Structural Equation Model of Factors Related to Death Anxiety for Chinese Patients with Cancer

Abstract

This study aimed to develop a model that specifies the predictive effects of factors on death anxiety among Chinese patients with cancer using structural equation modeling. Using convenience sampling, data were collected from 353 cancer patients. Self-administered questionnaires included Social Support Rating Scale, Rosenberg Self-Esteem Scale, Connor Davidson Resilience Scale, Templer’s Death Anxiety Scale, and socio-demographic factors. The results showed that social support, self-esteem, and resilience significantly impacted death anxiety. The final model fitted the data acceptably (χ² = 37.319, df =31, p = 0.201). Social support mediated death anxiety through self-esteem and resilience. Resilience mediated the buffer effect of self-esteem on death anxiety as an intermediary factor. Findings suggest the need for further studies to explore effective interventions to provide social support and improve self-esteem and resilience among patients with cancer to alleviate death anxiety.

Keywords

death anxiety resilience self-esteem social support structural equation modeling

Background

The North American Nursing Diagnosis Association International definition for death anxiety is a “vague uneasy feeling of discomfort or dread generated by perceptions of a real or imagined threat to one’s existence” (Carpenito, 2001). Conte et al. categorize death anxiety as a fear of unknown events and pain (Conte et al., 1982), while Hunsberger and Jackson view death anxiety as an emotional response to a psychological threat (Hunsberger& Jackson, 2005). For patients with cancer, death anxiety can be especially distressing because the nature of the disease triggers cognitive schemas relative to personal death and dying.

Although patients with high levels of death anxiety are vulnerable to depressive symptoms, heightened uncertainties, and diminished QOL (Sherman et al., 2010; Tong et al., 2016), patients with low levels of death anxiety are better able to communicate with family members, discuss end-of-life topics or unfinished business, and get through the final stage of life (Brown et al., 2014). Caregivers of cancer patients also validate the relationship between death anxiety and QOL as they report changes to QOL and levels of stress for both themselves and their patients (Soleimani et al., 2017). Therefore, research on cancer patients’ death anxiety is not only conductive to developing a modern medical model but also forms part of the inevitable trend of developing targeted measures to improve the QOL for all parties.

Researchers have been increasingly explored the relationship between the death anxiety of cancer patients and its influencing factors. The development of a death anxiety scale provides a basis for the evaluation of death anxiety and the analysis of influencing factors (Abdel-Khalek, 2004; Beydag, 2012). Demographic and disease-related factors are helpful in screening for high-risk groups, but exploring intervention variables may be more important. Therefore, this study explores the path interaction between death anxiety and other factors, which is the basis and key to constructing an effective intervention program for death anxiety.

Social support plays an important role for patients with terminal illnesses. Family support forms an important part of patients’ social structure and is vital in managing the negative emotions incited by death anticipation as the disease progresses. Proper interpersonal communication can help alleviate death anxiety (Smith et al., 1983). In their investigation of gynecological cancer patients and their caregivers, Uslu-sahan (2019) and others found that social support was negatively correlated with death anxiety.

Self-esteem is another factor related to death anxiety. According to the anxiety-buffer hypothesis, the flexibility of self-esteem provides self-regulating mechanisms to help persons alleviate anxiety. When self-esteem is weak or challenged, the individual worries that they may “spill” their inner fear and thus trigger various defensive behaviors (Greenberg et al., 2003). Low self-esteem is negatively related to death anxiety among healthy individuals, while high self-esteem can alleviate death anxiety (Routledge et al., 2010). Over the years, studies have reported on the relationship between death anxiety and self-esteem in cancer patients (Neel et al., 2015; Chen et al., 2018).

Resilience is understood as positive adaptation, or the ability to maintain or regain mental health, despite experiencing adversity (Fletcher & Sarkar, 2013). Many studies (Arredondo & Caparrós, 2021; Edo-Gual et al., 2015) have confirmed the mediating role of resilience in regulating psychological states among student samples. Among breast cancer patients, Wu and colleagues showed that resilience was an important protective factor for these patients’ mental health, and important in helping their psychological growth in their fight against cancer (Wu et al., 2018). Resilience partially mediated the relationship between anxiety and subjective support, and the relationship between depression, subjective support, and support utilization (Hu et al., 2018). However, whether resilience can also play a mediating role in the adaptive strategies against cancer patients’ death anxiety is yet to be confirmed.

Researchers have reported an association between social support, self-esteem, and resilience among different samples. For instance, Aprilianto et al. (2021) investigated the correlation between family social support and the self-esteem of breast cancer patients undergoing neoadjuvant chemotherapy. Their results indicated a strong positive correlation between family social support and patients’ self-esteem. Correspondingly, Tian et al.’s study (2021) of Chinese lung cancer patients found social support had a direct effect on self-esteem. Numerous cross-sectional studies revealed a positive association between social support and resilience (Gamble et al., 2021; Zahid et al., 2021). These included Aizpurua-Perez I and colleagues’ (2020) systematic review that concluded social support as a protective factor could enhance resilience. Additionally, research on protective factors of resilience among breast cancer patients based on social-ecological systems theory found resilience was positively correlated with self-esteem (Zhang, 2015). Similar conclusions were obtained among cancer patients undergoing radiotherapy, as social support was found to have an indirect influence on resilience through uncertainty and self-esteem (Jun et al., 2015).

Cancer creates a level of stress, which causes patients to develop death anxiety because of the psychological adjustment. According to the theory of resilience, the action of these stressors coupled with the protective factors in the environment (external support) can play a buffer role. After the buffer, internal factors (self-esteem) and external factors (support) interact to adapt to the stressful event. This is called an individual-environmental process. A reorganization of cancer patients’ mental flexibility is posited to improve death anxiety. The purpose of this study, therefore, is to contribute to the literature by structurally examining the impact of resilience, self-esteem, and social support on death anxiety, to improve the understanding of death anxiety among cancer patients, and develop a model for possible interventions.

This study aims to test the conceptual model summarized by the following questions:

(1)

Question 1 (Q1): What was the effect of social support on cancer patients' death anxiety?

(2)

Question 2 (Q2): What was the effect of resilience on cancer patients' death anxiety?

(3)

Question 3 (Q3): What was the effect of self-esteem on cancer patients’ death anxiety?

(4)

Question 4 (Q4): What was the effect of social support on self-esteem?

(5)

Question 5 (Q5): What was the effect of self-esteem on resilience?

(6)

Question 6 (Q6): What was the in-sample predictive power of the model?

Method

Study Design

This study followed a descriptive cross-sectional research design. A self-administered questionnaire collected data on factors related to cancer patients’ death anxiety, that is, social support, self-esteem, resilience, and socio-demographic and clinical characteristics.

Participants

A convenience sampling method was employed to recruit participants from two academic cancer hospitals between June and August 2020 in Beijing, China. Inclusion criteria were: (a) ≥18 years, (b) diagnosed with cancer through pathological examination, (c) well enough physically to answer the questionnaires accurately, and (d) voluntary participation. Exclusion criteria were: (a) patients with a psychiatric history or cognitive disorders, (b) inability to complete the questionnaires due to communication disorders, or physical weakness, and (3) diagnosed with other serious life-threatening diseases.

A total of 360 questionnaires were distributed but only 353 were returned, and 330 were included in the final analysis (effective rate 91.67%). Ethical approval was granted by the Institutional Biomedical Ethics Committee under protocol 2020KT19. The study procedures followed all ethical standards and were undertaken within the context of the Helsinki declaration. All participants provided written informed consent.

Measurements

The questionnaires aimed to measure the constructs depicted in the hypothesized model (see Figure. 1). The latent constructs included were social support, self-esteem, and resilience in addition to the measured constructs of death anxiety.

Figure 1.

Hypothetical model of death anxiety among patients with cancer.

Demographic and Clinical Characteristics

The general information section of the questionnaire was designed by the researchers and collected socio-demographic data such as age, sex, educational status, region, marital status, and working status. Interpersonal information included data about primary caregivers, and the experience of losing a loved relative/friend, while disease-related data consisted of the degree of understanding the disease, stage of disease, and daily living activities.

Death Anxiety

Death anxiety was measured by the Templer death anxiety scale (T-DAS), which was developed by Templer(1970). Yang et al. (2016) developed a Chinese cross-cultural adaptation of the T-DAS into CT-DAS, which had good reliability and validity. The CT-DAS originally consisted of 15 true/false questions. The performance of the 5-point Likert-type of CT-DAS (CL-TDAS) was evaluated and validated among colorectal cancer patients. A high score represents a high degree of death anxiety. The Cronbach`s alpha coefficient for the whole scale was 0.828, which indicated good reliability.

Social Support

Social support was measured by the Social Support Rating Scale (SSRS) (Xiao, 1994). The 10-item version of the Chinese SSRS developed by Xiao is a commonly used instrument to measure social support in China and contains three factors: objective support, subjective support, and availability. The scale employs a 4-point rating system with the score ranging from one to four for most items. A representative item is “How many close friends do you have to get support and help?” Higher scores indicate higher levels of social support. In this study, the Cronbach’s α coefficient was 0.740 for the overall scale.

Self-esteem

Self-esteem was assessed using the 10-item Rosenberg Self-esteem Scale (RSES) (Rosenberg, 1965), which assesses a person’s global self-worth by incorporating both positive and negative feelings about the self. It also employs a 4-point rating scale (1 = strongly disagree, 4 = strongly agree). The total score of self-esteem is calculated by summing the score of each item, with higher scores indicating higher self-esteem. The Cronbach’s α coefficient was 0.853 for the overall scale.

Resilience

Resilience was evaluated using the CD-RISC, which was first developed by Connor and Davidson(2003) and later revised into a Chinese version by Yu and colleagues (2007). The 25-item scale contains three conceptually distinct subscales: strength, tenacity, and optimism. The scale is scored based on a 5-point Likert scale ranging from 0 (not true at all) to 4 (true nearly all the time) and the obtain scores were added up to a score range of 0 to 100, with higher scores denoting greater resilience. It exhibited strong internal consistency with a Cronbach’s alpha = 0.919 in the current sample.

Study Power

GPower 3.1 was used to test whether the sample size was sufficient (Faul et al., 2007). A prior power analysis, linear multiple regression was used to compute the required sample size, with a fixed model and a single regression coefficient applied on the following data: the number of predictors (n = 10), the effect size (f2 = 0.15), and the probability of alpha error (0.05). The limited sample size of cancer patients needed was 125, the power of the study obtained was 99.02%, with a degree of freedom of 114. The sample size (n = 330) was sufficient.

Statistical analysis

The IBM SPSS Statistics 18.0 (IBM Corp., USA) was used for data analysis. Less than 2% of the data were missing but were replaced by mean imputation. Results from Little’s MCAR test showed that these values were missing completely at random (p > .05). Descriptive statistics and Pearson correlation were used for statistical analysis. The hypothesized model [Figure. 1] was tested using structural equation modeling (SEM) with IBM SPSS AMOS version 21.0 (IBM Corp., USA). Maximum likelihood estimation was used for parameter estimation. The ratio of cases to model parameters for SEM recommendations should be more than 10:1 (Wu, 2010). Of the 330 cases, we tested a model with 10 parameters for a ratio of 33:1, which was acceptable.

The model fit indices were as follows: Chi-square (χ2), degrees of freedom (df), p (probability level), root mean square error of approximation (RMSEA), goodness-of-fit index (GFI), adjusted GFI (AGFI), parsimony GFI (PGFI), normed fit index (NFI), comparative fit index (CFI), and standardized root mean square residual (SRMR). The critical values for GFI, AGFI, NFI, and CFI were 0.90 or higher (Wu, 2010). Low values (between 0 and 0.06) for RMSEA and (below 0.10) for SRMR indicated a good fitting model (Kline, 2011). Low values (<5.00) for χ2/df were preferred (Wu, 2010).

Results

Characteristics of the Sample

Baseline characteristics of study participants are shown in Table 1. The mean (SD) age of participants was 50.76± 13.78 years. More than half of the respondents were male (57.9%); the majority were married (87.9%).

Table 1.

Baseline Characteristics of Study Participants.

		n(%)	Mean	SD	p-Values
Patient individual factors
Age		19–83 (range)	50.76	13.78	0.003**
Gender	Female	139 (42.1)	41.85	10.91	0.017*
	Male	191 (57.9)	39.10	9.73
Education status (n=328)	Primary school	16 (4.8)	37.00	10.39	0.374
	Secondary school	63 (19.2)	40.33	10.99
	High school	149 (45.4)	39.72	9.82
	University	100 (30.6)	41.35	10.45
Marital status	Unmarried	25 (7.6)	42.38	8.48	0.234
	Married	290 (87.9)	40.27	10.29
	Widowed/divorced	15 (4.5)	36.63	13.07
Working status (n=329)	Employed	121 (36.8)	42.04	9.92	0.039*
	Unemployed	82 (24.9)	40.22	10.91
	Retired	126 (38.3)	38.70	10.07
Religious belief	No	309 (93.6)	40.18	10.11	0.593
	Yes	21 (6.4)	41.43	13.25
Experience of losing loved relative/friend (n=328)	No	199 (60.7)	39.78	10.89	0.255
	Yes	128 (39.3)	41.11	9.43
Interpersonal factors
Patient-primary caregiver relationship	Relatives	311(94.2)	40.44	10.32	0.207
Patient-primary caregiver relationship	Unrelated person	19(5.8)	37.36	10.19
Disease-related factors
Stage of cancer	I	32 (9.7)	41.29	9.55	0.124
	II	46 (13.9)	43.39	11.03
	III	69 (20.9)	39.74	10.73
	IV	183 (55.5)	39.49	10.02
Understanding of the disease (n=329)	Not at all	11 (3.4)	44.26	5.76	0.211
	Partly understand	106 (32.2)	41.03	10.94
	Fully understand	212 (64.4)	39.60	10.11
Daily living activities	Independence	227 (68.8)	39.26	10.07	0.010*
Daily living activities	Mild dependence	77 (23.3)	41.62	9.92
	Moderate dependence	18 (5.5)	44.68	7.85
	Severe dependence	5 (1.5)	51.40	18.31
	Full dependence	3 (0.9)	36.00	20.42

Notes: * p<0.05;**p<0.001.

Descriptive Statistics for Major Study Variables

The mean score of the death anxiety scale was (40.26 ± 10.32); this followed the normal distribution. The age and death anxiety showed a negative correlation (r = −0.163, p = .003); the death anxiety scores of patients of different genders, working statuses, and daily living activities had statistical differences (p < .05), which are shown in Table 1. The mean, standard deviation, and correlation of major variables are shown in Table 2.

Table 2.

Correlation Between Major Study Variables.

	Death anxiety	Self-esteem	Objective support	Subjective support	Availability	Tenacity	Strength	Optimism
Death anxiety	1
Self-esteem	−0.368^**	1
Objective support	−0.099	0.207^**	1
Subjective support	−0.073	0.181^**	0.404^**	1
Availability	0.000	0.186^**	0.262^**	0.280^**	1
Tenacity	−0.271^**	0.456^**	0.152^**	0.223^**	0.186^**	1
Strength	−0.414^**	0.522^**	0.163^**	0.197^**	0.184^**	0.812^**	1
Optimism	−0.344^**	0.398^**	0.130^*	0.201^**	0.192^**	0.550^**	0.588^**	1
Mean±SD	40.26±10.32	31.74±4.09	10.22±3.50	24.88±5.37	6.93±1.87	36.31±7.69	24.30±4.62	10.39±2.50

Notes: * p<0.05;**p<0.001.

Test of the Hypothesized Model

Although the hypothesized model fit the data acceptably, the model fitting index was poor [χ² = 76.368, df = 38, Table 3]. p < .001 indicated that the model was inconsistent with the covariance data.

Table 3.

Fitting Indicators of the Models.

Fitting indicator	p	χ²/df	RMSEA	GFI	AGFI	PGFI	NFI	CFI	SRMR
Reference	>0.05	<5.00	<0.06	>0.90	>0.90	>0.05	>0.90	>0.90	<0.10
The hypothesized model	0.000	2.001	0.055	0.961	0.933	0.554	0.909	0.951	0.055
The final model	0.201	1.204	0.025	0.979	0.963	0.552	0.957	0.992	0.454

The hypothesized model was revised according to the literature review, clinical experience, and the principle of model medication. The final model (Figure 2) excluded three paths from the hypothesized model: the direct paths from objective support and availability to death anxiety and the direct path from working status to death anxiety. The model was further simplified. Social support and resilience were included as an overall significant variable and seemed theoretically reasonable. Gender as a dichotomous variable was also excluded. A direct path from death anxiety to tenacity was included. The final model had a better fitting index [Table 3, χ² = 37.319, df =31, p = 0.201, RMSEA = 0.025, GFI = 0.979, AGFI = 0.963, PGFI = 0.552, NFI = 0.957, CFI = 0.992, SRMR = 0.0454].

Figure 2.

Test of hypothesized model.

Table 4 showed the path coefficients. Direct effect, indirect effect, and total effect were statistically significant (p < .05). Self-esteem and resilience mediated social support and death anxiety. Social support had a significant positive direct effect on resilience (β = 0.19) and self-esteem (β = 0.32). Resilience had a significant negative direct effect on death anxiety (β = −0.33), and self-esteem had a significant negative direct effect on death anxiety (β = −0.17). Self-esteem had a significant positive direct effect on resilience (β = −0.50) and death anxiety was positively associated with the tenacity of the resilience (β = 0.15). Collectively, 23.7% of the variance in death anxiety was explained by the final model.

Table 4.

Effect estimates of the final model.

Path	Direct Effect				Indirect Effect			Total Effect
Path	Standard coefficients	SE	B (95%CI)	p	SE	B (95%CI)	p	Standard coefficients	SE	B (95%CI)	p
Social support→Self-esteem	0.324	0.149	0.166–0.473	0.010	—	—	—	0.324	0.071	0.166–0.473	0.010
Social support→resilience	0.187	0.249	0.039–0.361	0.016	0.037	0.083–0.246	0.010	0.349	0.075	0.220–0.502	0.008
Social support→death anxiety	—	—	—	—	0.033	−0.262–−0.120	0.004	−0.172	0.033	−0.262	0.004
Self-esteem→death anxiety	−0.172	0.153	−0.314–−0.063	0.006	0.039	−0.241–0.090	0.012	−0.338	0.052	−0.451–−0.231	0.011
Self-esteem→resilience	0.499	0.100	0.410–0.591	0.012	—	—	—	0.499	0.049	0.410–0.591	0.012
Resilience→death anxiety	−0.332	0.091	−0.465–−0.188	0.012	—	—	—	−0.332	0.072	−0.465–−0.188	0.012
Age→death anxiety	−0.126	0.036	−0.209–−0.040	0.023	—	—	—	−0.126	0.045	−0.209–−0.040	0.023
Daily living activity→death anxiety	0.128	0.675	0.004–0.247	0.045	—	—	—	0.128	0.066	0.004–0.247	0.045
Death anxiety→ tenacity	0.153	0.030	0.075–0.237	0.009	—	—	—	0.153	0.040	0.075–0.237	0.009

Discussion

Death anxiety has progressively received researchers' attention and is described as a complex psychological state. This study explored the interrelationship between social support, self-esteem, resilience, and death anxiety among Chinese patients with cancer. Our SEM indicated that under the stress of cancer diagnosis, social support, self-esteem, and resilience influenced death anxiety. The model highlighted resilience as a key intermediary factor that mediated a buffer effect of self-esteem on death anxiety. In addition, the social transaction between human beings and the environment played a key role in resilience, which suggests social support should be provided to improve patients’ self-esteem and promote resilience.

Social support encompasses objective support, subjective support, and also a level of social support utilization. Social support is an important protective factor of resilience (Kumpfer, 2002). We posit that patients with cancer need more support systems, which can improve their resilience. The study explained that the ability of individuals to actively access and use available support was key to facilitating flexible reorganization in the process of coping with stress from a cancer diagnosis. The coefficient between resilience and death anxiety was negative and large, which proved that patients’ death anxiety could be alleviated after good resilience reorganization. Although no direct effect was found between social support and death anxiety, there was an indirect effect. Therefore, we theorize that the effective enhancement of cancer patients’ social support can alleviate death anxiety. In both the hypothesized model and the final model of this study, attention should be paid to improving subjective support levels and encouraging patients to evaluate available resources reasonably. This is in addition to the necessary objective support. Both patients and care providers must learn to make better use of support during the intervention or when seeking assistance.

Self-esteem may be considered as a person’s own positive or negative attitude (Rosenberg, 1965). Its effect on death anxiety is explained in the previously proposed theory (Greenberg et al., 2003), and this conclusion was confirmed by the results of this study. On the one hand, the direct effect coefficient from self-esteem to resilience was positive and large. The positive effect of self-esteem on resilience revealed that in the process of patients facing cancer stressors, self-esteem to resilience reorganization was important. On the other hand, self-esteem mediated the effect of social support on death anxiety as an intermediary variable. Although self-esteem is a product of constantly internalizing other people’s evaluations and opinions of oneself, once it is formed, it becomes a relatively stable part of the self-system. As French psychologists, Christophe André and François Lelord (2015) pointed out in L^′ Eatime De Sol, three pillars of self-esteem contain self-love, self-concept, and self-confidence. In the face of cancer stress, using self-acceptance helps patients to improve self-love, especially focusing on the most effective short-term intervention to improve self-confidence, so as to promote higher levels of resilience reorganization.

The final model of this study showed that resilience had a direct effect on death anxiety, which meant higher resilience helped alleviate death anxiety among cancer patients. Simultaneously, this study found that death anxiety had a direct effect on tenacity and impacted cancer patients’ resilience. Tenacity implies that a resilient person consciously integrates behaviors such as controlling, goal-setting, and decision-making when they are drawn into a situation of frustration and setback (Yu & Zhang, 2007) These new findings remind oncologists, nurses, and other health care providers to be on the alert for patients with high death anxiety. During the treatment of cancer and its related complications stages, we should pay attention to patients’ emotional control ability, help them establish the correct disease control objectives, and provide more information to assist them to make appropriate decisions.

Limitations

The first limitation of the present study is that it analyzed the death anxiety of patients with cancer at a single point in time. As such, any speculation regarding causal relationships must be made with caution. A follow-up survey related to the patients’ death anxiety should be conducted in the future. The second limitation is related to the fact that data for this research were collected via a questionnaire survey. In questionnaire surveys, respondents’ choices for responses are limited. It can be discussed further in combination with qualitative research more factors about deep psychology in the future.

Conclusions

Death come to human mind in the moment he/she was diagnosed cancer. The perception of confronting personal mortality may evoke death anxiety. Therefore, an understanding of the interaction path of death anxiety and the factors that contribute to heightening the emotion to stressful levels is the basis and motivation to construct an effective intervention program. Preliminary results supported the model and showed that good social support, self-esteem, and resilience contributed to relieving death anxiety. Social support mediated death anxiety through self-esteem and resilience, while resilience as an intermediary factor mediated the buffer effect of self-esteem on death anxiety. Findings suggest the need for further research to explore effective intervention to provide social support, improve self-esteem, and enhance the resilience of patients with cancer to alleviate death anxiety.

Footnotes

Acknowledgments

We would like to express our heartfelt appreciation for all the patients who cooperated with this study. This study was supported by a research grant from Science Foundation of Peking University Cancer Hospital 2020-18.

We would like to thank TopEdit (.com) for its linguistic assistance during the preparation of this manuscript.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science Foundation of Peking University Cancer Hospital (2020-18).

ORCID iDs

Hong Yang

Wenhua Yu

Youhui Gu

Xiaoting Hou

Yuhan Lu

Author Biographies

Hong Yang, MSN, RN, is nurse deputy director working at the nursing department of Peking University Cancer Hospital & Institute. Her ongoing research focuses on psychological nursing, application of information management system in cancer patients' symptom management, and psychological, personal, and environmental factors associated with the death anxiety.

Zhanying Wang, BS, RN, is a senior nurse in the Day Oncology Unit of Peking University Cancer Hospital & Institute. Her research interests are psychological support and chemotherapy related symptom management.

Wenhua Yu, MSN, RN, is working at Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute. Her research focuses on symptoms management among cancer patients and enhanced recovery after surgery.

Youhui Gu, BS, RN, is the teaching secretary of the nursing department of Peking University Cancer Hospital & Institute. Her main research interests are nursing education and quality of life of oncology nurses.

Jinxing Shao, BS, RN, nurse in charge of Peking University Cancer Hospital & Institute, has been engaged in clinical nursing for 19 years. she is engaged in clinical cancer nursing, and her interest direction is hospice nursing and pain nursing for cancer patients in the terminal period.

Yaru Zhang, MSN, RN, is working in Peking University Cancer Hospital & Institute, Her research focus on quality of life and symptom management among cancer patients during radiotherapy.

Xiaoting Hou, MSN, RN, is working at Department of Thoracic Oncology I, Peking University Cancer Hospital & Institute. Her research focuses on symptoms management among cancer patients, hospice & palliative care and evidence translation and implementation.

Dongqin Kang, MSN, RN, Oncology Certified Nurse, is working at Department of Integrative Medicine & Geriatric Oncology, Peking University Cancer Hospital & Institute. Research interests: symptoms management and nursing of elderly patients with cancer.

Yuhan Lu, MSN, RN, associate professor, the director of nursing department of Peking University Cancer Hospital & Institute. She has specialized in the field of cancer nursing , hospice & palliative care, and nursing management research for many years, collaborating with colleagues on practical guidance and published several professional books leading change to quality care of cancer nursing.

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