European Journal of Cardiovascular Medicine

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycaemia resulting from defects in insulin secretion, insulin action, or both. It represents one of the most significant global health burdens, with an estimated 422 million people affected worldwide as of 2014, over 90% of whom are diagnosed with type 2 diabetes mellitus (T2DM) [1]. In India alone, the number of individuals living with diabetes is approximately 77 million, making it the country with the second highest diabetic population globally, following China. The increasing prevalence, especially in urban populations, is largely attributed to rapid urbanization, sedentary lifestyles, and dietary transitions [2].

Adherence to prescribed medication is a cornerstone in the management of chronic illnesses such as diabetes. Medication adherence is defined as the extent to which patients take medications as prescribed by their healthcare providers, both in terms of timing and dosage [3]. Poor adherence to antidiabetic medications not only impairs glycemic control but also increases the risk of diabetes-related complications, including cardiovascular diseases, nephropathy, neuropathy, and retinopathy. Moreover, non-adherence significantly contributes to increased healthcare utilization and costs, and can ultimately result in premature morbidity and mortality [4].

Despite the availability of effective pharmacological treatments, adherence rates among diabetic patients remain suboptimal. The World Health Organization (WHO) estimates that in developing countries, the average adherence rate for chronic disease therapies is below 50% [5]. Multiple factors contribute to non-adherence, including patient-related beliefs, lack of knowledge about the disease, fear of side effects, forgetfulness, and dissatisfaction with healthcare services [6].

Given the critical role of adherence in effective diabetes management, it is essential to identify the extent of medication adherence and the underlying causes of non-adherence within the local population. This study was conducted in a tertiary care hospital in Visakhapatnam, Andhra Pradesh, to evaluate adherence levels among diabetic patients and to investigate the contributing factors to poor adherence using the Malaysia Medication Adherence Assessment Tool (MYMAAT-21), a validated questionnaire [7]. The findings aim to provide insight into patient behaviors and perceptions, and to guide interventions that could enhance medication adherence and, consequently, improve clinical outcomes.

Study Design and Setting

This study employed a cross-sectional observational design to assess medication adherence among diabetic patients. The research was conducted at the Medicine and Endocrinology departments of a tertiary healthcare hospital in Visakhapatnam, Andhra Pradesh, India

Study Population

The study included patients diagnosed with diabetes mellitus, aged 18 to 60 years, who were either visiting outpatient departments or admitted in inpatient units. Patients were considered eligible if they had been prescribed a consistent antidiabetic medication regimen for at least three months.

Inclusion Criteria

• Patients aged 18–60 years
• Diagnosed with diabetes mellitus
• Prescribed antidiabetic medication for at least 3 months
• Provided informed consent
• Recent HbA1c% reading available (within the last 6 months)

Exclusion Criteria

• Patients below 18 or above 60 years
• Unwilling or unable to provide informed consent
• Caregiver-dependent patients
• Patients without a recent HbA1c reading (older than 6 months)

Sample Size and Sampling Technique

A total of 205 patients were included in the study using a convenience sampling method.

Data Collection Tool

Medication adherence was assessed using the Malaysia Medication Adherence Assessment Tool (MYMAAT-21). This tool consists of 21 items covering five key domains:

1. Medication-taking behaviour
2. Perceived utility
3. Perceived barriers
4. Socio-cognitive factors
5. Perceived severity and susceptibility

Responses were recorded on a five-point Likert scale ranging from "strongly disagree" to "strongly agree", scored from 5 to 1 respectively. The total score ranged from 21 to 105, with higher scores indicating better adherence.

• Score > 95: Good adherence
• Score ≤ 95: Moderate or poor adherence

In addition to questionnaire responses, demographic data (age, gender) and the latest HbA1c% readings of participants were documented.

Data Collection Procedure

Participants were first briefed on the nature and purpose of the study and were required to sign an informed consent form. Each participant then completed the MYMAAT-21 questionnaire with assistance from the investigator when required. The data were anonymized to ensure confidentiality.

Statistical Analysis

Data were entered into Microsoft Excel and analyzed using SPSS software. Descriptive statistics such as frequencies, percentages, means, and standard deviations were used to summarize the data. The relationship between medication adherence and HbA1c% levels was assessed. The sensitivity and specificity of MYMAAT-21 in predicting poor glycemic control (HbA1c > 8%) were calculated.

Ethical Considerations

Approval for the study was obtained from the Institutional Ethics Committee and hospital administration. All participants were assured of confidentiality, and no identifying information was used in the analysis or reporting.

1.Baseline Demographic Characteristics of the Study Population

A total of 205 diabetic patients were included in the study. The mean age of the participants was 54.42 years with a standard deviation of 10.78 years, indicating that most of the study population was in the middle-aged to older adult category. The sample had a slightly higher proportion of female participants (54.6%) compared to males (45.4%). The mean HbA1c level across the study population was 8.49% (SD = 1.29), suggesting suboptimal glycemic control on average among the participants.

Table 1. Baseline Demographic and Clinical Characteristics of Participants (N = 205)

2. Medication Adherence Status

Based on the MYMAAT-21 scoring system, 114 out of 205 patients (55.6%) demonstrated good adherence to their prescribed antidiabetic medications, while 91 patients (44.4%) were categorized as moderately or poorly adherent. This indicates that nearly half of the diabetic patients surveyed exhibited some level of non-adherence, underscoring the need for focused interventions.

Figure1: Medication Adherence Status among Diabetic Patients

A bar chart displaying the distribution of adherence and non-adherence among the participants.

To statistically evaluate whether the observed adherence rate significantly differed from a neutral benchmark of 50%, a binomial test was conducted. The result was not statistically significant (p = 0.124), suggesting that the observed adherence rate of 55.6% does not significantly differ from a 50% adherence proportion (two-tailed binomial test).

3. Gender-wise Comparison of Medication Adherence

Among the 205 participants, 93 were male and 112 were female. Of the males, 49 (52.7%) were adherent and 44 (47.3%) were non-adherent. Among the females, 65 (58.0%) were adherent and 47 (42.0%) were non-adherent. While female patients exhibited a slightly higher adherence rate compared to males, the difference was not statistically significant.

Figure 2. Medication Adherence by Gender

A clustered bar chart showing the number of adherent and non-adherent patients by gender.

To test for an association between gender and adherence status, a Chi-square test of independence was performed. The result was not statistically significant, χ²(1, N = 205) = 0.39, p = 0.531, indicating no significant relationship between gender and medication adherence in this sample.

The odds ratio (OR) was calculated to be 0.81, suggesting that males had slightly lower odds of being adherent compared to females. However, this difference was not statistically meaningful

4. HbA1c% Levels vs Medication Adherence

Glycemic control, as measured by HbA1c levels, was significantly associated with medication adherence. Among the adherent group, 81 patients (71%) had HbA1c levels below 8%, while 33 (29%) had levels above 8%. In contrast, in the non-adherent group, only 10 patients (11%) had HbA1c levels below 8%, whereas 81 (89%) had poorly controlled glycemia with HbA1c > 8%.

Figure 3. HbA1c% Levels in Adherent vs Non-Adherent Patients

A grouped bar chart illustrating the distribution of glycemic control (HbA1c <8% or >8%) among adherent and non-adherent patients.

A Chi-square test of independence revealed a statistically significant association between medication adherence and glycemic control, χ²(1, N = 205) = 71.54, p< 0.001. This strongly suggests that patients who adhere to their medication regimens are more likely to achieve better glycemic control.

Furthermore, the odds ratio (OR) was calculated to be 19.88, indicating that adherent patients were nearly 20 times more likely to have an HbA1c level <8% compared to non-adherent patients. This highlights the clinical impact of medication adherence on diabetic outcomes.

5. Diagnostic Accuracy of MYMAAT-21 Compared to HbA1c% Levels

To evaluate the diagnostic accuracy of the MYMAAT-21 adherence tool, its classification of patients as adherent or non-adherent was compared against their most recent HbA1c% levels, using a cutoff of 8% as the threshold for good glycemic control.

The following definitions were used:

• True Positives (TP): Patients with HbA1c < 8% who were classified as adherent
• True Negatives (TN): Patients with HbA1c > 8% who were classified as non-adherent
• False Positives (FP): Patients with HbA1c > 8% but classified as adherent
• False Negatives (FN): Patients with HbA1c < 8% but classified as non-adherent

Table2: Confusion Matrix: MYMAAT-21 vs HbA1c%

Using the values from the confusion matrix, the following diagnostic metrics were calculated:

• Sensitivity: 89.01% — proportion of patients with controlled HbA1c correctly identified as adherent
• Specificity: 71.05% — proportion of patients with uncontrolled HbA1c correctly identified as non-adherent
• Positive Predictive Value (PPV): 71.05%
• Negative Predictive Value (NPV): 89.01%
• Overall Accuracy: 79.02%

These results demonstrate that MYMAAT-21 performs well as a screening tool, especially in identifying patients who are achieving glycemic control. The high sensitivity and NPV suggest that patients classified as adherent are indeed more likely to maintain target HbA1c levels, supporting the use of this questionnaire in clinical settings to monitor medication-taking behaviour.

6. Key Behavioural Patterns from MYMAAT-21

Patient-reported behaviours were assessed using the 21-item MYMAAT-21 tool, which explores various dimensions influencing medication adherence, including beliefs, perceptions, and barriers. Several behavioural trends emerged among the non-adherent population, offering insight into why patients might not follow their prescribed treatment regimens.

Figure 5. Key Behavioural Reasons for Non-Adherence (MYMAAT-21 Responses)

A horizontal bar chart showing the percentage of patients who agreed or strongly agreed with statements reflecting common non-adherence behaviours.

The most commonly reported behaviours contributing to non-adherence included:

• Missing follow-up appointments (35%)
• Reducing medication when feeling better (26%)
• Taking medication alternatively (24%)
• Fear of over-reliance on medication (24%)
• Frequently forgetting to take medication (23%)

Other reported issues included lack of interest in taking medication (15%), belief that the illness was not serious (11%), and perceived ineffectiveness of medication (10%).

These findings underscore the multifactorial nature of non-adherence, combining behavioural, psychological, and perceptual elements. Addressing these concerns through targeted education and behavioural interventions may help improve long-term adherence.

This study assessed medication adherence and its relationship with glycemic control among diabetic patients in a tertiary healthcare hospital using the MYMAAT-21 tool and HbA1c% levels. The findings provide insight into adherence behaviours, gender and glycemic trends, and the diagnostic utility of MYMAAT-21 in a real-world clinical setting.

Overall Adherence Rate and Comparison

In our study, 55.6% of diabetic patients demonstrated good adherence to antidiabetic medications. This aligns with reports from Sharma et al. in North India, who observed an adherence rate of 54.5% among type 2 diabetic patients [14]. Our findings also mirror results from Ethiopia, where 52.5% adherence was reported by Mitiku et al. [8]. Conversely, Murwanashyaka et al. reported significantly lower adherence (37.1%) in a Rwandan cohort [9], reflecting regional variations influenced by health literacy, system structure, and medication availability.

While a binomial test did not show a statistically significant deviation from a 50% benchmark (p = 0.124), the observed adherence still emphasizes the urgent need to address non-adherence in nearly half of the patient population.

Gender-wise Comparison of Adherence

Females in this study showed slightly better adherence (58%) than males (52.7%), though this difference was not statistically significant. This echoes the findings of Karymsakov et al., who also noted minor gender differences without a significant impact on adherence levels [10]. Although some literature suggests that gender-based social roles and health-seeking behaviour may influence adherence, studies including Unni et al. found no consistent gender-based adherence patterns in multi-year U.S. data [12].

Glycemic Control and Adherence Correlation

A strong and statistically significant association was observed between medication adherence and glycemic control. Among adherent patients, 71% had HbA1c < 8%, while 89% of non-adherent patients had HbA1c > 8% (χ² = 71.54, p< 0.001). This aligns with existing evidence that links non-adherence to suboptimal glycemic outcomes. Mann et al. highlighted that beliefs about treatment efficacy and disease perception are key drivers of adherence, directly influencing metabolic outcomes [15]. Similarly, Jufar et al. found non-adherence to be strongly associated with poor glycemic control in Ethiopian public hospitals [16].

These results support the WHO’s earlier conclusion that low adherence rates significantly contribute to worsening chronic disease progression, especially in resource-limited settings [8].

Diagnostic Performance of MYMAAT-21

The MYMAAT-21 tool showed robust diagnostic capability with 89.0% sensitivity, 71.1% specificity, and an overall accuracy of 79.0% when benchmarked against HbA1c% values. These values surpass the specificity reported in the original MYMAAT validation study (43%) and suggest better contextual performance in this Indian tertiary care setting. Structured adherence tools like MYMAAT have also been supported by Sharma et al. as practical and scalable options for monitoring adherence in outpatient settings [14].

Behavioural Insights from MYMAAT-21

MYMAAT-21 responses highlighted several behavioural drivers of non-adherence:

• Missed follow-up appointments (35%)
• Intentional dose reduction when feeling better (26%)
• Alternate intake of medication (24%)
• Forgetfulness (23%)
• Fear of medication over-reliance (24%)

These behaviours are consistent with findings from multiple international studies. Mitiku et al. and Murwanashyaka et al. both reported high levels of intentional dose skipping and poor appointment compliance due to low health literacy and perception of disease [8,9]. Karymsakov et al. emphasized that many patients perceive treatment as unnecessary once symptoms improve, a trend echoed in our results [10].

Such behavioural insights emphasize the importance of individualized patient education, counselling about long-term disease consequences, and addressing cognitive and emotional barriers to sustained medication adherence.

This study highlights that a substantial proportion (44.4%) of diabetic patients attending a tertiary healthcare facility exhibited moderate to poor adherence to their prescribed medications. Medication adherence was strongly associated with better glycemic control, as evidenced by significantly lower HbA1c levels among adherent patients. The findings reaffirm the utility of structured self-report tools such as MYMAAT-21 in identifying patients at risk for non-adherence, especially when paired with objective clinical markers like HbA1c%.

Behavioural insights drawn from the MYMAAT-21 responses suggest that forgetfulness, intentional dose skipping, missed follow-up appointments, and misconceptions about disease severity or treatment efficacy are key barriers to adherence. These findings are consistent with similar studies conducted in both Indian and international contexts, further underscoring the universal and multifactorial nature of medication non-adherence.

The high sensitivity and reasonable specificity of MYMAAT-21 in predicting glycemic control support its integration into routine clinical screening, particularly in resource-constrained settings. Targeted educational interventions, patient-centered counselling, and strengthened follow-up systems are essential strategies for addressing the root causes of non-adherence and improving long-term diabetes outcomes.

Limitations

This study was limited by its cross-sectional design, which restricts causal inference between adherence and glycemic control. Data were collected using a self-reported questionnaire, which may be subject to recall and social desirability bias. Additionally, the use of convenience sampling at a single tertiary care centre may limit the generalizability of the findings to broader populations.

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