Abstract
Background
This study aimed to investigate the knowledge, attitude, and practice (KAP) of gout among patients with gout in the Xiamen region and identify factors influencing these domains to inform targeted interventions.
Methods
A cross-sectional study was conducted in Xiamen, China. From November 2023 to January 2024, questionnaires were distributed via Questionnaire Star to collect demographic information and KAP scores. Structural Equation Modeling (SEM) was employed to explore the interrelationships among knowledge, attitudes, and practices.
Results
A total of 622 questionnaires were collected, of which 462 were valid, with an effective rate of 74.3%. The median scores (IQR) were as follows: Knowledge, Median = 16, IQR: 10–22; Attitude, Median = 34, IQR: 29–37; and Practice, Median = 40, IQR: 33–47. Significant positive correlations were found among the three domains: knowledge and attitude (r = 0.456, p < 0.001), knowledge and practice (r = 0.539, p < 0.001), and attitude and practice (r = 0.477, p < 0.001). Structural Equation Modeling demonstrated a good model fit (RMSEA = 0.068, SRMR = 0.069, TLI = 0.839, CFI = 0.851) and revealed that knowledge had a significant direct effect on practice (β = 0.644, p < 0.001), while the indirect effects through attitude were not statistically significant.
Conclusion
Patients with gout demonstrated limited knowledge and moderately negative attitudes toward gout, yet relatively proactive self-management practices.
Keywords: attitude, cross-sectional survey, gout, knowledge, patient education, practice
Background
Gout is a prevalent chronic inflammatory disease that has shown a rising global burden, affecting approximately 1–4% of adults worldwide and imposing substantial public health and economic challenges. In China, recent epidemiological surveys report a prevalence ranging from 1.1 to 3.5%, with an increasing trend in coastal and urban regions. Gout typically progresses from hyperuricemia to advanced gout characterized by tophi and chronic arthritis (1–3). Dietary patterns such as high intake of seafood, red and processed meats, and refined grains contribute to elevated serum uric acid levels and increased gout risk (4). The warm and humid subtropical climate of Xiamen may further predispose residents to gout flares by influencing purine metabolism and dehydration-related uric acid concentration. Xiamen, a coastal Chinese city with a hot and humid climate, faces unique factors influencing gout’s prevalence and impact. China’s healthcare system operates through a hierarchical structure, with large hospitals serving as tertiary care centers and community health centers providing primary care services (5). There is an increasing trend toward managing chronic diseases like gout at the grassroots level.
A Knowledge, Attitude, and Practices (KAP) survey is a research instrument employed to assess a group’s understanding, attitudes, and behaviors regarding a specific subject, including diseases. This methodology serves as a diagnostic tool to gain insights into what is understood, believed, and put into practice in relation to various health conditions. In the context of health literacy, the KAP model is based on the premise that knowledge positively influences attitudes, subsequently shaping behaviors, which can have a direct impact on disease incidence and management (6–9). Previous KAP (Knowledge, Attitudes, and Practices) studies focusing on healthcare professionals have indicated that there may be a deficit in their understanding and implementation of best treatment practices for gout, potentially leading to inadequate patient education about the condition (10–12). Considering the increased incidence of gout in recent decades, coupled with an aging population, there is a growing concern that these factors may contribute to the escalating prevalence of gout (13). KAP surveys are valuable tools for assessing the knowledge, attitudes, and practices related to gout management in coastal urban populations. However, most previous studies have focused on healthcare providers rather than patients with gout (14), highlighting the need to investigate patient-level KAP patterns and influencing factors in regions such as Xiamen.
Therefore, this study aimed to investigate the KAP of gout among patients with gout in the Xiamen region, to provide a scientific basis for developing targeted health education and intervention strategies suited to the local dietary and environmental context.
Methods
Study design and subjects
This cross-sectional study was conducted from November 2023 to January 2024 among patients with gout at two medical institutions in Xiamen: Xiamen University Affiliated Xiang’an Hospital, and the Xiamen Haicang District Shitang Community Health Service Center. Ethical approval was obtained from the Medical Ethics Committee of Xiamen University, and informed consent was obtained from all participants. Participants were recruited using convenience sampling. Inclusion criteria for patients with gout encompassed individuals aged 18–80 years who had experienced at least one acute gout attack. Exclusion criteria involved the exclusion of questionnaires with completion times shorter than 2 s per single-choice question or 3 s per multiple-choice question, following established standards for online survey quality control (15).
Questionnaire distribution
The survey was conducted from November 2023 to January 2024 at Xiamen University Affiliated Xiang’an Hospital and Xiamen Haicang District Shitang Community Health Service Center, which served as the main survey sites. Before distribution, investigators received standardized online training on questionnaire administration and data quality control. Participant identity and data authenticity were verified through WeChat account and IP address matching to ensure reliability. The questionnaire was designed and distributed via the Questionnaire Star online platform and completed by participants through WeChat scanning. To ensure data quality, the platform was configured so that each WeChat account was permitted only one submission, and each IP address was restricted to one submission; any duplicate submissions were automatically flagged and excluded. Questionnaires with abnormal completion times (less than 110 s or more than 1800 s) or logical errors were also excluded as invalid responses. All items were mandatory before data export and verification (15).
Procedures
The design of the questionnaire drew upon insights and methodologies from previously published literature (16–18) and the “Merck Manual Gout1 “as valuable references content validity was assessed by a panel of three rheumatologists and two epidemiologists, who reviewed the questionnaire for relevance, clarity, and comprehensiveness. Subsequently, a pilot test was conducted on 53 gout patients with similar demographic characteristics to the target population (but excluded from the final study) to assess clarity and completion time. This resulted in a Cronbach’s α coefficient of 0.906, indicating a high level of internal consistency and reliability. The four-dimensional structure (demographics, knowledge, attitude, practice) reflects the standard KAP model framework, with a preceding demographic section to capture baseline variables.
The final questionnaire, conducted in Chinese, encompassed four distinct dimensions. Firstly, there was a section gathering Participant demographic information, comprising 14 questions. The Knowledge dimension followed, featuring 15 questions: seven single-choice questions (questions 1, 3–5, 13–15) were scored as 1 for correct responses and 0 for unclear or incorrect answers, while eight multiple-choice questions (questions 2, 6–12) carried a score range of 0–43. The Attitude dimension included 11 questions, all utilizing a five-point Likert scale, with scores ranging from 1 to 5, reflecting the degree of attitude, resulting in an overall score range of 11–55. Lastly, the Practice dimension consisted of 10 questions, also employing a five-point Likert scale, with scores ranging from 1 to 5, corresponding to the level of action taken, and yielding a total score range of 10–50.
Sample size calculation
The sample size was calculated using the formula n = (z2p(1-p))/d2, where z = 1.96 at 5% level of significance and 5% acceptable margin of error (d = 0.05). The proportion of the expected population was set at 50%. Based on this calculation, the minimum required sample size was 384 (19).
Statistical analyses
Statistical analysis was conducted using SPSS 26.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test confirmed that KAP scores were not normally distributed (p < 0.05); therefore, non-parametric tests were used. Comparisons between two groups were performed using the Mann–Whitney U test; for variables with three or more groups, the Kruskal-Wallis H test was used. Logistic regression was used to perform univariate and multivariate analysis. Due to the observed skewed distribution of scores, median values provided more robust measures of central tendency and more representative cut-off points than means. Dichotomizing scores at the median allowed us to use logistic regression to identify factors associated with scores in the upper half of the distribution. Pearson correlation analysis was employed to assess the correlations between knowledge, attitude, and practice scores. In multivariate analysis, median score was used as the cut-off value. Variables with p < 0.25 in univariate analysis were entered into multivariate logistic regression, as this more permissive threshold retains potentially explanatory variables that may be important in the adjusted model. Two-sided p < 0.05 were considered statistically significant in this study. Structural Equation Modeling (SEM) was used to explore the relationships between knowledge, attitudes, and practice, providing a framework for understanding these interrelationships and identifying key pathways. Statistical analysis was conducted using SPSS 26.0 (RRID:SCR_002865).
Results
Basic characteristics of participants
A total of 622 questionnaires were collected. Of these, 101 were excluded due to abnormal completion times (<110 s or more than 1800 s), and 59 were excluded because of logical errors (such as selecting both “correct” and “not sure” for the same question). Finally, 462 valid questionnaires were included, resulting in an effective response rate of 74.3%. Among the respondents, 251 (54.3%) were male, 337 (72.9%) had a university education or above, and 365 (79.0%) were employed full-time. The median scores (IQR) were as follows: Knowledge, Median = 16, IQR: 10–22; Attitude, Median = 34, IQR: 29–37; and Practice, Median = 40, IQR: 33–47, indicating generally low knowledge, moderately negative attitudes, and relatively proactive practices toward gout management (Table 1).
Table 1.
Baseline table.
| Characteristics | N (%) | Knowledge (K) | Attitude (A) | Practice (P) | |||
|---|---|---|---|---|---|---|---|
| Median (IQR) | P | Median (IQR) | P | Median (IQR) | P | ||
| Total | 462 | 16 (10, 22) | 34 (29, 37) | 40 (33, 47) | |||
| Gender | 0.884 | 0.924 | 0.716 | ||||
| Male | 251 (54.3) | 16 (9, 22) | 33 (29, 37) | 41 (32, 46) | |||
| Female | 211 (45.7) | 16 (10, 22) | 34 (29, 37) | 40 (34, 48) | |||
| Age (years) | 0.041 | <0.001 | 0.023 | ||||
| 18–24 | 59 (12.8) | 14 (5, 18) | 29 (27, 34) | 36 (30, 46) | |||
| 25 ~ 34 | 136 (29.4) | 16 (11, 22) | 34 (29, 37) | 39.5 (32, 47) | |||
| 35 ~ 50 | 181 (39.2) | 16 (9, 23.5) | 34 (30, 37.5) | 41 (36, 47.5) | |||
| 51 and above | 86 (18.6) | 16 (12, 21.3) | 33 (31, 37) | 41 (36.8, 46) | |||
| Marital status | 0.005 | 0.001 | <0.001 | ||||
| Unmarried | 109 (23.6) | 14 (6, 18.5) | 32 (27, 36) | 37 (30, 46) | |||
| Married | 345 (74.7) | 16 (11, 23) | 34 (30, 37) | 41 (36, 47) | |||
| Divorced | 5 (1.1) | 13 (5.5, 13.5) | 32 (26.5, 34.5) | 38 (28.5, 42.5) | |||
| Widowed | 3 (0.6) | 16 (1, 17) | 28 (28, 32) | 28 (26, 30) | |||
| Education | 0.306 | 0.494 | 0.229 | ||||
| Primary School | 19 (4.1) | 13 (2, 19) | 32 (28, 36) | 38 (30, 47) | |||
| Junior High School | 44 (9.5) | 16 (9.5, 18.8) | 33 (29.5, 36) | 38 (34.3, 43.5) | |||
| High School | 62 (13.4) | 16.5 (10.8, 23.3) | 33 (29, 37) | 40 (31, 46) | |||
| University and above | 337 (72.9) | 16 (10, 22) | 34 (29, 37) | 41 (34, 47) | |||
| Employment | 0.043 | 0.663 | 0.020 | ||||
| Full-time | 365 (79.0) | 16 (9, 22) | 34 (29, 37) | 40 (32.5, 47) | |||
| Part-time | 41 (8.9) | 14 (9.5,23) | 33 (27, 37) | 41 (35,50) | |||
| Retired | 50 (10.8) | 17.5 (14, 23) | 33 (30.8, 37) | 41.5 (36, 45.3) | |||
| Loss of labour force | 6 (1.3) | 1 (0, 17.5) | 31.5 (27, 34.8) | 30 (28.8, 32) | |||
| Frequency of gout medical consultations | 0.241 | 0.502 | 0.080 | ||||
| At least every 3 months | 61 (13.2) | 16 (14, 22) | 33 (27, 37) | 43 (38, 48) | |||
| Every 6 months | 62 (13.4) | 16 (11.8,23.3) | 33 (28.8, 36) | 38 (33.8,45) | |||
| Annually | 69 (14.9) | 17 (10.5, 22.5) | 33 (30, 37) | 40 (32.5, 44.5) | |||
| Less than once a year | 270 (58.4) | 15 (8, 22) | 34 (29, 37) | 40.5 (32, 48) | |||
| Age of Onset | 0.005 | 0.073 | 0.158 | ||||
| 18–24 years | 74 (16.0) | 14.5 (7.8, 20.3) | 32 (27, 36.3) | 36.5 (30, 49) | |||
| 25–34 years | 152 (32.9) | 18 (11,24) | 34 (29, 38) | 41 (35,47) | |||
| 35–50 years | 164 (35.5) | 16 (10.3, 22) | 34 (31, 37) | 41 (37, 46) | |||
| 51 years and above | 72 (15.6) | 14 (7.3, 18) | 33 (29, 37) | 40 (32.3, 46) | |||
| Family History | <0.001 | 0.610 | 0.065 | ||||
| Yes | 61 (13.2) | 18 (13,25) | 34 (28.5, 37) | 40 (36,48.5) | |||
| No | 301 (65.2) | 16 (11, 22) | 34 (29, 37) | 41 (34, 47) | |||
| Unclear | 100 (21.6) | 13 (4, 20.8) | 33 (29, 37) | 38 (31, 46) | |||
| Alcohol History | 0.008 | 0.290 | 0.191 | ||||
| Yes | 227 (49.1) | 17 (12,23) | 34 (29, 37) | 41 (35,47) | |||
| No | 217 (47.0) | 15 (7, 21) | 33 (29, 37) | 40 (32, 46) | |||
| Unclear | 18 (3.9) | 14.5 (0, 23.3) | 30.5 (27.8, 35.3) | 35.5 (30, 46.3) | |||
| Times of Gout Attacks in the Past Year | 0.035 | 0.506 | 0.432 | ||||
| No attacks | 250 (54.1) | 14 (7, 22) | 33.5 (29, 37) | 40 (32, 48) | |||
| 1 attack | 92 (19.9) | 16.5 (11,22) | 34 (30, 37) | 40.5 (35.3,47) | |||
| 2 attacks | 65 (14.1) | 17 (12, 23) | 33 (28.5, 37) | 38 (31, 45) | |||
| 3–5 attacks | 30 (6.5) | 18.5 (13, 22.3) | 34 (30.5, 37.3) | 40 (36.8, 46.3) | |||
| More than 5 attacks | 25 (5.4) | 16 (14.5, 20.5) | 35 (32, 40) | 42 (38.5, 46.5) | |||
| Duration of Gout | <0.001 | 0.004 | 0.031 | ||||
| Less than 1 year | 227 (49.1) | 13 (6, 20) | 33 (27, 36) | 40 (31, 48) | |||
| 1–2 years | 102 (22.1) | 18 (13,25) | 34 (30.8, 37) | 40 (35,46) | |||
| 2–5 years | 71 (15.4) | 17 (13, 22) | 33 (31, 37) | 40 (34, 44) | |||
| 5–10 years | 36 (7.8) | 20 (14.3, 24) | 36 (32, 38.8) | 45 (40, 47) | |||
| More than 10 years | 26 (5.6) | 16.5 (13.5, 23.3) | 33.5 (27.8, 38.3) | 43.5 (38, 48.3) | |||
| Type of Gout | <0.001 | 0.061 | <0.001 | ||||
| Acute gout | 132 (28.6) | 21 (15, 25.8) | 34 (31, 37) | 42 (37, 47) | |||
| Chronic gout | 58 (12.6) | 18.5 (13, 23) | 35 (27.8, 38.3) | 44 (38,48) | |||
| Unclear | 272 (58.9) | 13.5 (6, 18) | 33 (29, 36) | 38 (31, 46) | |||
| Previous Medications Taken (Multiple Choices Allowed) | |||||||
| Colchicine | 115 (24.9) | ||||||
| Nonsteroidal anti-inflammatory drugs (NSAIDs) | 70 (15.2) | ||||||
| Analgesics | 143 (31.0) | ||||||
| Corticosteroids | 17 (3.7) | ||||||
| Allopurinol | 52 (11.3) | ||||||
| Benzbromarone | 67 (14.5) | ||||||
| Sodium bicarbonate | 48 (10.4) | ||||||
| Traditional Chinese medicine | 213 (46.1) | ||||||
| Do you have any Gout-related Complications? (Multiple Choices Allowed) | |||||||
| Hypertension | 113 (24.5) | ||||||
| Hyperlipidemia | 126 (27.3) | ||||||
| Diabetes | 43 (9.3) | ||||||
| Obesity | 168 (36.4) | ||||||
| Coronary heart disease | 17 (3.7) | ||||||
| Chronic kidney disease | 20 (4.3) | ||||||
| Hyperthyroidism | 18 (3.9) | ||||||
| Hypothyroidism | 11 (2.4) | ||||||
| Anemia | 109 (23.6) | ||||||
| Psoriasis | 19 (4.1) | ||||||
Bold values indicate P < 0.05.
Distribution analysis of the KAP scores
Most respondents (approximately 65.5%) exhibited limited knowledge of gout, particularly in aspects related to medication and disease management. About two-thirds (67.1%) correctly distinguished gout from osteoporosis (Q1), but more than half (around 58.2%) were unable to identify uric acid–lowering drugs such as allopurinol or benzbromarone (Q12) or recognize medications that may trigger gout attacks (Q10). Fewer than half (43.6%) understood the necessity of long-term urate-lowering therapy (Q14), and 52.3% were unclear about the difference between chronic and acute gout (Q3), indicating substantial gaps in treatment awareness and disease understanding.
Participants’ attitudes toward gout were mixed. Nearly half (48.9%) disagreed with the misconception that gout mainly affects older men (Q1) and recognized the importance of following physicians’ advice for long-term treatment (Q4). However, 56.4% underestimated the systemic effects of gout beyond joint pain (Q7), and 41.7 remained concerned about the potential harm of long-term medication use (Q5). Overall, while moderate awareness of lifestyle management existed, inconsistencies in perception reflected uncertainty and ambivalence toward gout control.
In terms of practice, self-management behaviors were generally suboptimal. Fewer than half (46.8%) reported regularly improving their diet and exercise habits or attending medical follow-ups, and only a minority (29.7%) maintained continuous urate-lowering therapy or sought reliable disease-related information (Q11, Q14). Although some patients practiced dietary moderation and hydration, gaps remained in consistent adherence and proactive disease monitoring. These findings suggest that while certain awareness and behaviors exist, deficiencies persist across all three KAP dimensions. Detailed item-level data are presented in Supplementary Tables S1, S2 and Supplementary Figure S1.
Analysis of factors associated with KAP scores
Correlation analysis showed that significant positive correlations were found between knowledge and attitude (r = 0.456, P < 0.001), as well as knowledge and practice (r = 0.539, P < 0.001). Meanwhile, there were also positive correlations between attitude and practice (r = 0.477, P < 0.001) (Table 2).
Table 2.
Correlation analysis.
| Characteristics | Knowledge | Attitude | Practice |
|---|---|---|---|
| Knowledge | 1.000 | / | / |
| Attitude | 0.456 (P < 0.001) | 1.000 | / |
| Practice | 0.539 (P < 0.001) | 0.477 (P < 0.001) | 1.000 |
Multivariate logistic regression analysis
Multivariate logistic regression analysis was performed using variables with p < 0.25 in univariate analysis as entry criteria. Results showed that being retired (OR = 3.935, 95% CI: 1.494–10.362, p = 0.006), having 3–5 gout attacks in the past year (OR = 0.379, 95% CI: 0.144–0.999, p = 0.050), gout duration of 1–2 years (OR = 1.958, 95% CI: 1.034–3.709, p = 0.039), 2–5 years (OR = 2.159, 95% CI: 1.006–4.633, p = 0.048), and 5–10 years (OR = 4.288, 95% CI: 1.582–11.622, p = 0.004), and being unsure of their gout type (OR = 0.326, 95% CI: 0.193–0.551, p < 0.001) were independently associated with knowledge scores (Supplementary Table S3). For attitude scores, being aged 25–34 years (OR = 2.376, 95% CI: 1.075–5.251, p = 0.032) and 35–50 years (OR = 2.490, 95% CI: 1.015–6.108, p = 0.046), and having a gout duration of 5–10 years (OR = 2.459, 95% CI: 1.119–5.406, p = 0.025) were independently associated with more positive attitudes (Supplementary Table S4). For practice scores, being unsure of their gout type (OR = 0.430, 95% CI: 0.262–0.706, p < 0.001) was independently associated with suboptimal practice (Supplementary Table S5). Regarding the total KAP score, being married (OR = 2.152, 95% CI: 1.029–4.501, p = 0.042), consulting every 6 months compared with at least every 3 months (OR = 0.412, 95% CI: 0.174–0.975, p = 0.044), having a gout duration of 5–10 years (OR = 5.110, 95% CI: 1.754–14.891, p = 0.003), and having an unclear gout type (OR = 0.251, 95% CI: 0.140–0.450, p < 0.001) were independently associated with overall KAP performance (Supplementary Table S6).
Structural equation modeling
The fit of the SEM model yielded good indices demonstrating good model fit (RMSEA value: 0.068, SRMR value: 0.069, TLI value: 0.839, and CFI value: 0.851) (Table 3), and the results of the mediation analysis shown that knowledge had direct effect on practice (β = 0.644, p < 0.001). However, the direct effect of knowledge on attitude, the direct effect of attitude on practice, and the indirect effect of knowledge on practice were not significant (Table 4 and Figure 1).
Table 3.
Model fitting index.
| Indicators | Reference | Results |
|---|---|---|
| RMSEA | <0.08 | 0.068 |
| SRMR | <0.08 | 0.069 |
| TLI | >0.80 | 0.839 |
| CFI | >0.80 | 0.851 |
Table 4.
The direct and indirect effects among the variables.
| Model paths | Total effects | Direct effect | Indirect effect | |||
|---|---|---|---|---|---|---|
| β (95%CI) | P | β (95%CI) | P | β (95%CI) | P | |
| Attitude | ||||||
| Knowledge | 0.046 (−0.060, 1.151) | 0.397 | 0.046 (−0.060, 1.151) | 0.397 | ||
| Practice | ||||||
| Knowledge | 0.642 (0.577, 0.706) | <0.001 | 0.644 (0.579, 0.708) | <0.001 | −0.002 (−0.008, 0.004) | 0.537 |
| Attitude | −0.043 (−0.126, 0.040) | 0.308 | −0.043 (−0.126, 0.040) | 0.308 | ||
Figure 1.
Structural equation model of factors influencing knowledge, attitude, and practice.
Discussion
Patients with gout had insufficient knowledge, negative attitude and proactive practice toward gout. Although our findings show significant positive correlations between knowledge, attitudes, and practice scores, the SEM analysis revealed that knowledge has a direct effect on practice, while the direct effects of attitudes on practice and indirect effects through attitudes were not statistically significant. The observed direct path from knowledge to practice suggests that patients’ behaviors are primarily driven by clinical guidance. This aligns with the Chinese healthcare context, where respect for medical authority often leads to high behavioral compliance regardless of personal attitudes.
This study examined knowledge, attitude, and practice scores among gout patients, revealing significant variations across different patient demographics and clinical characteristics. A previous study developed a web-based, patient-tailored tool designed to enhance adherence to urate-lowering therapy in patients with gout, which received positive evaluations (20).
These findings have crucial implications for improving gout management. Differences in knowledge, attitudes, and practices were observed across patient subgroups according to age, marital status, employment status, gout duration, and gout type, indicating the need for tailored educational approaches (21, 22). Notably, age-stratified analysis revealed significant differences across KAP domains: younger patients (18–24 years) demonstrated lower knowledge and less positive attitudes compared with middle-aged groups (25–50 years), who showed higher odds of adequate knowledge and more positive attitudes toward gout management. This may reflect greater health literacy, more frequent healthcare engagement, and longer cumulative disease experience in middle-aged patients. These findings underscore the importance of age-targeted educational interventions, particularly for younger patients who may underestimate the chronic and systemic nature of gout. Disease duration and attack frequency also emerged as important determinants of KAP. Patients with a gout duration of 1–2 years and 5–10 years demonstrated significantly higher knowledge scores compared with those diagnosed within the past year, suggesting that accumulated disease experience fosters greater understanding of gout pathophysiology and management. Similarly, patients with longer disease duration (5–10 years) exhibited more positive attitudes, likely reflecting adaptation to chronic illness and greater engagement with healthcare providers over time. In contrast, newly diagnosed patients may struggle to adjust to lifestyle and dietary modifications, highlighting the need for intensive early education at the point of diagnosis. Regarding attack frequency, the multivariate analysis showed that patients who experienced 3–5 attacks in the past year had lower odds of adequate knowledge (OR = 0.379, p = 0.050), which may reflect a paradox in which frequent flares are associated with disease uncertainty or inadequate management rather than improved understanding. These indicators, disease duration and attack frequency, reflect patients’ prior disease experience and cognitive engagement with their condition, and should be considered when designing individualized patient education programs. For example, younger and employed patients tended to have lower knowledge and practice scores, while longer disease duration was associated with more positive attitudes, suggesting that patient experience influences understanding and behavior (23, 24). Compared with a study in Saudi Arabia (25), where patients showed high satisfaction, adequate knowledge, and positive attitudes toward gout management, patients in Xiamen demonstrated greater knowledge gaps and more negative perceptions, particularly regarding medication adherence and dietary control. These regional contrasts highlight the influence of sociocultural and healthcare contexts on disease perception and self-management (22, 26). Notably, many participants remained unclear about the distinction between acute and chronic gout, the appropriate use of uric acid–lowering drugs, and the role of exercise in gout prevention and flare control, with 56.7% believing that intense physical activity could trigger attacks (27). Furthermore, while patients generally demonstrated awareness of dietary triggers (e.g., 80.7% correctly identified seafood as a trigger), significant gaps persisted in medication-related knowledge. Only 11.9% correctly identified febuxostat, 34.2% identified benzbromarone, and 31.2% identified allopurinol as uric acid–lowering drugs, highlighting a critical deficit in pharmacological knowledge. Low adherence to long-term urate-lowering therapy remains a critical challenge, as many patients discontinue medication during asymptomatic periods, which can worsen disease progression. These findings underscore the urgent need for evidence-based patient education and behavioral interventions that strengthen disease-specific knowledge and promote long-term adherence (28–30). Healthcare providers should prioritize patient-centered strategies that correct misconceptions about gout’s age and gender susceptibility (26, 31), emphasize the importance of sustained urate-lowering therapy, and integrate dietary and lifestyle counseling into clinical practice to enhance adherence and outcomes.
The findings on self-management practices among patients with gout provide valuable insights into how individuals actively engage in disease control. Many patients demonstrated proactive behaviors such as dietary adjustments, regular exercise, weight management, and adherence to ULT, reflecting a strong sense of responsibility and awareness toward gout management. Their efforts to monitor purine-rich foods, follow medical advice, and seek reliable information about gout and prescribed medications indicate a positive orientation toward self-care. However, important gaps remain: while dietary awareness was relatively high (e.g., 80.7% correctly identified seafood as a trigger), medication-related knowledge and adherence were notably deficient. Only 45.9% of patients were aware that chronic gout often requires lifelong urate-lowering therapy, and fewer than half correctly identified common uric acid–lowering drugs. Patients with longer disease duration (5–10 years) generally performed better in self-management, likely due to accumulated experience and repeated clinical interactions, whereas newly diagnosed patients faced greater challenges in adapting to the required lifestyle and pharmacological changes. This highlights the critical window of early disease management, where targeted education on medication adherence, particularly regarding allopurinol and other urate-lowering agents, can have the greatest impact. The SEM results further revealed that knowledge exerts a direct influence on practice, underscoring the pivotal role of patient education in improving self-management behaviors (22, 24, 32). Although attitudes toward gout also correlated positively with practice, their mediating effect was limited, suggesting that practical guidance and actionable knowledge may have a more immediate impact on behavior than attitudinal shifts. These findings highlight the importance of targeted, evidence-based educational interventions that provide clear, context-specific instructions and address common misconceptions about gout. By integrating patient-centered education with behavioral strategies—such as lifestyle counseling, peer support, and social-environmental considerations—healthcare providers can promote sustained adherence and achieve more effective, holistic management of gout.
The multivariate logistic regression analysis reveals important insights for improving clinical practice in gout management. Retirees are more likely to possess adequate gout knowledge, highlighting the need for tailored education for the non-retired population. Uncertainty about gout type is associated with suboptimal practice, emphasizing the importance of clear communication to ensure adherence to recommended actions. Surprisingly, younger age groups (25–34 years and 35–50 years) exhibit more positive attitudes toward gout management, suggesting potential effectiveness in attitude-focused interventions for these demographics. Individuals managing gout for an extended duration demonstrate a more positive attitude, indicating the significance of sustained patient engagement and support during long-term management (23, 24).
Limitations
Limitations of this study include the use of a cross-sectional design, which restricts our ability to establish causal relationships between variables. The data collected relied on self-reported responses from participants, which may introduce recall and social desirability biases. Additionally, the study was conducted in a specific geographic region (Xiamen), which may limit the generalizability of the findings to broader populations. Furthermore, the reliance on questionnaires to assess knowledge, attitude, and practice may not capture the full complexity of patient experiences and behaviors related to gout. The relatively high educational status of participants (72.9% with university education or above) may limit the generalizability of our findings to less educated populations, who may face greater challenges in accessing and understanding gout information and implementing self-management practices. The use of an online survey platform (Questionnaire Star via WeChat) may have reduced participation among older adults or individuals without smartphones, potentially introducing a digital access bias that could affect the representativeness of the sample. However, several measures were taken to mitigate this limitation: WeChat has an extremely high penetration rate in China (over 1.3 billion monthly active users), trained researchers were present at both survey sites to assist participants with questionnaire completion, and the platform was configured to allow only one submission per WeChat account and per IP address to prevent duplicate entries. Nonetheless, this potential selection bias should be considered when interpreting the findings. Regarding data quality, a total of 160 questionnaires were excluded from the final analysis. The primary reasons for exclusion were abnormal completion times (<110 s or >1800 s; n = 101) and logical errors in responses such as simultaneously selecting a correct answer and “unclear” for the same item (n = 59), rather than system failures. Of the excluded questionnaires, 28 (17.5%) were from participants aged 51 years and older, which is within the expected range and does not suggest disproportionate exclusion of older participants. No major technical failures of the survey platform were identified during the data collection period.
Conclusion
Patients with gout had insufficient knowledge, negative attitude and proactive practice toward gout. To optimize clinical practice in gout management, it is advisable to prioritize comprehensive patient education, particularly targeting the non-retired population. Additionally, addressing any negative attitudes observed among younger individuals and enhancing communication concerning gout types and treatment recommendations are essential steps for improving overall patient care in gout management.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Edited by: Cristina Hernández-Díaz, Centro de Investigación y Atención en Reumatología con Ecografía, Mexico
Reviewed by: Esteban Cruz, National Autonomous University of Mexico, Mexico
Somesh Kumar Saxena, SAM Global University, India
Abbreviations: KAP, Knowledge, Attitude, and Practice; SEM, Structural Equation Modeling; IQR, Interquartile Range.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Medical Ethics Committee of Xiamen University (no. XAHLL2023019). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
YY: Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing, Supervision. YS: Writing – original draft, Formal analysis, Data curation, Writing – review & editing. WL: Writing – original draft, Software, Resources, Writing – review & editing. QL: Software, Writing – original draft, Resources, Writing – review & editing. XD: Validation, Writing – original draft, Writing – review & editing, Visualization. YL: Validation, Writing – original draft, Writing – review & editing, Visualization.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1786934/full#supplementary-material
References
- 1.Zhou J, Yuan Y, Xu P, Yi B, Chen H, Su W. Host-population microbial diversity scaling of Chinese gut microbiomes in gout patients. Evol Bdioinformatics Online. (2022) 18:5858. doi: 10.1177/11769343221095858, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Lei Y, Yang G, Du F, Yi J, Quan L, Liu H, et al. Formulation and evaluation of a drug-in-adhesive patch for transdermal delivery of colchicine. Pharmaceutics. (2022) 14:2245. doi: 10.3390/pharmaceutics14102245, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Cross M, Ong KL, Culbreth GT, Steinmetz JD, Cousin E, Lenox H, et al. Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the global burden of disease study 2021. Lancet Rheumatol. (2024) 6:e507–17. doi: 10.1016/S2665-9913(24)00117-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhang Y, Yang R, Dove A, Li X, Yang H, Li S, et al. Healthy lifestyle counteracts the risk effect of genetic factors on incident gout: a large population-based longitudinal study. BMC Med. (2022) 20:138. doi: 10.1186/s12916-022-02341-0, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Burns LR, Liu GG. China's healthcare system and reform. Cham: Springer; (2017). [Google Scholar]
- 6.Khalid A, Haque S, Alvi S, Ferdous M, Genereux O, Chowdhury N, et al. Promoting health literacy about Cancer screening among Muslim immigrants in Canada: perspectives of imams on the role they can play in community. J Prim Care Community Health. (2022) 13:3051. doi: 10.1177/21501319211063051, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Koni A, Taha S, Daifallah A, Koni M, Abutaha S, Sabateen A, et al. A cross-sectional evaluation of knowledge, attitudes, practices, and perceived challenges among Palestinian pharmacists regarding COVID-19. SAGE Open Med. (2022) 10:20503121211069278. doi: 10.1177/20503121211069278, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Shubayr MA, Kruger E, Tennant M. Oral health providers' views of oral health promotion in Jazan, Saudi Arabia: a qualitative study. BMC Health Serv Res. (2023) 23:214. doi: 10.1186/s12913-023-09170-8, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kaplan A, Bülbül E. Evaluation of health literacy level and personal factors in disease self-management of emergency department patients with chronic diseases. Int Emerg Nurs. (2024) 77:101523. doi: 10.1016/j.ienj.2024.101523, [DOI] [PubMed] [Google Scholar]
- 10.Harrold LR, Mazor KM, Negron A, Ogarek J, Firneno C, Yood RA. Primary care providers' knowledge, beliefs and treatment practices for gout: results of a physician questionnaire. Rheumatology. (2013) 52:1623–9. doi: 10.1093/rheumatology/ket158, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Vaidya B, Pudasaini K, Baral R, Nakarmi S. Diagnosis and management of gout by clinicians in Nepal: a web-based survey. Rheumatol Int. (2021) 41:1311–9. doi: 10.1007/s00296-020-04712-3, [DOI] [PubMed] [Google Scholar]
- 12.Zhao M, Jian J, Yang D, Sun H, Liu L, Yan Z, et al. Knowledge, attitudes, and practices of gouty arthritis in the general population aged > 30. BMC Med Educ. (2024) 24:775. doi: 10.1186/s12909-024-05690-x, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Cross M, Ong KL, Culbreth GT, Steinmetz JD, Cousin E, Lenox H. Global, regional, and national burden of gout, 1990-2020, and projections to 2050: a systematic analysis of the global burden of disease study 2021. Lancet Rheumatol. (2024) 6:e507–17. doi: 10.1016/s2665-9913(24)00117-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Taha MM, Matar MS, Elhag EAA, Hamadouk RM, Ahmed KO, Yousef BA. Physicians’ knowledge, attitudes, and practices in gout management: a cross-sectional study at soba teaching hospital. J. Adv. Med. Pharm. Sci. (2025) 27:53–64. doi: 10.9734/jamps/2025/v27i5777 [DOI] [Google Scholar]
- 15.Jaeger SR, Cardello AV. Factors affecting data quality of online questionnaires: issues and metrics for sensory and consumer research. Food Qual Prefer. (2022) 102:1046762022. doi: 10.1016/j.foodqual.2022.104676 [DOI] [Google Scholar]
- 16.Ashiq K, Bajwa M, Qayyum M, Ashiq S, Yasmeen A, Abid F, et al. Knowledge, attitude and practices assessment of the gout patients residing in Lahore, Pakistan. Pakistan J. Med. Dentistry. (2022) 11:76–822022. doi: 10.36283/PJMD11-2/012 [DOI] [Google Scholar]
- 17.Dalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet. (2021) 397:1843–55. doi: 10.1016/s0140-6736(21)00569-9, [DOI] [PubMed] [Google Scholar]
- 18.Dalbeth N, Merriman TR, Stamp LK. Gout. Lancet. (2016) 388:2039–52. doi: 10.1016/s0140-6736(16)00346-9 [DOI] [PubMed] [Google Scholar]
- 19.Charan J, Kaur R, Bhardwaj P, Singh K, Ambwani SR, Misra S. Sample size calculation in medical research: a primer. Annal. Nat. Acad. Med. Sci. (2021) 57:74–80. doi: 10.1055/s-0040-1722104, 3785596 [DOI] [Google Scholar]
- 20.Te Kampe R, Boonen A, Jansen TL, Elling JM, Flendrie M, van Eijk-Hustings Y, et al. Development and usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy in gout. BMC Med Inform Decis Mak. (2022) 22:95. doi: 10.1186/s12911-022-01833-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Pascart T, Grandjean A, Capon B, Legrand J, Namane N, Ducoulombier V, et al. Monosodium urate burden assessed with dual-energy computed tomography predicts the risk of flares in gout: a 12-month observational study: MSU burden and risk of gout flare. Arthritis Res Ther. (2018) 20:210. doi: 10.1186/s13075-018-1714-9, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Serlachius A, Schache K, Kieser A, Arroll B, Petrie K, Dalbeth N. Association between user engagement of a Mobile health app for gout and improvements in self-care behaviors: randomized controlled trial. JMIR Mhealth Uhealth. (2019) 7:e15021. doi: 10.2196/15021, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Nguyen AD, Frensham LJ, Wong MX, Meslin SM, Martin P, Lau AY, et al. mHealth app patient testing and review of educational materials designed for self-management of gout patients: descriptive qualitative studies. JMIR Mhealth Uhealth. (2018) 6:e182. doi: 10.2196/mhealth.9811, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yin Y, Wang H, Fan CF, Chen H. Potential development of a Mobile application for gout self-management: what support Do patients need? Patient Prefer Adherence. (2021) 15:2231. doi: 10.2147/ppa.s310689, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Khormi AAM, Basalem AA, Al Muaddi AMZ, Alaskar AM, Algahtani RAS, Alharbi AS, et al. Knowledge and attitudes of gout patients and their perspectives about diagnosis and management: a cross-sectional study in Saudi Arabia. Immun Inflamm Dis. (2023) 11:e1010. doi: 10.1002/iid3.1010, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhang Y, Chen S, Yuan M, Xu Y, Xu H. Gout and diet: a comprehensive review of mechanisms and management. Nutrients. (2022) 14:3525. doi: 10.3390/nu14173525, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Singh JA. Disease modification in gout: a qualitative study of gout expert rheumatologists. Rheumatology Adv. Prac. (2022) 6:107. doi: 10.1093/rap/rkab107, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Doherty M, Jenkins W, Richardson H, Sarmanova A, Abhishek A, Ashton D, et al. Efficacy and cost-effectiveness of nurse-led care involving education and engagement of patients and a treat-to-target urate-lowering strategy versus usual care for gout: a randomised controlled trial. Lancet. (2018) 392:1403–12. doi: 10.1016/s0140-6736(18)32158-5, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Singh JA, Edwards NL. Gout management and outcomes during the COVID-19 pandemic: a cross-sectional internet survey. Ther. Adv. Musculoskeletal Dis. (2020) 12:124. doi: 10.1177/1759720x20966124, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Fang YJ, Wu TY, Lin CL, Su CY, Li JR, Chung YL, et al. Effects of urate-lowering therapy on risk of hyperlipidemia in gout by a population-based cohort study and on in vitro hepatic lipogenesis-related gene expression. Mediat Inflamm. (2020) 2020:8890300. doi: 10.1155/2020/8890300, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Song Y, Kang ZW, Liu Y. Multiple gouty tophi in the head and neck with normal serum uric acid: a case report and review of literatures. World J Clin Cases. (2022) 10:1373–80. doi: 10.12998/wjcc.v10.i4.1373, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Zhang MY, Niu JQ, Wen XY, Jin QL. Liver failure associated with benzbromarone: a case report and review of the literature. World J Clin Cases. (2019) 7:1717–25. doi: 10.12998/wjcc.v7.i13.1717, [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.