Evidence map›Paper›PMID 40105049›Full record

ReviewCurrent opinion in pulmonary medicine2025

Optimizing adherence to medication to improve outcomes in asthma.

Pamela Rackow, Amelia Drennan, Hilary Pinnock, Alexandra L Dima

Abstract readReview
In one paragraph

Review in Current opinion in pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

4 authors.

Pamela RackowUniversity of Stirling, Faculty of Natural Sciences, Psychology, Stirling.
Amelia DrennanUniversity of Stirling, Faculty of Natural Sciences, Psychology, Stirling.
Hilary PinnockUsher Institute, The University of Edinburgh, Edinburgh, UK.
Alexandra L DimaAvedis Donabedian Research Institute (FAD) - Universitat Autònoma de Barcelona (UAB).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewAdherence to medication is essential for asthma control and reducing the risk of exacerbations. Research has accumulated in recent years on causes and consequences of adherence and effective interventions. This review highlights current advances in adherence research and their potential for clinical practice.

findingsOptimizing adherence to medication can be achieved through interventions that identify individual barriers and train the care team in offering tailored support. Digital technologies that facilitate remote monitoring, patient-provider communication and care coordination are increasingly being integrated into asthma care. SUMMARY: Adherence determinants reported cover individual, social and health service-related factors. Age and attitudes toward adherence are crucial determinants. Patients' and caregivers' mental health is relevant for adherence and clinical outcomes, highlighting the importance of integrating this aspect into holistic asthma management. Single-site care arrangements are beneficial for adherence. Tailoring adherence interventions to individual needs, using brief questionnaires to assess barriers and recommending evidence-based strategies to address them, have been found useful and feasible across care settings. Digital technologies such as smart inhaler systems and telemedicine-enhanced care have been shown to be effective in randomized controlled trials, yet implementation research highlights challenges to sustaining support on the long-term.

Indexed as

Anti-Asthmatic AgentsAsthmaMedication AdherenceCaregiversHumansTelemedicineAnti-Asthmatic Agents

Identifiers

PMID40105049
PMCPMC11957441

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.