A novel algorithm to compute adherence from electronic adherence monitoring devices

一种计算电子依从性监测设备依从性的新算法

基本信息

批准号：
10516828
负责人：
Meghan Eileen McGrady
金额：
$ 22.3万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-06 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10516828
关键词：
Address Adherence Adult Adverse effects Algorithm Design Algorithms Antineoplastic Agents Behavior Biological Cessation of life Child Clinical Clinical Trials Code Complex Computer software Data Development Diagnosis Dose Ensure Feedback Focus Groups Funding Grant Hospitals Hour Intravenous Lead Malignant Neoplasms Manuals Measurement Measures Methods Modification Monitoring Clinical Trials Names National Cancer Institute Nausea Oncology Oral Outcome Parents Patient Noncompliance Patient Self-Report Patients Persons Pharmaceutical Preparations Pharmacy facility Procedures Process Proxy Publishing Records Regimen Relapse Reporter Reproducibility Research Research Personnel Risk Running Science Scientific Advances and Accomplishments Sensitivity and Specificity Series System Testing Time Treatment Protocols United States United States National Institutes of Health Visit algorithm development anti-cancer anticancer research cancer therapy chemotherapy computerized data processing cost design human error improved innovation medication compliance microchip monitoring device multidisciplinary novel novel therapeutics pill satisfaction side effect social therapeutic development tool uptake usability

项目摘要

PROJECT SUMMARY/ABSTRACT Medication adherence is a current scientific priority of the National Cancer Institute and a top priority in clinical trials. Electronic adherence monitoring devices (EAMDs) are pill bottles or boxes that contain a computer chip that records the dates and times of each bottle/box opening (or “actuation”). Due to their accuracy and ability to capture day-to-day medication-taking behavior, EAMDs are increasingly cited (i.e., by the FDA) as the preferred measure of daily anticancer medication adherence in research. Unfortunately, researchers currently lack the tools they need to accurately and efficiently convert raw EAMD actuations into adherence data and current methods of completing this task are unstandardized, error-prone, and burdensome. The purpose of this R21 is to develop a novel algorithm to convert EAMD actuations into adherence data. To maximize potential uptake and usability, we will engage a multidisciplinary adherence science focus group to inform the design of the algorithm and associated user interface (Aim 1). We will then evaluate the accuracy of the algorithm in classifying daily adherence data (Aim 2) and obtain quality of use feedback (Aim 3) to inform final refinements prior to large-scale testing.

项目概要/摘要药物依从性是美国国家癌症研究所当前的科学重点，也是临床的重中之重电子依从性监测设备 (EAMD) 是包含计算机芯片的药瓶或盒子。由于其准确性和能力，记录每个瓶子/盒子打开（或“启动”）的日期和时间。由于 EAMD 能够捕捉日常用药行为，因此越来越多地（即被 FDA）引用为不幸的是，研究人员目前是研究中每日抗癌药物依从性的首选衡量标准。缺乏准确有效地将原始 EAMD 驱动转换为依从性数据所需的工具，当前完成此任务的方法不标准化、容易出错且繁重。 R21 将开发一种新颖的算法，将 EAMD 驱动转换为依从性数据，以最大限度地发挥潜力。吸收和可用性，我们将聘请多学科依从性科学焦点小组来为设计提供信息然后我们将评估算法和相关的用户界面（目标 1）。完善每日依从性数据（目标 2）并获取使用质量反馈（目标 3）以告知最终结果在大规模测试之前。