Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study

Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study Xin Wang, Yuqing Yang, Xuehan Song, Weiyan Xu, Ziyi Geng, Hailing Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8820419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective To explore core symptoms and nursing interventions in the frailty-depression network of patients with gastrointestinal cancer Using Network Analysis Models to Inform Precision Intervention and Care. Design: A cross-sectional survey was conducted among patients with gastrointestinal cancer undergoing treatment in the oncology department of a tertiary general hospital in Shandong Province from February to June 2023, with concurrent assessments of frailty and depression symptoms. Methods The Fried Frailty Scale and Patient Health Questionnaire were used to assess frailty and depression status, respectively. A total of 238 patients with gastrointestinal cancer completed the questionnaires. Network analysis of the relationship was conducted using R software, including network relationship analysis, core symptom analysis, and evaluation of network structure accuracy and stability. Results Network analysis revealed the strongest associations between “FP2 (slowed gait) and FP4 (low physical activity),” “PHQ8 (bradykinesia/agitation) and PHQ9 (suicidal ideation)”, and “PHQ8 (bradykinesia/agitation) and PHQ6 (guilt)” which exhibited the strongest correlations. PHQ4 (lack of energy) exhibited the highest predictability and expected impact. Conclusion This study employed symptom network analysis to explore the frailty and depression relationship network among patients with gastrointestinal cancer.“PHQ4 (lack of energy)” was the most central node. Additionally, gender differences should be considered to develop scientifically grounded psychological interventions that improve patients' psychological well-being and mitigate their frailty and depression. Impact: This study employed network analysis to explore the association between frailty and depression in patients with gastrointestinal cancer. This methodology overcomes the limitations of traditional univariate analyses by visualizing and quantifying complex relationships among symptoms. It identifies the most central and influential symptoms and connection pathways within the network, offering a novel perspective and specific targets for precise clinical intervention Gastrointestinal Neoplasms Frailty Syndrome Depression Network Analysis Clinical Nursing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 | Introduction Gastrointestinal tumors represent a significant global health burden with persistently high incidence and mortality rates. Frailty and depression are two prevalent and severely impactful conditions among patients with cancer, particularly those with gastrointestinal malignancies. These conditions interact reciprocally and negatively affect treatment tolerance, quality of life, and overall prognosis. However, existing research often examines frailty and depression in isolation or is limited to specific age cohorts, failing to reveal the complex symptomatic interplay within the gastrointestinal cancer population. This gap hinders the development of precise intervention strategies for these patients. Therefore, this study aimed to employ network analysis models to elucidate the intricate interrelationships between fatigue and depression symptoms in patients with gastrointestinal cancer and conducted an in-depth analysis of gender differences. This approach seeks to identify core intervention targets, thereby providing a foundation for developing more effective care strategies for patients with gastrointestinal cancer. 2 | Background Gastrointestinal tumors are benign and malignant tumors originating in the stomach, colon, rectum, gastrointestinal stromal tissue, small intestine, and other sites, with the stomach and colon/rectum being the most common locations [ 1 ]. In February 2024, the WHO International Agency for Research on Cancer released its latest survey findings [ 2 ], indicating that in 2022, 970, 000 (4.9%) new cases of gastric cancer and 1.9 million (9.6%) new cases of colorectal cancer were reported, with respective death tolls of 660,000 and 990,000 individuals. These cancers are the fifth and second leading causes of cancer-related death worldwide. In 2021, China recorded 1,957,948 new gastrointestinal cancer cases, with an incidence rate of 137.62 per 100,000 population. By 2050, the burden of gastrointestinal cancer in China is projected to surge significantly, with the total number of cases increasing by 49.06% [ 3 ]. Research indicates [ 3 ] that the burden of gastrointestinal tumors in China is growing rapidly and will continue to rise, posing a substantial mortality threat to the population in the future. Frailty is a multidimensional syndrome characterized by multisystem organ dysfunction, reduced physiological reserve, increased vulnerability, and diminished stress resistance [ 4 ]. It primarily manifests as unintentional weight loss, decreased walking speed, reduced grip strength, diminished physical activity levels, and self-reported fatigue [ 5 ]. Studies have indicated a strong association between frailty and cancer, with frailty incidence rates among patients with cancer ranging from 6% to 86% [ 6 ]. Research indicates [ 7 ] that frailty increases all-cause mortality, reduces tolerance to cancer treatments, and may lead to diminished quality of life, repeated hospitalizations, and poor prognosis, adversely affecting outcomes in patients with gastrointestinal cancer [ 1 ]. Depression occurs significantly more frequently in patients with cancer than in the general population, directly impacting treatment adherence, efficacy, and quality of life, making it a critical issue in the comprehensive management of cancer [ 8 ]. Research indicates [ 9 , 10 ] a significant bidirectional causal relationship between frailty and depression in older adults. Their potential comorbidity mechanisms may involve multiple factors, with depression exerting a significant positive influence on frailty in patients with cancer; that is, more severe depression is correlated with more severe frailty. Furthermore, patients with gastrointestinal cancer frequently experience impaired nutrient absorption, chronic inflammation, and treatment-related stress injuries, leading to markedly elevated rates of frailty. The prevalence of postoperative frailty reaches 58.7% among older adult patients, with depression prevalence in the frail group soaring to 62.79%, far exceeding the 16.48% rate observed in healthy individuals [ 11 ]. Given that frailty and depression jointly constitute high-risk factors for poor prognosis in patients with gastrointestinal cancer, there is an urgent need to analyze the mechanisms underlying their mutual influence. Network analysis methods can model the relationships between symptoms as a network, thereby visualizing and quantifying the connections between different symptoms and disorders [ 12 ]. Simultaneously, network analysis aids in identifying the most central and influential symptoms within symptom networks, which may serve as potential intervention targets [ 13 ]. Currently, most studies are limited to examining the univariate relationship between frailty and depression and fail to analyze the internal heterogeneity of symptoms [ 14 ]. Furthermore, existing studies are confined to older patients with gastrointestinal cancer, presenting age-bias limitations [ 15 , 16 ] that prevent accurate measurement of the correlation between frailty and depression symptoms in the entire gastrointestinal cancer population. 2 | The Study 2.1 | Objectives This study aimed to construct a network of depressive and frailty symptoms among patients with gastrointestinal cancer, facilitating a comprehensive understanding of the interrelationships between these symptoms and providing a basis for developing relevant preventive strategies. 2.2 | Research Question (1) What are the core symptoms and intrinsic associations within the frailty and depression network among patients with gastrointestinal cancer? (2) Are there any gender differences? (3) How can targeted nursing interventions be developed? 3 | Methods/Methodology 3.1 | Design A cross-sectional survey was conducted among patients with gastrointestinal cancer undergoing treatment in the oncology department of a tertiary general hospital in Shandong Province from February to June 2023, with concurrent assessments of frailty and depressive symptoms of the patients. 3.2 | Study size The scale used in this study includes 14 symptoms. To ensure the stability of the symptom network, the minimum sample size required, calculated using the formula [N + N×(N-1)/2], is 105 cases. Previous studies have shown that a larger sample size in network analysis leads to a more reliable symptom network. Therefore, a total of 238 cases were finally included in this study. 3.3 | Study Setting and Sampling From February to June 2023, patients with gastrointestinal cancer meeting inclusion and exclusion criteria were selected using convenience sampling at the Oncology Department of a Grade III Class A general hospital in Shandong Province. Ultimately, 238 patients with gastrointestinal tumors were enrolled in this study. 3.4 | Inclusion and Exclusion Criteria The inclusion criteria were as follows: (1) age ≥ 18 years; (2) pathologically diagnosed with gastrointestinal cancer; (3) undergoing initial chemotherapy for gastrointestinal cancer; (4) conscious, without cognitive impairment, and able to communicate normally; and (5) voluntarily participating in the study with informed consent. The exclusion criteria were as follows: (1) patients with tumors other than gastrointestinal tumors; (2) patients unable to cooperate with frailty measurement; (3) patients with severe organ failure; and (4) patients receiving radiotherapy or immune-targeted therapy alone. 3.5 | Instrument with Validity and Reliability The Fried Frailty Phenotype (FP) was proposed by Fried et al. [ 17 ] in 2001 based on the frailty cycle theoretical model. It measures frailty in research subjects through five indicators: ① Weight loss (more than 4.5 kg or 5% of body weight within one year). ②Reduced walking speed (measured using a stopwatch for the time taken to walk 4.6 m at a normal pace on level ground; the criteria were determined based on height and walking time for men and women). ③Reduced grip strength (measured using a dynamometer; criteria determined based on the BMI range and grip strength values for men and women). ④Reduced physical activity (a. Inability to complete light household tasks due to fatigue on three days within the past week. Men: <2.5 hours of leisurely walking per week; women: <2 hours of leisurely walking per week. Participation in predominantly sedentary activities [e.g., watching TV and sitting while chatting] for most activities within the past week. Meeting any of these three criteria indicated reduced physical activity). ⑤Self-reported fatigue (scored based on days experiencing either feeling exhausted doing tasks or being unable to walk). A score of 2–3 points for either indicates fatigue). Each item is answered “Yes” or “No”; ‘Yes’ scores 1 point, “No” scores 0 points. The total score ranged from 0–5. A score of ≥ 3 indicates frailty status, categorizing patients into non-frail and frail groups based on their scores. One or two positive indicators (1–2 points) indicated pre-frailty, while no symptoms (0 points) indicated non-frailty. This scale is widely used in the Chinese cancer population. The Patient Health Questionnaire-9 (PHQ-9) was revised by Kroenke et al. [ 18 ]. This unidimensional scale comprises nine items (loss of interest, low mood, sleep disturbances, lack of energy, poor appetite or overeating, feeling like a failure or having let yourself or your family down, and difficulty concentrating). Each question assesses the frequency of specific depressive symptoms experienced over the past two weeks (“Not at all”= 0, “A few days” = 1, “Most days”= 2, “Almost every day”= 3). The total score ranges from 0 to 27, with higher scores indicating more severe depressive symptoms. Scores of 0–4, 5–9, 10–14, 15–19, and 20–27 indicate no, mild, moderate, moderate-to-severe, and severe depression, respectively. The Cronbach's α coefficient for this scale was 0.758 [ 18 ]. 3.6 | Data collection and Data Analysis 3.6.1 | Data collection A sociodemographic and clinical questionnaire was used to collect baseline information on the research participants. The sociodemographic data included age, gender, ethnicity, educational attainment, employment status, marital status, living arrangements, whether living alone, per capita monthly household income, method of medical expense coverage, and primary caregiver. Disease-related data included the number of medications taken, number of comorbid conditions, smoking history, drinking history, height, weight, BMI, tumor location, metastasis status, tumor stage (TNM staging), differentiation grade and surgical approach. 3.6.2 | Data Analysis Data analysis was performed using the SPSS software (version 25.0). Two-tailed tests were conducted with α = 0.05, and P < 0.05 was considered statistically significant. Network analysis was completed using R software (version 4.3.1). During data preprocessing, outliers were identified using the corrected Z-score method (|Z|>3.29). After inspection, the outliers constituted less than 1% of the data points. For detected outliers, multivariate imputation by chained equations (MICE) was applied [ 19 ] to minimize their impact on subsequent analyses. A vulnerability-depression relationship network model was constructed using scores from 14 variables across two scales: the PHQ-9 and the FP. In this model, each variable is represented as a node in the network, with edges between nodes signifying the interactions between the variables. Edge weights are defined as partial correlation coefficients that reflect the unique association between variables after controlling for the influence of other variables. Given that the variables included in this study encompass both dichotomous (FP scale) and ordinal (PHQ-9 scale, four-level scoring) measurement types, we employed mixed graphical models to conduct network analysis of the data [ 19 ], thereby more accurately modeling conditional dependencies within the mixed data. Model selection combined the extended Bayesian information criterion (EBIC) (gamma = 0.5) [ 20 ] with the least absolute shrinkage and selection operator (LASSO) to obtain a more robust, sparse, partial correlation network [ 21 ]. The following core metrics were computed for each node in the network: (1) Strength, defined as the sum of the absolute values of a node's edge weights, which characterizes its relative importance within the network. This metric is presented as a standardized z-score, where positive values indicate above-average importance and negative values indicate below-average importance; (2) proximity, reflecting the average shortest distance from a node to all other nodes in the network, used to assess information propagation efficiency; (3) intermediary degree, which counts the number of shortest paths passing through a node, reveals its critical role as a “bridge” in the network; and (4) expected influence, quantifying the breadth and depth of other nodes a node can influence through its network connections, reflects the node's global importance or propagation potential within the network [ 22 ]. To ensure the reliability of the centrality metrics, this study employed the bootstrap method to calculate the Centrality Stability Coefficient (CS). The CS coefficient must satisfy ≥ 0.25 as the minimum acceptable threshold and ≥ 0.5 to achieve ideal explanatory validity [ 23 ]. Furthermore, the bridging strength of all nodes was calculated using the R package NetworkTools. Bridging strength refers to the sum of the absolute values of the edge weights connecting a node to all other nodes in the network. This metric aids in identifying nodes that play crucial roles in connecting different communities. To examine gender differences in the frailty-depression network, we conducted a network comparison using a permutation test. Overall network strength was compared using the Network Comparison Test R software (version 4.3.1), employing a permutation test to compare the overall strength of the networks in the male and female subsamples. This test randomly permutes the gender labels of the sample to construct a null distribution of overall strength differences under the null hypothesis (no gender difference). It then calculates the extreme position of the observed difference within this distribution to yield an exact p-value (p < 0.001). Comparisons of specific edge weights were similarly conducted using the aforementioned software, performing cross-gender edge-by-edge permutation tests for each edge weight in the network. To control the risk of false positives from multiple comparisons, the raw p-values obtained from all edge comparisons underwent Bonferroni-Holm correction. All analyses were performed under consistent network sparsity parameters (gamma = 0.5) to ensure observed differences did not stem from instability inherent to the network estimation itself. 3.7 | Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of the School of Nursing and Rehabilitation, Shandong University (Approval No. 2022-R-067). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. The research participants' information was kept strictly confidential, with no disclosure of personal details or privacy. 4 | Results 4.1 | General Characteristics of Patients with Gastrointestinal Cancer and Incidence of Depression and Fatigue This study enrolled 238 patients with gastrointestinal tumors at baseline, ranging in age from 34 to 85 years, with a mean age of (60.69 ± 10.56) years. Among them, 91 (38.24%) were aged < 60 years, and 147 (61.76%) were aged ≥ 60 years. The male-to-female ratio was 158 males (66.39%) and 80 females (33.61%). Less than 1% of patients belonged to ethnic minorities. Over half had an educational attainment of junior high school or below. The other demographic characteristics are presented in Table 1 . The mean FP and PHQ-9 total scores for the study participants were 1.92 (SD = 1.31) and 5.10 (SD = 3.92), respectively. The node names, means, standard deviations, and node strengths for specific item scores are presented in Table 2 . Table 1 Demographic characteristics of patients with gastrointestinal tumors(N = 238) Variable Group Frequency Percentage Age <60 91 38.24% ≥ 60 147 61.76% Sex Male 158 66.39% Female 80 33.61% Ethnicity Han 236 99.16% Hui 2 0.84% Education Level Junior high school and below 142 59.69% High school or junior college 58 24.37% Junior college/bachelor's degree and above 38 15.97% Employment Status Employed 38 15.97% Retired 75 31.51% Unemployed 21 8.82% Other 104 43.70% Marital Status Married 231 97.06% Divorced/Widowed 7 2.94% Living Arrangements Rural 96 40.34% Township 19 7.98% County seat 80 33.61% Urban 43 18.07% Living Alone No 235 98.74% Yes 3 1.26% Average Monthly Household Income <2000 92 38.66% 2000–3999 67 28.15% 4000–5999 41 17.23% 6000–7999 19 7.98% ≥ 8000 19 7.98% Method of Covering Medical Expenses Urban and Rural Residents' Medical Insurance 140 58.82% Employee Medical Insurance 87 36.55% Other 11 4.62% Primary Caregiver Spouse 128 53.78% Children 90 37.82% Other 20 8.40% Table 2 Descriptive statistics of measurement items Items Items context Items M(SD) Item Strength FP1 FP2 FP3 FP4 FP5 PHQ1 PHQ2 PHQ3 PHQ4 PHQ5 PHQ6 PHQ7 PHQ8 PHQ9 Weight loss Slowed gait Low grip strength Low physical activity Fatigue Loss of pleasure Low mood Sleep problems Lack of energy Changes in appetite Guilt Difficulty concentrating Bradykinesia/Agitation Suicidal ideation 0.68(0.47) 0.30(0.46) 0.41(0.49) 0.33(0.47) 0.21(0.41) 0.88(0.90) 0.64(0.71) 0.84(0.97) 0.97(0.86) 0.92(0.99) 0.19(0.46) 0.29(0.67) 0.31(0.57) 0.05(0.26) 0.00 0.28 0.12 0.54 0.60 0.79 0.77 0.21 1.10 0.45 0.60 0.48 0.90 0.56 FP: Fatigue Phenotype; PHQ: Patient Health Questionnaire The node colors correspond to different symptom categories, with green representing frailty symptoms and pink representing depressive symptoms. The thickness of the edges was positively correlated with the edge weight values. Thicker edges indicate higher weights, indicating a stronger association between the two symptoms. 4.2 | Network Structure and Centrality Metrics Analysis This study constructed a network structure comprising 14 nodes representing frailty and depression (Fig. 1 ). Network analysis revealed the following edges with higher weights among connections between different symptoms: - The strongest link within frailty symptoms was between FP2 (slowed gait) and FP4 (low physical activity) (edge weight = 0.85); - The strongest link within depression symptoms was between PHQ4 (lack of energy) and PHQ5 (abnormal appetite) (edge weight = 0.70); followed by PHQ8 (bradykinesia/agitation) with PHQ9 (suicidal ideation) and PHQ6 (guilt) (edge weights = 0.78 and 0.72, respectively); the strongest edge between fatigue and depression was FP5 (fatigue) with PHQ4 (lack of energy) (edge weight = 0.65). PHQ4 Lack of energy) exhibited the highest predictability (standardized value = 1.01). Although FP1 (weight loss) had a negative z-value for bridging strength, its node strength was zero, indicating that this symptom neither played a significant bridging role nor formed stable connections with other nodes in the network.(Figure 4 ) The results of the centrality analysis are shown in Fig. 2 . The PHQ4 (lack of energy) and PHQ8 (bradykinesia/agitation) exhibited the strongest node strengths. Simultaneously, these two nodes (PHQ4 and PHQ8) demonstrated a greater expected influence than the other nodes across the entire network. Within the separate symptom networks for fatigue and depression, the nodes with the highest node strength were fatigue condition FP5 (fatigue) and FP4 (low physical activity) for the fatigue network, and PHQ4 (lack of energy), PHQ8 (bradykinesia/akathisia), PHQ1 (anhedonia), and PHQ2 (depressed mood) for the depression network. Regarding network proximity and betweenness centrality, depression symptoms PHQ4 (lack of energy) and PHQ8 (bradykinesia/akathisia) exhibited higher proximity and stronger betweenness centrality across all nodes. 4.3 | Analysis of Gender Differences In the comparative analysis of frailty-depression relationship network models among patients with gastrointestinal cancer, male participants exhibited significantly lower overall network strength than female participants (male participants: 0.55 vs. female participants: 2.32, p < 0.001), with extensive differences in edge weights (M = 0.088, p < 0.001) (Fig. 3 ). To understand this finding, we examined the statistically significant differences at the edge level between male and female participants after applying the Bonferroni-Holm correction. The results revealed 21 statistically significant edges (corrected p < 0.05), with 19 significantly stronger in women and two significantly stronger in men. Specifically, stronger connections in female patients included FP2-FP4 (low slow gait-physical activity, p < 0.001), FP4-FP5 (low physical activity-fatigue, p < 0.001), and PHQ4-PHQ5 (lack of energy-appetite changes, p < 0.01). Stronger connections were observed in male patients between PHQ3-PHQ8 (sleep problems-bradykinesia/agitation, p < 0.001) and PHQ8-PHQ9 (bradykinesia/agitation-suicidal ideation, p < 0.001). 4.4 | Stability Test Results for Network Centrality Measures The center stability coefficient indicates that the CS coefficients for node strength, node proximity, node expected influence, and node bridging strength were all ≥ 0.70, while the CS coefficient for node betweenness was 0.68. All values were significantly greater than 0.50, demonstrating that the network model exhibited good robustness (Fig. 5 ). 5 | Discussion The prevalence of frailty among patients with gastrointestinal cancer in this study was 34.03%, consistent with domestic reports ranging from 21% to 39% [24–26]. Research has confirmed [27] that frailty in patients with gastrointestinal cancer triggers a series of health issues, ultimately leading to long-term adverse outcomes such as disability, cognitive decline, and even death. Therefore, future studies should further explore and clarify the frailty status of patients with gastrointestinal cancer to provide data to support proactive frailty screening and rehabilitation interventions in this population. The detection rate of depressive symptoms was 23.11%, slightly higher than the 16%-22% range reported in recent surveys [28–30]. Research has shown [29] that the prevalence of depression among patients with colorectal cancer (32%) is significantly higher than that among patients with other types of cancer. This may be partly attributed to the high proportion of patients with colon cancer in this study (40.76% of tumors were located in the intestine). Additionally, the lack of standardized depression assessment tools currently used in China may partially explain the variations in the reported depression rates. Furthermore, by examining the network structure of the frailty-depression relationship in patients with gastrointestinal cancer, this study revealed that frailty status is not only closely associated with depressive symptoms but also that the variables within depressive symptoms themselves exhibit strong interconnections, highlighting the complex relationships among internalizing symptoms. These findings provide further evidence for the improvement of frailty and depression in patients with gastrointestinal cancer. They also underscore the importance of considering changes in related symptoms alongside individual symptoms, suggesting that this may be a key target for healthcare providers and families in their attention and intervention efforts. 5.1 | Nursing staff should enhance management of core symptoms in the frailty-depression relationship among patients with gastrointestinal cancer Research indicates that PHQ4 (lack of energy) is not only the most central symptom within the frailty-depression network but also exhibits the highest proximity and anticipated influence. Patients experiencing fatigue commonly report feeling tired, lacking energy, and finding even minor tasks exhausting. This suggests that fatigue is a central and highly influential symptom within the frailty-depression psychopathological network, consistent with previous studies [31,32]. This finding suggests that fatigue is a key symptom of the fatigue-depression syndrome in patients with gastrointestinal cancer. This is because patients with cancer typically exhibit poor baseline health due to tumor cell invasion, chemotherapy/radiation therapy, and surgical stress [1]. Patients with gastrointestinal cancer also experience reduced digestive organ function, leading to decreased food intake and malnutrition. This triggers an insufficient bodily energy supply, resulting in fatigue, accelerated muscle breakdown, and symptoms such as exhaustion and weight loss, which become a significant stressor on patients' physiological reserves. Second, the sensation of insufficient energy prevents patients from completing daily activities, triggering a “subjective feeling of fatigue” that induces self-doubt and a low mood, thereby exacerbating depression [33]. Simultaneously, this “subjective fatigue” diminishes patients' motivation to engage in activities, leading them to voluntarily reduce their activity levels and further increasing the risk of frailty. However, relevant studies have found [34,35] that most oncology healthcare providers lack training in assessing fatigue and energy depletion, demonstrating significant gaps in their recognition and management skills of these symptoms. This highlights the need for nursing staff to strengthen their commitment to patient-centered care principles and practices. Consequently, systematic training should be enhanced for oncology teams to evaluate patients' depressive symptoms, improve their assessment skills, prioritize individualized patient needs and experiences, and implement timely, targeted interventions. PHQ8 (bradykinesia/agitation) was also a core symptom in the relationship network between fatigue and depression, with its mediating effect and expected influence second only to PHQ4 (lack of energy). This differs from the findings of other studies [36,37], which agree that PHQ4 (lack of energy) is one of the most important core symptoms, followed by PHQ2 (depressed mood), but disagree on the significance of PHQ8 (bradykinesia/agitation). This discrepancy may arise because other studies examined isolated depressive symptom clusters, where core symptoms tended to cluster around the core affective dimension (PHQ2 depressed mood) and foundational somatic dimension (PHQ4 Lack of energy). As a secondary somatic symptom of depression, PHQ8 (bradykinesia/agitation) was overshadowed by the centrality. This study, however, examines the “frailty-depression network.” Core frailty features like “muscle loss and reduced activity capacity” have direct pathological links to PHQ8's “bradykinesia.” This makes the PHQ8 a pivotal bridge connecting “frailty-related physical symptoms” with “depressive mood/physical symptoms,” significantly enhancing its centrality. Second, patients with gastrointestinal cancer in this study exhibited a higher proportion of elderly individuals (43.70% aged ≥65 years). This demographic inherently experiences physiological motor decline, which, when compounded by tumor effects and treatment impacts, significantly elevates the incidence and severity of PHQ8 symptoms. Therefore, timely assessment of nutritional status is essential for patients with gastrointestinal cancer. Dietary structures should be adjusted based on nutritional needs, and nutritional supplementation should be provided. It is also crucial to provide exercise guidance tailored to the patient's physical conditions. This includes a series of aerobic exercises, such as balance training, strength training, and stretching exercises (e.g., walking in circles and stair climbing) [1], as well as resistance training at appropriate frequencies and intensities. Examples include seated leg raises and wall-push-ups. Such interventions enhance physical function, alleviate fatigue, and ultimately improve patient prognosis and recovery outcomes. FP1 (weight loss) exhibits the lowest centrality strength across the entire network. As FP1 is an objective quantitative indicator, over 80% of the nodes in the network represent subjective perception metrics. According to symptom network research patterns [36-38], subjective symptoms form strong associations more readily due to the “sensation superposition effect.” In contrast, the linkage between objective indicators and subjective symptoms requires multiple transformations from objective to subjective perception. This process exhibits low conversion efficiency and significant individual variation, resulting in FP1 having a much lower connection frequency with other nodes than subjective symptoms. Second, weight loss typically occurs early in the disease course or before treatment and generally progresses gradually. In contrast, other frailty and depressive symptoms often emerge after treatment (e.g., following chemotherapy) or during disease progression, potentially fluctuating with treatment cycles [39]. Due to prolonged exposure to adverse effects from chemotherapy, radiotherapy, or surgery causing muscle wasting, and psychological stress exacerbating emotional disturbances, the central intensity of FP1 (weight loss) may appear less pronounced than expected. However, it is noteworthy that weight loss triggers a cascade of reactions that significantly increase the mortality risk and hinder cancer patient recovery [40,41]. Thus, early clinical monitoring and weight management are critically important. Regularly monitor patients' nutritional intake and use smart body fat scales to track weight changes. For patients undergoing long-term treatment, nutritional status and physical parameters (e.g., muscle mass and body fat percentage) should be dynamically monitored over time, and personalized interventions should be implemented based on these dimensional changes. 5.2 | Nursing staff should closely monitor the strong association and highly predictive symptoms within the frailty-depression relationship in patients with gastrointestinal tumors. The symptom network analysis results of this study revealed the strongest connection between FP2 (slowed gait) and FP4 (low physical activity), consistent with previous studies [42,43]. This may reflect the shared etiology or mutual influence of the two conditions. Step speed in patients with cancer shows a significant positive correlation with objective physical activity [44], indicating that a slower step speed correlates with lower activity levels. Given that most patients with gastrointestinal cancer are elderly, factors such as age and cancer-related fatigue [45] contribute to slower step speed, further increasing the fall risk. Patients may develop a fear of falling and experience a sense of disability due to this condition. Consequently, they actively reduce their daily activities [46]. The resulting lack of skeletal muscle exercise leads to weakened muscle strength, which further impairs the walking speed. This creates a bidirectional cycle between the two variables, forming a mutually reinforcing feedback loop that perpetuates the cycle. As walking speed gradually decreases, patients exhibit stronger resistance to activity, continuously strengthening the feedback loop and deepening the connection between the two conditions. PHQ8 (bradykinesia/agitation) and PHQ9 (suicidal ideation), as well as PHQ8 (bradykinesia/agitation) and PHQ6 (guilt), exhibit strong correlations. When patients experience bradykinesia and restricted mobility, they develop a profound sense of “incapacitation.” This indicates an inability to adequately care for themselves, leading to a significant decline in their quality of life and difficulty fulfilling Maslow's basic physiological and safety needs [47]. When fundamental needs remain unmet, anxiety and pessimism inevitably arise. Patients attribute their “inability to care for themselves” to “being a burden on their family,” resulting in intense feelings of “guilt.” Research indicates [48] that suicidal ideation correlates with a low quality of life, with older adult cancer patients being particularly vulnerable. Additionally, patients with gastrointestinal tumors undergo prolonged treatment cycles [49]. Due to extended therapy, patients may experience physical dysfunction that limits social engagement, leading to severe social isolation and loneliness [47]. These factors leave patients' needs for emotional connection, respect, and belonging unmet, fueling their guilt and suicidal thoughts. Consequently, these two symptom clusters exhibited strong connectivity within the network. The results of this study indicate that the PHQ4 (Lack of energy) is the node with the highest predictive power for the frailty-depression network. This may be attributed to the node possessing the highest node strength and strongest bridging function within the network. Second, studies [50–53] have found that chronic fatigue, through sustained activation of the hypothalamic-pituitary-adrenal (HPA) axis, triggers abnormal cortisol secretion. This, in turn, drives a vicious cycle of “fatigue-stress-depression,” leading to a series of chain reactions. Therefore, the PHQ4 (lack of energy) exhibited the highest predictive value within the network, suggesting that the degree of improvement in fatigue symptoms can serve as a sensitive indicator for evaluating treatment efficacy for frailty and depression. This approach offers a more comprehensive assessment than monitoring mood or physical fitness changes alone. Nursing staff can monitor changes in fatigue to identify individuals at risk of frailty and depression early. For patients at high risk of fatigue, personalized interventions can be implemented (e.g., exercise rehabilitation therapy, psychological care such as cognitive behavioral therapy, and health education). Second, preventively identifying strongly correlated symptoms and using them as entry points for early psychological interventions combined with pharmacotherapy can weaken symptom interactions, sever strong symptom linkages, enhance intervention efficiency and effectiveness, and control the progression of frailty and depression conditions [54]. 5.3 | Healthcare providers should pay attention to gender differences in the frailty-depression network among patients with gastrointestinal tumors This study found that after standardizing for network density and node count,the global network strength in females was 4.2 times higher than that in males, with significantly shorter characteristic path lengths. Research hypothesized [55,56] that estrogen fluctuations enhance the efficiency of inflammatory signal transmission to the central nervous system, making peripheral fatigue signals more likely to trigger the activation of central emotional nodes. Furthermore, women exhibit significantly higher depressive symptoms than men, which may explain why female symptom networks are stronger than male ones. These findings indicate that symptom comorbidity mechanisms exhibit sex dependency, providing primary targets for subsequent precision interventions. Concurrently, this disparity involves distinct gender-heterogeneous connectivity patterns: females form highly clustered subnetworks centered on “fatigue-low mood,” a clustering characteristic potentially linked to heightened female sensitivity to somatic symptoms. Furthermore, estrogen exhibits synergistic effects on cognitive and emotional functions, particularly under psychosocial stress, potentially underpinning the association between ovarian hormone fluctuations and depression in women. In contrast, males exhibit a linear risk trajectory of “sleep disturbance-bradykinesia/agitation-suicidal ideation.” This aligns with prior research indicating a significantly higher suicide risk in males than in females [55], [56]. Studies [57, 58] have indicated that anxiety, depression, and insomnia are positively correlated in patients with cancer. Male suicidal ideation is not solely driven by a depressive mood but is more closely associated with material factors and externalizing impulses. Externalizing impulses, such as sleep deprivation and motor abnormalities, serve as critical drivers of suicidal urges, thereby forming the “sleep-motor retardation/agitation-suicide” linear network structure. Therefore, for female patients with gastrointestinal tumors, the focus should be on emotional counseling and physical training only. Future research should pay greater attention to whether female patients lack positive emotional experiences. During health education and nursing interventions, patients should be guided to make positive evaluations of situations, and timely psychological counseling should be provided to encourage patients to express and release their emotions. Targeting the “fatigue-low mood” pathway, combining aerobic exercise to reduce fatigue symptoms with positive mental health education is recommended. Psychological interventions should prioritize enhancing self-worth, providing precise rehabilitation guidance, improving patients' cognitive processes, and preventing emotional dysregulation. For male patients, sleep interventions and suicide risk screening should be emphasized. Precise strategies should be developed to prioritize sleep interventions, closely monitor patients' sleep patterns, conduct regular sleep quality assessments and suicide risk evaluations, and strengthen communication with patients. For patients with sleep disorders, psychological interventions such as cognitive behavioral therapy and relaxation training [59], as well as acupoint application combined with progressive muscle relaxation training [60], can be employed to improve sleep quality and broadly reduce the overall severity of the frailty-depression symptom network. 5.4 | Strength and Limitations of the Work This study overcomes the limitations of traditional single-variable research by employing network analysis to comprehensively dissect the association between frailty and depression in patients with gastrointestinal cancer. This study systematically constructs the first symptom network model linking these two conditions, clearly identifying core symptoms and strongly connected pathways, thereby providing a novel perspective for elucidating the mechanisms of comorbidity. Furthermore, it identifies core nodes within the fatigue and depression networks and highlights gender-specific differences. This provides evidence-based support for developing personalized precision nursing intervention strategies, with significant implications for clinical translation. This study has certain limitations. First, as a cross-sectional study, the constructed network of frailty-depression states in patients with gastrointestinal cancer cannot reveal changes in symptoms over time or establish causal relationships between symptoms. Second, the single-center sample may introduce selection bias, necessitating future multi-center studies to validate the findings. The gender analysis of symptom networks included only 80 female patients, potentially limiting the reliability and representativeness of the gender-specific results. Future studies should increase the representation of female patients. Third, symptom assessment relied on patient self-reports without objective physiological validation (e.g., cortisol levels, muscle strength testing), potentially introducing a reporting bias. Finally, neither age nor self-reported socioeconomic status was included as a covariate in the network analysis calculations to control for confounding effects on the frailty-depression association. 5.5 | Recommendations for Further Research To address the aforementioned shortcomings, future research should adopt longitudinal designs to track symptom changes, conduct multi-center recruitment to enhance generalizability, include more female participants to ensure credible gender comparisons, combine self-reports with objective measures (e.g., cortisol, grip strength), and control for covariates such as age and socioeconomic status in network models. 5.6 | Implications for policy and practice Healthcare policies should prioritize systematic screening for fatigue and depression in patients with gastrointestinal cancer. Nursing practice should adopt targeted, gender-sensitive interventions focusing on core symptoms, such as fatigue, integrating nutritional support, exercise, and psychological care to improve patient outcomes. 6 | Conclusion Using symptom network analysis, this study identified the network-associated characteristics and core symptoms of frailty and depression in patients with gastrointestinal cancer. Future research should prioritize highly interconnected nodes and core symptoms within the symptom network, block the transmission pathways of core symptoms, and implement interventions that effectively reduce positive affect limitation and alleviate fatigue. Such approaches may yield beneficial effects that help limit other symptoms of the disease. Healthcare providers should pay greater attention to these symptoms and consider them as intervention targets. Developing scientifically grounded psychological interventions could promote improvements in both mental health status and frailty levels among patients with gastrointestinal cancer. Declarations Funding: This work was supported by the Research on Strategies to Improve Nutritional Literacy Among patients with gastrointestinal cancer from an Active Aging Perspective, Shandong Provincial Federation of Social Sciences 2023 Collaborative Project in Humanities and Social Sciences, (No. 2023-JKZX-19) Author Contribution Author ContributionsAll authors have made substantial intellectual contributions to the manuscript and meet the ICMJE criteria for authorship. Specific contributions are as follows:● Xin Wang: Conceptualization, Methodology, Formal analysis, Investigation, Writing–Original Draft, Writing–Review & Editing, Project administration.（ [email protected] ）● Yuqing Yang: Methodology, Software, Review of Relevant Literature , Writing–Review & Editing.（ [email protected] ）● Xuehan Song: Investigation, Resources, Writing–Original Draft (Introduction and Background).（ [email protected] ）● Ziyi Geng: Investigation, Resources, Writing–Original Draft (Introduction and Background).（ [email protected] ）● Weiyan Xu: Investigation, Resources, Data Curation.（ [email protected] ）● Hailing Yang: Supervision, Validation, Writing–Review & Editing, Funding acquisition.（ [email protected] ）● Yuanyuan Chen: Supervision, Conceptualization, Methodology, Validation, Writing–Review & Editing, Funding acquisition.（ [email protected] ）All authors have approved the final article and confirmed that all eligible authors are listed as authors. References Xu WY. 2024. Longitudinal Study on Pre-Chemotherapy Frailty Status and Its Impact on Long-Term Chemotherapy Toxicity in Patients with Gastrointestinal Cancer. Shandong University. 10.27272/d.cnki.gshdu.2024.001852 Global cancer burden. growing, amidst mounting need for services[J]. Saudi Med J. 2024;45(3):326–7. LU Y, ZHANG J, ZHOU Z, et al. Age-Period-Cohort Analysis on the Burden of Gastrointestinal Cancers in China: Trends, Risk Factors, and Predictions[J/OL]. Healthc (Basel Switzerland). 2025;13(10):1096. 10.3390/healthcare13101096 . Li KY, He X, Zhong LY et al. 2025. Analysis of Frailty Status and Influencing Factors in Elderly Patients with Brain Tumors. Journal of Nurses Training: 1–9. http://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/52.1063.R.20251121.1905.005 BUTA B J, WALSTON J D, GODINO J G, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments[J/OL]. Ageing Res Rev. 2016;26:53–61. 10.1016/j.arr.2015.12.003 . Guo Y, Yi MX, Man JX, et al. A Meta-Analysis of Influencing Factors of Frailty in Cancer Patients. Chin Gen Pract. 2023;26(8):989–96. Fu J. Relationship Between Frailty and Nutritional Risk Status and Analysis of Influencing Factors in Elderly Patients with Lung Cancer Undergoing Chemotherapy. Health Vocat Educ. 2025;43(18):128–31. 10.20037/j.issn.1671-1246.2025.18.34 . YE L, LADBURY C, TAM A, et al. Prevalence of major depression, suicidal ideation, and mental health treatment among cancer survivors[J/OL]. Palliat Support Care. 2024;1–8. 10.1017/S1478951524000944 . Zhang HP, Lin ZH, Min ZX, et al. Current Status and Influencing Factors of Frailty in Patients with Digestive System Tumors. J Nurs Sci. 2022;37(8):11–5. Wang MQ, Lei S, Fei XY, et al. Correlation Between Frailty and Depressive Disorders in the Elderly and Research Progress on Exercise Interventions. Chin J Nerv Mental Dis. 2025;51(10):627–31. Li JJ, Mei Z, Mei W, et al. Study on the Relationship Between Postoperative Frailty and Quality of Postoperative Recovery in Elderly Patients with Gastrointestinal Tumors. Int J Geriatr. 2025;46(2):156–61. ROBINAUGH D J, HASLBECK J M B WALDORPLJ, et al. Advancing the network theory of mental disorders: A computational model of panic disorder[J/OL]. Psychol Rev. 2024;131(6):1482–508. 10.1037/rev0000515 . Xi C, Wei ZC, Qi ZA et al. 2025. A Network Analysis of Insomnia, Anxiety, Depressive Symptoms and Non-Suicidal Self-Injury Behavior in Adolescents. Journal of Central South University (Medical Science): 1–10. http://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/43.1427.R.20251115.0910.006 LIANG Y, CHEN J, ZHANG KR et al. Network analysis of frailty, depression, and clinical outcomes in gastrointestinal cancer: A cross-sectional study[J/OL]. Front Oncol, 2024(14). 10.3389/fonc.2024.1289765 Li JJ, Mei Z, Mei W, et al. Relationship Between Postoperative Frailty and Quality of Postoperative Recovery in Elderlypatients with gastrointestinal cancer. Int J Geriatr. 2025;46(2):156–61. FENG Y, SU M, WANG J, et al. Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study[J/OL]. J Geriatric Oncol. 2024;15(7):101833. 10.1016/j.jgo.2024.101833 . FRIED L P, TANGEN C M, WALSTON J et al. Frailty in older adults: evidence for a phenotype[J/OL]. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 2001, 56(3): M146–156. 10.1093/gerona/56.3.m146 KROENKE K, SPITZER R L, WILLIAMS JB. The PHQ-9: validity of a brief depression severity measure[J/OL]. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x . Guan M, Liu J, Li X et al. The impact of depressive and anxious symptoms on non-suicidal self-injury behavior in adolescents: a network analysis. BMC Psychiatry. 2024;24(1):229. Published 2024 Mar 26. 10.1186/s12888-024-05599-1 DE RON J, FRIED E I EPSKAMPS. Psychological networks in clinical populations: investigating the consequences of Berkson’s bias[J/OL]. Psychol Med. 2021;51(1):168–76. 10.1017/S0033291719003209 . EPSKAMP S, FRIED E I.. A tutorial on regularized partial correlation networks[J/OL]. Psychol Methods. 2018;23(4):617–34. 10.1037/met0000167 . EPSKAMP S, BORSBOOM D, FRIED E I. Estimating psychological networks and their accuracy: A tutorial paper[J/OL]. Behav Res Methods. 2018;50(1):195–212. 10.3758/s13428-017-0862-1 . HASLBECK J M B, FRIED E I. How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets[J/OL]. Psychol Med. 2017;47(16):2767–76. 10.1017/S0033291717001258 . ZHANG F, YAN Y. Frailty as a predictor of adverse outcomes in patients with gastric cancer: A systematic review and meta-analysis of 75,357 patients[J/OL]. Ageing Res Rev. 2024;101:102528. 10.1016/j.arr.2024.102528 . TANG W Z, TAN Z K K, QIU L Y, et al. Prevalence and unfavorable outcome of frailty in older adults with gastric cancer: a systematic review and meta-analysis[J/OL]. Support Care Cancer. 2024;32(2):115. 10.1007/s00520-024-08306-8 . ARORA S, FOWLER M E, HARMON C, et al. Differences in Pretreatment Frailty Across Gastrointestinal Cancers in Older Adults: Results From the Cancer and Aging Resilience Evaluation Registry[J/OL]. JCO Oncol Pract. 2022;18(11):e1796–806. 10.1200/OP.22.00270 . Guo Y, Yu DL, Yi MX, et al. Research Progress on Frailty Interventions in Patients with Digestive Tract Tumors. J Clin Med Pract. 2023;27(14):143–8. ZAMANI M, ALIZADEH-TABARI S. Anxiety and depression prevalence in digestive cancers: a systematic review and meta-analysis[J/OL]. BMJ Supportive Palliat Care. 2023;13(e2):e235–43. 10.1136/bmjspcare-2021-003275 . MEJAREH Z N ABDOLLAHIB, HOSEINIPALANGI Z, et al. Global, regional, and national prevalence of depression among cancer patients: A systematic review and meta-analysis[J/OL]. Indian J Psychiatry. 2021;63(6):527–35. 10.4103/indianjpsychiatry.indianjpsychiatry_77_21 . SONG L, PANG Y, HE Y, et al. Impact of Psychological Factors on Survival in Metastatic Esophagogastric Cancer: Secondary Analysis of a Randomized Controlled Trial[J/OL]. JCO Oncol Pract. 2025;21(7):962–9. 10.1200/OP-24-00514 . ZAMANI M, ALIZADEH-TABARI S. Anxiety and depression prevalence in digestive cancers: a systematic review and meta-analysis[J/OL]. BMJ supportive Palliat care. 2023;13(e2):e235–43. 10.1136/bmjspcare-2021-003275 . SONG L, PANG Y, HE Y, et al. Impact of Psychological Factors on Survival in Metastatic Esophagogastric Cancer: Secondary Analysis of a Randomized Controlled Trial[J/OL]. JCO Oncol Pract. 2025;21(7):962–9. 10.1200/OP-24-00514 . Wang SL, Qiang WZ. 2005. Analysis of Perioperative Emotions in Malignant Tumor Patients. J Clin Psychiatry (1): 19–20. Yang J, Juan ZL. Cancer-Related Fatigue Levels and Its Influencing Factors in Patients with Digestive Tract Tumors. Chin J Curr Adv Gen Surg. 2018;21(1):61–3. Wu J, Mei CL, Wei LY, et al. Current Status and Influencing Factors of Cancer-Related Fatigue in Gastric Cancer Patients Undergoing Chemotherapy. Chin Nurs Res. 2025;39(13):2278–85. HARTUNG T J, FRIED E I, MEHNERT A, et al. Frequency and network analysis of depressive symptoms in patients with cancer compared to the general population[J/OL]. J Affect Disord. 2019;256:295–301. 10.1016/j.jad.2019.06.009 . WANG K, DIAO M, YANG Z, et al. Identification of Core Symptom Cluster in Patients With Digestive Cancer: A Network Analysis[J/OL]. Cancer Nurs. 2025;48(1):E55–63. 10.1097/NCC.0000000000001280 . SHIM E, HA H, SUH Y, et al. Network analyses of associations between cancer-related physical and psychological symptoms and quality of life in gastric cancer patients[J/OL]. Psycho-oncology. 2021;30(6):946–53. 10.1002/pon.5681 . Fearon K, Arends J, Baracos V. Understanding the mechanisms and treatment options in cancer cachexia. Nat Rev Clin Oncol. 2013;10(2):90–9. https://doi.org/10.1038/nrclinonc.2012.209 . LI PUKLINLS, CARTMEL F. Post-diagnosis weight trajectories and mortality among women with breast cancer[J/OL]. npj Breast Cancer. 2023;9(1):98. 10.1038/s41523-023-00603-5 . BONOMI PD, CRAWFORD J, DUNNE R F, et al. Mortality burden of pre-treatment weight loss in patients with non‐small‐cell lung cancer: A systematic literature review and meta‐analysis[J/OL]. J Cachexia Sarcopenia Muscle. 2024;15(4):1226–39. 10.1002/jcsm.13477 . ROMERO S A D, JONES L, BAUML JM, et al. The association between fatigue and pain symptoms and decreased physical activity after cancer[J/OL]. Support Care Cancer. 2018;26(10):3423–30. 10.1007/s00520-018-4203-4 . Zhang HP, Lin ZH, Min ZX, et al. Current Status and Influencing Factors of Frailty in Patients with Digestive System Tumors. J Nurs Sci. 2022;37(8):11–5. Yao JF, Li GS, Min GA. Influencing Factors of Functional Status and Its Relationship with Frailty in Elderly Patients with Gastrointestinal Tumors. China Med Herald. 2025;22(32). 10.20047/j.issn1673-7210.2025.32.11 . Tan RY, Hua HM, Yi PS, et al. Data Mining and Analysis of Fall Incidents in Hospitalized Cancer Patients. Chin Gen Pract Nurs. 2025;23(23):4411–5. OĞUZ S, ERTÜRK G, POLAT M G, et al. The effect of kinesiophobia on physical activity, balance, and fear of falling in patients with Parkinson’s disease[J/OL]. Physiother Theory Pract. 2023;39(4):865–72. 10.1080/09593985.2022.2028325 . ZHANG P, WANG L, ZHOU Q, et al. A network analysis of anxiety and depression symptoms in Chinese disabled elderly[J/OL]. J Affect Disord. 2023;333:535–42. 10.1016/j.jad.2023.04.065 . COSTANTINI A, POMPILI M, INNAMORATI M, et al. Psychiatric Pathology and Suicide Risk in Patients with Cancer[J/OL]. J Psychosoc Oncol. 2014;32(4):383–95. 10.1080/07347332.2014.917136 . JOENSUU H, ERIKSSON M, SUNDBY HALL K, et al. Survival Outcomes Associated With 3 Years vs 1 Year of Adjuvant Imatinib for Patients With High-Risk Gastrointestinal Stromal Tumors: An Analysis of a Randomized Clinical Trial After 10-Year Follow-up[J/OL]. JAMA Oncol. 2020;6(8):1241. 10.1001/jamaoncol.2020.2091 . TRAUTMANN A. Mécanismes sous-jacents à la fatigue chronique, un symptôme trop souvent négligé: II. De l’immunité dérégulée à la neuroinflammation et ses conséquences[J/OL]. médecine/sciences. 2021;37(11):1047–54. 10.1051/medsci/2021170 . ROINE I, FONCEA L M, LEDERMANN W, et al. Slow recovery of cerebrospinal fluid glucose and protein concentrations distinguish pneumococcal from Haemophilus influenzae and meningococcal meningitis in children[J]. Pediatr Infect Dis J. 1995;14(10):905–7. CUI L, LI S, WANG S, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment[J/OL]. Signal Transduct Target Therapy. 2024;9(1):30. 10.1038/s41392-024-01738-y . KRISTIANSEN MS, STABURSVIK J, O’LEARY E C et al. Clinical symptoms and markers of disease mechanisms in adolescent chronic fatigue following Epstein-Barr virus infection: An exploratory cross-sectional study[J/OL]. Brain, Behavior, and Immunity, 2019, 80: 551–563. 10.1016/j.bbi.2019.04.040 Hou JY, Li Y, Jia L, et al. Network Analysis of Post-Traumatic Stress Disorder Symptoms and Nursing Strategies in First-Episode Stroke Patients. Chin J Nurs. 2024;59(8):953–9. ALBERT K M, NEWHOUSE PA, Estrogen. Stress, and Depression: Cognitive and Biological Interactions[J/OL]. Ann Rev Clin Psychol. 2019;15(1):399–423. 10.1146/annurev-clinpsy-050718-095557 . KUEHNER C. Why is depression more common among women than among men?[J/OL]. Lancet Psychiatry. 2017;4(2):146–58. 10.1016/S2215-0366(16)30263-2 . VÖRÖS V, OSVÁTH P. [Gender differences in suicidal behavior][J]. Neuropsychopharmacologia Hungarica: A Magyar Pszichofarmakologiai Egyesulet Lapja = Official. J Hung Association Psychopharmacol. 2004;6(2):65–71. Li SY. Analysis of Anxiety, Depression, Insomnia Status, Their Impact on Quality of Life and Related Factors in Malignant Tumor Patients. China Medical University (1). 10.27652/d.cnki.gzyku.2020.001005 Zheng LH. Nursing Intervention Strategies for Sleep Disorders in Patients in the Radiotherapy Department of a Cancer Hospital. Sci Guide Dly January. 2025;9:B03. 10.28511/n.cnki.nkxdb.2025.000612 . Wang LJ, Jiao S. Analysis of the Effect of Acupoint Application Combined with Progressive Relaxation Training on Sleep Disorders in Chemotherapy Patients with Digestive Tract Malignancies. Heilongjiang J Traditional Chin Med. 2025;54(2):93–5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor invited by journal 12 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 08 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8820419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602474397,"identity":"e3350aca-a386-46a4-9640-1d3b3fde7ece","order_by":0,"name":"Xin Wang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":602474400,"identity":"228d9ce9-a26f-4bb3-9a18-f3161149dc31","order_by":1,"name":"Yuqing Yang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Yang","suffix":""},{"id":602474402,"identity":"f3c01853-5f00-4375-97cd-11630af8615b","order_by":2,"name":"Xuehan Song","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xuehan","middleName":"","lastName":"Song","suffix":""},{"id":602474405,"identity":"06a9d45e-078a-4516-aed5-c56cd9e5990f","order_by":3,"name":"Weiyan Xu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiyan","middleName":"","lastName":"Xu","suffix":""},{"id":602474408,"identity":"cb113b00-e253-41eb-9e7b-856b65737f99","order_by":4,"name":"Ziyi Geng","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Ziyi","middleName":"","lastName":"Geng","suffix":""},{"id":602474409,"identity":"2e3a85c6-d90a-4800-87d7-1dd596c49294","order_by":5,"name":"Hailing Yang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Hailing","middleName":"","lastName":"Yang","suffix":""},{"id":602474410,"identity":"62f2ab42-983f-43a7-a0c3-a91bfde66ba0","order_by":6,"name":"Yuanyuan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACNv7mgw8+GNjU97M3Nj5IqKghrIVP4liy4YyCNMaZPYebDR6cOUZYixxDjpkwz4fDjBtmuLdJPmxhJsJhDMfSmHkM0pgNJBjbKhIb2Bj427sT8Gthbj72cI6BDZu5dGPbjcQdMgwSZ85uIGRLusEbgzQeyzkHgVrOsDEYSOQS0pJjJsFjcFjC4EZiW0FiGzNxWiSBWgxAWhiI0wIOZIO0BMmeg80SCWeO8RD0i3w/KCr/2CTws7c//PijokaOv70XvxYMwEOa8lEwCkbBKBgFWAEANy1O32/doggAAAAASUVORK5CYII=","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-02-08 09:13:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8820419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8820419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104472371,"identity":"0ec08df5-ad13-4a35-9e4d-a1ddc15d3571","added_by":"auto","created_at":"2026-03-12 07:32:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted undirected network diagram of frailty and depression in patients with gastrointestinal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe node colors correspond to different symptom categories, with green representing frailty symptoms and pink representing depressive symptoms. The thickness of the edges was positively correlated with the edge weight values. Thicker edges indicate higher weights, indicating a stronger association between the two symptoms.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/07c567c075c3fc91276cd981.png"},{"id":104472370,"identity":"2da43050-c155-410b-8063-04b590447aa5","added_by":"auto","created_at":"2026-03-12 07:32:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLine Graphs of Strength, Closeness, Betweenness, and Expected Influence Indices for Each Node of Frailty and Depressive Symptoms in Patients with Gastrointestinal Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEntry strength is a standardized z-score, with positive values above the mean and negative values below the mean.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/d2c462ae502c62f05d3d2bf1.png"},{"id":104472373,"identity":"653cc9b3-014c-40d2-98c4-61e1d1beeace","added_by":"auto","created_at":"2026-03-12 07:32:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of gender differences in frailty-depressive symptoms among patients with gastrointestinal tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe node colors correspond to different symptom categories: green represents frailty, and pink represents depression. The edge colors indicate different correlation types: red denotes positive correlations, and blue denotes negative correlations. The thickness of the ed\u003cstrong\u003eges is positively correlated with \u003c/strong\u003ethe \u003cstrong\u003eedge weights\u003c/strong\u003e; \u003cstrong\u003ethe thicker the edge, the higher the weight, \u003c/strong\u003eindicating\u003cstrong\u003e a stronger association between the two connected symptoms.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/c8cd6badd54916c151ee3d29.png"},{"id":104472375,"identity":"03fbe0c6-f34f-44bb-b74e-40aa4a56c9a0","added_by":"auto","created_at":"2026-03-12 07:32:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork Structure and Node Bridge Strength Diagram of Frailty and Depression in patients with gastrointestinal cance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe left figure displays the topological structure of the symptom network: nodes are color-coded to distinguish between debilitating, depressive, and bridging symptoms. The right figure presents a line chart of the normalized bridging strength for each symptom node: the horizontal axis represents bridging strength values standardized using Z-scores, while the vertical axis ranks symptom nodes from the highest to lowest normalized bridging strength.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/bcbbe879570612870005e82e.png"},{"id":104472372,"identity":"7c7e7893-99bf-4691-9f19-170f30ce8327","added_by":"auto","created_at":"2026-03-12 07:32:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrap Method Based on Sampling Without Replacement Validates the Structural Stability of the Frailty-Depression Relationship Network in patients with gastrointestinal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe horizontal axis denotes the proportion of participants resampled without replacement, and the vertical axis indicates the mean Pearson correlation between each centrality index (bridge strength, betweenness, and expected influence) computed from the resampled data and the corresponding index derived from the original full sample.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/0f51a3edb9931f60edf8dd34.png"},{"id":104808581,"identity":"c2f7dc39-b989-40b5-9fb8-71ecf30527cf","added_by":"auto","created_at":"2026-03-17 12:38:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1827950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8820419/v1/e64289f8-cd67-4439-8bc8-8da83c6cfc7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study","fulltext":[{"header":"1 | Introduction","content":"\u003cp\u003eGastrointestinal tumors represent a significant global health burden with persistently high incidence and mortality rates. Frailty and depression are two prevalent and severely impactful conditions among patients with cancer, particularly those with gastrointestinal malignancies. These conditions interact reciprocally and negatively affect treatment tolerance, quality of life, and overall prognosis. However, existing research often examines frailty and depression in isolation or is limited to specific age cohorts, failing to reveal the complex symptomatic interplay within the gastrointestinal cancer population. This gap hinders the development of precise intervention strategies for these patients. Therefore, this study aimed to employ network analysis models to elucidate the intricate interrelationships between fatigue and depression symptoms in patients with gastrointestinal cancer and conducted an in-depth analysis of gender differences. This approach seeks to identify core intervention targets, thereby providing a foundation for developing more effective care strategies for patients with gastrointestinal cancer.\u003c/p\u003e"},{"header":"2 | Background","content":"\u003cp\u003eGastrointestinal tumors are benign and malignant tumors originating in the stomach, colon, rectum, gastrointestinal stromal tissue, small intestine, and other sites, with the stomach and colon/rectum being the most common locations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In February 2024, the WHO International Agency for Research on Cancer released its latest survey findings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], indicating that in 2022, 970, 000 (4.9%) new cases of gastric cancer and 1.9\u0026nbsp;million (9.6%) new cases of colorectal cancer were reported, with respective death tolls of 660,000 and 990,000 individuals. These cancers are the fifth and second leading causes of cancer-related death worldwide. In 2021, China recorded 1,957,948 new gastrointestinal cancer cases, with an incidence rate of 137.62 per 100,000 population. By 2050, the burden of gastrointestinal cancer in China is projected to surge significantly, with the total number of cases increasing by 49.06% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Research indicates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that the burden of gastrointestinal tumors in China is growing rapidly and will continue to rise, posing a substantial mortality threat to the population in the future.\u003c/p\u003e \u003cp\u003eFrailty is a multidimensional syndrome characterized by multisystem organ dysfunction, reduced physiological reserve, increased vulnerability, and diminished stress resistance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It primarily manifests as unintentional weight loss, decreased walking speed, reduced grip strength, diminished physical activity levels, and self-reported fatigue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies have indicated a strong association between frailty and cancer, with frailty incidence rates among patients with cancer ranging from 6% to 86% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Research indicates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] that frailty increases all-cause mortality, reduces tolerance to cancer treatments, and may lead to diminished quality of life, repeated hospitalizations, and poor prognosis, adversely affecting outcomes in patients with gastrointestinal cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepression occurs significantly more frequently in patients with cancer than in the general population, directly impacting treatment adherence, efficacy, and quality of life, making it a critical issue in the comprehensive management of cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research indicates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] a significant bidirectional causal relationship between frailty and depression in older adults. Their potential comorbidity mechanisms may involve multiple factors, with depression exerting a significant positive influence on frailty in patients with cancer; that is, more severe depression is correlated with more severe frailty. Furthermore, patients with gastrointestinal cancer frequently experience impaired nutrient absorption, chronic inflammation, and treatment-related stress injuries, leading to markedly elevated rates of frailty. The prevalence of postoperative frailty reaches 58.7% among older adult patients, with depression prevalence in the frail group soaring to 62.79%, far exceeding the 16.48% rate observed in healthy individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given that frailty and depression jointly constitute high-risk factors for poor prognosis in patients with gastrointestinal cancer, there is an urgent need to analyze the mechanisms underlying their mutual influence.\u003c/p\u003e \u003cp\u003eNetwork analysis methods can model the relationships between symptoms as a network, thereby visualizing and quantifying the connections between different symptoms and disorders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Simultaneously, network analysis aids in identifying the most central and influential symptoms within symptom networks, which may serve as potential intervention targets [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Currently, most studies are limited to examining the univariate relationship between frailty and depression and fail to analyze the internal heterogeneity of symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, existing studies are confined to older patients with gastrointestinal cancer, presenting age-bias limitations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] that prevent accurate measurement of the correlation between frailty and depression symptoms in the entire gastrointestinal cancer population.\u003c/p\u003e"},{"header":"2 | The Study","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 | Objectives\u003c/h2\u003e \u003cp\u003eThis study aimed to construct a network of depressive and frailty symptoms among patients with gastrointestinal cancer, facilitating a comprehensive understanding of the interrelationships between these symptoms and providing a basis for developing relevant preventive strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 | Research Question\u003c/h2\u003e \u003cp\u003e(1) What are the core symptoms and intrinsic associations within the frailty and depression network among patients with gastrointestinal cancer?\u003c/p\u003e \u003cp\u003e(2) Are there any gender differences?\u003c/p\u003e \u003cp\u003e(3) How can targeted nursing interventions be developed?\u003c/p\u003e \u003c/div\u003e"},{"header":"3 | Methods/Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 | Design\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted among patients with gastrointestinal cancer undergoing treatment in the oncology department of a tertiary general hospital in Shandong Province from February to June 2023, with concurrent assessments of frailty and depressive symptoms of the patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 | Study size\u003c/h2\u003e \u003cp\u003eThe scale used in this study includes 14 symptoms. To ensure the stability of the symptom network, the minimum sample size required, calculated using the formula [N\u0026thinsp;+\u0026thinsp;N\u0026times;(N-1)/2], is 105 cases. Previous studies have shown that a larger sample size in network analysis leads to a more reliable symptom network. Therefore, a total of 238 cases were finally included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 | Study Setting and Sampling\u003c/h2\u003e \u003cp\u003eFrom February to June 2023, patients with gastrointestinal cancer meeting inclusion and exclusion criteria were selected using convenience sampling at the Oncology Department of a Grade III Class A general hospital in Shandong Province. Ultimately, 238 patients with gastrointestinal tumors were enrolled in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 | Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) pathologically diagnosed with gastrointestinal cancer; (3) undergoing initial chemotherapy for gastrointestinal cancer; (4) conscious, without cognitive impairment, and able to communicate normally; and (5) voluntarily participating in the study with informed consent. The exclusion criteria were as follows: (1) patients with tumors other than gastrointestinal tumors; (2) patients unable to cooperate with frailty measurement; (3) patients with severe organ failure; and (4) patients receiving radiotherapy or immune-targeted therapy alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 | Instrument with Validity and Reliability\u003c/h2\u003e \u003cp\u003eThe Fried Frailty Phenotype (FP) was proposed by Fried et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in 2001 based on the frailty cycle theoretical model. It measures frailty in research subjects through five indicators: ① Weight loss (more than 4.5 kg or 5% of body weight within one year). ②Reduced walking speed (measured using a stopwatch for the time taken to walk 4.6 m at a normal pace on level ground; the criteria were determined based on height and walking time for men and women). ③Reduced grip strength (measured using a dynamometer; criteria determined based on the BMI range and grip strength values for men and women). ④Reduced physical activity (a. Inability to complete light household tasks due to fatigue on three days within the past week. Men: \u0026lt;2.5 hours of leisurely walking per week; women: \u0026lt;2 hours of leisurely walking per week. Participation in predominantly sedentary activities [e.g., watching TV and sitting while chatting] for most activities within the past week. Meeting any of these three criteria indicated reduced physical activity). ⑤Self-reported fatigue (scored based on days experiencing either feeling exhausted doing tasks or being unable to walk). A score of 2\u0026ndash;3 points for either indicates fatigue). Each item is answered \u0026ldquo;Yes\u0026rdquo; or \u0026ldquo;No\u0026rdquo;; \u0026lsquo;Yes\u0026rsquo; scores 1 point, \u0026ldquo;No\u0026rdquo; scores 0 points. The total score ranged from 0\u0026ndash;5. A score of \u0026ge;\u0026thinsp;3 indicates frailty status, categorizing patients into non-frail and frail groups based on their scores. One or two positive indicators (1\u0026ndash;2 points) indicated pre-frailty, while no symptoms (0 points) indicated non-frailty. This scale is widely used in the Chinese cancer population.\u003c/p\u003e \u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) was revised by Kroenke et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This unidimensional scale comprises nine items (loss of interest, low mood, sleep disturbances, lack of energy, poor appetite or overeating, feeling like a failure or having let yourself or your family down, and difficulty concentrating). Each question assesses the frequency of specific depressive symptoms experienced over the past two weeks (\u0026ldquo;Not at all\u0026rdquo;= 0, \u0026ldquo;A few days\u0026rdquo; = 1, \u0026ldquo;Most days\u0026rdquo;= 2, \u0026ldquo;Almost every day\u0026rdquo;= 3). The total score ranges from 0 to 27, with higher scores indicating more severe depressive symptoms. Scores of 0\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14, 15\u0026ndash;19, and 20\u0026ndash;27 indicate no, mild, moderate, moderate-to-severe, and severe depression, respectively. The Cronbach's α coefficient for this scale was 0.758 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 | Data collection and Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 | Data collection\u003c/h2\u003e \u003cp\u003eA sociodemographic and clinical questionnaire was used to collect baseline information on the research participants. The sociodemographic data included age, gender, ethnicity, educational attainment, employment status, marital status, living arrangements, whether living alone, per capita monthly household income, method of medical expense coverage, and primary caregiver. Disease-related data included the number of medications taken, number of comorbid conditions, smoking history, drinking history, height, weight, BMI, tumor location, metastasis status, tumor stage (TNM staging), differentiation grade and surgical approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 | Data Analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using the SPSS software (version 25.0). Two-tailed tests were conducted with α\u0026thinsp;=\u0026thinsp;0.05, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Network analysis was completed using R software (version 4.3.1). During data preprocessing, outliers were identified using the corrected Z-score method (|Z|\u0026gt;3.29). After inspection, the outliers constituted less than 1% of the data points. For detected outliers, multivariate imputation by chained equations (MICE) was applied [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to minimize their impact on subsequent analyses. A vulnerability-depression relationship network model was constructed using scores from 14 variables across two scales: the PHQ-9 and the FP. In this model, each variable is represented as a node in the network, with edges between nodes signifying the interactions between the variables. Edge weights are defined as partial correlation coefficients that reflect the unique association between variables after controlling for the influence of other variables. Given that the variables included in this study encompass both dichotomous (FP scale) and ordinal (PHQ-9 scale, four-level scoring) measurement types, we employed mixed graphical models to conduct network analysis of the data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], thereby more accurately modeling conditional dependencies within the mixed data. Model selection combined the extended Bayesian information criterion (EBIC) (gamma\u0026thinsp;=\u0026thinsp;0.5) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with the least absolute shrinkage and selection operator (LASSO) to obtain a more robust, sparse, partial correlation network [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The following core metrics were computed for each node in the network: (1) Strength, defined as the sum of the absolute values of a node's edge weights, which characterizes its relative importance within the network. This metric is presented as a standardized z-score, where positive values indicate above-average importance and negative values indicate below-average importance; (2) proximity, reflecting the average shortest distance from a node to all other nodes in the network, used to assess information propagation efficiency; (3) intermediary degree, which counts the number of shortest paths passing through a node, reveals its critical role as a \u0026ldquo;bridge\u0026rdquo; in the network; and (4) expected influence, quantifying the breadth and depth of other nodes a node can influence through its network connections, reflects the node's global importance or propagation potential within the network [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To ensure the reliability of the centrality metrics, this study employed the bootstrap method to calculate the Centrality Stability Coefficient (CS). The CS coefficient must satisfy\u0026thinsp;\u0026ge;\u0026thinsp;0.25 as the minimum acceptable threshold and \u0026ge;\u0026thinsp;0.5 to achieve ideal explanatory validity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the bridging strength of all nodes was calculated using the R package NetworkTools. Bridging strength refers to the sum of the absolute values of the edge weights connecting a node to all other nodes in the network. This metric aids in identifying nodes that play crucial roles in connecting different communities.\u003c/p\u003e \u003cp\u003eTo examine gender differences in the frailty-depression network, we conducted a network comparison using a permutation test. Overall network strength was compared using the Network Comparison Test R software (version 4.3.1), employing a permutation test to compare the overall strength of the networks in the male and female subsamples. This test randomly permutes the gender labels of the sample to construct a null distribution of overall strength differences under the null hypothesis (no gender difference). It then calculates the extreme position of the observed difference within this distribution to yield an exact p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparisons of specific edge weights were similarly conducted using the aforementioned software, performing cross-gender edge-by-edge permutation tests for each edge weight in the network. To control the risk of false positives from multiple comparisons, the raw p-values obtained from all edge comparisons underwent Bonferroni-Holm correction. All analyses were performed under consistent network sparsity parameters (gamma\u0026thinsp;=\u0026thinsp;0.5) to ensure observed differences did not stem from instability inherent to the network estimation itself.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 | Ethics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was reviewed and approved by the Ethics Committee of the School of Nursing and Rehabilitation, Shandong University (Approval No. 2022-R-067). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. The research participants' information was kept strictly confidential, with no disclosure of personal details or privacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 | Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 | General Characteristics of Patients with Gastrointestinal Cancer and Incidence of Depression and Fatigue\u003c/h2\u003e \u003cp\u003eThis study enrolled 238 patients with gastrointestinal tumors at baseline, ranging in age from 34 to 85 years, with a mean age of (60.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.56) years. Among them, 91 (38.24%) were aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and 147 (61.76%) were aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. The male-to-female ratio was 158 males (66.39%) and 80 females (33.61%). Less than 1% of patients belonged to ethnic minorities. Over half had an educational attainment of junior high school or below. The other demographic characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean FP and PHQ-9 total scores for the study participants were 1.92 (SD\u0026thinsp;=\u0026thinsp;1.31) and 5.10 (SD\u0026thinsp;=\u0026thinsp;3.92), respectively. The node names, means, standard deviations, and node strengths for specific item scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of patients with gastrointestinal tumors(N\u0026thinsp;=\u0026thinsp;238)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school or junior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior college/bachelor's degree and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving Arrangements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTownship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCounty seat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving Alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAverage Monthly Household Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;3999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000\u0026ndash;5999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6000\u0026ndash;7999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMethod of Covering Medical Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban and Rural Residents' Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Caregiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of measurement items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems context\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems M(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem Strength\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP1\u003c/p\u003e \u003cp\u003eFP2\u003c/p\u003e \u003cp\u003eFP3\u003c/p\u003e \u003cp\u003eFP4\u003c/p\u003e \u003cp\u003eFP5\u003c/p\u003e \u003cp\u003ePHQ1\u003c/p\u003e \u003cp\u003ePHQ2\u003c/p\u003e \u003cp\u003ePHQ3\u003c/p\u003e \u003cp\u003ePHQ4\u003c/p\u003e \u003cp\u003ePHQ5\u003c/p\u003e \u003cp\u003ePHQ6\u003c/p\u003e \u003cp\u003ePHQ7\u003c/p\u003e \u003cp\u003ePHQ8\u003c/p\u003e \u003cp\u003ePHQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003cp\u003eSlowed gait\u003c/p\u003e \u003cp\u003eLow grip strength\u003c/p\u003e \u003cp\u003eLow physical activity\u003c/p\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003cp\u003eLoss of pleasure\u003c/p\u003e \u003cp\u003eLow mood\u003c/p\u003e \u003cp\u003eSleep problems\u003c/p\u003e \u003cp\u003eLack of energy\u003c/p\u003e \u003cp\u003eChanges in appetite\u003c/p\u003e \u003cp\u003eGuilt\u003c/p\u003e \u003cp\u003eDifficulty concentrating\u003c/p\u003e \u003cp\u003eBradykinesia/Agitation\u003c/p\u003e \u003cp\u003eSuicidal ideation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.47)\u003c/p\u003e \u003cp\u003e0.30(0.46)\u003c/p\u003e \u003cp\u003e0.41(0.49)\u003c/p\u003e \u003cp\u003e0.33(0.47)\u003c/p\u003e \u003cp\u003e0.21(0.41)\u003c/p\u003e \u003cp\u003e0.88(0.90)\u003c/p\u003e \u003cp\u003e0.64(0.71)\u003c/p\u003e \u003cp\u003e0.84(0.97)\u003c/p\u003e \u003cp\u003e0.97(0.86)\u003c/p\u003e \u003cp\u003e0.92(0.99)\u003c/p\u003e \u003cp\u003e0.19(0.46)\u003c/p\u003e \u003cp\u003e0.29(0.67)\u003c/p\u003e \u003cp\u003e0.31(0.57)\u003c/p\u003e \u003cp\u003e0.05(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e0.45\u003c/p\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e0.48\u003c/p\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFP: Fatigue Phenotype; PHQ: Patient Health Questionnaire\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe node colors correspond to different symptom categories, with green representing frailty symptoms and pink representing depressive symptoms. The thickness of the edges was positively correlated with the edge weight values. Thicker edges indicate higher weights, indicating a stronger association between the two symptoms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 | Network Structure and Centrality Metrics Analysis\u003c/h2\u003e \u003cp\u003eThis study constructed a network structure comprising 14 nodes representing frailty and depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Network analysis revealed the following edges with higher weights among connections between different symptoms: - The strongest link within frailty symptoms was between FP2 (slowed gait) and FP4 (low physical activity) (edge weight\u0026thinsp;=\u0026thinsp;0.85); - The strongest link within depression symptoms was between PHQ4 (lack of energy) and PHQ5 (abnormal appetite) (edge weight\u0026thinsp;=\u0026thinsp;0.70); followed by PHQ8 (bradykinesia/agitation) with PHQ9 (suicidal ideation) and PHQ6 (guilt) (edge weights\u0026thinsp;=\u0026thinsp;0.78 and 0.72, respectively); the strongest edge between fatigue and depression was FP5 (fatigue) with PHQ4 (lack of energy) (edge weight\u0026thinsp;=\u0026thinsp;0.65). PHQ4 Lack of energy) exhibited the highest predictability (standardized value\u0026thinsp;=\u0026thinsp;1.01). Although FP1 (weight loss) had a negative z-value for bridging strength, its node strength was zero, indicating that this symptom neither played a significant bridging role nor formed stable connections with other nodes in the network.(Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the centrality analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The PHQ4 (lack of energy) and PHQ8 (bradykinesia/agitation) exhibited the strongest node strengths. Simultaneously, these two nodes (PHQ4 and PHQ8) demonstrated a greater expected influence than the other nodes across the entire network. Within the separate symptom networks for fatigue and depression, the nodes with the highest node strength were fatigue condition FP5 (fatigue) and FP4 (low physical activity) for the fatigue network, and PHQ4 (lack of energy), PHQ8 (bradykinesia/akathisia), PHQ1 (anhedonia), and PHQ2 (depressed mood) for the depression network. Regarding network proximity and betweenness centrality, depression symptoms PHQ4 (lack of energy) and PHQ8 (bradykinesia/akathisia) exhibited higher proximity and stronger betweenness centrality across all nodes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 | Analysis of Gender Differences\u003c/h2\u003e \u003cp\u003eIn the comparative analysis of frailty-depression relationship network models among patients with gastrointestinal cancer, male participants exhibited significantly lower overall network strength than female participants (male participants: 0.55 vs. female participants: 2.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with extensive differences in edge weights (M\u0026thinsp;=\u0026thinsp;0.088, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To understand this finding, we examined the statistically significant differences at the edge level between male and female participants after applying the Bonferroni-Holm correction. The results revealed 21 statistically significant edges (corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with 19 significantly stronger in women and two significantly stronger in men. Specifically, stronger connections in female patients included FP2-FP4 (low slow gait-physical activity, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FP4-FP5 (low physical activity-fatigue, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PHQ4-PHQ5 (lack of energy-appetite changes, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Stronger connections were observed in male patients between PHQ3-PHQ8 (sleep problems-bradykinesia/agitation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PHQ8-PHQ9 (bradykinesia/agitation-suicidal ideation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 | Stability Test Results for Network Centrality Measures\u003c/h2\u003e \u003cp\u003eThe center stability coefficient indicates that the CS coefficients for node strength, node proximity, node expected influence, and node bridging strength were all \u0026ge;\u0026thinsp;0.70, while the CS coefficient for node betweenness was 0.68. All values were significantly greater than 0.50, demonstrating that the network model exhibited good robustness (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 | Discussion","content":"\u003cp\u003eThe prevalence of frailty among patients with gastrointestinal cancer in this study was 34.03%, consistent with domestic reports ranging from 21% to 39% [24–26]. Research has confirmed [27] that frailty in patients with gastrointestinal cancer triggers a series of health issues, ultimately leading to long-term adverse outcomes such as disability, cognitive decline, and even death. Therefore, future studies should further explore and clarify the frailty status of patients with gastrointestinal cancer to provide data to support proactive frailty screening and rehabilitation interventions in this population. The detection rate of depressive symptoms was 23.11%, slightly higher than the 16%-22% range reported in recent surveys [28–30]. Research has shown [29] that the prevalence of depression among patients with colorectal cancer (32%) is significantly higher than that among patients with other types of cancer. This may be partly attributed to the high proportion of patients with colon cancer in this study (40.76% of tumors were located in the intestine). Additionally, the lack of standardized depression assessment tools currently used in China may partially explain the variations in the reported depression rates. Furthermore, by examining the network structure of the frailty-depression relationship in patients with gastrointestinal cancer, this study revealed that frailty status is not only closely associated with depressive symptoms but also that the variables within depressive symptoms themselves exhibit strong interconnections, highlighting the complex relationships among internalizing symptoms. These findings provide further evidence for the improvement of frailty and depression in patients with gastrointestinal cancer. They also underscore the importance of considering changes in related symptoms alongside individual symptoms, suggesting that this may be a key target for healthcare providers and families in their attention and intervention efforts.\u003c/p\u003e\n\u003cp\u003e5.1 | Nursing staff should enhance management of core symptoms in the frailty-depression relationship among patients with gastrointestinal cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch indicates that PHQ4 (lack of energy) is not only the most central symptom within the frailty-depression network but also exhibits the highest proximity and anticipated influence. Patients experiencing fatigue commonly report feeling tired, lacking energy, and finding even minor tasks exhausting. This suggests that fatigue is a central and highly influential symptom within the frailty-depression psychopathological network, consistent with previous studies [31,32]. This finding suggests that fatigue is a key symptom of the fatigue-depression syndrome in patients with gastrointestinal cancer. This is because patients with cancer typically exhibit poor baseline health due to tumor cell invasion, chemotherapy/radiation therapy, and surgical stress [1]. Patients with gastrointestinal cancer also experience reduced digestive organ function, leading to decreased food intake and malnutrition. This triggers an insufficient bodily energy supply, resulting in fatigue, accelerated muscle breakdown, and symptoms such as exhaustion and weight loss, which become a significant stressor on patients' physiological reserves. Second, the sensation of insufficient energy prevents patients from completing daily activities, triggering a “subjective feeling of fatigue” that induces self-doubt and a low mood, thereby exacerbating depression [33]. Simultaneously, this “subjective fatigue” diminishes patients' motivation to engage in activities, leading them to voluntarily reduce their activity levels and further increasing the risk of frailty. However, relevant studies have found [34,35] that most oncology healthcare providers lack training in assessing fatigue and energy depletion, demonstrating significant gaps in their recognition and management skills of these symptoms. This highlights the need for nursing staff to strengthen their commitment to patient-centered care principles and practices. Consequently, systematic training should be enhanced for oncology teams to evaluate patients' depressive symptoms, improve their assessment skills, prioritize individualized patient needs and experiences, and implement timely, targeted interventions.\u003c/p\u003e\n\u003cp\u003ePHQ8 (bradykinesia/agitation) was also a core symptom in the relationship network between fatigue and depression, with its mediating effect and expected influence second only to PHQ4 (lack of energy). This differs from the findings of other studies [36,37], which agree that PHQ4 (lack of energy) is one of the most important core symptoms, followed by PHQ2 (depressed mood), but disagree on the significance of PHQ8 (bradykinesia/agitation). This discrepancy may arise because other studies examined isolated depressive symptom clusters, where core symptoms tended to cluster around the core affective dimension (PHQ2 depressed mood) and foundational somatic dimension (PHQ4 Lack of energy). As a secondary somatic symptom of depression, PHQ8 (bradykinesia/agitation) was overshadowed by the centrality. This study, however, examines the “frailty-depression network.” Core frailty features like “muscle loss and reduced activity capacity” have direct pathological links to PHQ8's “bradykinesia.” This makes the PHQ8 a pivotal bridge connecting “frailty-related physical symptoms” with “depressive mood/physical symptoms,” significantly enhancing its centrality. Second, patients with gastrointestinal cancer in this study exhibited a higher proportion of elderly individuals (43.70% aged ≥65 years). This demographic inherently experiences physiological motor decline, which, when compounded by tumor effects and treatment impacts, significantly elevates the incidence and severity of PHQ8 symptoms. Therefore, timely assessment of nutritional status is essential for patients with gastrointestinal cancer. Dietary structures should be adjusted based on nutritional needs, and nutritional supplementation should be provided. It is also crucial to provide exercise guidance tailored to the patient's physical conditions. This includes a series of aerobic exercises, such as balance training, strength training, and stretching exercises (e.g., walking in circles and stair climbing) [1], as well as resistance training at appropriate frequencies and intensities. Examples include seated leg raises and wall-push-ups. Such interventions enhance physical function, alleviate fatigue, and ultimately improve patient prognosis and recovery outcomes.\u003c/p\u003e\n\u003cp\u003eFP1 (weight loss) exhibits the lowest centrality strength across the entire network. As FP1 is an objective quantitative indicator, over 80% of the nodes in the network represent subjective perception metrics. According to symptom network research patterns [36-38], subjective symptoms form strong associations more readily due to the “sensation superposition effect.” In contrast, the linkage between objective indicators and subjective symptoms requires multiple transformations from objective to subjective perception. This process exhibits low conversion efficiency and significant individual variation, resulting in FP1 having a much lower connection frequency with other nodes than subjective symptoms. Second, weight loss typically occurs early in the disease course or before treatment and generally progresses gradually. In contrast, other frailty and depressive symptoms often emerge after treatment (e.g., following chemotherapy) or during disease progression, potentially fluctuating with treatment cycles [39]. Due to prolonged exposure to adverse effects from chemotherapy, radiotherapy, or surgery causing muscle wasting, and psychological stress exacerbating emotional disturbances, the central intensity of FP1 (weight loss) may appear less pronounced than expected. However, it is noteworthy that weight loss triggers a cascade of reactions that significantly increase the mortality risk and hinder cancer patient recovery [40,41]. Thus, early clinical monitoring and weight management are critically important. Regularly monitor patients' nutritional intake and use smart body fat scales to track weight changes. For patients undergoing long-term treatment, nutritional status and physical parameters (e.g., muscle mass and body fat percentage) should be dynamically monitored over time, and personalized interventions should be implemented based on these dimensional changes.\u003c/p\u003e\u003cp\u003e5.2 | Nursing staff should closely monitor the strong association and highly predictive symptoms within the frailty-depression relationship in patients with gastrointestinal tumors.\u003c/p\u003e\n\u003cp\u003eThe symptom network analysis results of this study revealed the strongest connection between FP2 (slowed gait) and FP4 (low physical activity), consistent with previous studies [42,43]. This may reflect the shared etiology or mutual influence of the two conditions. Step speed in patients with cancer shows a significant positive correlation with objective physical activity [44], indicating that a slower step speed correlates with lower activity levels. Given that most patients with gastrointestinal cancer \u0026nbsp;are elderly, factors such as age and cancer-related fatigue [45] contribute to slower step speed, further increasing the fall risk. Patients may develop a fear of falling and experience a sense of disability due to this condition. Consequently, they actively reduce their daily activities [46]. The resulting lack of skeletal muscle exercise leads to weakened muscle strength, which further impairs the walking speed. This creates a bidirectional cycle between the two variables, forming a mutually reinforcing feedback loop that perpetuates the cycle. As walking speed gradually decreases, patients exhibit stronger resistance to activity, continuously strengthening the feedback loop and deepening the connection between the two conditions.\u003c/p\u003e\n\u003cp\u003ePHQ8 (bradykinesia/agitation) and PHQ9 (suicidal ideation), as well as PHQ8 (bradykinesia/agitation) and PHQ6 (guilt), exhibit strong correlations. When patients experience bradykinesia and restricted mobility, they develop a profound sense of “incapacitation.” This indicates an inability to adequately care for themselves, leading to a significant decline in their quality of life and difficulty fulfilling Maslow's basic physiological and safety needs [47]. When fundamental needs remain unmet, anxiety and pessimism inevitably arise. Patients attribute their “inability to care for themselves” to “being a burden on their family,” resulting in intense feelings of “guilt.” Research indicates [48] that suicidal ideation correlates with a low quality of life, with older adult cancer patients being particularly vulnerable. Additionally, patients with gastrointestinal tumors undergo prolonged treatment cycles [49]. Due to extended therapy, patients may experience physical dysfunction that limits social engagement, leading to severe social isolation and loneliness [47]. These factors leave patients' needs for emotional connection, respect, and belonging unmet, fueling their guilt and suicidal thoughts. Consequently, these two symptom clusters exhibited strong connectivity within the network.\u003c/p\u003e\n\u003cp\u003eThe results of this study indicate that the PHQ4 (Lack of energy) is the node with the highest predictive power for the frailty-depression network. This may be attributed to the node possessing the highest node strength and strongest bridging function within the network. Second, studies [50–53] have found that chronic fatigue, through sustained activation of the hypothalamic-pituitary-adrenal (HPA) axis, triggers abnormal cortisol secretion. This, in turn, drives a vicious cycle of “fatigue-stress-depression,” leading to a series of chain reactions. Therefore, the PHQ4 (lack of energy) exhibited the highest predictive value within the network, suggesting that the degree of improvement in fatigue symptoms can serve as a sensitive indicator for evaluating treatment efficacy for frailty and depression. This approach offers a more comprehensive assessment than monitoring mood or physical fitness changes alone. Nursing staff can monitor changes in fatigue to identify individuals at risk of frailty and depression early. For patients at high risk of fatigue, personalized interventions can be implemented (e.g., exercise rehabilitation therapy, psychological care such as cognitive behavioral therapy, and health education). Second, preventively identifying strongly correlated symptoms and using them as entry points for early psychological interventions combined with pharmacotherapy can weaken symptom interactions, sever strong symptom linkages, enhance intervention efficiency and effectiveness, and control the progression of frailty and depression conditions [54].\u003c/p\u003e\n\u003cp\u003e5.3 | Healthcare providers should pay attention to gender differences in the frailty-depression network among patients with gastrointestinal tumors\u003c/p\u003e\n\u003cp\u003eThis study found that after standardizing for network density and node count,the global network strength in females was 4.2 times higher than that in males, with significantly shorter characteristic path lengths. Research hypothesized [55,56] that estrogen fluctuations enhance the efficiency of inflammatory signal transmission to the central nervous system, making peripheral fatigue signals more likely to trigger the activation of central emotional nodes. Furthermore, women exhibit significantly higher depressive symptoms than men, which may explain why female symptom networks are stronger than male ones. These findings indicate that symptom comorbidity mechanisms exhibit sex dependency, providing primary targets for subsequent precision interventions. Concurrently, this disparity involves distinct gender-heterogeneous connectivity patterns: females form highly clustered subnetworks centered on “fatigue-low mood,” a clustering characteristic potentially linked to heightened female sensitivity to somatic symptoms. Furthermore, estrogen exhibits synergistic effects on cognitive and emotional functions, particularly under psychosocial stress, potentially underpinning the association between ovarian hormone fluctuations and depression in women. In contrast, males exhibit a linear risk trajectory of “sleep disturbance-bradykinesia/agitation-suicidal ideation.” This aligns with prior research indicating a significantly higher suicide risk in males than in females [55], [56]. Studies [57, 58] have indicated that anxiety, depression, and insomnia are positively correlated in patients with cancer. Male suicidal ideation is not solely driven by a depressive mood but is more closely associated with material factors and externalizing impulses. Externalizing impulses, such as sleep deprivation and motor abnormalities, serve as critical drivers of suicidal urges, thereby forming the “sleep-motor retardation/agitation-suicide” linear network structure.\u003c/p\u003e\n\u003cp\u003eTherefore, for female patients with gastrointestinal tumors, the focus should be on emotional counseling and physical training only. Future research should pay greater attention to whether female patients lack positive emotional experiences. During health education and nursing interventions, patients should be guided to make positive evaluations of situations, and timely psychological counseling should be provided to encourage patients to express and release their emotions. Targeting the “fatigue-low mood” pathway, combining aerobic exercise to reduce fatigue symptoms with positive mental health education is recommended. Psychological interventions should prioritize enhancing self-worth, providing precise rehabilitation guidance, improving patients' cognitive processes, and preventing emotional dysregulation. For male patients, sleep interventions and suicide risk screening should be emphasized. Precise strategies should be developed to prioritize sleep interventions, closely monitor patients' sleep patterns, conduct regular sleep quality assessments and suicide risk evaluations, and strengthen communication with patients. For patients with sleep disorders, psychological interventions such as cognitive behavioral therapy and relaxation training [59], as well as acupoint application combined with progressive muscle relaxation training [60], can be employed to improve sleep quality and broadly reduce the overall severity of the frailty-depression symptom network.\u003c/p\u003e\n\u003cp\u003e5.4 | Strength and Limitations of the Work\u003c/p\u003e\n\u003cp\u003eThis study \u0026nbsp;overcomes \u0026nbsp; the limitations of traditional single-variable research by employing network analysis to \u0026nbsp;comprehensively \u0026nbsp;dissect the association between frailty and depression in patients with gastrointestinal cancer. \u0026nbsp;This study \u0026nbsp; systematically constructs the first symptom network model linking these two conditions, clearly identifying core symptoms and strongly connected pathways, thereby providing a novel perspective for elucidating the mechanisms of comorbidity. Furthermore, it identifies core nodes within the fatigue and depression networks and highlights gender-specific differences. This provides evidence-based support for developing personalized precision nursing intervention strategies, \u0026nbsp;with \u0026nbsp;significant implications for clinical translation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has certain limitations. First, as a cross-sectional study, the constructed network of frailty-depression states in patients with gastrointestinal cancer \u0026nbsp;cannot reveal changes in symptoms over time or establish causal relationships between symptoms. Second, the single-center sample may introduce selection bias, necessitating future multi-center studies to validate the findings. The gender analysis of symptom networks included only 80 female patients, potentially limiting the reliability and representativeness of the gender-specific results. Future studies should increase the representation of female patients. Third, symptom assessment relied on patient self-reports without objective physiological validation (e.g., cortisol levels, muscle strength testing), potentially introducing a reporting bias. Finally, neither age nor self-reported socioeconomic status was included as a covariate in the network analysis calculations to control for confounding effects on the frailty-depression association.\u003c/p\u003e\n\u003cp\u003e5.5 | Recommendations for Further Research\u003c/p\u003e\n\u003cp\u003eTo address the aforementioned shortcomings, future research should adopt longitudinal designs to track symptom changes, conduct multi-center recruitment to enhance generalizability, \u0026nbsp;include more female participants to ensure credible gender comparisons, combine self-reports with objective measures (e.g., cortisol, grip strength), and control for covariates such as age and socioeconomic status in network models.\u003c/p\u003e\n\u003cp\u003e5.6 | Implications for policy and practice\u003c/p\u003e\n\u003cp\u003eHealthcare policies should prioritize systematic screening for fatigue and depression in patients with gastrointestinal cancer. Nursing practice should adopt targeted, gender-sensitive interventions focusing on core symptoms, such as fatigue, integrating nutritional support, exercise, and psychological care to improve patient outcomes.\u003c/p\u003e"},{"header":"6 | Conclusion","content":"\u003cp\u003eUsing symptom network analysis, this study identified the network-associated characteristics and core symptoms of frailty and depression in patients with gastrointestinal cancer. Future research should prioritize highly interconnected nodes and core symptoms within the symptom network, block the transmission pathways of core symptoms, and implement interventions that effectively reduce positive affect limitation and alleviate fatigue. Such approaches may yield beneficial effects that help limit other symptoms of the disease. Healthcare providers should pay greater attention to these symptoms and consider them as intervention targets. Developing scientifically grounded psychological interventions could promote improvements in both mental health status and frailty levels among patients with gastrointestinal cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Research on Strategies to Improve Nutritional Literacy Among patients with gastrointestinal cancer from an Active Aging Perspective, Shandong Provincial Federation of Social Sciences 2023 Collaborative Project in Humanities and Social Sciences, (No. 2023-JKZX-19)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionsAll authors have made substantial intellectual contributions to the manuscript and meet the ICMJE criteria for authorship. Specific contributions are as follows:● Xin Wang: Conceptualization, Methodology, Formal analysis, Investigation, Writing\u0026ndash;Original Draft, Writing\u0026ndash;Review \u0026amp; Editing, Project administration.（[email protected]）● Yuqing Yang: Methodology, Software, Review of Relevant Literature , Writing\u0026ndash;Review \u0026amp; Editing.（[email protected]）● Xuehan Song: Investigation, Resources, Writing\u0026ndash;Original Draft (Introduction and Background).（[email protected]）● Ziyi Geng: Investigation, Resources, Writing\u0026ndash;Original Draft (Introduction and Background).（[email protected]）● Weiyan Xu: Investigation, Resources, Data Curation.（[email protected]）● Hailing Yang: Supervision, Validation, Writing\u0026ndash;Review \u0026amp; Editing, Funding acquisition.（[email protected]）● Yuanyuan Chen: Supervision, Conceptualization, Methodology, Validation, Writing\u0026ndash;Review \u0026amp; Editing, Funding acquisition.（[email protected]）All authors have approved the final article and confirmed that all eligible authors are listed as authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu WY. 2024. Longitudinal Study on Pre-Chemotherapy Frailty Status and Its Impact on Long-Term Chemotherapy Toxicity in Patients with Gastrointestinal Cancer. Shandong University. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.27272/d.cnki.gshdu.2024.001852\u003c/span\u003e\u003cspan address=\"10.27272/d.cnki.gshdu.2024.001852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal cancer burden. growing, amidst mounting need for services[J]. Saudi Med J. 2024;45(3):326\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLU Y, ZHANG J, ZHOU Z, et al. Age-Period-Cohort Analysis on the Burden of Gastrointestinal Cancers in China: Trends, Risk Factors, and Predictions[J/OL]. Healthc (Basel Switzerland). 2025;13(10):1096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare13101096\u003c/span\u003e\u003cspan address=\"10.3390/healthcare13101096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi KY, He X, Zhong LY et al. 2025. Analysis of Frailty Status and Influencing Factors in Elderly Patients with Brain Tumors. Journal of Nurses Training: 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/52.1063.R.20251121.1905.005\u003c/span\u003e\u003cspan address=\"http://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/52.1063.R.20251121.1905.005\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBUTA B J, WALSTON J D, GODINO J G, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments[J/OL]. Ageing Res Rev. 2016;26:53\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2015.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2015.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Yi MX, Man JX, et al. A Meta-Analysis of Influencing Factors of Frailty in Cancer Patients. Chin Gen Pract. 2023;26(8):989\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu J. Relationship Between Frailty and Nutritional Risk Status and Analysis of Influencing Factors in Elderly Patients with Lung Cancer Undergoing Chemotherapy. Health Vocat Educ. 2025;43(18):128\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20037/j.issn.1671-1246.2025.18.34\u003c/span\u003e\u003cspan address=\"10.20037/j.issn.1671-1246.2025.18.34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYE L, LADBURY C, TAM A, et al. Prevalence of major depression, suicidal ideation, and mental health treatment among cancer survivors[J/OL]. Palliat Support Care. 2024;1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1478951524000944\u003c/span\u003e\u003cspan address=\"10.1017/S1478951524000944\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang HP, Lin ZH, Min ZX, et al. Current Status and Influencing Factors of Frailty in Patients with Digestive System Tumors. J Nurs Sci. 2022;37(8):11\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang MQ, Lei S, Fei XY, et al. Correlation Between Frailty and Depressive Disorders in the Elderly and Research Progress on Exercise Interventions. Chin J Nerv Mental Dis. 2025;51(10):627\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Mei Z, Mei W, et al. Study on the Relationship Between Postoperative Frailty and Quality of Postoperative Recovery in Elderly Patients with Gastrointestinal Tumors. Int J Geriatr. 2025;46(2):156\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eROBINAUGH D J, HASLBECK J M B WALDORPLJ, et al. Advancing the network theory of mental disorders: A computational model of panic disorder[J/OL]. Psychol Rev. 2024;131(6):1482\u0026ndash;508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/rev0000515\u003c/span\u003e\u003cspan address=\"10.1037/rev0000515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi C, Wei ZC, Qi ZA et al. 2025. A Network Analysis of Insomnia, Anxiety, Depressive Symptoms and Non-Suicidal Self-Injury Behavior in Adolescents. Journal of Central South University (Medical Science): 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/43.1427.R.20251115.0910.006\u003c/span\u003e\u003cspan address=\"http://link--cnki--net--https.cnki.tsyxy.qfclo.com:2222/urlid/43.1427.R.20251115.0910.006\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIANG Y, CHEN J, ZHANG KR et al. Network analysis of frailty, depression, and clinical outcomes in gastrointestinal cancer: A cross-sectional study[J/OL]. Front Oncol, 2024(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2024.1289765\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2024.1289765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Mei Z, Mei W, et al. Relationship Between Postoperative Frailty and Quality of Postoperative Recovery in Elderlypatients with gastrointestinal cancer. Int J Geriatr. 2025;46(2):156\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFENG Y, SU M, WANG J, et al. Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study[J/OL]. J Geriatric Oncol. 2024;15(7):101833. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jgo.2024.101833\u003c/span\u003e\u003cspan address=\"10.1016/j.jgo.2024.101833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFRIED L P, TANGEN C M, WALSTON J et al. Frailty in older adults: evidence for a phenotype[J/OL]. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 2001, 56(3): M146\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gerona/56.3.m146\u003c/span\u003e\u003cspan address=\"10.1093/gerona/56.3.m146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKROENKE K, SPITZER R L, WILLIAMS JB. The PHQ-9: validity of a brief depression severity measure[J/OL]. J Gen Intern Med. 2001;16(9):606\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1046/j.1525-1497.2001.016009606.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1525-1497.2001.016009606.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan M, Liu J, Li X et al. The impact of depressive and anxious symptoms on non-suicidal self-injury behavior in adolescents: a network analysis. BMC Psychiatry. 2024;24(1):229. Published 2024 Mar 26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-024-05599-1\u003c/span\u003e\u003cspan address=\"10.1186/s12888-024-05599-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDE RON J, FRIED E I EPSKAMPS. Psychological networks in clinical populations: investigating the consequences of Berkson\u0026rsquo;s bias[J/OL]. Psychol Med. 2021;51(1):168\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0033291719003209\u003c/span\u003e\u003cspan address=\"10.1017/S0033291719003209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPSKAMP S, FRIED E I.. A tutorial on regularized partial correlation networks[J/OL]. Psychol Methods. 2018;23(4):617\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/met0000167\u003c/span\u003e\u003cspan address=\"10.1037/met0000167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPSKAMP S, BORSBOOM D, FRIED E I. Estimating psychological networks and their accuracy: A tutorial paper[J/OL]. Behav Res Methods. 2018;50(1):195\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3758/s13428-017-0862-1\u003c/span\u003e\u003cspan address=\"10.3758/s13428-017-0862-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHASLBECK J M B, FRIED E I. How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets[J/OL]. Psychol Med. 2017;47(16):2767\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0033291717001258\u003c/span\u003e\u003cspan address=\"10.1017/S0033291717001258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG F, YAN Y. Frailty as a predictor of adverse outcomes in patients with gastric cancer: A systematic review and meta-analysis of 75,357 patients[J/OL]. Ageing Res Rev. 2024;101:102528. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2024.102528\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2024.102528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTANG W Z, TAN Z K K, QIU L Y, et al. Prevalence and unfavorable outcome of frailty in older adults with gastric cancer: a systematic review and meta-analysis[J/OL]. Support Care Cancer. 2024;32(2):115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00520-024-08306-8\u003c/span\u003e\u003cspan address=\"10.1007/s00520-024-08306-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eARORA S, FOWLER M E, HARMON C, et al. Differences in Pretreatment Frailty Across Gastrointestinal Cancers in Older Adults: Results From the Cancer and Aging Resilience Evaluation Registry[J/OL]. JCO Oncol Pract. 2022;18(11):e1796\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/OP.22.00270\u003c/span\u003e\u003cspan address=\"10.1200/OP.22.00270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Yu DL, Yi MX, et al. Research Progress on Frailty Interventions in Patients with Digestive Tract Tumors. J Clin Med Pract. 2023;27(14):143\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZAMANI M, ALIZADEH-TABARI S. Anxiety and depression prevalence in digestive cancers: a systematic review and meta-analysis[J/OL]. BMJ Supportive Palliat Care. 2023;13(e2):e235\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjspcare-2021-003275\u003c/span\u003e\u003cspan address=\"10.1136/bmjspcare-2021-003275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMEJAREH Z N ABDOLLAHIB, HOSEINIPALANGI Z, et al. Global, regional, and national prevalence of depression among cancer patients: A systematic review and meta-analysis[J/OL]. Indian J Psychiatry. 2021;63(6):527\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/indianjpsychiatry.indianjpsychiatry_77_21\u003c/span\u003e\u003cspan address=\"10.4103/indianjpsychiatry.indianjpsychiatry_77_21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSONG L, PANG Y, HE Y, et al. Impact of Psychological Factors on Survival in Metastatic Esophagogastric Cancer: Secondary Analysis of a Randomized Controlled Trial[J/OL]. JCO Oncol Pract. 2025;21(7):962\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/OP-24-00514\u003c/span\u003e\u003cspan address=\"10.1200/OP-24-00514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZAMANI M, ALIZADEH-TABARI S. Anxiety and depression prevalence in digestive cancers: a systematic review and meta-analysis[J/OL]. BMJ supportive Palliat care. 2023;13(e2):e235\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjspcare-2021-003275\u003c/span\u003e\u003cspan address=\"10.1136/bmjspcare-2021-003275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSONG L, PANG Y, HE Y, et al. Impact of Psychological Factors on Survival in Metastatic Esophagogastric Cancer: Secondary Analysis of a Randomized Controlled Trial[J/OL]. JCO Oncol Pract. 2025;21(7):962\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/OP-24-00514\u003c/span\u003e\u003cspan address=\"10.1200/OP-24-00514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang SL, Qiang WZ. 2005. Analysis of Perioperative Emotions in Malignant Tumor Patients. J Clin Psychiatry (1): 19\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Juan ZL. Cancer-Related Fatigue Levels and Its Influencing Factors in Patients with Digestive Tract Tumors. Chin J Curr Adv Gen Surg. 2018;21(1):61\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Mei CL, Wei LY, et al. Current Status and Influencing Factors of Cancer-Related Fatigue in Gastric Cancer Patients Undergoing Chemotherapy. Chin Nurs Res. 2025;39(13):2278\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHARTUNG T J, FRIED E I, MEHNERT A, et al. Frequency and network analysis of depressive symptoms in patients with cancer compared to the general population[J/OL]. J Affect Disord. 2019;256:295\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2019.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2019.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG K, DIAO M, YANG Z, et al. Identification of Core Symptom Cluster in Patients With Digestive Cancer: A Network Analysis[J/OL]. Cancer Nurs. 2025;48(1):E55\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/NCC.0000000000001280\u003c/span\u003e\u003cspan address=\"10.1097/NCC.0000000000001280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHIM E, HA H, SUH Y, et al. Network analyses of associations between cancer-related physical and psychological symptoms and quality of life in gastric cancer patients[J/OL]. Psycho-oncology. 2021;30(6):946\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pon.5681\u003c/span\u003e\u003cspan address=\"10.1002/pon.5681\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFearon K, Arends J, Baracos V. Understanding the mechanisms and treatment options in cancer cachexia. Nat Rev Clin Oncol. 2013;10(2):90\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrclinonc.2012.209\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2012.209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI PUKLINLS, CARTMEL F. Post-diagnosis weight trajectories and mortality among women with breast cancer[J/OL]. npj Breast Cancer. 2023;9(1):98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41523-023-00603-5\u003c/span\u003e\u003cspan address=\"10.1038/s41523-023-00603-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBONOMI PD, CRAWFORD J, DUNNE R F, et al. Mortality burden of pre-treatment weight loss in patients with non‐small‐cell lung cancer: A systematic literature review and meta‐analysis[J/OL]. J Cachexia Sarcopenia Muscle. 2024;15(4):1226\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcsm.13477\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eROMERO S A D, JONES L, BAUML JM, et al. The association between fatigue and pain symptoms and decreased physical activity after cancer[J/OL]. Support Care Cancer. 2018;26(10):3423\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00520-018-4203-4\u003c/span\u003e\u003cspan address=\"10.1007/s00520-018-4203-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang HP, Lin ZH, Min ZX, et al. Current Status and Influencing Factors of Frailty in Patients with Digestive System Tumors. J Nurs Sci. 2022;37(8):11\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao JF, Li GS, Min GA. Influencing Factors of Functional Status and Its Relationship with Frailty in Elderly Patients with Gastrointestinal Tumors. China Med Herald. 2025;22(32). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20047/j.issn1673-7210.2025.32.11\u003c/span\u003e\u003cspan address=\"10.20047/j.issn1673-7210.2025.32.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan RY, Hua HM, Yi PS, et al. Data Mining and Analysis of Fall Incidents in Hospitalized Cancer Patients. Chin Gen Pract Nurs. 2025;23(23):4411\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOĞUZ S, ERT\u0026Uuml;RK G, POLAT M G, et al. The effect of kinesiophobia on physical activity, balance, and fear of falling in patients with Parkinson\u0026rsquo;s disease[J/OL]. Physiother Theory Pract. 2023;39(4):865\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/09593985.2022.2028325\u003c/span\u003e\u003cspan address=\"10.1080/09593985.2022.2028325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG P, WANG L, ZHOU Q, et al. A network analysis of anxiety and depression symptoms in Chinese disabled elderly[J/OL]. J Affect Disord. 2023;333:535\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.04.065\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.04.065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOSTANTINI A, POMPILI M, INNAMORATI M, et al. Psychiatric Pathology and Suicide Risk in Patients with Cancer[J/OL]. J Psychosoc Oncol. 2014;32(4):383\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07347332.2014.917136\u003c/span\u003e\u003cspan address=\"10.1080/07347332.2014.917136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJOENSUU H, ERIKSSON M, SUNDBY HALL K, et al. Survival Outcomes Associated With 3 Years vs 1 Year of Adjuvant Imatinib for Patients With High-Risk Gastrointestinal Stromal Tumors: An Analysis of a Randomized Clinical Trial After 10-Year Follow-up[J/OL]. JAMA Oncol. 2020;6(8):1241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2020.2091\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2020.2091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTRAUTMANN A. M\u0026eacute;canismes sous-jacents \u0026agrave; la fatigue chronique, un sympt\u0026ocirc;me trop souvent n\u0026eacute;glig\u0026eacute;: II. De l\u0026rsquo;immunit\u0026eacute; d\u0026eacute;r\u0026eacute;gul\u0026eacute;e \u0026agrave; la neuroinflammation et ses cons\u0026eacute;quences[J/OL]. m\u0026eacute;decine/sciences. 2021;37(11):1047\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1051/medsci/2021170\u003c/span\u003e\u003cspan address=\"10.1051/medsci/2021170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eROINE I, FONCEA L M, LEDERMANN W, et al. Slow recovery of cerebrospinal fluid glucose and protein concentrations distinguish pneumococcal from Haemophilus influenzae and meningococcal meningitis in children[J]. Pediatr Infect Dis J. 1995;14(10):905\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCUI L, LI S, WANG S, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment[J/OL]. Signal Transduct Target Therapy. 2024;9(1):30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-024-01738-y\u003c/span\u003e\u003cspan address=\"10.1038/s41392-024-01738-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKRISTIANSEN MS, STABURSVIK J, O\u0026rsquo;LEARY E C et al. Clinical symptoms and markers of disease mechanisms in adolescent chronic fatigue following Epstein-Barr virus infection: An exploratory cross-sectional study[J/OL]. Brain, Behavior, and Immunity, 2019, 80: 551\u0026ndash;563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbi.2019.04.040\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2019.04.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou JY, Li Y, Jia L, et al. Network Analysis of Post-Traumatic Stress Disorder Symptoms and Nursing Strategies in First-Episode Stroke Patients. Chin J Nurs. 2024;59(8):953\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eALBERT K M, NEWHOUSE PA, Estrogen. Stress, and Depression: Cognitive and Biological Interactions[J/OL]. Ann Rev Clin Psychol. 2019;15(1):399\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-clinpsy-050718-095557\u003c/span\u003e\u003cspan address=\"10.1146/annurev-clinpsy-050718-095557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKUEHNER C. Why is depression more common among women than among men?[J/OL]. Lancet Psychiatry. 2017;4(2):146\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2215-0366(16)30263-2\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(16)30263-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026Ouml;R\u0026Ouml;S V, OSV\u0026Aacute;TH P. [Gender differences in suicidal behavior][J]. Neuropsychopharmacologia Hungarica: A Magyar Pszichofarmakologiai Egyesulet Lapja\u0026thinsp;=\u0026thinsp;Official. J Hung Association Psychopharmacol. 2004;6(2):65\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi SY. Analysis of Anxiety, Depression, Insomnia Status, Their Impact on Quality of Life and Related Factors in Malignant Tumor Patients. China Medical University (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.27652/d.cnki.gzyku.2020.001005\u003c/span\u003e\u003cspan address=\"10.27652/d.cnki.gzyku.2020.001005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng LH. Nursing Intervention Strategies for Sleep Disorders in Patients in the Radiotherapy Department of a Cancer Hospital. Sci Guide Dly January. 2025;9:B03. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.28511/n.cnki.nkxdb.2025.000612\u003c/span\u003e\u003cspan address=\"10.28511/n.cnki.nkxdb.2025.000612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang LJ, Jiao S. Analysis of the Effect of Acupoint Application Combined with Progressive Relaxation Training on Sleep Disorders in Chemotherapy Patients with Digestive Tract Malignancies. Heilongjiang J Traditional Chin Med. 2025;54(2):93\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastrointestinal Neoplasms, Frailty Syndrome, Depression, Network Analysis, Clinical Nursing","lastPublishedDoi":"10.21203/rs.3.rs-8820419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8820419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore core symptoms and nursing interventions in the frailty-depression network of patients with gastrointestinal cancer Using Network Analysis Models to Inform Precision Intervention and Care.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted among patients with gastrointestinal cancer undergoing treatment in the oncology department of a tertiary general hospital in Shandong Province from February to June 2023, with concurrent assessments of frailty and depression symptoms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Fried Frailty Scale and Patient Health Questionnaire were used to assess frailty and depression status, respectively. A total of 238 patients with gastrointestinal cancer completed the questionnaires. Network analysis of the relationship was conducted using R software, including network relationship analysis, core symptom analysis, and evaluation of network structure accuracy and stability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNetwork analysis revealed the strongest associations between \u0026ldquo;FP2 (slowed gait) and FP4 (low physical activity),\u0026rdquo; \u0026ldquo;PHQ8 (bradykinesia/agitation) and PHQ9 (suicidal ideation)\u0026rdquo;, and \u0026ldquo;PHQ8 (bradykinesia/agitation) and PHQ6 (guilt)\u0026rdquo; which exhibited the strongest correlations. PHQ4 (lack of energy) exhibited the highest predictability and expected impact.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study employed symptom network analysis to explore the frailty and depression relationship network among patients with gastrointestinal cancer.\u0026ldquo;PHQ4 (lack of energy)\u0026rdquo; was the most central node. Additionally, gender differences should be considered to develop scientifically grounded psychological interventions that improve patients' psychological well-being and mitigate their frailty and depression.\u003c/p\u003e\u003ch2\u003eImpact:\u003c/h2\u003e \u003cp\u003eThis study employed network analysis to explore the association between frailty and depression in patients with gastrointestinal cancer. This methodology overcomes the limitations of traditional univariate analyses by visualizing and quantifying complex relationships among symptoms. It identifies the most central and influential symptoms and connection pathways within the network, offering a novel perspective and specific targets for precise clinical intervention\u003c/p\u003e","manuscriptTitle":"Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:32:13","doi":"10.21203/rs.3.rs-8820419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T12:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285604794261632759157240154675987425955","date":"2026-03-13T17:19:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T10:06:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-12T11:31:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T11:38:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T11:37:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2026-02-08T08:51:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3814d43-4123-4db3-9cfd-c3db1aa6bb81","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T07:32:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:32:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8820419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8820419","identity":"rs-8820419","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}