Titre
Using a Semiautomated Procedure (CleanADHdata.R Script) to Clean Electronic Adherence Monitoring Data: Tutorial.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Bandiera, C.
Co-première auteure/Co-premier auteur
Pasquier, J.
Co-première auteure/Co-premier auteur
Locatelli, I.
Auteure/Auteur
Schneider, M.P.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
2561-326X
Statut éditorial
Publié
Date de publication
2024-05-22
Volume
8
Première page
e51013
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
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.
We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
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.
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.
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.
We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
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.
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.
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.
PID Serval
serval:BIB_C7DDCF71752B
PMID
Open Access
Oui
Date de création
2024-05-24T07:54:09.929Z
Date de création dans IRIS
2025-05-20T23:49:02Z
Fichier(s)![Vignette d'image]()
En cours de chargement...
Nom
38776539_BIB_C7DDCF71752B.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
126.72 KB
Format
Adobe PDF
PID Serval
serval:BIB_C7DDCF71752B.P001
URN
urn:nbn:ch:serval-BIB_C7DDCF71752B5
Somme de contrôle
(MD5):c2dd653e0a4ce08f736991be3c57f9a4