Abstract
Age-related macular degeneration (AMD) is a leading cause of vision impairment. Although modifiable risk factors accumulate early in life, current research predominantly focuses on older adults. This study aimed to assess the levels of knowledge, attitudes, and practices regarding AMD among adults aged 30 years and above in Nanchong, Sichuan Province, China. A cross-sectional study was conducted between November 2023 and August 2024 at Nanchong Central Hospital among adults aged 30 years and above, employing a validated and self-designed questionnaire (Cronbach’s α = 0.838). The survey collected demographic data and measured scores for knowledge, attitudes, and practices. Participants were recruited through convenience sampling from outpatients in the ophthalmology department. The study included 424 participants, with 48 (11.32%) diagnosed with AMD. The mean knowledge, attitude, and practice scores were 5.41 ± 5.20 (range = 0–22), 32.43 ± 4.15 (range = 9–45), and 20.74 ± 5.01 (range = 7–35), respectively. Significant positive correlations were observed between knowledge and attitude (r = 0.423, P < .001), knowledge and practice (r = 0.576, P < .001), and attitude and practice (r = 0.492, P < .001). Mediation analysis indicated that knowledge exerted significant direct effects on attitude (β = 0.435, P = .020) and practice (β = 0.719, P = .012). Attitude also showed a significant direct effect on practice (β = 0.360, P = .004). In addition, a significant indirect effect of knowledge on practice through attitude was observed (β = 0.156, P = .005), confirming the mediating role of attitude. Overall, the participants demonstrated low levels of AMD knowledge, moderately positive attitudes, and inactive practices. These findings underscore the need to move beyond simply increasing AMD awareness toward promoting sustained preventive eye health behaviors among adults aged 30 years and above. Integrating practical risk-reduction guidance and appropriate eye-care practices into public health education may help facilitate earlier recognition of AMD risk and more proactive eye health management.
Keywords: age-related macular degeneration, China, cross-sectional study, health behavior, health education, knowledge, attitudes, practice
1. Introduction
Age-related macular degeneration (AMD) is a chronic, degenerative, and progressive disease that affects the macula, the region of the retina responsible for central vision. AMD is generally defined as a progressive degenerative disease of the macula leading to central vision loss. Individuals aged 50 years and older are at the highest risk of developing AMD, with the risk increasing significantly with advancing age.[1] Globally, AMD is a leading cause of vision impairment, affecting an estimated 196 million people worldwide, with prevalence rates ranging from 7% to 12% among middle-aged and older adults.[2] It is associated with several traditional cardiovascular risk factors, including aging, smoking, obesity, high cholesterol, and hypertension.[3,4] Clinically, AMD often presents as a gradual loss of central vision and is associated with significant psychosocial impairments, including reduced quality of life and increased rates of depression.[5]
Patient education on precautionary measures, such as regular eye examinations, smoking cessation, maintaining a healthy diet, and managing systemic conditions, plays an important role in reducing the risk of AMD progression.[6] An adult’s proactive management of lifestyle factors can substantially impact the progression of AMD. For instance, practices such as adhering to a diet rich in leafy greens and omega-3 fatty acids, regular physical activity, smoking cessation, and maintaining a healthy weight have all been linked to a lower risk of progression.[7,8] Promoting these preventive practices may help maintain long-term visual health and potentially slow disease progression. Although AMD predominantly affects older adults, many of its modifiable risk factors, including smoking, diet, blood pressure, and other lifestyle-related exposures, accumulate throughout adulthood.[9] Consequently, adults aged 30 years and above may represent an important preventive or preclinical target population for health education and risk reduction. Evaluating knowledge, attitudes, and practices (KAP) within this group may help identify gaps in awareness and preventive behaviors before the typical onset of AMD, thereby supporting earlier health education and long-term prevention strategies. Improved awareness and adoption of healthy behaviors have been shown to influence the progression and risk profile of AMD.[7,9]
The KAP framework is a well-established approach for assessing a population’s understanding, beliefs, and behaviors regarding health issues. It operates on the premise that knowledge positively influences attitudes, which in turn shape health-related behaviors.[10] In the case of AMD, however, awareness and proactive preventive measures remain notably low, particularly among aging populations. Identifying gaps in AMD awareness is essential for designing targeted educational interventions, ultimately enhancing patient adherence to preventive measures and reducing the overall burden of AMD.
While previous studies have examined AMD awareness in general populations, including surveys conducted in Beijing[11] and Singapore,[12] research focusing on adults before the typical onset age remains limited. Because modifiable risk factors accumulate across the lifespan, preventive behaviors adopted earlier in adulthood may have long-term implications for eye health.[9] By assessing KAP among adults aged 30 years and above as a preventive or preclinical target group, this study provides insight into early awareness and preventive behaviors related to AMD. Establishing KAP baselines in this population may support earlier counseling, targeted education, and long-term prevention strategies.
2. Materials and methods
2.1. Study design and participants
This cross-sectional study was conducted between November 2023 and August 2024 at Nanchong Central Hospital, targeting outpatients in the ophthalmology department. The study protocol was approved by the Medical Ethics Committee of Nanchong Central Hospital (Ethics Approval Number: 2023 Review No. 089), and written informed consent was obtained from all participants.
Participants were recruited through convenience sampling from outpatients in the ophthalmology department. The inclusion criteria were adults aged 30 years and above, as this group was considered a preventive or preclinical target population for evaluating AMD-related awareness and preventive education needs. In addition, the age of 30 could capture adults who are already likely to have established lifestyle and metabolic exposures; and willingness to participate in this study. There was no upper age limit for inclusion. Those with limited comprehension ability or inability to understand the questionnaire content were excluded.
2.2. Questionnaire
The questionnaire was developed based on a comprehensive review of the literature[13–15] and was tested in a pilot study among 28 respondents, yielding a Cronbach’s α reliability coefficient of 0.838. Content relevance was reviewed by 3 experts to ensure that each item was appropriate and related to the study topic; however, no formal content validation procedure was conducted. Face validity was evaluated during the pilot study by asking respondents whether any questionnaire items were difficult to understand or answer, and no major issues were reported. In addition, during the pilot study, participants were asked whether any items were difficult to understand due to medical terminology or sentence complexity, and their feedback indicated that the questionnaire was understandable to the target population. The final questionnaire (Supplemental Questionnaire, Supplemental Digital Content 1), in Chinese, consisted of 4 sections: demographic characteristics (including age, body mass index [BMI], gender, residence, education level, type of occupation, smoking status, presence of cardiovascular-related diseases, family history of AMD, and current diagnosis of AMD), knowledge, attitude, and practice. In this study, the diagnosis of AMD was based on self-reported information collected through the questionnaire from both on-site and online respondents. The knowledge dimension assessed participants’ understanding of AMD pathogenesis, risk factors, symptoms, diagnostic methods, complications, and treatment options; the attitude dimension evaluated their perceptions and concerns related to AMD and eye health; and the practice dimension captured their preventive and health-seeking behaviors related to AMD. In the knowledge section, responses were scored on a scale of 2, 1, or 0, representing levels of understanding from “well understood” to “unclear.” The attitude section included 9 questions, yielding a 5-point Likert scale from strongly agree (5 points) to strongly disagree (1 point), with a total possible score ranging from 9 to 45. The practice section contained 7 questions, also using a 5-point Likert scale, with scores ranging from strongly agree (5 points) to strongly disagree (1 point), giving a total score range of 7 to 35. Following Bloom cutoff criteria, participants who scored above 80% of the total were categorized as having adequate knowledge, positive attitude, and proactive practice. Those falling within the range of 60% to 80% of the total were classified as having moderate knowledge, attitude, and practice. Scores below 60% reflected inadequate knowledge, negative attitude, and inactive practice,[16] consistent with Bloom cutoff classification. These cutoff thresholds follow Bloom established criteria and are widely applied in KAP studies to categorize levels of knowledge, attitude, and practice.
The questionnaires were collected through both online and on-site methods. The online questionnaire was created using “Wenjuanxing,” a popular online survey platform in China (http://www.wjx.cn). A link to the questionnaire was distributed through the researchers’ social media networks, allowing participants to access the survey by scanning a quick-response code that directed them to the electronic form. To ensure data integrity, only 1 submission was permitted per account. The on-site questionnaire was distributed to patients admitted to the ophthalmology clinic at Nanchong Central Hospital. During the on-site distribution, researchers could provide assistance for those unfamiliar with mobile technology or the internet. Data from both methods were entered into a single database to avoid duplicate entries.
2.3. Sample size calculation
The required sample size was estimated using Cochran’s formula for categorical data:
n0 = Z2·P(1 − P)/e2, where Z = 1.96 for a 95% confidence level, P = .5, and e = 0.05. The minimum estimated sample size was 384 participants.
2.4. Statistical analysis
Data analysis was performed using SPSS 27.0 (IBM) and AMOS 26.0 (IBM). Continuous variables were presented as mean ± standard deviation, while categorical variables and individual question responses were expressed as frequencies and percentages (n, %). Continuous variables were compared using the Mann–Whitney U test or Kruskal–Wallis H test where appropriate. Spearman correlation analysis and structural equation model were conducted to examine the relationships and interactions among KAP scores. A two-sided P value of less than .05 was considered statistically significant.
3. Results
3.1. Demographic characteristics
Among the 424 participants, 222 (52.36%) had a BMI greater than 23.9 kg/m2, with a mean age of 54.82 ± 16.22 years. In addition, 338 participants (79.72%) resided in urban areas, 218 (51.42%) were employed in nonpermanent jobs, 136 (32.08%) were smokers, 214 (50.47%) had cardiovascular diseases, and 48 (11.32%) were diagnosed with AMD (Table 1). The analysis based on 424 questionnaires showed a Kaiser–Meyer-Olkin measure of sampling adequacy of 0.900 (P < .001), supporting the suitability of the data for factor analysis. In addition, confirmatory factor analysis was conducted to evaluate the construct validity of the questionnaire. The model demonstrated an acceptable fit to the data (chi-square minimum/degrees of freedom = 2.926, root mean square error of approximation = 0.067, incremental fit index = 0.928, Tucker–Lewis index = 0.904, comparative fit index = 0.927), indicating good construct validity (Table S1, Supplemental Digital Content 2, and Fig. S1, Supplemental Digital Content 3).
Table 1.
Basic information on the population.
| Variables | N (%) or mean ± SD | Knowledge, mean ± SD | P value | Attitude, mean ± SD | P value | Practice, mean ± SD | P value |
|---|---|---|---|---|---|---|---|
| N = 424 | 5.41 ± 5.20 | 32.43 ± 4.15 | 20.74 ± 5.01 | ||||
| Age | 54.82 ± 16.22 | ||||||
| BMI | <.001 | .01 | .018 | ||||
| Standard or below | 202 (47.64) | 6.34 ± 5.54 | 32.90 ± 4.21 | 21.33 ± 5.45 | |||
| Overweight or above (BMI > 23.9) | 222 (52.36) | 4.57 ± 4.72 | 32.00 ± 4.05 | 20.21 ± 4.51 | |||
| Gender | .237 | .325 | <.001 | ||||
| Male | 186 (43.87) | 4.91 ± 4.66 | 32.14 ± 3.77 | 19.74 ± 5.00 | |||
| Female | 238 (56.13) | 5.80 ± 5.56 | 32.65 ± 4.41 | 21.53 ± 4.88 | |||
| Residence | <.001 | <.001 | <.001 | ||||
| Urban | 338 (79.72) | 5.96 ± 5.44 | 32.99 ± 3.94 | 21.54 ± 4.70 | |||
| Rural | 86 (20.28) | 3.26 ± 3.29 | 30.20 ± 4.21 | 17.60 ± 4.98 | |||
| Education | <.001 | <.001 | <.001 | ||||
| Primary school or below | 104 (24.53) | 2.42 ± 2.46 | 29.36 ± 2.85 | 17.46 ± 4.53 | |||
| Middle school | 78 (18.40) | 3.44 ± 3.40 | 31.41 ± 3.41 | 19.38 ± 4.43 | |||
| High school/vocational school | 80 (18.87) | 5.11 ± 4.30 | 32.38 ± 3.11 | 22.06 ± 4.71 | |||
| Associate degree | 69 (16.27) | 6.07 ± 3.07 | 33.97 ± 4.38 | 22.23 ± 3.84 | |||
| Bachelor’s degree | 69 (16.27) | 9.13 ± 6.16 | 35.80 ± 4.14 | 23.07 ± 4.69 | |||
| Master’s degree or above | 24 (5.66) | 13.21 ± 8.18 | 35.08 ± 2.34 | 23.96 ± 4.84 | |||
| Type of occupation | <.001 | <.001 | <.001 | ||||
| Fixed employment | 119 (28.07) | 9.44 ± 6.88 | 34.91 ± 4.13 | 22.97 ± 4.76 | |||
| Non-fixed employment | 218 (51.42) | 3.14 ± 2.90 | 30.71 ± 3.50 | 18.64 ± 4.48 | |||
| Retired | 87 (20.52) | 5.61 ± 3.33 | 33.33 ± 3.66 | 22.95 ± 4.30 | |||
| Smoking status | .001 | <.001 | <.001 | ||||
| Yes | 136 (32.08) | 3.98 ± 3.49 | 31.44 ± 3.42 | 18.29 ± 4.72 | |||
| No | 288 (67.92) | 6.09 ± 5.71 | 32.89 ± 4.38 | 21.90 ± 4.72 | |||
| Presence of cardiovascular-related diseases | <.001 | <.001 | <.001 | ||||
| Yes | 214 (50.47) | 3.78 ± 3.47 | 31.15 ± 3.78 | 19.87 ± 4.92 | |||
| No | 210 (49.53) | 7.08 ± 6.06 | 33.72 ± 4.11 | 21.63 ± 4.94 | |||
| Family history of age-related macular degeneration | .002 | .364 | .036 | ||||
| Yes | 13 (3.07) | 8.69 ± 3.86 | 33.92 ± 4.70 | 23.38 ± 5.66 | |||
| No | 411 (96.93) | 5.31 ± 5.20 | 32.38 ± 4.13 | 20.66 ± 4.97 | |||
| Current diagnosis of age-related macular degeneration | <.001 | .072 | .021 | ||||
| Yes | 48 (11.32) | 8.00 ± 3.06 | 31.56 ± 3.57 | 22.13 ± 4.31 | |||
| No | 376 (88.68) | 5.08 ± 5.32 | 32.54 ± 4.21 | 20.56 ± 5.07 |
BMI = body mass index, SD = standard deviation.
3.2. Knowledge, attitude, and practice
The mean scores for knowledge, attitude, and practice were 5.41 ± 5.20 (possible range = 0–22), 32.43 ± 4.15 (possible range = 9–45), and 20.74 ± 5.01 (possible range = 7–35), respectively. Analysis of demographic characteristics revealed significant differences in participants’ knowledge, attitude, and practice scores based on BMI (P < .001, P = .010, P = .018), residence (P < .001 for all), education (P < .001 for all), type of occupation (P < .001 for all), smoking status (P = .001, P < .001, P < .001), and the presence of cardiovascular diseases (P < .001 for all). Differences in knowledge scores were significantly associated with a family history of AMD (P = .002) and current diagnosis (P < .001). Variations in practice scores were associated with gender (P < .001), family history (P = .036), and current diagnosis (P = .021; Table 1).
3.3. Responses to knowledge, attitude, and practice
The distribution of knowledge responses indicated that the 3 questions with the highest percentages of “unclear” responses were as follows: “Are you aware that the main complications of age-related macular degeneration include endophthalmitis, cataracts, and glaucoma?” (K5) at 84.43%, “Are you aware that age-related macular degeneration is classified into dry and wet forms and progresses through early, intermediate, and late stages?” (K4) at 83.25%, and “Are you aware that the primary diagnostic tests for age-related macular degeneration are optical coherence tomography and fundus fluorescein angiography?” (K6) at 75.47%. Regarding information sources on AMD (K12), the most commonly reported were new media (62.97%) and hospital lectures and education programs (57.31%; Table 2).
Table 2.
Knowledge.
| N (%) | |||
|---|---|---|---|
| Very knowledgeable | Heard of it | Unclear | |
| 1. Are you aware that age-related macular degeneration is a progressive neurodegenerative disease that primarily affects the macular region of the retinal area of the eye? | 27 (6.37) | 146 (34.43) | 251 (59.2) |
| 2. In addition to age being the primary risk factor, are you aware that smoking, sun exposure, improper eye use, and cardiovascular diseases are all risk factors for the development of age-related macular degeneration? | 28 (6.6) | 80 (18.87) | 316 (74.53) |
| 3. Are you aware that the main symptoms of AMD include vision, decreased visual acuity, visual field blind spots, flashes of light, or difficulty adapting to darkness? | 55 (12.97) | 72 (16.98) | 297 (70.05) |
| 4. Are you aware that AMD is divided into dry and wet forms, progressing through early, intermediate, and late stages? | 20 (4.72) | 51 (12.03) | 353 (83.25) |
| 5. Are you aware that AMD is often accompanied by other age-related eye conditions, such as cataracts, glaucoma, or, in cases of intravitreal treatment, endophthalmitis? | 20 (4.72) | 46 (10.85) | 358 (84.43) |
| 6. Are you aware that the primary diagnostic methods for AMD are optical coherence tomography (OCT) and fundus fluorescein angiography (FFA)? | 28 (6.6) | 76 (17.92) | 320 (75.47) |
| 7. Are you aware that treatments for AMD include intravitreal injections of anti-vascular endothelial growth factor drugs, photodynamic therapy, and laser photocoagulation surgery? | 32 (7.55) | 86 (20.28) | 306 (72.17) |
| 8. Are you aware that regular fundus examinations are essential after treatment to evaluate disease progression or improvement? | 113 (26.65) | 188 (44.34) | 123 (29.01) |
| 9. Are you aware that consuming foods rich in antioxidants, green vegetables, and fish may help prevent or slow down the progression of age-related macular degeneration? | 34 (8.02) | 122 (28.77) | 268 (63.21) |
| 10. Are you aware that foods high in antioxidants include those rich in vitamin C, vitamin E, and beta-carotene? | 76 (17.92) | 196 (46.23) | 152 (35.85) |
| 11. Are you aware that regular monocular vision tests and routine fundus examinations are important as you age to monitor visual function and retinal health? | 66 (15.57) | 234 (55.19) | 124 (29.25) |
| 12. From which sources do you typically obtain information about AMD? | |||
| Medical books and materials | 65 (15.33) | ||
| Hospital lectures and education | 243 (57.31) | ||
| New media (WeChat, Weibo, etc) | 267 (62.97) | ||
| Multimedia (television) | 136 (32.08) | ||
| Relatives and friends | 71 (16.75) | ||
AMD = age-related macular degeneration.
Attitudinal responses showed that 66.51% of participants did not consider themselves ill despite having eye problems (A3), while 58.25% disagreed with the statement, “I believe that it is inevitable to develop these diseases as one ages, which makes me feel anxious” (A1). In addition, 31.37% were unconcerned about the potential impact of the disease on various aspects of life (A2), and 27.12% lacked confidence in adhering to treatment due to the high costs (A9; Table 3).
Table 3.
Attitudes.
| N (%) | |||||
|---|---|---|---|---|---|
| Strongly agree | Agree | Neutral | Disagree | Strongly disagree | |
| 1. I believe that the development of these diseases as one ages, which makes me feel anxious. | 11 (2.59) | 100 (23.58) | 62 (14.62) | 247 (58.25) | 4 (0.94) |
| 2. I am concerned that various aspects of my life will be affected in various aspects, if I become ill. | 19 (4.48) | 235 (55.42) | 36 (8.49) | 133 (31.37) | 1 (0.24) |
| 3. If I experience any eye issues, I tend to overthink and believe that I am ill. | 15 (3.54) | 78 (18.4) | 47 (11.08) | 282 (66.51) | 2 (0.47) |
| 4. I believe that regular eye examinations are essential as one ages to detect and diagnose conditions as early as possible. | 132 (31.13) | 272 (64.15) | 16 (3.77) | 3 (0.71) | 1 (0.24) |
| 5. I believe that maintaining good eye care in daily life is very important. | 137 (32.31) | 257 (60.61) | 26 (6.13) | 3 (0.71) | 1 (0.24) |
| 6. I believe that controlling underlying conditions is crucial for the prevention of age-related macular degeneration. | 77 (18.16) | 279 (65.8) | 64 (15.09) | 4 (0.94) | |
| 7. I believe that quitting smoking and reducing alcohol consumption can help prevent age-related macular degeneration. | 64 (15.09) | 240 (56.6) | 108 (25.47) | 12 (2.83) | |
| 8. If diagnosed, I am determined to adhere to treatment and follow-up care. | 57 (13.44) | 349 (82.31) | 17 (4.01) | 1 (0.24) | |
| 9. If diagnosed, even if the treatment is costly, I am confident in my ability to continue with the treatment. | 46 (10.85) | 193 (45.52) | 70 (16.51) | 115 (27.12) | |
Practice responses indicated that 57.31% never used a magnifying glass for reading or chose books with larger print (P4), 57.31% did not proactively seek information about AMD (P7), and 22.41% never underwent regular eye examinations, such as vision tests and fundus examinations, to monitor their eye health (P1; Table 4).
Table 4.
Practice.
| N (%) | |||||
|---|---|---|---|---|---|
| Always | Often | Sometimes | Occasionally | Never | |
| 1. I will undergo regular eye examinations, such as vision tests and fundus examinations, to stay updated about the condition of my eyes. | 23 (5.42) | 88 (20.75) | 100 (23.58) | 118 (27.83) | 95 (22.41) |
| 2. If I experience symptoms such as decreased or distorted vision, I will seek medical attention immediately. | 192 (45.28) | 140 (33.02) | 59 (13.92) | 23 (5.42) | 10 (2.36) |
| 3. I will consume more fresh fruits and vegetables to maintain a balanced diet. | 78 (18.4) | 141 (33.25) | 87 (20.52) | 98 (23.11) | 20 (4.72) |
| 4. I will use a magnifying glass for reading and writing or choose books with larger print. | 8 (1.89) | 27 (6.37) | 58 (13.68) | 88 (20.75) | 243 (57.31) |
| 5. I will avoid straining my eyes in dim light conditions, such as reading, writing, or using a mobile phone. | 133 (31.37) | 72 (16.98) | 122 (28.77) | 57 (13.44) | 40 (9.43) |
| 6. I will protect my eyes by avoiding prolonged exposure to UV rays. | 156 (36.79) | 85 (20.05) | 88 (20.75) | 37 (8.73) | 58 (13.68) |
| 7. I will actively gather information about age-related macular degeneration. | 24 (5.66) | 34 (8.02) | 37 (8.73) | 86 (20.28) | 243 (57.31) |
UV = ultraviolet.
3.4. Correlations and interaction among knowledge, attitude, and practice
Correlation analysis revealed significant positive correlations between knowledge and attitude (r = 0.423, P < .001), and between knowledge and practice (r = 0.576, P < .001). Furthermore, a significant correlation was found between attitude and practice (r = 0.492, P < .001; Table 5).
Table 5.
Correlation analysis.
| Knowledge | Attitudes | Practice | |
|---|---|---|---|
| Knowledge | 1 | ||
| Attitudes | 0.423 (P < .001) | 1 | |
| Practice | 0.576 (P < .001) | 0.492 (P < .001) | 1 |
Mediation analysis indicated that knowledge was directly associated with attitude (β = 0.435, P = .020) and practice (β = 0.719, P = .012). Attitude was also directly associated with practice (β = 0.360, P = .004). In addition, a significant indirect association between knowledge and practice through attitude was observed (β = 0.156, P = .005), confirming the mediating role of attitude (Table 6 and Fig. 1).
Table 6.
Mediating effect significance test for the final mode.
| Model paths | Standardized total effects | Standardized direct effects | Standardized indirect effects | |||
|---|---|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | β (95% CI) | P value | |
| Knowledge → attitude | 0.435 (0.304–0.526) | .020 | 0.435 (0.304–0.526) | .020 | ||
| Knowledge → practice | 0.875 (0.808–0.935) | .012 | 0.719 (0.619–0.804) | .012 | 0.156 (0.105–0.233) | .005 |
| Attitude → practice | 0.360 (0.248–0.520) | .004 | 0.360 (0.248–0.520) | .004 | ||
CI = confidence interval.
Figure 1.
The structural equation model.
4. Discussion
Adults aged 30 years and above, considered in this study as a preventive or preclinical target population rather than a group at immediate risk of AMD, demonstrated low levels of knowledge about AMD, along with moderate attitudes and relatively inactive preventive and management practices. These findings highlight the potential value of targeted educational interventions to improve awareness and encourage early preventive behaviors related to AMD in clinical settings.
Previous studies underscore significant gaps in AMD awareness. Similar findings were reported in Beijing and Singapore, where population-based surveys demonstrated low awareness of AMD and its risk factors despite public education efforts, indicating that insufficient knowledge remains a widespread issue across different regions. A study in Beijing found that only 6.8% of residents were aware of AMD, and even among those familiar with the disease, only 35% recognized smoking as a key risk factor.[11] Meanwhile, a survey in Singapore showed that public awareness of AMD remained low even after nationwide education campaigns, with many respondents still unable to identify major risk factors such as smoking.[12] Similarly, research on AMD patients revealed that while many had a general understanding of the condition, a large proportion still lacked important information.[17] Although AMD primarily affects older adults, insufficient awareness and preventive behaviors in earlier adulthood may limit opportunities for early risk-factor modification and long-term eye health promotion.
The correlation analyses and structural equation modeling demonstrated significant associations among knowledge, attitude, and practice. Knowledge was significantly associated with both attitude and practice, while attitude appeared to function as a potential mediator in the association between knowledge and practice. Literature on eye health and chronic disease management also supports the notion that improved knowledge can foster more positive attitudes and better health practices.[18] Therefore, improving knowledge about AMD may be associated with more positive attitudes and greater engagement in proactive health behaviors. Although improving knowledge may represent an important first step, increased awareness does not necessarily translate into meaningful behavior change, particularly among adults aged 30 years and above who may face competing health priorities or long-standing lifestyle habits. Previous research on chronic disease prevention has similarly shown that awareness alone is often insufficient to drive sustained behavioral modification. Therefore, while identifying gaps in AMD-related knowledge is important, future interventions may benefit from incorporating behavioral change strategies and supportive environments to increase the likelihood that improved awareness is accompanied by proactive preventive practices.
The significant differences in KAP scores based on demographic and health-related variables provide additional insights. Participants with higher BMI had lower knowledge and practice scores, consistent with evidence linking elevated BMI to reduced health literacy and fewer health-promoting behaviors.[19] While the differences in knowledge and practice scores were significant, the lack of a notable difference in attitude implies that participants’ weight may not influence their perceptions of AMD, but it does affect their behaviors and understanding of the disease.
Similarly, significant differences were observed between urban and rural participants, with those living in urban areas demonstrating higher KAP scores. This discrepancy is likely attributable to greater access to healthcare resources, education, and information in urban settings, as has been reported in other health studies.[20,21] Rural residents may face barriers such as limited access to eye care specialists and educational programs, which could explain their lower scores. In terms of education, participants with higher educational levels had significantly better KAP scores, reinforcing the established relationship between education and health literacy.[22,23] The observed differences in KAP scores based on occupation type further underscore the impact of socioeconomic factors, as individuals with stable employment tend to have more stability and access to healthcare resources compared to those with unstable employment.
The differences between smokers and nonsmokers, as well as those with and without cardiovascular diseases, add further context. Smokers and participants with cardiovascular conditions had significantly lower KAP scores, which may be attributed to a general disregard for health-promoting behaviors or a lower prioritization of eye health due to other health concerns.[24,25] Interestingly, while these groups showed significant differences in both knowledge and practice, their attitudes toward AMD did not differ as much.
In the knowledge dimension, a large portion of participants were unaware of critical aspects of AMD, including its complications, stages, and diagnostic methods. This widespread lack of understanding is similar to findings from other studies in which patients struggled to grasp the complexities of AMD, particularly the distinctions between dry and wet forms and the importance of regular screenings. Given that the majority of participants reported relying on new media for information, this highlights the need for more accurate and accessible health information to be disseminated through these channels. Future educational interventions should focus on simplifying and clarifying the key elements of AMD, particularly through social media platforms, which were found to be a primary source of information for participants.[9,26,27]
The attitude dimension revealed that while most participants held generally positive views regarding the importance of eye health and regular examinations, a significant number expressed concerns about treatment costs and their ability to adhere to long-term care. This is consistent with research on other chronic diseases where financial barriers are a common deterrent to treatment adherence.[28] To address these concerns, healthcare systems should explore offering more affordable options for AMD management, such as subsidized eye exams and treatments. In addition, patient education should emphasize the cost-effectiveness of early intervention and regular monitoring, which can prevent more expensive and invasive treatments in the later stages of the disease.[29,30]
In terms of practices, a notable proportion of participants reported never engaging in key behaviors such as regular eye exams, using appropriate reading aids, or proactively seeking information about AMD. These results reflect the broader trend of low health-seeking behaviors, particularly in relation to preventive care. Comparisons with other studies indicate that this issue is not unique to AMD but is common across many noncommunicable diseases, where patients often fail to take proactive measures unless symptoms are severe. To improve these practices, healthcare providers should incorporate regular eye screenings into routine checkups, especially for older adults and those with risk factors for AMD. In addition, public health campaigns could promote eye health through community outreach programs, particularly targeting rural and lower-educated populations.[31–33]
Beyond clinical implications, our findings have important public health relevance. The substantial gaps in early AMD-related knowledge and preventive behaviors observed in this study indicate a need for structured population-level interventions. From both clinical and public health perspectives, the findings of this study offer several important applications. Clinically, understanding the levels of AMD-related KAP among adults aged 30 years and above can help healthcare providers identify groups with insufficient awareness and tailor early educational interventions or counseling strategies accordingly. Increased awareness and proactive behaviors may support earlier detection and better management of AMD, potentially improving long-term visual outcomes. From a public health standpoint, the identified gaps in knowledge and preventive behaviors highlight the need for community-based health education programs, integration of AMD awareness into broader chronic disease prevention initiatives, and targeted outreach to populations with lower health literacy. These findings can serve as a foundation for developing comprehensive, population-level AMD prevention strategies and for informing policy decisions related to visual health promotion.
This study has several limitations. First, as a cross-sectional design, it only provides a snapshot in time, limiting the ability to infer causal relationships among KAP related to AMD. Second, the use of self-reported questionnaires, including the self-reported diagnosis of AMD, may introduce response and misclassification biases. Third, both on-site and online recruitment methods may have introduced selection bias: participants were mainly ophthalmology outpatients, who may possess higher baseline awareness of eye diseases than the general population, and the online distribution through the researchers’ social networks (primarily WeChat) may have attracted individuals with greater health awareness or even multiple members from the same family, potentially affecting response independence and inflating awareness estimates. Furthermore, the study was conducted in a single hospital in Nanchong, Sichuan, and the outpatient-based sample may differ from a population-based AMD sample, which limits the generalizability of the findings. In addition, several important clinical characteristics of AMD were not collected or analyzed in this study, including disease duration, the number of anti-vascular endothelial growth factor injections received, and clear clinical stratification between dry and wet forms of AMD. Lastly, only a very small number of participants reported a family history of AMD, restricting the ability to draw meaningful conclusions regarding familial influence. These missing clinical variables further limit the clinical interpretability of the findings and should be addressed in future studies with more detailed ophthalmologic data collection.
5. Conclusion
The participants demonstrated low levels of AMD knowledge, moderately positive attitudes, and inactive practices. These findings underscore the need to move beyond simply increasing AMD awareness toward promoting sustained preventive eye health behaviors among adults aged 30 years and above. Integrating practical risk-reduction guidance and appropriate eye-care practices into public health education may help facilitate earlier recognition of AMD risk and more proactive eye health management.
Author contributions
Conceptualization: Lan Wang.
Data curation: Lan Wang, Yi Lu.
Formal analysis: Lan Wang, Yi Lu, Hui Tang.
Writing – review & editing: Lan Wang, Hui Tang.
Abbreviations:
- AMD
- age-related macular degeneration
- BMI
- body mass index
- KAP
- knowledge, attitudes, and practices
The study protocol was approved by the Medical Ethics Committee of Nanchong Central Hospital (Ethics Approval Number: 2023 Review No. 089), and written informed consent was obtained from all participants. The authors confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
This study was supported by Research Project of the Sichuan Provincial Primary Health Development Research Center (Project Number: SWFZ23-C-95).
The authors have no conflicts of interest to declare.
All data generated or analyzed during this study are included in this published article (and its supplementary information files).
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000050700).
How to cite this article: Wang L, Lu Y, Tang H. Knowledge, attitudes, and practices regarding age-related macular degeneration among adults aged 30 years and above: A cross-sectional study. Medicine 2026;105:39(e50700).
Contributor Information
Lan Wang, Email: 187195689@qq.com.
Yi Lu, Email: 253026628@qq.com.
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