PDF(Pubmed)
Abstract：
BACKGROUND: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence.
OBJECTIVE: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
METHODS: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients\' demographic data. The script formats the data longitudinally and calculates each day\'s medication implementation.
RESULTS: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence.
CONCLUSIONS: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.
OBJECTIVE: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
METHODS: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients\' demographic data. The script formats the data longitudinally and calculates each day\'s medication implementation.
RESULTS: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence.
CONCLUSIONS: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.
摘要：
背景：患者对药物的依从性可以使用交互式数字健康技术（如电子监护仪（EM））进行评估。必须表征治疗方案的变化和EM使用随时间的偏差，以建立药物依从性的实际水平。
目的：我们开发了计算机脚本CleanADHdata。R清理原始EM依从性数据，本教程是用户指南。
方法：除了原始EM数据之外，我们收集了依从性开始和停止监测日期，并确定了处方方案，根据规定的治疗方案，每天预期的EM开放次数，EM使用偏差，和患者人口统计数据。脚本对数据进行纵向格式化，并计算每天的用药执行情况。
结果：我们提供了10名患者的模拟数据集，在中位187天（IQR135-342天）的时间内使用了15种EMs。EM原始数据清理前后患者实施的中位数为83.3%(IQR71.5%-93.9%)和97.3%(IQR95.8%-97.6%)，分别(Δ+14%)。这种差异足以认为EM数据清理能够避免数据误解并在实现和持久性方面为依从性分析提供清理的数据集。
结论：CleanADHdata。R脚本是一个半自动程序，增加了标准化和可重复性。该脚本在数字健康领域具有更广泛的适用性，因为它可用于清理使用各种数字技术收集的依从性数据。
目的：我们开发了计算机脚本CleanADHdata。R清理原始EM依从性数据，本教程是用户指南。
方法：除了原始EM数据之外，我们收集了依从性开始和停止监测日期，并确定了处方方案，根据规定的治疗方案，每天预期的EM开放次数，EM使用偏差，和患者人口统计数据。脚本对数据进行纵向格式化，并计算每天的用药执行情况。
结果：我们提供了10名患者的模拟数据集，在中位187天（IQR135-342天）的时间内使用了15种EMs。EM原始数据清理前后患者实施的中位数为83.3%(IQR71.5%-93.9%)和97.3%(IQR95.8%-97.6%)，分别(Δ+14%)。这种差异足以认为EM数据清理能够避免数据误解并在实现和持久性方面为依从性分析提供清理的数据集。
结论：CleanADHdata。R脚本是一个半自动程序，增加了标准化和可重复性。该脚本在数字健康领域具有更广泛的适用性，因为它可用于清理使用各种数字技术收集的依从性数据。
