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饮食类应用程序的行为改变技术、干预特点及可用性

Behaviour change techniques, intervention features and usability of diet apps.

作者信息

Pavlicek Richard, Cradock Kevin A

机构信息

Department of Health and Nutrition Science, Atlantic Technological University, Ash Lane, Sligo, Ireland.

出版信息

Prev Med Rep. 2025 May 2;54:103085. doi: 10.1016/j.pmedr.2025.103085. eCollection 2025 Jun.

Abstract

OBJECTIVE

Identify the behaviour change techniques and intervention features in popular diet apps.

METHODS

The most popular diet apps were identified from the top 200 ranked apps in the Health & Fitness sections of the App Store and Google Play in September 2023. Selected apps were used for one week and their content analysed for the presence of behaviour change techniques and intervention features. Apps were rated using the Mobile App Rating Scale score.

RESULTS

Thirteen apps with 23 app versions (free & premium) were included. The mean number of behaviour change techniques was 18.3 ± 5.8. The most frequently coded behaviour change techniques were predominantly from the 'Goals and planning' and 'Feedback and monitoring' categories. Apps contained 21.1 ± 6.1 intervention features and scored a mean Mobile App Rating Scale rating of 3.8 ± 0.3. There was a strong, statistically significant correlation ( = 0.69;  = 0.01) between the number of behaviour change techniques and the Mobile App Rating Scale rating. Analysis identified discrepancies between the Mobile App Rating Scale rating and the App Store and Google Play ranking systems.

CONCLUSIONS

Selected apps contained a high number of behaviour change techniques and intervention features. Most included apps lacked an evidence base and safety features. App engagement, optimal use of time, safety features and app ranking systems require further research to provide evidence-based recommendations.

摘要

目的

识别流行饮食应用程序中的行为改变技术和干预特征。

方法

从2023年9月应用商店和谷歌Play健康与健身板块排名前200的应用程序中确定最受欢迎的饮食应用程序。选择的应用程序使用一周,并分析其内容中行为改变技术和干预特征的存在情况。应用程序使用移动应用程序评分量表进行评分。

结果

纳入了13个应用程序,共23个应用程序版本(免费和付费)。行为改变技术的平均数量为18.3 ± 5.8。编码最频繁的行为改变技术主要来自“目标与计划”和“反馈与监测”类别。应用程序包含21.1 ± 6.1个干预特征,移动应用程序评分量表的平均评分为3.8 ± 0.3。行为改变技术的数量与移动应用程序评分量表评分之间存在强的、具有统计学意义的相关性(r = 0.69;p = 0.01)。分析发现移动应用程序评分量表评分与应用商店和谷歌Play排名系统之间存在差异。

结论

所选应用程序包含大量行为改变技术和干预特征。大多数纳入的应用程序缺乏证据基础和安全功能。应用程序的参与度、时间的最佳利用、安全功能和应用程序排名系统需要进一步研究以提供基于证据的建议。

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