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

从移动医疗数据推断持久行为变化的方法论和数据驱动方法

• 批准号: 10029357
• 负责人: Donald Hedeker
• 金额: $ 53.29万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-17 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10029357
• 关键词: Address; Aftercare; Algorithms; Back; Behavior; Behavior Therapy; Behavioral; Body Weight Changes; Body Weight decreased; Chronic Disease; Clinical; Computer software; Confusion; Consumption; Data; Decision Making; Diet; Diet Monitoring; Disease; Dose; Feedback; Frequencies; Goals; Habits; Health Promotion; Health Promotion Sciences; Health behavior; Health behavior change; Individual; Intercept; Intervention; Knowledge; Learning; Location; Machine Learning; Maintenance; Malignant Neoplasms; Measurable; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Participant; Pattern; Persons; Physical activity; Process; Psychological Theory; Relapse; Research; Risk Behaviors; Risk Factors; Running; Science; Statistical Data Interpretation; Statistical Models; Techniques; Terminology; Testing; Time; Vegetables; analytical tool; base; behavior change; behavioral phenotyping; cancer risk; cardiometabolism; control theory; cost; cost effective; cost efficient; digital; dosage; effective intervention; exercise intervention; improved; individual variation; interest; intervention cost; mHealth; personalized medicine; post intervention; predictive modeling; prevent; rate of change; relapse risk; theories; treatment duration; weight loss intervention

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.

抽象的 贫穷的 癌症， 冗长， 达到 饮食和体育锻炼（PA）行为，是心脏代谢疾病和最普遍的风险因素和 可以治疗以预防疾病。但是，大多数饮食，PA和减肥干预措施都是昂贵的， 和繁重的。如果我们能告诉人们何时 可持续的健康行为模式变化，以使治疗可以逐渐减少，然后停止 没有行为复发。可以假定习惯形成理论来解决这个问题，但他们 事实证明没有行动来指导治疗决策，因为它们没有指定可衡量的标准 可靠地检测到持久行为模式的采集。因此，我们建议确定行为模式 在干预后持续存在之前并预测维持目标级别的行为改善 结束。确定干预措施是否持久地纳入行为所需的测量值 作为饮食，PA和减肥干预措施的一部分收集改善。具体来说，参与者 在评估中传达时，以数字方式自我监控自己的行为，以告知他们 朝着目标迈进。我们将分析6个MHealth试验中收集的自我监控措施 超过1,600名参与者和每天147,000多个观察中的14年，以评估何时 干预已持久地涉及针对性行为，这是通过其可靠的持久性证明的 干涉。我们将使用位置标尺建模不仅在绝对级别（位置）中量化更改 一种行为，但也处于其内部变异性（比例）。我们认为持久行为的诱导 改变需要改善位置（健康行为增加；不健康的行为减少） 并减少规模（即行为一致性提高）。 AIM 1将应用现有位置量表 测试有效干预措施将改善位置并减少规模的假设的方法 所有试验中的有针对性行为。因为现有方法仅在小组级别测量量表，并且 无法衡量我们需要个性化治疗的个人行为一致性的变化 适应，AIM 2将扩展位置量表方法，以实现对变更率的个人估计 行为一致性。将分析从新方法中得出的估计，以了解哪些参数 干预期间的行为改变与维护后处理最相关。最后，AIM 3将 将机器学习应用于从扩展位置尺度混合模型中的估计，以建立范围和 可预测行为维护后处理后的行为模式。这些结果设备通过指导有关所需剂量的实时调查来告知行为互入科学并提高治疗效率 和行为治疗的持续时间。