Methodological and data-driven approach to infer durable behavior change from mHealth data

摘要 贫困 癌症， 冗长， 实现 饮食和体力活动（PA）行为，心脏代谢疾病的最普遍的危险因素， 可以用来预防疾病。然而，大多数饮食，PA和减肥干预措施都是昂贵的， 和负担。如果我们能告诉人们 一个可持续的健康行为改变模式，这样治疗就可以逐渐减少，然后停止 没有行为复发。习惯形成的理论可能被认为可以解决这个问题，但它们 没有被证明可用于指导治疗决策，因为它们没有指定可测量的标准， 可靠地检测持久行为模式的获取。因此，我们建议识别行为模式， 预测并预测干预后持续存在的目标水平行为改善的维持 形接头.判断干预是否持久地诱导了行为 作为饮食、PA和减肥干预措施的一部分收集改善。具体而言，参与者 不断自我监测他们的行为数字化，而评估是中继回来，告诉他们 朝着目标前进。我们将分析在6个移动健康试验中收集的自我监测措施， 在1，600多名参与者和147，000多名日常观察中进行了14年的研究，以评估 干预持久地夹带了有针对性的行为，正如其可靠的持久性所证实的那样， 干预我们将使用位置规模模型来量化变化，不仅在绝对水平（位置）， 一种行为，但也在其内部的人的可变性（规模）。我们认为持久行为的诱导 改变需要位置的改善（健康行为的增加;不健康行为的减少） 并且规模减小（即，行为的一致性）。目标1将采用现有的位置比例 方法来检验假设，有效的干预措施将改善的位置和减少的规模， 所有试验中的目标行为。由于现有的方法仅在组水平上测量规模， 我们无法测量个体行为一致性的变化， 适应，目标2将扩展位置尺度方法，使个人估计的变化率， 行为一致性将分析从新方法得出的估计值，以了解 干预期间的行为变化与维持治疗后最相关。最后，Aim 3将 将机器学习应用于来自扩展的位置-尺度混合模型的估计，以建立范围， 预测治疗后行为维持的行为模式。这些结果将为行为干预科学提供信息，并通过指导所需剂量的实时决策来提高治疗效率 和行为治疗的持续时间。

Abstract Poor cancers, lengthy, achieve diet and physical activity (PA) behaviors, the most prevalent risk factors for cardiometabolic diseases and can be treated to prevent disease. However, most diet, PA, and weight loss interventions are costly, and burdensome. Theseinterventions could be more cost-efficient if we could tell when people a sustainable pattern of health behavior change so that treatment could be tapered and then stopped without behavioral relapse. Theories of habit formation might be assumed to address this problem, but they have not proved actionable to guide treatment decisions because they do not specify measurable criteria to reliably detect acquisition of a durable behavior pattern. Hence, we propose to identify behavior patterns that precede and predict maintenance of target-level behavioral improvement that persist after an intervention ends. The measurements needed to tell whether an intervention has durably entrained behavioral improvement are collected as part of diet, PA, and weight loss interventions. Specifically, participants continuously self-monitor their behavior digitally while assessments are relayed back to inform them about progress toward goals. We will analyze self-monitoring measures collected in 6 mHealth trials, conducted over 14 years among over 1,600 participants and more than 147,000 daily observations, to assess when an intervention has durably entrained targeted behaviors, as validated by their reliable persistence post- intervention. We will use location scale modeling to quantify change not only in the absolute level (location) of a behavior but also in its within-person variability (scale). We posit that the induction of durable behavior change requires both improvement in location (increases for healthy behaviors; decreases for unhealthy ones) and decrease in scale (i.e., increased behavioral consistency). Aim 1 will apply existing location scale methods to test the hypothesis that effective interventions will improve the location and reduce the scale of targeted behaviors across all trials. Because existing methods only measure scale at the group level and cannot measure the change in an individual's behavioral consistency that we need to personalize treatment adaptation, Aim 2 will extend location scale methods to enable individual estimation of the rate of change in behavioral consistency. Estimates derived from the new method will be analyzed to learn which parameters of behavior change during intervention are most associated with maintenance post-treatment. Finally, Aim 3 will apply machine learning to estimates from the extended location-scale mixed models to establish ranges and behavioral patterns that predict behavioral maintenance post-treatment. These resultswill inform behaviorinterventionscience and improve treatment efficiency by guiding real-timedecisions about the needed dosage and duration of behavioral treatments.