JMIR Publications

ABSTRACT

Background:

Patient adherence to medications can be assessed using digital technology such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterised 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 (https://github.com/jpasquier/CleanADHdata) formats the data longitudinally and calculates each day’s medication implementation. We provided a simulated dataset for 10 patients, for which 15 EMs were used over a median period of 187 days (IQR 135–342).

Results:

The median patient imple-mentation before and after EM raw data cleaning was respectively 83.3% (IQR 71.5–93.9) and 97.3% (IQR 95.8–97.6), Δ+14%.

Conclusions:

This difference is substantial enough to consider EM data cleaning to avoid data misinterpretation and provide a cleaned dataset for the adherence analysis in terms of implementation and persistence. Moreover, a semi-automated procedure increases standardisation and reproducibility.