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

• 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
• 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

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.

项目总结/文摘