The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature
Open accessAbstract
BACKGROUND: Many patients experience difficulties in adhering to long-term treatment. Although patients' reasons for not being adherent are diverse, one of the most commonly reported barriers is forgetfulness. Reminding patients to take their medication may provide a solution. Electronic reminders (automatically sent reminders without personal contact between the healthcare provider and patient) are now increasingly being used in the effort to improve adherence. OBJECTIVE: To examine the effectiveness of interventions using electronic reminders in improving patients' adherence to chronic medication. METHODS: A comprehensive literature search was conducted in PubMed, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials. Electronic searches were supplemented by manual searching of reference lists and reviews. Two reviewers independently screened all citations. Full text was obtained from selected citations and screened for final inclusion. The methodological quality of studies was assessed. RESULTS: Thirteen studies met the inclusion criteria. Four studies evaluated short message service (SMS) reminders, seven audiovisual reminders from electronic reminder devices (ERD), and two pager messages. Best evidence synthesis revealed evidence for the effectiveness of electronic reminders, provided by eight (four high, four low quality) studies showing significant effects on patients' adherence, seven of which measured short-term effects (follow-up period <6 months). Improved adherence was found in all but one study using SMS reminders, four studies using ERD and one pager intervention. In addition, one high quality study using an ERD found subgroup effects. CONCLUSION: This review provides evidence for the short-term effectiveness of electronic reminders, especially SMS reminders. However, long-term effects remain unclear.
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Two possible approaches to promote discussion among reviewers are generic reminders-which are often ineffective and ignored-and personalized reminders-which are generally known to be more effective, but require effort by the meta-reviewers.
“Two possible approaches to promote discussion among reviewers are generic reminders-which are often ineffective and ignored-and personalized reminders-which are generally known to be more effective (Vervloet et al., 2012), but require effort by the meta-reviewers.”
From What Can Natural Language Processing Do for Peer Review? (2024) · cites this paper as Vervloet et al., 2012
Several meta-analyses corroborate the effectiveness of SMS for medication adherence.
“Several meta-analyses corroborate the effectiveness of SMS for medication adherence [18-20].”
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- Authors
- Vervloet, M., Linn, A. J., van Weert, J. C. M., de Bakker, D. H., Bouvy, M. L., & van Dijk, L.
- Affiliations
- Netherlands Institute for Health Services Research (NL)
- University of Amsterdam (NL)
- Utrecht University (NL)
- Corresponding author
- Vervloet, Marcia
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-09
- Volume
- 19
- Issue
- 5
- Pages
- 696-704
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC3422829
- OpenAlex
- W2152680984
- Cited by
- 455
- References
- 63
- Citations by year
- 2026: 15 · 2025: 14 · 2024: 24 · 2023: 25 · 2022: 32 · 2021: 40 · 2020: 44 · 2019: 32 · 2018: 41 · 2017: 40 · 2016: 42 · 2015: 40 · 2014: 37 · 2013: 26 · 2012: 3
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacy and Medical Practices
- Keywords
- electronic reminders, medication adherence, chronic medication, pagers, forgetfulness, adherence interventions, long-term treatment
- MeSH terms
- Audiovisual Aids, Automation, Humans, Reminder Systems, Telemedicine, Cell Phone, Medication Adherence, Text Messaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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