Skip to main content
Download PDF

Dietary knowledge-attitude-practice status in hemodialysis patients: a latent profile analysis

BMC Public Health volume 24, Article number: 836 (2024) Cite this article

Abstract

Background

Hemodialysis patients require a reasonable dietary intake to manage their disease progression effectively. However, there is limited research on these patients’ overall dietary knowledge, attitude, and practice (KAP) status. This study aimed to investigate the dietary KAP status and latent profiles in hemodialysis patients and identify sociodemographic and disease-related factors associated with these profiles and dietary practice.

Methods

A multicenter cross-sectional study involving 425 hemodialysis patients was conducted. A dietary KAP questionnaire in hemodialysis patients was used to evaluate the dietary KAP of the patients. A structural equation model was employed to analyze the correlations between dietary knowledge, attitude, and practice. Multiple linear regression analysis was used to identify factors associated with dietary practice scores. Latent profile analysis was conducted to determine the latent profiles of dietary KAP, and binary logistic regression was used to explore the sociodemographic and disease-related characteristics associated with each KAP profile in hemodialysis patients.

Results

The normalized average scores for dietary knowledge, attitude, and practice in hemodialysis patients were 0.58, 0.82, and 0.58, respectively. The structural equation model revealed significant positive correlations between dietary knowledge and attitude, and attitude and practice. Attitude played an indirect effect between knowledge and practice. Gender, cerebrovascular disease, and dietary attitude scores were identified as independent influencing factors for dietary practice scores. Two dietary KAP profiles were developed: a profile with general knowledge and attitude but low practice (40.2%) and a profile with general knowledge and attitude and high practice (59.8%). Binary logistic regression analysis indicated gender and monthly income per household significantly predicted membership in each KAP profile.

Conclusions

The dietary practice of hemodialysis patients requires improvement. It is necessary to develop more individualized dietary interventions for these patients. Further exploration is needed to understand the motivation of patients to change their dietary behavior.

Peer Review reports

Background

Chronic kidney disease (CKD) is a significant global health problem, affecting approximately 14.3% of the population [1]. With its high prevalence, poor prognosis, and expensive treatment, CKD has become a significant concern [2]. As the disease progresses, CKD can lead to end-stage renal disease (ESRD) [3]. Hemodialysis (HD) is the most commonly used treatment for ESRD patients [4]. However, HD has limitations in terms of filtration and regulatory functions. Consequently, HD patients often experience a range of complications, including volume overload, hypertension, electrolyte disturbances, mineral and bone disorders, and malnutrition [5, 6]. These complications are closely related to the dietary intake of HD patients, which in turn affects disease control and mortality [7, 8]. Therefore, HD patients must adopt appropriate dietary practices to reduce complications and improve their prognosis.

The Knowledge-Attitude-Practice (KAP) theory is a widely recognized behavioral intervention theory that explains healthy behavior [9]. According to this theory, changing behavior involves two key steps: establishing beliefs and changing attitudes. The KAP theory has been extensively applied in developing questionnaires and investigating the KAP status in specific populations [10,11,12]. It has also been used in health education programs to prevent primary and chronic diseases [13,14,15,16]. Our research team has developed a dietary KAP questionnaire for HD patients based on the KAP theory. The questionnaire underwent validation through Delphi expert consultation and preliminary implementation in HD patients, demonstrating good reliability and validity [17]. However, the final version of the questionnaire has not been used to report the dietary KAP status in HD patients. Investigating the dietary KAP status of HD patients could help health professionals identify weaknesses in dietary management and develop corresponding interventions to improve patients’ dietary practice. Although a previous single-center survey investigated the KAP regarding hemodialysis and its complications in HD patients, it did not include dietary-related questions [18]. Therefore, more studies are needed to investigate the dietary KAP status of HD patients.

Latent profile analysis (LPA) is a person-centered approach using continuous variables to divide samples into meaningful subgroups based on similar characteristics [19]. It helps identify underlying characteristics of individuals based on their response patterns to specific topics, enabling a better understanding of individuals with different profiles [20, 21]. By employing LPA, we can explore the dietary KAP profiles of HD patients and identify sociodemographic and disease-related characteristics associated with different profiles. This approach will provide insights into the dietary KAP of HD patients.

Therefore, based on the KAP theory, this study aims to explore the latent profiles of dietary KAP and the corresponding sociodemographic and disease-related characteristics of each KAP profile in HD patients through LPA analysis. Additionally, we aim to investigate the dietary KAP status and the factors influencing dietary practices in HD patients. The findings of this study will provide essential evidence for healthcare professionals to develop targeted intervention strategies and improve the dietary KAP of HD patients.

Methods

Study design

This multicenter cross-sectional study employed convenience sampling to recruit 425 HD patients from five hospitals in Beijing, China, between June 1st, 2021, and July 31st, 2021.

Participants

Participants informed consent was obtained from all participants. Eligible patients were 18 years or older, clinically diagnosed with ESRD, had undergone hemodialysis treatment for more than 3 months, had independent oral feeding without chewing and swallowing disorders, were conscious, and had unimpeded communication. Patients with acute comorbidities during the data collection period were excluded. It is approximated that there are 20–25 factors that affect dietary practice scores. Therefore, to avoid violating the principle that at least 10 cases are required for one variable in the multiple linear regression, a minimum sample size of 200–250 patients was needed [22]. The study received approval from the Medical Ethical Committee of Peking University Third Hospital (IRB2021–084-02).

Data collection

Sociodemographic information

A self-designed questionnaire was used to collect sociodemographic data, including age, gender, marital status, education, occupational status, dialysis duration, payment method, primary disease, and comorbidities.

Dietary knowledge-attitude-practice questionnaire in hemodialysis patients

The questionnaire consisted of three sub-scales: dietary knowledge, attitude, and practice. The knowledge sub-scale comprised four dimensions: dietary potassium intake, protein energy intake, volume control, and dietary phosphorus intake. The attitude sub-scale was not dimensioned, while the practice sub-scale included four dimensions: dietary control, dietary monitoring, cooking skills, and selection of alternative condiments. The questionnaire contained 34 items, with 12 items in the knowledge sub-scale, 7 in the attitude sub-scale, and 15 in the practice sub-scale. Each knowledge item was scored as 0 or 1 (12 multiple-choice questions, with 1 point for a correct answer and 0 for a wrong answer); each attitude item was scored on a scale of 0 to 4 (0 for strongly disagree, 4 for strongly agree); and each practice item was scored on a scale of 0 to 4 (0 for never, 4 for always) as well. Higher scores indicated better dietary knowledge, attitude, and practice. The questionnaire demonstrated good content validity (0.79), with a Cronbach’s α coefficient of 0.868 and retest reliability of 0.845 [17]. The questionnaire is shown in the Appendix.

Statistical analysis

SPSS25.0 was utilized for statistical description and analysis. Normally distributed continuous data were presented as the mean with standard deviation (SD), while non-normally distributed continuous variables were expressed using the median [interquartile range (IQR)]. Categorical data were presented as numbers (percentages). The two-tailed t-test and Mann-Whitney test were employed to compare two independent variables, while a one-way analysis of variance was used to compare multiple independent variables. The Bonferroni correlation was applied for post hoc tests. Categorical data were analyzed using the chi-square test. The correlation between continuous variables was examined using Spearman analysis. Multiple linear regression analysis was conducted to identify factors associated with dietary practice scores, and binary logistic regression analysis was performed to determine the independent influencing factors of KAP profiles. A two-tailed P-value < 0.05 was considered statistically significant.

Mplus 7.0 was employed to analyze latent profiles of dietary KAP in HD patients. The scores of 34 items of dietary KAP in HD patients were used as exogenous variables. Sequentially, 1 ~ 5 profiles were selected for analysis, and the fitting effect of the final model was evaluated using the following three indicators [23]. Firstly, the information evaluation index compared the difference between the expected value and the actual value with the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) to determine the model fit. Smaller statistical values indicated a better fit. Secondly, the classification evaluation index assessed classification accuracy through the information entropy (Entropy), which ranges from 0 to 1. A value closer to 1 indicated a more accurate classification. Lastly, the likelihood ratio test compared the fitting differences between k-1 and k-category models using the Lo-Mendell-Rubin likelihood ratio test (LMR) and bootstrapped likelihood ratio test (BLRT). If P <  0.05 for LMR and BLRT, the k-category model is superior to the k-1 category model. While the above evaluation metrics provide a reference for profile decision-making, the interpretability of each category should also be considered when determining the best model.

AMOS 26.0 was utilized to test the mediating role of attitude between knowledge and practice. Bootstrapping was used to test the significance of the mediating effect. Non-significant and significant direct effects denoted fully mediated and partially mediated effects, respectively. We conducted 2000 bootstrap resamples and used 95% confidence intervals (CIs) to test for direct and indirect effects. The fit between the hypothesized model and the data was assessed by calculating absolute and incremental fit indices. A good model yields a minimum discrepancy divided by the degree of freedom (CMIN/df) <  3, a root mean square error of approximation (RMSEA) <  0.05 or at least 0.08, and goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), Tucker-Lewis index (TLI) and comparative fit index (CFI) >  0.90.

Findings

Sociodemographic and disease-related information of hemodialysis patients

A total of 425 HD patients were recruited. 60.7% (258/425) patients were male, and 39.3% (167/425) were female, with age ranged from (23 ~ 97) years. See Table 1.

Table 1 The sociodemographic and disease-related information of HD patients
Full size table

Dietary knowledge, attitude and practice status of hemodialysis patients

In the knowledge sub-scale, the mean scores per item in each dimension, in descending order, were “protein energy intake” “dietary phosphorus intake” “dietary potassium intake” and “volume control”. In the practice sub-scale, the mean scores per item in each dimension, in descending order, were “dietary control” “cooking skills” “selection of alternative condiments” and “dietary monitoring”. More details were shown in Table 2.

Table 2 Dietary knowledge, attitude and practice scores of HD patients
Full size table

Influencing factors of dietary practice scores in hemodialysis patients

Univariate factors influencing dietary practice scores in hemodialysis patients

The results showed that gender, marital status, monthly income per household, payment method, and cerebrovascular diseases were significantly correlated to the dietary practice scores (P <  0.05); dietary attitude scores were positively related to dietary knowledge scores (r = 0.265, P <  0.001), and dietary practice scores (r = 0.237, P <  0.001) in HD patients. See Table 3.

Table 3 Univariate factors influencing dietary practice scores in HD patients
Full size table

Independent influencing factors of dietary practice scores in hemodialysis patients

Using “male, no spouse, monthly income per household ≤ 1000 RMB, local medical insurance, and no cerebrovascular disease” as the reference group, the results showed that the gender (β = 0.233, P <  0.001), cerebrovascular disease (β = 0.154, P = 0.002), and dietary attitude scores (β = 0.146, P = 0.003) were the independent influencing factors of dietary practice scores, and the R2 = 0.079. See Table 4.

Table 4 Independent influencing factors of dietary practice scores in HD patients
Full size table

The structural equation model of dietary knowledge, attitude, and practice in hemodialysis patients

The structural equation model fit was ideal, with CMIN/df = 2.461, GFI = 0.937, AGFI = 0.913, TLI = 0.957, NFI = 0.941, CFI = 0.964, and RMSEA = 0.059 (Table 5). All models’ paths were statistically significant (P <  0.05), except for the knowledge-behavior path where P >  0.05. The R2 = 0.04. The structural equation model was shown in Fig. 1.

Table 5 Structural equation model of dietary knowledge, attitude, and practice in HD patients
Full size table
Fig. 1
figure 1

The structural equation model of dietary knowledge, attitude, and practice in hemodialysis patients. ***P < 0.001; **P < 0.01; *P < 0.05. Depiction: Rectangle shows observed variables, ellipses indicate potential variables, and circles represent residual terms

Full size image

Profiles of dietary knowledge, attitude, and practice in hemodialysis patients

Normalize the scores for each dimension by using SPSS software. According to the LMR, the maximum number of classes to consider was two and BLRT was favorable each time to increase the number of classes. Nevertheless, the 2-class model had lower AIC, BIC, and aBIC values than the first. Furthermore, the 3-class model fitted better than the 2-class model. However, one category in the 3-class model had very low category probabilities (0.02). More detailed information can be found in Table 6. Considering the potential use of class membership as a variable for further analysis and the need for parsimony, the 2-class model was retained. Figure 2 illustrated the pattern of KAP scores for each identified profile. The two profiles, namely the “general knowledge and attitude - high practice profile” and the “general knowledge and attitude - low practice profile”, were primarily discriminated by different patterns of scores on the KAP dimensions.

Table 6 Fit indices for the latent profile analysis of dietary KAP in HD patients
Full size table
Fig. 2
figure 2

Description of the selected LPA profiles of the dietary knowledge-attitude-practice in hemodialysis patients. Depiction: Dietary knowledge, entries 1–4 in the horizontal coordinate; Dietary attitude, entry 5 in the horizontal coordinate; Dietary practice, entries 6–9 in the horizontal coordinate; Range of standardized scores, vertical coordinate; Class 1, general knowledge and attitude - high practice profile; Class 2, general knowledge and attitude - low practice profile

Full size image

Factors influencing dietary knowledge, attitude, and practice profiles in hemodialysis patients

Univariate factors influencing dietary knowledge, attitude, and practice profiles in hemodialysis patients

The results showed that gender (X2 = 5.359, P = 0.021) and monthly income per household (X2 = 11.623, P = 0.020) were significantly associated with KAP profiles. More details were shown in Table 7.

Table 7 Univariate factors influencing dietary KAP profiles in HD patients
Full size table

Independent influencing factors of dietary knowledge, attitude, and practice profiles in hemodialysis patients

The assignment of variables was shown in Table 8. The binary multivariate logistic regression results indicated that gender and monthly income per household were independent influencing factors of dietary KAP profiles in HD patients (P < 0.05). See Table 9.

Table 8 The assignment of variables
Full size table
Table 9 Independent influencing factors of dietary KAP profiles in HD patients
Full size table

Discussion

The findings revealed that among the dietary KAP dimensions, HD patients had the highest score in the attitude dimension and the lowest in the knowledge and practice dimension. It suggested that while some patients had a positive attitude toward dietary management, they struggled to translate it into actual practice. It might be because dietary practice was related to various sociodemographic and disease-related factors in addition to dietary attitude (See Table 2). The linear regression model further indicated that female patients with cerebrovascular disease and better dietary attitudes exhibited better dietary practices, which aligned with previous studies [24, 25]. The possible explanation was that male patients often engaged in more social activities involving table culture, while women tended to be more health-conscious [24]. Additionally, dietary management played a crucial role in preventing and controlling cerebrovascular disease [26], and there were often common dietary risk factors for various diseases. Therefore, patients with complications might exhibit better dietary practices.

The results indicated that patients had the worst dietary knowledge of volume control. In terms of dietary practice, they had the worst management of dietary monitoring (mainly about monitoring water and salt intake and weight gain). The awareness, knowledge and skills related to volume control among HD patients still need further improvement. Other factors, such as comorbidities and poly-medication treatments, might also contribute to poor volume control [27]. Strict volume control was crucial for HD patients. Excessive water and salt intake could lead to volume overload, increasing the risk of high blood pressure, heart disease, and in severe cases, even left heart failure and death [28]. Therefore, it was essential to strengthen health education and interventions related to water and salt intake and volume control in HD patients in the future.

This study’s structural equation model revealed significant positive correlations between dietary knowledge and attitude, as well as attitude and practice. Moreover, the study found that attitude mediated between knowledge and practice, indicating that attitude influenced knowledge translation into behavior. These findings were consistent with the KAP theoretical model and previous findings [29, 30]. According to the KAP model, dietary behavioral changes in HD patients involved three successive processes: knowledge acquisition, belief generation, and behavior formation. Patients with sufficient dietary knowledge were more likely to develop a positive attitude toward dietary management and transfer to improved dietary behavior. Therefore, dietary education could promote positive dietary behavior among HD patients. While the study provided valuable insights, further exploration was needed to understand the specific mechanisms and factors involved in transforming dietary knowledge, attitude, and practice in HD patients.

The R2 value of the linear regression model was only 0.079, although higher than the R2 (0.04) of the structural equation model, suggesting that the KAP theory might have limitations in explaining dietary behavior change in hemodialysis patients. There were likely several other environmental and social factors that should have been considered in this study. However, previous studies demonstrated that the KAP theory could effectively explain behaviors such as influenza vaccine uptake among healthcare workers (R2 = 0.69) [29] and nutrition labeling among residents (R2 = 0.545) [31]. The disparity in results could be attributed to the dietary behavior being a relatively ingrained habit formed over a long period, influenced by various social, cultural, environmental, financial, and traditional factors [32]. Consequently, changing dietary behavior become more challenging. A previous study by Gao et al. [33] exploring the behavioral pathways explaining oral health disparity in children also revealed that patients’ health behavior was associated with not only knowledge and attitude but also ethnicity and socioeconomic status. Therefore, developing dietary interventions that consider individuals’ personalized characteristics, dietary knowledge and attitude was crucial. The KAP model could also be refined and revised to cater to specific behaviors by considering environmental, sociodemographic, and disease-related characteristics. New behavioral theory models could be developed to better explain and guide dietary behavior change.

This study identified two distinct profiles of dietary KAP patterns among HD patients: a low-practice profile and a high-practice profile. Interestingly, the study found that while the knowledge and attitude toward dietary intake were similar in both profiles, there was a significant difference in dietary practice. Changing patients’ knowledge and attitude might not change dietary behavior. Dietary practice was a complex behavior influenced by various personal and social factors [34,35,36]. Further analysis of the profiles revealed that the high practice profile consisted of more female patients with higher monthly household income, indicating that patients with higher socioeconomic status tended to have better dietary KAP. This could be attributed to their increased access to healthcare resources and higher expectations for quality of life [37,38,39]. Consequently, they were more likely to understand and adhere to health education and dietary management requirements, as well as had better conditions and resources to manage their dietary intake in daily life. In contrast, patients with lower socioeconomic status faced barriers to accessing healthcare resources. Studies found that higher prices deter city dwellers from making sustainable food choices, even when they were interested in sustainability [40]. Impoverished populations often struggled to afford nutrient-rich fresh foods, further exacerbating health disparities [41]. Efforts should be made to improve access to healthcare resources and promote health equity for patients with lower socioeconomic status, including increasing access to healthy foods.

Study limitations

The participants were limited to HD patients in Beijing, China, and there might be variations in dietary KAP among patients from different geographical areas. In this regard, this study was designed to be a multicenter study conducted at different levels of hospitals to compensate for this shortcoming. Additionally, the cross-sectional study could not verify the causal relationship. Future multicenter, longitudinal clinical studies with large samples are needed to confirm these findings and causal relationships.

Furthermore, the linear regression and structural equation models explained only a small proportion of the variance in dietary practice among HD patients. Given the complexity of dietary behavior, future investigations should consider the roles of biological, environmental, policy, and other factors to gain a more comprehensive understanding of dietary behavior change. Additionally, combining qualitative and quantitative studies could provide insights into the motivations behind dietary behavior change in dialysis patients.

Conclusion

This study found that HD patients’ dietary knowledge and attitude were better than their actual dietary practice. The study also identified dietary attitude as a positive mediator between dietary knowledge and practice. However, the R2 value of the structural equation model was small, indicating that other factors influence dietary practice. The study showed that dietary practice in HD patients is affected by dietary attitude, gender, and cerebrovascular diseases. The high practice profile consisted of more female patients with higher incomes. Therefore, Individualized interventions are needed to improve dietary practice, considering factors such as attitude, gender, and socioeconomic status. Future research should consider a broader population and explore additional factors influencing dietary behavior.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Abbreviations

aBIC:

Adjusted bayesian information criterion

AGFI:

Adjusted goodness of fit index

AIC:

Akaike information criterion

BIC:

Bayesian information criterion

BLRT:

Bootstrapped likelihood ratio test

CFI:

Comparative fit index

CIs:

Confidence intervals

CKD:

Chronic kidney disease

CMIN/df:

Discrepancy divided by the degree of freedom

ESRD:

End-stage renal disease

GFI:

Goodness of fit index

HD:

Hemodialysis

IQR:

Interquartile range

KAP:

Knowledge-attitude-practice

LMR:

Lo-Mendell-Rubin likelihood ratio test

LPA:

Latent profile analysis

NFI:

Normed fit index

RMSEA:

Root mean square error of approximation

SD:

Standard deviation

TLI:

Tucker-Lewis index

References

  1. Ene-Iordache B, Perico N, Bikbov B, Carminati S, Remuzzi A, Perna A, et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob Health. 2016;4(5):e307–19.

    Article  PubMed  Google Scholar 

  2. Russo D, Morrone L, Di Iorio B, Andreucci M, De Gregorio MG, Errichiello C, et al. Parathyroid hormone may be an early predictor of low serum hemoglobin concentration in patients with not advanced stages of chronic kidney disease. J Nephrol. 2015;28(6):701–8.

    Article  CAS  PubMed  Google Scholar 

  3. Guha A, Maddox WR, Colombo R, Nahman NS Jr, Kintziger KW, Waller JL, et al. Cardiac implantable electronic device infection in patients with end-stage renal disease. Heart Rhythm. 2015;12(12):2395–401.

    Article  PubMed  Google Scholar 

  4. Malchesky PS. Renal support:a time to reassess the direction! Artif Organs. 2019;43(7):615–7.

    Article  PubMed  Google Scholar 

  5. Jiang N, Fang W, Gu AP, Yuan JZ, Yang XX, Lin AW, et al. Improving diet recipe and cooking methods attenuates hyperphosphatemia in patients undergoing peritoneal dialysis. Nutr Metab Cardiovasc Dis. 2015;25(9):846–52.

    Article  CAS  PubMed  Google Scholar 

  6. Goncalves FA, de Jesus JS, Cordeiro L, Piraciaba MCT, de Araujo LKRP, Steller Wagner Martins C, et al. Hypokalemia and hyperkalemia in patients on peritoneal dialysis:incidence and associated factors [J]. Int Urol Nephrol. 2020;52(2):393–8.

    Article  CAS  PubMed  Google Scholar 

  7. Fernández-Martín JL, Martínez-Camblor P, Dionisi MP, Floege J, Ketteler M, London G, et al. Improvement of mineral and bone metabolism markers is associated with better survival in haemodialysis patients: the COSMOS study. Nephrol Dial Transplant. 2015;30:1542–51.

    Article  PubMed  Google Scholar 

  8. Khor BH, Sualeheen A, Sahathevan S, Chinna K, Gafor AHA, Bavanandan S, et al. Association of dietary patterns with serum phosphorus in maintenance haemodialysis patients: a cross-sectional study. Sci Rep. 2020;10:12278.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cleland J. A critique of KAP studies and some suggestions for their improvement. Stud Fam Plan. 1973;4:42–7.

    Article  CAS  Google Scholar 

  10. Zhou W, Zheng Q, Huang M, Zhang C, Zhang H, Yang L, et al. Knowledge, attitude, and practice toward delirium and subtype assessment among Chinese clinical nurses and determinant factors: a multicentre cross-section study. Front Psychiatry. 2023;13:1017283.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hu H, Xu Y, Shao Y, Liang Y, Wang Q, Luo S, et al. A latent profile analysis of residents’ knowledge, attitude, and practice toward common chronic diseases among ethnic minority area in China. Front Public Health. 2022;10:940619.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Li Z, Jin H, Chen W, Sun Z, Jing L, Zhao X, et al. Influencing factors of knowledge, attitude, and practice regarding medical nutrition therapy in patients with diabetes: a National Cross-Sectional Study in urban China. J Diabetes Res. 2017;2017:8948452.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Herbuela VRDM, de Guzman FS, Sobrepeña GD, Claudio ABF, Tomas ACV, Arriola-Delos Reyes CM, et al. Knowledge, attitude, and practices regarding dengue fever among pediatric and adult in-patients in metro manila, Philippines. Int J Environ Res Public Health. 2019;16:23.

    Article  Google Scholar 

  14. Datiko DG, Habte D, Jerene D, Suarez P. Knowledge, attitudes, and practices related to TB among the general population of Ethiopia: findings from a national cross-sectional survey. PLoS One. 2019;14:e0224196.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Anyolitho MK, Poels K, Huyse T, Tumusiime J, Mugabi F, Tolo CU, et al. Knowledge, attitudes, and practices regarding schistosomiasis infection and prevention: a mixed-methods study among endemic communities of western Uganda. PLoS Negl Trop Dis. 2022;16:e0010190.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Eghbali-Babadi M, Feizi A, Khosravi A, Sarrafzadegan N. The effect of training on knowledge, attitude, and practice in patients with hypertension; the application of the expanded chronic care model: a clinical trial study. Iran Red Crescent Med J. 2018;20:e61693.

    Article  Google Scholar 

  17. Zhang Y, Zhao Y, Zhang X, Zhu J, Men H, Zhao W, et al. Development and validation of dietary KAP questionnaire for hemodialysis patients. Chi J Nurs Edu. 2023;20(4):409–14. (in Chinese)

    Google Scholar 

  18. Xu F, Zhuang B, Wang Z, Wu H, Hui X, Peng H, et al. Knowledge, attitude, and practice of patients receiving maintenance hemodialysis regarding hemodialysis and its complications: a single-center, cross-sectional study in Nanjing. BMC Nephrol. 2023;24(1):275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Marsh HW, Lüdtke O, Trautwein U, Morin AJS. Classical latent profile analysis of academic self-concept dimensions: synergy of person-and variable centered approaches to theoretical models of self-concept. Struct Equ Model Multidiscip J. 2009;16:191–225.

    Article  MathSciNet  Google Scholar 

  20. MacLean E, Dendukuri N. Latent class analysis and the need for clear reporting of methods. Clin Infect Dis. 2021;73:e2285–6.

    Article  PubMed  Google Scholar 

  21. Sinha P, Calfee CS, Delucchi KL. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit Care Med. 2021;49:e63–79.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kendall MGMG. Kendall’s advanced theory of statistics. London: Edward Arnold, New York: Halsted Press; 2004.

  23. Kim SY. Determining the number of latent classes in single-and multi-phase growth mixture models. Struct Equ Modeling. 2014;21:263–79.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  24. Chan YM, Zalilah MS, Hii SZ. Determinants of compliance behaviours among patients undergoing hemodialysis in Malaysia. PLoS One. 2012;7(8):e41362.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kugler C, Vlamick H, Haverich A, Maes B. Nonadherence with diet and fluid restrictions among adults having hemodialysis. J Nurs Sch. 2005;37(1):25–9.

    Article  Google Scholar 

  26. Qin C, Lv J, Yu C, Guo Y, Bian Z, Gao M, et al. Dietary patterns and cardiometabolic diseases in 0.5 million Chinese adults: a 10-year cohort study. Nutr J. 2021;20(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Flythe JE, Chang TI, Gallagher MP, Lindley E, Madero M, Sarafidis PA, et al. Blood pressure and volume management in dialysis: conclusions from a kidney disease: improving global outcomes (KDIGO) controversies conference. Kidney Int. 2020;97(5):861–76.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ok E, Asci G, Chazot C, Ozkahya M, Mees EJ. Controversies and problems of volume control and hypertension in haemodialysis. Lancet. 2016;388(10041):285–93.

    Article  PubMed  Google Scholar 

  29. Chen S, Jiang Y, Tang X, Gan L, Xiong Y, Chen T, et al. Research on knowledge, attitudes, and practices of influenza vaccination among healthcare Workers in Chongqing, China-based on structural equation model. Front Public Health. 2022;10:853041.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhang H, Zhao C, Song C, Wu Y, Wei D, Li X. Knowledge, attitude, and practice of healthcare workers on early gastrointestinal cancer in China. Front Public Health. 2023;11:1191699.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Liao Y, Yang J. Status of nutrition labeling knowledge, attitude, and practice (KAP) of residents in the community and structural equation modeling analysis. Front Nutr. 2023;10:1097562.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Plackett B. Changing diets at scale. Nature. 2020;588(7837):S70–2.

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Gao XL, Hsu CY, Xu YC, Loh T, Koh D, Hwarng HB. Behavioral pathways explaining oral health disparity in children. J Dent Res. 2010;89(9):985–90.

    Article  PubMed  Google Scholar 

  34. Cohen DA, Babey SH. Contextual influences on eating behaviours: heuristic processing and dietary choices. Obes Rev. 2012;13(9):766–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Adamson AJ, Mathers JC. Effecting dietary change. Proc Nutr Soc. 2004;63(4):537–47.

    Article  PubMed  Google Scholar 

  36. Shepherd R, Shepherd R. Resistance to changes in diet. Proc Nutr Soc. 2002;61(2):267–72.

    Article  PubMed  Google Scholar 

  37. Sacker A, Bartley M, Firth D, Fitzpatrick R. Dimensions of social inequality in the health of women in England: occupational, material and behavioural pathways. Soc Sci Med. 2001;52:763–81.

    Article  CAS  PubMed  Google Scholar 

  38. Laaksonen M, Roos E, Rahkonen O, Martikainen P, Lahelma E. Influence of material and behavioural factors on occupational class differences in health. J Epidemiol Community Health. 2005;59:163–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Chan MF. The impact of health care resources, socioeconomic status, and demographics on life expectancy: a cross-country study in three southeast Asian countries. Asia Pac J Public Health. 2015;27(2):NP972–83.

    Article  MathSciNet  PubMed  Google Scholar 

  40. Rejman K, Kaczorowska J, Halicka E, Laskowski W. Do Europeans consider sustainability when making food choices? A survey of polish city-dwellers. Public Health Nutr. 2019;22(7):1330–9.

    PubMed  PubMed Central  Google Scholar 

  41. Drewnowski A. Fat and sugar: an economic analysis. J Nutr. 2003;133(3):838S–40S.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the patients who participated in this study and the dialysis room staff for their tremendous cooperation.

Funding

The Beijing Health Technologies Promotion Program, Grant number: BHTPP202058.

Author information

Authors and Affiliations

  1. Department of Nursing, Peking University Third Hospital, Beijing, China

    Zhiqian Chen, Xinxin Chen, Xiaoyu Zhang & Chunyan Su

  2. Department of Nephrology, Peking University Third Hospital Yanqing Hospital, Beijing, China

    Na Xu

  3. Department of Nephrology, Peking University Third Hospital, Beijing, China

    Xinxin Chen, Xiaoyu Zhang & Chunyan Su

  4. Department of Nephrology, Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China

    Shuqing Yin

  5. Department of Nephrology, Beijing Haidian Hospital, Beijing, China

    Guanghui Xiao

  6. Department of Nephrology, Peking University International Hospital, Beijing, China

    Li Luo

  7. Department of Nephrology, Fuxing Hospital, The Eighth Clinical Medical College, Capital Medical University, Beijing, China

    Qun Liu

Authors
  1. Zhiqian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Na Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinxin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuqing Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guanghui Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Li Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chunyan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CS and QL contributed to conception and study design. XC, SY, LL, GX, NX and XZ contributed to data extraction and database management. XC, ZC and CS contributed to the analysis and interpretation of data. ZC, QL, NX and CS contributed to drafting and revising of the final manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qun Liu or Chunyan Su.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Medical Ethical Committee of Peking University Third Hospital (IRB2021–084-02). Informed consent was obtained from all subjects who participated in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Xu, N., Chen, X. et al. Dietary knowledge-attitude-practice status in hemodialysis patients: a latent profile analysis. BMC Public Health 24, 836 (2024). https://doi.org/10.1186/s12889-024-18066-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-024-18066-z

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us