Evidence map›Paper›PMID 32929109›Full record

Trial reportScientific reports2020

A data-driven typology of asthma medication adherence using cluster analysis.

Holly Tibble, Amy Chan, Edwin A Mitchell, Elsie Horne, Dimitrios Doudesis, Rob Horne, Mehrdad A Mizani, Aziz Sheikh, Athanasios Tsanas

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Scientific reports, 2020. 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
1.6field-weighted citation impact, top 17% of its field
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, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Medication Adherence in Children with Asthma.Patient preference and adherence · 2024
    Review
  4. Article
  5. Review
  6. Article
  7. 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

9 authors at 4 institutions in 2 countries.

Holly TibbleUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK. Holly.Tibble@ed.ac.uk.
Amy ChanSchool of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Edwin A MitchellDepartment of Paediatrics: Child and Youth Health, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand.
Elsie HorneUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
Dimitrios DoudesisUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
Rob HorneAsthma UK Centre for Applied Research, Usher Institute, Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK.
Mehrdad A MizaniUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
Aziz SheikhUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
Athanasios TsanasUsher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
University of Edinburgh · GBInstitute of Occupational Medicine · GBUniversity of Auckland · NZAsthma UK · GB

Funding

Medical Research Council MC_PC_19004
6 · The paper itself

Abstract

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).

Indexed as

Drug MonitoringAdministration, InhalationAdolescentAnti-Asthmatic AgentsAsthmaChildCluster AnalysisFemaleHumansMaleMonitoring, AmbulatoryNebulizers and VaporizersAnti-Asthmatic Agents

Identifiers

PMID32929109
PMCPMC7490405
OpenAlexW3084802220

What OpenQuestion holds

Textmetadata
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.