• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

德国消费者与受监管的移动健康应用程序评论的评级分析与BERTopic建模

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany.

作者信息

Uncovska Marie, Freitag Bettina, Meister Sven, Fehring Leonard

机构信息

Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany.

Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany.

出版信息

NPJ Digit Med. 2023 Jun 21;6(1):115. doi: 10.1038/s41746-023-00862-3.

DOI:10.1038/s41746-023-00862-3
PMID:37344556
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10285024/
Abstract

Germany introduced prescription-based mobile health (mHealth) apps in October 2020, becoming the first country to offer them fully reimbursed by health insurance. These regulated apps, known as DiGAs, undergo a rigorous approval process similar to pharmaceuticals, including data protection measures and sometimes clinical trials. This study compares the user experience of DiGAs with non-prescription mHealth apps in Germany, analyzing both average app store ratings and written reviews. Our study pioneers the use of BERTopic for sentiment analysis and topic modeling in the mHealth research domain. The dataset comprises 15 DiGAs and 50 comparable apps, totaling 17,588 German-language reviews. Results reveal that DiGAs receive higher contemporary ratings than non-regulated apps (Android: 3.82 vs. 3.77; iOS: 3.78 vs. 3.53; p < 0.01; non-parametric Mann-Whitney-Wilcoxon test). Key factors contributing to positive user experience with DiGAs are customer service and personalization (15%) and ease of use (13%). However, challenges for DiGAs include software bugs (24%) and a cumbersome registration process (20%). Negative user reviews highlight concerns about therapy effectiveness (11%). Excessive pricing is the main concern for the non-regulated group (27%). Data privacy and security receive limited attention from users (DiGAs: 0.5%; comparators: 2%). In conclusion, DiGAs are generally perceived positively based on ratings and sentiment analysis of reviews. However, addressing pricing concerns in the non-regulated mHealth sector is crucial. Integrating user experience evaluation into the review process could improve adherence and health outcomes.

摘要

德国于2020年10月推出了基于处方的移动健康(mHealth)应用程序,成为首个提供由医疗保险全额报销的此类应用程序的国家。这些受监管的应用程序被称为数字健康应用程序(DiGAs),要经过类似于药品的严格审批流程,包括数据保护措施,有时还包括临床试验。本研究比较了德国数字健康应用程序与非处方移动健康应用程序的用户体验,分析了应用商店的平均评分和书面评论。我们的研究率先在移动健康研究领域使用BERTopic进行情感分析和主题建模。数据集包括15个数字健康应用程序和50个可比应用程序,共有17588条德语评论。结果显示,数字健康应用程序的当代评分高于非受监管应用程序(安卓系统:3.82对3.77;iOS:3.78对3.53;p<0.01;非参数曼-惠特尼-威尔科克森检验)。数字健康应用程序带来积极用户体验的关键因素是客户服务和个性化(15%)以及易用性(13%)。然而,数字健康应用程序面临的挑战包括软件漏洞(24%)和繁琐的注册流程(20%)。负面用户评论突出了对治疗效果的担忧(11%)。价格过高是非受监管组的主要担忧(27%)。数据隐私和安全受到用户的关注有限(数字健康应用程序:0.5%;比较对象:2%)。总之,基于评分和评论的情感分析,数字健康应用程序总体上得到了积极评价。然而,解决非受监管移动健康领域的价格担忧至关重要。将用户体验评估纳入审查过程可以提高依从性和健康结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/ae37e364760c/41746_2023_862_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/80676bab3946/41746_2023_862_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/d7d93b47fb7b/41746_2023_862_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/7a897160bf5b/41746_2023_862_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/36c49fa5e1a3/41746_2023_862_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/f0bee45ed89d/41746_2023_862_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/eb568e91a966/41746_2023_862_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/ae37e364760c/41746_2023_862_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/80676bab3946/41746_2023_862_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/d7d93b47fb7b/41746_2023_862_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/7a897160bf5b/41746_2023_862_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/36c49fa5e1a3/41746_2023_862_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/f0bee45ed89d/41746_2023_862_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/eb568e91a966/41746_2023_862_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6c/10285024/ae37e364760c/41746_2023_862_Fig7_HTML.jpg

相似文献

1
Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany.德国消费者与受监管的移动健康应用程序评论的评级分析与BERTopic建模
NPJ Digit Med. 2023 Jun 21;6(1):115. doi: 10.1038/s41746-023-00862-3.
2
Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.德国患者对处方和全额报销的移动健康应用程序的接受度:基于 UTAUT2 的在线调查研究。
J Med Syst. 2023 Jan 27;47(1):14. doi: 10.1007/s10916-023-01910-x.
3
Digital Health Applications (DiGAs) on a Fast Track: Insights From a Data-Driven Analysis of Prescribable Digital Therapeutics in Germany From 2020 to Mid-2024.数字健康应用(DiGAs)快速发展:2020 年至 2024 年年中德国可处方数字治疗药物的数据分析洞察。
J Med Internet Res. 2024 Aug 29;26:e59013. doi: 10.2196/59013.
4
Evaluating Asthma Mobile Apps to Improve Asthma Self-Management: User Ratings and Sentiment Analysis of Publicly Available Apps.评估哮喘移动应用程序以改善哮喘自我管理:公共可用应用程序的用户评分和情绪分析。
JMIR Mhealth Uhealth. 2020 Oct 29;8(10):e15076. doi: 10.2196/15076.
5
mHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis.用于心理健康筛查与诊断的移动健康解决方案:基于情感和主题分析的应用程序用户观点综述
Front Psychiatry. 2022 Apr 27;13:857304. doi: 10.3389/fpsyt.2022.857304. eCollection 2022.
6
A large scale analysis of mHealth app user reviews.移动健康应用程序用户评论的大规模分析。
Empir Softw Eng. 2022;27(7):196. doi: 10.1007/s10664-022-10222-6. Epub 2022 Oct 12.
7
Exploring the Far Side of Mobile Health: Information Security and Privacy of Mobile Health Apps on iOS and Android.探索移动健康的另一面:iOS 和 Android 上移动健康应用的信息安全和隐私。
JMIR Mhealth Uhealth. 2015 Jan 19;3(1):e8. doi: 10.2196/mhealth.3672.
8
User Reviews of Depression App Features: Sentiment Analysis.抑郁症应用程序功能的用户评价:情感分析。
JMIR Form Res. 2021 Dec 14;5(12):e17062. doi: 10.2196/17062.
9
Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality.流行的循证商业心理健康应用程序:参与度、功能、美学和信息质量分析。
JMIR Mhealth Uhealth. 2021 Jul 14;9(7):e29689. doi: 10.2196/29689.
10
Health Internet Technology for Chronic Conditions: Review of Diabetes Management Apps.慢性病的健康互联网技术:糖尿病管理应用程序综述
JMIR Diabetes. 2021 Aug 31;6(3):e17431. doi: 10.2196/17431.

引用本文的文献

1
[Digital health applications for depressive disorders in Germany : A narrative review of the evidence and integration into treatment].德国抑郁症的数字健康应用:证据的叙述性综述及纳入治疗
Nervenarzt. 2025 Aug 13. doi: 10.1007/s00115-025-01879-7.
2
Employee Preference and Use of Employee Mental Health Programs: Mixed Methods Study.员工对员工心理健康项目的偏好与使用情况:混合方法研究
JMIR Hum Factors. 2025 May 5;12:e65750. doi: 10.2196/65750.
3
Artificial intelligence tools in supporting healthcare professionals for tailored patient care.

本文引用的文献

1
Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.德国患者对处方和全额报销的移动健康应用程序的接受度:基于 UTAUT2 的在线调查研究。
J Med Syst. 2023 Jan 27;47(1):14. doi: 10.1007/s10916-023-01910-x.
2
A large scale analysis of mHealth app user reviews.移动健康应用程序用户评论的大规模分析。
Empir Softw Eng. 2022;27(7):196. doi: 10.1007/s10664-022-10222-6. Epub 2022 Oct 12.
3
German Mobile Apps for Patients With Psoriasis: Systematic Search and Evaluation.德国面向银屑病患者的移动应用程序:系统检索与评估。
支持医疗保健专业人员进行个性化患者护理的人工智能工具。
NPJ Digit Med. 2025 Apr 16;8(1):210. doi: 10.1038/s41746-025-01604-3.
4
From diabetes care to prevention: review of prediabetes apps in the DACH region.从糖尿病护理到预防:对DACH地区糖尿病前期应用程序的综述。
Mhealth. 2025 Jan 17;11:8. doi: 10.21037/mhealth-24-57. eCollection 2025.
5
Think-Aloud Testing of a Companion App for Colonoscopy Examinations: Usability Study.结肠镜检查辅助应用程序的有声思维测试:可用性研究
JMIR Hum Factors. 2025 Feb 12;12:e67043. doi: 10.2196/67043.
6
Mobile Applications for Longitudinal Data Collection: Web-based Survey Study of Former Intensive Care Patients.用于纵向数据收集的移动应用程序:对 former 重症监护患者的基于网络的调查研究 (注:这里“former”不太明确准确意思,可能是“既往的”之类含义,具体结合上下文理解)
J Med Syst. 2025 Jan 31;49(1):18. doi: 10.1007/s10916-025-02151-w.
7
Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation.使用马尔可夫队列模拟对德国用于抑郁症的移动健康应用程序进行成本效益分析。
NPJ Digit Med. 2024 Nov 17;7(1):321. doi: 10.1038/s41746-024-01324-0.
8
A transparent and standardized performance measurement platform is needed for on-prescription digital health apps to enable ongoing performance monitoring.处方数字健康应用程序需要一个透明且标准化的性能测量平台,以实现持续的性能监测。
PLOS Digit Health. 2024 Nov 15;3(11):e0000656. doi: 10.1371/journal.pdig.0000656. eCollection 2024 Nov.
9
Topic identification and content analysis of internet medical policies under the background of Healthy China 2030.“健康中国 2030”背景下互联网医疗政策的主题识别与内容分析。
Health Res Policy Syst. 2024 Sep 30;22(1):132. doi: 10.1186/s12961-024-01226-3.
10
Exploring public opinion on health effects of prepared dishes in China through social media comments.通过社交媒体评论探索中国公众对预制菜健康影响的看法。
Front Public Health. 2024 Sep 12;12:1424690. doi: 10.3389/fpubh.2024.1424690. eCollection 2024.
JMIR Mhealth Uhealth. 2022 May 26;10(5):e34017. doi: 10.2196/34017.
4
A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts.LDA、NMF、Top2Vec和BERTopic用于揭秘推特帖子的主题建模比较
Front Sociol. 2022 May 6;7:886498. doi: 10.3389/fsoc.2022.886498. eCollection 2022.
5
The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model-Based Content Analysis.印度肥胖相关移动健康应用的质量:基于 PRECEDE-PROCEED 模型的内容分析。
JMIR Mhealth Uhealth. 2022 May 11;10(5):e15719. doi: 10.2196/15719.
6
Review and Analysis of German Mobile Apps for Inflammatory Bowel Disease Management Using the Mobile Application Rating Scale: Systematic Search in App Stores and Content Analysis.使用移动应用程序评级量表对德国炎症性肠病管理的移动应用程序进行回顾和分析:应用商店的系统搜索和内容分析。
JMIR Mhealth Uhealth. 2022 May 3;10(5):e31102. doi: 10.2196/31102.
7
Attitudes Toward Mobile Apps for Pandemic Research Among Smartphone Users in Germany: National Survey.德国智能手机用户对大流行研究移动应用程序的态度:全国性调查。
JMIR Mhealth Uhealth. 2022 Jan 24;10(1):e31857. doi: 10.2196/31857.
8
Physicians' Attitudes Toward Prescribable mHealth Apps and Implications for Adoption in Germany: Mixed Methods Study.医生对可开方的移动医疗应用程序的态度及其在德国采用的影响:混合方法研究。
JMIR Mhealth Uhealth. 2021 Nov 23;9(11):e33012. doi: 10.2196/33012.
9
The Role of Physicians in Digitalizing Health Care Provision: Web-Based Survey Study.医生在医疗保健数字化提供中的作用:基于网络的调查研究。
JMIR Med Inform. 2021 Nov 11;9(11):e31527. doi: 10.2196/31527.
10
User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling.用户对饮食追踪应用程序的看法:评论内容分析与主题建模
J Med Internet Res. 2021 Apr 22;23(4):e25160. doi: 10.2196/25160.