Purpose: Poor medication adherence can negatively affect health outcomes of patients with asthma from medication and significantly increase the healthcare costs. Management of adherence to inhalers remains a challenging topic in the long-term management of patients with asthma. We aim to evaluate the role of asthma control test (ACT) in the management of adherence to inhalers in outpatients with asthma.
Patients and Methods: Six hundred twenty-seven outpatients with asthma admitted to the clinic of respiratory medicine in a tertiary hospital in northwestern China during 2016 to 2019 were randomly divided into observation group (n= 315) and control (n= 312) and received standard inhalant therapy for 6 months and lung function test before and after treatment. The patients in the observation group took ACT questionnaires at the end of each month, while the patients in control only took an ACT at the end of the last month. The ‘Test of Adherence to Inhalers’ (TAI) questionnaire was used to evaluate the patients’ adherence to inhalant therapy.
Results: All patients completed the study. The ACT scores in the observation group showed a gradual increase month by month. The TAI results indicated that adherence to inhalers of patients in the observation group was significantly better than that in control and the patients’ non-adherence pattern in the observation group, with significantly lower erratic non-adherence, was also different from that in control. After 6 months of treatment, the lung function indexes and their relative improvement and the ACT scores in the observation group were significantly better or higher than those in control.
Conclusion: The once-monthly self-reported ACT can effectively improve the adherence to inhalers of outpatients with asthma mainly by addressing erratic non-adherence and improve the treatment effects, and thus deserves widespread use in the treatment adherence management in patients with asthma.
Keywords: asthma control test, ACT, medication adherence, Test of Adherence to Inhalers, TAI, self-management, China