Clustering medication adherence behavior based on beliefs in medicines and illness perceptions in patients taking...

The prevalence of medication non-adherence is 50% in chronic disease conditions and varies from 30% to 70% in asthma maintenance medications. A major drawback in addressing medication non-adherence is the short time available for patient consultations, which limits the ability of the clinician in identifying the problem. Thus, this study explores how medication adherence behavior can be clustered and identifies the unique characteristics of each cluster so that clinicians can recognize the cluster characteristics in patients to provide targeted interventions. The study objectives were to: (1) cluster patients' medication adherence behavior with asthma maintenance medications based on their beliefs in medicines and illness perceptions, and (2) describe the characteristics of the patients in each cluster based on psychosocial, clinical, and demographic characteristics.

A cross-sectional online survey design on a convenience sample of adult individuals who were taking asthma maintenance medications.

Self-reported medication adherence using Morisky scale, beliefs in medicines using Beliefs in Medicines Questionnaire, and illness perceptions using the Brief Illness Perceptions Questionnaire.

The cluster analysis with 392 subjects resulted in five clusters based on patients' beliefs in medicines and their illness perceptions. The clusters formed had distinct characteristics that lend themselves to monitoring or for which targeted interventions can be framed to improve medication adherence.

The study only examined asthma maintenance medications limiting the generalizability of the study. Also, all the data collected including medication adherence were self-reported data from an online panel. This can cause selection bias and lack of generalizability.

The study demonstrated how the concept of 'non-adherence' is different for different patients and the need for tailored interventions for each type of non-adherence. With the limited consultation time available for clinicians to communicate with the patients, identifying the characteristics of patients in different clusters can assist clinicians in providing appropriate targeted interventions.