Abstract
INTRODUCTION: Long-term affective disorders are characterized by poor illness comprehension, low treatment adherence, high relapse rates, severe impairment, and psychosocial dysfunction. While complex treatments for depressive disorders are available, more than 50% of patients don't adhere them. In addition to pharmacotherapy, enhancing medication adherence is crucial and beneficial for the bipolar illness treatment. Objectively measuring medication adherence is the first step to improving it. Non-adherence has been linked to several variables, although the data is inconsistent depending on patient subgroups. This comprehensive study examines the sociodemographic, clinical, personality, and quality of life characteristics that medication adherence. Factors such as early start, rapid cycling, increasing severity and outcomes, and higher suicidality are associated with affective disorder. Even during remission, affective disorder patients have low low quality of life (QoL). GOALS AND OBJECTIVES: The primary aim is to determine which sociodemographic factors influence medication compliance and quality of life. A total of 120 eligible patients were split into two groups, a control and an experimental groups of 60 each. Both control and experimental groups were pre-tested using the WHO QoL Brief and sociodemographic data. Unlike the control group, which received normal treatment, the experiment group participated in seven sessions, twice-weekly group psychoeducational therapy for three months. Both groups were evaluated immediately six months following the intervention. Chi-square and three-way ANOVA with Bonferroni 't' test for post-hoc comparisons. A P value of 0.05 or less was significant. Sigma Plot 14.5 (Sy stat Software Inc., San Jose, USA) was used for statistical analysis. RESULTS: A substantial correlation found between medication compliance and education level (χ(2) = 9.174, P = 0.027). Additionally, age, gender, and important life events were found to influence the Hogan drug attitude inventory (HDAI) and WHOQOL-BREF.