ArticleAllergy, asthma & immunology research2017
Development and Validation of an Attitudinal-Profiling Tool for Patients With Asthma.
Article in Allergy, asthma & immunology research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Uncovering the Burden of Rhinitis in Patients Purchasing Nonprescription Short-Acting β-Agonist (SABA) in the Community.Pharmacy (Basel, Switzerland) · 2023Article
- Burden and unmet needs in asthma care in the Asia-Pacific region.Public health challenges · 2022Article
- Digital technologies and adherence in respiratory diseases: the road ahead.The European respiratory journal · 2018Review
- Personalising care of adults with asthma from Asia: a modified e-Dephi consensus study to inform management tailored to attitude and control profiles.NPJ primary care respiratory medicine · 2017Article
- Effective deployment of technology-supported management of chronic respiratory conditions: a call for stakeholder engagement.Pragmatic and observational research · 2017Article
- Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research.Therapeutic advances in respiratory diseaseReview
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Authors and funding
8 authors.
Funding
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Abstract
purposeTo develop a profiling tool which accurately assigns a patient to the appropriate attitudinal cluster for the management of asthma.
methodsAttitudinal data from an online survey of 2,467 patients with asthma from 8 Asian countries/region, aged 18-50 years, having had ≥2 prescriptions in the previous 2 years and access to social media was used in a discriminant function analysis to identify a minimal set of questions for the Profiling Tool. A split-sample procedure based on 100 sets of randomly selected estimation and validation sub-samples from the original sample was used to cross-validate the Tool and assess the robustness of its predictive accuracy.
resultsOur Profiling Tool contained 10 attitudinal questions for the patient and 1 GINA-based level of asthma control question for the physician. It achieved a predictive accuracy of 76.2%. The estimation and validation sub-sample accuracies of 76.7% and 75.3%, respectively, were consistent with the tool's predictive accuracy at 95% confidence level; and their 1.4 percentage-points difference set upper-bound estimate for the degree of over-fitting.
conclusionsThe Profiling Tool is highly predictive (>75%) of the attitudinal clusters that best describe patients with asthma in the Asian population. By identifying the attitudinal profile of the patient, the physician can make the appropriate asthma management decisions in practice. The challenge is to integrate its use into the consultation workflow and apply to areas where Internet resources are not available or patients who are not comfortable with the use of such technology.
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