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应用程序寻求理论：一项关于癌症生存者移动应用程序中使用健康行为改变理论的研究结果。

Apps seeking theories: results of a study on the use of health behavior change theories in cancer survivorship mobile apps.

机构信息

Texas A&M School of Public Health, Health Promotion and Community Health Sciences, Texas A&M University, College Station, TX, TX, United States.

出版信息

JMIR Mhealth Uhealth. 2015 Mar 27;3(1):e31. doi: 10.2196/mhealth.3861.

DOI:10.2196/mhealth.3861
PMID:25830810
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4393507/
Abstract

BACKGROUND: Thousands of mobile health apps are now available for use on mobile phones for a variety of uses and conditions, including cancer survivorship. Many of these apps appear to deliver health behavior interventions but may fail to consider design considerations based in human computer interface and health behavior change theories. OBJECTIVE: This study is designed to assess the presence of and manner in which health behavior change and health communication theories are applied in mobile phone cancer survivorship apps. METHODS: The research team selected a set of criteria-based health apps for mobile phones and assessed each app using qualitative coding methods to assess the application of health behavior change and communication theories. Each app was assessed using a coding derived from the taxonomy of 26 health behavior change techniques by Abraham and Michie with a few important changes based on the characteristics of mHealth apps that are specific to information processing and human computer interaction such as control theory and feedback systems. RESULTS: A total of 68 mobile phone apps and games built on the iOS and Android platforms were coded, with 65 being unique. Using a Cohen's kappa analysis statistic, the inter-rater reliability for the iOS apps was 86.1 (P<.001) and for the Android apps, 77.4 (P<.001). For the most part, the scores for inclusion of theory-based health behavior change characteristics in the iOS platform cancer survivorship apps were consistently higher than those of the Android platform apps. For personalization and tailoring, 67% of the iOS apps (24/36) had these elements as compared to 38% of the Android apps (12/32). In the area of prompting for intention formation, 67% of the iOS apps (34/36) indicated these elements as compared to 16% (5/32) of the Android apps. CONCLUSIONS: Mobile apps are rapidly emerging as a way to deliver health behavior change interventions that can be tailored or personalized for individuals. As these apps and games continue to evolve and include interactive and adaptive sensors and other forms of dynamic feedback, their content and interventional elements need to be grounded in human computer interface design and health behavior and communication theory and practice.

摘要

背景：现在有数千种移动健康应用程序可用于手机，用于各种用途和情况，包括癌症生存。其中许多应用程序似乎提供了健康行为干预措施，但可能没有考虑基于人机界面和健康行为改变理论的设计因素。 目的：本研究旨在评估移动电话癌症生存应用程序中健康行为改变和健康沟通理论的存在方式和应用方式。 方法：研究团队选择了一组基于标准的移动电话健康应用程序，并使用定性编码方法对每个应用程序进行评估，以评估健康行为改变和沟通理论的应用。每个应用程序都使用源自 Abraham 和 Michie 的 26 种健康行为改变技术分类法的编码进行评估，并根据特定于信息处理和人机交互的 mHealth 应用程序的特征进行了一些重要更改，例如控制理论和反馈系统。 结果：共对基于 iOS 和 Android 平台的 68 个移动电话应用程序和游戏进行了编码，其中 65 个是唯一的。使用 Cohen's kappa 分析统计，对于 iOS 应用程序，评分者间的可靠性为 86.1（P<.001），对于 Android 应用程序，为 77.4（P<.001）。在大多数情况下，iOS 平台癌症生存应用程序中包含基于理论的健康行为改变特征的分数始终高于 Android 平台应用程序的分数。在个性化和定制方面，67%的 iOS 应用程序（24/36）具有这些元素，而 38%的 Android 应用程序（12/32）具有这些元素。在意图形成提示方面，67%的 iOS 应用程序（34/36）表示具有这些元素，而 16%的 Android 应用程序（5/32）具有这些元素。 结论：移动应用程序作为提供健康行为改变干预措施的一种方式正在迅速兴起，可以针对个人进行定制或个性化。随着这些应用程序和游戏继续发展，并包括交互式和自适应传感器以及其他形式的动态反馈，它们的内容和干预元素需要基于人机界面设计以及健康行为和沟通理论与实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/45c15ce608a5/mhealth_v3i1e31_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/f86017bfdb0b/mhealth_v3i1e31_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/f16e19c2c1be/mhealth_v3i1e31_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/3b18643e1751/mhealth_v3i1e31_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/bd6c0beccc06/mhealth_v3i1e31_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/79c07b56d7ce/mhealth_v3i1e31_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/cc3cf0e9d827/mhealth_v3i1e31_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/b8d13a996590/mhealth_v3i1e31_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/2d709aae0cc2/mhealth_v3i1e31_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/45c15ce608a5/mhealth_v3i1e31_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/f86017bfdb0b/mhealth_v3i1e31_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/f16e19c2c1be/mhealth_v3i1e31_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/3b18643e1751/mhealth_v3i1e31_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/bd6c0beccc06/mhealth_v3i1e31_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/79c07b56d7ce/mhealth_v3i1e31_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/cc3cf0e9d827/mhealth_v3i1e31_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/b8d13a996590/mhealth_v3i1e31_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/2d709aae0cc2/mhealth_v3i1e31_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d024/4393507/45c15ce608a5/mhealth_v3i1e31_fig9.jpg

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