Evidence map›Paper›PMID 38911591›Full record

ArticlePatient preference and adherence2024

Improving Assessment of Adherence Behaviors and Drivers: Targeted Literature Review and Concept Elicitation Interviews in Multiple Countries and Disease Populations.

Sarah Bentley, Elizabeth Exall, Lucy Morgan, Nicolas Roche, Kamlesh Khunti, Rebecca Rossom, James Piercy, Rob Arbuckle, Victoria S Higgins

Abstract read
In one paragraph

Article in Patient preference and adherence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

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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

9 authors.

Sarah BentleyPatient-Centered Outcomes, Adelphi Values, Bollington, UK.
Elizabeth ExallPatient-Centered Outcomes, Adelphi Values, Bollington, UK.
Lucy MorganPatient-Centered Outcomes, Adelphi Values, Bollington, UK.ORCID 0000-0002-6679-454X
Nicolas RocheRespiratory Medicine, Cochin Hospital, APHP Centre University of Paris, Institut Cochin (UMR1016), Paris, France.ORCID 0000-0002-3162-5033
Kamlesh KhuntiDiabetes Research Centre, Leicester University, Leicester, UK.ORCID 0000-0003-2343-7099
Rebecca RossomHealthPartners Institute, University of Minnesota Medical School, Minneapolis, MN, USA.
James PiercyAdelphi Real World, Bollington, UK.
Rob ArbucklePatient-Centered Outcomes, Adelphi Values, Bollington, UK.ORCID 0000-0003-0345-2523
Victoria S HigginsAdelphi Real World, Bollington, UK.ORCID 0000-0002-3901-7390

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Medication adherence is crucial for achieving clinical goals. Medication adherence drivers and behaviors were explored across multiple conditions, countries, and medication schedules/modalities to develop a conceptual model of medication adherence, which could later be used to support development of a patient-reported outcome (PRO) measure of adherence. Patients and Methods: Targeted review of qualitative literature identified important medication adherence concepts. Fifty-seven qualitative concept elicitation interviews were conducted (USA n=21, Spain n=18, Germany n=18). Participants were prescribed medication for: hypertension (n=9), asthma (n=8), multiple myeloma (n=8), psoriasis (n=8), diabetes (n=7), depression (n=7), multiple sclerosis (n=7), and/or schizophrenia (n=6). Thematic analysis of verbatim transcripts was performed. Expert clinicians (n=3) provided input throughout. Results: Nine qualitative articles were selected for review from 2168 screened abstracts. Forty-two medication adherence concepts were reported and grouped into 10 domains. Eight forms of medication adherence were reported during interviews, along with 27 drivers of non-adherence, all of which were incorporated into a conceptual model. Participants reported skipping medication doses (n=36/57; 63.2%) or taking medication later in the day than prescribed (n=29/57; 50.9%). Common drivers of non-adherence included forgetfulness (n=35/57; 61.4%), being out of the usual routine (n=31/57; 54.4%) and being busy (n=22/57; 38.6%). US participants were more likely to report non-adherence due to low perceived efficacy (n=6/21, 28.6%) and cost (n=5/21, 23.8%) than German (n=1/18, 5.6%; n=0/18, 0.0%) or Spanish (n=2/18, 11.1%; n=1/18, 5.6%) participants. Conclusion: Findings highlight the diverse forms and drivers of medication non-adherence, informing the development of a comprehensive conceptual model of medication adherence. The conceptual model builds on and advances previous models of medication adherence and can be used by healthcare professionals to understand and interpret barriers to medication adherence and how best to support patients in taking their medication as intended.

Indexed as

interviewsliterature reviewmedication adherencequalitative research

Identifiers

PMID38911591
PMCPMC11192640

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Registered trials

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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 OpenQuestion graph.