Evidence map›Paper›PMID 38970987›Full record

ArticleArtificial intelligence in medicine2024

Vision-based estimation of fatigue and engagement in cognitive training sessions.

Yanchen Wang, Adam Turnbull, Yunlong Xu, Kathi Heffner, Feng Vankee Lin, Ehsan Adeli

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Artificial intelligence in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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.

Yanchen WangDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Adam TurnbullDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Yunlong XuDepartment of Neurobiology, University of Chicago, Chicago, IL, USA.
Kathi HeffnerSchool of Nursing, University of Rochester, Rochester, NY, USA.
Feng Vankee LinDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Ehsan AdeliDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA. Electronic address: eadeli@stanford.edu.

Funding

Targeting Autonomic Flexibility to Enhance Cognitive Training Outcomes in Older Adults with Mild Cognitive ImpairmentR01NR015452 · NINR · UNIVERSITY OF ROCHESTER · PI HEFFNER, KATHI L, LIN, FENG VANKEE · 2015 to 2024
$4.4M
vmPFC's role in adherence to cognitive trainingR61AG081723 · NIA · STANFORD UNIVERSITY · PI HEFFNER, KATHI L, LIN, FENG VANKEE · 2023 to 2024
$988k
A facial expression-based personalization engine (FPE) for monitoring and modulating real-time effective engagement in cognitive training in older adults at risk for AD/ADRDR61AG084471 · NIA · STANFORD UNIVERSITY · PI ADELI, EHSAN, LIN, FENG VANKEE · 2023 to 2024
$702k
NIA NIH HHS R61 AG081723NIA NIH HHS R61 AG084471NINR NIH HHS R01 NR015452
6 · The paper itself

Abstract

Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. The effectiveness of CCT is often affected by a lack of effective engagement. Mental fatigue is a the primary factor for compromising effective engagement in CCT, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can constantly monitor and reliably detect mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment using their video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy (79.58%) and precision (0.82) compared to the prior models for binary and multi-class classification of mental fatigue. We also validated our model by significantly relating to reaction time across CCT tasks (Waldχ

Indexed as

Cognitive DysfunctionCognitive TrainingMental FatigueAgedAged, 80 and overCognitionFemaleHumansMaleVideo RecordingCognitive trainingComputer visionDisengagementFacial gesturesFatigue detection

Identifiers

PMID38970987
PMCPMC11305905

What Socratic holds

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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.