Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

背景：虽然一般认为强化计划对行为改变很有价值，但17- 19维护，20-22和坚持，23，24仍然需要更多的研究来更好地理解 这些过程的行动机制(MOA)，以便更强大的数字卫生干预可以 有系统地、有规模地将它们结合起来。持续调整支持的父级R01方法 包括两种状态：依赖于连续增援计划的启动状态和 转换为使用可变配筋计划的维护状态。主要用途： 通过计算，系统地研究加固计划的动态MOA，对行为变化的影响 建模和严格的二次分析。假设：我们假设1)具体的“黑匣子” 动态模型可以确定关键的测量社会认知理论(SCT)结构，如自我效能感，即 对理解行为维护很重要；2)灰箱/半物理模型的使用，包括 增强进度表到模型中，将解释更大比例的差异，以及3)按需建模 同时摄动随机逼近(MOD-SPSA)方法将识别最优模型 结构，以及可调参数的估计方法，更好地符合非线性假设。方法： 我们将在之前经过验证的SCT动态模型的基础上构建，该模型是父R01的基础，但 添加了对模型结构中包含的钢筋明细表的关键见解。然后, 使用从母公司R01临床试验的前100名参与者产生的数据(请参阅我们的研究战略 确保试验完整性的方法)，我们将进行“黑箱”自回归动态模型，这确实 不包含领域先验知识，节省SCT变量选择。接下来，我们将进行灰箱建模， 它非常类似于动态结构方程模型，因为它将先前的领域知识结合到 数学模型。灰箱建模的步骤/天的百分比方差的增加是指示性的 关于强化学习的MOA的先前领域知识的附加值，因此是对这一点的稳健测试 动态MOA。最后，我们将使用MoD-SPSA25作为识别最佳功能、模型结构和 估计方法中的参数可调，以检查潜在的非线性相互作用和关系。 影响：这项研究具有许多协同效益，包括：1)它将产生严格的科学 更好地理解MOA、增援计划和行为维护的证据；2)它将 制定关键的新方法，动态地实施不同的强化计划，以培养行为 通过数字健康干预进行维护；以及3)对于父母R01，本研究将允许 以及需要改进的技术，特别强调改善行为维护。因此，这一点 补充保持在原家长奖励的范围内，并将最大限度地发挥知识的影响 特别是对行为理论测试的行为维护提出了更高的要求。

Background: While reinforcement schedules are, in general, understood to be valuable for behavioral change,17- 19 maintenance,20-22 and adherence,23,24 there is still much more research needed to better understand the mechanisms of action (MoA) for these processes, such that more robust digital health interventions can incorporate them, systemically and at scale. The Parent R01 approach for continuously adapting support includes two states: the initiation state in which it relies on a continuous reinforcement schedule and a maintenance state in which it shifts to the use of a variable reinforcement schedule. Primary purpose: To systematically study the dynamic MoA of reinforcement schedules, on behavior change, via computational modeling and rigorous secondary analyses. Hypotheses: We hypothesize that 1) idiographic “black-box” dynamical models can identify key measured social cognitive theory (SCT) constructs like self-efficacy, that are important to understanding behavioral maintenance; 2) the use of grey-box/semi-physical models, incorporating reinforcement schedules into models, will explain a larger portion of variance, and 3) Model-on-Demand with Simultaneous Perturbation Stochastic Approximation (MoD-SPSA) approaches will identify optimal model structure, and adjustable parameters in the estimation method that better fit non-linear assumptions. Methods: We will build on our previously validated SCT dynamical model, which is foundational to the Parent R01, but with added incorporation of key insights about reinforcement schedules incorporated into the model structures. Then, using the data generated from first 100 participants of the Parent R01 clinical trial (see research strategy for our approach to ensure trial integrity), we will conduct “black-box” auto-regressive dynamical models, which does not incorporate prior domain knowledge, save SCT variable selection. Next, we will conduct “grey-box” modeling, which is much like a dynamical structural equation model in that it incorporates prior domain knowledge into the mathematical model. Increased percent variance explained of steps/day of the grey-box modeling is indicative of the added value of prior domain knowledge about the MoA of reinforcement learning, thus a robust test of this dynamic MoA. Finally, we will use MoD-SPSA25 as an aid for identifying optimal features, model structure, and adjustable parameters in the estimation method, to examine potential nonlinear interactions and relationships. Implications: This research has a number of synergistic benefits including: 1) it will generate rigorous scientific evidence for better understanding the MoA, reinforcement schedules, for behavioral maintenance; 2) it will produce key novel ways to operationalize, dynamically, different reinforcement schedules for fostering behavioral maintenance via digital health interventions; and 3) for the Parent R01, this research will allow the approaches and techniques to be refined with a specific emphasis on improving behavior maintenance. Thus, this supplement remains within the original scope of the parent award and will maximize the impact of the knowledge gained from it, especially for advancing the behavior maintenance with the behavioral theory testing.