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）可降低膀胱，乳腺，结肠， 子宫内膜癌、食管癌、胃癌和肾癌，并且有中度证据表明肺癌。个人 40岁以上不活动的人患癌症的风险很高58，65，但只有1/3符合PA指南;5-15 因此，他们是一个重要的目标群体。虽然存在有效的巴勒斯坦权力机构干预措施， 16-18从而确定需要采取干预措施， 考虑到动态的、特殊的PA决定因素，以支持每个人的PA。作为回应，我们 开发JustWalk，一种模块化自适应移动的健康（mHealth）干预， 调整以支持每个人的PA。JustWalk基于社会认知理论（SCT），具有N-of-1 适应驱动的SCT的数学动力学模型，我们已经开发和验证。JustWalk 基于我们对控制工程方法的创新使用，我们可以执行N-of-1自适应，我们称之为 控制优化试验（COT）。我们有一个数字平台和经验证明我们的下一步： 在随机对照试验（RCT）中评估是否使用COT方法持续优化PA 对每个个体的干预上级相同但缺乏COT方法的干预。初级 目的：评价COT-1组中中度至剧烈强度PA（MVPA）的分钟/周差异， 12个月时优化组与非COT组。假设：我们假设每周的分钟数显著高于 通过ActiGraph测量的干预组（COT）相对于对照组（非COT）的MVPA（功率为 效应量≥0.32）。方法：我们将对386名年龄在40岁以上的不活动的成年人进行这项随机对照试验， 超重/肥胖。这是一个高危人群，他们将受益于PA干预预防癌症， 由于PA的特殊性和动态性， 在这个群体中宣告。将在基线、6个月和12个月时使用髋关节佩戴式 ActiGraph用于评估MVPA的分钟/周，根据指南进行合理说明。结论：本研究高度 重要的是，我们的干预将是第一个可扩展的PA干预，直接基于SCT， 由SCT的数学动态模型版本驱动的N-of-1自适应。此外，有利的结果将 证明我们的COT方法用于其他复杂和高度特殊的动态行为，如体重 管理、吸烟或滥用药物。最后，我们的工作应该提高对参与的理解 数字健康工具。这项研究是高度创新的，因为我们将是第一个进行COT， 经验性地评估其在RCT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.