Health-related quality of life in Chinese kidney transplant recipients assessed by Chinese medicine quality of life-11 dimensions (CQ-11D)

Abstract

Background

Health-related quality of life (HRQoL) in kidney transplant recipients (KTRs) is a key concern influenced by physiological, social, and cultural factors. However, yet commonly used assessment tools in previous studies do not adequately capture the unique sociocultural context of Chinese populations. The objective of this study was to assess the HRQoL in KTRs using the CQ-11D questionnaire and analyze factors associated with HRQoL to enhance the HRQoL of KTRs.

Methods

We conducted a cross-sectional study. Data were collected from two tertiary hospitals in Beijing and Changsha, China. Demographic and clinical data were collected using a structured questionnaire. The CQ-11D was used to measure HRQoL. Other scales, including the Constitution in Chinese Medicine Questionnaire (CCMQ), the Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI), were used to assess TCM constitution, psychological distress, and sleep quality, respectively. Data were analyzed using IBM SPSS 26.0, including bivariate analysis, generalized linear models, and multiple regression analysis.

Results

A total of 215 KTRs were enrolled. The median CQ-11D health utility value was 0.918. There were differences across the 11 dimensions of the CQ-11D in KTRs. After the age of 40, the proportion of reported problems generally increased with age. This trend was particularly evident in dimensions such as SY, SM, and JS, where individuals over 50 reported significantly more problems than other age groups. The biased constitutions and poor sleep quality were associated with a higher likelihood of reporting problems in most CQ-11D dimensions. Factors such as sleep quality, TCM constitution, and regular exercise were significantly associated with HRQoL.

Conclusion

Sleep quality, TCM constitution, and regular physical activity were associated with HRQoL of KTRs. Healthcare professionals should prioritize interventions targeting these factors, especially in older KTRs and those with a biased constitution, to improve their HRQoL.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-026-02520-0.

Introduction

Currently, kidney transplantation (KT) has become the preferred treatment for patients with chronic kidney disease (CKD), offering higher survival rates and better clinical outcomes compared to dialysis [1, 2]. The 1-year and 5-year survival rates of kidney transplant recipients (KTRs) are approximately 97% and 88.4%, respectively [3]. Although kidney transplantation improves patient survival, its high cost, along with post-transplant complications such as rejection and infections, significantly impacts patients’ sleep quality and psychological well-being, ultimately leading to a severe decline in their quality of life [4, 5].

Health-related quality of life (HRQoL) is regarded as an indicator of individual well-being and effectively measures the potential health benefits brought by various interventions [6, 7]. It is considered a subset of quality of life (QoL). However, sociocultural differences across countries affect the factors influencing HRQoL, limiting the applicability of universal measurement tools. Previous studies have used universal tools to measure the HRQoL of KTRs, which may not adequately reflect sociocultural differences. The Chinese medicine quality of life-11 dimensions (CQ-11D) is a measurement tool based on the theory and the health concept of Traditional Chinese Medicine (TCM). It not only assesses quality of life but also evaluates the effects of TCM interventions, reflecting the characteristics and preferences of the Chinese population [8]. Importantly, CQ-11D is a preference-based instrument that generates health utility values anchored on a full-health to worse-than-death scale, enabling quantitative comparisons across populations and supporting economic evaluations. Previous researches have confirmed the feasibility, reliability, and validity of the CQ-11D instrument [9, 10]. Although previous studies in Chinese general and chronic disease populations have demonstrated that CQ-11D scores correlate well with these general HRQoL measures (SF-36, EQ-5D) [11, 12], suggesting acceptable construct validity of the CQ-11D. The CQ-11D’s 11 dimensions (covering physical health, psychological well-being, sleep quality, and daily functioning) align with the common symptom burden and health concerns observed in KTRs. To date, no studies have applied CQ-11D specifically in KTRs, highlighting the need to explore its feasibility and utility in this population.

In addition to using a culturally relevant preference-based HRQoL measure, this study incorporates the concept of TCM constitution, which reflects an individual’s stable tendency toward specific physiological and psychological characteristics shaped by both innate and environmental factors [13]. These constitutions are classified into nine subtypes under two categories: the neutral constitution and the biased constitutions (Qi-Deficient, Yin-Deficient, Phlegm-Dampness, Blood-Stasis, Yang-Deficient, Dampness-Heat, Qi-Depression, and Special) [14]. The neutral constitution represents an optimal state of health characterized by harmony between yin and yang and adequate circulation of qi and blood. Individuals with this constitution typically have a moderate body build, a healthy complexion, and stable emotions. They demonstrate good physical and psychological well-being and strong adaptability to both physical and social environments. The remaining eight constitution types are classified as biased constitutions, which are characterized by disrupted regulation of bodily functions, physiological disharmony, and impaired organ performance. These patterns indicate that the body is in a suboptimal health condition or affected by disease. Differences in constitutions can increase susceptibility to certain pathogenic factors, affecting physiological function and overall QoL [15]. Previous study [16] has shown that different traditional constitution types are associated with HRQoL. Individuals with a neutral constitution tend to demonstrate better psychological adjustment and higher quality of life. In contrast, those with biased constitutions may experience both mental and physical disruption, which can negatively affect daily functioning and overall well-being. This TCM constitution theory has been applied at various levels of public health management in China, offering a new approach to maintaining health and preventing disease [17]. However, evidence regarding its association with preference-based HRQoL among KTRs remains limited.

This study aims to assess the HRQoL of KTRs and identify associated factors, to inform targeted interventions and improve their quality of life and post-transplant outcomes.

Methods

Study design, setting, and ethical statement

This cross-sectional study used convenience sampling and was conducted from January to April 2024 in the outpatient clinics of two tertiary hospitals.

Ethical approval was granted by the Ethics Committee of Beijing University of Chinese Medicine (Approval No. 2023BZYLL1212).

Participants

Screening survey

KTRs attending outpatient clinics at two tertiary hospitals were consecutively screened. Inclusion criteria were: (a) had successfully undergone kidney transplantation and were in regular follow-up; (b) fully conscious, with adequate reading and communication abilities; (c) provided informed consent and agreed to participate voluntarily. Exclusion criteria included: (a) recipients of multi-organ transplants; (b) severe diseases involving the heart, brain, lungs, or other vital organs.

Sample size

The required sample size was determined through an a priori power analysis using G*Power 3.1.9.7 [18, 19]. For a multiple linear regression model, assuming f² = 0.15, α = 0.05, power = 0.80, and 18 predictors, the minimum required sample size was calculated to be 150. Considering a potential 20% attrition rate or invalid responses, we targeted a minimum recruitment of 188 participants. Consequently, a total of 215 KTRs were enrolled.

Measures

Measurements of demographics

The demographic questionnaire, designed by the researchers, included both personal characteristics and disease-related information. The demographic variables included gender, age, body mass index (BMI), education level, employment status, income and marital status. The disease-related variables included history of smoking and alcohol history, post-transplant duration, organ source, and complications.

Measurement of HRQoL

HRQoL was assessed using the CQ-11D [8], which consists of 2 dimensions based on TCM’s theory of body (XING) and spirit (SHEN) unity. The “XING” dimension includes the first eight items: XD (movement and self-care); SY (appetite); DB (stool); SM (sleep quality); JS (vitality, including feeling alive, energetic, and focused); TY (dizziness, ranging from lightheadedness to severe vertigo and instability); XH (palpitations or restlessness); TT (pain); and PL (fatigue). And “SHEN” dimension includes the last three items: FZ (irritability); JL (anxiety and nervousness); and YY (feelings of frustration, disinterest, and low energy). Each item is rated on a four-level scale. Four levels (Levels 1–4) reflect the severity of issues reported, where higher levels denote more severe problems. A level of 1 indicates that there is no problem with the dimension, and levels of 2–4 indicate having self-reported “any problems” [10].

Health utility values were calculated using the published CQ-11D Chinese value set, applying dimension and level-specific decrements to derive the overall utility score [10, 20]. Each of the 11 items has four response levels, and levels 2–4 are associated with predefined utility decrements estimated from the valuation study. The overall utility score was calculated by subtracting the sum of the level-specific decrements from 1 according to the following tariff algorithm:

where “U” represents the health utility value, “n” stands for health condition state, and β_ij denotes the utility decrement associated with level j of dimension i. For example, if a respondent reports the health state 13,112,121,223, the corresponding decrements are applied as follows: U(13112121223) = 1-0-0.102-0-0-0.022-0-0.007-0-0-0.006-0.052 = 0.811.

The best possible health state (“11111111111”) corresponds to a utility value of 1, while the worst health state (“44444444444”) corresponds to a utility value of -0.868. Therefore, the total score index ranges from − 0.868 to 1, with higher values indicating better individual health status [20]. The CQ-11D demonstrates excellent reliability and validity. The overall Cronbach’s alpha of the scale is 0.820. The correlation coefficients between the body (XING) and total scores, and between the Shen (SHEN) and total scores, are 0.940 and 0.826, respectively [9]. The specific dimensions of the scale and the formula for calculating the utility values [10] are detailed in Appendix 1.

The CQ-11D was developed based on the World Health Organization’s concept of quality of life and the TCM view of health. Its item pool was generated through literature review and patient interviews, and then refined through expert consultation and pilot testing to form the final 11-dimension descriptive system [8]. Although the CQ-11D was not originally developed as a kidney disease–specific instrument, its dimensions cover common physical and psychological concerns that are frequently reported by kidney transplant recipients, supporting its use for assessing HRQoL in this population.

Measurement of TCM constitution

The Constitution in Chinese Medicine Questionnaire (CCMQ) [21] was used to assess TCM constitutions, which includes 9 subscales and 60 items to identify 9 types of TCM constitutions. Each item is scored on a five-point Likert scale, with a maximum score of 5 and a minimum score of 1. The subscales are scored in two stages: first by calculating the raw score, then by deriving a transformed score using a specific formula. The transformed score determines the individual’s physique type [22]. The scale demonstrates strong reliability, with test-retest reliability ranging from 0.77 to 0.90 and internal consistency coefficients from 0.72 to 0.80 across the nine subscales [23].

Measurements of anxiety and depression

The Hospital Anxiety and Depression Scale (HADS) [24] was employed to assess anxiety and depression over the past month. The HADS comprises two subscales: anxiety (HADS-A) and depression (HADS-D), with a total of 14 items. Each item is scored from 0 to 3 points, and the total score for each subscale ranges from 0 to 21 points. A score of 0–7 is considered normal, 8–10 indicates mild symptoms, 11–14 reflects moderate symptoms, and 15–21 represents severe symptoms [25]. The Cronbach’s α for the anxiety and depression subscales ranged from 0.68 to 0.93 (mean 0.83) and 0.67 to 0.90 (mean 0.82), respectively. Additionally, the construct validity correlations range from 0.49 to 0.83 [26].

Measurements of sleep quality

The Pittsburgh Sleep Quality Index (PSQI) [27] was used to assess the sleep quality over the past month. The scale consists of 19 self-assessment items and 5 other-assessment items, covering 7 domains, including subjective sleep quality, sleep latency, and sleep duration. Each factor is scored on a scale from 0 to 3, with the total score ranging from 0 to 21. A total score of below 5 indicates good sleep, a score of 6–10 suggests mild disorder, 11–15 indicates moderate disorder, and a score above 15 reflects severe disorder. The Chinese version has a Cronbach’s α of 0.845, and test-retest reliability of 0.994 [28].

Data collection

Data were collected using standardized questionnaires at two transplant centers during routine outpatient follow-up visits. To ensure consistency across centers, identical eligibility criteria, survey instruments, and standardized instructions were used. In Beijing, questionnaires were administered offline in person. Trained researchers explained the study purpose using a standardized script, obtained written informed consent, and checked completed questionnaires for completeness on site to minimize missing data. In Changsha, questionnaires were administered online via the Questionnaire Star platform using the same questionnaire content and order. To reduce potential information bias and duplicate responses inherent to online administration, submissions were restricted to one per device/IP address/social media account (e.g., WeChat), and questionnaires were excluded if completion time was implausibly short or if responses showed uniform answering patterns. In addition, data were independently transcribed into an Excel database by two researchers and cross-validated to minimize data entry errors. These procedures were implemented to mitigate potential biases related to different administration modes (online vs. offline) between the two centers.

Statistical analysis

We used IBM SPSS 26.0 software (SPSS Inc., IBM, Chicago, IL, USA) to perform statistical analysis. Frequencies and percentages were used to describe categorical data. The normally distributed variable (only age) was described as mean ± SD; non-normal variables as median [P₂₅, P₇₅]. Since each CQ-11D dimension was measured on a four-level ordered categorical scale (Levels 1–4), ordinal regression with a cumulative probit link (ordered probit model) was employed to analyze the 11 individual CQ-11D dimensions. These analyses were exploratory. This approach is more appropriate than ordinary least squares regression for ordinal outcomes because it accounts for the ranked nature of response categories without assuming equal distances between adjacent levels [29]. To account for multiple testing, the false discovery rate (FDR) was controlled using the Benjamini-Hochberg procedure. Results are reported as regression coefficients (β) with standard errors, 95% CI, P-values and adjusted P.

The associations between different variables and CQ-11D scores were tested using Mann-Whitney U test or Kruskal-Wallis H test, and Spearman correlation analysis. Multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF) and tolerance statistics. A VIF value of > 5 was considered to indicate significant multicollinearity. As no problematic multicollinearity was identified (VIF range: 1.05–2.90), all selected predictors were retained in the final model. The total CQ-11D health utility value was analyzed using multiple linear regression to examine associations with demographic and clinical factors. This was defined as the primary analysis. The selection of independent variables for the multiple linear regression model was based on a combination of clinical relevance and the results of univariate analyses. P < 0.05 was considered statistically significant without the need of adjustment for multiple testing.

Results

Characteristics of participants

The study included 215 KTRs, of which 122 were male (56.74%) and 93 were female (43.26%). Mean age was 45.23 ± 11.79 years. Median BMI was 22.86 (20.31, 25.26). The post-transplant duration ranged from 3 months to 277 months. The median number of immunosuppressive medications was 3 (2, 3). Among the 215 participants, 117 (54.4%) were recruited from Beijing and 98 (45.6%) from Changsha. Participants from Changsha had a significantly longer post-transplant duration and higher anxiety and depression scores than those from Beijing (P < 0.05). However, CQ-11D utility scores did not significantly differ between the two centers (P > 0.05). Additional demographic and clinical data appear in Table 1.

Table 1.

CQ-11D index scores and relate characteristics of all participants (n = 215)

Characteristics	Classification	n	%	Index scores (P25, P75)	Z/H	P
Gender	Male	122	56.74	0.918 (0.831, 0.967)	-1.451	0.147
	Female	93	43.26	0.885 (0.823, 0.958)
Education level	High school and below	89	41.40	0.905 (0.817, 0.961)	1.368	0.505
	Junior college/ University	112	52.09	0.918 (0.830, 0.965)
	Master’s degree and above	14	6.51	0.922 (0.869, 0.972)
Occupation	Unemployed	50	23.26	0.894 (0.820, 0.960)	1.472	0.479
	Employed	134	62.33	0.918 (0.831, 0.967)
	Retirement	31	14.41	0.884 (0.848, 0.965)
Marital status	Unmarried	34	15.81	0.923 (0.825, 0.961)
	Married	164	76.28	0.918 (0.830, 0.965)	2.106	0.551
	Divorced	11	5.12	0.906 (0.829, 0.963)
	Widowed	6	2.79	0.760 (0.516, 0.942)
Monthly per capita household income, RMB	<3000	36	16.74	0.878 (0.798, 0.928)
	3000~6000	82	38.14	0.905 (0.823, 0.960)	9.586	0.022
	6000~10,000	48	22.33	0.905 (0.823, 0.961)
	>10,000	49	22.79	0.958 (0.867, 0.976)
History of smoking	No	169	78.60	0.918 (0.842, 0.965)	-2.312	0.021
	Yes	46	21.40	0.867 (0.797, 0.931)
History of drinking	No	173	80.47	0.918 (0.825, 0.961)	-0.293	0.769
	Yes	42	19.53	0.918 (0.848, 0.965)
Source of transplanted kidney	Deceased donor kidney	198	92.09	0.918 (0.826, 0.965)	-0.093	0.926
	Living donor kidney	17	7.91	0.925 (0.823, 0.961)
Complication	No	73	33.95	0.929 (0.867, 0.972)	-2.814	0.005
	Yes	142	66.05	0.882 (0.818, 0.960)
Exercise regularly	No	110	51.16	0.882 (0.817, 0.961)	-2.130	0.033
	Yes	105	48.84	0.918 (0.848, 0.967)
TCM constitution	Biased constitutions	59	27.44	0.878 (0.816, 0.951)	-5.788	<0.001
	Neutral constitution	156	72.56	0.961 (0.908, 0.989)

Description of psychological and sleep quality

The median total score on the HADS was 14 (7, 21), with median scores of 8 (3, 12) for the anxiety dimension and 7 (3, 9) for the depression dimension. Sleep quality was assessed using the PSQI, and the median score for PSQI was 6 (4, 9). Figure 1 presents severity levels for anxiety, depression, and sleep quality.

Fig. 1.

Percentage distribution of severity levels in anxiety (assessed by HADS-A), depression (assessed by HADS-D), and sleep quality (assessed by PSQI). A: Severity distribution of anxiety. B: Severity distribution of depression. C: Severity distribution of sleep quality

Factors differences in the 11 dimensions of CQ-11D

The proportions of participants reporting problems (selecting levels of 2–4) varied across the 11 dimensions. Except for the items XD (14.42%) and TT (49.30%), the proportion of participants reporting problems for the other items exceeded 50%. The highest proportions of reporting problems were in SM (81.4%), PL (79.5%), and JS (76.7%). The proportions of participants reporting problems for each item are shown in Fig. 2.

Fig. 2.

Frequencies of each level in the CQ-11D dimensions. Note: Responses for each dimension are categorized into four levels (Levels 1–4), reflecting the severity of issues reported, where higher levels denote more severe problems. Level 1 indicates that participants reported no problem related to the item, while Levels 2–4 indicate that participants reported any problem related to the item

The proportion of reporting problems in each item of the CQ-11D was analyzed by grouping participants according to gender, age, and physique, as shown in Fig. 3, Supplementary Table 1, and Supplementary Fig. 1. From age 40 onward, the proportion of reporting problems generally increased with age. This trend was particularly evident in the SY, SM, and JS dimensions, where individuals over 50 reported significantly higher proportions of problems compared to younger age groups. The “SHEN” dimension (PL, FZ, JL) had consistently high problem rates (56.90%-82.35%) across all age groups.

Fig. 3.

Percentage of reported problems in 11 health dimensions of CQ-11D presented by sex, age, and constitution of TCM groups. A: The percentage of reported problems in 11 health dimensions of CQ-11D was presented by the sex group. B: The percentage of reported problems in 11 health dimensions of CQ-11D was presented by age group. C: The percentage of reported problems in 11 health dimensions of CQ-11D was presented by TCM constitution group

Additionally, recipients with biased constitution generally had a higher proportion of reporting problems compared to those with neutral constitution. Especially elderly patients (> 60) with a biased constitution showed high problem rates across various items, indicating elevated health risks. Although no significant overall gender differences were found, females over 60 years of age reported higher proportions of problems in the SM, JS, TY, XH, TT, PL, FZ, and JL dimensions. Males aged 30–40 years, on the other hand, reported higher proportions of problems in SY, DB, JS, and PL.

Table 2 presents the results of the ordered probit model for sociodemographic characteristics and health-related variables. The ordered probit models for the 11 dimensions showed good model fit. After controlling the false discovery rate using the Benjamini–Hochberg procedure, several associations remained statistically significant. The detailed regression results, adjusted P, and model fit statistics for each dimension are provided in Supplementary Table 2.

Table 2.

Associations between characteristics and distribution of 4 levels in CQ-11D dimensions

Item		XD	SY	DB	SM	JS	TY	XH	TT	PL	FZ	JL
Gender	Female	-0.596	-0.185	0.245	0.235	0.446	0.626*	0.439	0.478	0.196	-0.103	0.385
	Male	0	0	0	0	0	0	0	0	0	0	0
Education level	Master’s degree and above	-5.558*	1.065	-0.103	0.466	0.159	-0.252	0.340	1.107*	0.348	0.848	0.525
	Junior college/ University	-0.100	-0.011	-0.176	-0.092	0.133	0.408	-0.027	0.405	0.291	-0.022	0.052
	High school and below	0	0	0	0	0	0	0	0	0	0	0
Occupation	Retirement	-0.379	-0.897	-0.873	-0.428	-0.291	-0.920*	0.088	-0.365	-0.095	-0.065	-0.017
	Employed	-0.656	0.010	0.097	-0.402	0.200	-0.638	0.111	-0.057	0.071	0.079	0.025
	Unemployed	0	0	0	0	0	0	0	0	0	0	0
Marital status	Widowed	-0.334	-0.862	-0.215	1.142	-0.431	-0.137	-0.670	-0.400	-0.324	-0.507	-0.791
	Divorced	-0.452	-0.271	-0.034	-0.056	-0.558	0.200	-0.052	0.198	0.834	0.327	0.638
	Married	-1.354	-0.568	-0.310	0.017	0.239	0.606	-0.190	0.376	-0.018	0.308	0.277
	Unmarried	0	0	0	0	0	0	0	0	0	0	0
Income each person/month, RMB	>10,000	0.022	-0.408	0.129	-0.590	-0.676	-1.089*	-0.043	-0.950*	0.223	-0.496	-0.632
	6000~10,000	0.567	0.031	0.548	0.130	-0.356	-0.363	0.341	-0.952*	0.274	-0.181	-0.143
	3000–6000	0.649	0.052	0.400	-0.056	-0.301	-0.344	0.309	-0.276	-0.121	-0.358	-0.186
	<3000	0	0	0	0	0	0	0	0	0	0	0
History of smoking	Yes	0.409	-0.132	0.014	0.195	0.146	-0.008	-0.139	0.009	0.541	0.218	0.213
	No	0	0	0	0	0	0	0	0	0	0	0
History of drinking	Yes	0.134	0.043	0.226	-0.653	-0.249	0.216	0.283	0.117	-0.605	-0.361	-0.354
	No	0	0	0	0	0	0	0	0	0	0	0
Source of kidney	Kinsfolk	0.265	1.056**	0.190	-0.480	0.170	0.087	-0.250	-0.079	0.114	0.323	0.347
	Hospital	0	0	0	0	0	0	0	0	0	0	0
Complication	Yes	0.648	0.135	0.216	-0.107	0.198	0.097	0.025	0.044	0.568*	0.003	0.433
	No	0	0	0	0	0	0	0	0	0	0	0
Regular exercise	Yes	-0.668	-0.280	-0.049	-0.111	-0.582**	-0.197	-0.308	0.039	-0.511*	-0.429	-0.379
	No	0	0	0	0	0	0	0	0	0	0	0
Constitution of TCM	Biased constitutions	-0.359	0.401	0.324	-0.313	0.997**	0.911*	1.065*	0.962*	0.890*	0.872*	1.021*
	Neutral constitution	0	0	0	0	0	0	0	0	0	0	0
Age		0.033	0.040**	0.019	0.011	0.015	0.002	-0.002	0.002	-0.002	-0.003	-0.008
BMI		-0.037	-0.067**	0.002	0.005*	<0.001	-0.011	0.001	0.002	-0.001	-0.002	<0.001
Time after transplantation		-0.019*	-0.004	< 0.001	0.001	<0.001	0.004	0.001	0.001	0.001	0.002	<0.001
Number of medications used		-0.422	-0.085	-0.025	0.118	-0.007	-0.099	0.012	-0.039	0.018	0.068	-0.095
Anxiety		0.071	-0.042	-0.047	0.008	0.026	0.034	-0.028	-0.011	0.031	-0.003	0.049
Depression		-0.021	0.093	0.084	-0.004	0.138	0.013	0.067	0.069	-0.007	0.010	0.002
PSQI		0.100	0.102**	0.108*	0.356*	0.138**	0.024	0.049	0.060	0.087*	0.088*	0.117*

Note: ^*** P < 0.001; ^** P < 0.01; ^* P < 0.05

In the “SHEN” dimension (PL, FZ, JL), recipients with a biased constitution showed a significantly higher likelihood of reporting problems (β = 0.872~1.021, adjusted P < 0.05). Regular physical exercise was associated with a lower likelihood of reporting problems in JS and PL (β = −0.582 and − 0.511, adjusted P = 0.008 and 0.012). KTRs with complications had a significantly higher likelihood of reporting problems in PL (β = 0.568, adjusted P = 0.012).

In the “XING” dimension, certain items (JS, TY, XH, TT) showed an increased likelihood of reporting problems associated with the biased constitution (β = 0.911 ~ 1.065, adjusted P < 0.05). Females were also more likely to report problems in TY than males (β = 0.626, adjusted P = 0.042). The likelihood of reporting SY problems increased with age (β = 0.040, adjusted P = 0.006).

Regarding socioeconomic factors, retired recipients reported fewer problems in TY (β = −0.920, adjusted P = 0.042), while higher income was associated with a lower likelihood of reporting problems in TY and TT (β = −1.089 and − 0.950, adjusted P = 0.012 and 0.036).

Factors associated with CQ-11D health utility value

The median CQ-11D health utility value was 0.918 (P₂₅ = 0.826, P₇₅ = 0.965). For comparison, population norms of the CQ-11D in the Chinese general population reported a mean utility value of 0.897 (SD = 0.142), with age-specific means ranging from 0.921 (15~24 years) to 0.796 (≥ 75 years) [10]. In chronic disease populations, CQ-11D score was 0.855 ± 0.168 [12], indicating that the KTRs’ scores in our study were slightly higher than those observed in patients with other chronic conditions. Due to the absence of concurrent measurements with established HRQoL instruments such as SF-36 or EQ-5D in this study, direct assessment of convergent validity was not possible. However, the median CQ-11D utility observed in our KTRs (0.918) was close to the general and chronic disease population reference values [10, 12]. CQ-11D index scores varied according to the participants’ sociodemographic characteristics, socioeconomic status, and health-related conditions. Lower scores were associated with low income, smoking, post-transplant complications, lack of regular physical exercise, biased constitution, anxiety and depression, and poor sleep quality (P < 0.05), as detailed in Table 1 and 3. Recipients with a monthly per capita household income below 3000 yuan had the lowest HRQoL score (0.878) among all income groups. Recipients who experienced post-transplant complications had a lower CQ-11D index score (0.882), while those with a neutral constitution had a higher CQ-11D index score (0.961).

Table 3.

Correlation analysis between CQ-11D health utility values and continuous variables (n = 215)

Item	r	P	95% CI
Age	-0.092	0.178	[-0.232, 0.053]
BMI	0.062	0.364	[-0.065, 0.184]
Post-transplant duration	-0.046	0.499	[-0.197, 0.095]
Number of medications used	-0.032	0.641	[-0.181, 0.099]
Anxiety	-0.213	0.002	[-0.348, -0.069]
Depression	-0.230	0.001	[-0.367, -0.098]
PSQI	-0.638	<0.001	[-0.718, -0.542]

Based on significant variables from univariate analyses and clinical relevance, the following variables were included as independent predictors in the multiple linear regression model: monthly per capita household income, history of smoking, complications, regular exercise, TCM constitution, anxiety, depression, and sleep quality (PSQI scores). CQ-11D health utility values were used as the dependent variable. Sleep quality, regular exercise, and the biased constitution were the main factors influencing the health utility value of KTRs (P < 0.01). Regression results are shown in Table 4.

Table 4.

Multivariate analysis of CQ-11D health utility values (n = 215)

Item	β	P	95% CI
PSQI	-0.017	<0.001	[-0.022, -0.013]
Exercise regularly	0.047	0.002	[0.017, 0.077]
Constitution of TCM	-0.049	0.007	[-0.085, -0.013]

Note: R² = 0.342, Adjusted R² = 0.332, F = 36.488, P<0.001

The associations between regular exercise, TCM constitution, and sleep quality with HRQoL remained stable after additional adjustment, indicating robustness of the findings. Details are shown in Table 5.

Table 5.

Multivariable linear regression analysis of factors associated with CQ-11D utility scores adjusted for center and post-transplant duration (n = 215)

Item	β	P	95% CI
PSQI	-0.016	<0.001	[-0.020, -0.012]
Exercise regularly	0.044	0.002	[0.014, 0.074]
Constitution of TCM	-0.050	0.006	[-0.086, -0.014]
Center	0.052	0.013	[0.011, 0.093]
Depression	-0.007	0.037	[-0.013, 0.000]

Note: R² = 0.390, Adjusted R² = 0.361, F = 13.069, P<0.001

Interaction terms between study center and the main explanatory variables (sleep quality, regular exercise, and TCM constitution) were tested. A significant interaction was observed only for TCM constitution (β = 0.263, P = 0.001). Therefore, stratified analyses were conducted according to study center. Among participants from Beijing, regular exercise, TCM constitution, and sleep quality were significantly associated with CQ-11D utility scores. Among participants from Changsha, only sleep quality remained significantly associated with CQ-11D utility scores, although the direction of associations for exercise and constitution was consistent with those observed in the Beijing center. The results can be found in Table 6.

Table 6.

Stratified multivariable regression by center

Item	Beijing			Changsha
	β	P	95% CI	β	P	95% CI
PSQI	-0.016	<0.001	[-0.022, -0.009]	-0.016	<0.001	[-0.021, -0.011]
Exercise regularly	0.059	0.011	[0.014, 0.104]	0.012	0.487	[-0.022, 0.047]
Constitution of TCM	-0.070	0.008	[-0.120, -0.019]	-0.033	0.148	[-0.079, -0.012]
Depression	-0.013	<0.001	[-0.020, -0.006]	-0.0005	0.860	[-0.006, 0.005]

Note: Center = Beijing: R² = 0.446, Adjusted R² = 0.426, F = 22.532, P<0.001. Center = Changsha: R² = 0.331, Adjusted R² = 0.302, F = 11.505, P<0.001

Discussion

Principal findings and contributions

This study provides one of the first applications of the CQ-11D in KTRs and offers both overall utility estimates and item-level problem profiles in a Chinese cultural context. Compared with previous HRQoL studies in transplantation that mainly relied on generic instruments, our approach adds value by combining a culturally grounded measure with an analysis of TCM constitution, sleep quality, and physical activity as correlates of HRQoL. Furthermore, by examining the CQ-11D items separately using ordinal regression, we were able to identify domain-specific burden patterns and their associated factors, which may be overlooked when only a single overall score is reported.

Participant characteristics

In this study, male KTRs slightly outnumbered females. Participants ranged widely in age, with most in the young to middle-aged groups. The demographic distribution was similar to that reported by Barutcu et al. [30]. Some recipients also reported poor sleep quality, which is linked to post-transplant recovery, mental health, and immunosuppressive therapy efficacy. However, a higher proportion reported poor sleep quality (61.40%) compared to Barutcu et al. (48.1%) [30]. The incidence of anxiety and depression was also high, possibly due to a shorter postoperative period, financial burden, and the unique sociocultural context of KTRs in China. Notably, the HADS scale identified anxiety and depression in some recipients, and approximately half of the participants experienced anxiety and depression. This finding aligns with Temür et al. [31] who also reported prevalent psychosocial issues among KTRs. From a TCM perspective, 72.56% of KTRs had a biased constitution. Therefore, we recommend incorporating traditional Chinese wellness practices based on the recipients’ constitutional types to help alleviate negative emotions. For example, Five-Element Music Therapy may serve as an effective self-regulation method [32].

Dimension-specific differences in CQ-11D scores in KTRs

Except for the XD (14.42%) and TT (49.30%) items, over 50% of KTRs reported problems in the other items. Most health issues were reported in SM (81.4%), PL (79.5%), and JS (76.7%) items. The item-level profile observed in our KTRs cohort partially aligns with CQ-11D findings from the Chinese general population [10], where PL and SM were among the most frequently reported problem items. Notably, JS problems were also common in the general population, and the relatively high burden observed in KTRs may additionally reflect transplant-specific challenges, such as long-term immunosuppression, post-transplant complications, and uncertainty during recovery. Moreover, the findings indicate that additional attention may be warranted for recipients aged > 50 years, particularly regarding SY, SM, and JS items. Notably, the proportion of reporting problems in the “SHEN” dimension (PL, FZ, JL) remained high across all age groups (56.90%-82.35%). Rodrigue et al. [33] found that KTRs experienced fatigue and poor sleep quality, lowering the QoL. Depression among KTRs (11.8%–41.6%) has been linked to long-term immunosuppressant side effects, recovery uncertainty, limited rehabilitation knowledge, and financial stress [34–36].

Except for the first two dimensions, recipients with a biased constitution reported more problems than those with a neutral constitution, particularly over age 60. In TCM, a neutral constitution describes strong, healthy individuals with balanced physiques and robust energy [13]. Biased constitutions, shaped by congenital and acquired factors, reflect imbalances in yin-yang or qi-blood, increasing disease vulnerability and problem reporting. TCM constitution can influence various aspects of health, and plays an important role in disease prevention and treatment [37]. Therefore, healthcare professionals may adjust TCM constitution via diet, Chinese medicine, traditional techniques, and exercise to restore balance and reduce health problems.

The overall rate of reporting problems did not significantly differ between genders. However, Pan et al. [10] found females more prone to physical and mental issues, possibly due to differing disease profiles. Men aged 30–40 and women over 60 showed more problems across dimensions, indicating that these groups need targeted clinical attention.

The ordered probit model showed that the scores of 11 items varied by demographic, socioeconomic, and disease characteristics. Associated factors varied, but sleep quality and TCM constitution affected HRQoL across most items. Poor sleep quality was linked to increased problem reporting. Lee et al. [38] found poor sleep independently associated with anxiety and depression, impairing emotional and physiological function, and increasing problem likelihood. Additionally, regular physical exercise was associated with fewer problems in JS and PL. Physical exercise benefits both physical and mental health of KTRs and enhances their emotional well-being and QoL. Cicognani et al. [39] reported that organ transplant recipients who engaged in regular exercise had comparable physical health to healthy peers and better psychological outcomes than non-exercisers. Luan et al. [40] found that long-term moderate-intensity aerobic exercise effectively reduced anxiety and depression. Furthermore, in TCM, appropriate exercise promotes qi and blood flow, helping relieve negative emotions such as depression, irritability, and anxiety [41]. Healthcare professionals may recommend appropriate forms of exercise based on the specific conditions of KTRs, such as Taijiquan, Baduanjin, swimming, walking, and yoga.

HRQoL levels and associated factors in KTRs

This study did not include concurrent measurements with established HRQoL instruments such as SF-36 or EQ-5D, and therefore direct assessment of convergent validity in KTRs was not possible. However, previous research in general and chronic disease populations has shown that CQ-11D scores correlate well with general HRQoL measures, suggesting acceptable construct validity [11, 12]. Although direct comparisons across instruments should be interpreted cautiously due to differences in scaling and measurement frameworks, the overall HRQoL level observed in our study appears broadly comparable to those reported in previous studies of KTRs using SF-36 (81 ± 15.1) [42] and EQ-5D-5 L (95.6 ± 3.25) [43]. These findings support the feasibility of applying CQ-11D in KTRs, and its content domains appear relevant to the symptom burden and functional challenges experienced by this population.

Univariate analysis showed that lower CQ-11D utility values were associated with low income, smoking, post-transplant complications, lack of regular physical exercise, biased constitution, anxiety, depression, and poor sleep quality. In sensitivity analyses, these associations remained stable after additional adjustment for study center, post-transplant duration, and depression, suggesting that the observed relationships were robust and not substantially influenced by potential confounding factors. The multivariate analysis identified sleep quality, regular physical exercise, and TCM constitution were independently associated with CQ-11D utility values among KTRs. Specifically, good sleep quality was associated with better HRQoL, while poor sleep was associated with worse physical, emotional, and functional health, contributing to anxiety and depression [30]. Moreover, poor sleep may also link to frailty risk, further reducing HRQoL [4]. Our study also found that regular physical exercise was associated with better HRQoL, in line with prior studies [39]. suggesting that physical activity is related to improved physical function and psychological well-being in transplant recipients [39, 40]. In addition, TCM constitution was associated with individual quality of life [44]. Studies further suggest that individuals with different TCM constitutions may experience different sleep quality due to variations in the yin–yang balance and qi–blood circulation [37, 45]. Luo Yuhao et al. [45] found that TCM constitution classification could affect quality of life by influencing sleep quality. However, TCM constitution is modifiable. TCM theory emphasizes that constitution depends on both congenital and acquired factors [13]. Exercise can adjust the biased constitution to promote health, prevent disease, or delay progression [46]. In addition, an interaction between study center and TCM constitution was observed, suggesting that the association between constitution and HRQoL may differ across centers. In the stratified analyses, regular exercise and TCM constitution were significantly associated with CQ-11D utility scores among participants from the Beijing center, whereas only sleep quality remained significant in the Changsha center. Nevertheless, the direction of the associations for exercise and constitution was similar in both centers. The lack of statistical significance in the Changsha subgroup may be related to the smaller sample size or differences in post-transplant duration and psychological status between the two centers. These findings suggest that although the overall associations were consistent, the magnitude of the effects may vary across study settings.

Our findings suggest that sleep quality, physical activity, and constitution-related health status may represent actionable targets for supportive management in KTRs. In routine outpatient follow-up, a feasible intervention framework could include: (1) risk stratification and screening, using brief tools such as PSQI and HADS together with CQ-11D profiles to identify patients with poor sleep and psychosocial distress; (2) sleep-focused management, including structured sleep hygiene education, review of modifiable contributors, and timely referral for evidence-based treatments such as cognitive behavioral therapy for insomnia when appropriate; (3) physical activity promotion, through individualized exercise prescriptions aligned with functional status and comorbidities (e.g., progressive walking or low-impact aerobic training combined with resistance exercises), supported by goal setting, self-monitoring, and follow-up reinforcement; and (4) constitution-informed supportive care, where constitution assessment can be used to tailor health education and self-management support (e.g., personalized lifestyle counseling and symptom monitoring) without implying disease-specific therapeutic efficacy [13]. For example, healthcare professionals can consider patients’ TCM constitution when providing guidance about diet and exercise, thereby improving sleep quality and HRQoL.

Strength and limitations

This study utilized the culturally appropriate CQ-11D to assess HRQoL among KTRs, providing additional evidence for its application in this population. However, several limitations should be considered. First, due to the cross-sectional design, temporal relationships between predictors and HRQoL could not be established, and the observed associations should not be interpreted as causal. Second, participants were recruited using convenience sampling from two tertiary centers, which may limit the representativeness and generalizability of the findings. Third, several variables were self-reported and may therefore be subject to reporting bias. Fourth, residual confounding cannot be ruled out because some clinically important variables were not captured in sufficient detail, such as renal function indicators, immunosuppressive regimens, rejection or infection history, and comorbidities, which may influence HRQoL in KTRs. The lack of detailed information on these variables may therefore have resulted in residual confounding and could partially influence the observed associations. In addition, this study did not include concurrent HRQoL assessments using established instruments (e.g., SF-36, EQ-5D, or KDQOL), preventing direct evaluation of convergent validity in this population. Future multicenter longitudinal studies with more comprehensive clinical data and multiple HRQoL measures are warranted to further validate these findings.

Conclusion

This study applied CQ-11D to assess the HRQoL of KTRs, revealing variation in reporting problems across demographic, social, and health-related factors. Sleep quality, TCM constitution, and regular exercise were significantly associated with KTRs’ HRQoL. These factors may serve as potential targets for clinical assessment and supportive management to improve HRQoL, which should be further evaluated in longitudinal and interventional studies.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Data availability

The data that support the findings of this study are available from the lead corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was reviewed and approved by the Ethics Committee of Beijing University of Traditional Chinese Medicine, ethical review approval number: 2023BZYLL1212. Informed consent was obtained from all individual participants included in the study.

Ethical explanation

All the procedures in this study were implemented in accordance with relevant laws and institutional guidelines, and have been approved by the corresponding institutional committees and informed consent of patients. Ethical examination and approval are approved by the Ethics Committee of Beijing University of Traditional Chinese Medicine. (Project No.: 2023BZYLL1212)

Informed consent

Participant were fully informed of the study and gave their informed written consent before participation. This study was conducted with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.