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Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory

机译:用Fittle +系统掌握健康行为的掌握:基于证据的干预和理论

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摘要

We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of these interventions on achieving behavior-change goals. Our approach in the Fittle+ systems rests on the idea that to master the complex fabric of a new healthy lifestyle, one must weave together a new set of healthy habits that over-ride the old unhealthy habits. To achieve these aims, we have developed a series of mHealth platforms that provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that provide additional support to the acquisition and maintenance of healthy habits. We present experimental evidence collected so far for statistically significant improvements in behavior change in eating, exercise, and physical activity for the following scaffolding interventions: guided mastery, teaming, self-affirmation, and implementation intentions. We also present predictive computational ACT-R models of daily individual behavior goal success for data collected in guided mastery and implementation intention studies that address goal-striving and habit formation mechanisms.
机译:我们提出了一系列MHEALTE应用程序和研究作为Fittle +项目的一部分。该研究方案具有(1)的双重目标,将可扩展的证据行为 - 更改干预措施转到MHEALT和评估它们和(2)发展理论上基于的预测模型,以更好地了解这些干预措施对实现行为的影响的动态 - 改变目标。我们在Fittle +系统中的方法依赖于掌握新的健康生活方式的复杂面料的想法,必须一起编织一套新的健康习惯,这些习惯过度乘坐旧的不健康习惯。为实现这些目标,我们开发了一系列MHEATH平台,提供了脚手架干预:行为改变技术和相关的MHECHEATH互动(例如,短信提醒;聊天对话框;用户界面功能;等)为采集提供额外支持维持健康习惯。我们提出了迄今为止收集的实验证据,以便在攻击干预措施的饮食,运动和身体活动方面的统计学意义改善:指导掌握,团队,自我肯定和实施意图。我们还提出了日常单独行为的预测计算ACT-R模型,为引导掌握和实施意向研究中收集的数据进行数据,以解决目标争取和习惯形成机制。

著录项

  • 来源
    《Human-computer interaction》 |2021年第2期|73-106|共34页
  • 作者单位

    Inst Human & Machine Cognit Pensacola FL 32502 USA;

    Palo Alto Res Ctr Palo Alto CA USA;

    Ant Financial Serv Grp Hangzhou Peoples R China;

    Facebook Menlo Pk CA USA;

    Palo Alto Res Ctr Palo Alto CA USA;

    Univ Calif Santa Cruz NIDR Santa Cruz CA 95064 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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