Development and Validation of an Attitudinal-Profiling Tool for Patients With Asthma

Aileen David-Wang, David Price, Sang Heon Cho, James Chung-Man Ho, Chong Kin Liam, Glenn Neira, Pei-Li Teh

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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Purpose: To develop a profiling tool which accurately assigns a patient to the appropriate attitudinal cluster for the management of asthma. Methods: Attitudinal 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. Results: Our 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. Conclusions: The 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.
Original languageEnglish
Pages (from-to)43-51
Number of pages9
JournalAllergy, Asthma & Immunology Research
Issue number1
Early online date29 Aug 2016
Publication statusPublished - Jan 2017


  • asthma
  • discriminant analysis
  • disease management
  • Asia


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