Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

背景：强有力的证据表明，体力活动(PA)可降低患膀胱癌、乳癌、结肠癌、 子宫内膜癌、食道癌、胃癌和肾癌，还有中度肺癌的证据。个人 40岁以上不活动的人患癌症的风险高达58，65岁，但只有三分之一的人符合PA的指导方针；5-15 因此，他们是一个重要的目标群体。虽然存在有效的PA干预措施，但干预措施通常是有效的 仅对某些个人或仅限于有限的时间，16-18因此确定了干预措施的必要性，可以 考虑动态的、特殊的PA决定因素，以支持每个人的PA。作为回应，我们 开发了Just Walk，这是一种模块化的自适应移动健康(MHealth)干预，使每天的N-of-1 调整以支持每个人的PA。Just Walk以社会认知理论(SCT)为基础，N-of-1 由我们开发和验证的SCT数学动力学模型驱动的自适应。只要走就行了 可以基于我们对控制工程方法的创新使用来执行N-of-1适应，我们称之为 控制优化试验(COT)。我们为我们的下一步行动提供了一个数字平台和经验证明： 在随机对照试验(RCT)中，评估是否使用COT方法持续优化PA 对每个个体的干预优于相同但缺乏COT方法的干预。主要 目的：评价不同年龄组和不同年龄组儿童中、重度PA(MVPA)分钟数/周的差异。 与非COT组相比，12个月时进行了优化。假设：我们假设每周的分钟数要高得多 干预臂(COT)中MVPA相对于对照(非COT)的MVPA，通过Actigraph测量(为 ≥的效果大小为0.32)。方法：我们将对386名40岁以上不活跃的成年人进行RCT。 超重/肥胖。这是一个高危群体，他们将受益于PA的癌症预防和干预 谁将从适应性干预中受益，因为PA的特殊和动态性质是 在这个群体中发音。评估将在基线、6个月和12个月时使用髋部佩戴的 用于评估MVPA每周分钟数的活动表，由指南证明是合理的。含义：这项研究具有很高的 意义重大，因为我们的干预将是第一个完全基于SCT的可扩展PA干预 由SCT的数学动态模型版本驱动的N-of-1适应。此外，有利的结果将 有理由将我们的COT方法用于其他复杂且高度特殊的动态行为，例如重量 管理、吸烟或药物滥用。最后，我们的工作应该提高对参与的理解 使用数字健康工具。这项研究具有很高的创新性，因为我们将是第一个进行COT和 经验性地评估其在随机对照试验中的效用。

Background: Strong evidence indicates physical activity (PA) reduces risk of bladder, breast, colon, endometrium, esophagus, gastric, and renal cancer, and there is moderate evidence for lung cancer. Individuals aged 40+ who are inactive are at high risk of developing cancers 58,65 but only 1/3 meet guidelines for PA;5-15 thus, they are an important group to target. While effective PA interventions exist, interventions often work only for some individuals or only for a limited time,16-18 thus establishing the need for interventions that can account for dynamic, idiosyncratic PA determinants in order to support each person’s PA. In response, we developed JustWalk, a modular adaptive mobile health (mHealth) intervention that makes daily N-of-1 adjustments to support PA for each person. JustWalk is based on Social Cognitive Theory (SCT) with N-of-1 adaptation driven by a mathematical dynamical model of SCT, which we have developed and validated. JustWalk can perform N-of-1 adaptation based on our innovative use of control engineering methods, which we call a control optimization trial (COT). We have a digital platform and empirical justification for our next step: to evaluate, in a randomized controlled trial (RCT), whether using a COT approach to continuously optimize a PA intervention to each individual is superior to an intervention that is identical but lacks the COT methods. Primary purpose: Evaluate differences in minutes/week of moderate-to-vigorous intensity PA (MVPA) among the COT- optimized vs. non-COT groups at 12 months. Hypotheses: We hypothesize significantly higher minutes/week of MVPA in the intervention arm (COT) relative to control (non-COT) as measured via ActiGraph (powered for effect size of ≥0.32). Methods: We will conduct this RCT with 386 adults aged 40+ who are inactive and overweight/obesity. This is a high-risk group who would benefit from a PA intervention for cancer prevention and who would benefit from an adaptive intervention because of the idiosyncratic and dynamic nature of PA that is pronounced within this group. Assessments will be conducted at baseline, 6, and 12-months using a hip-worn ActiGraph for assessing minutes/week of MVPA, as justified by guidelines. Implications: This research is highly significant because our intervention would be the first scalable PA intervention squarely grounded in SCT with N-of-1 adaptation driven by a mathematical dynamical model version of SCT. Further, favorable results would justify use of our COT methods for other complex and highly idiosyncratic and dynamic behaviors such as weight management, smoking, or substance abuse. Finally, our work should improve understanding of engagement with digital health tools. This research is highly innovative as we would be the first to conduct a COT and to empirically evaluate its utility in an RCT.