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Secondary analysis of electronically monitored medication adherence data for a cohort of hypertensive African-Americans

机译：一项针对非裔美国人高血压人群的电子监控药物依从性数据的二级分析

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Background: Electronic monitoring devices (EMDs) are regarded as the “gold standard” for assessing medication adherence in research. Although EMD data provide rich longitudinal information, they are typically not used to their maximum potential. Instead, EMD data are usually combined into summary measures, which lack sufficient detail for describing complex medication-taking patterns. This paper uses recently developed methods for analyzing EMD data that capitalize more fully on their richness.Methods: Recently developed adaptive statistical modeling methods were used to analyze EMD data collected with medication event monitoring system (MEMS?) caps in a clinical trial testing the effects of motivational interviewing on adherence to antihypertensive medications in a cohort of hypertensive African-Americans followed for 12 months in primary care practices. This was a secondary analysis of EMD data for 141 of the 190 patients from this study for whom MEMS data were available.Results: Nonlinear adherence patterns for 141 patients were generated, clustered into seven adherence types, categorized into acceptable (for example, high or improving) versus unacceptable (for example, low or deteriorating) adherence, and related to adherence self-efficacy and blood pressure. Mean adherence self-efficacy was higher across all time points for patients with acceptable adherence in the intervention group than for other patients. By 12 months, there was a greater drop in mean post-baseline blood pressure for patients in the intervention group, with higher baseline blood pressure values than those in the usual care group.Conclusion: Adaptive statistical modeling methods can provide novel insights into patients’ medication-taking behavior, which can inform development of innovative approaches for tailored interventions to improve medication adherence.
机译：背景：电子监测设备（EMD）被视为评估研究中药物依从性的“金标准”。尽管EMD数据提供了丰富的纵向信息，但通常不会最大限度地利用它们。取而代之的是，EMD数据通常被合并为摘要度量，这些度量缺乏足够的细节来描述复杂的用药模式。本文使用最近开发的方法来分析EMD数据，以更充分地利用它们的丰富性。方法：最近开发的自适应统计建模方法用于在用药事件监测系统（MEMS？）瓶盖收集的EMD数据中进行临床试验，以测试其效果一项针对非裔美国人高血压人群坚持服用降压药物的动机访谈，随访了12个月。这是对该研究中MEMS数据可用的190位患者中141位患者的EMD数据的二级分析。结果：生成了141位患者的非线性依从性模式，分为7种依从性类型，分为可接受的类别（例如，高或高）改善）与不可接受（例如低或恶化）的依从性相关，并与依从性自我效能感和血压有关。干预组中可接受依从性的患者在所有时间点的平均依从性自我效能均高于其他患者。到12个月时，干预组患者的基线后平均血压下降幅度更大，基线血压值高于常规护理组。结论：自适应统计建模方法可以为患者的病情提供新的见解服药行为，可以为针对性干预措施的创新方法开发提供信息，以改善药物依从性。

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