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一种提高无特定健康状况用户对移动健康应用中自我数据报告依从性的方法。

An approach to boost adherence to self-data reporting in mHealth applications for users without specific health conditions.

作者信息

Aguiar Maria, Cejudo Ander, Epelde Gorka, Chaves Deisy, Trujillo Maria, Artola Garazi, Ayala Unai, Bilbao Roberto, Tueros Itziar

机构信息

Multimedia and Computer Vision Group, Universidad del Valle, Cali, Colombia.

Digital Health and Biomedical Technologies, Vicomtech Foundation, Basque Research and Technology Alliance, Donostia-San Sebastián, Spain.

出版信息

BMC Med Inform Decis Mak. 2025 Jan 10;25(1):16. doi: 10.1186/s12911-024-02833-4.

Abstract

BACKGROUND

The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objective of this study is to evaluate the effect of gamification features in a mHealth app that includes the most common categories of behavior change techniques for the self-report of lifestyle data. The data reported by the user can be manual (i.e., diet, activity, and weight) and automatic (Fitbit wearable devices). As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect.

METHODS

In this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. With a first group of users, we perform a comparative analysis in terms of adherence and system usability scale of two versions of the app, both including the most common categories of behavior change techniques but the second version having added gamification features. Then, with a second group of participants and the best mHealth app version, a longer study is carried out and user adherence, the system usability scale and user feedback are analyzed.

RESULTS

In the first stage study, results have shown that the app version with gamification features has achieved a higher adherence, as the percentage of days active was higher for most of the users and the system usability scale score is 80.67, which is categorized as rank A. The app also exceeded the expectations of the users by about 70% for the app version with gamification functionalities. In the second stage of the study, an adherence of 76.25% is reported after 8 weeks and 58% at the end of the pilot for the mHealth app. Similarly, for the wearable device, an adherence of 74.32% is achieved after 8 weeks and 81.08% is obtained at the end of the pilot. We hypothesize that these specific wearable devices have contributed to a decreased system usability scale score, reaching 62.89 which is ranked as C.

CONCLUSION

This study evidences the effectiveness of the gamification category of behavior change techniques in increasing the overall user adherence, expectations, and perceived usability. In addition, the results provide quantitative results on the effect of the most common categories of behavior change techniques for the self-report of lifestyle data. Therefore, a higher duration in the study has shown several limitations when capturing lifestyle data, especially when including wearable devices such as Fitbit.

摘要

背景

用于公共卫生或医疗保健目的的移动健康(mHealth)应用程序的普及极大地改变了人类生活,改善了生活方式行为和慢性病管理。本研究的目的是评估一款mHealth应用程序中游戏化功能的效果,该应用程序包含用于自我报告生活方式数据的最常见行为改变技术类别。用户报告的数据可以是手动的(即饮食、活动和体重)和自动的(Fitbit可穿戴设备)。作为次要目标,这项工作旨在探讨考虑更长研究持续时间时依从性的差异,并对游戏化效果进行比较分析。

方法

在本研究中,通过对两个用户组的分析来评估各种行为改变技术策略的有效性。对于第一组用户,我们对应用程序的两个版本在依从性和系统可用性量表方面进行了比较分析,两个版本都包含最常见的行为改变技术类别,但第二个版本添加了游戏化功能。然后,对于第二组参与者和最佳的mHealth应用程序版本,进行了更长时间的研究,并分析了用户依从性、系统可用性量表和用户反馈。

结果

在第一阶段研究中,结果表明具有游戏化功能的应用程序版本实现了更高的依从性,因为大多数用户的活跃天数百分比更高,系统可用性量表得分为80.67,属于A级。对于具有游戏化功能的应用程序版本,该应用程序也超出了用户预期约70%。在研究的第二阶段,mHealth应用程序在8周后报告的依从性为76.25%,试点结束时为58%。同样,对于可穿戴设备,8周后实现了74.32%的依从性,试点结束时为81.08%。我们假设这些特定的可穿戴设备导致系统可用性量表得分降低,达到62.89,被列为C级。

结论

本研究证明了行为改变技术的游戏化类别在提高总体用户依从性、预期和感知可用性方面的有效性。此外,结果提供了关于用于自我报告生活方式数据的最常见行为改变技术类别的效果的定量结果。因此,在捕获生活方式数据时,更长的研究持续时间显示出一些局限性,特别是当包括Fitbit等可穿戴设备时。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e63/11721516/a11bb7528b6f/12911_2024_2833_Fig1_HTML.jpg

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