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

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS BMC Medical Informatics and Decision Making Pub Date : 2025-01-10 DOI:10.1186/s12911-024-02833-4
Maria Aguiar, Ander Cejudo, Gorka Epelde, Deisy Chaves, Maria Trujillo, Garazi Artola, Unai Ayala, Roberto Bilbao, Itziar Tueros
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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.

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一种在移动医疗应用程序中为没有特定健康状况的用户提供自我数据报告的方法。
背景:以公共卫生或医疗为目的的移动健康(mHealth)应用程序的普及,极大地改变了人类的生活方式,改善了生活方式行为和慢性病管理。本研究的目的是评估移动健康应用程序中游戏化功能的影响,该应用程序包括最常见的行为改变技术类别,用于生活方式数据的自我报告。用户报告的数据可以是手动(即饮食、活动和体重)和自动(Fitbit可穿戴设备)。作为次要目标,本研究旨在探讨在考虑较长研究时间时依从性的差异,并对游戏化效应进行比较分析。方法:在本研究中,通过对两个用户群体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,我们根据应用的两个版本的依从性和系统可用性进行比较分析,这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,对第二组参与者和最佳移动健康应用程序版本进行更长的研究,并分析用户依从性,系统可用性规模和用户反馈。结果:在第一阶段的研究中,结果显示,具有游戏化功能的应用版本获得了更高的依从性,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,被归类为a级。该应用也超出了用户对具有游戏化功能的应用版本的预期约70%。在研究的第二阶段,8周后的依从性为76.25%,试验结束时为58%,移动健康应用程序。同样,对于可穿戴设备,8周后的依从性为74.32%,试验结束时为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下降,达到62.89,排名为c。结论:本研究证明了行为改变技术的游戏化类别在提高整体用户依从性、期望和感知可用性方面的有效性。此外,该结果还提供了最常见的行为改变技术类别对生活方式数据自我报告的影响的定量结果。因此,研究中较长的持续时间表明,在捕获生活方式数据时存在一些局限性,特别是在包括Fitbit等可穿戴设备时。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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