafi, Myeounggon Lee, Mohammad Dehghan Rouzi, J Ray Runyon, Esther M Sternberg, Bonnie J LaFleur

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In one paragraph

Article in Gerontology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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

6 authors.

Bijan NajafiCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, Geffen College of Medicine, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Myeounggon LeeCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, Geffen College of Medicine, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Mohammad Dehghan RouziCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, Geffen College of Medicine, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
J Ray RunyonAndrew Weil Center for Integrative Medicine, University of Arizona, COM, Tucson, Arizona, USA.
Esther M SternbergAndrew Weil Center for Integrative Medicine, University of Arizona, COM, Tucson, Arizona, USA.
Bonnie J LaFleurR. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona, USA.

Funding

Regulatory and Human Study Operations (RHSO) Core CU19AG065169 · NIA · UNIVERSITY OF ARIZONA · PI HUENTELMAN, MATT · 2021 to 2025
$59.8M
NIA NIH HHS U19 AG065169
6 · The paper itself

Abstract

introductionCognitive frailty, the concurrent presence of mild cognitive impairment and physical frailty, poses a significant risk for adverse outcomes in older adults. Traditional assessments that rely on extensive walking tests or specialized equipment are impractical for routine or remote evaluations. This study evaluated a 20-s video-based Upper Frailty Meter (vFM) test, incorporating dual-task conditions, as a feasible tool for identifying cognitive frailty.

methodsData from 413 participants aged 50-79 years in the Healthy Minds for Life cohort were analyzed across four sites: the University of Arizona, Johns Hopkins University, Emory University, and the University of Miami. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA), whereas frailty indices were derived from the vFM test. Participants performed repetitive elbow flexion extension under single-task (physical task only) and dual-task (physical task with concurrent cognitive exercise) conditions. Frailty phenotypes, including slowness, weakness, and exhaustion, were quantified using AI-based video kinematic analysis. Logistic regression and receiver operating characteristic (ROC) analyses evaluated the model's predictive accuracy for cognitive frailty.

resultsParticipants classified as cognitive frailty group (n = 53, 12.8%) demonstrated significantly higher frailty index scores compared to robust individuals (p < 0.001). Among all vFM-derived parameters, the dual-task slowness phenotype demonstrated the strongest correlation with MoCA scores (r = -0.282, p < 0.001) and emerged as the most predictive single marker for distinguishing the cognitive frailty group, demonstrating high clinical applicability (area under the curve [AUC] = 0.87). Combining single-task and dual-task metrics further enhanced predictive accuracy (AUC = 0.91), achieving sensitivity and specificity rates exceeding 85%. This combined approach significantly differentiated cognitive frailty from robust status, outperforming models based on age alone or single-task metrics.

conclusionThe 20-s vFM test offers a practical, noninvasive, easy-to-implement, and accessible solution for objectively evaluating cognitive frailty, demonstrating high predictive accuracy in distinguishing at-risk individuals. Its integration into telehealth platforms could enhance early detection and enable timely interventions, promoting healthier aging trajectories. Further longitudinal studies are recommended to validate its utility in tracking cognitive and physical decline over time.

Indexed as

Cognitive DysfunctionFrailtyGeriatric AssessmentAgedCognitionCohort StudiesFemaleFrail ElderlyHumansMaleMiddle AgedROC CurveVideo RecordingCognitive frailtyDigital healthHealthy agingPrecision agingTelehealth

Identifiers

PMID40359928
PMCPMC12277066

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Registered trials

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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 OpenQuestion graph.