Article(id=1324077712104628874, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202510349, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761580800000, receivedDateStr=2025-10-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614326739, onlineDateStr=2026-11-02, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614326739, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614326739, creator=13701087609, updateTime=1793614326739, updator=13701087609, issue=Issue{id=1324077560820282023, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614290671, creator='13701087609', updateTime=1793614391469, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077983656444429, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077983660638734, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1082, endPage=1088, ext={EN=ArticleExt(id=1324077712394035851, articleId=1324077712104628874, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Digital exclusion, social participation, and depression symptoms among Chinese older adults, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the long-term relationship between digital exclusion and depressive symptoms in middle-aged and older adults in China, to evaluate whether social participation serves as a mediator in this connection, and to guide interventions to reduce depression within this group.

Methods

A longitudinal analysis was conducted using data from the 2011-2020 waves of the China Health and Retirement Longitudinal Study (CHARLS). Self-reported data were used to evaluate digital exclusion and social participation, while the presence of depressive symptoms was measured with the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D 10). Differences in the incidence of depressive symptoms among groups were assessed using Kaplan-Meier estimates and the log-rank test. Cox proportional hazards models were used to analyze the longitudinal association between digital exclusion and depressive symptoms, and causal mediation analysis was employed to test the mediating effect.

Results

A total of 8 808 participants were included in the study. At baseline, 8 334 (94.6%) individuals were classified as digital exclusion group. Over a median follow-up of 7.00 years, 3 969 participants developed depressive symptoms. The survival analysis revealed that the cumulative incidence of depressive symptoms was significantly higher in the digital exclusion group than that in the digital inclusion group (Log-rank P<0.05). After controlling for confounders, Cox regression analysis showed that digital exclusion remained an independent risk factor for depressive symptoms (HR=1.37, 95% CI: 1.11-1.69). The mediation analysis found that social participation partially mediated the association between digital exclusion and depressive symptoms.

Conclusion

Digital exclusion is associated with an increased risk of depressive symptoms among middle-aged and older Chinese adults. This association may be partially mediated by social participation. Promoting digital inclusion and encouraging social engagement may be potential strategies for the prevention and intervention of depression in this vulnerable group.

, authors=Li LIU1, Jing QI2, Xiao-hong LU1, Fang YU1, Pei-yuan QIU2, authorsList=Li LIU, Jing QI, Xiao-hong LU, Fang YU, Pei-yuan QIU, authorCompany=null, correspAuthors=Pei-yuan QIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324077713488749202, articleId=1324077712104628874, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国中老年人数字排斥与抑郁症状的关联研究, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

本研究旨在探究中国中老年人群数字排斥与抑郁症状的纵向关联,并检验社会参与在两者关联之间的中介效应,以期为该人群抑郁症状的识别与干预提供实证支持。

方法

研究使用CHARLS数据库2011—2020年的连续追踪数据。数字排斥和社会参与通过受访者的自我报告进行评估,抑郁症状采用10项简版流行病学研究中心抑郁量表进行测量。采用Kaplan-Meier生存曲线和Log-rank检验比较数字排斥组和数字包容组抑郁症状发病风险,并构建Cox比例风险模型分析其纵向关联。进一步采用因果中介分析检验社会参与的中介作用。

结果

本研究共纳入8 808名研究对象。基线时,8 334(94.6%)名研究对象被分为数字排斥。在为期约7.00年的随访过程中,共观察到3 969名研究对象出现抑郁症状。生存分析显示,数字排斥组的抑郁症状发病风险高于数字包容组(log-rank P<0.05)。控制混杂因素后,Cox模型分析结果显示,数字排斥与抑郁症状的发生风险呈正相关(HR=1.37, 95% CI: 1.11~1.69)。中介分析证实,社会参与在数字排斥与抑郁症状的关联中起部分中介作用。

结论

数字排斥与中国中老年人发生抑郁症状的风险增加有关。社会参与在这一关联中起到了部分中介作用。促进数字包容、鼓励社会参与可能是预防和干预中老年人抑郁症状的有效策略。

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刘利(1981—),女,硕士,副教授,研究方向:预防医学、健康教育与健康促进、社会医学

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邱培媛,E-mail:
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J Alzheimers Dis Rep, 2024, 8(1): 387-397., articleTitle=Effect of Internet use on cognitive function of Middle-Aged and elderly adults in China: evidence from China family panel studies, refAbstract=null)], funds=[Fund(id=1324077718740017861, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, awardId=2022NSFSC0668, language=CN, fundingSource=四川省自然科学基金面上项目(2022NSFSC0668), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077713782350483, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, xref=1., ext=[AuthorCompanyExt(id=1324077713790739092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, companyId=1324077713782350483, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Preventive Medicine, Sichuan College of Traditional Chinese Medicine, Mianyang, Sichuan 621000,China), AuthorCompanyExt(id=1324077713803322005, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, companyId=1324077713782350483, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.四川中医药高等专科学校预防医学教研室,四川 绵阳 621000)]), AuthorCompany(id=1324077713883013782, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, xref=2., ext=[AuthorCompanyExt(id=1324077713891402391, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, companyId=1324077713883013782, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.四川大学公共卫生学院流行病与卫生统计学系)])], figs=[ArticleFig(id=1324077717527863993, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=EN, label=Fig.1, caption=The selection and inclusion process of participants, figureFileSmall=14EEAZ2jEVZ6wygeF/+Kug==, figureFileBig=WJAO+8z6SfWTJxRF5HKBDw==, tableContent=null), ArticleFig(id=1324077717594972858, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=CN, label=图1, caption=研究样本筛选及纳入流程, figureFileSmall=14EEAZ2jEVZ6wygeF/+Kug==, figureFileBig=WJAO+8z6SfWTJxRF5HKBDw==, tableContent=null), ArticleFig(id=1324077717800493755, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=EN, label=Fig.2, caption=K-M curves for the incidence of depressive symptoms in the digital exclusion group and the digital inclusion group, figureFileSmall=MuzokDqdrhg+KZqGPj9ZnA==, figureFileBig=0EBREDe3iFYD3DgoehY79A==, tableContent=null), ArticleFig(id=1324077717884379836, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=CN, label=图2, caption=数字排斥组和数字包容组发生抑郁症状的K-M曲线图, figureFileSmall=MuzokDqdrhg+KZqGPj9ZnA==, figureFileBig=0EBREDe3iFYD3DgoehY79A==, tableContent=null), ArticleFig(id=1324077717968265917, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=EN, label=Fig.3, caption=Results of the mediation analysis of social participation, figureFileSmall=28XzRvu/PTv02CDv9JhkBA==, figureFileBig=zGC1cr6+wSP71vwd1XA7fA==, tableContent=null), ArticleFig(id=1324077718047957694, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=CN, label=图3, caption=社会参与的中介分析结果, figureFileSmall=28XzRvu/PTv02CDv9JhkBA==, figureFileBig=zGC1cr6+wSP71vwd1XA7fA==, tableContent=null), ArticleFig(id=1324077718123455167, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=EN, label=Table 1, caption=

The characteristics comparison of the digital exclusion group versus the digital inclusion group [n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
特征数字排斥(n=8 334)数字包容(n=474)总样本(n=8 808)Z/χ2P值
年龄(岁)9.521<0.001
<605 136(61.6)393(82.9)5 529(62.8)
60~692 224(26.7)68(14.3)2 292(26.0)
70~79868(10.4)12(2.5)880(10.0)
≥80106(1.3)1(0.2)107(1.2)
性别14.831<0.001
男4 493(53.9)299(63.1)4 792(54.4)
女3 841(46.1)175(36.9)4 016(45.6)
受教育程度-30.971<0.001
初中及以下7 223(86.7)165(34.8)7 388(83.9)
高中/中专973(11.7)198(41.8)1 171(13.3)
大专及以上138(1.7)111(23.4)249(2.8)
婚姻状况2.7810.095
已婚7 634(91.6)445(93.9)8 079(91.7)
未婚700(8.4)29(6.1)729(8.3)
社会经济地位-21.712<0.001
Q1(最低)2 189(26.3)14(3.0)2 203(25.0)
Q22 169(26.0)32(6.8)2 201(25.0)
Q32 112(25.3)91(19.2)2 203(25.0)
Q4(最高)1 864(22.4)337(71.1)2 201(25.0)
居住地352.920<0.001
城市3 413(41.0)403(85.0)3 816(43.3)
农村4 921(59.0)71(15.0)4 992(56.7)
吸烟1.1830.553
从不4 789(57.5)272(57.4)5 061(57.5)
曾经796(9.6)52(11.0)848(9.6)
现在2 749(33.0)150(31.6)2 899(32.9)
饮酒70.534<0.001
从不4 622(55.5)180(38.0)4 802(54.5)
曾经636(7.6)28(5.9)664(7.5)
现在3 076(36.9)266(56.1)3 342(37.9)
共病-0.2880.773
无共病3 115(37.4)172(36.3)3 287(37.3)
单一共病2 581(31.0)152(32.1)2 733(31.0)
多重共病2 638(31.7)150(31.6)2 788(31.7)
BADL4.4010.036
未受损8 181(98.2)472(99.6)8 653(98.2)
受损153(1.8)2(0.4)155(1.8)
IADL24.744<0.001
未受损7 837(94.0)472(99.6)8 309(94.3)
受损497(6.0)2(0.4)499(5.7)
记忆疾病3.1890.074
无8 261(99.1)474(100.0)8 735(99.2)
有73(0.9)0(0.0)73(0.8)
社会参与126.380<0.001
未参与4 028(48.3)103(21.7)4 131(46.9)
参与4 306(51.7)371(78.3)4 677(53.1)
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数字排斥与数字包容组的特征比较[n(%)]

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特征数字排斥(n=8 334)数字包容(n=474)总样本(n=8 808)Z/χ2P值
年龄(岁)9.521<0.001
<605 136(61.6)393(82.9)5 529(62.8)
60~692 224(26.7)68(14.3)2 292(26.0)
70~79868(10.4)12(2.5)880(10.0)
≥80106(1.3)1(0.2)107(1.2)
性别14.831<0.001
男4 493(53.9)299(63.1)4 792(54.4)
女3 841(46.1)175(36.9)4 016(45.6)
受教育程度-30.971<0.001
初中及以下7 223(86.7)165(34.8)7 388(83.9)
高中/中专973(11.7)198(41.8)1 171(13.3)
大专及以上138(1.7)111(23.4)249(2.8)
婚姻状况2.7810.095
已婚7 634(91.6)445(93.9)8 079(91.7)
未婚700(8.4)29(6.1)729(8.3)
社会经济地位-21.712<0.001
Q1(最低)2 189(26.3)14(3.0)2 203(25.0)
Q22 169(26.0)32(6.8)2 201(25.0)
Q32 112(25.3)91(19.2)2 203(25.0)
Q4(最高)1 864(22.4)337(71.1)2 201(25.0)
居住地352.920<0.001
城市3 413(41.0)403(85.0)3 816(43.3)
农村4 921(59.0)71(15.0)4 992(56.7)
吸烟1.1830.553
从不4 789(57.5)272(57.4)5 061(57.5)
曾经796(9.6)52(11.0)848(9.6)
现在2 749(33.0)150(31.6)2 899(32.9)
饮酒70.534<0.001
从不4 622(55.5)180(38.0)4 802(54.5)
曾经636(7.6)28(5.9)664(7.5)
现在3 076(36.9)266(56.1)3 342(37.9)
共病-0.2880.773
无共病3 115(37.4)172(36.3)3 287(37.3)
单一共病2 581(31.0)152(32.1)2 733(31.0)
多重共病2 638(31.7)150(31.6)2 788(31.7)
BADL4.4010.036
未受损8 181(98.2)472(99.6)8 653(98.2)
受损153(1.8)2(0.4)155(1.8)
IADL24.744<0.001
未受损7 837(94.0)472(99.6)8 309(94.3)
受损497(6.0)2(0.4)499(5.7)
记忆疾病3.1890.074
无8 261(99.1)474(100.0)8 735(99.2)
有73(0.9)0(0.0)73(0.8)
社会参与126.380<0.001
未参与4 028(48.3)103(21.7)4 131(46.9)
参与4 306(51.7)371(78.3)4 677(53.1)
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The longitudinal relationship between digital exclusion and depressive symptoms

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分组HR (95%CI)
模型1模型2模型3模型4
数字包容对照组对照组对照组对照组
数字排斥2.23(1.83~2.71)1.40(1.14~1.73)1.38(1.12~1.71)1.37(1.11~1.69)
P值<0.0010.0020.0020.003
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数字排斥与抑郁症状的纵向关联

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分组HR (95%CI)
模型1模型2模型3模型4
数字包容对照组对照组对照组对照组
数字排斥2.23(1.83~2.71)1.40(1.14~1.73)1.38(1.12~1.71)1.37(1.11~1.69)
P值<0.0010.0020.0020.003
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Association between digital exclusion and depressive symptoms based on multiple imputation

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分组HR (95%CI)
模型1模型2模型3模型4
数字包容对照组对照组对照组对照组
数字排斥2.14(1.84~2.49)1.39(1.18~1.63)1.37(1.17~1.61)1.35(1.15~1.59)
P值<0.001<0.001<0.001<0.001
), ArticleFig(id=1324077718635160260, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077712104628874, language=CN, label=表3, caption=

多重插补后数字排斥与抑郁症状的关联

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分组HR (95%CI)
模型1模型2模型3模型4
数字包容对照组对照组对照组对照组
数字排斥2.14(1.84~2.49)1.39(1.18~1.63)1.37(1.17~1.61)1.35(1.15~1.59)
P值<0.001<0.001<0.001<0.001
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中国中老年人数字排斥与抑郁症状的关联研究
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刘利 1 , 祁婧 2 , 卢晓红 1 , 余芳 1 , 邱培媛 2
现代预防医学 | 健康与社会行为 2026,53(6): 1082-1088
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现代预防医学 |健康与社会行为 2026 , 53 (6) : 1082 -1088
中国中老年人数字排斥与抑郁症状的关联研究
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刘利1, 祁婧2, 卢晓红1, 余芳1, 邱培媛2
作者信息
  • 1.四川中医药高等专科学校预防医学教研室,四川 绵阳 621000
  • 2.四川大学公共卫生学院流行病与卫生统计学系
通讯作者:
邱培媛,E-mail:
作者简介:

刘利(1981—),女,硕士,副教授,研究方向:预防医学、健康教育与健康促进、社会医学

Digital exclusion, social participation, and depression symptoms among Chinese older adults
Li LIU1, Jing QI2, Xiao-hong LU1, Fang YU1, Pei-yuan QIU2
Affiliations
  • Department of Preventive Medicine, Sichuan College of Traditional Chinese Medicine, Mianyang, Sichuan 621000,China
出版时间: 2026-03-25 doi: 10.20043/j.cnki.MPM.202510349
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目的

本研究旨在探究中国中老年人群数字排斥与抑郁症状的纵向关联,并检验社会参与在两者关联之间的中介效应,以期为该人群抑郁症状的识别与干预提供实证支持。

方法

研究使用CHARLS数据库2011—2020年的连续追踪数据。数字排斥和社会参与通过受访者的自我报告进行评估,抑郁症状采用10项简版流行病学研究中心抑郁量表进行测量。采用Kaplan-Meier生存曲线和Log-rank检验比较数字排斥组和数字包容组抑郁症状发病风险,并构建Cox比例风险模型分析其纵向关联。进一步采用因果中介分析检验社会参与的中介作用。

结果

本研究共纳入8 808名研究对象。基线时,8 334(94.6%)名研究对象被分为数字排斥。在为期约7.00年的随访过程中,共观察到3 969名研究对象出现抑郁症状。生存分析显示,数字排斥组的抑郁症状发病风险高于数字包容组(log-rank P<0.05)。控制混杂因素后,Cox模型分析结果显示,数字排斥与抑郁症状的发生风险呈正相关(HR=1.37, 95% CI: 1.11~1.69)。中介分析证实,社会参与在数字排斥与抑郁症状的关联中起部分中介作用。

结论

数字排斥与中国中老年人发生抑郁症状的风险增加有关。社会参与在这一关联中起到了部分中介作用。促进数字包容、鼓励社会参与可能是预防和干预中老年人抑郁症状的有效策略。

数字排斥  /  抑郁症状  /  中老年人  /  CHARLS
Objective

To explore the long-term relationship between digital exclusion and depressive symptoms in middle-aged and older adults in China, to evaluate whether social participation serves as a mediator in this connection, and to guide interventions to reduce depression within this group.

Methods

A longitudinal analysis was conducted using data from the 2011-2020 waves of the China Health and Retirement Longitudinal Study (CHARLS). Self-reported data were used to evaluate digital exclusion and social participation, while the presence of depressive symptoms was measured with the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D 10). Differences in the incidence of depressive symptoms among groups were assessed using Kaplan-Meier estimates and the log-rank test. Cox proportional hazards models were used to analyze the longitudinal association between digital exclusion and depressive symptoms, and causal mediation analysis was employed to test the mediating effect.

Results

A total of 8 808 participants were included in the study. At baseline, 8 334 (94.6%) individuals were classified as digital exclusion group. Over a median follow-up of 7.00 years, 3 969 participants developed depressive symptoms. The survival analysis revealed that the cumulative incidence of depressive symptoms was significantly higher in the digital exclusion group than that in the digital inclusion group (Log-rank P<0.05). After controlling for confounders, Cox regression analysis showed that digital exclusion remained an independent risk factor for depressive symptoms (HR=1.37, 95% CI: 1.11-1.69). The mediation analysis found that social participation partially mediated the association between digital exclusion and depressive symptoms.

Conclusion

Digital exclusion is associated with an increased risk of depressive symptoms among middle-aged and older Chinese adults. This association may be partially mediated by social participation. Promoting digital inclusion and encouraging social engagement may be potential strategies for the prevention and intervention of depression in this vulnerable group.

Digital exclusion  /  Depressive symptoms  /  Chinese elderly  /  CHARLS
刘利, 祁婧, 卢晓红, 余芳, 邱培媛. 中国中老年人数字排斥与抑郁症状的关联研究. 现代预防医学, 2026 , 53 (6) : 1082 -1088 . DOI: 10.20043/j.cnki.MPM.202510349
Li LIU, Jing QI, Xiao-hong LU, Fang YU, Pei-yuan QIU. Digital exclusion, social participation, and depression symptoms among Chinese older adults[J]. Modern Preventive Medicine, 2026 , 53 (6) : 1082 -1088 . DOI: 10.20043/j.cnki.MPM.202510349
随着人均预期寿命的延长和生育率的持续下降,中国的老龄化问题日益突出。依据2023年《中国统计年鉴》的数据,我国老年人口规模持续增长,65岁及以上人群占全国人口约15%[1]。在老龄化快速发展的背景下,老年人心理健康问题日益突出,我国老年人中约有五分之一患有抑郁症[2]。抑郁症可能导致老年人认知功能下降、生活能力受限,增加家庭照护负担和社会经济压力[3]。因此,对老年人群进行抑郁症的早期干预,不仅能够改善个体心理健康状况,还能减轻家庭与社会负担,为健康老龄化提供重要支撑。
“数字排斥”是指个体缺乏访问或使用信息与通信技术,尤其是互联网的机会或能力[4]。在经济较为发达、市场体系完善的国家,互联网使用率普遍较高,但在发展中国家,互联网使用率仍偏低[5]。相关报告指出,60岁及以上老年人是主要的数字排斥群体[6],而数字排斥与多种健康不良结局相关,如身体功能受限、幸福感下降等[5-7]。
既往研究发现,数字排斥与抑郁风险增加相关[8-12],但是,数字排斥与老年人抑郁症状之间关联的作用机制尚缺乏研究。数字排斥被证明会限制老年人的社会互动[13-14],而社会参与水平的降低则是老年抑郁症状的危险因素[15]。尽管目前有研究探索了社会参与在数字排斥与抑郁症状之间的中介作用,但均为横断面设计[11]。本研究利用2011—2020年中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)的纵向数据,分析数字排斥与抑郁症状的关系,并探讨社会参与的中介效应,旨在为揭示两者之间的关联及关联机制提供科学依据。
研究数据来源于2011—2020年五轮中国健康与养老追踪调查(CHARLS)。本研究纳入了年龄≥45岁,且至少完成两轮调查的中老年人。排除标准为:(1)基线抑郁评估信息缺失的受访者;(2)基线数字排斥信息缺失的受访者;(3) 随访中抑郁信息缺失的受访者;(4) 基线时已存在抑郁症状的受访者;(5) 协变量信息缺失的受访者。最终筛选出8 808名符合条件的参与者进入本研究(见图1)。
本研究采用10项简版流行病学研究中心抑郁量表(CES-D 10)对抑郁症状进行测量。该量表通过10个条目评估受访者在过去一周中的情绪,每个条目均设四个选项,分别为:“很少或者没有”、“不太多”、“有时或有一半的时间”和“大多数时间”,依次记为0分、1分、2分和3分。其中,条目“我对未来充满希望”和“我感到愉快”为反向计分。总分范围为0至30分,将总分≥10作为抑郁症状的判定标准[16]。
数字排斥来源于受访者的自我报告。参考既往研究[5,17],我们依据问题“您在过去一个月中是否使用过互联网?”进行构建:回答“否”的受访者归为“数字排斥组”,回答“是”的受访者归为“数字包容组”。
本研究采用自我报告的方式评估社会参与,主要考察受访者在过去一个月中是否参加过以下6类社交活动:1)串门、跟朋友交往;2)棋牌娱乐或去社区活动室;3)跳舞、健身、练气功等;4)参加社团组织活动;5)志愿者活动或者慈善活动;6)教育或培训课程。受访者参与至少一项活动,则社会参与变量赋值为1,否则赋值为0。
依据既往文献,本研究在模型中对以下混杂因素进行了控制[10-11]。社会人口学因素包括年龄、性别、婚姻状态、教育背景、居住地类别以及社会经济地位,其中社会经济地位依据年度人均家庭消费支出的四分位数进行划分[18]。行为与生活方式涵盖吸烟和饮酒状况。健康相关因素则包括共病情况、记忆疾病,以及日常生活能力。共病根据患有慢病的数量分为三类:无共病、单一慢病和多重共病。日常生活能力水平分别通过工具性日常生活活动能力(Instrumental Activities of Daily Living, IADL)和基础性日常生活活动能力(Basic Activities of Daily Living, BADL)进行评估,IADL通过做家务、做饭、购物、自己吃药和管钱这五项指标进行评估,若任意一项选择了有困难自己可完成/有困难需要帮助完成和无法完成,则判定为IADL受损。BADL则通过六项指标评估,即穿衣、洗澡、进食、起床、上厕所和控制大小便,若任一项目选择了有困难自己可完成/有困难需要帮助完成和无法完成,则判定为BADL受损。
首先,对研究对象的基线特征进行描述。分类变量以频数和百分比呈现,采用卡方检验分析无序分类变量的组间差异,Mann-Whitney U检验分析有序等级变量的组间差异。采用Kaplan-Meier(K-M)曲线计算生存概率,log-rank 检验探索数字排斥和数字包容组抑郁症状发生风险的差异。采用Cox比例风险模型探索基线的数字排斥与抑郁症状的纵向关联,并报告风险比(HR)及95%置信区间(CI)。本研究采用逐步控制混杂因素的方式建模:模型1为不调整任何变量的模型;模型2在模型1的基础上,纳入社会人口学因素,包括年龄、性别、婚姻状态、教育背景、居住地类别以及社会经济地位;模型3在模型2的基础上进一步加入行为与生活方式(吸烟和饮酒状况);模型4为全调整模型,在模型3基础上,进一步纳入健康相关因素(共病、记忆疾病、BADL、IADL)。使用Schoenfeld残差检验对比例风险假设进行验证。经检验,该研究整体模型和协变量均符合比例风险假设。为探讨社会参与在数字排斥与抑郁症状之间的中介作用,本研究采用R软件的regmedint包进行中介分析[19],其中通过logistic回归模型估计数字排斥与社会参与之间的关联,Cox模型估计社会参与抑郁症状之间的关联,并控制了所有混杂变量。为检验结果的稳健性,研究采用多重插补对缺失值进行填补后进行敏感性分析。本研究采用STATA/SE 15.0进行数据清理,R软件(版本4.1.3)进行数据分析。所有检验均采用双侧检验,检验水准α=0.05。
研究纳入的8 808名研究对象中,8 334(94.6%)名研究对象被分为数字排斥组,474(5.4%)名研究对象为数字包容组。研究人群中60岁以下为5 529(62.8%)名,女性4 016(45.6%)名。组间比较显示,数字排斥组的参与者年龄偏高,女性比例(46.1% vs 36.9%)与农村居住比例(59.0% vs 15.0%)均更高。此外,数字排斥组在受教育程度和社会经济地位上普遍更低(P<0.05)。两组在饮酒行为、BADL/IADL受损情况以及社会参与度方面的基线分布亦存在统计学差异(P<0.05)。详见表1。
在中位数为7.00年的随访期间内,共有3 969例(45.06%)研究对象出现抑郁症状。Kaplan-Meier生存分析结果显示,数字排斥组的累积抑郁症状发生风险高于数字包容组(P<0.001)(见图2)。
Cox回归分析结果显示,在模型2中,数字排斥组发生抑郁症状的风险是数字包容组的1.40(95% CI: 1.14~1.73)倍。进一步调整生活方式因素后,模型3显示数字排斥组发生抑郁症状的风险是数字包容组的1.38(95% CI: 1.12~1.71)倍。在上述模型的基础上继续调整共病、BADL、IADL和记忆疾病之后,数字排斥仍然是抑郁症状的危险因素(见表2)。
本研究探索了社会参与的中介效应,将数字排斥与抑郁症状的总效应(HR=1.38, 95% CI: 1.12~1.70)分成通过社会参与的自然间接效应(HR=1.03, 95% CI: 1.02~1.05)和自然直接效应(HR=1.34, 95% CI: 1.09~1.65)。社会参与的中介效应占比为10.75%,说明在数字排斥与抑郁症状的关联中,有10.75%的效应是通过社会参与介导。(图3)
为了验证研究结果的稳健性,采用多重插补的方法对缺失值进行了填补。研究结果显示,数字排斥与抑郁症状相关。研究结果具有稳健性。
本研究发现,基线时8 334(94.6%)名研究对象自报存在数字排斥,这一比例明显高于其他国家的数据。例如,在丹麦,60岁及以上人群中仅有23.8%自报数字排斥;在英国,该比例为65.5%[5]。总体而言,本研究提示发达国家的数字排斥率普遍低于发展中国家,这与既往研究结果一致[5]。造成这一差异的原因可能是我国部分地区数字基础设施相对不足以及老年人群体的数字技能和使用意愿有限。
本研究发现数字排斥与中老年人群未来发生抑郁症状的风险增加有关。即使控制了混杂因素,数字排斥组的风险仍比数字包容组高37%,与既往研究结果一致。一项基于全球五个中老年人群的纵向研究发现,数字排斥组的风险仍比数字包容组高约13%~55%[10]。目前,关于数字排斥增加抑郁症状风险的机制尚不完全明确。
首先,有研究认为互联网为老年人提供了便捷的沟通渠道,使他们能够与远方的家人和朋友保持联系,从而减少孤独感和社会隔离,提升心理健康水平[14,20-21]。相反,缺乏互联网使用的老年人往往缺少与亲人保持联系的途径,容易产生孤独,进而增加抑郁风险[20]。本研究的中介分析结果也支持这一观点,提示社会参与是数字排斥影响心理健康的重要路径之一。
其次,随着年龄增长,中老年人常面临社交圈缩小、人际关系减少等挑战[22]。而互联网的使用则为他们提供了从“社交回避”转向“积极参与”的机会[13]。借助社交媒体和门户网站,中老年人能够获取信息、分享生活,并拓展社会关系[23-24]。这种社会参与的增加不仅能增强自我认同感和归属感,还能改善心理健康[25]。因此,社会参与在一定程度上解释了数字排斥与抑郁风险的关联。然而,鉴于其中介效应占比仅为 10.75%,提示数字排斥对抑郁症状的影响可能还通过其他机制实现。
此外,数字排斥可能限制中老年人获取互联网医疗服务。随着信息通信技术的发展,电子健康、移动健康和远程医疗等新型医疗服务逐渐普及。然而,不使用互联网的老年人可能难以获得这些医疗资源,进而影响心理健康。最后,数字排斥可能减少认知刺激,例如通过互联网学习新技能、参与益智活动或在线娱乐[26]。当老年人因数字排斥而缺乏这些认知刺激时,可能更易出现心理健康问题[27]。
本研究的优势在于:首先,基于CHARLS这一大型全国代表性队列,采用了纵向设计,并在研究开始时排除了已有抑郁症状的个体,有助于更准确地进行因果推断;其次,在纵向研究框架下进一步考察了社会参与的中介作用,为理解数字排斥影响抑郁症状的潜在机制提供了新的证据。尽管如此,本研究仍存在局限性。首先,虽然研究已经控制了已知的混杂因素,但仍可能存在未控制的残余混杂。其次,本研究中的数字排斥、社会参与等变量均依赖于受访者的自我报告,可能引起回忆偏倚,但在大规模人群研究中,自我报告仍是常用且可行的测量方式。第三,本研究对数字排斥的定义基于单一问题,可能存在测量误差。受访者对“使用互联网”的理解可能存在认知偏差,例如部分老年人虽然日常使用微信视频通话等特定应用,但并不认为自己在使用“互联网”,这可能导致数字排斥发生率的高估。最后,本研究主要关注基线数字排斥状态对随后抑郁症状发生风险的预测作用,未考虑随访期间受访者互联网使用行为的动态变化。这种动态变化可能对心理健康产生累积或即时影响,未来研究可尝试探索两者间的动态相互作用。
综上,本研究发现数字排斥与中老年人抑郁症状发生风险的增加相关。应通过加强基础数字基础设施建设、推动信息化产业发展、开展数字技能培训和教育补偿等举措,改善弱势群体的互联网使用状况。这不仅有助于满足其信息获取和娱乐需求,拓展持续学习的机会,还能促进与家人朋友的沟通,提升社会参与度,最终在一定程度上降低抑郁症状的发生风险。
  • 四川省自然科学基金面上项目(2022NSFSC0668)
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2026年第53卷第6期
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doi: 10.20043/j.cnki.MPM.202510349
  • 接收时间:2025-10-28
  • 首发时间:2026-11-02
  • 出版时间:2026-03-25
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  • 收稿日期:2025-10-28
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四川省自然科学基金面上项目(2022NSFSC0668)
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    1.四川中医药高等专科学校预防医学教研室,四川 绵阳 621000
    2.四川大学公共卫生学院流行病与卫生统计学系

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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
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