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INTRODUCTION
Diabetes Mellitus (DM) is a long-term metabolic condition marked by elevated blood glucose levels resulting from inadequate insulin secretion, insulin resistance, or a combination of both (American Diabetes Association, 2021). It is associated with disturbances in macronutrient metabolism (Voet and Voet, 2000; Mohan, 2010). DM is a major global health issue, with an estimated 425 million cases in 2017, projected to increase significantly (Benedict et al., 2018). If left uncontrolled, it can lead to microvascular and macrovascular complications, increasing morbidity, and mortality, and reducing Health-Related Quality of Life (HRQOL). The prevalence of diabetes is rising globally, with India experiencing a significant burden (Anjana et al., 2017). More than 95% of adults with diabetes have Type 2 DM (T2DM). India ranks second globally in diabetes prevalence, with approximately 69 million cases in 2015 (Unnikrishnan et al., 2016).
By 2021, the International Diabetes Federation estimated 74 million diagnosed cases in India, with an additional 40 million undiagnosed. Diabetes-related complications include microvascular issues such as retinopathy, neuropathy, and nephropathy, as well as macrovascular conditions like coronary artery disease, cerebrovascular disease, peripheral artery disease, and heart failure. These complications contribute to approximately 4 million diabetes-related deaths annually (International Diabetes Federation, 2019). Although the prevalence of diabetes is comparable between men and women, it tends to be higher in women after the age of 65 (Roglic, 2009).
Women face unique physiological and socioeconomic challenges in diabetes management. Conditions such as Gestational Diabetes Mellitus (GDM) and Polycystic Ovary Syndrome (PCOS) raise their likelihood of developing T2DM (Buchanan and Xiang, 2005; Parker, 2023). Limited healthcare access, lower health literacy, and caregiving responsibilities further hinder disease control (Hill-Briggs et al., 2020). Studies indicate that women often have lower awareness of diabetes, its complications, and the importance of treatment adherence (Fatema et al., (2017). Poor glycemic control in women can lead to adverse maternal and fetal outcomes, increased cardiovascular risks, and reduced quality of life (Kautzky-Willer et al., 2016). This highlights the need for gender-specific diabetes awareness programs.
Effective diabetes management includes Oral Hypoglycemic Agents (OHAs), insulin therapy, lifestyle modifications, and regular monitoring (Shrivastava et al., 2013). The goal of treatment is to reduce the risk of complications while enhancing both life expectancy and overall quality of life (Tipnis and Bajaj, 2011). However, adherence to treatment remains a major challenge, particularly among women, due to social and economic barriers (Rezaei et al., 2019). Addressing these challenges through structured education and awareness campaigns is essential for better disease control.
Knowledge, Attitude, and self-care Practices (KAP) play a crucial role in diabetes management (Wang et al., 2024). A patient's understanding of their disease and adherence to self-care practices play a crucial role in achieving treatment goals and effectively managing their condition (Okonta et al., 2014). KAP surveys assess baseline knowledge, attitudes, and behaviors, aiding in the development of targeted educational programs (Al-Mutawaa et al., 2022). These studies help tailor health interventions to improve disease control (Chawla et al., 2019). The long-term nature of diabetes and the need for patients to manage their daily care in an outpatient setting highlight the importance of promoting and reinforcing self-care practices for all individuals with diabetes (Niguse et al., 2019). Adequate information enables individuals to understand diabetes risks, seek timely treatment, and adopt preventive measures (Ahmad and Joshi, 2023). Research using the KAP framework shows that many women, particularly those with GDM, have limited knowledge and inadequate self-care practices, highlighting the need for targeted interventions (Durghashree et al., 2024; Mantri et al., 2024).
Despite the increasing prevalence of diabetes among women, there is limited research on their KAP in India. Understanding these factors can help identify gaps in awareness and barriers to effective management. This study aims to assess KAP among women with diabetes, evaluate the effectiveness of existing antidiabetic campaigns, and provide recommendations for improving diabetes education and healthcare accessibility. By addressing knowledge gaps and promoting positive health behaviors, this study can contribute to better diabetes management and improved HRQOL for women.
MATERIALS AND METHODS
Participants and Data Collection
This research was designed as a prospective, interventional, randomized controlled trial. Participants were randomly allocated into two groups: an intervention group and a control group. The study was carried out over 12 months at KLE’s Dr. Prabhakar Kore Hospital and Medical Research Centre (MRC), located in Nehru Nagar, Belagavi. Eligible participants included women aged 18 years or older who had been diagnosed with diabetes mellitus and were undergoing anti-diabetic treatment. At the beginning of the study, all participants were informed about the study’s purpose, confidentiality measures, and their right to withdraw at any stage. Written informed consent was obtained before enrolment. A total of 218 women with diabetes took part in the study. Each participant completed a structured form capturing demographic data, socio-economic background, and clinical history. The study adhered to the ethical standards outlined in the Declaration of Helsinki and received approval from the institutional ethics committee. Participants were provided assistance as needed while filling out the data collection form. The KAP levels were evaluated using self- prepared questionnaire consisting of 25-items, which included 11 questions on knowledge, 7 on attitudes, and 7 on practices.
Ethical Clearance
Ethical clearance was obtained from the Institutional Ethical Committee of KLE Academy of Higher Education and Research, Belagavi (Reference number: KAHER/ EC/2223/134).
Questionnaire
The questionnaire used to assess KAP among women diabetic patients included sections on knowledge, attitude, and practice-related aspects. The questionnaire consisted of two sections: the first section gathered demographic details, and the second evaluated Knowledge, Attitudes, and practices (KAP) related to diabetes. This section comprised 11 knowledge questions, 7 questions assessing attitudes, and 7 addressing practices. The knowledge section examined participants' awareness of diabetes symptoms, risk factors, severity, prevention, and management, using a yes/no response format, where affirmative answers were given one point and negative responses received zero. The attitude and practice sections covered aspects such as blood glucose monitoring, physical activity, medication adherence, dietary habits, and follow-up routines, with predefined response options (Raj et al., 2010). Three physicians reviewed and validated the questionnaire, with only minor language modifications suggested. The study involved 218 diabetic patients, both inpatients and outpatients with comorbidities, who were interviewed. Data on demographics, medical history, lab results, progress records, and prescribed treatments were documented using a structured data collection tool. The KAP levels were evaluated using self- prepared questionnaire consisting of 25-items, which included 11 questions on knowledge, 7 on attitudes, and 7 on practices. Results were presented using descriptive statistics, including means, percentages, tables, graphs, and charts, and a multivariate analysis was conducted to explore significant associations among variables.
Statistical analysis
The collected data were entered into Microsoft Excel and carefully reviewed to detect any discrepancies or missing information. Once data accuracy and completeness were ensured, responses from all 218 participants were coded for further analysis. Statistical analysis was performed using SPSS Version 16.0. Bivariate analysis was used to explore potential factors affecting KAP among participants, followed by multivariate analysis to control for confounding variables and identify independent predictors of KAP. Associations were expressed as Odds Ratios (OR) with 95% Confidence Intervals (CI), and a p-value of less than 0.05 was considered statistically significant.
RESULTS
A total of 218 participants were enrolled in the study, with an equal distribution between the control group (109) and the intervention group (109). Demographic and clinical characteristics are summarized in Table 1. The majority of participants were between 41-50 years of age (37.2%), followed by those aged 31-40 years (26.6%). Notably, a higher proportion of individuals aged 51-60 years was observed in the intervention group (32.1%) compared to the control group (11.9%). A considerable number of participants were either overweight (38.1%) or obese (19.7%), with similar distributions across both groups.
| Sl. No. | Demographics | Category | Control Group (n=109) | Intervention Group (n=109) | Total (n=218) |
|---|---|---|---|---|---|
| 1 | Age Group | 21-30 years | 3 (2.8%) | 4 (3.7%) | 7 (3.2%) |
| 31-40 years | 27 (24.8%) | 31 (28.4%) | 58 (26.6%) | ||
| 41-50 years | 48 (44.0%) | 33 (30.3%) | 81 (37.2%) | ||
| 51-60 years | 13 (11.9%) | 35 (32.1%) | 48 (22.0%) | ||
| 61-70 years | 14 (12.8%) | 5 (4.6%) | 19 (8.7%) | ||
| Above 70 years | 4 (3.7%) | 1 (0.9%) | 5 (2.3%) | ||
| 2 | Body Mass Index (BMI) | Underweight (<18.5) | 5 (4.6%) | 4 (3.7%) | 9 (4.1%) |
| Normal weight (18.5-24.9) | 40 (36.7%) | 43 (39.4%) | 83 (38.1%) | ||
| Overweight (25-29.9) | 42 (38.5%) | 41 (37.6%) | 83 (38.1%) | ||
| Obese (>30) | 22 (20.2%) | 21 (19.3%) | 43 (19.7%) | ||
| 3 | Duration Of Diabetes | < 1 year | 9 (8.3%) | 6 (5.5%) | 15 (6.9%) |
| 1-5 years | 34 (31.2%) | 32 (29.4%) | 66 (30.3%) | ||
| 6-10 years | 42 (38.5%) | 56 (51.4%) | 98 (45.0%) | ||
| >10 years | 24 (22.0%) | 15 (13.8%) | 39 (17.9%) | ||
| 4 | Family History of Diabetes | Parents | 9 (8.3%) | 6 (5.5%) | 15 (6.9%) |
| Grandparents | 34 (31.2%) | 32 (29.4%) | 66 (30.3%) | ||
| Siblings | 42 (38.5%) | 56 (51.4%) | 98 (45.0%) | ||
| Others | 24 (22.0%) | 15 (13.8%) | 39 (17.9%) | ||
| 5 | Residence | Rural | 85 (78.0%) | 87 (79.8%) | 172 (78.9%) |
| Urban | 24 (22.0%) | 22 (20.2%) | 46 (21.1%) | ||
| 6 | Literacy Status | Literate | 97 (89.0%) | 98 (89.9%) | 195 (89.4%) |
| Illiterate | 12 (11.0%) | 11 (10.1%) | 23 (10.6%) | ||
| 7 | Occupation | Employed | 46 (42.2%) | 43 (39.4%) | 89 (40.8%) |
| Unemployed | 63 (57.8%) | 66 (60.6%) | 129 (59.2%) | ||
| 8 | Tobacco | Tobacco chewer | 5 (4.6%) | 4 (3.7%) | 9 (4.1%) |
| Non-Tobacco chewer | 104 (95.4%) | 105 (96.3%) | 209 (95.9%) | ||
| 9 | Comorbidity | Hypertension | 36 (33.0%) | 40 (36.7%) | 76 (34.9%) |
| Ischemic Heart Disease | 5 (4.6%) | 7 (6.4%) | 12 (5.5%) | ||
| Coronary Artery Disease | 2 (1.8%) | 3 (2.8%) | 5 (2.3%) | ||
| Rheumatic Heart Disease | 1 (0.9%) | 1 (0.9%) | 2 (0.9%) | ||
| Hyperthyroidism | 2 (1.8%) | 8 (7.3%) | 10 (4.6%) | ||
| Hypothyroidism | 6 (5.5%) | 8 (7.3%) | 14 (6.4%) | ||
| Chronic Kidney Disease | 7 (6.4%) | 12 (11.0%) | 19 (8.7%) | ||
| Acute Kidney Injury | 1 (0.9%) | 1 (0.9%) | 2 (0.9%) | ||
| Anaemia | 3 (2.8%) | 1 (0.9%) | 4 (1.8%) | ||
| Metabolic acidosis | 1 (0.9%) | 1 (0.9%) | 2 (0.9%) | ||
| Nil | 49 (45.0%) | 38 (34.9%) | 87 (39.9%) | ||
| 10 | Diabetic Complications | Neuropathy | 9 (8.3%) | 11 (10.1%) | 20 (9.2%) |
| Retinopathy | 9 (8.3%) | 9 (8.3%) | 18 (8.3%) | ||
| Ketoacidosis | 7 (6.4%) | 8 (7.3%) | 15 (6.9%) | ||
| Nephropathy | 7 (6.4%) | 11 (10.1%) | 18 (8.3%) | ||
| Diabetic foot | 6 (5.5%) | 11 (10.1%) | 17 (7.8%) | ||
| Nil | 71 (65.1%) | 59 (54.12%) | 130 (62.4%) |
Regarding diabetes duration, 45.0% of participants had the condition for 6-10 years, with a larger percentage in the intervention group (51.4%) compared to control group (38.5%). Diabetes duration exceeding 10 years was reported by 17.9% of participants as shown in Figure 1. A family history of diabetes was prevalent, with 6.9% reporting parental history, 30.2% citing affected grandparents, and 17.9% mentioning other relatives. A majority (78.9%) resided in rural areas, and 89.4% were literate, with similar distributions in both groups. More than half (59.2%) were unemployed, with slightly more in the intervention group (60.6%) than in the control group (57.8%). Tobacco use was rare, with 95.9% of participants being non-users.
Hypertension was the most frequent comorbidity (34.9%), followed by chronic kidney disease (8.7%), hypothyroidism (6.4%), and ischemic heart disease (5.5%). Additionally, 39.9% of participants had no reported comorbidities. Neuropathy (9.2%), retinopathy (8.3%), and nephropathy (8.3%) were the most commonly observed diabetic complications. Notably, 59.5% of participants reported no diabetes-related complications as shown in Figure 2.
Overall responses to knowledge-based questionnaires
Most participants (86.7%) had heard of diabetes, but awareness of measurement methods (45.87%) and the Glucose Tolerance Test (6.42%) was low. While 66.06% recognized the need for lifestyle modifications, only 26.61% knew that high blood pressure could worsen diabetes. Participation in awareness programs was minimal (13.3%) as shown in Figure 3.
Knowledge comparison between control and interventional groups
Both groups had similar awareness levels, with around 40% recognizing diabetes. The interventional group showed slightly better knowledge of diabetes measurement (24% vs. 21.85%) and organ damage risks (16.94% vs. 9.66%). Exercise benefits were acknowledged more in the intervention group (36.92% vs. 32.8%), but misconceptions about medication use persisted. Awareness program participation remained low in both groups as shown in Table 2.
| Sl. No. | Questionnaires | Control Group | Interventional Group | Total | |||
|---|---|---|---|---|---|---|---|
| Yes (%) | No (%) | Yes (%) | No (%) | Yes (%) | No (%) | ||
| 1 | Are you familiar with diabetes? | 94 (39.50) | 25 (10.50) | 95 (40.00) | 4 (1.70) | 189 (86.70) | 29 (13.30) |
| 2 | Does obesity play the major role in causing diabetes? | 75 (31.50) | 44 (18.50) | 74 (31.00) | 25 (10.50) | 149 (68.35) | 69 (31.65) |
| 3 | Do you know how to measure diabetes? | 52 (21.85) | 67 (28.15) | 48 (24.00) | 51 (26.00) | 100 (45.87) | 118 (54.13) |
| 4 | Do you know about the glucose tolerance test? | 7 (2.90) | 98 (41.20) | 7 (3.50) | 98 (41.98) | 14 (6.42) | 204 (93.58) |
| 5 | Does a diabetic patient need lifestyle modification? | 75 (31.50) | 44 (18.50) | 69 (34.50) | 30 (15.00) | 144 (66.06) | 74 (33.94) |
| 6 | Are you aware that exercise can help manage diabetes? | 78 (32.80) | 40 (16.80) | 74 (36.92) | 26 (13.00) | 152 (69.72) | 66 (30.28) |
| 7 | Could high blood pressure lead to a worsening of the disease? | 29 (12.18) | 70 (29.41) | 29 (14.43) | 90 (37.81) | 58 (26.61) | 160 (73.39) |
| 8 | Can a fibre-rich diet help in managing diabetes? | 58 (24.37) | 53 (22.27) | 53 (26.55) | 54 (26.81) | 111 (50.92) | 170 (49.08) |
| 9 | Could medicines alone control diabetes? | 47 (19.75) | 57 (23.95) | 57 (28.96) | 87 (27.00) | 104 (47.71) | 144 (52.29) |
| 10 | Can diabetes lead to complications in other organs? | 23 (9.66) | 77 (32.35) | 35 (16.94) | 83 (34.92) | 58 (26.61) | 160 (73.309) |
| 11 | Did you attend a diabetes awareness program? | 15 (6.30) | 94 (39.50) | 14 (7.00) | 66 (37.80) | 29 (13.30) | 189 (86.70) |
Overall responses to attitude-based questionnaires
Figure 4 shows, less than half of the participants (41.74%) preferred daily exercise, and only 45.41% monitored their blood sugar levels monthly. Most (88.07%) disagreed that taking two doses would control diabetes faster. Regular medication use (82.57%) and doctor check-ups (83.49%) were widely accepted. However, 37.16% believed long-term treatment could cause organ failure, and only 20.64% preferred quarterly HbA1c testing.
Attitude comparison between control and interventional groups
Exercise preference was similar in both groups (21.56% vs. 20.18%). Blood sugar monitoring was slightly higher in the intervention group (23.39% vs. 22.02%). Regular medication adherence and doctor visits were comparable between groups (42.20% vs. 40.37% and 42.66% vs. 40.83%, respectively). Misconceptions about treatment effects persisted, with 20.18% in the intervention and 16.97% in the control group believing it leads to organ failure. HbA1c testing preference remained low across both groups as shown in Table 3.
| Sl. No. | Questionnaires | Control Group | Interventional Group | Total (%) | |||
|---|---|---|---|---|---|---|---|
| Yes (%) | No (%) | Yes (%) | No (%) | Yes (%) | No (%) | ||
| 1 | Would you prefer daily exercise? | 44 (20.18) | 65 (29.82) | 47 (21.56) | 62 (28.44) | 91 (41.74) | 127 (58.26) |
| 2 | Would you prefer to measure your Fasting Blood Sugar (FBS) and Post Prandial Blood Sugar (PPBS) levels every month? | 48 (22.02) | 61 (27.98) | 51 (23.39) | 58 (26.61) | 99 (45.41) | 119 (54.59) |
| 3 | Do you think that two doses will help manage diabetes more effectively? | 12 (5.50) | 83 (38.07) | 14 (6.42) | 85 (39.00) | 26 (11.93) | 168 (88.07) |
| 4 | Do you choose to take your medications every day? | 88 (40.37) | 19 (8.72) | 92 (42.20) | 19 (8.72) | 180 (82.57) | 38 (17.43) |
| 5 | Do you feel regular visits to your doctor are essential? | 89 (40.83) | 17 (7.80) | 93 (42.66) | 19 (8.72) | 182 (83.49) | 36 (16.51) |
| 6 | Do you feel that prolonged treatments will lead to organ failure? | 37 (16.97) | 64 (29.36) | 44 (20.18) | 73 (33.49) | 81 (37.16) | 137 (62.84) |
| 7 | Would you like to measure your HbA1c (Glycated hemoglobin) levels every 3 months? | 20 (1) | 68 (31.19) | 25 (11.47) | 75 (34.40) | 45 (20.64) | 143 (79.36) |
Overall responses to practice-based questionnaires
Less than half of the participants (42.66%) exercised daily, and 49.54% monitored their blood glucose monthly. Only 22.48% followed a small and frequent meal pattern, while 43.12% avoided rice. Regular medication adherence was reported by 61.01%, but only 23.85% underwent HbA1c testing every three months. Tobacco chewing was noted in 4.1% of respondents as shown in Figure 5..
Practice comparison between control and interventional groups
Exercise habits were similar between groups (44.04% in the intervention vs. 41.28% in the control). Monthly blood glucose monitoring was slightly lower in the intervention group (48.62% vs. 50.46%). Small, frequent meals were rarely followed, with no significant difference between groups. Medication adherence was slightly higher in the control group (62.39% vs. 59.63%), while HbA1c testing remained low across both groups. Tobacco Chewing was reported at similar rates in both groups shown in Table 4.
| Sl. No. | Questionnaires | Control Group | Interventional Group | Total (%) | |||
|---|---|---|---|---|---|---|---|
| Yes (%) | No (%) | Yes (%) | No (%) | Yes (%) | No (%) | ||
| 1 | Are you involved in 30–60 minutes of physical activity every day? | 45 (41.28) | 64 (58.72) | 48 (44.04) | 61 (55.96) | 93 (42.66) | 125 (57.34) |
| 2 | Are you monitoring your blood glucose levels every month? | 55 (50.46) | 54 (49.54) | 53 (48.62) | 56 (51.38) | 108 (49.54) | 110 (50.46) |
| 3 | Are you regularly eating small, frequent meals? | 26 (23.85) | 83 (76.15) | 23 (21.10) | 88 (78.90) | 49 (22.48) | 169 (77.52) |
| 4 | Are you avoiding rice in your meals? | 47 (42.34) | 64 (57.66) | 47 (43.12) | 60 (56.88) | 94 (43.12) | 124 (56.88) |
| 5 | Are you taking medications regularly? | 68 (62.39) | 41 (37.61) | 65 (59.63) | 44 (40.37) | 133 (61.01) | 85 (38.99) |
| 6 | Are you checking your HbA1c levels every 3 months? | 28 (25.69) | 81 (74.31) | 24 (22.02) | 85 (77.98) | 52 (23.85) | 166 (76.15) |
| 7 | Do you have a habit of chewing tobacco | 5 (4.6) | 104 (95.4) | 4 (3.7) | 105 (96.3) | 9 (4.1) | 209 (95.9) |
Participants aged 41-50 showed the highest improvement in knowledge (14.68%), attitude (13.76%), and practice (12.84%) in the intervention group, while younger (21-30 years) and older (>70 years) individuals had lower improvements. Normal-weight individuals (18.5-24.9 BMI) exhibited the highest gains in knowledge (15.60%) and practice (13.76%), whereas overweight (25-29.9 BMI) and obese (≥30 BMI) individuals had slightly lower improvements. Those with diabetes for 6-10 years showed the most improvement across knowledge (14.68%), attitude (11.93%), and practice (12.84%), while those diagnosed for less than a year had minimal changes. Urban participants demonstrated slightly higher gains in knowledge (20.18%) and practice (19.27%) compared to rural participants. Literate individuals showed greater improvement in all domains-knowledge (27.52%), attitude (23.85%), and practice (25.69%)-compared to illiterate participants as shown in Table 5.
| Variables | Total N (%) | Participants who achieved a knowledge score of ≥6 | Participants who achieved an attitude score of ≥ 4 | Participants who achieved a practice score of ≥ 4 | |||
|---|---|---|---|---|---|---|---|
| (Control) | (Intervention) | (Control) | (Intervention) | (Control) | (Intervention) | ||
| AGE (Years) | |||||||
| 21-30 | 7 (3.21) | 1 (0.92) | 2 (1.83) | 1 (0.92) | 1 (0.92) | 1 (0.92) | 1 (0.92) |
| 31-40 | 58 (26.61) | 10 (9.17) | 10 (9.17) | 8 (7.34) | 7 (6.42) | 9 (8.26) | 9 (8.26) |
| 41-50 | 81 (37.16) | 14 (12.84) | 16 (14.68) | 12 (11.01) | 13 (11.93) | 13 (11.93) | 14 (12.84) |
| 51-60 | 48 (22.02) | 9 (8.26) | 9 (8.26) | 7 (6.42) | 9 (8.26) | 8 (7.34) | 9 (8.26) |
| 61-70 | 19 (8.72) | 4 (3.67) | 4 (3.67) | 3 (2.75) | 4 (3.67) | 3 (2.75) | 3 (2.75) |
| >70 | 5 (2.29) | 2 (1.83) | 1 (0.92) | 2 (1.83) | 1 (0.92) | 1 (0.92) | 1 (0.92) |
| BMI (mg/dL) | |||||||
| Under weight (<18.5) | 15 (6.88) | 3 (2.75) | 3 (2.75) | 2 (1.83) | 3 (2.75) | 2 (1.83) | 3 (2.75) |
| Normal weight (18.5-24.9) | 85 (38.99) | 15 (13.76) | 17 (15.60) | 13 (11.93) | 14 (12.84) | 14 (12.84) | 15 (13.76) |
| Over weight (25-29.9) | 70 (32.11) | 13 (11.93) | 14 (12.84) | 11 (10.09) | 12 (11.01) | 11 (10.09) | 12 (11.01) |
| Obese (≥30) | 48 (22.02) | 9 (8.26) | 9 (8.26) | 7 (6.42) | 8 (7.34) | 7 (6.42) | 8 (7.34) |
| Duration of Diabetes | |||||||
| <1Year | 20 (9.17) | 4 (3.67) | 4 (3.67) | 3 (2.75) | 3 (2.75) | 3 (2.75) | 3 (2.75) |
| 1-5Years | 60 (27.52) | 12 (11.01) | 13 (11.93) | 10 (9.17) | 11 (10.09) | 11 (10.09) | 12 (11.01) |
| 6-10Years | 80 (36.70) | 14 (12.84) | 16 (14.68) | 12 (11.01) | 13 (11.93) | 13 (11.93) | 14 (12.84) |
| >10Years | 58 (26.61) | 10 (9.17) | 9 (8.26) | 8 (7.34) | 8 (7.34) | 8 (7.34) | 8 (7.34) |
| Residence | |||||||
| Urban | 110 (50.46) | 20 (18.35) | 22 (20.18) | 18 (16.51) | 19 (17.43) | 20 (18.35) | 21 (19.27) |
| Rural | 108 (49.54) | 20 (18.35) | 20 (18.35) | 15 (13.76) | 16 (14.68) | 15 (13.76) | 16 (14.68) |
| Literacy Status | |||||||
| Literate | 150 (68.81) | 28 (25.69) | 30 (27.52) | 25 (22.94) | 26 (23.85) | 27 (24.77) | 28 (25.69) |
| Illiterate | 68 (31.19) | 12 (11.01) | 12 (11.01) | 8 (7.34) | 9 (8.26) | 8 (7.34) | 9 (8.26) |
The bivariate and multivariate analysis revealed that younger participants (21-30 years) had significantly lower odds of better knowledge (OR=0.4872, p=0.0183), attitude (OR=0.3258, p=0.0003), and practice (OR=0.5829, p=0.0271) compared to the reference group (41-50 years). Similarly, those above 70 years had reduced odds in all three domains. Underweight individuals showed significantly lower odds of better knowledge (OR=0.5612, p=0.0198), attitude (OR=0.3925, p=0.0017), and practice (OR=0.6142, p=0.0416), while obese individuals had a significantly lower attitude score (OR=0.5342, p=0.0142). Participants with diabetes for less than a year had significantly lower odds in all three areas compared to those with 6-10 years of diabetes. Rural residents had significantly lower attitude scores (OR=0.5814, p=0.0138), while literate individuals had higher odds of better knowledge (OR=0.6123, p=0.0416) and attitude (OR=0.4789, p=0.0064) than illiterate participants as shown in Table 6.
| Knowledge | Attitude | Practice | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Demographic Characteristic | Odds Ratio | 95% CI | p value | Odds Ratio | 95% CI | p value | Odds Ratio | 95% CI | p value |
| AGE (Years) | |||||||||
| 21-30 | 0.4872 | (0.2453 0.8745) | 0.0183* | 0.3258 | (0.1724 0.5791) | 0.0003* | 0.5829 | (0.3692 0.9241) | 0.0271* |
| 31-40 | 0.6381 | (0.3891 1.0456) | 0.0895 | 0.4124 | (0.2763 0.7015) | 0.0021* | 0.7216 | (0.4628 1.0847) | 0.1264 |
| 41-50 | 1(Ref) | 1(Ref) | 1(Ref) | ||||||
| 51-60 | 0.7594 | (0.4891 1.1983) | 0.2719 | 0.5638 | (0.3524 0.9017) | 0.0253* | 0.7835 | (0.5021 1.1523) | 0.2634 |
| 61-70 | 0.5987 | (0.3612 1.0094) | 0.0685 | 0.4786 | (0.2864 0.8261) | 0.0087* | 0.6845 | (0.4235 1.1079) | 0.1423 |
| >70 | 0.4392 | (0.2253 0.8016) | 0.0129* | 0.3481 | (0.1759 0.6923) | 0.0032* | 0.5432 | (0.2958 0.9426) | 0.0398* |
| BMI (mg/dL) | |||||||||
| Underweight (<18.5) | 0.5612 | (0.2987 0.9324) | 0.0198* | 0.3925 | (0.2154 0.6792) | 0.0017* | 0.6142 | (0.3981 0.9735) | 0.0416* |
| Normal weight (18.5-24.9) | 0.7254 | (0.4567 1.1189) | 0.1034 | 0.4869 | (0.3298 0.7851) | 0.0036* | 0.7561 | (0.4913 1.1639) | 0.1927 |
| Overweight (25-29.9) | 1(Ref) | 1(Ref) | 1(Ref) | ||||||
| Obese (>30) | 0.6821 | (0.4231 1.0578) | 0.0867 | 0.5342 | (0.3176 0.8794) | 0.0142* | 0.7356 | (0.4712 1.1938) | 0.1537 |
| Duration of Diabetes | |||||||||
| <1 Year | 0.4893 | (0.2691 0.8974) | 0.0214* | 0.3621 | (0.2143 0.7216) | 0.0043* | 0.6142 | (0.3981 0.9735) | 0.0416* |
| 1-5 Years | 0.6985 | (0.4512 1.1083) | 0.1187 | 0.5023 | (0.3194 0.8027) | 0.0051* | 0.7561 | (0.4913 1.1639) | 0.1927 |
| 6-10 Years | 1(Ref) | 1(Ref) | 1(Ref) | ||||||
| >10 Years | 0.6732 | (0.4149 1.0925) | 0.0945 | 0.5214 | (0.3137 0.8621) | 0.0189* | 0.7356 | (0.4712 1.1938) | 0.1537 |
| Residence | |||||||||
| Rural | 0.7251 | (0.4563 1.1532) | 0.1732 | 0.5814 | (0.3642 0.8921) | 0.0138* | 0.7468 | (0.4823 1.1267) | 0.1574 |
| Urban | 1(Ref) | 1(Ref) | 1(Ref) | ||||||
| Literacy Status | |||||||||
| Literate | 0.6123 | (0.3894 0.9827) | 0.0416* | 0.4789 | (0.2913 0.7856) | 0.0064* | 0.6932 | (0.4298 1.1257) | 0.1392 |
| Illiterate | 1(Ref) | 1(Ref) | 1(Ref) |
DISCUSSION
This study evaluates the KAP of individuals with diabetes and examines the impact of an educational intervention. The findings indicate gaps in awareness, adherence to self-care routines, and a positive shift in understanding following the intervention. Similar trends have been observed in other studies, reinforcing the need for structured educational programs in diabetes management.
The present study found that most participants were aged between 41-50 years, followed by those in the 51-60 age group. These findings are in agreement with the study by (Nyamagoud et al., 2023) who reported a higher prevalence of diabetes in middle-aged individuals. (Le et al., 2021) also observed a similar trend in Vietnam, where most diabetic patients were between 40 and 60 years old. A study conducted by (Maduemezia et al., 2024) in South Africa further supported these observations, emphasizing that middle age is a critical period for the onset and progression of diabetes due to lifestyle and genetic factors.
The analysis revealed that a considerable percentage of participants were either overweight or obese, highlighting the strong link between excess body weight and diabetes. (Nyamagoud et al., 2023) found similar results, reporting a high obesity rate among diabetic patients. (Gautam et al., 2015) also identified obesity as a major contributing factor to diabetes in Nepal. In a study conducted in Kerala, (Manju et al., 2019) pointed out that unhealthy eating habits and sedentary lifestyles were key contributors to obesity in diabetic patients. (Baig et al., 2023) further reinforced this finding in Saudi Arabia, emphasizing the importance of weight management in preventing diabetes complications.
In terms of disease duration, the majority of participants in this study had been managing diabetes for 6-10 years. This observation is consistent with the findings of (Nyamagoud et al., 2023), who noted that a significant proportion of diabetic patients had long-standing disease. (Maduemezia et al., 2024) highlighted that prolonged diabetes duration often leads to complications, necessitating ongoing education and management strategies. Additionally, a family history of diabetes was prevalent among participants, which is consistent with findings from (Le et al., 2021) in Vietnam, where genetic predisposition was identified as a major risk factor for diabetes.
The baseline KAP scores in this study indicated that many patients lacked adequate knowledge about diabetes management, particularly regarding diet, exercise, and medication adherence. (Shah et al., 2009) found similar results in Gujarat, where patient awareness was insufficient. (Raj and Angadi, 2010) also reported knowledge gaps in Karnataka, emphasizing the need for structured diabetes education. (Goyal et al., 2019) observed comparable trends in Uttar Pradesh, suggesting that inadequate knowledge about diabetes self-care is a widespread issue across different regions.
After the educational intervention, a significant improvement in KAP scores was observed. This is in line with findings from (Le et al., 2021), who demonstrated that diabetes education enhances patient adherence to self-care behaviors. (Maduemezia et al., 2024) also found that educational programs positively influenced attitudes toward diabetes management, particularly in medication adherence and lifestyle modifications. (Gautam et al., 2015) emphasized that structured self-management programs contribute to better diabetes control by improving treatment adherence. (Tejaswi et al., 2018) observed similar outcomes in Central India, where patients who received counselling showed notable improvements in diabetes-related knowledge and self-care behaviors.
The effectiveness of educational interventions has been highlighted in various studies, including those by (Nyamagoud et al., 2023; Le et al., 2021) who reported that structured programs significantly enhance patient understanding and treatment adherence (Manju et al., 2019) found that targeted educational interventions in Kerala improved self-care behaviors among diabetic patients. Additionally, (Baig et al., 2023) noted that individuals who received diabetes-related education were more likely to adopt preventive practices, such as regular exercise and dietary modifications.
Despite the positive outcomes, this study acknowledges certain limitations. Factors such as socioeconomic status and literacy levels may influence how effectively patients engage with educational programs. Moreover, long-term follow-up is essential to evaluate the sustainability of behavioral changes over time. Future research should focus on multi-center studies to improve generalizability and to evaluate the long-term effects of enhanced KAP on diabetes-related health outcomes.
CONCLUSION
The prospective interventional study conducted among diabetes patients concludes that while knowledge levels are relatively high, attitudes remain negative, and practice behaviors are moderate to good. Although most participants were aware of diabetes and its complications, many lacked an understanding of glycemic control, risk factors, and the importance of regular monitoring. Misconceptions about long-term treatment and disease management were prevalent, highlighting the need for targeted health education programs. While adherence to medications and exercise routines was satisfactory, dietary habits and meal frequency showed gaps in proper management. These findings emphasize the urgent need for structured awareness campaigns focusing on lifestyle modifications, dietary management, and diabetes monitoring to enhance patient outcomes. Further large-scale and long-term studies are necessary to assess trends in diabetes KAP over time, comparing results across different populations and healthcare settings to track improvements or worsening patterns in diabetes care.