Background:Lung cancer,one of the most prevalent and deadly malignancies worldwide,not only poses a significant physical burden but also a profound psychological challenge to patients.Among these psychological challen...Background:Lung cancer,one of the most prevalent and deadly malignancies worldwide,not only poses a significant physical burden but also a profound psychological challenge to patients.Among these psychological challenges,the fear of recurrence stands out as a particularly distressing issue.This fear,often rooted in the patients’past experiences with the disease and its treatment,can significantly impact their quality of life,mental health,and even compliance with follow-up care.Moreover,this fear can be exacerbated by the lack of understanding and support from healthcare professionals and family members,further isolating patients and compounding their psychological burden.Therefore,understanding and addressing the fear of recurrence in lung cancer patients is crucial for improving their overall well-being and outcomes.Aims:This study aims to develop a symptom network model for fear of recurrence in early-stage lung cancer patients,analyzing symptom correlations to enhance healthcare providers’understanding and management of these symptoms,thereby improving patient outcomes and quality of life.Design:A cross-sectional study design was used.Method:We employed convenience sampling to recruit 551 lung cancer patients from the Thoracic Surgery Department of a tertiary hospital in Beijing between January 2023 and December 2023.A cross-sectional study was conducted using the General Information Questionnaire,Fear of Disease Progression Scale,and Level of Hope Scale.Network analysis was performed with JASP 0.18.3.0 using the EBICglasso method,and centrality metrics including Betweenness,Closeness,Degree centrality,and Expected influence were calculated.Results:Symptom network analysis identified fear of family impact and future work disruption as central to recurrence fear in these patients.Gender-based analysis revealed‘fear of being unable to continue work’as central in males,while‘fear of affecting family members’was central in females.Among adolescents,concerns about future work,medication side effects,and family impact showed the highest expected influence.In contrast,older patients predominantly feared major treatment implications.One-way ANOVA indicated that older age correlated with reduced recurrence fear,and higher hope levels significantly mitigated this fear.Conclusion:This study broadens understanding of fear of recurrence across demographic variables like gender and age,elucidating symptom interrelations and impacts.Future strategies should focus on patient-specific differences in recurrence fear to formulate targeted interventions.Relevance to Clinical Practice:Through in-depth analysis of the symptom network,healthcare professionals can more comprehensively understand the psychological responses of lung cancer patients when they face the risk of recurrence,and then formulate more precise and personalized treatment plans.At the same time,doctors and nurses can adjust treatment strategies in a timely manner according to the changes in the patient’s symptom network and provide more comprehensive psychological support,thus enhancing the patient’s treatment adherence and outcome.Patient Contribution:People who were invited to participate voluntarily completed a range of questionnaires.展开更多
BACKGROUND A psychological sense of coherence(SOC)in percutaneous coronary intervention(PCI)patients is important for disease prognosis,and there is considerable variation between their symptoms.In contrast,network an...BACKGROUND A psychological sense of coherence(SOC)in percutaneous coronary intervention(PCI)patients is important for disease prognosis,and there is considerable variation between their symptoms.In contrast,network analysis provides a new approach to gaining insight into the complex nature of symptoms and symptom clusters and identifying core symptoms.AIM To explore the psychological coherence of symptoms experienced by PCI patients,we aim to analyze differences in their associated factors and employ network analysis to characterize the symptom networks.METHODS A total of 472 patients who underwent PCI were selected for a cross-sectional study.The objective was to investigate the association between general patient demographics,medical coping styles,perceived stress status,and symptoms of psychological coherence.Data analysis was conducted using a linear regression model and a network model to visualize psychological coherence and calculate a centrality index.RESULTSPost-PCI patients exhibited low levels of psychological coherence, which correlated with factors such as education,income, age, place of residence, adherence to medical examinations, perceived stress, and medical coping style.Network analysis revealed that symptoms within the sense of psychological coherence were strongly interconnected,particularly with SOC2 and SOC8, demonstrating the strongest correlations. Among these, SOC10 emergedas the symptom with the highest intensity, centrality, and proximity, identifying it as the most central symptom.CONCLUSIONThe network model has strong explanatory power in describing the psychological consistency symptoms ofpatients after PCI, identifying the central SOC symptoms, among which SOC10 is the key to overall SOCenhancement, and there is a strong positive correlation between SOC2 and SOC8, emphasizing the need to considerthe synergistic effect of symptoms in intervention measures.展开更多
Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive s...Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.展开更多
Prior empirical evidence supports the close association between problematic social networking sites use(PSNS)and online aggressive behavior(OAB).However,few studies have examined the potential symptom connection betwe...Prior empirical evidence supports the close association between problematic social networking sites use(PSNS)and online aggressive behavior(OAB).However,few studies have examined the potential symptom connection between these two issues as part of a multidimensional mechanism.The current study aims to explore the underlying network structure between PSNS use and OAB and conduct a simulated intervention using the NodeIdentifyR algorithm(NIRA),taking gender into account to further inform the implementation of intervention measures.A total of 1325 participants completed questionnaires that assessed SNS addictive tendencies and online aggression.The symptom-specific intervention simulation analysis was conducted to clarify which symptoms can alleviate or exacerbate the overall performance of PSNS and OAB.Findings indicated that there were significantly different symptom connections in both groups,with no symptom linked to the two behaviors in the male group,while“insomnia”and“instrumental overt aggression”linked the two in the female group(p<0.05).In addition,“dual existence”may be the most effective alleviating intervention target for both groups(NIRA=1.34 for males and NIRA=1.44 for females).Besides,“virtual friend anxiety”and“online relationship satisfaction”should be considered separately for preventive care when dealing with males(NIRA=1.07)and females(NIRA=1.37).The findings offer significant implications for gender-specific strategies to alleviate PSNS use and OAB linkage.展开更多
目的·探究大学生抑郁症状与焦虑症状的共病情况及其与社交网络成瘾之间的关联。方法·通过在上海市5所高等院校进行的针对本科生的横断面问卷调查,收集调查对象的人口学信息,采用患者健康问卷9条目(Patient Health Questionnai...目的·探究大学生抑郁症状与焦虑症状的共病情况及其与社交网络成瘾之间的关联。方法·通过在上海市5所高等院校进行的针对本科生的横断面问卷调查,收集调查对象的人口学信息,采用患者健康问卷9条目(Patient Health Questionnaire-9,PHQ-9)、广泛性焦虑量表(Generalized Anxiety Disorder,GAD-7)和青少年社交网络成瘾评估量表评估抑郁、焦虑及社交网络成瘾情况。采用Logistic回归对大学生社交网络成瘾进行多因素分析。采用潜在剖面分析对大学生的抑郁症状和焦虑症状进行分类,识别大学生抑郁和焦虑情况的异质性,并通过稳健三步法校正分类误差。运用χ^(2)检验和Logistic回归分析明确各剖面的社交网络成瘾率差异是否存在统计学意义。结果·共纳入调查对象1768名,23.53%的调查对象存在抑郁和焦虑共病的情况,66.29%的调查对象有社交网络成瘾。调整人口学因素后,大学生抑郁和焦虑共病组(n=416)发生社交网络成瘾的概率是无抑郁或焦虑组(n=618)的6.093倍(OR=6.093,95%CI 4.426~8.389),仅有抑郁或焦虑组(n=734)发生社交网络成瘾的概率是无抑郁或焦虑组的2.442倍(OR=2.442,95%CI 1.947~3.064)。大学生抑郁和焦虑症状的潜在剖面可划分为中水平组(45.4%)、低水平组(46.3%)和高水平组(8.3%)。3个剖面的社交网络成瘾率差异存在统计学意义,中水平组和高水平组较低水平组更易发生社交网络成瘾(均P<0.001)。结论·大学生抑郁和焦虑情况存在异质性,且易出现抑郁合并焦虑的情况。对于高抑郁、高焦虑情况的大学生,其社交网络成瘾情况也较严重。因此,应高度关注大学生的心理健康状况,针对抑郁合并焦虑症状严重的大学生,就其社交网络成瘾问题开展精准化干预工作。展开更多
目的通过网络分析探究老年脑卒中患者多维症状网络,识别核心症状并分析多维症状的影响因素,为制订精准症状管理提供依据。方法2024年1-5月,采用便利抽样法选取安徽省4所三级甲等医院收治的500例老年脑卒中患者为研究对象,采用一般资料...目的通过网络分析探究老年脑卒中患者多维症状网络,识别核心症状并分析多维症状的影响因素,为制订精准症状管理提供依据。方法2024年1-5月,采用便利抽样法选取安徽省4所三级甲等医院收治的500例老年脑卒中患者为研究对象,采用一般资料调查表、脑卒中症状体验量表(stroke patients’symptom experience scale,Stroke-SES)、多维健康心理控制源量表(multidimensional health locus of control,MHLC)、简易疾病感知问卷(brief illness perception questionnaire,BIPQ)对其进行调查。通过R语言构建症状网络并分析中心性指标,采用单因素分析、相关分析及多元线性回归分析探讨多维症状的影响因素。结果老年脑卒中患者多维症状发生率为19.00%~94.60%。症状网络分析显示,“自理能力下降”强度中心性和预期影响均最高。多元线性回归分析显示,身体活动水平、健康内部控制、健康机遇控制、疾病感知是症状严重程度的影响因素(均P<0.01),解释总变异的63.24%。结论自理能力下降是老年脑卒中患者多维症状网络中的核心症状,受多重因素影响;医护人员可基于核心症状及其相关影响因素制订精准干预措施,以提升症状管理效能。展开更多
基金supported by Beijing Hope Run Special Fund of Cancer Foundation of China(LC2022C05).
文摘Background:Lung cancer,one of the most prevalent and deadly malignancies worldwide,not only poses a significant physical burden but also a profound psychological challenge to patients.Among these psychological challenges,the fear of recurrence stands out as a particularly distressing issue.This fear,often rooted in the patients’past experiences with the disease and its treatment,can significantly impact their quality of life,mental health,and even compliance with follow-up care.Moreover,this fear can be exacerbated by the lack of understanding and support from healthcare professionals and family members,further isolating patients and compounding their psychological burden.Therefore,understanding and addressing the fear of recurrence in lung cancer patients is crucial for improving their overall well-being and outcomes.Aims:This study aims to develop a symptom network model for fear of recurrence in early-stage lung cancer patients,analyzing symptom correlations to enhance healthcare providers’understanding and management of these symptoms,thereby improving patient outcomes and quality of life.Design:A cross-sectional study design was used.Method:We employed convenience sampling to recruit 551 lung cancer patients from the Thoracic Surgery Department of a tertiary hospital in Beijing between January 2023 and December 2023.A cross-sectional study was conducted using the General Information Questionnaire,Fear of Disease Progression Scale,and Level of Hope Scale.Network analysis was performed with JASP 0.18.3.0 using the EBICglasso method,and centrality metrics including Betweenness,Closeness,Degree centrality,and Expected influence were calculated.Results:Symptom network analysis identified fear of family impact and future work disruption as central to recurrence fear in these patients.Gender-based analysis revealed‘fear of being unable to continue work’as central in males,while‘fear of affecting family members’was central in females.Among adolescents,concerns about future work,medication side effects,and family impact showed the highest expected influence.In contrast,older patients predominantly feared major treatment implications.One-way ANOVA indicated that older age correlated with reduced recurrence fear,and higher hope levels significantly mitigated this fear.Conclusion:This study broadens understanding of fear of recurrence across demographic variables like gender and age,elucidating symptom interrelations and impacts.Future strategies should focus on patient-specific differences in recurrence fear to formulate targeted interventions.Relevance to Clinical Practice:Through in-depth analysis of the symptom network,healthcare professionals can more comprehensively understand the psychological responses of lung cancer patients when they face the risk of recurrence,and then formulate more precise and personalized treatment plans.At the same time,doctors and nurses can adjust treatment strategies in a timely manner according to the changes in the patient’s symptom network and provide more comprehensive psychological support,thus enhancing the patient’s treatment adherence and outcome.Patient Contribution:People who were invited to participate voluntarily completed a range of questionnaires.
基金Supported by the Self-funded Research Project of Health Commission of Guangxi Zhuang Autonomous Region,No.Z-A20220509.
文摘BACKGROUND A psychological sense of coherence(SOC)in percutaneous coronary intervention(PCI)patients is important for disease prognosis,and there is considerable variation between their symptoms.In contrast,network analysis provides a new approach to gaining insight into the complex nature of symptoms and symptom clusters and identifying core symptoms.AIM To explore the psychological coherence of symptoms experienced by PCI patients,we aim to analyze differences in their associated factors and employ network analysis to characterize the symptom networks.METHODS A total of 472 patients who underwent PCI were selected for a cross-sectional study.The objective was to investigate the association between general patient demographics,medical coping styles,perceived stress status,and symptoms of psychological coherence.Data analysis was conducted using a linear regression model and a network model to visualize psychological coherence and calculate a centrality index.RESULTSPost-PCI patients exhibited low levels of psychological coherence, which correlated with factors such as education,income, age, place of residence, adherence to medical examinations, perceived stress, and medical coping style.Network analysis revealed that symptoms within the sense of psychological coherence were strongly interconnected,particularly with SOC2 and SOC8, demonstrating the strongest correlations. Among these, SOC10 emergedas the symptom with the highest intensity, centrality, and proximity, identifying it as the most central symptom.CONCLUSIONThe network model has strong explanatory power in describing the psychological consistency symptoms ofpatients after PCI, identifying the central SOC symptoms, among which SOC10 is the key to overall SOCenhancement, and there is a strong positive correlation between SOC2 and SOC8, emphasizing the need to considerthe synergistic effect of symptoms in intervention measures.
基金supported by Beijing High Level Public Health Technology Talent Construction Project(Discipline Backbone-01-028)the Beijing Municipal Science&Technology Commission(No.Z181100001518005)+2 种基金the Capital's Funds for Health Improvement and Research(CFH 2024-2-1174)the University of Macao(MYRG-GRG2023-00141-FHS,CPG2025-00021-FHS)the Science and Technology Plan Foundation of Guangzhou(No.202201011663).
文摘Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.
基金supported by Research Grant from“National Center for Mental Health,China-East China Normal University”Mental Health Digital Governance Innovation Institute.
文摘Prior empirical evidence supports the close association between problematic social networking sites use(PSNS)and online aggressive behavior(OAB).However,few studies have examined the potential symptom connection between these two issues as part of a multidimensional mechanism.The current study aims to explore the underlying network structure between PSNS use and OAB and conduct a simulated intervention using the NodeIdentifyR algorithm(NIRA),taking gender into account to further inform the implementation of intervention measures.A total of 1325 participants completed questionnaires that assessed SNS addictive tendencies and online aggression.The symptom-specific intervention simulation analysis was conducted to clarify which symptoms can alleviate or exacerbate the overall performance of PSNS and OAB.Findings indicated that there were significantly different symptom connections in both groups,with no symptom linked to the two behaviors in the male group,while“insomnia”and“instrumental overt aggression”linked the two in the female group(p<0.05).In addition,“dual existence”may be the most effective alleviating intervention target for both groups(NIRA=1.34 for males and NIRA=1.44 for females).Besides,“virtual friend anxiety”and“online relationship satisfaction”should be considered separately for preventive care when dealing with males(NIRA=1.07)and females(NIRA=1.37).The findings offer significant implications for gender-specific strategies to alleviate PSNS use and OAB linkage.
文摘目的通过网络分析探究老年脑卒中患者多维症状网络,识别核心症状并分析多维症状的影响因素,为制订精准症状管理提供依据。方法2024年1-5月,采用便利抽样法选取安徽省4所三级甲等医院收治的500例老年脑卒中患者为研究对象,采用一般资料调查表、脑卒中症状体验量表(stroke patients’symptom experience scale,Stroke-SES)、多维健康心理控制源量表(multidimensional health locus of control,MHLC)、简易疾病感知问卷(brief illness perception questionnaire,BIPQ)对其进行调查。通过R语言构建症状网络并分析中心性指标,采用单因素分析、相关分析及多元线性回归分析探讨多维症状的影响因素。结果老年脑卒中患者多维症状发生率为19.00%~94.60%。症状网络分析显示,“自理能力下降”强度中心性和预期影响均最高。多元线性回归分析显示,身体活动水平、健康内部控制、健康机遇控制、疾病感知是症状严重程度的影响因素(均P<0.01),解释总变异的63.24%。结论自理能力下降是老年脑卒中患者多维症状网络中的核心症状,受多重因素影响;医护人员可基于核心症状及其相关影响因素制订精准干预措施,以提升症状管理效能。
