1 University of Auckland, Auckland, New Zealand.
2 Dublin City University, Dublin, Ireland.
Health Educ Behav. 2018 Jun;45(3):331-348. doi: 10.1177/1090198117742438. Epub 2017 Dec 7.
Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals' behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.
很少有促进身体活动 (PA) 的干预措施能够动态适应个体行为的变化。针对行为决定因素的干预措施与更高的效果相关联,并且应该反映出随着时间的推移行为的变化。本文描述了应用两种框架来协助开发一种自适应的基于证据的智能手机干预措施,旨在影响 PA 和久坐行为 (SB)。干预映射用于确定影响 PA 采用和最佳行为改变技术 (BCT) 的决定因素。行为干预技术用于翻译和操作 BCT 及其传递模式。该干预措施基于综合行为改变模型,关注九个决定因素,包含 33 个 BCT,并包括三个主要组件:(1) 通过现有的智能手机应用程序自动捕获日常 PA 和 SB;(2) 根据个体的 PA 和 SB 将其分类为活动概况;(3) 通过概念验证应用程序以动态方式传递行为改变内容。本文举例说明了如何使用两种互补的框架来指导移动健康行为改变计划的开发。这种方法可以指导未来 mHealth 计划的开发。