SCH:INT: Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term Medication

到2022年，美国将有近400万乳腺癌幸存者。长期坚持 内分泌治疗对于激素受体阳性乳腺癌的幸存者至关重要， 预防癌症复发的药物。尽管这些药物有挽救生命的好处， 坚持度低。服药行为同时受到多尺度因素的影响，包括 个人和环境因素，以及患者的其他行为模式。尽管在智能 和互联健康，很少有人尝试开发和提供个性化的干预措施， 药物依从性。现有的基于技术的干预措施侧重于不依从的认知原因 一些人经历的药物治疗（例如，忘记），但未能考虑到相互作用 在认知因素和其他类型的因素之间（即，环境的、行为的）， 坚持。此外，很少有技术驱动的干预措施被评估为有效性， 支持药物依从性。用于了解多尺度因子与 个性化干预对这些因素的影响最终会改善药物治疗 坚持。这个三阶段项目将通过开发和 采用多尺度建模和干预（MMI）系统。首先，一个由传感器丰富的 智能手机、无线药物事件监测系统（MEMS）、无线信标和可穿戴设备 将开发收集药物依从性的原位数据的传感器，以提供连续的、非侵入性的 依从性评估和多尺度因素监测。第二，MMI系统将 部署到乳腺癌幸存者中，以模拟依从性和多尺度因素之间的关系， 识别与服药行为相关的模式，并制定干预措施。第三，概念验证 将通过人类受试者研究证明MMI，受试者接受多尺度 干预措施。这项拟议中的研究具有重大的公共卫生意义，因为预计将增加 我们对乳腺癌幸存者药物依从性的理解，从而提供了一个总体框架， 这将适用于多种癌症口服化疗。

By 2022, there will be nearly 4 million breast cancer survivors in the USA. Adherence to long-term endocrine therapy is crucial for survivors of hormone receptor-positive breast cancer prescribed these daily medications to prevent cancer recurrence. Despite the life-saving benefits of these medications, rates of adherence are low. Medication-taking behavior is simultaneously influenced by multiscale factors, including personal and environmental factors, and a patient's other behavioral patterns. Despite advances in smart and connected health, there have been few attempts to develop and deliver personalized interventions for medication adherence. Existing technology-based interventions focus on cognitive reasons for non-adherence to medications experienced by some people (e.g., forgetting), but fail to account for interactions between cognitive factors and other types of factors (i.e., environmental, behavioral) that contribute to adherence. Furthermore, few technology-driven interventions have been assessed for efficacy in supporting medication adherence. Tools to understand interactions between multiscale factors and the effect that personalized interventions have on these factors would ultimately improve medication adherence. This 3-phase project will overcome these fundamental scientific barriers by developing and employing a Multiscale Modeling and Intervention (MMI) system. First, a system consisting of sensor-rich smartphones, wireless medication event monitoring systems (MEMS), wireless beacons, and wearable sensors that collect in situ data on medication adherence, will be developed to provide continuous, noninvasive adherence assessment and multiscale monitoring of factors. Second, the MMI system will be deployed to breast cancer survivors to model relationships between adherence and multiscale factors, identify patterns associated with medication-taking behavior, and develop interventions. Third, a proof-of-concept for MMI will be demonstrated through a human subjects study, with subjects receiving multiscale interventions. The proposed research has significant public health implications as it is expected to increase our understanding of medication adherence in breast cancer survivors, thus providing a general framework that will be applicable to oral chemotherapy use across multiple cancers.