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Article(id=1241329579084804742, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241329570129956900, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202502350, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739808000000, receivedDateStr=2025-02-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773885634442, onlineDateStr=2026-03-19, pubDate=1752076800000, pubDateStr=2025-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773885634442, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773885634442, creator=13701087609, updateTime=1773885634442, updator=13701087609, issue=Issue{id=1241329570129956900, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='13', pageStart='2305', pageEnd='2496', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773885632307, creator=13701087609, updateTime=1773885763730, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241330121425080472, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241329570129956900, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241330121425080473, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241329570129956900, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2437, endPage=2443, ext={EN=ArticleExt(id=1241329579567149751, articleId=1241329579084804742, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Network analysis of the relationship between obesity-related eating behaviors and family health in adolescents, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct a contemporaneous network model of adolescent obesity-related eating behaviors and family health, identifying potential intervention targets to provide reference for effective management of obesity-related eating behaviors and prevention of obesity-related diseases.

Methods

A multi-stage sampling method was used to select 1 065 adolescents from 120 Chinese cities between July and September 2021. Data were collected using a general information questionnaire, Chinese version of the Sakata Eating Behavior Scale, and Chinese version of the Family Health Scale. Network analysis was performed using R language to explore relationships between obesity-related eating behaviors and family health.

Results

The network model revealed FH2 (family health lifestyle) as the node with the highest expected influence (EI=0.970). EB6 (others’ eating behavior stimulates my eating desire) showed the highest bridge expected influence (BEI=0.048), while FH3 (family health resources, BEI=-0.397) and EB1 (irregular mealtime, BEI=-0.235) exerted strong negative connecting effects in the network.

Conclusion

Interactive relationships exist between adolescent obesity-related eating behaviors and family health. Targeted interventions focusing on eating behavior regulation, integration of family health resources, and establishment of regular mealtimes may improve dietary behavior management and prevent obesity-related diseases in adolescents.

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目的

构建青少年肥胖相关饮食行为与家庭健康的同期网络模型，旨在识别潜在的干预靶点，为高效管理青少年肥胖相关饮食行为与预防肥胖相关疾病提供参考依据。

方法

通过多阶段抽样选取2021年7—9月来自中国120个城市的1 065例青少年作为研究对象，采用一般资料调查表、中文版坂田饮食行为量表和中文版家庭健康量表进行调查。基于R语言进行网络分析，探索肥胖相关饮食行为与家庭健康间的关系。

结果

基于网络模型发现，家庭健康生活方式(FH2)是预期影响值最高的节点[expected influence (EI)=0.970]；桥梁预期影响值最高的节点是别人的进食行为激发我的进食欲望[EB6, bridge expected influence (BEI)=0.048]；而家庭健康资源(FH3,BEI=-0.397)和没有固定的用餐时间(EB1,BEI=-0.235)则在网络中发挥了较强的负向连接作用。

结论

青少年肥胖相关饮食行为与家庭健康之间存在相互作用关系，饮食行为调控、整合家庭健康资源与规划固定的用餐时间等针对性的家庭健康生活方式，有助于提高青少年饮食行为管理效率，预防肥胖及其相关疾病发生。

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李瑜，E-mail:
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赖湘瑜（2002—），女，硕士在读，研究方向：健康行为与健康促进

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赖湘瑜（2002—），女，硕士在读，研究方向：健康行为与健康促进

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赖湘瑜（2002—），女，硕士在读，研究方向：健康行为与健康促进

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Current Diabetes Reports, 2022, 22(4): 147-155., articleTitle=The impact of meal timing on risk of weight gain and development of obesity: a review of the current evidence and opportunities for dietary intervention, refAbstract=null), Reference(id=1241329767358722467, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, doi=null, pmid=null, pmcid=null, year=2022, volume=168, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[26], rfOrder=28, authorNames=Loth KA, Ji Z, Wolfson J, journalName=Appetite, refType=null, unstructuredReference=Loth KA, Ji Z, Wolfson J, et al. COVID-19 pandemic shifts in food-related parenting practices within an ethnically/racially and socioeconomically diverse sample of families of preschool-aged children[J] . 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JAMA Netw Open, 2023, 6(4): e236331., articleTitle=Effect of longer family meals on children's fruit and vegetable intake: A randomized clinical trial, refAbstract=null)], funds=[Fund(id=1241329764175245634, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, awardId=粤教高函[2024]30号, language=CN, fundingSource=2024年度广东省本科高校教学质量与教学改革工程项目(粤教高函[2024]30号), fundOrder=null, country=null), Fund(id=1241329764284297542, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, awardId=研生[2024]89号, language=CN, fundingSource=2024年研究生课程思政建设项目(研生[2024]89号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241329756650664915, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, xref=1., ext=[AuthorCompanyExt(id=1241329756659053525, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, companyId=1241329756650664915, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China), AuthorCompanyExt(id=1241329756667442134, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, companyId=1241329756650664915, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.广州中医药大学护理学院，广东 广州 510006)]), AuthorCompany(id=1241329756831020003, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, xref=2., ext=[AuthorCompanyExt(id=1241329756839408612, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, companyId=1241329756831020003, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.广州中医药大学教务部)])], figs=[ArticleFig(id=1241329762946314501, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=EN, label=Figure 1, caption=Network model of obesity-related eating behavior-family health in adolescents, figureFileSmall=S3sslver9Jk9S1Jf8lFGcw==, figureFileBig=yCaDaMoThwnaO7NmdfoaAQ==, tableContent=null), ArticleFig(id=1241329763030200588, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=CN, label=图1, caption=青少年肥胖相关饮食行为-家庭健康网络模型

注：蓝色边和红色边分别表示正相关和负相关；蓝色节点代表肥胖相关饮食行为，黄色节点代表家庭健康；EB1=没有固定的用餐时间，EB2=只有吃十分饱才满足，EB3=吃饭速度快，EB4=认为自己比别人更容易长胖，EB5=喜欢高油脂的食物，EB6=别人的进食行为激发我的进食欲望，EB7=购买食物的数量超过需要才满意，FH1=家庭/社会/情绪健康过程，FH2=家庭健康生活方式，FH3=家庭健康资源，FH4=家庭外部社会支持。

, figureFileSmall=S3sslver9Jk9S1Jf8lFGcw==, figureFileBig=yCaDaMoThwnaO7NmdfoaAQ==, tableContent=null), ArticleFig(id=1241329763160224020, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=EN, label=Figure 2, caption=Results of centrality analysis of network, figureFileSmall=UAZ+ITWYJo6tqLESSteM4Q==, figureFileBig=GIsXK4kEDJhPpQm4jr7s1g==, tableContent=null), ArticleFig(id=1241329763298636058, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=CN, label=图2, caption=网络中心性分析结果

注：图A为网络的节点EI值；图B为网络的节点BEI值。

, figureFileSmall=UAZ+ITWYJo6tqLESSteM4Q==, figureFileBig=GIsXK4kEDJhPpQm4jr7s1g==, tableContent=null), ArticleFig(id=1241329763432853793, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=EN, label=Figure 3, caption=Stability tests of the network’s node EI, BEI and edge weights, figureFileSmall=TMsaVfKeYhThTlP5W9AZUA==, figureFileBig=4pnAMvpi5z1e7djZq38a/w==, tableContent=null), ArticleFig(id=1241329763567071528, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=CN, label=图3, caption=网络的节点EI、BEI及边缘权重的稳定性检验

注：图A为节点EI、BEI的稳定性检验，横轴表示抽取样本的比例，纵轴表示原始样本的平均CS系数；图B为边缘权重的稳定性检验，红线表示按顺序排序的边缘权重，灰色区域表示95%CI，灰色区域越窄则95%CI越小，表示边缘权重的准确性越高。

, figureFileSmall=TMsaVfKeYhThTlP5W9AZUA==, figureFileBig=4pnAMvpi5z1e7djZq38a/w==, tableContent=null), ArticleFig(id=1241329763676123438, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=EN, label=Figure 4, caption=Differences test of the network’s node EI, BEI and edge weights, figureFileSmall=hKIbwulm1zILcILtA8t8RA==, figureFileBig=Oggx4AaLVeTspFRoGLgdfw==, tableContent=null), ArticleFig(id=1241329763801952562, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=CN, label=图4, caption=网络的节点EI、BEI及边缘权重的差异性检验

注：图A为节点EI的差异性检验；图B为节点BEI的差异性检验；图C为边缘权重的差异性检验；白框表示节点的EI和BEI值；黑框和灰框分别表示节点、边缘权重之间存在或不存在显著性差异；彩色方框表示边缘权重的强弱，从红色(负相关)到白色(代表较弱的边)，最后是蓝色(正相关)。

, figureFileSmall=hKIbwulm1zILcILtA8t8RA==, figureFileBig=Oggx4AaLVeTspFRoGLgdfw==, tableContent=null), ArticleFig(id=1241329763902615861, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=EN, label=Table 1, caption=

Differences in obesity-related eating behaviors and family health scores among general information of adolescents［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目青少年肥胖相关饮食行为家庭健康
总样本1 06516.75±4.3638.48±6.65
性别
男442 (41.5)16.77±4.5737.92±6.57
女623 (58.5)16.73±4.2038.87±6.68
t值0.135-2.287
P值0.8920.022
常住地
农村312 (29.3)16.86±4.3037.77±6.52
城镇753 (70.7)16.70±4.3838.77±6.68
t值0.532-2.245
P值0.5950.025
文化程度
小学及以下112 (10.5)15.79±4.2439.98±6.48
初中或高中595 (55.9)16.56±4.1638.57±6.61
大专及以上358 (33.6)17.35±4.6337.84±6.71
F值6.7974.586
P值0.0010.010
是否独生子女
是334 (31.4)16.83±4.5938.75±6.59
否731 (68.6)16.71±4.2438.35±6.67
t值0.4390.907
P值0.6600.365
家庭类型
核心家庭885 (83.1)16.62±4.3038.73±6.55
其他形式的家庭(包括单亲家庭、主干家庭等)180 (16.9)17.38±4.5637.21±7.00
t值-2.1592.820
P值0.0310.005
家庭人均月收入(元)
≤1 500127 (11.9)17.69±4.2135.32±6.48
1 500 ～ 4 500453 (42.5)16.82±4.0638.05±6.38
≥4 500485 (45.5)16.43±4.6239.70±6.63
F/H值6.787**24.417*
P值0.034<0.001
BMI
体重过轻52 (4.9)15.94±4.5739.88±6.23
正常体重854 (80.2)16.39±4.2738.54±6.70
超重106 (10.0)18.80±3.7937.97±6.35
肥胖53 (5.0)19.11±4.7237.00±6.68
F值16.2321.886
P值<0.0010.130
), ArticleFig(id=1241329764020056377, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241329579084804742, language=CN, label=表1, caption=

青少年一般资料在肥胖相关饮食行为与家庭健康得分中的差异［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目青少年肥胖相关饮食行为家庭健康
总样本1 06516.75±4.3638.48±6.65
性别
男442 (41.5)16.77±4.5737.92±6.57
女623 (58.5)16.73±4.2038.87±6.68
t值0.135-2.287
P值0.8920.022
常住地
农村312 (29.3)16.86±4.3037.77±6.52
城镇753 (70.7)16.70±4.3838.77±6.68
t值0.532-2.245
P值0.5950.025
文化程度
小学及以下112 (10.5)15.79±4.2439.98±6.48
初中或高中595 (55.9)16.56±4.1638.57±6.61
大专及以上358 (33.6)17.35±4.6337.84±6.71
F值6.7974.586
P值0.0010.010
是否独生子女
是334 (31.4)16.83±4.5938.75±6.59
否731 (68.6)16.71±4.2438.35±6.67
t值0.4390.907
P值0.6600.365
家庭类型
核心家庭885 (83.1)16.62±4.3038.73±6.55
其他形式的家庭(包括单亲家庭、主干家庭等)180 (16.9)17.38±4.5637.21±7.00
t值-2.1592.820
P值0.0310.005
家庭人均月收入(元)
≤1 500127 (11.9)17.69±4.2135.32±6.48
1 500 ～ 4 500453 (42.5)16.82±4.0638.05±6.38
≥4 500485 (45.5)16.43±4.6239.70±6.63
F/H值6.787**24.417*
P值0.034<0.001
BMI
体重过轻52 (4.9)15.94±4.5739.88±6.23
正常体重854 (80.2)16.39±4.2738.54±6.70
超重106 (10.0)18.80±3.7937.97±6.35
肥胖53 (5.0)19.11±4.7237.00±6.68
F值16.2321.886
P值<0.0010.130
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基于网络分析的青少年肥胖相关饮食行为与家庭健康的关系研究
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赖湘瑜 1 , 刘嘉琳 1 , 赵耀 1 , 伍丽华 1 , 吴心雨 1 , 楚慧耘 1 , 何康 1 , 廖源 2 , 李瑜 1
现代预防医学 | 健康与社会行为 2025,52(13): 2437-2443
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现代预防医学 | 健康与社会行为 2025, 52(13): 2437-2443
基于网络分析的青少年肥胖相关饮食行为与家庭健康的关系研究
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赖湘瑜1, 刘嘉琳1, 赵耀1, 伍丽华1, 吴心雨1, 楚慧耘1, 何康1, 廖源2, 李瑜1
作者信息
  • 1.广州中医药大学护理学院，广东 广州 510006
  • 2.广州中医药大学教务部

    • 赖湘瑜（2002—），女，硕士在读，研究方向：健康行为与健康促进

通讯作者:

李瑜，E-mail:
Network analysis of the relationship between obesity-related eating behaviors and family health in adolescents
Xiang-yu LAI1, Jia-lin LIU1, Yao ZHAO1, Li-hua WU1, Xin-yu WU1, Hui-yun CHU1, Kang HE1, Yuan LIAO2, Yu LI1
Affiliations
  • School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
出版时间: 2025-07-10 doi: 10.20043/j.cnki.MPM.202502350
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摘要
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目的

构建青少年肥胖相关饮食行为与家庭健康的同期网络模型，旨在识别潜在的干预靶点，为高效管理青少年肥胖相关饮食行为与预防肥胖相关疾病提供参考依据。

方法

通过多阶段抽样选取2021年7—9月来自中国120个城市的1 065例青少年作为研究对象，采用一般资料调查表、中文版坂田饮食行为量表和中文版家庭健康量表进行调查。基于R语言进行网络分析，探索肥胖相关饮食行为与家庭健康间的关系。

结果

基于网络模型发现，家庭健康生活方式(FH2)是预期影响值最高的节点[expected influence (EI)=0.970]；桥梁预期影响值最高的节点是别人的进食行为激发我的进食欲望[EB6, bridge expected influence (BEI)=0.048]；而家庭健康资源(FH3,BEI=-0.397)和没有固定的用餐时间(EB1,BEI=-0.235)则在网络中发挥了较强的负向连接作用。

结论

青少年肥胖相关饮食行为与家庭健康之间存在相互作用关系，饮食行为调控、整合家庭健康资源与规划固定的用餐时间等针对性的家庭健康生活方式，有助于提高青少年饮食行为管理效率，预防肥胖及其相关疾病发生。

青少年  /  肥胖相关饮食行为  /  家庭健康  /  网络分析
Objective

To construct a contemporaneous network model of adolescent obesity-related eating behaviors and family health, identifying potential intervention targets to provide reference for effective management of obesity-related eating behaviors and prevention of obesity-related diseases.

Methods

A multi-stage sampling method was used to select 1 065 adolescents from 120 Chinese cities between July and September 2021. Data were collected using a general information questionnaire, Chinese version of the Sakata Eating Behavior Scale, and Chinese version of the Family Health Scale. Network analysis was performed using R language to explore relationships between obesity-related eating behaviors and family health.

Results

The network model revealed FH2 (family health lifestyle) as the node with the highest expected influence (EI=0.970). EB6 (others’ eating behavior stimulates my eating desire) showed the highest bridge expected influence (BEI=0.048), while FH3 (family health resources, BEI=-0.397) and EB1 (irregular mealtime, BEI=-0.235) exerted strong negative connecting effects in the network.

Conclusion

Interactive relationships exist between adolescent obesity-related eating behaviors and family health. Targeted interventions focusing on eating behavior regulation, integration of family health resources, and establishment of regular mealtimes may improve dietary behavior management and prevent obesity-related diseases in adolescents.

Adolescent  /  Obesity-related eating behavior  /  Family health  /  Network analysis
赖湘瑜, 刘嘉琳, 赵耀, 伍丽华, 吴心雨, 楚慧耘, 何康, 廖源, 李瑜. 基于网络分析的青少年肥胖相关饮食行为与家庭健康的关系研究. 现代预防医学, 2025 , 52 (13) : 2437 -2443 . DOI: 10.20043/j.cnki.MPM.202502350
Xiang-yu LAI, Jia-lin LIU, Yao ZHAO, Li-hua WU, Xin-yu WU, Hui-yun CHU, Kang HE, Yuan LIAO, Yu LI. Network analysis of the relationship between obesity-related eating behaviors and family health in adolescents[J]. Modern Preventive Medicine, 2025 , 52 (13) : 2437 -2443 . DOI: 10.20043/j.cnki.MPM.202502350
正文
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肥胖症是全球致死致残的主要危险因素之一，已成为严重威胁人类健康的重大公共卫生问题。近年来，随着生活方式与饮食结构的改变，全球青少年肥胖率呈持续上升趋势[1]。青少年肥胖的流行不仅诱发代谢综合征和心脑血管事件，还会引发一系列心理和社会问题[2]。影响青少年肥胖的因素是多方面的，其中肥胖相关饮食行为作为可干预因素引起了广泛关注。肥胖相关饮食行为是一种显著导致体重异常增加的不良饮食模式，主要表现为大量摄入高脂肪食物和情绪化进食等行为[3]。受身体意象恶化和体重污名等多种社会心理因素的影响，具有肥胖相关饮食行为的青少年发生抑郁和焦虑的风险显著增加[4]。可见，早期识别与干预肥胖相关饮食行为是促进青少年身心健康发展的关键环节。国内外研究发现，家庭健康相关因素对于防控青少年肥胖相关饮食行为至关重要[5-6]。家庭健康是一种以家庭为单位的资源，由家庭成员的健康、能力和互动及家庭的生理、社会、情感、经济和医疗资源交叉发展而成[7]。然而，尽管家庭健康在青少年肥胖防控中的作用逐渐受到关注，但既往研究多限于将变量视为一个整体的“总分模型”，忽略了变量各项指标之间复杂的交互作用关系[8]。因此，家庭健康与青少年肥胖相关饮食行为之间的复杂关系及作用机制尚不明确。网络分析是基于网络理论的统计新范式，以节点代表观测变量，以连线的粗细代表节点间的关联强弱，能够直观地对节点之间的复杂相互作用进行集体表征，并通过计算各类定量指标识别关键的中心和桥梁节点，帮助研究人员了解变量之间表观层面的复杂机制[9]。基于此，本研究旨在通过网络分析方法识别潜在的干预靶点，揭示青少年肥胖相关饮食行为与家庭健康之间的复杂关系，以期为制定针对性的防控策略提供科学依据。
1　对象与方法
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1.1　研究对象
本研究数据来源于北京大学公共卫生学院于2021年7—9月开展的“2021年中国居民心理与行为调查”项目。该项目采用多阶段抽样纳入了中国全部省份的120个城市的居民(港澳台地区除外)，通过问卷星平台(https://www.wjx.cn/)向城市居民发放调查问卷，并基于第七次全国人口普查的数据结果进行配额抽样(配额属性为性别、年龄、城乡分布)，使样本基本符合人口特征。本研究选取1 065例青少年作为研究对象。纳入标准：（1）年龄12～18岁；（2）自愿参加研究，填写知情同意书；（3）理解问卷所表达的含义，可自行完成或在调查员帮助下完成问卷调查。排除标准：（1）神志不清、精神异常者；（2）不愿合作者。本研究已通过暨南大学医学伦理委员会审查(JNUKY-2021-018)。
1.2　研究工具
1.2.1　一般资料调查表
选取性别、常住地、文化程度、独生子女情况、家庭类型、家庭人均月收入、身体质量指数（body mass index，BMI）作为一般资料。其中，依据美国疾病预防控制中心的标准[10]，按BMI百分位数将BMI划分为四个等级：体重过轻(BMI<P5)、正常体重(P5≤BMI<P85)、超重(P85≤BMI<P95)以及肥胖(BMI≥P95)，该标准已被验证适用于中国青少年群体的BMI分类[6,11]。
1.2.2　中文版坂田饮食行为量表(Sakata eating behavior scale short form, EBS-SF)
由Tayama等[3]学者编制、我国学者葛蒲等[12]汉化，是用于评估个体饮食行为的测量工具。EBS-SF共包括7个条目，采用Likert 4级计分法，赋分1～4分，总分7～28分，得分越高表明肥胖相关饮食行为水平更高。本研究中，EBS-SF的Cronbach α系数为0.835。
1.2.3　中文版家庭健康量表(short form of the family health scale, FHS-SF)
由Crandall等[13]学者编制、我国学者王飞等[14]汉化，是用于评估个体家庭健康水平的测量工具。FHS-SF包含4个维度：家庭/社会/情绪健康过程、家庭健康生活方式、家庭健康资源、家庭外部社会支持，共10个条目。采用Likert 5级计分法，赋分1～5分，总分10～50分，得分越高表明家庭健康状况越好。本研究中，FHS-SF的Cronbach α系数为0.847。
1.3　统计学方法
符合正态分布的计量资料采用（均数±标准差）表示，计数资料采用频数和百分比表示，检验水准α=0.05。网络分析在R 4.3.3中进行。首先，使用R qgraph包构建网络模型。基于EBICglasso函数正则化估计网络，剔除网络中连接相对较弱的边并凸显有意义的边，使网络更简洁。根据EBIC超参数设置的建议，将其设定为0.5[15]。在网络中，每个节点(node)代表一个观测变量，连接节点的每条边(edge)表示对其他所有节点条件化处理后两个节点之间的相关性。边的颜色和粗细表明关系的方向和强弱。蓝色边和红色边分别表示正相关和负相关，线条越粗且颜色饱和度更强则表示相关性较强。其次，使用R networktools包计算网络的节点/桥梁中心性指标，确定网络模型的中心节点和桥梁节点。由于网络模型同时具有正向和负向边，本研究选择预期影响值(expected influence, EI)和桥梁预期影响值(bridge expected influence, BEI)作为节点重要性的主要评价指标[16]。最后，使用R bootnet包辅助性验证网络模型的准确性和稳定性。通过自举边缘权重的95%置信区间(confidence intervals, CI)检验边缘的准确性；并计算相关性稳定 (correlation stability,CS)系数测试EI和BEI的稳定性，CS系数不宜低于0.25，高于0.5则表示有足够的稳定性[17]。
2　结果
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2.1　青少年一般资料在肥胖相关饮食行为与家庭健康得分中的差异比较
本研究共纳入青少年1 065例。其中男性442人(41.5%)，女性623人(58.5%)，平均BMI为(20.25±3.11) kg/m2。单因素分析结果显示，文化程度、家庭类型、家庭人均月收入、BMI在肥胖相关饮食行为得分中存在显著差异；性别、常住地、文化程度、家庭类型、家庭人均月收入在家庭健康得分中存在显著差异。见表1。
2.2　网络分析结果
2.2.1　青少年肥胖相关饮食行为-家庭健康网络模型可视化
青少年肥胖相关饮食行为-家庭健康网络模型如图1所示。该网络共包含11个节点和36条边缘，其网络密度为0.655 (36/55)。其中，蓝色和黄色节点分别代表肥胖相关饮食行为的条目和家庭健康的维度。
2.2.2　网络的节点中心性及桥梁中心性指标
中心性分析结果显示，在节点中心性指标中，FH2(家庭健康生活方式)表现出最高的EI值(EI=0.970)，其次为FH1 (家庭/社会/情绪健康过程，EI=0.956)和EB2 (只有吃十分饱才满足，EI=0.911)，见图2A。由图2B可见，在桥梁中心性指标中，EB6 (别人的进食行为激发我的进食欲望)表现出正向的桥梁中心性(BEI=0.048)，在网络中发挥正向连接作用。而其余节点在网络中均展现出负BEI值，其中FH3 (家庭健康资源，BEI=-0.397)和EB1 (没有固定的用餐时间，BEI=-0.235)在网络中发挥了较强的负向连接作用。因此，FH2 (家庭健康生活方式)是青少年肥胖相关饮食行为-家庭健康网络的中心节点；EB6 (别人的进食行为激发我的进食欲望)、FH3 (家庭健康资源)和EB1(没有固定的用餐时间)为重要的桥梁节点。
2.2.3　网络的稳定性及差异性检验
网络的稳定性估计如图3A所示，节点EI和BEI的曲线下降趋势平缓，经计算CS系数均大于0.75，说明75%的样本可以在不显著改变网络结构的情况下从分析中剔除。由图3B可见边缘权重具有较窄的95%CI，表明整个网络估计边缘权重的可靠性较高。网络的差异性检验结果如图4所示，在网络中，大多数节点的EI、BEI与边缘权重的差异具有统计学意义。由此可见，网络的准确性和稳定性较好。
3　讨论
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本研究基于网络分析方法，构建了中国青少年肥胖相关饮食行为与家庭健康的同期网络模型，并揭示了二者间的相互作用关系。研究结果表明，在该网络中，家庭健康生活方式(FH2)处于最核心的地位，与其他节点联系最为紧密。家庭健康生活方式体现了家庭健康的内在维度，包括健康行为模式以及促进健康的选择和习惯。这些行为和习惯通过家庭互动和模范作用影响其他家庭成员，尤其是对处于饮食习惯塑造关键期的青少年。研究发现，家庭提供的餐食和健康食物的可得性促进健康的饮食行为，家庭成员共同参与餐食准备与讨论，不仅有助于增强家庭互动，还能培养青少年对健康食物的偏好，从而降低肥胖风险[18]。此外，家庭健康生活方式不仅影响青少年的饮食行为，还对其社会行为和心理健康产生积极作用。健康的家庭生活方式可以通过减少青少年时期的药物使用量和提升体育锻炼的参与度，进一步降低慢性疾病的发生率[19-20]。值得注意的是，家庭成员共同参与体育活动使青少年的自信心和社交能力得到提升，有助于减少焦虑和抑郁情绪[18]。由此可见，这种多维度的健康影响指明了推广和实施FH2是预防青少年肥胖相关饮食行为和实现更广泛健康效益的关键策略。
本研究发现别人的进食行为激发我的进食欲望(EB6)、家庭健康资源(FH3)和没有固定的用餐时间(EB1)是青少年肥胖相关饮食行为-家庭健康网络的重要桥梁节点。EB6这一节点揭示了社会环境对青少年饮食行为的潜在影响。既往研究也发现了类似现象，强调了社会饮食行为与情绪调节之间的联系[21]。在社交情境中，个体的饮食行为更容易受到同伴的食物选择和份量的影响。这是一种社会压力的表现形式，即个体可能因同伴的期望和判断而改变自身的饮食行为。学校是青少年主要的社交情景，其特有的集体用餐模式可能进一步强化此类社会效应。而这种社会压力与情绪调节有关，不仅可能导致社交活动中的过度进食，还可能促使个体选择不健康的食物[22]。由此可见，社交环境中同伴行为的驱动显著影响青少年的饮食行为，提示医疗保健人员应重点关注如何帮助青少年提升抵御不良社交影响的能力，使其在面对社交压力时能够做出更健康的饮食选择。
FH3以其显著的负BEI值在网络中发挥了最强的负向连接作用，表明越充足的家庭健康资源与越低水平的肥胖相关饮食行为相关，这一发现与Lee等[23]的研究一致。家庭健康资源是指家庭在维持和促进健康方面所拥有的各种资源，包括经济资源、社会支持和医疗资源等。研究表明，许多低收入家庭的父母倾向于选择不健康的高能量食品，这反映了社会经济地位与饮食质量之间的正相关关系[18]。具体而言，低社会经济地位的家庭在选择食物时，首要关注的是食品的价格而非其健康价值及质量。这种关注点的偏移可能会潜移默化地引导青少年因较低的成本而选择不健康的食品，进而增加肥胖及相关慢性疾病的风险。此外，家庭提供的社会支持可提升青少年的适应能力，缓解因情绪问题导致的不健康饮食行为，如情绪化进食[24]。由此可见，FH3具有较高的干预价值，及时评估青少年的家庭健康资源并针对性干预可以改善其饮食质量，降低肥胖风险。
EB1同样以其负BEI值在网络中发挥了负向连接作用。缺乏规律的用餐时间与青少年肥胖风险增加和不健康饮食行为密切相关，这与既往的研究[25]结果类似。不规律的用餐时间通过影响新陈代谢效率、干扰生物节律以及使激素水平和肠道菌群失调等机制来影响肥胖。家庭作为青少年成长的重要环境，其健康理念和生活方式对青少年有显著影响。研究表明，良好的家庭健康状况可以改善青少年的肥胖相关饮食行为[6]。Loth等[26]指出，家庭不设定固定的用餐时间会导致青少年饮食不规律，更容易摄入非营养性食物，特别是甜味和咸味零食，长此以往有加重肥胖的风险。反之，规律的家庭聚餐不仅是一个具有塑造作用的学习环境，影响着青少年的食物选择与偏好，还能通过营造积极的用餐氛围增强家庭互动和情感联系，促进家庭整体健康[27]。因此，家庭应重视为青少年建立规律的用餐时间和健康的饮食环境，优化餐食结构，以改善青少年的饮食行为和肥胖风险；此外，学校与社区应加强公共卫生宣传，开展饮食教育和运动支持等活动，共同为青少年创造健康的成长环境。
综上所述，本研究应用网络分析方法探索了中国青少年肥胖相关饮食行为与家庭健康的同期网络特征，发现两者间存在相互作用关系。饮食行为调控、整合家庭健康资源与规划固定的用餐时间等针对性的家庭健康生活方式，有助于提高青少年饮食行为管理效率，预防肥胖及其相关疾病发生。然而，这些措施在不同家庭背景下的实施效果可能受资源获取、文化观念及生活方式等因素影响。家庭的社会经济地位可能影响健康食材的获取；文化观念的差异可能影响对健康行为的认知与接受程度；家庭生活方式的差异可能制约固定用餐时间的规划执行。因此，未来在推广家庭健康干预时，有必要结合家庭背景特征设计个性化的干预方案，确保实施的可持续性，为青少年肥胖防控提供精准高效的实践指导。本研究的局限性在于：首先，横断面研究设计无法确定因果关系，未来还需开展纵向研究以追踪变量随时间的动态变化规律和潜在因果路径；其次，本研究未纳入体育活动水平、学校环境等可能对青少年饮食行为与家庭健康产生影响的潜在协变量，可能导致结果偏倚；最后，本研究主要采用自我报告的测量方式，存在一定的回忆性偏倚，未来研究可以结合客观的生理测量和行为观察以减少此类偏差。
基金
收起
  • 2024年度广东省本科高校教学质量与教学改革工程项目(粤教高函[2024]30号)
  • 2024年研究生课程思政建设项目(研生[2024]89号)
文献
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doi: 10.20043/j.cnki.MPM.202502350
  • 接收时间：2025-02-18
  • 首发时间：2026-03-19
  • 出版时间：2025-07-10
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  • 收稿日期：2025-02-18
基金
2024年度广东省本科高校教学质量与教学改革工程项目(粤教高函[2024]30号)
2024年研究生课程思政建设项目(研生[2024]89号)
作者信息
    1.广州中医药大学护理学院，广东 广州 510006
    2.广州中医药大学教务部

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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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