Herth Hope Index trajectory and its predictors in middle-aged and elderly patients with lung cancer: A prospective longitudinal observational study

doi:10.21203/rs.3.rs-4686422/v1

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Herth Hope Index trajectory and its predictors in middle-aged and elderly patients with lung cancer: A prospective longitudinal observational study

https://doi.org/10.21203/rs.3.rs-4686422/v1

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Objective

For middle-aged and elderly patients with lung cancer undergoing radiotherapy, the care and support received from family members or close social supporters during treatment significantly influence the physical and mental state and treatment outcomes of the patient. Hope, a positive emotional state, reflects patients’ attitudes and expectations toward their condition, treatment process, and prognosis. The level of hope may vary among middle-aged and elderly patients with lung cancer undergoing radiotherapy. Therefore, exploring individual differences and developmental changes during radiotherapy is important. This study aims to evaluate whether heterogeneity exists in the levels of hope among middle-aged and elderly patients with lung cancer during thoracic radiotherapy, identify related predictive factors, and understand the level of family care received by patients at this stage, to provide targeted interventions for patients with lung cancer with different levels of hope.

Methods

A total of 124 patients with lung cancer were included. Their levels of hope and extent of family care were evaluated at four time points. Growth mixture models, univariate analysis, and multivariate logistic regression were used to study the trajectory in the level of hope, its association with outcomes, and its predictive factors.

Results

Two hope trajectory classes were identified: the slow decline (27.4%) and the rapid decline (72.6%). Age, marital status, level of education, and extent of family care were factors influencing the hope trajectory categories during radiotherapy for middle-aged and elderly patients with lung cancer (P < 0.05).

Conclusion

The trajectories of hope among middle-aged and elderly patients with lung cancer during radiotherapy were heterogeneous. Healthcare providers can tailor interventions based on the factors that influence these hope trajectory categories.

Biological sciences/Psychology/Human behaviour

Biological sciences/Cancer/Lung cancer

Lung cancer

Radiotherapy period

Hope level

Latent category

Predictive factor analysis

Growth mixture model

Lung cancer is currently the leading malignant tumor in terms of both global incidence and mortality. Patients aged ≥ 45 years account for more than 70% of patients with lung cancer[1], and global population aging is becoming an increasingly prominent issue[2]. Lung cancer prevention and control are extremely challenging. Surgery, medication, and radiotherapy are the three mainstay treatment approaches for lung cancer. Radiotherapy using high-energy radiation to kill cancer cells or inhibit their growth is an important treatment modality for middle-aged and elderly patients with lung cancer, who may face a high surgical risk and low medication tolerance[3].

With the rise and development of radiotherapy in combination with targeted therapy and immunotherapy, the application of radiotherapy for the treatment of lung cancer is becoming increasingly widespread. Radiotherapy can be used in combination with other treatment modalities or as a standalone treatment for lung cancer[4]. For patients with early-stage non-small-cell lung cancer who are not suitable for or who refuse to undergo surgery, radiotherapy is recommended as the primary treatment. For patients with locally advanced non-small-cell lung cancer with limited stage or stage Ⅲb/Ⅲc who cannot undergo surgery, the five-year survival rate can reach 43% with comprehensive radiotherapy and chemotherapy[5]. For some larger tumors, radiotherapy may play an adjuvant role before or after surgery, further improving the local control rate and survival rate of patients with lung cancer^[4]. Kong et al.[6] proposed that radiotherapy can be beneficial for most patients with lung cancer.

With the rise of the biopsychosocial model, the psychological state of patients with cancer is receiving increasing attention. However, psychological studies involving patients with lung cancer have mainly focused on patients undergoing surgical or medicinal treatment[7–8]. Compared with surgery or medication, the effectiveness and adverse symptoms of radiotherapy progress more slowly and subtly, with longer average hospital stays. These characteristics may lead to different psychological states among patients undergoing radiotherapy. Existing research has mostly focused on negative emotions, such as anxiety, depression, and psychological distress[9]. Although these studies have promoted the progress of negative psychology, the excessive focus on negative emotions may lead to the pathological development of psychology, overlooking the positive qualities of human psychology[10]. Hope, as one of the core concepts of positive psychology[11–12], may unleash tremendous psychological potential and reduce people’s negative thoughts around the concept of disease.

Many foreign studies have explicitly emphasized the importance of the level of hope of patients with cancer[11], and the dynamic nature of hope encourages us to consider its developmental patterns. The heterogeneity among individuals is also worthy of research attention. Therefore, this prospective study explores the levels of hope of patients with lung cancer undergoing radiotherapy. We use the latent class growth model (LCGM) to analyze the development trajectory of patients’ levels of hope from the day before radiotherapy to the end of radiotherapy. We also explore its influencing factors, aiming to provide a reference for nursing staff to formulate targeted measures to enhance patients’ levels of hope. We propose two hypotheses. First, we hypothesize that the level of hope of middle-aged and elderly patients with lung cancer during radiotherapy changes dynamically and that there is group consistency. Second, we hypothesize that different patients’ levels of hope are correlated with the extent of family care that they receive, and that there are multiple factors influencing patients’ levels of hope.

Study design

This prospective longitudinal study was conducted in Hubei Province, China, between April 2021 and May 2022. The study was approved by the institutional review board of our institution. All recruited participants provided written informed consent. The participants were investigated longitudinally 1 day before radiotherapy (T1), 10th radiotherapy (T2), 20th radiotherapy (T3), and 24 hours after the last radiotherapy (T4). This study adhered to the reporting guidelines as outlined in the Reporting Observational Longitudinal Research statement[13].

Study participants

We used the convenience sampling method and recruited 135 patients with lung cancer. The inclusion criteria were (a) a diagnosis of lung cancer by cytology or pathology; (b) aged ≥ 45 years; (c) radiotherapy at the thoracic mediastinum and undergoing first-time radiotherapy alone; (d) more than 5 weeks when planning radiotherapy; (e) total radiotherapy dose from 50 Gy to 70 Gy; (f) ability to communicate verbally and in writing; and (g) voluntary participation in the study. The exclusion criteria were (a) the presence of tumors in other organs; (b) mental illness, severe medical conditions, or a diagnosis of other types of cancer or cognitive dysfunction; (c) knowledge of their own illness; (d) an expected survival time of < 1 year, as assessed by doctors; and (e) participation in other clinical intervention studies.

Ethical considerations

This study was performed in line with the principles of the Declaration of Helsinki.Approval was granted by the Ethics Committee of Xiangyang No. 1 People’s Hospital, Hubei University of Medicine(Approval number: XYYYE20240084) .

Measures

Demographic and clinical characteristics

An information sheet was designed to collect the participants’ demographic data, including age, sex, residential location, level of education, marital status, work status, medical insurance type, monthly household income (RMB, yuan), and smoking status.

Patients’ clinical variables included tumor histology, tumor–node–metastasis (TNM) classification, comorbidities, number of radiotherapy sessions, and cancer stage. The cancer stage was mainly based on the TNM classification[14].

The Herth Hope Index

The Herth Hope Index (HHI) was developed by Herth[15]. It is mostly used to determine the level of hope of patients with cancer. The HHI contains three dimensions (positive attitude, positive behavior, and positive relationship) with a total of 12 items. The HHI is scored on a 4-point Likert scale ranging from “very against” to “very agree.” The final score is the sum of the item scores, with a higher score indicating a higher level of hope. Zhao et al. previously translated the HHI into Chinese[16], and the Cronbach’s ɑ coefficient is 0.97.

Family Adaptation, Partnership, Growth, Affection, Resolve Scale

The Family Adaptation, Partnership, Growth, Affection, Resolve (APGAR) Scale questionnaire was designed by Dr. Smilkstein[17] in 1978. The APGAR Scale is a subjective tool that can be used to explore patients’ satisfaction with their own family functioning. The questionnaire consists of five items: fitness, cooperation, growth, emotion, and intimacy. Each item is rated on a 3-point Likert scale ranging from 0 (rarely) to 2 (often), with a total score of 0–10 points. A score of 7–10 points indicates good family function, 4–6 points indicates moderate family function impairment, and 0–3 points indicates severe family function impairment. Lv et al.[18] previously translated the APGAR Scale into Chinese, and the Cronbach’s ɑ coefficient ranges from 0.830 to 0.857.

Data collection

The researchers explained the research objectives and methods to the eligible patients and obtained their consent. General information and disease information were obtained from the hospital’s medical records system. To ensure the objectivity and accuracy of the data collection, nursing graduate students who had received nursing research training were responsible for data collection. At T1, the purpose of the survey was explained to the patients and they were informed that the survey included four items. The contact information of the patient or their primary caregiver was obtained, and we clarified that the patient was aware of all relevant details and agreed to cooperate. At T2–T4, the patient was contacted by phone to determine the specific time for data collection. Unified guidelines were used to explain the filling method to the patients. If the patients were unable to independently fill out the questionnaire, the researchers read out the items sentence by sentence and made objective records. All questionnaires were collected in a timely manner and checked item by item. Any omissions or errors in the content were verified with the patient on the spot. A group meeting was held every two weeks to discuss the problems encountered during questionnaire collection and make timely adjustments and improvements.

Statistical analyses

Statistical analyses were performed using IBM SPSS Statistics (v.29.0; IBM Corp, Armonk, NY, US) and Mplus v.8.3. Normally distributed measurement data are expressed as the mean ± standard deviation, while non-normally distributed measurement data are expressed as the median and quartile.

Sociodemographic and clinical variables were compared between the classes by the analysis of variance (ANOVA), Kruskal–Wallis test, chi-square test, or Fisher’s exact test, as appropriate. P < 0.05 was considered statistically significant. The Holm–Bonferroni correction method was applied to adjust for multiple comparisons.

We fitted the trajectories of the level of hope at four follow-up points using the growth mixture model (GMM). The optimal model was judged by information evaluation, fit test, and classification correctness indices[19]. The information evaluation indices included the Akaike information criterion (AIC), Bayesian information criterion (BIC), and sample size-adjusted BIC (aBIC). The better the GMM fit, the lower the values of the above indices. The Lo-Mendell-Rubin (LMR) test and the bootstrapped likelihood ratio test (BLRT) are commonly used model fit test indicators. When the number of latent categories is k, the model with k latent categories is considered better if the P value from the LMR or BLRT test is < 0.05. If P > 0.05, the model with k − 1 latent categories is selected. In the classification correctness index, entropy (range 0–1) indicates the accuracy of the model in classifying individuals into the correct categories. When entropy is ≥ 0.8, the model is considered to have high classification accuracy[20]. In addition, the class probability (≥ 5% of the sample) is also considered.

Patient characteristics

Initially, a total of 135 middle-aged and elderly patients with lung cancer met the criteria for admission and were willing to participate in the study. Five patients were withdrawn from the study the day before radiotherapy. After one year of follow-up, one patient abandoned treatment, one patient withdrew from the study, and four patients died. Therefore, 124 patients with complete follow-up data were finally included in the analysis. The follow-up details are shown in Fig. 1, and the participants’ baseline characteristics are shown in Table 1.

Table 1

Baseline characteristics of study population overall and by HHI trajectory groups
Characteristics	Total (n = 124)	Class1 (n = 34)	Class2 (n = 90)	²/t	P
Sex, n (%)				0.762	0.385
Male	101(81.5)	26(76.5)	75(83.3)
Female	23(18.5)	8(23.5)	15(16.7)
Age, n (%)				13.489	< 0.01
45–59	61(49.2)	8(23.5)	53(58.9)
> 60	63(50.8)	26(76.5)	37(41.1)
residential location, n (%)				1.553	0.215
Rural	69(55.6)	22(64.7)	47(52.2)
Urban	55(44.4)	12(35.3)	43(47.8)
Educational level,n (%)				17.376	< 0.01
≤ Primary	52(41.9)	7(20.6)	45(50.0)
Junior	50(40.3)	15(44.1)	35(38.9)
Senior	19(15.3)	9(26.5)	10(11.1)
≥ College	3(2.4)	3(8.8)	0(0)
Work status, n (%)
Full-time job	4(3.2)	2(5.9)	2(2.2)	1.051	0.307
Retired or other	120(96.8)	32(94.1)	88(97.8)
Marital status, n (%)
Married	104(83.9)	33(97.1)	71(78.9)	6.228	0.014
Unmarried or divorced or widowed	20(16.1)	1(2.9)	19(21.1)
Medical insurance type, n (%)				0.337	0.563
Public health services	4(3.2)	0(0)	4(4.4)
Urban basic medical	57(46)	18(52.9)	39(43.3)
New agricultural cooperative medical	47(37.9)	13(38.2)	34(37.8)
Other	16(12.9)	3(8.8)	13(14.4)
Monthly household income (RMB, Yuan), n (%)				0.001	0.972
< 2000	21(16.9)	5(14.7)	16(17.8)
2000–5999	86(69.4)	25(73.5)	61(67.8)
≥ 6000	17(13.7)	4(11.8)	13(14.4)
Cancer stage, n (%)				1.015	0.316
Ⅰ(T1-2N0M0)	8(6.5)	3(8.8)	5(5.6)
Ⅱ(T1-2N1M0)	22(17.7)	5(14.7)	17(18.9)
ⅢA(T3N0M0;T1-3N2M0)	31(25)	7(20.6)	24(26.7)
ⅢB(T1-4N3M0;T4N1M0)	40(32.3)	8(23.5)	32(35.6)
IV(T1-4N1-3M1)	23(18.5)	11(32.4)	12(13.3)
Pathology Type, n (%)				1.725	0.191
Small cell lung cancer	24(19.4)	4(11.8)	20(22.2)
Non small cell lung cancer(Squamous cell carcinoma, adenocarcinoma, large cell carcinoma)	100(80.6)	30(88.2)	70(77.8)
The status of the tumor node metastasis, n (%)				1.972	0.163
Yes	113(91.1)	29(85.3)	84(93.3)
No	11(8.9)	5(14.7)	6(6.7)
Comorbidity, n (%)				1.051	0.307
Yes	120(96.8)	32(94.1)	88(97.8)
No	4(3.2)	2(5.9)	2(2.2)
Smoking, n (%)				0.365	0.547
Yes	106(85.5)	28(82.4)	78(86.7)
No	18(14.5)	6(17.6)	12(13.3)
The number of radiotherapy, n (%)
20–24 times (low dose)	4(3.2)	2(5.9)	2(2.2)	1.643	0.202
25–28 times (moderate dose)	84(67.7)	18(52.9)	66(73.3)
29–33 times (high dose)	36(29)	14(41.2)	22(24.4)
APGAR,mean (SD)	9.46 ± 2.47	8.21 ± 2.19	9.93 ± 2.42	13.152	< 0.01

Class 1: “slow-decreasing HHI”; Class 2: “rapid-decreasing HHI”;

Notes: SD: Standard Deviation

N (%). Based on χ2 test of association

Mean (SD). Based on analysis of variance test of association

Bold prompt P < 0.1

Herth Hope Index trajectories

The HHI scores at the four time points (T1–T4) were 38.85 ± 5.19, 35.02 ± 6.10, 30.10 ± 6.86, and 29.21 ± 6.13, respectively, and after repeated-measures ANOVA, the F value was 208.58 and P < 0.01.

The level of hope of the respondents was used as the explicit index to analyze the hope level trajectory category of middle-aged and elderly patients with lung cancer. When two categories were retained, the AIC, BIC, and aBIC were minimal, with the largest information entropy. BLRT was significant, suggesting that these two categories were superior to the other potential categories. These two categories accounted for more than 25% of cases, and the sample was representative, so the model of these two potential categories was selected. Model fit information for the HHI trajectory is presented in Table 2.

Figure 2 shows the details of the two distinct HHI trajectories. Based on the above model, trajectory maps were drawn for two potential categories with the desired level as the vertical axis and time as the horizontal axis. There were 34 patients (27.4%) in Class 1 (slow decline group), with a low expected score at baseline and a slow decline throughout the entire radiotherapy period. Class 2 (rapid decline group) included 90 patients (72.6%), with a high expected score at baseline and a rapid decline throughout the entire radiotherapy period.

Table 2

The fitting results of the potential category analysis model for the HHI trajectory of middle-aged and elderly lung cancer patients
Class	AIC	BIC	SCBIC	H	BLRT	CP
1	2941.884	2967.266	2938.808	-	-	1.000
2	2932.279	2966.122	2928.178	0.792	<0.001	0.274/0.726
3	2932.860	2975.165	2927.734	0.745	0.667	0.202/0.669/ 0.129
4	2932.537	2983.302	2926.385	0.730	0.250	0.153/0.202/0.113/0.532
5	2933.386	2992.612	2926.209	0.776	0.667	0.452/0.121/0.089/0.185/0.153
Notes: AIC༚Akaike Information Criterion
BIC: Bayesian Information Criterion
SCBIC: Sample-Corrected Bayesian Information Criterion
H: Information Entropy
BLRT: Bootstrap Likelihood Ratio Test
CP: Class Proportion

Predictors of the Herth Hope Index trajectories

We chose Class 1 as the reference group for the multivariate logistic regression analysis. To avoid omissions, we included factors with P values of < 0.1 in the univariate analysis (Table 1), including age, level of education, marital status, and APGAR score, as the independent variables in the multivariate logistic regression analysis. The results showed that age, level of education, marital status, and APGAR score were predictors of the HHI trajectory in middle-aged and elderly patients with lung cancer (all P < 0.05). Additional details are provided in Table 3.

Table 3

Logistic regression analysis for predictors of HHI trajectories in middle-aged and elderly lung cancer patients
Variables	B	S.E	Beta	P	OR	95%CI
Constant	1.792	1.617	1.228	0.268	-	-
APGAR	0.343	0.114	9.069	0.003	1.409	1.127ཞ1.762
Educational level	-1.131	0.326	12.001	<0.001	0.323	0.170ཞ0.612
Marital status	-2.415	1.212	3.971	0.046	0.089	0.008ཞ0.961
Age	1.322	0.526	6.322	0.012	3.753	1.339ཞ10.520
Class 1 served as the reference group

The present study is the first to use LCGM to describe the HHI trajectory during radiotherapy in middle-aged and elderly patients with lung cancer. We identified two different HHI trajectory categories. In addition, we found that age, level of education, marital status, and APGAR score were predictors of the HHI trajectory.

At present, there is a lack of consensus on the development trajectory of the HHI for middle-aged and elderly patients with lung cancer during radiotherapy, particularly regarding the timing of specific points of change in the HHI. However, the present study of patients with lung cancer provides valuable insights. The results of this study show that patients had the highest level of hope at T1, with a score of 38.85 ± 5.19 points. This observation is consistent with the study of Zhao et al.[21] Over time, the level of hope of patients with lung cancer during radiotherapy showed a decreasing trend, but the level of hope at T4 remained moderate. This indicates that in the face of their diagnosis, patients with lung cancer are not passive victims; rather, they are actively trying to identify ways to improve their lives. In addition, among the three dimensions of hope, the positive attitude dimension consistently scored the highest, with T1–T4 values of 13.62 ± 2.29, 12.93 ± 2.37, 10.48 ± 2.96, and 10.47 ± 2.81 points, respectively (all P < 0.001), indicating that patients with lung cancer have a positive attitude toward the disease. The positive relationship dimension showed the largest decrease, with a value of 3.629 (P < 0.001), and significant differences were observed in the pairwise comparisons between T1 and T3. However, the degree of decline in the later stage (2.121) was significantly greater than in the earlier stage (1.508) (P < 0.001). This may be because the population of this study was middle-aged and elderly patients, with many children not being around during their radiotherapy and caregivers mainly being caregivers. The study shows that the trust and cooperation between caregivers and patients in China needs to be improved, as described previously[22]. The traditional concept of family companionship in middle-aged and elderly patients is of great significance. In addition, various economic expenditures are prone to rejection toward caregivers, and communication between patients and their family members is reduced. With the progress of treatment, especially in the later stages, various factors increase the probability of subtle disharmony between patients and other people. Nursing staff can correctly evaluate the relationship between middle-aged and elderly patients with lung cancer undergoing radiotherapy and other people, especially the patients’ families, and targeted measures can be taken to improve the intimate relationship between patients and other individuals.

This study used a mixed growth model to identify heterogeneity among middle-aged and elderly patients with lung cancer in terms of their levels of hope, and the desired horizontal trajectory was divided into two potential categories. Among them, patients in Class 1 (72.6%) had a higher level of hope before radiotherapy than patients in Class 2 (27.4%). As radiotherapy progresses, it is hoped that patients with higher levels will decline faster than those with lower levels. These findings are consistent with the previous literature[23]. As defined by Snyder, “hope” as a positive motivational status is a type of individual thought, behavioral tendency, and cognitive characteristic[24]. This indicates that most patients have a positive attitude toward radiotherapy, which is a new type of treatment. However, with the extension of radiotherapy time, somatic symptoms related to radiotherapy begin to appear[25], resulting in anxiety about disease progression, which may result in a significant decrease in the level of hope[26]. This study divided the level of hope into two categories. As existing dynamic research on the level of hope is in the exploratory stage, few similar conclusions have been proposed. Considering the importance of the level of hope of patients with cancer, the conclusions of this study have certain guiding significance for clinical nursing staff. The author hopes that nursing staff will exercise critical thinking in their work to further confirm and improve the research results in the future.

In terms of predictors, according to our fndings, patients with lung cancer with a higher level of education and a marital status of unmarried, divorced, or widowed had a Class 1 level of hope, while younger patients and those with more family care had higher levels of hope before radiotherapy, but these individuals demonstrated a faster decline in the level of hope with radiotherapy. In terms of age, the results of our study were contrary to those of previous studies[27–28]. We found that the level of hope decreased with aging in patients with lung cancer. Only patients undergoing radiotherapy for the first time were included in the present study, and this new treatment method brought hope to the patients. In the current aging population, patients aged 45–60 years are still the main labor force within families, and they long to continue to take on family responsibilities after the disease improves in the early stage of radiotherapy, with a high level of hope. However, as radiotherapy progresses, these patients are affected by various factors, such as the environment, and they begin to adopt the role of a patient, facing the disease objectively and calmly. The influence of age differences in the hope level is reduced.

Patients who were married were more likely to enter the slow decline group. Modern cancer treatment has shifted toward a family-centered model[29]. A good family support system is an important external resource for hospitalized patients to effectively cope with disease[30]. Spouses, who are often the most powerful supporters, can provide sustained emotional support to patients, promote their psychological and physical health, and help alleviate negative emotions. The more care patients receive from their families, the higher their level of hope for the disease. Therefore, clinical medical workers should encourage the spouses of patients with cancer to actively participate in cancer treatment, promote supportive care in the family context, and provide coping resources to the affected individual to maintain their positive attitude.

This study demonstrates that the extent of family care significantly impacts the level of hope of middle-aged and elderly patients with lung cancer undergoing radiotherapy. Specifically, patients with a high level of family care exhibit a slow decline in levels of hope, whereas those with a low level of family care show a rapid decline in their levels of hope.

Patients in Class 1 received continuous care and support from their families. This included not only emotional encouragement, but also practical assistance and positive communication in daily life. Family support alleviates patients’ anxiety and stress to some extent, enhancing their psychological resilience. This allows them to maintain a relatively high level of hope despite the physical discomfort and side effects caused by treatment. This finding is consistent with previous studies emphasizing the positive impact of social support on the psychological health of patients with cancer[31]. In contrast, patients in Class 2 felt lonely and helpless due to insufficient family care. Their psychological stress was not effectively relieved, leading to a swift decline in the level of hope. This suggests that patients with insufficient family care are more susceptible to negative emotions and have lower expectations for treatment outcomes. Therefore, clinical caregivers should pay attention to the family environment of patients with lung cancer undergoing radiotherapy and implement appropriate family interventions[32]. Such interventions may include encouraging family members to actively participate in the patient’s treatment process, providing emotional support and practical help, enhancing interactions and support through family therapy and psychological counseling, and educating family members to actively and comprehensively support the patient.

This study also found that the level of education significantly affected the development trajectory of the level of hope of middle-aged and elderly patients with lung cancer. Patients with a higher level of education exhibited a slow decline in hope, while those with a lower level of education showed a rapid decline.

Patients in Class 1 generally had better information access and comprehension, allowing them to thoroughly understand disease and treatment-related knowledge, reducing their fear of the unknown. Additionally, highly educated patients often possessed good problem-solving abilities and coping strategies, enabling them to face treatment challenges rationally and maintain a relatively stable level of hope. This aligns with the existing literature highlighting the positive impact of education on patients’ health behaviors and psychological states[33]. Conversely, patients in Class 2 had limited access to information due to their lower level of education, making it difficult for them to fully understand the disease and treatment process, leading to fear and anxiety. These patients may have lacked effective coping strategies and felt helpless and desperate when facing treatment discomfort and side effects, resulting in a rapid decline in their levels of hope. Therefore, for patients with lower levels of education, clinical caregivers should emphasize health education by providing easy-to-understand educational materials and explanations to help patients understand the disease and the treatment process. Offering more health education and psychological support could help the patient to better understand the treatment process and expected outcomes, strengthening their coping abilities and confidence.

In this study, we identified two HHI trajectories and investigated their predictive factors in patients with lung cancer. The findings provide compelling evidence to identify time-sensitive periods for the delivery of interventions to vulnerable populations. The influencing factors identified in this study may inform future research directions and guide the development of tailored interventions that aim to improve the HHI score in patients with lung cancer.

Acknowledgement

We would like to thank the support from the Xiangyang No. 1 People’s Hospital, Hubei University of Medicine.We thank all the participants of this research.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ran Wang ,Yumei Zhou , Huili Xu and Miao Xu . The frst draft of the manuscript was written by Wang Ran and Huili Xu and all authors commented on previous versions of the manuscript. All authors read and approved the fnal manuscript.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing Interest

The authors declare no competing interests.

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No competing interests reported.

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Herth Hope Index trajectory and its predictors in middle-aged and elderly patients with lung cancer: A prospective longitudinal observational study

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Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Methods

Study design

Study participants

Ethical considerations

Measures

Demographic and clinical characteristics

The Herth Hope Index

Family Adaptation, Partnership, Growth, Affection, Resolve Scale

Data collection

Statistical analyses

RESULTS

Patient characteristics

Herth Hope Index trajectories

Predictors of the Herth Hope Index trajectories

Discussion

Conclusions

Declarations

References

Additional Declarations

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Version 1