Evidence map›Paper›PMID 41078609›Full record

ArticleDigital health

Self-administered lexical retrieval therapy app in post-stroke aphasia: A pilot study evaluating feasibility, usability, and effectiveness.

SaeByeol Kim, Minjung Kim, Sooyoun Cho, Jungwan Kim, Jinwoo Kim, Yuyoung Kim

Abstract read
In one paragraph

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

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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

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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.

SaeByeol KimHuman Computer Interaction Lab, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0000-6245-9640
Minjung KimHuman Computer Interaction Lab, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0007-9727-9376
Sooyoun ChoHuman Computer Interaction Lab, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0009-3653-6847
Jungwan KimDepartment of Speech and Language Pathology, Daegu University, Gyeongsan, Republic of Korea.
Jinwoo KimHuman Computer Interaction Lab, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-0162-5446
Yuyoung KimHuman Computer Interaction Lab, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-2309-8058

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Post-stroke aphasia impairs language abilities. Although digital therapy apps offer accessible self-administered alternatives, the effectiveness of effortful lexical retrieval in such therapies remains unclear. Objective: This study aimed to evaluate the feasibility, usability, and preliminary effectiveness of a tablet-based lexical retrieval therapy app designed to support effortful lexical retrieval in individuals with post-stroke aphasia. Methods: Patients with post-stroke aphasia were randomly assigned to receive lexical retrieval therapy app or workbook-based cognitive exercises for two weeks. Feasibility was assessed through adherence and engagement and usability through the system usability scale (SUS). Preliminary effectiveness of the proposed method was measured by improvements in retrieval performance and the Boston Naming Test (BNT). Results: The study included 17 participants, with the intervention group (9/17) showing an 88.9% retention rate. They showed significantly higher post-intervention lexical retrieval scores than the control group (F Conclusions: This study provides preliminary evidence that a self-administered app using effortful therapy can improve lexical retrieval in post-stroke aphasia, highlighting its potential as an accessible alternative to clinician-led interventions. Future research should explore long-term adherence, adaptive strategies, and their lasting therapeutic effects.

Indexed as

Aphasiadigital therapyeffortful learningeffortful word retrievalnamingstroke

Identifiers

PMID41078609
PMCPMC12508555

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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.