Poor adherence to medication and a lack of understanding of medication instructions are major barriers to the treatment of T2DM. Poor adherence to medication and life-style recommendations may significantly contribute to the economic burden of this disease. The rate of high adherence identified in Bandung City was 20.9%. This result was lower than that reported in another study conducted in the United States of America (79%) [21]. This adherence rate was possibly associated with the poor quality of pharmaceutical care provided to patients. Pharmacists should provide important information including what to do if a dose is missed or an adverse effect is caused by the prescribed medication. As reported in our previous study, the average dispensing time in community pharmacy in Bandung City, Indonesia, was 62 s (varying widely from 3 to 435 s), which is above the recommended 60 s [22]. Therefore, pharmacists should allocate more time for patient consultation. In addition, another study revealed that pharmaceutical care intervention for diabetes treatment in Indonesia contributed to improvements of 17.01%; 6.73%; and 6.31% in 2 h postprandial glucose, HDL and triglyceride parameters, respectively, relative to treatment without the provision of pharmaceutical care [23].
The primary objective of diabetes management is to improve patient QOL. This study established the association between adherence and diabetes-specific QOL (and vice versa). The D-39 diabetes specific-QOL instrument has been suggested for use in research and clinical practice [14]. Furthermore, this instrument not only reflects the QOL in diabetes patients but also allows patients to frame responses based on their own personal conceptualization of QOL. These properties result in an instrument that is focused on the patients, which is important in any patient-assessed QOL measure.
A number of previous studies have attempted to analyze the association between adherence and various diabetes specific-QOL measures. However, within these studies, associations have proven to be weak [24]. Only a few domains have been analyzed [25], or only type 1 diabetes was assessed [26]. Furthermore, previous studies have focused on singular aspects of QOL (e.g., distress [24]), and neglected other key components of QOL, such as physical and social functioning [13].
This study overcame the limitations of previous studies and the results of this study enhance the current body of evidence regarding the positive association between adherence and diabetes-specific-QOL in patients. The results of the Kruskall-Wallis test showed that adherence to prescribed medication was significantly associated with diabetes-specific-QOL and vice versa (p = 0.009) in participating patients. Furthermore, the multiple linear regression model results suggested that adherence was significantly associated with diabetes specific-QOL. These results suggest that increased occurence of high adherence to diabetes-prescribed medicine was associated with better QOL and vice versa. This result also indicated the beneficial effects of the use of anti-diabetic pharmacological therapies by patients, which may have been associated with decreased pain and other diabetes-related complications.
The results of this study are in accordance with those of Farias et al. [9], suggesting that QOL in patients with DM may influence treatment adherence, satisfactorily improve clinical outcomes and reduce morbidity and mortality rates and disease progression. However, the relationship between QOL and treatment adherence remains contradictory. Some studies [27, 28] have shown that the highest QOL level in patients with DM was associated with better treatment adherence; however, other research has not identified this association [29].
Based on our previous study, the predictors of low adherence in Indonesia were complementary and alternative medicine usage [adjusted odds ratio (ORadj) 6.16; 2.44–15.52], gender (ORadj 2.57; 1.05–6.31), and age (ORadj 4.25; 1.53–11.31) [30]. In patients, adherence to medication may be associated with relieved symptoms in the short term and managed disease in the longer term, thus resulting in better social functioning and improved QOL. In contrast, a previous study by Martinez [31] showed that there was no association between medication adherence and QOL measured by the World Health Organization Quality of Life questionnaire (WHOQOL-100) in T2DM patients. That study recommended that it was necessary to investigate psychological predictors of therapy adherence behavior in T2DM patients. These different findings may be a result of differences in the instruments used, patients socio-demographics and healthcare settings.
The results of the multiple regression analysis also revealed that patient income was significantly associated with diabetes-specific QOL. As estimated, it was found that patients with lower incomes had a significantly lower QOL. This result is in agreement with prior studies by Glasgow et al. [32] and Ferrans et al. [33]. The decreased QOL in this population showed the need to reform the treatment of low-income diabetes patients.
Of all the diabetes-specific-QOL domain scores, the social burden (27.5%) was lower than the overall QOL score which indicated better QOL. In the instrument used, lower score is associated with better QOL. In contrast, a previous study showed a substantial effect (50% or more) of social functioning on QOL in patients [34]. A probable explanation for this difference is the presence of full support from the patient’s family or social support from the work environment. Social support could encourage improved psychological conditions and better adherence to prescribed diabetes medication. Better family and social support has been found to be predictive of higher adherence and better QOL [35]. Further, all the patients in this study were T2DM patients who had experienced a lower level of impact on their social life than that previously reported in type 1 diabetes patients, in whom more social control problems have been reported [36]. Thus, it is possible that patients did not worry that diabetes might limit their social relationships or friendships.
On the other hand, sexual functioning scores were higher than overall QOL scores which indicated poor QOL. In the instrument used, higher score is associated with poor QOL. This result is in accordance with a previous study that reported the sexual dysfunctions associated with diabetes have been known to decrease QOL in both males and females [37]. Sexual dysfunction has been frequently identified in diabetes mellitus patients. The prevalence of erectile dysfunction among diabetic men has been estimated to be 35–90% [38]. The topic of sexual activity is very sensitive to Indonesians and not pleasant to talk about to the public. A study of the help-seeking patterns (on the issues of sexual behavior and dysfunction) in urban populations in China, Taiwan, South Korea, Japan, Thailand, Singapore, Malaysia, Indonesia and the Philippines showed that although sexual dysfunction was frequent, socio-cultural factors seemed to prevent suffering individuals from seeking treatment [39]. A self-reported instrument is, however, still the best way to investigate complicated information. Low adherence to medications could have limited the effects of medication on pain management and sexual functioning. There were slight differences between the mean scores in the energy and mobility, diabetes control, and anxiety and worry domains. However, these domains were proven to not have an effect on diabetes patients in this study.
In this study, we restricted to only T2DM patients who used oral medication to prevent any confounding associated with insulin use that might affect adherence. We also stratified predictors into categories to minimize confounding and facilitate interpretation of the data. However, this study still had several limitations. First, the sample size of 91 patients may have limited the power of the analyses, thus, the generalizability of this study may also be limited. Subsequent studies should not only use larger and more diverse samples to ensure sufficient power and generalizability but also should use a case control study design with groups defined according adherence to therapy. In addition, other information that may influence adherence should also be assessed, such as the use of other medications and the frequency of dosage, costs, and side effects of medication. Second, our data relied on the respondents’ self-reported data regarding their medication adherence and may have been subject to recall bias. This possibility, however, should have been minimized as the MMAS has been a validated and is a self-reported instrument most widely used to assess adherence.