认知障碍老年人认知训练移动应用:应用商店搜索和质量评估。

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Leyi Wu, Jiajuan Pan, Chuwen Dou, An Gu, An Huang, Hong Tao, Xiaoyan Wang, Chen Zhang, Lina Wang
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引用次数: 0

摘要

背景:随着人口老龄化,认知障碍越来越普遍。移动应用程序为提供认知训练干预提供了一个可扩展的平台。然而,它们的质量参差不齐,缺乏严格的评估,因此需要进一步的研究来指导优化,并确保它们在改善认知健康结果方面的有效应用。目的:本研究旨在评价为老年人认知障碍设计的认知训练应用程序的特点和质量。方法:采用预设的检索词和检索标准对谷歌Play Store和Apple App Store进行综合检索,检索时间为2024年7月13日。两名独立评审员使用移动应用评级量表(MARS)对符合条件的应用程序进行质量评估,并通过二次加权kappa (К)评估评价者的可靠性。Kruskal-Wallis测试分析了每个维度在各个子组之间的MARS分数差异,并应用Spearman相关性来检查用户星级评分与总体平均分数之间的关系。结果:共确定了4822个潜在应用程序,其中24个符合资格标准。其中,13款(54%)游戏同时在两个平台上运行,5款(21%)游戏只在bb0 Play Store上运行,6款(25%)游戏只在苹果App Store上运行。值得注意的是,5款(20.8%)应用程序提供了用户定制的培训模块,8款(33%)应用程序有医疗专业人员参与开发。研究者间一致性高(k=0.88;95% ci, 0.80-0.95)。基于MARS维度的全球质量得分范围为2.38至4.13,24个应用程序的平均值(SD)为3.57(0.43),表明质量总体上可以接受。功能维度得分最高,而参与度得分最低。脑HQ和Peak得分在4分以上,被评为良好,而记忆训练师、认知技能训练和银杏记忆和大脑训练得分在3分以下,被评为不足。Spearman相关性显示平均分与应用评分之间无显著关联。结论:目前用于认知障碍老年人的认知训练应用程序表现出中等质量,但有相当大的可变性。在参与和信息方面需要改进。未来的发展应优先考虑提高用户参与度,纳入个性化功能,并让卫生保健专业人员和专家参与进来,以与循证指南保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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