Dong Yuwen, Deshpande Sunil, Rivera Daniel E, Downs Danielle S, Savage Jennifer S
Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.
Exercise Psychology Laboratory, Department of Kinesiology, Penn State University, University Park, PA, USA.
Proc Am Control Conf. 2014 Jun;2014:4198-4203. doi: 10.1109/ACC.2014.6859462.
Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.
控制工程提供了一种系统且高效的方法，用于优化被称为适应性或“即时”行为干预的个性化定制治疗与预防策略的有效性。这些干预措施的性质要求按类别水平分配剂量，先前的工作已使用基于混合逻辑动态（MLD）的混合模型预测控制（HMPC）方案对此进行了处理。然而，涉及序贯决策的适应性行为干预的某些要求在文献中尚未得到全面探讨。本文通过将序贯决策策略的要求表示为混合整数线性约束，提出了对用于HMPC的传统MLD框架的扩展。这通过用户指定的剂量序列表、一次操纵一个输入以及以低于测量采样间隔的时间间隔分配剂量的切换时间策略来实现。为孕期体重增加（GWG）干预开发的一个模型用于说明这些序贯决策策略的生成及其对实施涉及多个组成部分的适应性行为干预的有效性。
