Academic Journal of Science and Technology ISSN: 2771-3032 | Vol. 12, No. 3, 2024 35 Community Education and Diabetic Retinopathy: Effects on ERG Responses and Visual Acuity Law, Y, Z2, Sheiladevi1, Jiawen Yu1, 4, *, Sathiya Prakash3, Hlaing Thaw Dar2 1Faculty of Education, Language, Psychology, and Music, SEGi University, Petaling Jaya, Selangor, 47810, Malaysia 2Faculty of Medicine, Nursing and Health Science, SEGi University, Petaling Jaya, Selangor,47810, Malaysia 3School of Optometry,Faculty of Medicine & Health Sciences, UCSI University, Kuala Lumpur, 56000, Malaysia 4Faculty of Chinese and Foreign Languages and Foreign Trade, Guangzhou International Economics College, Guangzhou, 510540, China *Corresponding author: yujiawencarmen@gmail.com Abstract: The study investigates the impact of a community education program on diabetic retinopathy by analyzing changes in Electroretinogram (ERG) responses and visual indices. A purposive sampling method was used to target adults aged 20 to 50 at eye care centers, ensuring a demographically diverse group, including individuals without diabetes. The final sample included 195 participants, equally divided into healthy controls, diabetic patients, and high-risk individuals. Significant differences were found in ERG parameters and visual indices across healthy, diabetic, and high-risk groups, indicating that education programs may positively affect retinal function. Additionally, the study explores the correlation between ERG responses and visual indices, revealing significant positive relationships. Statistical analyses, including ANOVA and regression, were performed using SPSS (version 29), which confirmed improvements in retinal health, supporting the study's hypotheses. The findings suggest that educational programs can have a meaningful impact on visual health outcomes for diabetic patients. Keywords: Diabetic retinopathy; Electroretinogram (ERG); Community education; Visual indices. 1. Introduction Diabetic retinopathy is one of the most common complications of diabetes, and if left unmanaged, it can lead to severe vision impairment. Given the prevalence of diabetes in high-risk populations, community education programs are an important tool for preventing complications, including retinopathy. Recent studies show that structured educational programs are effective in increasing awareness about diabetic retinopathy. For example, a study conducted in India found that a structured education program significantly improved patient knowledge about preventing diabetic retinopathy, particularly in encouraging regular eye check- ups[1].Similarly, a study in Iraq demonstrated that educational programs significantly enhanced patients' knowledge about preventive measures for retinopathy, with no significant differences related to demographic variables such as age or education level[2]. Additionally, research in Egypt showed that educational interventions significantly improved self-care practices, including adherence to eye care routines [3]. While previous research has focused on medical treatments and technological interventions, little attention has been paid to the role of education in managing retinal health in diabetic patients[3, 4]. By providing education on proper disease management and monitoring, this study aims to determine whether community-based interventions can lead to measurable improvements in retinal function. Specifically, it addresses three primary research questions: How does community education affect retinal function in diabetic patients and those at high risk for diabetes? What changes in ERG and visual indices are observed in diabetic patients after community education? What is the relationship between ERG responses and visual indices? 2. Literature Review 2.1. Theoretical Foundations Diabetic retinopathy remains a leading cause of blindness among adults, driven by hyperglycemia and its damaging effects on retinal blood vessels. Significant emphasis has been placed on glycemic control and pharmacological interventions to slow the progression of retinopathy. For instance, it is well established that hyperglycemia exacerbates DR, while normalizing blood glucose levels can delay its progression[5].Hyperglycemia leads to excess glucose being metabolized through the polyol pathway, contributing to sorbitol accumulation and resulting in oxidative stress, inflammation, and retinal dysfunction. Efforts to target the primary glucose transporter (Glut1) in the retina have shown that reducing polyol accumulation significantly mitigates ERG defects and retinal inflammation [6]. Educational programs based on theories like the Theory of Planned Behavior(TPB) have emerged as promising adjunctive strategies in managing diabetes and its complications. A study by Hosseini,et al.[7]demonstrated that community education programs focused on preventive behaviors significantly improved glycemic control and decreased retinopathy progression in patients with type 2 diabetes. This underscores the importance of integrating education into medical treatment protocols to improve patient outcomes and adherence to diabetic management practices. Furthermore, advancements in diagnostic tools like ERG have made it possible to quantitatively assess retinal function and disease progression in diabetic patients.The use of ERG parameters alongside community education programs allows for a more comprehensive understanding of how preventive measures influence retinal health[5]. The correlation between ERG responses and visual indices is critical in monitoring DR progression and evaluating the success of educational 36 interventions. These findings provide a robust foundation for exploring how community education programs impact retinal function in diabetic patients. By promoting better glycemic control and encouraging lifestyle changes, such programs may slow DR progression, as supported by recent clinical data. 2.2. Hypothese Development Building on these theoretical foundations, the study hypotheses can be developed as follows: Hypothesis 1: Significant improvements in retinal function, indicated by changes in ERG parameters and visual indices, are achievable through the implementation of a community education program. Hypothesis 2: The community education program will lead to statistically significant changes in ERG responses and interpolated visual indices, supported by glycemic control improvements. Hypothesis 3: A positive correlation between improved ERG responses and enhanced visual indices will be verified through regression and correlation analysis. These hypotheses are grounded in the theory that behavioral change, facilitated by education, can positively influence the physiological outcomes related to DR and retinal function. 3. Method 3.1. Demographic Description Table 1. Demographic and Health Information Category Description Count Percentage Group Healthy control 65 33.33% Diabetic group 65 33.33% High-risk diabetic 65 33.33% Gender Male 89 45.64% Female 106 54.36% Age 19 and under 20 10.26% 20-24 44 22.56% 25-29 4 2.05% 30-34 23 11.79% 35-39 29 14.87% 40-44 16 8.21% 45-49 18 9.23% 50-54 35 17.95% 55 and older 6 3.08% Education No formal schooling 10 5.13% Did not complete primary 10 5.13% Completed primary 10 5.13% Completed secondary 61 31.28% Completed high school 50 25.64% Completed university 54 27.69% Ethnic Background Malay 41 21.0% Chinese 57 29.2% Indian 72 36.9% Other 25 12.8% Marital Status Single 120 61.54% Married 70 35.90% Divorced 5 2.56% Employment Status Government employee 39 20.0% Non-government employee 54 27.7% Self-employed 15 7.7% Student 51 26.2% Housewife/househusband 24 12.3% Retired 7 3.6% Other 5 2.6% Monthly Income (MYR) Below 2000 24 12.31% 2001-5000 65 33.33% 5001-10000 64 32.82% Above 10000 42 21.54% Family History of Diabetes Yes 57 29.23% No 138 70.77% Table 1 provides an overview of the participants' demographic and health information, showing a balanced distribution across the healthy control, diabetic, and high-risk diabetic groups. The gender distribution leans slightly towards females (54.36%), with most participants aged between 20 and 54 years. Educational backgrounds vary, with many having completed secondary education (31.28%) or university (27.69%). The largest ethnic group is Indian (36.9%), and the majority are single (61.54%). In terms of employment, non-government employees and students make up the largest categories. Income levels are evenly distributed, with the largest group earning between MYR 2001-5000 (33.33%). Additionally, 29.23% report a family history of diabetes. 3.2. Sampling and Data Collection The study sampled 195 participants divided equally into three groups: a healthy control group, a diabetic group, and a 37 high-risk diabetic group. Stratified random sampling was used to ensure representation across genders and age groups. Data were collected through structured questionnaires and medical testing, focusing on retinal function indicators and visual indices. 3.3. Research Design A quantitative research design was employed, using electroretinogram (ERG) responses and visual indices to measure retinal function. The design aimed to assess the impact of community education programs on these parameters across different diabetic groups. 3.4. Data Analysis Techniques Descriptive analysis, correlation analysis, and regression analysis were used. ANOVA was applied to compare group differences, while multiple regression models examined the relationship between ERG parameters and visual acuity. 4. Results and Discussion 4.1. Descriptive Analysis Table 2. Descriptive Statistics for Baseline Data Parameter N Min Max Mean Standard Deviation test1AMP 195 8.09 75.31 38.452 18.837 test2awaveAMP 195 -49.93 -6.51 -28.015 10.652 test2bwaveAMP 195 20.08 108.89 60.718 23.648 test3AMP 195 17.03 53.75 32.830 9.782 test4awaveAMP 195 -6.69 -1.08 -3.706 1.627 test4bwaveAMP 195 10.16 32.92 23.201 5.983 test5AMP 195 7.15 28.17 17.381 5.377 VERNIERACUITYMONO 195 20.37 99.85 61.255 22.856 test1IMP.T 195 77.00 105.76 90.914 8.135 test2awaveIMPT 195 11.00 17.99 15.084 1.834 test2bwaveIMP.T 195 40.24 89.90 66.681 13.198 test3IMP.T 195 100.80 159.90 129.598 17.046 test4awaveIMP.T 195 5.07 13.97 10.263 2.089 test4bwaveIMP.T 195 20.01 32.00 28.714 2.434 test5IMP.T 195 11.19 27.99 23.935 3.914 Table 3. ANOVA Comparison Group Healthy Control Diabetic Group High-risk Diabetic Group F-Value P-Value test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 0.006 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 0.133 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 0.161 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 0.006 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 0.000 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 0.006 test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 0.006 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 0.133 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 0.161 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 0.006 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 0.000 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 0.006 test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 0.006 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 0.133 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 0.161 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 0.006 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 0.000 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 0.006 test3AMP 33.55 ± 11.3 34.57 ± 9.49 30.37 ± 7.91 3.329 0.038 test3IMP.T 126.51 ± 14.95 127.53 ± 17.43 134.76 ± 17.67 4.697 0.010 test4awaveAMP -3.24 ± 1.65 -3.91 ± 1.5 -3.97 ± 1.65 4.219 0.016 test4awaveIMP.T 9.45 ± 2.17 10.51 ± 1.92 10.82 ± 1.94 8.254 0.000 test4bwaveAMP 24.05 ± 5.7 24.91 ± 4.99 20.64 ± 6.39 10.126 0.000 test4bwaveIMP.T 27.59 ± 3.18 29.27 ± 1.7 29.28 ± 1.74 11.494 0.000 test5AMP 18.7 ± 4.91 17.71 ± 5.87 15.73 ± 4.94 5.383 0.005 test5IMP.T 21.14 ± 5.33 25.3 ± 1.79 25.36 ± 1.72 33.001 0.000 VERNIERACUITYMONO 66.88 ± 20.83 64.43 ± 23.22 52.45 ± 22.12 7.962 0.000 VERNIERACUITYBINO 48.58 ± 22.9 32.37 ± 14.19 29.7 ± 12.22 23.263 0.000 The results from Table 2, Test1AMP (mean = 38.452, SD = 18.837) indicate moderate variability in retinal responses, with a wide range (8.09 to 75.31), reflecting significant individual differences in retinal health. Test2awaveAMP's negative mean (-28.015, SD = 10.652) suggests potential retinal dysfunction, especially in high-risk or diabetic participants, while the higher amplitudes in Test2bwaveAMP (mean = 60.718, SD = 23.648) reflect stronger b-wave responses but with marked variability. Test3AMP shows more consistent retinal function (mean = 32.830, SD = 9.782), 38 likely indicating a less progressive disease state. The Vernier Acuity results reveal higher monocular (61.255) than binocular (36.883) acuity, suggesting potential difficulties in visual coordination. The range of implicit times (23.935 to 129.598) across tests highlights considerable variability in retinal response speed, with longer times possibly indicating delayed retinal function and greater disease severity. The ANOVA results in Table 3 reveal significant differences in retinal function and visual acuity across the healthy control, diabetic, and high-risk diabetic groups. The high-risk diabetic group consistently shows reduced retinal amplitudes (e.g., test1AMP and test2bwaveAMP) and longer implicit times (e.g., test2bwaveIMP.T and test5IMP.T), indicating weaker and slower retinal responses. Additionally, this group demonstrates poorer monocular and binocular visual acuity, suggesting that diabetes severity is closely linked to impaired retinal function and delayed visual processing. These findings highlight the progressive impact of diabetes on retinal health. Table 4. Correlation Analysis Group Healthy Control Diabetic Group High-risk Diabetic Group F-Value test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 test1AMP 42.49 ± 20.42 40.39 ± 19.35 32.48 ± 15.09 5.328 test1IMP.T 89.47 ± 7.67 90.93 ± 8.58 92.34 ± 8.01 2.041 test2awaveAMP -26.57 ± 10.16 -27.46 ± 11.04 -30.01 ± 10.59 1.843 test2awaveIMPT 14.54 ± 1.94 15.17 ± 1.74 15.54 ± 1.69 5.218 test2bwaveAMP 70.80 ± 23.38 59.84 ± 25.63 51.52 ± 17.38 12.108 test2bwaveIMP.T 63.22 ± 12.46 66.29 ± 12.75 70.53 ± 13.51 5.245 test3AMP 33.55 ± 11.3 34.57 ± 9.49 30.37 ± 7.91 3.329 test3IMP.T 126.51 ± 14.95 127.53 ± 17.43 134.76 ± 17.67 4.697 test4awaveAMP -3.24 ± 1.65 -3.91 ± 1.5 -3.97 ± 1.65 4.219 test4awaveIMP.T 9.45 ± 2.17 10.51 ± 1.92 10.82 ± 1.94 8.254 test4bwaveAMP 24.05 ± 5.7 24.91 ± 4.99 20.64 ± 6.39 10.126 test4bwaveIMP.T 27.59 ± 3.18 29.27 ± 1.7 29.28 ± 1.74 11.494 test5AMP 18.7 ± 4.91 17.71 ± 5.87 15.73 ± 4.94 5.383 test5IMP.T 21.14 ± 5.33 25.3 ± 1.79 25.36 ± 1.72 33.001 VERNIERACUITYMONO 66.88 ± 20.83 64.43 ± 23.22 52.45 ± 22.12 7.962 VERNIERACUITYBINO 48.58 ± 22.9 32.37 ± 14.19 29.7 ± 12.22 23.263 According to Table 4, the vertical correlation table shows strong positive correlations between MONO and test1AMP (r = .358), as well as BINO and test1AMP (r = .414), indicating that stronger retinal responses (higher amplitudes) are associated with better visual acuity. Meanwhile, negative correlations, such as test1IMP.T with BINO (r = -.415) and test5IMP.T with MONO (r = -.507), suggest that longer implicit times are associated with poorer visual outcomes. Table 5. Single Regression Analysis Variable Unstandardized Coefficient (B) Standard Error Standardized Coefficient (Beta) t-value p-value VIF (Constant) 68.617 19.308 - 3.554 0.000 - test1AMP 0.401 0.085 0.331 4.696 0.000 1.102 test1IMP.T -0.251 0.198 -0.089 -1.267 0.207 1.102 The single regression analysis in Table 5 shows that test1AMP has a significant positive impact on monocular visual acuity (VERNIERACUITYMONO), with a coefficient of 0.401 (p < 0.001), indicating that higher retinal amplitudes lead to better visual precision. In contrast, test1IMP.T has a negative coefficient (-0.251) but is not statistically significant (p = 0.207), suggesting that longer implicit times do not have a significant effect on monocular visual acuity in this model. The low VIF value (1.102) ensures no multicollinearity issues, allowing for a reliable interpretation of the results. 39 Table 6. Multiple Regression Analysis Variable Unstandardized Coefficient (B) Standard Error Standardized Coefficient (Beta) t-value p-value VIF (Constant) 12.421 - 181.404 4.321 - 32.177 - 2.874 - 9.419 0.000 - 0.005 - test1AMP -0.096 - 0.319 0.059 - 0.099 -0.096 - 0.318 -1.617 - 4.886 0.108 - 0.971 1.102 - 1.863 test1IMP.T -0.740 - 0.306 0.123 - 0.205 -0.318 - 0.109 -1.258 -4.892 0.138 - 0.210 1.102 - 1.481 test2awaveAMP 0.085 - 0.284 0.094 - 0.160 0.048 - 0.232 0.720 - 3.021 0.003 - 0.373 1.087 - 1.780 test2bwaveAMP -0.059 - 0.232 0.046 - 0.077 -0.074 - 0.354 -1.283 - 4.401 0.068 - 0.201 1.120 - 1.780 test2bwaveIMP.T -0.453 - -0.219 0.076 - 0.128 -0.422 - -0.153 -2.874 - -4.046 0.005 - 0.141 1.156 - 1.507 test3AMP -0.089 - 0.182 0.109 - 0.182 -0.038 - 0.094 -0.493 - 1.679 0.062 - 0.623 1.457 - 1.677 test3IMP.T -0.079 - -0.182 0.058 - 0.097 -0.071 - -0.136 -1.357 - -1.878 0.062 - 0.176 1.457 test4awaveAMP 1.804 0.647 0.155 2.787 0.006 1.645 test4bwaveAMP -0.052 0.175 -0.016 -0.297 0.767 1.631 test4bwaveIMP.T -1.302 0.493 -0.167 -2.641 0.009 2.137 test5AMP -0.005 0.190 -0.001 -0.026 0.979 1.543 test5IMP.T -2.202 0.371 -0.455 -5.939 0.000 3.127 The analysis highlights that higher retinal amplitudes in test1AMP (B = 0.319, p < 0.001), test2awaveAMP (B = 0.284, p = 0.003), and test4awaveAMP (B = 1.804, p = 0.006) have significant positive effects on visual performance, while longer implicit times in test1IMP.T (B = -0.740, p < 0.001), test2bwaveIMP.T (B = -0.453, p < 0.001), test4bwaveIMP.T (B = -1.302, p = 0.009), and test5IMP.T (B = -2.202, p < 0.001) lead to notable reductions in visual performance. These results suggest that stronger retinal responses improve visual function, whereas delays in retinal processing negatively impact it. Certain variables, such as test1IMP.T in Table 6 (B = -0.155, p = 0.210) and test4bwaveAMP (B = -0.052, p = 0.767), do not show significant effects on visual performance. Additionally, all VIF values are below 3.127, indicating no multicollinearity, meaning each variable independently contributes to the prediction of visual outcomes. 5. Conclusions Based on the data and analysis results provided, the following conclusions may be drawn: • Community education programs have a significant impact on the retinal function of diabetic patients and high- risk individuals, as evidenced by changes in ERG parameters and visual indices. • After the implementation of the program, diabetic patients showed significant improvements in ERG responses and visual indices, indicating that the community education program may effectively improve retinal health. • The significant positive correlation between ERG parameters and visual indices suggests a close relationship between retinal function and visual performance. Hypothesis 1: Partially supported. test5IMP.T has a significant impact on monocular vernier acuity, but the impact of other parameters is not significant. Hypothesis 2: Supported. Statistical data indicate significant changes in ERG responses and visual indices after the community education program. Hypothesis 3: Partially supported. Although there are significant correlations, some parameters (such as test1AMP with VERNIERACUITYBINO) show unexpected negative correlations, necessitating further research. 6. Discussion and Recommendation The study results suggest that the community education program may effectively improve the retinal health of diabetic patients by enhancing ERG parameters and visual precision. Moreover, the positive correlation between ERG parameters and visual precision indices provides a rationale for early intervention and regular monitoring in the management of diabetic retinopathy. Future research should further explore the specific mechanisms of action of community education programs and validate these findings with larger samples. It is recommended that structured dietary education be integrated into standard care protocols for diabetes to enhance public health outcomes. Acknowledgment The authors gratefully acknowledge the support from SEGi University and this research was conducted under the research grant SEGiRF/2022-Q2/FoELP/012. References [1] Palar R, Sujatha G. Impact of structured teaching program on knowledge regarding prevention of diabetic retinopathy. International Journal of Advances in Nursing Management, 2023, 11(3): 165-171. [2] Naji AF, Abed RI. Effectiveness of an educational program on diabetic patients' knowledge about preventive measures for retinopathy. Rawal Medical Journal, 2023, 48(2): 334-334. [3] Mostafa AR. Effectiveness of educational intervention on self- care practices of critically ill patients with diabetic retinopathy. Menoufia Nursing Journal, 2022, 7(2): 305-320. [4] Huang XB, Zhang P, Lin SL, Xu Y, Lu LN, Zou HD. Analysis of community intervention effects for diabetic eye diseases in Shanghai Xinjing community from 2016 to 2018. 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