Evidence mapPaperPMID 40614261Full record

ArticleJMIR mHealth and uHealth2025

Cognitive Training Mobile Apps for Older Adults With Cognitive Impairment: App Store Search and Quality Evaluation.

Leyi Wu, Jiajuan Pan, Chuwen Dou, An Gu, An Huang, Hong Tao, Xiaoyan Wang, Chen Zhang, Lina Wang

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2025. 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

9 authors.

Leyi WuSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0009-0005-8852-4216
Jiajuan PanSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0009-0004-1085-076X
Chuwen DouSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0009-0007-7277-5744
An GuSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0009-0005-1024-0336
An HuangSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0009-0008-5649-0404
Hong TaoCenter for Whole-Person Research, AdventHealth Whole-Person Research, Orlando, FL, United States.ORCID http://orcid.org/0000-0002-3521-3204
Xiaoyan WangDepartment of General Medicine, Community Health Service Center of Renhuangshan, Huzhou, China.ORCID http://orcid.org/0009-0003-3145-9666
Chen ZhangDepartment of General Medicine, Community Health Service Center of Renhuangshan, Huzhou, China.ORCID http://orcid.org/0009-0001-1050-7169
Lina WangSchool of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Erhuan East Road 759, Longquan Street, Wuxing District, Huzhou, 313000, China, 86 13587278357.ORCID http://orcid.org/0000-0002-8153-7015

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As the population ages, cognitive impairment is becoming increasingly prevalent. Mobile apps offer a scalable platform for delivering cognitive training interventions. However, their variable quality and lack of rigorous evaluation underscore the need for further research to guide optimization and ensure their effective application in improving cognitive health outcomes. Objective: This study aimed to evaluate the characteristics and quality of cognitive training apps designed for older adults with cognitive impairment. Methods: A comprehensive search of the Google Play Store and Apple App Store was conducted using predefined terms and inclusion criteria, with the search completed on July 13, 2024. Eligible apps were assessed for quality by two independent reviewers using the Mobile App Rating Scale (MARS), with interrater reliability evaluated via quadratic weighted kappa (К). The Kruskal-Wallis test analyzed differences in MARS scores across subgroups for each dimension, and Spearman correlation was applied to examine the relationship between user star ratings and overall mean scores. Results: A total of 4822 potential apps were identified, of which 24 met eligibility criteria. Among these, 13 (54%) were available on both platforms, 5 (21%) were exclusive to the Google Play Store, and 6 (25%) to the Apple App Store. Notably, 5 (20.8%) apps offered user-tailored training modules and 8 (33%) involved medical professionals in development. Interrater agreement was high (k=0.88; 95% CI, 0.80-0.95). Global quality scores based on the MARS dimensions ranged from 2.38 to 4.13, with a mean (SD) of 3.57 (0.43) across 24 apps, indicating generally acceptable quality. The functionality dimension received the highest score, while engagement scored the lowest. Brain HQ and Peak had scores above 4 and were rated as good, whereas Memory Trainer, Cognitive Skill Training, and Ginkgo Memory & Brain Training scored below 3 and were rated as insufficient. Spearman correlation showed no significant association between mean score and app rating. Conclusions: Current cognitive training apps for older adults with cognitive impairment demonstrate moderate quality with considerable variability. Improvements are needed in the engagement and information dimensions. Future development should prioritize enhancing user engagement, incorporating personalized features, and involving health care professionals and experts to align with evidence-based guidelines.

Indexed as

Cognitive Behavioral TherapyCognitive DysfunctionMobile ApplicationsAgedCognitive TrainingFemaleHumansMaleReproducibility of Resultscognitive trainingeHealthmobile app rating scalemobile appsolder adults

Identifiers

PMID40614261
PMCPMC12252145

What Socratic holds

Textmetadata
LicenceCC BY
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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.