Blog

Introduction

The increasing prevalence of chronic kidney disease (CKD) represents a major public health issue that incurs a substantial burden on the family and society. Despite dramatic improvements have been made in the treatment and management of CKD, it is still associated with a high morbidity and mortality rate. In addition to pharmacological treatments, lifestyle modifications, including dietary management, have been demonstrated to serve as a critical component in retarding CKD progression and reducing of mortality.¹ Current guidelines generally recommend 0.60–0.80 grams of protein per kilogram of body weight per day for individuals with advanced CKD (stage 3 or higher) who are at high risk of malnutrition due to low appetite, chronic micro-inflammatory state, and acidemia-induced muscle breakdown.² Therefore, dietary management that provides essential and adequate nutrient intakes while minimizing kidney burden is of critical importance.

The plant-based diet (PBD), characterized by a preponderance of plant-based foods with a comparatively lower proportion of animal-sourced foods, is gaining popularity due to its purported health effects and environmental sustainability.³ Observational studies have linked adherence to a healthful PBD pattern with a reduced prevalence of hypertension, diabetes, and cardiovascular disease (CVD).^4–6 Although traditional viewpoint holds that animal proteins possess a higher biological value and thus superior to plant proteins,⁷ an increasing number of studies have indicated that a PBD is cross-sectionally associated with kidney function and may decrease the risk of diabetic nephropathy in patients with type 2 diabetes.^8,9 Furthermore, a PBD represents the sole source of fiber capable of modulating gut microbiota and decreasing uremic toxins. Another notable advantage of PBD is its superior protein to phosphorus ratio compared to animal-based diets.¹⁰ To the best of our knowledge, the majority of previous studies have focused on the health effects of individual nutrients or nutrient quantities, with relatively less attention devoted to the assessment of diet quality.

We are aware that two earlier studies have already investigated the impact of PBD patterns on the mortality risk in patients with CKD.^11,12 However, both studies included a predominantly advanced CKD cohort. It is currently unknown whether a PBD pattern also exerts a significant influence on the mortality risk of early stages of CKD, in which studies of nutritional management are relatively insufficient. To bridge this gap in the literature, our study examined the association between PBD indices and all-cause or cardiovascular mortality in a cohort of community-dwelling adults with predominantly early stages of CKD without comorbid CVD.

Methods

Data Source and Participant Selection

Publicly available data from the US 1999–2018 National Health and Nutrition Examination Survey (NHANES), an ongoing biennial cross-sectional survey, were used to conduct this mortality follow-up study. Ethical approval was obtained from the NCHS Research Ethics Review Board, and all participants provided informed consent. Based on the item 1 and 2 of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects dated February 18, 2023, China, our study is exempted from ethical approval from our institution. Detailed descriptions of data acquisition can be found elsewhere.¹³

Adult participants with CKD, defined as an estimated glomerular filtration rate (eGFR) calculated from 2009 serum creatinine-based equation¹⁴ <60 mL/min/1.73m² or a urinary albumin-to-creatinine ratio (uACR) >30 mg/g, were potentially eligible for inclusion. We further excluded participants for the following reasons: age < 20 years, pregnancy at the time of interview, with a history of malignancy or CVD (including self-reported stroke, heart attack, coronary heart disease, congestive heart failure, and angina pectoris), dialysis at the time of interview, missing data of PDI or follow-up, and those with missing covariates. As illustrated in Figure 1, a total of 4098 CKD subjects with complete data were included in the final analysis.

Figure 1 The participant flow diagram. Abbreviations: CKD, chronic kidney disease; CVD, cardiovascular disease; NHANES, National Health and Nutrition Examination Survey; PDI, plant-based diet index.

Assessment of PBD Indices

The dietary intake data were derived from the first-round (1999–2002 cycles) or the average of the first-round and second-round (2003–2018 cycles) 24-hour dietary recall, as appropriate. Details of the construction of PBD indices have been delineated previously.¹⁵ First, a total of 17 food groups were categorized into the healthy PBD (whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and coffee), unhealthy PBD (fruit juices, refined grains, potatoes, sugar-sweetened beverages, sweets and desserts) and animal-based foods (animal fats, dairy products, eggs, fish or seafood, meat, and miscellaneous animal-based foods), primarily based on our current knowledge of the relationships between specific foods and certain chronic diseases. Consequently, the healthy PBD index (hPDI) and the unhealthy PBD index (uPDI) were generated by dividing each food intake into cohort-specific quintiles and assigning a score of 1 to 5. For the calculation of hPDI, positive scores were assigned to higher quintiles of healthy PBD (ie, the highest and lowest quintiles were assigned a score of 5 and 1, respectively), and higher quintiles of unhealthy and animal-based foods (ie, the highest and lowest quintiles were assigned a score of 1 and 5, respectively) received inverse scores. A similar approach was employed during the calculation of uPDI, wherein positive scores were assigned to unhealthy PBD and inverse scores were allocated to healthy and animal-based foods. To semiquantitatively assess the relative intake of plant-based foods and animal-based foods, a total PDI was scored by assigning positive scores to healthy and unhealthy PBD and negative scores to animal-based foods.

Outcome Ascertainment

Information regarding survival status was derived from the National Center for Health Statistics Public-Use Linked Mortality Files by linking to the National Death Index with a probabilistic matching algorithm. Follow-up time was censored at the recorded date of participant death, or at December 31, 2019 for those without a recorded death. The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes were employed to determine the leading cause of death. In this context, CVD mortality was defined by the ICD-10 codes I00–I09, I11, I13, I20–I51, or I60–I69.¹⁶

Covariates

We incorporated a range of participant-level characteristics into the analysis, including demographics, socioeconomic status, clinical features, relevant laboratory results, and medication use. Specifically, we retrieved participants’ age (continuous), sex (men vs women), race (non-Hispanic white vs other), marital status (single vs non-single), poverty-income ratio (continuous), education level (less than high school vs high school vs higher than high school), body mass index (continuous, calculated as body weight in kilograms divided by height in meters squared), physical activity (none vs moderate vs vigorous), smoking (yes vs no), drinking (yes vs no), and total energy intake (continuous). The comorbidity variables included diabetes and hypertension. Laboratory tests included glycated hemoglobin, total cholesterol and triglycerides. Medication use assessed incorporated statins, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers. The poverty-income ratio was calculated as annual household incomes divided by the family’s assigned poverty threshold. Smoking and drinking were defined as having smoked >100 cigarettes and consumed >12 drinks throughout their lifetime, respectively.

Statistical Analysis

Statistical analysis was performed in accordance with the analytic guideline that advocates appropriate weighting to obtain nationally representative estimates. Comparisons were made among total PDI tertiles (T1, lowest tertile; T2, medium tertile; T3, highest tertile) via the one-way analysis of variance or chi-squared test, as appropriate. The Kaplan–Meier survival curves were plotted to estimate the cumulative survival rate, which was then compared among different groups with the Log rank test. Following the assessment of the proportional hazard assumption using the Schoenfeld residual method, we examined the associations between total PDI, hPDI or uPDI with all-cause or CVD mortality using the Cox proportional hazards regression models, with results reported as hazard ratios (HRs) and 95% confidence intervals (95% CI). Potential non-linear associations between PBD indices and all-cause or CVD mortality were identified using the restricted cubic spline curves. In addition to the crude analysis without adjustments, two additional models adjusting for confounding factors were also generated. Model 1 was adjusted for participant’s age, sex, race, marital status, education level, and poverty-income ratio. Model 2 was further adjusted for body mass index, physical activity, smoking, drinking, hypertension, diabetes, glycated hemoglobin, total cholesterol, triglycerides, total energy intake, use of statins and angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, and eGFR and uACR. Subgroup analyses were also conducted stratified by participant’s age, sex, diabetes status, eGFR, and uACR. We conducted two sensitivity analyses: 1) in the first sensitivity analysis, participants who had deceased within 24 months of the follow-up were excluded to mitigate the risk of reverse causation; 2) in the second sensitivity analysis, we further adjusted for serum uric acid levels, dietary oxidative balance score,¹⁷ and dietary phosphorus to protein ratio.¹⁸ The statistical analyses were conducted using the R software (version 4.2.3), and a two-tailed P value < 0.05 was considered statistically significant.

Results

Baseline Participant Characteristics

Table 1 presents the characteristics of the 4098 participants (mean age 55.50 years, 40.38% men) and comparisons of participants stratified by total PDI tertiles. The mean eGFR and uACR for the entire cohort were 80.62 mL/min/1.73m² and 153.12 mg/g, respectively. In comparison to the lowest total PDI tertile, those within the highest total PDI tertile were significantly older, more likely to be women, with higher educational attainment, more likely to be physically inactive, with a lower smoking prevalence, and a lower triglyceride level.

Table 1 Comparison of Baseline Characteristics in Individuals with Chronic Kidney Disease Stratified by the Tertile of the Total Plant-Based Diet Index

Kaplan–Meier Survival Analysis

During a median follow-up period of 102 (interquartile range 58–154) months, 1191 (19.52%) participants deceased, of which 397 were determined to be died of CVD causes. Specifically, the mortality rates were 23.40%, 20.31%, and 15.85% for the total PDI T1, T2 and T3 groups, respectively. Of these, 161 (6.92%), 135 (7.16%) and 101 (5.78%) were attributed to CVD mortality. Kaplan–Meier survival curves (Figure 2) based on the total PDI, hPDI and uPDI tertiles showed significant differences in all-cause mortality rate among the three uPDI subgroups. All-cause mortality in the T1, T2 and T3 groups stratified by total PDI and hPDI, and CVD mortality stratified by total PDI, hPDI and uPDI tertiles were not statistically significant.

Figure 2 Kaplan–Meier survival curves for the associations between total plant-based dietary index (PDI, A and B), healthy PDI (hPDI, C and D), unhealthy PDI (uPDI, E and F) and all-cause mortality or cardiovascular mortality in US adults with chronic kidney disease.

Associations Between Total PDI, hPDI, uPDI and All-Cause or CVD Mortality

As shown in Tables 2 and 3, total PDI and hPDI, as either continuous or categorical variables, were not associated with all-cause or CVD mortality. In the fully adjusted Model 2, an one-unit increase in uPDI was associated with a 2% increased risk of all-cause mortality. Similarly, the HRs and 95% CIs for the uPDI T2 and T3 groups were 0.98 (0.79–1.20) and 1.45 (1.15–1.83), respectively (P for trend = 0.003), with the T1 group serving as the reference group. Correspondingly, the restricted cubic spline curves (Figure 3) also indicated a positive and linear relationship between uPDI and all-cause mortality.

Table 2 Associations Between Total PDI, hPDI, uPDI and All-Cause Mortality in US Adults with Chronic Kidney Disease

Table 3 Associations Between Total PDI, hPDI, uPDI and Cardiovascular Mortality in US Adults with Chronic Kidney Disease

Figure 3 Restricted cubic spline curves for the associations between total plant-based dietary index (PDI, A and B), healthy PDI (hPDI, C and D), unhealthy PDI (uPDI, E and F) and all-cause mortality or cardiovascular mortality in US adults with chronic kidney disease.

Subgroup Analysis

Subgroup analysis (Figure 4) showed that the associations between uPDI and all-cause mortality were more pronounced in women and non-diabetics than in men and diabetics. The relationship between uPDI and all-cause mortality remained consistent across the spectrum of participant’s age, eGFR, and uACR.

Figure 4 Subgroup analyses of the association between unhealthy plant-based dietary index and all-cause mortality in US adults with chronic kidney disease. Abbreviations: CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; uACR, urinary albumin-to-creatinine ratio.

Sensitivity Analysis

Both sensitivity analysis (Supplementary Tables 1–3) demonstrated that a higher uPDI was associated with increased all-cause mortality.

Discussion

This study demonstrated, for the first time, that a higher intake of unhealthy PBD is related to an increased risk of all-cause mortality, but not incident cardiovascular mortality, in a nationally representative sample of US community-dwelling adults with predominantly early-stage CKD, especially in women and in those without diabetes. The hPDI and total PDI did not appear to have a significant relation to mortality risk in this population. In addition, the sensitivity analysis indicated that the study results were robust and are unlikely to be affected by reverse causation. Taken together, these data indicated that intakes of unhealthy PBD seemed to outweigh adherence to healthy PBD in influencing mortality risk in US adults with early stage CKD.

The PBD has been previously shown to confer a range of potential health benefits, including weight management, blood pressure and glucose regulation, and a reduced risk of CVD.¹⁹ Furthermore, earlier studies have also linked PBD with decreased risk of specific chronic conditions, including metabolic syndrome, liver cirrhosis, and colorectal cancer.^20–22 A recent meta-analysis of 14 studies further demonstrated that the pooled relative risks for all-cause, CVD, and cancer mortality were 0.85, 0.85 and 0.91, respectively, for a higher hPDI, and 1.18, 1.19 and 1.10, respectively, for a higher uPDI,²³ underscoring the effects of PBD in mortality reduction. However, it should be noted that not all PBD are conducive to optimal health, and it is practically difficult to maintain a vegan diet for the majority of the US population. To overcome this limitation, Satija et al conceptualized a hierarchical dietary model consisting of total PDI, hPDI, and uPDI, which enabled the understanding of how gradually increasing plant foods, while decreasing animal foods, affects health.²⁴

The applicability of findings from studies of PBD in the general population to CKD patients has not yet been established, as CKD patients often exhibit unique dietary patterns and nutrient needs. Cross-sectional studies, such as the Tehran Lipid and Glucose Study and the Multiethnic Study of Atherosclerosis, have all reported a decreased incidence of CKD with a PBD.^25,26 Mechanistically, the beneficial effects of a plant-rich diet on kidney health may be related to its effect on inhibiting micro-inflammation, promoting intestinal mobility, modulating gut microbiota, reducing uremic toxin production, and providing nutrients of vitamins, minerals, fibers and phytochemicals.²⁷

To the best of our knowledge, the results of our study differ from those of several previous studies on the associations of PBD indices with mortality risk specifically in CKD patients.^11,12 In a report of 2539 CKD participants from the Chronic Renal Insufficiency Cohort Study, Amir et al found that the highest tertile of total PDI and hPDI exhibited a 26% and 21% reduced risk of all-cause mortality.¹² Analogous findings have also been observed in an analysis of 4807 CKD patients from the UK Biobank.¹¹ It is noteworthy that both studies exclusively focused on all-cause mortality, without investigating CVD mortality. This study bridged this knowledge gap by demonstrating that total PDI, hPDI, and uPDI were all unrelated to CVD mortality. While the current study, along with the two aforementioned studies, has all observed a deleterious effect of uPDI on mortality risk in CKD patients, this study diverges from others in its finding of no associations between total PDI, hPDI, and mortality risk. The potential explanation for this discrepancy may be attributed to variations in participant characteristics, such as the severity of CKD. Specifically, the mean eGFR of the included patients in this study is 80.62 mL/min/1.73m², which is significantly higher than that reported by Amir et al (43.4 mL/min/1.73m²) and by Thompson et al (59.5 mL/min/1.73m²).^11,12 More importantly, this study excluded participants with known diagnoses of CVD, whereas the other two studies did not. Consequently, the subjects in our study may represent a highly selected population with a more favorable cardiometabolic profile. This finding underscored that the consumption of unhealthy PBDs may exert a more substantial influence on the mortality risk in early-stage CKD without CVD than the adherence to a healthy PBD.

Our study showed that an unhealthy PBD has the potential to markedly elevate all-cause mortality risk, even among individuals with only mild CKD. An unhealthy PBD is typically high in refined and ultra-processed foods and less in healthy plants and animal-sourced foods. Its association with all-cause mortality in CKD patients may be attributable to its low micronutrient content, high caloric content, hyperorexia associated with high fat intake, and unfavorable effects of added sugars.²⁸

Subgroup analysis indicated that the association between uPDI and all-cause mortality in CKD patients were sex-specific that was only observed in women and non-diabetics. Similarly, Wang et al reported a stronger reverse relationship between hPDI and the aging process in females.²⁹ The potential explanation for these observed discrepancies include the differential influence of estrogen, which has been demonstrated to enhance fat transportation in women and promote serum triglyceride levels.³⁰ The consumption of sugar-sweetened beverages, a component of the uPDI, has been demonstrated to increase the risk of metabolic syndrome in women, but not in men,³¹ possibly due to the fact that triglycerides and lipoproteins in women appear to be more sensitive to changes in dietary carbohydrates or fats than in men.³² The attenuated effect of uPDI on all-cause mortality in individuals with diabetes may reflect post-diagnosis dietary improvements, as patients often adopt healthier eating habits after being diagnosed. Nevertheless, these findings underscore the importance of avoiding unhealthy plant-based diets, particularly in those without diabetes, to mitigate mortality risk.

Notable strengths of this study include large sample size and national representation, thus facilitating external generalization of study results to US CKD patients with similar characteristics. We contend that our analysis has broadened previous research by incorporating predominantly those with only mild renal impairment. Moreover, we have also adjusted for dietary oxidative balance score, dietary phosphorus intake, and uric acid, as previous studies have demonstrated that these factors could also influence outcomes in CKD.^33,34 There are, however, several limitations inherent in this study that should be pointed out. First, the dietary information was collected through self-report, which may introduce recall bias. Second, dietary patterns were collected using the 24-hour recall method, which may not always be a precise reflection of habitual dietary patterns or potential dietary changes over time. Third, PBD indices negative scored all animal-based foods, whereas animal-sourced proteins are generally preferred over plant-sourced proteins for protein bioavailability and malnutrition prevention.³⁵ Finally, the observational nature of the study may be susceptible to residual confounding that may deviate results.

In conclusion, this population-based study showed that higher adherence to an unhealthy PBD is associated with an augmented risk of all-cause mortality in US adults with predominantly early stages of CKD, particularly in women and non-diabetics. Our results underscore the critical importance of avoiding unhealthy PBD in the management of early stages of CKD. In the future, further interventional studies are warranted to ascertain whether reducing unhealthy PBD may indeed improve outcomes in patients with early stages of CKD.

Data Sharing Statement

The dataset used for the current analysis are publicly available from the National Health and Nutrition Examination Survey at https://www.cdc.gov/nchs/nhanes/.

Ethical Approval and Consent to Participate

The NHANES has been approved by the NCHS Research Ethics Review Board. Informed consent was obtained from all participants.

Funding

There is no funding to report.

Disclosure

All the authors have declared no competing interests.

References

1. Hu EA, Coresh J, Anderson CAM, et al. Adherence to healthy dietary patterns and risk of CKD progression and all-cause mortality: findings from the CRIC (Chronic Renal Insufficiency Cohort) Study. Am J Kidney Dis. 2021;77(2):235–244. doi:10.1053/j.ajkd.2020.04.019

2. Ikizler TA, Burrowes JD, Byham-Gray LD, et al. KDOQI clinical practice guideline for nutrition in CKD: 2020 update. Am J Kidney Dis. 2020;76(3 Suppl 1):S1–S107. doi:10.1053/j.ajkd.2020.05.006

3. Musicus AA, Wang DD, Janiszewski M, et al. Health and environmental impacts of plant-rich dietary patterns: a US prospective cohort study. Lancet Planet Health. 2022;6(11):e892–e900. doi:10.1016/S2542-5196(22)00243-1

4. Tomé-Carneiro J, Visioli F. Plant-based diets reduce blood pressure: a systematic review of recent evidence. Curr Hypertens Rep. 2023;25(7):127–150. doi:10.1007/s11906-023-01243-7

5. Sullivan VK, Kim H, Caulfield LE, Steffen LM, Selvin E, Rebholz CM. Plant-Based Dietary Patterns and Incident Diabetes in the Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care. 2024;47(5):803–809. doi:10.2337/dc23-2013

6. Kim H, Caulfield LE, Garcia-Larsen V, Steffen LM, Coresh J, Rebholz CM. Plant-based diets are associated with a lower risk of incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality in a general population of middle-aged adults. J Am Heart Assoc. 2019;8(16):e012865. doi:10.1161/JAHA.119.012865

7. Perraud E, Wang J, Salomé M, Huneau JF, Lapidus N, Mariotti F. Plant and animal protein intakes largely explain the nutritional quality and health value of diets higher in plants: a path analysis in french adults. Front Nutr. 2022;9:924526. doi:10.3389/fnut.2022.924526

8. Moloudpour B, Jam SA, Darbandi M, et al. Association between plant-based diet and kidney function in adults. J Ren Nutr. 2024;34(2):125–132. doi:10.1053/j.jrn.2023.09.002

9. Zarantonello D, Brunori G. The role of plant-based diets in preventing and mitigating chronic kidney disease: more light than shadows. J Clin Med. 2023;12(19):6137. doi:10.3390/jcm12196137

10. Carrero JJ, González-Ortiz A, Avesani CM, et al. Plant-based diets to manage the risks and complications of chronic kidney disease. Nat Rev Nephrol. 2020;16(9):525–542. doi:10.1038/s41581-020-0297-2

11. Thompson AS, Gaggl M, Bondonno NP, et al. Adherence to a healthful plant-based diet and risk of mortality among individuals with chronic kidney disease: a prospective cohort study. Clin Nutr. 2024;43(10):2448–2457. doi:10.1016/j.clnu.2024.09.021

12. Amir S, Kim H, Hu EA, et al. Adherence to plant-based diets and risk of CKD progression and all-cause mortality: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2024;83(5):624–635. doi:10.1053/j.ajkd.2023.09.020

13. Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv Nutr. 2016;7(1):121–134. doi:10.3945/an.115.009258

14. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi:10.7326/0003-4819-150-9-200905050-00006

15. Bigman G, Rusu ME, Kleckner AS, et al. Plant-based diets and their associations with physical performance in the baltimore longitudinal study of aging. Nutrients. 2024;16(23):4249. doi:10.3390/nu16234249

16. Kong X, Wang W. Association between Life’s Essential 8 and all-cause or cardiovascular-specific mortality in patients with rheumatoid arthritis. Clin Exp Rheumatol. 2024;42(7):1459–1466. doi:10.55563/clinexprheumatol/ppsp71

17. Lan Y, Tang H, Lin Z, Huang C, Chen L. Association of oxidative balance score with all-cause mortality among individuals with chronic kidney disease: a cohort study. J Health Popul Nutr. 2024;43(1):160. doi:10.1186/s41043-024-00657-6

18. Murtaugh MA, Filipowicz R, Baird BC, Wei G, Greene T, Beddhu S. Dietary phosphorus intake and mortality in moderate chronic kidney disease: NHANES III. Nephrol Dial Transplant. 2012;27(3):990–996. doi:10.1093/ndt/gfr367

19. Kim H, Rebholz CM. Plant-based diets for kidney disease prevention and treatment. Curr Opin Nephrol Hypertens. 2024;33(6):593–602. doi:10.1097/MNH.0000000000001015

20. Wiśniewska K, Okręglicka KM, Nitsch-Osuch A, Oczkowski M. Plant-based diets and metabolic syndrome components: the questions that still need to be answered-a narrative review. Nutrients. 2024;16(1):165. doi:10.3390/nu16010165

21. Tabar MS, Fotros D, Hekmatdoost A, et al. The association between plant-based diet indices and risk of mortality in patients with cirrhosis: a cohort study. BMC Gastroenterol. 2024;24(1):395. doi:10.1186/s12876-024-03475-6

22. Yarmand S, Rashidkhani B, Alimohammadi A, et al. A healthful plant-based diet can reduce the risk of developing colorectal cancer: case-control study. J Health Popul Nutr. 2024;43(1):111. doi:10.1186/s41043-024-00605-4

23. Tan J, Zhang S, Jiang Y, Li J, Yang C. Plant-based diet and risk of all-cause mortality: a systematic review and meta-analysis. Front Nutr. 2024;11:1481363. doi:10.3389/fnut.2024.1481363

24. Satija A, Bhupathiraju SN, Rimm EB, et al. Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med. 2016;13(6):e1002039. doi:10.1371/journal.pmed.1002039

25. Thompson AS, Tresserra-Rimbau A, Jennings A, et al. Adherence to a Healthful Plant-Based Diet and Risk of Chronic Kidney Disease Among Individuals with Diabetes. J Am Nutr Assoc. 2025;44(3):212–222. doi:10.1080/27697061.2024.2415917

26. Kim H, Caulfield LE, Garcia-Larsen V, et al. Plant-based diets and incident CKD and kidney function. Clin J Am Soc Nephrol. 2019;14(5):682–691. doi:10.2215/CJN.12391018

27. Storz MA. What makes a plant-based diet? A review of current concepts and proposal for a standardized plant-based dietary intervention checklist. Eur J Clin Nutr. 2022;76(6):789–800. doi:10.1038/s41430-021-01023-z

28. Zhuang P, Wang F, Yao J, et al. Unhealthy plant-based diet is associated with a higher cardiovascular disease risk in patients with prediabetes and diabetes: a large-scale population-based study. BMC Med. 2024;22(1):485. doi:10.1186/s12916-024-03683-7

29. Wang J, Yang C, Dong X, et al. Healthful plant-based diets are negatively associated with the rate of biological aging: a national study based on US adults. Nutr Res. 2024;132:112–124. doi:10.1016/j.nutres.2024.10.005

30. Weng J, Mao Y, Xie Q, Sun K, Kong X. Gender differences in the association between healthy eating index-2015 and hypertension in the US population: evidence from NHANES 1999–2018. BMC Public Health. 2024;24(1):330. doi:10.1186/s12889-023-17625-0

31. Kuo CT, Chen DR, Chan CC, Yeh YP, Chen HH. Sex differences in the association between sugar-sweetened beverages consumption and metabolic risks among the working-age population in Taiwan. Public Health Nutr. 2023;26(3):653–660. doi:10.1017/S1368980022001549

32. Sanchez BN, Volek JS, Kraemer WJ, Saenz C, Maresh CM. Sex differences in energy metabolism: a female-oriented discussion. Sports Med. 2024;54(8):2033–2057. doi:10.1007/s40279-024-02063-8

33. Yin Y, Zhao C, Niu Y, Qi J, Zhang Y, Lu B. Associations between oxidative balance score and chronic kidney disease events in US adults: a population-based study. Sci Rep. 2024;14(1):13743. doi:10.1038/s41598-024-64147-9

34. Chang AR, Lazo M, Appel LJ, Gutiérrez OM, Grams ME. High dietary phosphorus intake is associated with all-cause mortality: results from NHANES III. Am J Clin Nutr. 2014;99(2):320–327. doi:10.3945/ajcn.113.073148

35. Meroño T, Zamora-Ros R, Hidalgo-Liberona N, et al. Animal protein intake is inversely associated with mortality in older adults: the InCHIANTI Study. J Gerontol A Biol Sci Med Sci. 2022;77(9):1866–1872. doi:10.1093/gerona/glab334

Introduction

Gynecologic cancers (GCs) are malignancies of the female reproductive system, including cervical, ovarian, endometrial, vulvar, and vaginal cancers.¹ According to the 2022 Global Cancer Data Statistics, GCs accounted for 1.47 million new cases and 680,000 deaths worldwide.² The incidence and mortality of GCs have been steadily increasing, with an increasing prevalence among younger individuals. In China, GCs represented approximately 5% of all new cancer cases, with the number of cases projected to reach 400,000 by 2030.^3,4

Due to the unique characteristics of the affected anatomical areas, patients with GCs often face multiple challenges, including sexual dysfunction, infertility, and body image disturbances.⁵ These issues are frequently accompanied by psychological problems such as shame, fear, depression, and anxiety, all of which severely impact patients’ self-management efficacy and overall physical and mental health.^6,7 On the other hand, in traditional Chinese norms, if someone has a reproductive system disease, it will be considered that the patient’s private life is disorderly. Perceptions will prevent patients from seeking help from outside, and even delay medical treatment, causing the condition to deteriorate. Self-management efficacy refers to a patients’ confidence in their ability to manage their disease and adapt their lifestyle to address disease-related symptoms, treatment demands and physical and psychological changes.⁸ Studies suggest that higher self-management efficacy significantly reduces anxiety and depression while improving quality of life, underscoring its importance as a focus of cancer care.⁹

Benefit finding refers to an individual to identify positive and meaningful aspects during the process of adapting to disease.¹⁰ According to cognitive adaptation theory, individuals facing stressful events, such as illness, adapt by reshaping their perceptions of the situation. Benefit finding emerges from this cognitive adjustment process.^11,12 In a longitudinal study of colorectal cancer patients, patients had moderate-to-high levels of BF at baseline, and they felt more benefits over time, which were reflected in positive feelings about life prospects, close interpersonal relationships, and coping skills.¹³ Therefore, paying attention to patients’ BF in the context of the disease is conducive to improving their perception of social support and coping capabilities. Additionally, patients with a higher capacity for benefit finding are more effective at self-management and exhibit great confidence in confronting their disease through positive health behaviors.¹⁴

Social support, defined as the emotional and material assistance received from social networks, including family, relatives and friends, play a critical role in relieving psychological distress, enhancing resilience, and improving self-management efficacy.^15,16

External resources like social support can shape an individual’s cognition processes, influencing their perception and evaluation of the disease.¹⁷ Social support often encourages patients to adopt a more positive outlook, fostering benefit finding and promoting proactive behavioral and psychological adjustments to manage the disease.¹⁸

Given these dynamics, we hypothesize that benefit finding mediates the relationship between social support and self-management efficacy. However, the relationship among benefit finding, social support, and self-management efficacy remains underexplored, particularly regarding the mediating role of benefit finding as a positive psychological construct. Thus, this study aims to investigate from the perspective of positive psychology, the following: (1) the level of self-management efficacy among patients with gynecological cancers; (2) the correlations among benefit finding, social support, and self-management efficacy; (3) the mediating role of benefit finding in the relationship between social support and self-management efficacy. The findings are intended to to provide theoretical insights and practical guidance for healthcare professionals in offering effective psychological support to patients with gynecological cancers.

Materials and Methods

Participants

The study selected patients admitted to the gynecologic oncology ward of Shenzhen Maternal and Child Health Hospital between November 2023 and July 2024 using a convenience sampling method. The inclusion criteria were as follows: (1) diagnosed with stage I–III gynecological cancer; (2) aged 18–75 years; (3) capable of understanding and independently completing the questionnaires; and (4) willing to participate voluntarily and sign informed consent. The exclusion criteria included: (1) cognitive disorders or mental illnesses; and (2) other serious life-threatening illnesses or psychological and cognitive impairments.

According to the Kendall sample-size estimation method, the sample size should be 5 to 10 times the number of independent variables.¹⁹ This study included a total of 24 independent variables. Considering a 30% non-response rate, the final sample size was 180 cases.

Measurements

General Information Questionnaire

A self-designed general information questionnaire collected data in two parts: (1) socio-demographic information, including age, religion, education level, marital status, occupation, place of residence, monthly per capita household income, and type of health insurance; and (2) disease-related factors, including cancer type, stage, treatment modality, and time since diagnosis.

Chinese Version of the Benefit Finding Scale (BFS)

The Chinese version of BFS translated by Liu Chunchun et al²⁰ was used in this study. The BFS comprises 22 items distributed across 6 dimensions: acceptance (items 1–3), family relationships (items 4–5), worldview (items 6–9), personal growth (items 10–16), social relationships (items 17–19), and health behaviors (items 20–22). Each item is rated on a 5-point Likert scale with total scores ranging from 22 to 110. Higher scores indicate greater levels of benefit finding. The scale demonstrated excellent internal consistency, with a Cronbach’s α 0.95.

Chinese Self-Management Efficacy Scale for Cancer Patients (C-SUPPH)

The C-SUPPH, revised by Qian,⁸ measures cancer patients’ self-management efficacy. The scale contains 28 items across 3 dimensions: positive attitude, self-decision making, and self-stress reduction. Each item is scored on a 5-point Likert scale, yielding total scores ranging from 28 to 140. Higher scores reflect greater confidence in disease self-management. The Cronbach’s α for this scale was 0.973, indicating high reliability.

Social Support Rating Scale (SSRS)

The SSRS, developed by Xiao,²¹ evaluate social support through 3 dimensions: objective support, subjective support, and social support utilization. The scale includes 10 items, with total scores ranging from 12 to 66. Higher scores denote higher levels of social support. The SSRS demonstrated high internal consistency, with a Cronbach’s α of 0.92.

Data Analysis

The electronic questionnaire was created and distributed via SoJump. For statistical analyses, SPSS version 29.0 was employed. The analytical methods included: (1) Descriptive statistics were utilized to summarize general and other participant data; (2) Pearson correlation analysis was employed to examine correlations among benefit finding, self-management efficacy, and social support; (3) The mediating role of benefit finding in the relationship between social support and self-management efficacy was analyzed using hierarchical multiple regression. The significance of the mediating effect was tested with Hayes’s²² PROCESS macro for SPSS (Model 4, bootstrap sample size = 5000), and a significance threshold of P < 0.05 was applied.

Ethical Considerations

The study was approved by the Ethics Committee of Shenzhen Maternal and Child Health Hospital (SFYLS[2023]039). Informed consent was obtained from all participants in written form. Each patient signed an informed consent form that clearly outlined the study’s purpose, procedures, potential risks and benefits. Furthermore, it was explicitly stated that the research data would be maintained as completely anonymous and coded to protect the participants’ privacy. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Results

General Characteristics of Participants

The mean age of the 180 patients with gynecologic cancer was 47.32 ± 11.38 years. Among the demographic and clinical variables examined, the benefit finding score significantly differed only across occupational groups (P < 0.05). Detailed results are presented in Tables 1 and 2.

Table 1 Univariate Analysis of BF in Patients with Gynecological Cancer (N = 180)

Table 2 Multiple Comparisons of BF Scores in Different Occupational Groups (N = 180)

Scores for BF, Self⁃Management Efficacy, and Social Support

The mean scores for the key study variables were as follows: benefit finding, 62.63 (13.71); self-management efficacy, 92.04 (17.55); and social support, 42.63 (7.17). Detailed descriptive statistics are showed in Table 3.

Table 3 Descriptive Statistics of the Measured Variables (N = 180)

Correlation Analysis

Positive correlations were observed among the three key variables: benefit finding, self-management efficacy, and social support (r = 0.453, r = 0.253, and r = 0.268, respectively). Detailed results are showed in Table 4.

Table 4 Correlations Among BF, C-SUPPH, and SSRS (N = 180)

Mediating Effect of BF on the Relationship Between Social Support and Self⁃Management Efficacy

To assess the mediating effect of benefit finding, a three-step regression analysis was conducted: In step 1, social support (independent variable) significantly influenced self-management efficacy (dependent variable) (β = 0.268, P < 0.001). In step 2, social support significantly affected benefit finding (mediating variable) (β = 0.235, P = 0.002). In step 3, regression analysis was performed with both social support and benefit finding as independent variables and self-management efficacy as the dependent variable. Both social support (β = 0.171, P = 0.013) and benefit finding (β = 0.413, P = 0.001) significantly predicted self-management efficacy. These results confirm the mediating role of benefit finding. The Durbin–Watson statistic ranged from 1.578 to 1.975, indicating no residuals autocorrelation. Additionally, tolerance values (0.945–1.000) and variance inflation factors (1.000–1.058) indicated the absence of multicollinearity, confirming the suitability of the model for regression analysis. Detailed results are showed in Table 5.

Table 5 Mediating Effects of BF on the Relationship Between SSRS and C-SUPPH (N = 180)

Using Model 4 in PROCESS 4.0, we further validated the mediating effect of benefit finding between social support and self-management efficacy. Social support (independent variable), benefit finding (mediator), and self-management efficacy (dependent variable) were analyzed using 5000 bootstrap samples and 95% confidence intervals. The results indicated that benefit finding partially mediated the relationship between social support and self-management efficacy, with a mediating effect value of 0.238, accounting for 36.28% of total effect. The 95% confidence interval (0.050–0.512) excluded 0, indicating statistical significance. Detailed results are presented in Table 6 and Figure 1.

Table 6 Mediatory Model of BF (N = 180)

Figure 1 Mediatory model of benefit finding for the relationship between social support and Self-management efficacy. Note: ***P < 0.001, **P < 0.01, *P < 0.05.

Discussion

Levels of BF, Self-Management Efficacy, Social Support in Patients with Gynecologic Cancer

The mean benefit finding score in this study (mean = 62.63, SD = 13.71) was moderate, aligning with the finding of Manner.¹⁹ However, previous studies have reported inconsistent results regarding factors influencing BF in cancer patients.^23–25 One-way ANOVA indicated significant differences in BF scores across various occupational groups. Post-hoc analysis showed that self-employed individuals have significant differences with unemployed, workers, staff of enterprises and institutions, and professional technical personnel. Similarly, retirees have significant differences with the unemployed and workers. These findings may reflect that self-employed individuals and retirees, being outside highly competitive work environments and having stable incomes, experience less psychological burden and are more likely to undergo positive psychological changes when confronted with cancers.

Among the BF dimensions, the highest scores were observed in health behavior changes, indicating that patients are motivated to take positive adjustments to maintain their health, such as improved dietary habits and better compliance with medical advice. Conversely, the lowest scores were in the worldview dimension, possibly because patients primarily focus on the immediate impacts of the disease, such as survival, rather than long-term life goals. Cancer, often viewed as a psychosomatic disorder, can diminish patients’ sense of meaning and value in life. BF can help patients develop physiological and psychosocial resilience during cancer-related stress and mitigate negative emotions.²⁶ Healthcare providers should prioritize enhancing patients’ cognitive framework to foster benefit finding and support their mental and physical health.

The self-management efficacy score in this study (mean = 92.03, SD = 17.54) was moderate but lower than that reported in breast cancer patients.²⁷ This discrepancy may result from side effects of treatment for gynecologic cancers, such as long-term indwelling urinary catheters, lymphedema and constipation, which require more time and effort to manage. Second, the 5-year survival rate of gynecologic cancer patients is lower than that of breast cancer patients, which undermines patients’ confidence in their recovery. Regular assessments of patients’ self-management efficacy and influencing factors are recommended, alongside the implementation of individualized nursing interventions to improve self-efficacy.^28,29

The social support score was (42.63±7.17) was moderate but slightly lower than that reported in patients with other cancers.³⁰ Factors contributing to this include the impact of gynecologic cancers on sexual function, fertility, and the urinary system. Traditional Chinese cultural values, which place high importance on fertility and usually discourage open discussion about sexual health, may further limit patients’ willingness to seek support. Higher levels of social support have been shown to foster positive societal perceptions and encourage emotional interactions with family, healthcare professionals, and friends, all of which benefit the patient’s psychosocial well-being.

Correlation Between Benefit Finding, Self-Management Efficacy, and Social Support

Correlation analysis revealed a positive association between self-management efficacy and benefit finding (r = 0.453, P < 0.01). This interdependence suggests that higher self-management efficacy is linked to higher levels of benefit finding, while greater BF reinforces self-management efficacy. Patients who derive positive meaning from their disease are more motivated and confident in their self-management behaviors.³¹ Successful self-management, such as symptom control or improved quality of life, further improves patients’ ability to view their disease in a positive light, fostering personal growth and a reshaping of values.^32,33

Social support also plays a crucial role in promoting benefit finding.³⁴ The positive correlation between social support and benefit finding scores (r = 0.235, P < 0.01) indicates that patients with greater social support are more likely to derive positive meaning from their illness.³⁵ As Conley suggested, emotional support enables patients to find positive aspects in their health challenges.³⁶

Additionally, social support was positively correlated with self-management efficacy (r = 0.268, P < 0.01), consistent with previous research.³⁷ Patients with higher self-management efficacy tend to have more optimistic attitudes, better self-management skills, and enhanced communication abilities. Social support, inturn, bolsters their confidence and facilitates these behaviors.^38,39

Women are expected to take on more caring responsibilities in society and may experience greater emotional stress, self-neglect, increased psychological stress, and conflicts between their roles and identities after illness. These factors affect patients’ access to social support, disease perception, health behavior and treatment compliance, and indirectly affect individuals’ perceived support and BF.

Healthcare professionals should not only address the physical aspects of gynecologic cancers but also focus on fostering positive psychological adaptation. This includes helping patients discover the beneficial aspects of their disease and enhancing their self-management abilities. Coordinating resources to provide sufficient social support is also crucial for promoting patients’ recovery and overall wellbeing.

BF as a Partial Mediator Between Social Support and Self-Management Efficacy

The mediation analysis in this study revealed that the mediating effect of BF accounted for 36.28% of the total effect. This indicates that benefit finding partially mediates the relationship between social support and self-management efficacy. Social support for gynecologic cancer patients directly influences self-management efficacy while also exerting an indirect influence through BF.

BF involves identifying positive and meaningful aspects through cognitive adaptation when facing negative events such as illness. According to cognitive adaptation theory, individuals adjust their mental state through cognitive processes when confronting stress, which benefits finding representing a positive outcome of these adaptations.⁴⁰ Social support, as a vital external resource, significantly influences these cognitive processes. Positive perceptions derived from social support encourage proactive self-management behaviors, thereby enhancing self-management efficacy.

Cultural factors may also impact the extent to which patients leverage support. For instance, Chinese cultural norms, which of the favor subtle and restrained communication, may lead individuals to be hesitant in discussing sensitive topics or expressing emotions openly. Additionally, traditional perceptions linking reproductive system diseases to personal indiscretions may discourage patient seeking timely medical assistance or emotional support. This hesitancy can delay interventions and exacerbate their condition.

To address these barriers, it is essential to strengthen social support systems, foster benefit finding, and provide training in self-management skills. In outpatient follow-up or nurse-led education courses, on the one hand, face-to-face session can be implemented to teach patients strategies to cope with disease-related stress, such as mindfulness training and positive psychological exercises, etc. On the other hand, peer support groups or online communities can also be established to integrate patient self-management education, thereby enhancing patients’ benefit finding from the disease through multi-dimensional intervention. Such measures can significantly improve the self-management efficacy of gynecologic cancer patients, ultimately enhancing their recovery outcomes and quality of life.

Strengths and Limitations

This study investigated the levels of BF, social support, and self-management efficacy in patients with gynecologic cancer and explored the relationships among these variables. The findings offer foundational data for designing targeted care interventions.

However, the study has some limitations. First, the relatively small sample size from a single hospital may limit the generalizability of the mediation effect model. Second, as a cross-sectional study, it cannot establish causal relationships between variables. Also, since all the data came from self-report scales, there’s a chance of social desirability bias. Furthermore, while this study demonstrated that BF partially mediates the relationship between social support and self-management efficacy, other potential mediating variables remain unexplored.

Future research should involve a larger sample sizes and adopt longitudinal designs across multiple centers to examine dynamic relationships and confirm causality among benefit finding, social support, and self-management efficacy. Additionally, exploring other potential mediators could provide a more comprehensive understanding of the mechanisms underlying these relationships, offering a stronger theoretical basis for nursing interventions.

Conclusion

This study found that self-management efficacy among gynecologic cancer patients was moderate and positively correlated with benefit finding and social support. Moreover, BF was identified as a partial mediator between social support and self-management efficacy. By exploring the mechanisms of these interactions, the findings provide new perspectives on the psychological and behavioral responses of patients during their disease journey. Healthcare professionals can use this knowledge to design clinical interventions aimed at enhancing patients’ self-management efficacy by fostering benefit finding. Such approaches hold promise for improving both the quality of life and clinical outcomes for patients with gynecologic cancers.

Abbreviations

BF, benefit finding; GCs, gynecologic cancers; C-SUPPH, self-management efficacy; SSRS, social support.

Data Sharing Statement

The datasets generated and/or analysed during the current study are not publicly available due to confidentiality issues but are available from the corresponding author on reasonable request. Liyuan Sun ([email protected]) should be contacted if someone wants to request the data from this study.

Ethical Approval

Approval was obtained from the Ethics Committee of Shenzhen Maternal and Child Health Hospital (SFYLS[2023]039).

Acknowledgments

We thank all the participants in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was funded by the 2022 Shenzhen Science and Technology Innovation Commission Basic Research General Project(No. JCYJ20220531102612029).

Disclosure

All authors declare no conflicts of interest in this work.

References

1. Gynecological Cancer. In: Gu D, Dupre ME, editors. Encyclopedia of Gerontology and Population Aging. Cham: Springer International Publishing. 2021.

2. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–263. doi:10.3322/caac.21834

3. Duan R, Zhang H, Yu J, et al. Temporal trends and projections of gynecological cancers in China, 2007-2030. BMC Womens Health. 2023;23(1):346. doi:10.1186/s12905-023-02384-2

4. Jiang X, Tang H, Chen T. Epidemiology of gynecologic cancers in China. J Gynecol Oncol. 2018;29(1):e7. doi:10.3802/jgo.2018.29.e7

5. Pozzar RA, Hammer MJ, Cooper BA, et al. Symptom clusters in patients with gynecologic cancer receiving chemotherapy. Oncol Nurs Forum. 2021;48(4):441–452. doi:10.1188/21.ONF.441-452

6. Cianci S, Tarascio M, Arcieri M, et al. post treatment sexual function and quality of life of patients affected by cervical cancer: a systematic review. Medicina. 2023;59(4):704. doi:10.3390/medicina59040704

7. Galica J, Saunders S, Romkey-Sinasac C, et al. The needs of gynecological cancer survivors at the end of primary treatment: a scoping review and proposed model to guide clinical discussions. Patient Educ Couns. 2022;105(7):1761–1782. doi:10.1016/j.pec.2021.11.020

8. Qian H. A cross-sectional study on the level of self-care self-efficacy among cancer patients and analysis of its factors. Second Military Med Univ. 2011.

9. Manne SL, Kashy DA, Kissane DW, Ozga M, Virtue SM, Heckman CJ. Longitudinal course and predictors of communication and affect management self-efficacy among women newly diagnosed with gynecological cancers. Support Care Cancer. 2020;28(4):1929–1939. doi:10.1007/s00520-019-04989-6

10. St Fleur RG, Ream M, Walsh EA, Antoni MH. Cognitive behavioral stress management affects different dimensions of benefit finding in breast cancer survivors: a multilevel mediation model. Psychology Health. 2023;1–20.

11. Li X, Wen Y, Shu W, Shen Z, Huang Z. Qualitative research on the perception of benefit in gynecological cancer patients. J Nurs Educ Pract. 2024;14(5):16. doi:10.5430/jnep.v14n5p16

12. Taylor SE. Adjustment to threatening events: a theory of cognitive adaptation. Am Psychologist. 1983;38(11):1161–1173. doi:10.1037/0003-066X.38.11.1161

13. A ZL, Deng M, Handorf E, et al. Understanding benefit finding among patients with colorectal cancer: a longitudinal study [J]. Supportive Care Cancer. 2021;29(5):2355–2362.

14. Zhang J, Liu X, Zhou X, et al. The relationship between benefit finding and quality of life in patients with chronic obstructive pulmonary disease: the mediating effects of self-management. Int J Chron Obstruct Pulmon Dis. 2024;19:2011–2021. doi:10.2147/COPD.S465953

15. Liang D, Jia R, Zhou X, et al. The effectiveness of peer support on self-efficacy and self-management in people with type 2 diabetes: a meta-analysis. Patient Educ Couns. 2021;104(4):760–769. doi:10.1016/j.pec.2020.11.011

16. Shen A, Wu P, Qiang W, et al. Factors associated with lymphedema self-management behaviours among breast cancer survivors: a cross-sectional study. J Clin Nurs. 2023;32(19–20):7330–7345. doi:10.1111/jocn.16833

17. Zhao M, Na N, Xing N, et al. The impact of social support on benefit finding among patients with advanced lung cancer and their caregivers: based on actor-partner interdependence mediation model. Support Care Cancer. 2024;32(5):287. doi:10.1007/s00520-024-08435-0

18. Zheng L, Liu Y. Nursing Research Methods. Beijing: People’s Medical Publishing House; 2018.

19. Manne SL, Kashy DA, Virtue S, et al. Acceptance, social support, benefit-finding, and depression in women with gynecological cancer. Qual Life Res. 2018;27(11):2991–3002. doi:10.1007/s11136-018-1953-x

20. Liu C, Zhang L, Gudenkauf L. Cross-cultural adaptation of the Benefit Finding Scale (BFS) in Chinese Cancer Patients. Chin J Nurs. 2015;50(05):561–566. doi:10.3761/j.issn.0254-1769.2015.05.010

21. Xiao S. Theoretical basis and research application of “social support rating scale. J Clin Psychol Med. 1994;98-100(2).

22. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–1182. doi:10.1037/0022-3514.51.6.1173

23. Lassmann I, Dinkel A, Marten-Mittag B, et al. Benefit finding in long-term prostate cancer survivors. Support Care Cancer. 2021;29(8):4451–4460. doi:10.1007/s00520-020-05971-3

24. Zhang Z, Li X, Wang Z, Yang Y. Analysis of influencing factors and impact path of benefit finding in patients with cervical cancer and their spouses. Chin J Nurs. 2024;59(18):2214–2221. doi:10.3761/j.issn.0254-1769.2024.18.005

25. Zhu P, Chen C, Liu X, Gu W, Shang X. Factors associated with benefit finding and mental health of patients with cancer: a systematic review. Support Care Cancer. 2022;30(8):6483–6496. doi:10.1007/s00520-022-07032-3

26. Liu Z, Doege D, Thong MSY, et al. Distress mediates the relationship between cognitive appraisal of medical care and benefit finding/posttraumatic growth in long-term cancer survivors. Cancer. 2021;127(19):3680–3690. doi:10.1002/cncr.33684

27. Bi W, Wang H, Yang G, Zhu C. A longitudinal cohort study on benefit finding evolution in Chinese women breast cancer survivals. Sci Rep. 2021;11(1):20640. doi:10.1038/s41598-021-99809-5

28. Chen RY, Huang LC, Su CT, et al. Effectiveness of short-term health coaching on diabetes control and self-management efficacy: a quasi-experimental trial. Front Public Health. 2019;7:314. doi:10.3389/fpubh.2019.00314

29. He J, Xia J. Effect of a WeChat-based perioperative nursing intervention on risk events and self-management efficacy in patients with thyroid cancer. Am J Transl Res. 2021;13(7):8270–8277.

30. Liu Z, Zhang L, Cao Y, Xia W, Zhang L. The relationship between coping styles and benefit finding of Chinese cancer patients: the mediating role of distress. Eur J Oncol Nurs. 2018;34:15–20. doi:10.1016/j.ejon.2018.03.001

31. Wen KY, Ma XS, Fang C, et al. Psychosocial correlates of benefit finding in breast cancer survivors in China. J Health Psychol. 2017;22(13):1731–1742. doi:10.1177/1359105316637839

32. Karavasileiadou S, Almegwely W, Alanazi A, Alyami H, Chatzimichailidou S. Self-management and self-efficacy of women with gestational diabetes mellitus: a systematic review. Global Health Action. 2022;15(1):2087298. doi:10.1080/16549716.2022.2087298

33. Wang TJ, Chang SC, Hsu HH, et al. Efficacy of a self-management program on quality of life in colorectal cancer patients: a randomized controlled trial. Eur J Oncol Nurs. 2023;67:102431. doi:10.1016/j.ejon.2023.102431

34. Sun L, Tao Y, Zhu S, Liu K. A randomized controlled trial of WeChat-based cognitive behavioral therapy intervention to improve cancer-related symptoms in gynecological cancer survivors: study protocol. BMC Health Serv Res. 2022;22(1):1052. doi:10.1186/s12913-022-08443-y

35. Conley CC, Small BJ, Christie J, et al. Patterns and covariates of benefit finding in young Black breast cancer survivors: a longitudinal, observational study. Psychooncology. 2020;29(7):1115–1122. doi:10.1002/pon.5398

36. Zhu L, Ranchor AV, Helgeson VS, et al. Benefit finding trajectories in cancer patients receiving psychological care: predictors and relations to depressive and anxiety symptoms. Br J Health Psychol. 2018;23(2):238–252. doi:10.1111/bjhp.12283

37. Tan T, Shen Y, Zhou X, Zhou B, Cheng M. Correlation of quality of life with self-care efficacy and social support in patients with nasopharyngeal carcinoma after radiotherapy. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2019;44(6):672–678. doi:10.11817/j.issn.1672-7347.2019.06.010

38. Geng Z, Ogbolu Y, Wang J, Hinds PS, Qian H, Yuan C. Gauging the effects of self-efficacy, social support, and coping style on self-management behaviors in chinese cancer survivors. Cancer Nurs. 2018;41(5):E1–e10. doi:10.1097/NCC.0000000000000571

39. Zhong F, Pengpeng L, Qianru Z. grouping together to fight cancer: the role of wechat groups on the social support and self-efficacy. Front Public Health. 2022;10:792699. doi:10.3389/fpubh.2022.792699

40. Sun L, Liu K, Li X, Zhang Y, Huang Z. Benefit-finding experiences of cervical cancer survivors in rural Yunnan province, China: a qualitative study. Nurs open. 2022;9(6):2637–2645. doi:10.1002/nop2.962