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Article(id=1241771812133081374, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241771801366299468, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717171200000, receivedDateStr=2024-06-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773991071017, onlineDateStr=2026-03-20, pubDate=1729785600000, pubDateStr=2024-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773991071017, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773991071017, creator=13701087609, updateTime=1773991071017, updator=13701087609, issue=Issue{id=1241771801366299468, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='20', pageStart='3649', pageEnd='3840', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773991068450, creator=13701087609, updateTime=1773991676896, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241774353457681403, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241771801366299468, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241774353457681404, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241771801366299468, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3741, endPage=3747, ext={EN=ArticleExt(id=1241771812783198518, articleId=1241771812133081374, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The influence of social network characteristics on health-related behavior of stroke, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the influence of social networks on health-related behaviors in stroke patients.

Methods

From July to September 2023, participants living in Guangdong were selected as the survey respondents using convenient sampling method. The general information questionnaire, stroke health-related behavior questionnaire and social network questionnaire were used to collect data. Social network analysis was used to explore network characteristics and draw social network diagrams. Latent class analysis was used to explore health-related behavior patterns of stroke. Kruskal-Wallis H test and Bonferroni test were used to compare the differences in social networks of different health-related behavior patterns.

Results

Stroke health-related behaviors can be divided into three categories, namely "good health behavior group" (C1, 58.15%), "smoking and drinking group" (C2, 24.60%), and "sedentary lifestyle and imbalanced diet and sleep group" (C3, 17.25%). The network effective size, efficiency, and betweenness centrality of the "smoking and drinking group" and the "sedentary lifestyle and imbalanced diet and sleep group” were lower than that of the "good health behavior group" (P<0.05/3). In contrast, the density, network hierarchy, network constraint, percentage of individuals who smoked and drank, and percentage of individuals who did not exercise and ate healthily of the two groups were higher than that of the "good health behavior group" (P<0.05/3).

Conclusion

Social network characteristics with high betweenness centrality, low hierarchy, low constraint and high network efficiency can promote health-related behaviors of stroke. Building a social network with equality, low constraint and positive healthy interaction is helpful for community stroke prevention and treatment.

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目的

探讨社会网络对脑卒中健康相关行为模式的影响。

方法

采用一般资料调查表、脑卒中健康相关行为调查表和社会网络问卷，于2023年7~9月便利抽取广东省居民进行调查。采用社会网络分析法探究网络特征并绘制社会网络图，潜在类别分析探究脑卒中健康相关行为模式，Kruskal-Wallis H检验和Bonferroni检验比较不同健康相关行为模式社会网络的差异。

结果

脑卒中健康相关行为可分为3个类别，为“健康行为良好组”（C1, 58.15%）、“吸烟嗜酒组”（C2, 24.60%）和“饮食睡眠失衡-久坐组”（C3, 17.25%）。与“健康行为良好组”比较，“吸烟嗜酒组”和“饮食睡眠失衡-久坐组”的网络有效规模、网络效率和网络中介中心度较低（P<0.05/3）；而网络密度、网络等级度、网络约束度、吸烟饮酒者比例和不运动不健康饮食者比例较高（P<0.05/3）。

结论

具备高中介中心度、低等级度、低约束度和高网络效率的社会网络特征能促进脑卒中健康行为，构建平等、低约束和良性健康互动的社会网络有助于社区脑卒中防治工作。

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谭益冰，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=U1ZEl7wjUGRqIK8iPF8cig==, magXml=RGss+WJzgJrid5l+SEYF/g==, pdfUrl=null, pdf=TdtGBV+gJxI0XseDwOBzKw==, pdfFileSize=1055321, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=NLULRoez6skAeO5OXTnQjg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Ap2BLHmBK7QXSU3qdrGMlg==, mapNumber=null, authorCompany=null, fund=null, authors=

陈宏仪(2000—)，女，硕士在读，研究方向：慢病护理

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陈宏仪(2000—)，女，硕士在读，研究方向：慢病护理

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陈宏仪(2000—)，女，硕士在读，研究方向：慢病护理

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Nutrients, 2023, 15(15): 3305., articleTitle=Social network, food patterns, physical activity and associations with overweight and obesity in adolescents from a school in rural Brazil, refAbstract=null)], funds=[Fund(id=1241771824879571801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, awardId=pdjh2024a100; pdjh2023b0127; pdjh2022b0122, language=CN, fundingSource=广东省科技创新战略专项基金(pdjh2024a100; pdjh2023b0127; pdjh2022b0122), fundOrder=null, country=null), Fund(id=1241771824967652192, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, awardId=A2024295, language=CN, fundingSource=广东省医学科研基金项目(A2024295), fundOrder=null, country=null), Fund(id=1241771825055732581, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, awardId=202310572006, language=CN, fundingSource=大学生创新创业训练计划(202310572006), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241771815815680422, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, xref=1., ext=[AuthorCompanyExt(id=1241771815824069031, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, companyId=1241771815815680422, 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 511400, China), AuthorCompanyExt(id=1241771815828263336, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, companyId=1241771815815680422, 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 511400)]), AuthorCompany(id=1241771816063144376, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, xref=2., ext=[AuthorCompanyExt(id=1241771816084115900, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, companyId=1241771816063144376, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.广州中医药大学第五临床医学院)])], figs=[ArticleFig(id=1241771821415076606, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Fig.1, caption=The characteristic distribution of 3 potential categories of stroke health-related behaviors, figureFileSmall=3xfpqLy1Cz4U66ZDOmwPyQ==, figureFileBig=NLULRoez6skAeO5OXTnQjg==, tableContent=null), ArticleFig(id=1241771821486379781, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=图1, caption=脑卒中健康相关行为的3个潜在类别的特征分布, figureFileSmall=3xfpqLy1Cz4U66ZDOmwPyQ==, figureFileBig=NLULRoez6skAeO5OXTnQjg==, tableContent=null), ArticleFig(id=1241771821775786768, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Fig.2, caption=Social network diagram of good health behavior group, figureFileSmall=Vd9vHyYwFyz6znHXvzVxYA==, figureFileBig=yLiXJxfxocf8aNhpiOxR+g==, tableContent=null), ArticleFig(id=1241771821876450067, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=图2, caption=健康行为良好组社会网络关系图, figureFileSmall=Vd9vHyYwFyz6znHXvzVxYA==, figureFileBig=yLiXJxfxocf8aNhpiOxR+g==, tableContent=null), ArticleFig(id=1241771821989696281, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Fig.3, caption=Social network diagram of smoking and alcohol addiction group, figureFileSmall=KTLhI7EfPrK7YU9t9oJDmg==, figureFileBig=9Q2RZIFVF5zZ1B2X/WBCbQ==, tableContent=null), ArticleFig(id=1241771822102942494, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=图3, caption=吸烟嗜酒组社会网络关系图, figureFileSmall=KTLhI7EfPrK7YU9t9oJDmg==, figureFileBig=9Q2RZIFVF5zZ1B2X/WBCbQ==, tableContent=null), ArticleFig(id=1241771823570948905, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Fig.4, caption=Social network diagram of sedentary lifestyle and imbalanced diet and sleep group, figureFileSmall=e0ADl4yphCT5U+6OsHAvnQ==, figureFileBig=qzerMATGXg97zSu23+CEMA==, tableContent=null), ArticleFig(id=1241771823747109677, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=图4, caption=饮食睡眠失衡-久坐组社会网络关系图, figureFileSmall=e0ADl4yphCT5U+6OsHAvnQ==, figureFileBig=qzerMATGXg97zSu23+CEMA==, tableContent=null), ArticleFig(id=1241771823881327411, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Table 1, caption=

Basic information of research subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数构成比（%）变量人数构成比（%）
性别月收入
男性11235.78<2 000元10031.95
女性20164.222 000~3 999元10433.23
年龄4 000~5 999元5718.21
35~45岁5818.506 000~7 999元237.35
45~55岁7122.708 000~9 999元72.24
55~65岁8426.80>10 000元227.03
≥65岁10031.90体检情况
婚姻情况从不体检5116.29
未婚185.75几年体检一次9630.67
已婚26484.35一年一次14749.96
离异/丧偶319.90一年两次及以上196.07
教育程度参与保险情况
小学及以下16352.08有28189.78
中学8426.84无3210.22
高中或中专3310.54
大专及以上3310.54
居住类型
独居3611.50
非独居者27788.50
), ArticleFig(id=1241771824011350841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=表1, caption=

调查对象基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数构成比（%）变量人数构成比（%）
性别月收入
男性11235.78<2 000元10031.95
女性20164.222 000~3 999元10433.23
年龄4 000~5 999元5718.21
35~45岁5818.506 000~7 999元237.35
45~55岁7122.708 000~9 999元72.24
55~65岁8426.80>10 000元227.03
≥65岁10031.90体检情况
婚姻情况从不体检5116.29
未婚185.75几年体检一次9630.67
已婚26484.35一年一次14749.96
离异/丧偶319.90一年两次及以上196.07
教育程度参与保险情况
小学及以下16352.08有28189.78
中学8426.84无3210.22
高中或中专3310.54
大专及以上3310.54
居住类型
独居3611.50
非独居者27788.50
), ArticleFig(id=1241771824128791358, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Table 2, caption=

Results of model fitting for potential categories of stroke health-related behaviors（n=313)

, figureFileSmall=null, figureFileBig=null, tableContent=
ModelAICBICaBICEntropyLMRBLMR类别概率
12 673.6142 703.5842 678.2111.000
22 521.5792 585.2652 531.3460.918<0.001<0.0010.821/0.179
32 411.6932 509.0942 426.6300.884<0.001<0.0010.581/0.246/0.173
42 350.6752 481.7922 370.7830.9330.001<0.0010.150/0.540/0.176/0.134
52 319.3642 484.1972 344.6430.9320.0300.2300.157/0.105/0.077/0.572/0.089
), ArticleFig(id=1241771824246231875, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=表2, caption=

脑卒中健康相关行为的潜在类别模型拟合结果（n=313)

, figureFileSmall=null, figureFileBig=null, tableContent=
ModelAICBICaBICEntropyLMRBLMR类别概率
12 673.6142 703.5842 678.2111.000
22 521.5792 585.2652 531.3460.918<0.001<0.0010.821/0.179
32 411.6932 509.0942 426.6300.884<0.001<0.0010.581/0.246/0.173
42 350.6752 481.7922 370.7830.9330.001<0.0010.150/0.540/0.176/0.134
52 319.3642 484.1972 344.6430.9320.0300.2300.157/0.105/0.077/0.572/0.089
), ArticleFig(id=1241771824367866697, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Table 3, caption=

The social network characteristics of research subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
变量M(P25, P75)变量M(P25, P75)
网络结构特征网络成员的健康行为
有效规模3.00（2.60,3.80）吸烟者比例0.20（0.00,0.40）
网络密度0.67（0.53,0.76）饮酒者比例0.20（0.00,0.40）
等级度0.03（0.02,0.04）不运动者比例0.40（0.00,0.60）
约束度0.53（0.48,0.58）不健康饮食者比例0.00（0.00,0.40）
网络效率0.50（0.33,0.60）有健康负面影响比例0.00（0.00,0.20）
中介中心度3.50（1.67,6.00）尝试改变健康行为比例0.00（0.00,0.40）
网络关系特征
年龄相似度0.20（0.10,0.40）
婚姻相似度0.80（0.70,0.90）
文化相似度0.40（0.20,0.60）
关系连带强度28.40（26.80,29.60）
), ArticleFig(id=1241771824497890125, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=表3, caption=

调查对象的社会网络特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量M(P25, P75)变量M(P25, P75)
网络结构特征网络成员的健康行为
有效规模3.00（2.60,3.80）吸烟者比例0.20（0.00,0.40）
网络密度0.67（0.53,0.76）饮酒者比例0.20（0.00,0.40）
等级度0.03（0.02,0.04）不运动者比例0.40（0.00,0.60）
约束度0.53（0.48,0.58）不健康饮食者比例0.00（0.00,0.40）
网络效率0.50（0.33,0.60）有健康负面影响比例0.00（0.00,0.20）
中介中心度3.50（1.67,6.00）尝试改变健康行为比例0.00（0.00,0.40）
网络关系特征
年龄相似度0.20（0.10,0.40）
婚姻相似度0.80（0.70,0.90）
文化相似度0.40（0.20,0.60）
关系连带强度28.40（26.80,29.60）
), ArticleFig(id=1241771824611136335, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=EN, label=Table 4, caption=

Social network characteristics of health-related behaviors in different categories of stroke [M(P25, P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量健康行为良好组吸烟嗜酒组饮食睡眠失衡-久坐组H值P值
网络结构特征
有效规模3.40(3.00,3.80)2.60(2.20,3.00)a2.60(2.20,2.62)a34.50<0.01
网络密度0.60(0.53,0.68)0.73(0.67,0.80)a0.73(0.73,0.80)a35.64<0.01
等级度0.02(0.00,0.03)0.04(0.02,0.05)a0.04(0.02,0.05)a48.26<0.01
约束度0.51(0.48,0.55)0.55(0.53,0.61)a0.55(0.53,0.61)a13.04<0.01
网络效率0.60(0.40,0.70)0.40(0.30,0.50)a0.40(0.30,0.40)a36.56<0.01
中介中心度5.00(2.67,6.5)1.67(1.50,3.50)a1.67(1.50,2.06)a45.46<0.01
网络关系特征
年龄相似度0.25(0.10,0.40)0.30(0.10,0.40)b0.10(0.00,0.31)7.380.03
婚姻相似度0.80(0.79,0.90)0.80(0.60,0.90)0.80(0.60,0.90)4.280.08
文化相似度0.40(0.20,0.60)0.40(0.00,0.60)0.40(0.20,0.60)1.980.32
关系连带强度28.73(27.15,29.60)27.60(26.33,29.20)28.40(27.10,29.20)3.530.17
网络成员的健康行为
吸烟者比例0.20(0.00,0.30)0.40(0.00,0.60)ab0.20(0.00,0.40)15.74<0.01
饮酒者比例0.18(0.00,0.29)0.20(0.00,0.41)ab0.06(0.00,0.40)9.72<0.01
不运动者比例0.20(0.00,0.40)0.20(0.00,0.60)b0.60(0.00,0.80)a16.87<0.01
不健康饮食者比例0.00(0.00,0.30)0.00(0.00,0.40)b0.30(0.20,0.80)a30.32<0.01
有健康负面影响比例0.00(0.00,0.20)0.00(0.00,0.20)0.00(0.00,0.20)1.390.50
尝试改变健康行为比例0.00(0.00,0.80)0.00(0.00,0.20)0.00(0.00,0.25)5.880.05
), ArticleFig(id=1241771824715993942, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241771812133081374, language=CN, label=表4, caption=

不同类别脑卒中健康相关行为的社会网络特征[M(P25, P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量健康行为良好组吸烟嗜酒组饮食睡眠失衡-久坐组H值P值
网络结构特征
有效规模3.40(3.00,3.80)2.60(2.20,3.00)a2.60(2.20,2.62)a34.50<0.01
网络密度0.60(0.53,0.68)0.73(0.67,0.80)a0.73(0.73,0.80)a35.64<0.01
等级度0.02(0.00,0.03)0.04(0.02,0.05)a0.04(0.02,0.05)a48.26<0.01
约束度0.51(0.48,0.55)0.55(0.53,0.61)a0.55(0.53,0.61)a13.04<0.01
网络效率0.60(0.40,0.70)0.40(0.30,0.50)a0.40(0.30,0.40)a36.56<0.01
中介中心度5.00(2.67,6.5)1.67(1.50,3.50)a1.67(1.50,2.06)a45.46<0.01
网络关系特征
年龄相似度0.25(0.10,0.40)0.30(0.10,0.40)b0.10(0.00,0.31)7.380.03
婚姻相似度0.80(0.79,0.90)0.80(0.60,0.90)0.80(0.60,0.90)4.280.08
文化相似度0.40(0.20,0.60)0.40(0.00,0.60)0.40(0.20,0.60)1.980.32
关系连带强度28.73(27.15,29.60)27.60(26.33,29.20)28.40(27.10,29.20)3.530.17
网络成员的健康行为
吸烟者比例0.20(0.00,0.30)0.40(0.00,0.60)ab0.20(0.00,0.40)15.74<0.01
饮酒者比例0.18(0.00,0.29)0.20(0.00,0.41)ab0.06(0.00,0.40)9.72<0.01
不运动者比例0.20(0.00,0.40)0.20(0.00,0.60)b0.60(0.00,0.80)a16.87<0.01
不健康饮食者比例0.00(0.00,0.30)0.00(0.00,0.40)b0.30(0.20,0.80)a30.32<0.01
有健康负面影响比例0.00(0.00,0.20)0.00(0.00,0.20)0.00(0.00,0.20)1.390.50
尝试改变健康行为比例0.00(0.00,0.80)0.00(0.00,0.20)0.00(0.00,0.25)5.880.05
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社会网络特征对脑卒中健康相关行为模式的影响研究
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陈宏仪 1 , 麦倩华 1 , 郑镇泳 1 , 翁丹婷 1 , 晏镜婷 1 , 刘柄硕 2 , 谭益冰 1
现代预防医学 | 健康与社会行为 2024,51(20): 3741-3747
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现代预防医学 | 健康与社会行为 2024, 51(20): 3741-3747
社会网络特征对脑卒中健康相关行为模式的影响研究
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陈宏仪1, 麦倩华1, 郑镇泳1, 翁丹婷1, 晏镜婷1, 刘柄硕2, 谭益冰1
作者信息
  • 1.广州中医药大学护理学院，广东 广州 511400
  • 2.广州中医药大学第五临床医学院

    • 陈宏仪(2000—)，女，硕士在读，研究方向：慢病护理

通讯作者:

谭益冰，E-mail：
The influence of social network characteristics on health-related behavior of stroke
Hong-yi CHEN1, Qian-hua MAI1, zhen-yong ZHENG1, Dan-ting WENG1, Jing-ting YAN1, Bing-Shuo LIU2, Yi-bing TAN1
Affiliations
  • School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 511400, China
出版时间: 2024-10-25 doi: 10.20043/j.cnki.MPM.202406001
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摘要
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目的

探讨社会网络对脑卒中健康相关行为模式的影响。

方法

采用一般资料调查表、脑卒中健康相关行为调查表和社会网络问卷，于2023年7~9月便利抽取广东省居民进行调查。采用社会网络分析法探究网络特征并绘制社会网络图，潜在类别分析探究脑卒中健康相关行为模式，Kruskal-Wallis H检验和Bonferroni检验比较不同健康相关行为模式社会网络的差异。

结果

脑卒中健康相关行为可分为3个类别，为“健康行为良好组”（C1, 58.15%）、“吸烟嗜酒组”（C2, 24.60%）和“饮食睡眠失衡-久坐组”（C3, 17.25%）。与“健康行为良好组”比较，“吸烟嗜酒组”和“饮食睡眠失衡-久坐组”的网络有效规模、网络效率和网络中介中心度较低（P<0.05/3）；而网络密度、网络等级度、网络约束度、吸烟饮酒者比例和不运动不健康饮食者比例较高（P<0.05/3）。

结论

具备高中介中心度、低等级度、低约束度和高网络效率的社会网络特征能促进脑卒中健康行为，构建平等、低约束和良性健康互动的社会网络有助于社区脑卒中防治工作。

脑卒中  /  健康相关行为  /  社会网络  /  潜在类别分析
Objective

To explore the influence of social networks on health-related behaviors in stroke patients.

Methods

From July to September 2023, participants living in Guangdong were selected as the survey respondents using convenient sampling method. The general information questionnaire, stroke health-related behavior questionnaire and social network questionnaire were used to collect data. Social network analysis was used to explore network characteristics and draw social network diagrams. Latent class analysis was used to explore health-related behavior patterns of stroke. Kruskal-Wallis H test and Bonferroni test were used to compare the differences in social networks of different health-related behavior patterns.

Results

Stroke health-related behaviors can be divided into three categories, namely "good health behavior group" (C1, 58.15%), "smoking and drinking group" (C2, 24.60%), and "sedentary lifestyle and imbalanced diet and sleep group" (C3, 17.25%). The network effective size, efficiency, and betweenness centrality of the "smoking and drinking group" and the "sedentary lifestyle and imbalanced diet and sleep group” were lower than that of the "good health behavior group" (P<0.05/3). In contrast, the density, network hierarchy, network constraint, percentage of individuals who smoked and drank, and percentage of individuals who did not exercise and ate healthily of the two groups were higher than that of the "good health behavior group" (P<0.05/3).

Conclusion

Social network characteristics with high betweenness centrality, low hierarchy, low constraint and high network efficiency can promote health-related behaviors of stroke. Building a social network with equality, low constraint and positive healthy interaction is helpful for community stroke prevention and treatment.

Stroke  /  Health-related behaviors  /  Social network  /  Latent class analysis
陈宏仪, 麦倩华, 郑镇泳, 翁丹婷, 晏镜婷, 刘柄硕, 谭益冰. 社会网络特征对脑卒中健康相关行为模式的影响研究. 现代预防医学, 2024 , 51 (20) : 3741 -3747 . DOI: 10.20043/j.cnki.MPM.202406001
Hong-yi CHEN, Qian-hua MAI, zhen-yong ZHENG, Dan-ting WENG, Jing-ting YAN, Bing-Shuo LIU, Yi-bing TAN. The influence of social network characteristics on health-related behavior of stroke[J]. Modern Preventive Medicine, 2024 , 51 (20) : 3741 -3747 . DOI: 10.20043/j.cnki.MPM.202406001
正文
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脑卒中健康相关行为是预防脑卒中的重要措施[1]。健康相关行为与社会环境因素息息相关[2]，受到其家庭成员、朋友、同事和邻居的影响[3]。社会网络是指个体与周围个体相互之间关系的集合[4]，影响网络成员的行为和健康结局[5]。社会网络分析法（Social Network Analysis, SNA）对社会网络中个体之间关系进行量化分析，将模糊抽象的社会关系转变为可直接测量的网络指标，为社会网络在健康行为研究领域提供了新视角[6]。因此，本研究拟分析不同脑卒中健康行为模式的社会网络特征差异，为今后制定有效的脑卒中防治方案提供新思路。
1　对象与方法
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1.1　调查对象
于2023年7~9月，便利抽取广州中医药大学护理学院“三下乡”服务点接受义诊的居民作为调查对象。目前脑卒中防治“关口”逐渐前移，部分地区脑卒中筛查对象年龄≥35岁[7]。纳入标准为：（1）年龄≥35岁；（2）知情同意，自愿参加；（3）意识清楚，能进行正常沟通交流。排除标准：（1）患有严重并发症或合并症；（2）存在认知障碍或精神异常情况。共发放问卷339份，回收有效问卷313份，有效回收率92.33%。本研究已通过广东省中医院伦理委员会审批（YE2023-472-01）。
1.2　调查工具
1.2.1　一般资料调查表
自行设计收集调查对象基本信息，包括性别、年龄、文化程度、婚姻状况、居住状况、月收入、体检频率、医保状况。
1.2.2　脑卒中健康相关行为评定
参考《中国脑卒中防治指导规范（2021版）》[8]和相关研究文献[9]，结合专家意见选择以下8项指标：（1）吸烟：每天吸烟≥1支且持续时间>6个月。（2）过量饮酒：过去一年内每周饮酒1次，日均酒精摄入量超过推荐量（男性为25 g，女性为15 g）。（3）高盐饮食：食盐摄入>6 g/d。（4）高脂饮食：红肉（如猪、牛）类摄入75 g/d和（或）烹调植物油25 g/d。（5）高糖饮食：添加糖摄入>50 g/d。（6）体力活动：采用国际体力活动问卷短卷（International Physical Activity Questionnaire-Short Form, IPAQ-SF）[10]，调查过去1周低强度、中等强度和高强度体力活动的情况。体力活动代谢当量（metabolic equivalent, MET）赋值分别为低强度3.3、中等强度4.0和高等强度8.0。每周体力活动量=对应运动强度的MET×每天活动的时长×每周活动天数。根据国际体力活动委员的分级标准将体力活动水平分为低体力活动（<600 METs/周）和中高体力活动（≥600 METs/周）。（7）久坐：<5 h/d为非久坐组，≥5 h/d为久坐组[11]。（8）睡眠：根据平均每晚睡眠时间分为睡眠不足（<6 h）、睡眠正常（6~9 h）和睡眠过长（>9 h）[12]。
1.2.3　社会网络问卷
参照Burt[13]的社会网络问卷，运用个体提名生成法及提名诠释法进行编制并做适当修订。（1）提名生成法研究个体所拥有的支持关系。受访者列举至少5串名字（代号或字母），称之为关系人，为过去三个月与其互动频繁、共同参与健康活动、给予健康支持与动力者。（2）提名诠释法进一步探询关系人与受访者的关系。包括：①基本信息：网络成员的年龄、婚姻、教育程度；②成员间相熟关系：采用Liketer 3级评分，1~3分别代表“很不熟悉”到“非常熟悉”；③与关系人的关系连带强度：含认识久暂、互动频率、亲密程度三个方面，采用Liketer 5级评分，1~5分别代表“<0.5年”到“>10年”“很少”到“每周1次及以上”“很不亲密”到“非常亲密”。
本研究关注整体社会网络中的健康相关行为，参照Dhand等[14]研究，增加对关系人过去三个月吸烟、饮酒、运动、饮食、负面健康影响和改变健康行为（如戒烟、锻炼等）情况的调查。
1.3　资料收集方法
调查前，对调查员进行脑卒中健康相关行为定义与社会网络知识的培训，说明研究目的及填写问卷的注意事项，解释问卷条目并统一指导语。调查员到“三下乡”服务点现场发放问卷，取得调查对象知情同意后以面对面形式收集。问卷填写完毕后，由调查者检查无漏填、缺失后回收。
1.4　统计分析
采用SPSS 26.0进行数据分析。计数资料采用频数和百分比表示。计量资料进行正态性检验，符合正态分布的采用均数和标准差表示，不服从正态分布的采用中位数和四分位数表示。不同健康行为模式下社会网络特征的差异，采用多个独立样本的Kruskal-Wallis H秩和检验，事后多重比较采用Bonferroni检验，并根据比较次数校正检验水准（α=0.05/3）。
采用Mplus 8.3软件进行潜在类别分析，识别脑卒中健康相关行为模式。模型适配性检验指标包括：（1）Akaike信息准则（AIC）、Baysian信息准则（BIC）、样本校正的BIC（aBIC），数值越小表明模型拟合越好；（2）熵（Entropy）：取值0~1，越接近1表示分类准确度越高，≥0.8表示分类准确度>90%；（3）调整似然比检验（LMR）和基于Bootstrap的似然比检验（BLRT）：当P<0.05表示k个类别的模型优于k-1个类别的模型[15]。
采用Ucinet 6.0绘制可视化社会网络图和计算网络特征，衡量社会网络的指标包含网络结构特征和网络关系特征[16]。（1）网络结构特征指标：有效规模（除去网络中冗余信息后提名的人数）、密度（网络成员彼此连接的程度）、等级度（网络各成员不对称可到达的程度）、约束度（受网络中其他成员的影响程度）、效率（网络中存在多余连线的程度）和中介中心度（个体在网络中与其他成员的最短路径上联系的频率）。（2）网络关系特征指标：婚姻、文化、年龄的相似性和关系连带强度（成员间认识久暂、互动频率、亲密程度）。
2　结果
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2.1　基本情况
本次调查共纳入313人，基本情况见表1。
2.2　脑卒中健康相关行为的潜在类别分类
将脑卒中健康相关行为的8个指标作为外显变量建立潜在类别模型，拟合了5个潜在类别模型，见表2。结果显示，AIC、BIC、aBIC随着分类的不断增加而逐渐降低；类别数为5时，BLMR值尚未达到显著水平（P>0.05）。结合拟合指标和实际意义，以模型3作为最佳拟合模型。3个类别在8个指标的条件概率见图1。C1组的脑卒中相关健康行为均表现良好，故命名为“健康行为良好组”，占比58.15%（n=182）；C2组中69.90%~95.10%以上的人吸烟饮酒，明显高于其他两组，故命名为“吸烟嗜酒组”，占比24.60%（n=77）；C3组中38.70%~91.50%的人有高盐、高脂、高糖饮食行为，52.80%的人睡眠不足或过长，46.40%的人久坐，故命名为“饮食睡眠失衡-久坐组”，占比17.25%（n=54）。
2.3　社会网络特征
社会网络特征如表3所示。
2.4　社会网络特征对脑卒中健康相关行为模式的影响
Kruskal-Wallis H秩和检验结果显示，不同健康相关行为模式在网络结构特征、年龄相似度、网络成员的健康行为等社会网络特征上的差异有统计学意义（P<0.05），见表4。两两比较分析结果显示：（1）网络结构特征中，C2、C3组与C1组比较，网络密度、等级度、约束度较高；网络有效规模、网络效率、中介中心度较低（P<0.05/3）。（2）网络关系特征中，C2组与C3组比较，年龄相似度较高（P>0.05/3）。（3）网络成员的健康行为中，C2组与C1、C3组比较，吸烟者、饮酒者的比例较高。C3组与C2、C1组比较，不运动者、不健康饮食者的比例较高（P<0.05/3）。
2.5　社会网络关系图
为可视化受访者周围的健康行为环境，绘制社会网络关系图（见图2~图4）。采用N+阿拉伯数字（如N1，N2）标记每位关系人，每一条线代表两个节点间存在交流关系。黑色节点代表受访者，红色节点代表吸烟、饮酒、不运动或不健康饮食的关系人，白色节点代表无吸烟、无饮酒、运动或健康饮食行为的关系人。根据各组危害健康行为比例的低到高，将网络图依次由左上向右下排序。在健康行为良好组的社会网络关系图中，有较多受访者节点处于“结构洞”位置，即其他节点需通过该受访者将两个子群体相连（见图2）。而在吸烟嗜酒组、饮食睡眠失衡-久坐组网络关系图中，具备危害健康行为（红色节点）较多，且节点间连线较多，密度高，网络结构较闭塞（见图3~图4）。
3　讨论
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3.1　社会网络特征对脑卒中健康相关行为模式的影响
本研究结果显示，吸烟嗜酒组、饮食睡眠失衡-久坐组与健康行为良好组比较，网络密度、网络等级度和网络约束度较高，且网络效率较低。网络中没有直接连接或关系间断的行动者之间存在的空隙称为“结构洞”[17]。个体可从富含结构洞的网络中获得多样化信息和资源。当效率值越大，密度值、等级度值和约束度值越小时，网络越可能存在结构洞。因此，吸烟嗜酒组、饮食睡眠失衡-久坐组的网络中存在的结构洞较少，等级结构分明，且个体受到高度约束。赵溢洋[18]发现，高密度和多结构洞的网络结构正向影响青少年的健康相关行为，本研究与此结果存在差异。一方面因为吸烟嗜酒组、饮食睡眠失衡-久坐组的网络是围绕危害健康行为形成的。加之网络的高密度性，同伴都彼此联系容易促成网络内的共同规范和群体认同，危害健康行为的相互影响更加显著。另一方面，高约束的网络缺乏结构洞，影响中心个体获得社会资本；同时高等级网络中的“枢纽”“权威”可能形成群体压力，干扰中心个体的健康认知理念乃至行为方式，使得危害健康行为的传播力更强。因此，建议社区工作者采用简短社会网络问卷调查居民的社会网络特征，识别需重点干预的高危网络并定位关键人物，如等级度高、约束度高、中介度低的网络和高等级的“权威”个体，以构建含丰富结构洞、密度适中和平等交流的社会网络。
同时，本结果显示吸烟嗜酒组、饮食睡眠失衡-久坐组相较于健康行为良好组的网络有效规模较小。徐迪等[19]发现，网络规模越大，网络中可提供帮助的成员可能越多且获取信息的途径更为丰富，可获得的社会和情感支持可能越多。因此，应鼓励个体积极参加脑卒中防治活动，拓展结识社会支持网络成员的渠道，扩大社会网络规模。
另外，吸烟嗜酒组、饮食睡眠失衡-久坐组相较于健康行为良好组中介中心度较低，说明个体在网络中的桥梁性较低。陈天麒等[20]发现，桥梁性较高的个体具备较低的HIV阳性风险，可能是由于担任网络间“桥梁”的个体具有较高的艾滋病相关知识水平。个体成为健康知识转移的桥梁，对脑卒中防治知识与行为的传播有积极影响。因此，应发挥高中介中心度个体的桥梁作用，促进脑卒中防治的健康知识与理念的传播。
3.2　网络成员的关系特征对脑卒中健康相关行为模式的影响
本研究结果显示，吸烟嗜酒组与饮食睡眠失衡-久坐组比较，年龄相似度较高。既往研究显示[21]，年龄相仿的同伴影响个体是否饮酒、何时饮酒和饮酒多少。从众、社交、情感交流等是常见的饮酒动机。陈海德等[22]发现，同伴身份认同影响个体的吸烟动机。社会网络理论认为，个体倾向于与自己相似的人建立联系[23]。可能与同辈在人生阅历、兴趣爱好和价值观念上更相似有关，同质性与共情感更高，更容易建立烟酒伙伴关系。提示社区工作者在脑卒中防治工作中关注同辈力量，运用团体健康教育或积极的同伴影响管控烟酒行为。
3.3　网络成员的健康行为对脑卒中健康相关行为模式的影响
本研究结果显示，吸烟嗜酒组和饮食睡眠失衡-久坐组是危害健康行为所连接的社会网络，网络中吸烟饮酒比例或不健康饮食不运动的比例较高。与以往研究结果[24-25]相似，吸烟饮酒者更可能与同样吸烟饮酒的群体建立关系，其行为倾向于与群体一致。Aguirre等[26]发现，家庭、朋友等社会网络中的饮食行为、体育活动等可以相互“传染”，促成网络成员具备相似的健康行为，可能与成员间共同偏好、共同行为环境等有关。因此，应当重视个体周围群体的脑卒中健康行为情况，降低网络中危害健康行为比例，构建良好的社会网络环境。
本研究存在一定局限性。本研究仅在广东省进行调查，研究结果可能受地区影响，另外今后可以纳入更多社会心理变量，以更全面探讨社会网络特征对脑卒中健康行为影响。
基金
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  • 广东省科技创新战略专项基金(pdjh2024a100; pdjh2023b0127; pdjh2022b0122)
  • 广东省医学科研基金项目(A2024295)
  • 大学生创新创业训练计划(202310572006)
文献
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doi: 10.20043/j.cnki.MPM.202406001
  • 接收时间：2024-06-01
  • 首发时间：2026-03-20
  • 出版时间：2024-10-25
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  • 收稿日期：2024-06-01
基金
广东省科技创新战略专项基金(pdjh2024a100; pdjh2023b0127; pdjh2022b0122)
广东省医学科研基金项目(A2024295)
大学生创新创业训练计划(202310572006)
作者信息
    1.广州中医药大学护理学院，广东 广州 511400
    2.广州中医药大学第五临床医学院

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谭益冰，E-mail：
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2种不同金属材料的力学参数
科
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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