Evidence mapPaperPMID 39012851Full record

ReviewMedical science monitor : international medical journal of experimental and clinical research2024

Innovative Approaches to Enhance and Measure Medication Adherence in Chronic Disease Management: A Review.

Michał Gackowski, Magdalena Jasińska-Stroschein, Tomasz Osmałek, Magdalena Waszyk-Nowaczyk

Abstract readReview
In one paragraph

Review in Medical science monitor : international medical journal of experimental and clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Barriers to Immunosuppressant Medication Adherence in Thoracic Transplant Recipients: Initial Findings.International journal of environmental research and public health · 2025
    Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Michał GackowskiChair and Department of Pharmaceutical Technology, Poznań University of Medical Sciences, Poznań, Poland.ORCID 0009-0001-9254-3728
Magdalena Jasińska-StroscheinDepartment of Biopharmacy, Medical University of Łódź, Łódź, Poland.ORCID 0000-0001-6171-0733
Tomasz OsmałekChair and Department of Pharmaceutical Technology, Poznań University of Medical Sciences, Poznań, Poland.ORCID 0000-0002-6939-2888
Magdalena Waszyk-NowaczykPharmacy Practice and Pharmaceutical Care Division, Chair and Department of Pharmaceutical Technology, Poznań University of Medical Sciences, Poznań, Poland.ORCID 0000-0001-6607-5126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication non-adherence is a problem that affects up to 50% of patients with chronic diseases. The result is a failure to achieve therapeutic goals and an increased burden on the healthcare system. It is, therefore, highly appropriate to develop models to assess patient adherence to prescribed therapy. To date, there are many methods for doing this. However, several tools have been developed that subjectively or objectively, directly or indirectly, assess the level of patient adherence. Electronic medication packaging devices are among the most rapidly evolving methods of measuring adherence. Other emerging technologies include the use of artificial intelligence algorithms and ingestible biosensors. The former is being used to create applications for mobile phones and laptops. The latter appears to be the least susceptible to the risk of overestimating adherence but remains very expensive. Here, we present recent developments in measuring patient adherence, and provide details of achievements in objective methods for assessing adherence, such as electronic monitoring devices, video-observed therapy, and ingestible biosensors. A dedicated section on using artificial intelligence and machine learning in adherence measurement and reviewing questionnaires and scales used in specific diseases is also included. Methods are discussed along with their advantages and potential limitations. This article aimed to review current measures and future initiatives to improve patient medication adherence.

Indexed as

Medication AdherenceAlgorithmsArtificial IntelligenceChronic DiseaseDisease ManagementHumansSurveys and Questionnaires

Identifiers

PMID39012851
PMCPMC11302205

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.