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Outcome measures for adherence data from a medication event monitoring system: A literature review
Author(s) -
Hartman Linda,
Lems Willem F.,
Boers Maarten
Publication year - 2019
Publication title -
journal of clinical pharmacy and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.622
H-Index - 73
eISSN - 1365-2710
pISSN - 0269-4727
DOI - 10.1111/jcpt.12757
Subject(s) - medicine , context (archaeology) , outcome (game theory) , standardization , medline , event (particle physics) , set (abstract data type) , medical physics , medical emergency , intensive care medicine , computer science , paleontology , physics , mathematics , mathematical economics , quantum mechanics , political science , law , biology , programming language , operating system
Summary What is known Currently, medication bottles with an electronic cap are frequently used to measure medication adherence. This system is termed medication event monitoring system (MEMS). To our knowledge, the optimal method to summarize data from MEMS has not yet been determined. Objective Look for best practices on how to quantify adherence data from MEMS. Methods Review of PubMed, Embase and Cochrane databases for the articles on medication adherence with MEMS. Results Of 1493 identified articles, 207 were included in this review. The MEMS cap was used for a median of 3 months (IQR: 4; range: 1 week to 24 months) in various health conditions. Many different outcome measures were used. Most studies computed an adherence score, expressed as the percentage of days on which the correct dose of medication was taken. The threshold to mark people as adherent was most frequently, arbitrarily, set at 80% (range: 67%‐95%). We found no data to support a specific threshold. Discussion Although the commonly used definition of adherence has face validity, we found no validation studies, and not all studies used the same cut‐off for adherence. Ideally, a cut‐off should be defined and validated in the context of the specific drug and its pharmacokinetic and dynamic characteristics, and perhaps other contextual factors, rather than generically. In addition, there was large heterogeneity in the definition of what “correct intake” of medication is. What is new and conclusion Outcome measures for MEMS data lacked standardization, and no demonstrable effort to validate any definition against a relevant clinical outcome is available. Consensus on the definition of adherence is urgently needed.