Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

通过控制系统工程方法优化个性化和适应性移动医疗干预措施

• 批准号: 10826070
• 负责人: Eric Hekler
• 金额: $ 25.64万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-14 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10826070
• 关键词: Adherence; Award; Behavior; Behavioral; Clinical Trials; Computer Models; Data; Engineering; Ensure; Equation; Fostering; Health; Intervention; Knowledge; Learning; Maintenance; Measures; Methods; Modeling; Parents; Participant; Process; Psychological reinforcement; Reinforcement Schedule; Research; Self Efficacy; Structure; System; Techniques; Testing; behavior change; digital health; improved; insight; mHealth; mathematical model; novel; novel strategies; physical model; secondary analysis; social cognitive theory; theories

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.

背景：虽然强化时间表，一般来说，被认为是有价值的行为改变，17- 19维护，20-22和坚持，23，24仍然需要更多的研究来更好地了解 这些过程的行动机制（MoA），以便更强大的数字健康干预措施可以 将它们系统地、大规模地结合起来。用于持续调整支持的父R 01方法 包括两种状态：依赖于持续强化计划的初始状态和 维护状态，在这种状态下，它转向使用可变的强化计划。主要目的： 系统地研究强化计划的动态MoA，行为变化，通过计算 建模和严格的二次分析。假设：我们假设1）具体的“黑匣子” 动态模型可以识别关键的衡量社会认知理论（SCT）的结构，如自我效能， 对理解行为维护很重要; 2）使用灰箱/半物理模型， 强化计划到模型中，将解释更大部分的方差，以及3）按需模型， 同时扰动随机近似（MoD-SPSA）方法将识别最优模型 结构，以及估计方法中更好地拟合非线性假设的可调参数。研究方法： 我们将建立在我们先前验证的SCT动态模型基础上，该模型是父R 01的基础，但 增加了对模型结构中加固时间表的关键见解。然后， 使用从母体R 01临床试验的前100名参与者中生成的数据（参见我们的研究策略）， 方法，以确保审判的完整性），我们将进行“黑箱”自回归动态模型，它 不包含先前的领域知识，节省SCT变量选择。接下来，我们将进行“灰箱”建模， 这很像一个动态结构方程模型，因为它将先验领域知识结合到 数学模型灰箱建模步骤/天的百分比方差增加是指示性的 关于强化学习的MoA的先验领域知识的附加值，因此这是一个强大的测试。 动态MoA最后，我们将使用MoD-SPSA 25作为识别最佳特征、模型结构和 估计方法中的可调参数，以检查潜在的非线性相互作用和关系。 影响：这项研究有许多协同效益，包括：1）它将产生严格的科学 更好地理解MoA，强化时间表，行为维持的证据; 2）它将 产生关键的新方法来操作，动态地，不同的强化时间表，以促进行为 通过数字健康干预进行维护; 3）对于父母R 01，本研究将允许采用以下方法 和技术，以改善行为的维持具体的重点。因此，这 补充仍然在原来的范围内的母奖项，并将最大限度地提高知识的影响 特别是对促进行为理论测验的行为维持有重要意义。

项目成果

[A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control: illustration with Just Walk].

• DOI: 10.4995/riai.2022.16798
• 发表时间: 2022-06-29
• 期刊: REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL
• 影响因子: 1.5
• 作者: Cevallos, Daniel;Martin, Cesar A.;El Mistiri, Mohamed;Rivera, Daniel E.;Hekler, Eric
• 通讯作者: Hekler, Eric

Goal setting and achievement for walking: A series of N-of-1 digital interventions.

• DOI: 10.1037/hea0001044
• 发表时间: 2021-01
• 期刊: Health psychology : official journal of the Division of Health Psychology, American Psychological Association
• 作者: Chevance G;Baretta D;Golaszewski N;Takemoto M;Shrestha S;Jain S;Rivera DE;Klasnja P;Hekler E
• 通讯作者: Hekler E

Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity.

• DOI: 10.23919/acc53348.2022.9867350
• 发表时间: 2022-06
• 期刊: Proceedings of the ... American Control Conference. American Control Conference

Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation.

使用混合数据分区和离散同时扰动随机逼近的行为干预的具体动态建模。

• DOI: 10.23919/acc55779.2023.10156304
• 发表时间: 2023
• 期刊: Proceedings of the ... American Control Conference. American Control Conference
• 作者: Kha,RachaelT;Rivera,DanielE;Klasnja,Predrag;Hekler,Eric
• 通讯作者: Hekler,Eric

The frequency of using wearable activity trackers is associated with minutes of moderate to vigorous physical activity among cancer survivors: Analysis of HINTS data.

使用可穿戴活动追踪器的频率与癌症幸存者中度至剧烈体力活动的分钟数相关：HINTS 数据分析。

• DOI: 10.1016/j.canep.2023.102491
• 发表时间: 2024
• 期刊: Cancer epidemiology
• 影响因子: 2.6
• 作者: DeLaTorre,StevenA;Pickering,Trevor;Spruijt-Metz,Donna;Farias,AlbertJ
• 通讯作者: Farias,AlbertJ