Logo

American Heart Association

  1
  0


Final ID: TP45

Advancements in Digital Cognitive Assessments for Post-Stroke Patients: A Scoping Review

Abstract Body: Introduction: Standardized cognitive assessments such as the Montreal Cognitive Assessment (MOCA) and Mini-Mental State Examination (MMSE) are generally administered using paper-and-pencil methods. Technological advancements have digitized these exams and expanded cognitive testing capabilities in the post-stroke population. Methods: Studies from 2010-2022 were identified from PubMed, Embase, Web of Science, Cumulated Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and Google Scholar to include digital cognitive assessments utilized for acute and chronic ischemic and hemorrhagic stroke patients. The research questions aim to evaluate technical aspects of digital tests, digital tool effectiveness, cognitive domains assessed, study population characteristics, patient usability, and exam feasibility. The methodological framework for this review included research question identification, relevant study collection, final study selection, data extraction, analysis, and summary. Covidence was used to compile relevant studies. Results: 72 articles were included for final analysis. 8 different digital methods (e.g., tablet, computer, virtual reality) were used to assess cognition, with 26 studies creating a new cognitive test and 24 creating a cognitive test based on a standardized exam. Participants were tested in both acute and chronic phases (5 strictly in acute, 55 strictly in subacute/chronic, and 11 in both). 58% of articles assessed ischemic and hemorrhagic stroke participants, and 9 studies only tested aphasia patients. Exams consisted of a variety of cognitive domains, with the majority of studies testing multiple domains (e.g., executive functioning, attention, and visuospatial processing), and some studies testing only one cognitive domain. The average rate of digital test completion was 95%. Validation of the digital tool was compared with a standardized, paper-and-pencil test (e.g., MOCA, MMSE) in 48 articles (67%). An overall positive satisfaction with the digital test was seen in 8 articles that incorporated patient questionnaires. Conclusion: This review suggests that post-stroke digital cognitive assessments are feasible in the acute and post-acute settings across multiple domains similar to the MOCA and MMSE. Enhancements in these tools will expand access to testing and allow for increased identification of post-stroke cognitive impairment.
  • Bateh, Kaitlyn  ( Alabama College of Osteopathic Medicine , Dothan , Georgia , United States )
  • Peterson, Shenita  ( Emory University , Kennesaw , Georgia , United States )
  • Billinger, Sandra  ( KU Medical Center , Roeland Park , Kansas , United States )
  • Nahab, Fadi  ( Emory University , Kennesaw , Georgia , United States )
  • Hu, Xiao  ( Emory University , Kennesaw , Georgia , United States )
  • Saurman, Jessica  ( Emory University , Kennesaw , Georgia , United States )
  • Bartsch, Bria  ( KU Medical Center , Roeland Park , Kansas , United States )
  • Xu, Yuan  ( Emory University , Kennesaw , Georgia , United States )
  • Aboul-nour, Hassan  ( University of Kentucky , Lexington , Kentucky , United States )
  • Hanson, Alene  ( Georgetown University , Washington , District of Columbia , United States )
  • Guan, Emily  ( Johns Hopkins University , Baltimore , Maryland , United States )
  • Su, Kyrsten  ( Claremont McKenna College , Claremont , California , United States )
  • Bateh, Alexander  ( Emory University , Kennesaw , Georgia , United States )
  • Author Disclosures:
    Kaitlyn Bateh: DO NOT have relevant financial relationships | Shenita Peterson: DO NOT have relevant financial relationships | Sandra Billinger: DO NOT have relevant financial relationships | Fadi Nahab: DO NOT have relevant financial relationships | Xiao Hu: No Answer | Jessica Saurman: DO NOT have relevant financial relationships | Bria Bartsch: DO NOT have relevant financial relationships | Yuan Xu: No Answer | Hassan Aboul-Nour: DO NOT have relevant financial relationships | Alene Hanson: DO NOT have relevant financial relationships | Emily Guan: DO NOT have relevant financial relationships | Kyrsten Su: DO NOT have relevant financial relationships | Alexander bateh: DO NOT have relevant financial relationships
Meeting Info:
Session Info:

Brain Health Posters II

Thursday, 02/06/2025 , 07:00PM - 07:30PM

Poster Abstract Session

More abstracts on this topic:
A Novel Imaging Biomarker to Make Precise Outcome Predictions for Patients with Acute Ischemic Stroke

Mallavarapu Monica, Kim Hyun Woo, Iyyangar Ananya, Salazar-marioni Sergio, Yoo Albert, Giancardo Luca, Sheth Sunil, Jeevarajan Jerome

A First-in-Class Humanized Antibody Fragment Targeting Platelet Glycoprotein Ibα: A Comprehensive Preclinical Study of CA1001 for the Treatment of Acute Ischemic Stroke

Xu Xiaohong, Preeti Preeti, Yu Ruoying, Shaykhalishahi Hamed, Zhang Cheng, Shen Chuanbin, Li Bei, Tang Naping, Chang Yan, Xiang Qian, Cui Yimin, Lei Xi, Ni Heyu, Zhu Guangheng, Liu Zhenze, Hu Xudong, Slavkovic Sladjana, Neves Miguel, Ma Wenjing, Xie Huifang

More abstracts from these authors:
Predicting Post-Stroke Cognitive Impairment (PSCI) Using Multiple Machine Learning Approaches

Xie Yuzhang, Nahab Fadi, Ge Yi, Wu Yuhua, Saurman Jessica, Yang Carl, Hu Xiao

Digital Clock Drawing and Recall Enables Rapid Cognitive Screening in Acute Ischemic Stroke Care

Fedorov Alex, Saurman Jessica, Ro Jennifer, Ammar Abdulraheem Ahmed Jumah, Edwards Paula, Loring David, Hu Xiao, Nahab Fadi

You have to be authorized to contact abstract author. Please, Login
Not Available

Readers' Comments

We encourage you to enter the discussion by posting your comments and questions below.

Presenters will be notified of your post so that they can respond as appropriate.

This discussion platform is provided to foster engagement, and simulate conversation and knowledge sharing.

 

You have to be authorized to post a comment. Please, Login or Signup.


   Rate this abstract  (Maximum characters: 500)