Evaluating and Enhancing Diabetic Patients' Self-Care Practices, Disease-Related Knowledge, and Medication Adherence in Erode District: A Cross-Sectional Survey

Overview of Diabetes Mellitus

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both [1]. Persistent hyperglycemia damages multiple organ systems over time, particularly the cardiovascular system, kidneys, eyes, and peripheral nerves, leading to significant morbidity and mortality [2].

In 2022, approximately 14% of adults aged 18 years and older were living with diabetes globally, an increase from 7% in 1990. Alarmingly, 59% of adults aged 30 years and over living with diabetes were not taking medication for their condition [3].

Epidemiology of Diabetes

Diabetes is a significant public health concern worldwide. Its prevalence is rising due to aging populations, sedentary lifestyles, and increasing obesity rates. Globally, type 2 diabetes (T2DM) accounts for more than 90-95% of cases, whereas type 1 diabetes (T1DM) comprises 5-10% [3]. Developing countries face unique challenges, including limited healthcare infrastructure, low awareness, and economic barriers to treatment adherence [4].

The ICMR-INDIAB study revealed significant variations in diabetes prevalence across Indian states, with Tamil Nadu showing prevalence rates ranging from 10.4% to 13.5% in urban areas and 4.8% to 7.8% in rural areas [5].

Pathogenesis of Diabetic Complications

There are four possible mechanisms by which hyperglycemia causes neurotoxicity:

1. metabolic,
2. vascular,
3. immunologic-autoimmune, and
4. neurotrophic growth factor-deficiency pathways [6].

Metabolic Pathway

Both neurons and Schwann cells undergo apoptosis when reactive oxygen species (ROS) accumulate through any of these pathways [7].

Vascular Pathway

According to the EURODIAB Prospective Complications study, the occurrence of neuropathy is linked to modifiable cardiovascular risk factors such as increased triglyceride levels, body mass index, smoking, and hypertension [8].

Immunologic-Autoimmune Pathway

The proximal weakness of diabetic lumbosacral plexopathy and other asymmetrical localized mononeuropathies in diabetes are usually accompanied by vasculitis with lymphocytic infiltrate [9].

Neurotrophic and Growth Factor-Deficiency Pathway

In diabetic neuropathy, the local generation of neurotrophic growth factors is hampered as Schwann cells become dysfunctional or undergo apoptosis due to redox stress [10].

Diagnosis of Diabetes and Its Complications

Diabetic neuropathies are diagnosed using the patient's medical history, a clinical examination, and supplementary laboratory tests including:

• Assessment of muscle strength and reflexes
• Evaluation of sensitivity to position, vibration, temperature, and light touch
• Ultrasound to determine urinary tract function
• Electromyography to assess muscle response to electrical impulses
• Nerve conduction studies
• Skin biopsies to evaluate cutaneous nerve innervation
• Nerve and muscle biopsies for histopathological evaluation [11]

Management of Diabetes Mellitus

Lifestyle Modification

Medical Nutrition Therapy (MNT): A balanced diet focusing on portion control, reduced intake of refined carbohydrates, saturated fats, and increased consumption of fiber, fruits, and vegetables is crucial [12].

Physical Activity: At least 150 minutes of moderate-intensity aerobic activity per week, combined with resistance training, improves glycemic control and cardiovascular outcomes [13].

Pharmacological Therapy

Oral Hypoglycemic Agents: Metformin is the first-line therapy for most patients with T2DM due to its efficacy, safety, and cardiovascular benefits. Other classes include sulfonylureas, DPP-4 inhibitors, SGLT2 inhibitors, and GLP-1 receptor agonists, which may be used alone or in combination depending on patient characteristics [1].

Patient Education and Support

Studies have consistently demonstrated that DSMES improves self-care behaviors, medication adherence, and clinical outcomes [14,15].

Disease-Related Knowledge in Diabetes Mellitus

Patients who possess higher knowledge are more likely to engage in healthy behaviors and achieve better glycemic control [16].

A study by Murugesan et al. (2020) in India revealed that only 46% of diabetic patients had adequate knowledge about diabetes and its complications, highlighting a significant gap in awareness [17]. Similarly, Al-Qazaz et al. (2011) found that better knowledge was positively correlated with medication adherence and glycemic control among type 2 diabetic patients [18].

Importance of Disease-Related Knowledge

Understanding the impact of diet, physical activity, blood glucose monitoring, and medication adherence helps patients make informed decisions daily [19].

Knowledge about the mechanisms of action, expected benefits, and consequences of non-adherence reinforces the importance of taking medications consistently [20].

Empowered patients take responsibility for their own care, set achievable goals, and maintain motivation [21].

Self-Care Activities in Diabetes

The American Association of Diabetes Educators (AADE) identifies seven key self-care behaviors:

1. healthy eating,
2. being physically active,
3. monitoring blood glucose,
4. adherence to medications,
5. problem-solving in daily management,
6. healthy coping with stress, and
7. reducing risks (foot care, smoking cessation) [22].

Self-Monitoring of Blood Glucose (SMBG): Plays a critical role in adjusting diet, exercise, and medication. Yet, studies show that SMBG remains underutilized due to cost and lack of knowledge, especially in developing countries [23].

Foot Care: Diabetic foot problems are a major cause of hospitalization, but regular inspection, appropriate footwear, and prompt treatment can reduce risks. Research shows that patient adherence to foot care practices remains suboptimal [24].

Importance of Self-Care Management

Reduction of Healthcare Costs: Consistent self-care reduces hospital admissions and emergency visits related to diabetes complications. Poor self-care leads to uncontrolled blood sugar, increased complications, and higher medical costs [25].

Medication Adherence in Diabetes

According to the World Health Organization (2003), adherence to long-term therapy for chronic diseases averages only 50% in developed countries and is even lower in developing nations [26].

Importance of Medication Adherence

Reducing Hospitalizations and Healthcare Costs: Non-adherence significantly increases the risk of diabetes-related hospital admissions, emergency room visits, and overall healthcare costs [27].

Assessment Tools: DKQ-24, DSMQ, and MMAS-8

Diabetes Knowledge Questionnaire (DKQ-24)

DKQ-24 assesses patients' understanding of diabetes, including pathophysiology, complications, lifestyle, and medication. It is a 24-item questionnaire with true/false/don't know responses. Score interpretation: ≥75% = good knowledge; 50-74% = moderate; <50% = poor knowledge. Higher scores correlate with better self-management and adherence [28].

Diabetes Self-Management Questionnaire (DSMQ)

DSMQ evaluates self-care behaviors across four domains: glucose management, dietary control, physical activity, and healthcare use. It consists of 16 items rated on a 4-point Likert scale. Total score indicates overall self-care adherence. High DSMQ scores are associated with improved HbA1c levels [29].

Morisky Medication Adherence Scale (MMAS-8)

MMAS-8 assesses medication adherence using 8 questions regarding missed doses, timing, and self-discontinuation. Scores: 8 = high adherence; 6-7 = medium; <6 = low adherence. It is widely used in clinical practice and research for evaluating adherence patterns [30].

Rationale for the Study

Despite the availability of effective treatments, diabetes management remains suboptimal in many parts of India, particularly in rural and semi-urban areas. Erode District in Tamil Nadu represents a region with a significant diabetes burden but limited data on patient knowledge, self-care practices, and medication adherence patterns. Previous studies in Tamil Nadu have highlighted gaps in diabetes awareness and self-management [31,32], but few have implemented and evaluated structured counseling interventions in this specific geographic context.

This study addresses this gap by comprehensively assessing baseline knowledge, self-care behaviors, and medication adherence among diabetic patients in Erode District, implementing a structured counseling intervention, and evaluating its effectiveness in improving these critical parameters.

AIM AND OBJECTIVES

Aim

The aim of the study is to evaluate and enhance diabetic patients' self-care practices, disease-related knowledge, and medication adherence in Erode District through structured counseling interventions.

Primary Objectives

• To assess the knowledge of diabetes among diabetic patients using DKQ-24
• To study the patterns of self-care practice among diabetic patients using DSMQ
• To measure medication adherence among patients suffering from diabetes using MMAS-8

Secondary Objectives

• To study factors influencing self-care practice
• To determine the various risk factors influencing adherence to medication
• To educate patients to improve patient's knowledge, self-care practice, and medication adherence through structured counseling

MATERIALS AND METHODS

Study Design

This was a cross-sectional, community-based pre-post interventional study conducted to evaluate the effectiveness of structured counseling on diabetes-related knowledge, self-care practices, and medication adherence among patients with type 2 diabetes mellitus.

Study Site

The study was conducted in rural and urban areas of Erode District, Tamil Nadu, India. Erode District is located in the western part of Tamil Nadu and has a mixed population with diverse socioeconomic backgrounds, making it representative of the broader regional demographic.

Study Duration

The study was conducted over a period of six months, from April 2025 to September 2025.

Sample Size Determination

The sample size was determined using Cochran's formula for cross-sectional studies based on proportions:

n = Z²??α/? × p(1-p) / d²

Where:

• n = required sample size
• Z??α/? = standard normal value at desired confidence level (1.96 for 95% confidence)
• p = estimated prevalence or proportion (0.5 when unknown)
• d = allowable error (margin of precision) (0.064)

Calculation:

n = (1.96)² × 0.5(1-0.5) / (0.064)²

n = (3.8416) × 0.25 / 0.004096

n = 0.9604 / 0.004096

n = 235 participants

Assuming a 95% confidence level (Z = 1.96), expected proportion (p = 0.5), and allowable error (d = 0.064), the required sample size was 235 participants. To account for potential dropouts and incomplete responses, 250 questionnaires were distributed.

Study Criteria

Inclusion Criteria

• Diabetic patients who are more than 18 years of age
• Duration of illness of a minimum of 1 year
• Patients who are willing to participate in the study and provide informed consent

Exclusion Criteria

• Pregnant women
• Newly diagnosed diabetics (less than 1 year duration)
• Those who are not willing to participate in the study
• Patients who are severely ill or non-ambulatory
• Patients with communication problems
• Patients with physician-diagnosed psychiatric illness
• Patients older than 80 years (due to likelihood of greater recall bias)

Materials and Instruments

Face-to-face interviews were conducted to solicit sociodemographic and economic data, medication adherence, diabetic knowledge, and self-care behavior of participants. Validated and structured interviewer-administered questionnaires were used for data collection.

Diabetes Knowledge Questionnaire (DKQ-24)

The DKQ-24 is a validated, 24-item survey used to assess a person's understanding of diabetes. It is also used to evaluate the effectiveness of diabetes education by comparing a patient's knowledge before and after receiving instruction. The 24 questions are typically grouped into three areas:

• Basic knowledge: Questions about the general nature of the illness
• Blood sugar control: Items related to managing blood glucose
• Preventing complications: Questions about avoiding long-term health problems

Patients respond with "Yes," "No," or "I don't know" to each statement, making it accessible for individuals with lower literacy levels. "I don't know" is scored as an incorrect answer to prevent guessing. A total score is calculated based on the number of correct answers.

Scoring Interpretation:

• 20 and above: Adequate knowledge
• 14 to 19: Average knowledge
• 13 and below: Inadequate knowledge

Diabetes Self-Management Questionnaire (DSMQ)

The DSMQ consists of 16 items. Respondents are asked to reflect their self-management behaviors over the past weeks and rate the extent to which each statement applies to them on a 4-point scale (0-3). The questionnaire contains 7 positively worded items and 9 negatively worded (inversely scored) items. The inverse items must be recoded such that higher values indicate more effective self-care before summing item scores to scale scores.

Scoring Example for Inverse Items (Item 16):

• "Applies to me very much" = 3 points → Inverse score = 0 points
• "Applies to me to a considerable degree" = 2 points → Inverse score = 1 point
• "Applies to me to some degree" = 1 point → Inverse score = 2 points
• "Does not apply to me" = 0 points → Inverse score = 3 points

The 'Sum Scale' (SS) serves as a global measure of the quality of diabetes self-care, comprising all 16 items. A preliminary cut-off score ≤ 6.0 (total score) is proposed to facilitate the identification of suboptimal self-care.

Morisky Medication Adherence Scale (MMAS-8)

The MMAS-8 assesses medication adherence using 8 questions. Questions 1-7 are answered with a simple "Yes" or "No," while Question 8 uses a 5-point Likert scale. Scoring involves assigning points based on responses:

• For Questions 1-4 and 6-7: "No" = 1 point, "Yes" = 0 points
• For Question 5: "Yes" = 1 point, "No" = 0 points (reverse scored)
• Question 8: Never/Rarely = 1 point; Once in a while = 0.75 points; Sometimes = 0.5 points; Usually = 0.25 points; All the time = 0 points

Scoring Interpretation:

• Score 8: High adherence
• Score 6 to <8: Medium adherence
• Score <6: Low adherence

Sociodemographic Data Collection Form

A structured data collection form was used to record:

• Name (initials only for confidentiality)
• Gender (Male/ Female)
• Age
• Marital status (Married/ Unmarried/ Widowed/ Divorced)
• Education status (Illiterate/ Primary/ Secondary/ Graduate)
• Socioeconomic status (Employed/ Unemployed)

Data Collection Procedure

Data collection was conducted through interviews and questionnaire administration. The study objectives, procedures, potential risks and benefits, and confidentiality safeguards were explained in the local language (Tamil). Written informed consent was obtained from all participants before baseline assessment. Participants were given a copy of the consent form and contact details of the research team.

Baseline Assessment (Pre-Counseling)

After obtaining consent, baseline data were collected including:

• Sociodemographic details
• Clinical history
• Diabetes Knowledge (DKQ-24)
• Self-care practices (DSMQ)
• Medication Adherence (MMAS-8)

Intervention

Participants received a standardized structured counseling session focused on:

• Disease knowledge (nature of diabetes, causes, complications)
• Self-care behaviors (diet, physical activity, foot care, blood glucose monitoring)
• Medication adherence (importance of regular medication, consequences of non-adherence, strategies to remember doses)

The counseling was delivered by trained educators using an educational module and took approximately 20-30 minutes per participant. Educational materials included visual aids and take-home pamphlets summarizing key messages (see Appendix for patient education materials).

Follow-Up Assessment (Post-Counseling)

The primary follow-up assessment was scheduled at 3 weeks after counseling to measure short-term changes in knowledge, self-care practices, and adherence using the same instruments (DKQ-24, DSMQ, MMAS-8). The same assessor performed the post-test where possible to minimize measurement variability.

Minimizing Loss to Follow-Up

To maximize paired data completeness, the following strategies were employed:

• Appointment reminders via telephone and SMS
• Flexible follow-up scheduling
• Small travel reimbursements (where permitted by ethics)
• Up to two additional contact attempts for participants who missed planned follow-up
• Home visits considered for unreachable participants within logistic feasibility

Data Management

All questionnaires were checked at the point of collection for completeness. Data were double-entered into a secure database and validated. The primary analysis used paired-case data (participants with both baseline and post-intervention measures).

Statistical Analysis

Descriptive Statistics

Sociodemographic characteristics and baseline clinical data were summarized using frequencies, percentages, means, and standard deviations.

Comparative Analysis

• The primary paired comparison was conducted using the paired t-test for normally distributed data and the Wilcoxon signed-rank test for non-normally distributed data or data containing outliers.
• For categorical variables, the McNemar-Bowker test was used to assess changes in distribution before and after intervention.
• Effect sizes (paired Cohen's d) with 95% confidence intervals were reported for mean changes.
• For nonparametric results, median change and associated nonparametric effect-size measures were presented.

Statistical Significance

All tests were two-tailed, and a p-value < 0.05 was considered statistically significant.

Software

Analyses were performed using SPSS (Statistical Package for Social Sciences) version 26.0 (IBM Corp., Armonk, NY, USA).

Ethical Considerations

Ethical Approval

The study protocol was reviewed and approved by the Institutional Ethics Committee (IEC) of JKKMMRF's Annai JKK Sampoorani Ammal College of Pharmacy (Ref No: JKKM/ IEC/ M.Pharm/2025-19, dated 26 March 2025). The Ethics Committee is registered with the Drug Controller General of India (DCGI) with registration number "ECR/1918/Inst/TN/2024" under the New Drugs and Clinical Trials Rules, 2019, and operates in compliance with ICMR Guidelines and ICH-GCP Guidelines.

Informed Consent

Written informed consent was obtained from all participants before enrollment. The consent form (provided in both English and Tamil) explained:

• Purpose of the study
• Study procedures
• Potential risks and benefits
• Confidentiality safeguards
• Voluntary participation and right to withdraw at any time without consequences

Confidentiality

Confidentiality of participants was strictly maintained throughout the study. Data were stored in password-protected files accessible only to the research team. Participant names were replaced with unique identification numbers in the database.

Declaration of Helsinki

The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

RESULTS

Response Rate and Participant Flow

Out of 250 questionnaires distributed, 237 were returned as completed, resulting in a response rate of 95% for the study. All 237 participants completed both baseline and follow-up assessments, yielding complete paired data for analysis.

Sociodemographic Characteristics of Participants

Gender Distribution

The gender-wise distribution of patients is presented in Table 1 and Figure 1. It was observed that a large majority of the patients, 134 (56.5%), were female, whereas only 103 (43.5%) were male.

Table 1: Gender Distribution of Patients (N=237)

Sr. No	Gender	Number of Patients	Percentage (%)
1	Male	103	43.5
2	Female	134	56.5

Figure 1: Gender Distribution of Patients

Age Distribution

The age distribution of the study population ranged between 29 and 75 years, with the majority clustered in middle-aged categories (Table 2, Figure 2). The largest proportion of respondents were in the 51-60 years group (35.9%, n=85), followed by those aged 41-50 years (31.2%, n=74), highlighting a predominance of participants in their mid to late working years. Approximately 16.0% (n=38) of the sample fell within the 61-70 years group, while only 1.7% (n=4) were aged 71 years and above. Younger individuals were notably underrepresented, with 0.4% (n=1) below 30 years and 14.8% (n=35) between 31-40 years. Overall, the age profile demonstrates that the study primarily reflects a middle-aged and older adult demographic.

Figure 2: Age Distribution of Patients

Figure 3: Educational Status of Patients

Marital Status

The marital status distribution revealed that the vast majority of participants were married (82.7%, n=196), indicating strong representation of individuals in established households (Table 4, Figure 4). A smaller proportion were widowed (7.2%, n=17) and divorced (6.8%, n=16), reflecting participants who had experienced marital dissolution. Only 3.4% (n=8) of respondents were unmarried, showing limited participation of single individuals. This distribution suggests that the study population is predominantly composed of married adults, with relatively fewer participants representing other marital categories.

Table 6: Response to Knowledge-Based Questions (DKQ-24) Before and After Counseling

Sr. No	Question	Before Counseling Yes (N)	(%)	No (N)	(%)	After Counseling Yes (N)	(%)	No (N)	(%)
1	Eating too much sugar and other sweet foods is a cause of diabetes	237	100	0	0	237	100	0	0
2	The usual cause of diabetes is lack of effective insulin in the body	151	63.7	86	36.3	180	76.0	57	24.0
3	Diabetes is caused by failure of the kidneys to keep sugar out of the urine	142	59.9	95	40.1	207	87.3	30	12.7
4	Kidneys produce insulin	142	59.9	95	40.1	197	83.1	40	16.9
5	In untreated diabetes, the amount of sugar in the blood usually increases	237	100	0	0	237	100	0	0
6	If I am diabetic, my children have a higher chance of being diabetic	233	98.3	4	1.7	226	95.3	11	4.7
7	Diabetes can be cured	145	61.2	92	38.8	193	81.4	44	18.6
8	A fasting blood sugar level of 210 is too high	191	80.5	46	19.5	225	94.9	12	5.1
9	The best way to check my diabetes is by testing my urine	136	57.4	101	42.6	193	81.4	44	18.6
10	Regular exercise will increase the need for insulin or other diabetic medication	154	64.9	83	35.1	201	84.8	36	15.2
11	There are two main types of diabetes: Type 1 and Type 2	142	59.9	95	40.1	176	74.2	61	25.8
12	An insulin reaction is caused by too much food	144	60.8	93	39.2	198	83.5	39	16.5
13	Medication is more important than diet and exercise to control my diabetes	137	57.8	100	42.2	204	86.1	33	13.9
14	Diabetes often causes poor circulation	151	63.7	86	36.3	164	69.2	73	30.8
15	Cuts and abrasions on diabetics heal more slowly	122	51.5	115	48.5	172	74.7	65	25.3
16	Diabetics should take extra care when cutting their toenails	157	66.2	80	33.8	197	83.1	40	16.9
17	A person with diabetes should cleanse a cut with iodine and alcohol	131	55.3	106	44.7	138	58.3	99	41.7
18	The way I prepare my food is as important as the foods I eat	148	62.4	89	37.6	180	75.9	57	24.1
19	Diabetes can damage my kidneys	158	66.6	79	33.4	194	81.8	43	18.2
20	Diabetes can cause loss of feeling in my hands, fingers and feet	170	71.7	67	28.3	204	86.1	33	13.9
21	Shaking and sweating are signs of high blood sugar	138	58.2	99	41.8	198	83.5	39	16.5
22	Frequent urination and thirst are signs of low blood sugar	156	65.8	81	34.2	178	75.1	59	24.9
23	Tight elastic hose or socks are not bad for diabetics	146	62.6	91	38.4	198	83.5	39	16.5
24	A diabetic diet consists mostly of special foods	156	65.8	81	34.2	153	64.6	84	35.4

Table 7: Shift in Knowledge Levels Regarding Diabetes Following Counseling

Sr. No	Knowledge Level	Before Counseling (N=237)	%	After Counseling (N=237)	%
1	Average (14-19)	184	77.3	70	33.7
2	Low (≤13)	35	14.3	2	0.4
3	Adequate (≥20)	18	7.4	165	67.1

Table 8: Response to Diabetes Self-Management (DSMQ) - Pre vs Post Counseling

Sr. No	Subscale	Before (M ± SD)	After (M ± SD)	Mean Difference	t value	p value	Cohen's d
1	Glucose Management (GM)	7.84 ± 1.48	10.41 ± 1.56	2.56	22.57	<0.001	1.68
2	Dietary Control (DC)	7.89 ± 1.29	8.08 ± 1.29	0.19	7.34	<0.001	0.48
3	Physical Activity (PA)	6.41 ± 1.36	7.11 ± 0.80	0.70	7.63	<0.001	0.49
4	Health-Care Use (HU)	6.41 ± 2.11	7.24 ± 0.69	0.84	49.91	<0.001	3.24
5	Self-Care (Overall)	1.97 ± 1.00	2.27 ± 0.70	0.29	—	—	—

Glucose Management (GM)

Participants' GM scores improved significantly following counseling, increasing from 7.84 ± 1.48 before counseling to 10.41 ± 1.56 after counseling. The mean difference of 2.56 points was highly significant (t = 22.57, p < 0.001), with a very large effect size (Cohen's d = 1.68). This indicates substantial improvement in participants' ability to manage glucose levels.

Dietary Control (DC)

Dietary control improved slightly from 7.89 ± 1.29 to 8.08 ± 1.29, with a mean difference of 0.19 points. Despite the small change, the improvement was statistically significant (t = 7.34, p < 0.001), with a medium effect size (Cohen's d = 0.48), suggesting modest but meaningful improvements in dietary self-management.

Physical Activity (PA)

The PA subscale increased from 6.41 ± 1.36 to 7.11 ± 0.80, reflecting a mean difference of 0.70 points. This change was statistically significant (t = 7.63, p < 0.001), with a medium effect size (Cohen's d = 0.49), indicating moderate improvement in physical activity behaviors post-counseling.

Health-Care Use (HU)

HU scores rose from 6.41 ± 2.11 to 7.24 ± 0.69, corresponding to a mean difference of 0.84 points. This was highly significant (t = 49.91, p < 0.001), with an exceptionally large effect size (Cohen's d = 3.24), suggesting counseling led to a dramatic increase in appropriate healthcare utilization.

Self-Care (Overall)

Overall self-care improved from 1.97 ± 1.00 to 2.27 ± 0.70, with a mean difference of 0.29 points. The descriptive statistics suggest a positive trend, indicating improved overall self-care practices.

Figure 8: Response to Diabetes Self-Management - Pre vs Post Counseling

[Comparative bar chart showing improvement across all DSMQ subscales]

Comparison of Self-Care Status

Table 9 and Figure 9 present the comparison of self-care status before and after counseling based on the DSMQ cut-off scores.

Table 9: Comparison of Self-Care Status Before and After Counseling (N=237)

Before counseling, only 161 (67.9%) participants demonstrated optimal self-care, while 76 (32.1%) participants were categorized as having suboptimal self-care. However, after the counseling intervention, the number of participants with optimal self-care markedly increased to 236 (99.6%), with only 1 participant (0.4%) remaining in the suboptimal care category. This finding highlights the substantial improvement in self-care practices following counseling, indicating the effectiveness of the intervention.

[Bar chart showing: Before counseling: Optimal (67.9%), Suboptimal (32.1%); After counseling: Optimal (99.6%), Suboptimal (0.4%)]

Factors Influencing Self-Care Practice

Table 10 presents the comparison of self-care practice before and after counseling across sociodemographic factors, including gender, age, marital status, education, and socioeconomic status.

Table 10: Factors Influencing Self-Care Practice: A Comparison Before and After Counseling

Key Observations:

Before counseling, the majority of participants across all groups were concentrated in the Low self-care category, with relatively few in the Medium and almost none in the High category. This indicates that baseline knowledge and adherence to self-care practices were inadequate, regardless of demographic background.

After counseling, there was a marked and consistent improvement across all factors:

Gender: Both males and females showed significant improvement. Among females, those in the Medium category rose from 36 to 110, and in High from 3 to 9. Among males, High increased from 0 to 7, and Medium from 27 to 90.

Age: Participants in all age groups benefited. For example, in the 31-45 group, High increased from 1 to 7 and Medium from 29 to 67. In the 46-60 group, High rose from 2 to 6 and Medium from 25 to 100. Even participants aged 61+ improved, with Medium increasing from 9 to 32.

Marital Status: Married participants had the largest improvement, with Medium rising from 50 to 164. Single participants (unmarried, widowed, divorced) also improved, with Medium rising from 11 to 27.

Education: Clear benefits were observed in all education groups. Graduates saw High rise from 0 to 7, and Medium from 17 to 37. Participants with school-level education improved dramatically, with Medium increasing from 42 to 147. Even those with no education showed gains, with Medium rising from 4 to 16.

Socioeconomic Status: Both employed and unemployed groups shifted strongly from Low to Medium. Among the employed, Medium rose from 47 to 148, while among the unemployed it increased from 16 to 52.

Statistical Confirmation for Self-Care Improvement

• Wilcoxon Signed-Rank Test: Showed a highly significant overall improvement in self-care scores after counseling (W = 308.5, p < 0.001)
• McNemar-Bowker Test: Confirmed a highly significant shift in categorical distribution (χ² = 148.2, df = 3, p < 0.001), indicating that participants moved from Low to Medium and High categories after the intervention

Medication Adherence Assessment (MMAS-8)

Item-wise Response Analysis

Table 11 presents the comparison of medication adherence responses before and after counseling, assessed across eight questionnaire items. Mean scores with standard deviations were calculated for each item, and paired-sample t-tests were conducted to evaluate changes following the intervention. Effect sizes were reported using Cohen's d.

Table 11: Response for Medication Adherence (MMAS-8) - Pre vs Post Counseling

Key Findings:

The results indicate significant improvements in medication adherence after counseling across all items (p < 0.05 to p < 0.001):

• Forgetting to take medicine: Mean score increased from 0.637 ± 0.482 to 0.865 ± 0.343 (p < 0.001, d = 0.545)
• Missed days in past two weeks: Improved from 0.574 ± 0.496 to 0.705 ± 0.457 (p = 0.003, d = 0.275)
• Stopping medication without telling doctor: Improved from 0.578 ± 0.495 to 0.840 ± 0.368 (p < 0.001, d = 0.601)
• Forgetting medication when traveling: Improved from 0.586 ± 0.494 to 0.826 ± 0.380 (p < 0.001, d = 0.548)
• Took medication yesterday: Improved from 0.426 ± 0.496 to 0.793 ± 0.406 (p < 0.001, d = 0.814) – this showed one of the largest improvements
• Stopping when symptoms controlled: Improved from 0.591 ± 0.493 to 0.841 ± 0.366 (p < 0.001, d = 0.583)
• Feeling hassled about treatment: Improved from 0.638 ± 0.482 to 0.844 ± 0.364 (p < 0.001, d = 0.486)
• Difficulty remembering all medications: Improved from 0.621 ± 0.207 to 0.783 ± 0.153 (p < 0.001, d = 0.887) – this showed the largest effect size

Figure 10: Response for Medication Adherence - Pre vs Post Counseling

[Comparative bar chart showing improvement across all MMAS-8 items]

Factors Influencing Medication Adherence

Table 12 presents the comparison of medication adherence before and after counseling across sociodemographic factors.

Table 12 Factors Influencing Medication Adherence: A Comparison Before and After Counseling