Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods
Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods
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DOI：
10.1016/j.psychsport.2018.06.011
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发表时间：
2019-03-01
影响因子：
3.4
通讯作者：
Smyth, Joshua M.
中科院分区：
文献类型：
--
作者：
Conroy, David E.;Hojjatinia, Sarah;Smyth, Joshua M.
目的：身体活动干预的概念模型主要是基于多运动和少运动的人之间的差异。然而，身体活动是一种动态行为，这种模型对在瞬间水平上调节行为的因素或人们对个人干预尝试的反应并不敏感。我们展示了控制系统工程方法如何应用于开发基于文本信息的密集干预的行为反应的个性化模型。设计与方法：为了验证这种方法的概念，10名成年人在16周的时间里佩戴活动监测器，每天随机接收5条短信。信息内容从三种类型的信息中随机选择，旨在针对(1)与增加身体活动相关的社会认知过程，(2)与减少久坐行为相关的社会认知过程，或(3)与身体活动或久坐行为无关的一般事实。为每个参与者估计了一个动态系统模型，以检查对每种类型的文本消息的响应的大小和时间。结果：模型揭示了不同人之间以及工作日和周末之间对不同信息类型的异质反应。结论：这一概念验证表明，该模型中的参数可用于开发干预交付的个性化算法。更一般地说，这些结果证明了控制系统工程模型在优化身体活动干预方面的潜在效用。
Objectives: The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention.Design & method: To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message.Results: Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends.Conclusions: This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.