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Article(id=1324078288838205961, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509034, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756742400000, receivedDateStr=2025-09-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614464244, onlineDateStr=2026-11-02, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614464244, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614464244, creator=13701087609, updateTime=1793614464244, updator=13701087609, issue=Issue{id=1324078175000605624, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='2', pageStart='193', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614437103, creator='13701087609', updateTime=1793614526046, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078548130082942, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078548130082943, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=316, endPage=321, ext={EN=ArticleExt(id=1324078290713059850, articleId=1324078288838205961, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Impact of digital literacy on the depression trajectory of the elderly, China, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the correlation between digital literacy and the depression trajectory of the elderly in China.

Methods

Based on data from the three waves of the China Family Panel Studies (CFPS) from 2016 to 2020, this study applied a group-based trajectory model to construct the changing trajectory of depression among the elderly, and employed a multi-class logistic regression model to investigate the association between digital literacy and the depression trajectory.

Results

Three trajectory groups were identified: “long-term non-depression group” (64.87%), “depression slow-rising group” (29.75%), and “long-term high depression group” (5.38%). Compared with elderly individuals in the long-term non-depression group, the possibility that the depression trajectory belonged to the depression slow-rising group (RRR=0.905, 95% CI: 0.851-0.962) and the long-term high depression group (RRR=0.681, 95% CI: 0.510-0.919) was smaller if the digital literacy was higher.

Conclusion

There is heterogeneity in the depression trajectories of older adults, and digital literacy significantly influences these trajectories. The digital literacy of the elderly population should be enhanced to alleviate their depression.

, authors=Si-hui LI, Na CHEN, authorsList=Si-hui LI, Na CHEN, authorCompany=null, correspAuthors=Na CHEN, 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=1324078291556114959, articleId=1324078288838205961, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=数字素养对我国老年人抑郁轨迹的影响, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

探讨数字素养与我国老年人抑郁轨迹之间的关联。

方法

基于2016—2020年三期中国家庭追踪调查（CFPS）数据，应用组基轨迹模型构建老年人抑郁的变化轨迹，运用多分类logistic回归模型研究数字素养和抑郁轨迹之间的关联。

结果

识别出三个轨迹组：“长期无抑郁组”（64.87%）、“抑郁缓慢上升组”（29.75%）、“长期高抑郁组”（5.38%）。与长期无抑郁组的老年人相比，数字素养越高，抑郁轨迹归属于抑郁缓慢上升组（RRR=0.905，95% CI：0.851~0.962）和长期高抑郁组（RRR=0.681，95% CI：0.510~0.919）的可能性越小。

结论

老年人抑郁轨迹存在异质性，数字素养显著影响抑郁轨迹，应提升老年群体的数字素养以缓解其抑郁。

, authors=李思惠, 陈娜, authorsList=李思惠, 陈娜, authorCompany=null, correspAuthors=陈娜, authorNote=

李思惠（2002—），女，硕士在读，研究方向：公共管理

, correspAuthorsNote=
陈娜，E-mail:
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李思惠（2002—），女，硕士在读，研究方向：公共管理

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李思惠（2002—），女，硕士在读，研究方向：公共管理

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(In Chinese), articleTitle=Research on impact mechanism of Internet usage on health status of Chinese residents, refAbstract=null), Reference(id=1324078304424239714, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=6, pageStart=101, pageEnd=114, url=null, language=null, rfNumber=[25], rfOrder=45, authorNames=栗洪伟, journalName=企业经济, refType=null, unstructuredReference=栗洪伟．互联网使用如何影响家庭关系 ——基于CFPS数据的实证分析[J]．企业经济，2022,（6）：101-114., articleTitle=互联网使用如何影响家庭关系 ——基于CFPS数据的实证分析, refAbstract=null), Reference(id=1324078304566846051, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=6, pageStart=101, pageEnd=114, url=null, language=null, rfNumber=[25], rfOrder=46, authorNames=Li HW, journalName=Enterprise Economy, refType=null, unstructuredReference=Li HW. How Internet use affects family relationships: an empirical analysis based on CFPS data[J]. Enterprise Economy, 2022, (6): 101-114. (In Chinese), articleTitle=How Internet use affects family relationships: an empirical analysis based on CFPS data, refAbstract=null)], funds=[Fund(id=1324078296010465841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, awardId=ZT202202, language=CN, fundingSource=江苏省中医药科技发展计划专题研究项目(ZT202202), fundOrder=null, country=null), Fund(id=1324078296094351922, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, awardId=B-b/2024/01/107, language=CN, fundingSource=江苏省教育科学规划课题（重点项目）(B-b/2024/01/107), fundOrder=null, country=null), Fund(id=1324078296178238003, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, awardId=null, language=CN, fundingSource=江苏高校“青蓝工程”, fundOrder=null, country=null), Fund(id=1324078296262124084, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, awardId=2024YLFWYGL016, language=CN, fundingSource=南京中医药大学养老服务与管理学院（养老产业学院）专项研究项目(2024YLFWYGL016), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324078291824550416, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, xref=null, ext=[AuthorCompanyExt(id=1324078291832939025, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, companyId=1324078291824550416, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Elderly Care Service and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China), AuthorCompanyExt(id=1324078291841327634, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, companyId=1324078291824550416, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=南京中医药大学养老服务与管理学院，江苏 南京 210023)])], figs=[ArticleFig(id=1324078293204476453, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Fig.1, caption=Chinese elderly depression trajectory group, figureFileSmall=qfQ1GoYjVsy89j3ySE9Xuw==, figureFileBig=RbQEdLn6Kwevt6ep3WsPEA==, tableContent=null), ArticleFig(id=1324078293321916966, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=图1, caption=我国老年人抑郁轨迹组, figureFileSmall=qfQ1GoYjVsy89j3ySE9Xuw==, figureFileBig=RbQEdLn6Kwevt6ep3WsPEA==, tableContent=null), ArticleFig(id=1324078295108690471, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Table 1, caption=

Digital literacy indicator system

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标二级指标三级指标变量描述
数字装置应用是否使用数字设备是否移动上网1/0
对数字设备的偏好移动设备上网时长（标准化）-0.2~16.2
数字资源访问是否通过数字渠道获取信息是否通过Internet获取信息1/0
通过数字渠道获取信息的偏好在线信息渠道的重要性0~7
数字技能掌握是否使用数字技能是否在线学习1/0
是否在线购物1/0
是否在线社交1/0
是否在线娱乐1/0
对数字技能的偏好互联网对学习的重要性1~5
互联网对日常购物的重要性1~5
互联网对社交的重要性1~5
互联网对娱乐的重要性1~5
), ArticleFig(id=1324078295205159464, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=表1, caption=

数字素养指标体系

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标二级指标三级指标变量描述
数字装置应用是否使用数字设备是否移动上网1/0
对数字设备的偏好移动设备上网时长（标准化）-0.2~16.2
数字资源访问是否通过数字渠道获取信息是否通过Internet获取信息1/0
通过数字渠道获取信息的偏好在线信息渠道的重要性0~7
数字技能掌握是否使用数字技能是否在线学习1/0
是否在线购物1/0
是否在线社交1/0
是否在线娱乐1/0
对数字技能的偏好互联网对学习的重要性1~5
互联网对日常购物的重要性1~5
互联网对社交的重要性1~5
互联网对娱乐的重要性1~5
), ArticleFig(id=1324078295314211369, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Table 2, caption=

Model fitting evaluation indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别组群样本规模分配到这一组群的样本比例（%）估计的组群身份概率π（%）组群平均后验概率Avepp
长期无抑郁组3 82564.8763.530.918
抑郁缓慢上升组1 75429.7530.590.826
长期高抑郁组3175.385.880.872
), ArticleFig(id=1324078295381320234, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=表2, caption=

模型拟合评价指标

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别组群样本规模分配到这一组群的样本比例（%）估计的组群身份概率π（%）组群平均后验概率Avepp
长期无抑郁组3 82564.8763.530.918
抑郁缓慢上升组1 75429.7530.590.826
长期高抑郁组3175.385.880.872
), ArticleFig(id=1324078295469400619, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Table 3, caption=

Comparison of baseline characteristics among different depression trajectory groups

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类长期无抑郁组抑郁缓慢上升组长期高抑郁组F(χ2)值P值
年龄(岁，)67.25±5.8366.98±5.6867.54±5.791.9160.147
性别[n(%)]65.431<0.001
男2 144(56.05)735(41.90)112(35.33)
女1 681(43.95)1 019(58.10)205(64.67)
教育程度[n(%)]36.126<0.001
小学及以下2 441(63.82)1 273(72.58)248(78.23)
初中1 266(33.10)466(26.57)66(20.82)
高中81(2.12)8(0.46)3(0.95)
高中以上37(0.96)7(0.39)0
婚姻[n(%)]73.746<0.001
有配偶3 335(87.19)1 344(76.62)218(68.77)
无配偶490(12.81)410(23.38)99(31.23)
自评健康[n(%)]299.221<0.001
非常健康341(8.92)53(3.02)7(2.21)
很健康500(13.07)117(6.67)10(3.15)
比较健康1 391(36.37)439(25.03)49(15.46)
一般943(24.65)426(24.29)39(12.30)
不健康650(16.99)719(40.99)212(66.88)
慢性病[n(%)]96.066<0.001
有940(24.58)666(37.97)168(53.00)
无2 885(75.42)1 088(62.03)149(47.00)
吸烟[n(%)]5.3210.005
是1 116(29.18)444(25.31)78(24.61)
否2 709(70.82)1 310(74.69)239(75.39)
喝酒[n(%)]26.135<0.001
是772(20.18)234(13.34)32(10.09)
否3 053(79.82)1 520(86.66)285(89.91)
午休[n(%)]14.927<0.001
是2 403(62.82)1 011(57.64)158(49.84)
否1 422(37.18)743(42.36)159(50.16)
), ArticleFig(id=1324078295557481004, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=表3, caption=

不同抑郁轨迹组的基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类长期无抑郁组抑郁缓慢上升组长期高抑郁组F(χ2)值P值
年龄(岁，)67.25±5.8366.98±5.6867.54±5.791.9160.147
性别[n(%)]65.431<0.001
男2 144(56.05)735(41.90)112(35.33)
女1 681(43.95)1 019(58.10)205(64.67)
教育程度[n(%)]36.126<0.001
小学及以下2 441(63.82)1 273(72.58)248(78.23)
初中1 266(33.10)466(26.57)66(20.82)
高中81(2.12)8(0.46)3(0.95)
高中以上37(0.96)7(0.39)0
婚姻[n(%)]73.746<0.001
有配偶3 335(87.19)1 344(76.62)218(68.77)
无配偶490(12.81)410(23.38)99(31.23)
自评健康[n(%)]299.221<0.001
非常健康341(8.92)53(3.02)7(2.21)
很健康500(13.07)117(6.67)10(3.15)
比较健康1 391(36.37)439(25.03)49(15.46)
一般943(24.65)426(24.29)39(12.30)
不健康650(16.99)719(40.99)212(66.88)
慢性病[n(%)]96.066<0.001
有940(24.58)666(37.97)168(53.00)
无2 885(75.42)1 088(62.03)149(47.00)
吸烟[n(%)]5.3210.005
是1 116(29.18)444(25.31)78(24.61)
否2 709(70.82)1 310(74.69)239(75.39)
喝酒[n(%)]26.135<0.001
是772(20.18)234(13.34)32(10.09)
否3 053(79.82)1 520(86.66)285(89.91)
午休[n(%)]14.927<0.001
是2 403(62.82)1 011(57.64)158(49.84)
否1 422(37.18)743(42.36)159(50.16)
), ArticleFig(id=1324078295641367085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Table 4, caption=

The correlation between digital literacy and depression trajectory

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别模型1
RRR(95%CI)		模型2
RRR(95%CI)		模型3
RRR(95%CI)
长期无抑郁组1.0001.0001.000
抑郁缓慢上升组0.868(0.819~0.919)a0.866(0.817~0.918)a0.905(0.851~0.962)a
长期高抑郁组0.637(0.480~0.845)a0.641(0.482~0.852)a0.681(0.510~0.919)a
), ArticleFig(id=1324078295729447470, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=表4, caption=

数字素养与抑郁轨迹的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别模型1
RRR(95%CI)		模型2
RRR(95%CI)		模型3
RRR(95%CI)
长期无抑郁组1.0001.0001.000
抑郁缓慢上升组0.868(0.819~0.919)a0.866(0.817~0.918)a0.905(0.851~0.962)a
长期高抑郁组0.637(0.480~0.845)a0.641(0.482~0.852)a0.681(0.510~0.919)a
), ArticleFig(id=1324078295809139247, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=EN, label=Table 5, caption=

Subgroup analysis of RRR (95% CI) correlation between digital literacy and depression trajectory

, figureFileSmall=null, figureFileBig=null, tableContent=
分组长期无抑郁组抑郁缓慢上升组长期高抑郁组P交互
性别<0.001
男1.000(Ref)0.898(0.822~0.982)a0.809(0.600~1.091)
女1.000(Ref)0.911(0.835~0.993)a0.418(0.130~1.343)
教育程度<0.001
小学及以下1.000(Ref)0.904(0.828~0.988)a0.295(0.053~1.640)
初中1.000(Ref)0.898(0.814~0.992)a0.894(0.677~1.180)
高中1.000(Ref)1.040(0.778~1.390)-
高中以上1.000(Ref)1.906(0.626~5.804)-
婚姻<0.001
有配偶1.000(Ref)0.907(0.849~0.970)a0.631(0.408~0.976)a
无配偶1.000(Ref)0.887(0.761~1.035)0.756(0.508~1.126)
), ArticleFig(id=1324078295888831024, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078288838205961, language=CN, label=表5, caption=

数字素养与抑郁轨迹关联的RRR(95% CI)亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分组长期无抑郁组抑郁缓慢上升组长期高抑郁组P交互
性别<0.001
男1.000(Ref)0.898(0.822~0.982)a0.809(0.600~1.091)
女1.000(Ref)0.911(0.835~0.993)a0.418(0.130~1.343)
教育程度<0.001
小学及以下1.000(Ref)0.904(0.828~0.988)a0.295(0.053~1.640)
初中1.000(Ref)0.898(0.814~0.992)a0.894(0.677~1.180)
高中1.000(Ref)1.040(0.778~1.390)-
高中以上1.000(Ref)1.906(0.626~5.804)-
婚姻<0.001
有配偶1.000(Ref)0.907(0.849~0.970)a0.631(0.408~0.976)a
无配偶1.000(Ref)0.887(0.761~1.035)0.756(0.508~1.126)
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数字素养对我国老年人抑郁轨迹的影响
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李思惠 , 陈娜
现代预防医学 | 健康与社会行为 2026,53(2): 316-321
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现代预防医学 |健康与社会行为 2026 , 53 (2) : 316 -321
数字素养对我国老年人抑郁轨迹的影响
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李思惠（2002—），女，硕士在读，研究方向：公共管理

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李思惠, 陈娜
作者信息
  • 南京中医药大学养老服务与管理学院，江苏 南京 210023
通讯作者:
陈娜，E-mail:
作者简介:

李思惠（2002—），女，硕士在读，研究方向：公共管理

Impact of digital literacy on the depression trajectory of the elderly, China
Si-hui LI, Na CHEN
Affiliations
  • School of Elderly Care Service and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
出版时间: 2026-01-25 doi: 10.20043/j.cnki.MPM.202509034
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摘要
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目的

探讨数字素养与我国老年人抑郁轨迹之间的关联。

方法

基于2016—2020年三期中国家庭追踪调查（CFPS）数据，应用组基轨迹模型构建老年人抑郁的变化轨迹，运用多分类logistic回归模型研究数字素养和抑郁轨迹之间的关联。

结果

识别出三个轨迹组：“长期无抑郁组”（64.87%）、“抑郁缓慢上升组”（29.75%）、“长期高抑郁组”（5.38%）。与长期无抑郁组的老年人相比，数字素养越高，抑郁轨迹归属于抑郁缓慢上升组（RRR=0.905，95% CI：0.851~0.962）和长期高抑郁组（RRR=0.681，95% CI：0.510~0.919）的可能性越小。

结论

老年人抑郁轨迹存在异质性，数字素养显著影响抑郁轨迹，应提升老年群体的数字素养以缓解其抑郁。

数字素养  /  老年人  /  抑郁  /  组基轨迹模型
Objective

To explore the correlation between digital literacy and the depression trajectory of the elderly in China.

Methods

Based on data from the three waves of the China Family Panel Studies (CFPS) from 2016 to 2020, this study applied a group-based trajectory model to construct the changing trajectory of depression among the elderly, and employed a multi-class logistic regression model to investigate the association between digital literacy and the depression trajectory.

Results

Three trajectory groups were identified: “long-term non-depression group” (64.87%), “depression slow-rising group” (29.75%), and “long-term high depression group” (5.38%). Compared with elderly individuals in the long-term non-depression group, the possibility that the depression trajectory belonged to the depression slow-rising group (RRR=0.905, 95% CI: 0.851-0.962) and the long-term high depression group (RRR=0.681, 95% CI: 0.510-0.919) was smaller if the digital literacy was higher.

Conclusion

There is heterogeneity in the depression trajectories of older adults, and digital literacy significantly influences these trajectories. The digital literacy of the elderly population should be enhanced to alleviate their depression.

Digital literacy  /  Older adults  /  Depression  /  Group-based trajectory model
李思惠, 陈娜. 数字素养对我国老年人抑郁轨迹的影响. 现代预防医学, 2026 , 53 (2) : 316 -321 . DOI: 10.20043/j.cnki.MPM.202509034
Si-hui LI, Na CHEN. Impact of digital literacy on the depression trajectory of the elderly, China[J]. Modern Preventive Medicine, 2026 , 53 (2) : 316 -321 . DOI: 10.20043/j.cnki.MPM.202509034
正文
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我国人口老龄化进程不断加快，是全球老龄化最严重的国家之一[1]。根据国家统计局数据，截至2024年底，我国60岁及以上人口占比22.0%，老年群体的身心健康状况成为社会关注的焦点[2]。抑郁作为一种常见的心理障碍，不仅会降低老年人的生活满意度，还可能引发一系列生理疾病[3]，增加家庭和社会的照护负担[4]。
数字素养是指通过数字技术安全适当地获得、管理、理解、整合、沟通、评价和创造信息的能力，是个体融入数字社会、实现自我发展的关键保障[5]。随着信息技术的快速发展，数字技术正深刻改变着人们的生活方式与行为模式。数字素养作为个体适应数字化社会的核心能力，其重要性日益凸显。然而，据《第55次中国互联网络发展状况统计报告》显示，截至2024年12月，我国仍有47.5%的老年群体未接入互联网，且数字素养相对较低[6]。
在数字化与老龄化并行的社会背景下，数字素养与老年人抑郁之间的关联逐渐进入学界视野。目前，学界关于数字化对老年人心理健康的影响尚未形成统一，有学者认为其影响是积极的[7-10]，也有部分学者持相反观点[11-12]。且现有关于数字化对老年人抑郁的研究多是基于横截面数据，较少有发展轨迹的研究。因此，本研究基于中国家庭追踪调查（CFPS）数据探讨老年人数字素养与抑郁轨迹间的关系，为制定改善抑郁的干预措施提供参考依据。
1　资料和方法
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1.1　资料来源
数据来源于2016—2020年三期中国家庭追踪调查（CFPS）数据。CFPS样本覆盖25个省/市/自治区，是一项全国性、大规模、多学科的社会跟踪调查项目。该问卷中反映居民数字素养的问题比较新颖且完备，能够细分居民多方面的数字素养水平[13]。本研究对象为60岁及以上的老年人，共有9 358名60岁以上的老年人参与了2016年的基线调查，排除仅参与2016年一期调查的1 664名老人、2016年抑郁数据缺失者880名、两次随访调查抑郁数据均缺失者797名和其他核心变量缺失者121名，共纳入5 896名研究对象。
1.2　变量选取
因变量：老年人抑郁水平，数据来源于CFPS中涉及抑郁量表（CES-D）的相关题目。共包括八道题：“我感到情绪低落”、“我觉得做任何事都很费劲”、“我的睡眠不好”、“我感到愉快”、“我感到孤独”、“我生活快乐”、“我感到悲伤难过”、“我觉得生活无法继续”[13]。其中积极题目反向计分，最终得分在8~32分，得分越高，表示抑郁水平越高。将总分≤15分定义为无抑郁症状，总分>15分定义为有抑郁症状[14-15]。
自变量：老年人数字素养，依据2016年基期数据，参考已有研究[16]构建的具体指标见表1。通过应用因子分析法并遵循特征值大于1的准则，提取5个公共因子，将每个因子方差贡献率在累积方差贡献率中的比例作为权重来计算因子得分，进而得出数字素养的综合得分，得分越高，表示数字素养越高，最终得分在-1.10~8.48分。
控制变量：年龄、性别、教育程度、婚姻、自评健康、慢性病、吸烟（过去1个月是否有吸烟行为）、喝酒（过去1个月每周是否有3次以上饮酒行为）、午休情况[13,17]。
1.3　统计学方法
本研究使用Stata 17.0软件进行统计分析。首先，通过组基轨迹模型拟合我国老年人抑郁的轨迹，从少亚组数、高阶项开始拟合，若无统计学意义则继续拟合低阶项，直至每条轨迹参数均显著。根据贝叶斯信息准则（Bayesian Information Criterions，BIC）、平均后验概率（Average Posterior Probability，AvePP）、每个亚组个体数量占比最终选择最优模型。采用卡方检验和方差分析比较不同轨迹亚组的基线特征，采用多分类logistic回归来研究数字素养和抑郁轨迹之间的关联，检验水准α=0.05。
2　结果
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2.1　调查对象的抑郁轨迹分组
基于GBTM模型，根据每条轨迹相关参数的显著性、贝叶斯信息准则、平均后验概率以及各亚组个体数量占比，最终确定了最佳轨迹类型数为3组（BIC=-41 058.73），见图1，分别是“长期无抑郁组（n=3 825，64.87%）”、“抑郁缓慢上升组（n=1 754，29.75%）”和“长期高抑郁组（n=317，5.38%）”。三个轨迹组的平均验后分组概率均大于0.7，各亚组内个体成员占比均不小于5%，模型拟合较为理想。见表2。
2.2　不同抑郁轨迹组基线特征比较
不同轨迹分组下老年人的性别、教育程度、婚姻、自评健康等存在显著差异。男性、小学及以下学历、有配偶、比较健康、无慢性病、不吸烟、较少饮酒、有午休习惯的老年人更有可能处于长期无抑郁状态。见表3。
2.3　数字素养和抑郁轨迹的关联
本研究以数字素养作为自变量，抑郁轨迹归属类别作为因变量，构建多分类logistic回归模型，以“长期无抑郁组”为参照组，依次调整年龄、性别、教育程度、婚姻、自评健康等控制变量，结果见表4。在控制人口学特征、健康行为变量后，老年人的数字素养每增加一个单位，相对于“长期无抑郁组”，“抑郁缓慢上升组”的相对风险比（Relative Risk Ratio, RRR）为0.905；“长期高抑郁组”的相对风险比（Relative Risk Ratio, RRR）为0.681。
2.4　亚组分析
性别、教育程度、婚姻和数字素养的相乘交互项均具有统计学意义(P交互<0.001)。从性别来看，数字素养对“抑郁缓慢上升组”的男性(RRR=0.898, 95% CI 0.822~0.982)和女性(RRR=0.911, 95% CI 0.835~0.993)抑郁轨迹均有显著影响；在教育程度方面，数字素养的提升显著降低“抑郁缓慢上升组”小学及以下(RRR=0.904,95% CI 0.828~0.988)和初中学历(RRR=0.898,95% CI 0.814~0.992)老年群体的抑郁轨迹风险；从婚姻状况来看，数字素养的提升显著降低有配偶老年群体的抑郁缓慢上升(RRR=0.907,95% CI 0.849~0.970)及长期高抑郁轨迹风险(RRR=0.631,95% CI 0.408~0.976)。见表5。
3　讨论
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本研究基于CFPS的三期随访数据，利用组基轨迹模型发现了我国老年人的抑郁发展轨迹存在异质性[18]。识别出了3个不同的抑郁轨迹组，分别为“长期无抑郁组”、“抑郁缓慢上升组”和“长期高抑郁组”。
本研究发现，数字素养对我国老年人抑郁发展轨迹有显著影响。数字素养越高的老年人越倾向于低抑郁轨迹，以上结论与已有研究结论一致[19-21]。随着年龄的增加，老年人面临着身体机能下降、社会角色转变、丧偶、独居等情况，面临着比其他社会群体更严重的社会隔离，在此过程中可能会产生抑郁。具备数字素养的老年人可以借助数字设备打破地域限制，与距离远、无法时常见面的亲友联系；数字素养高的老人亦可以通过各类软件进行娱乐，丰富精神生活，减少孤独感，进而缓解抑郁[22]。
研究还发现，数字素养对男性、初中学历、有配偶的老年人抑郁轨迹的影响更为显著，在性别与学历维度与已有研究结果相似[23-24]。从传统性别角色看，男性老年人更倾向于通过工具性活动维持社会连接与自我价值感，而非情感性倾诉。数字素养能直接赋能男性老年人的工具性需求，同时，通过线上平台社交弥补线下缺失的情感互动。相比小学及以下学历的老年人，初中学历老年人有更好的文化基础及学习能力，能更高效融入数字生活，通过线上社交、信息获取缓解孤独与认知封闭。而高中学历及以上群体可能因教育经历，对数字技术有更高的期待或更复杂的需求，数字素养提升的“边际效益”在抑郁改善上被分散。初中学历群体需求更聚焦于基础数字功能带来的“连接感”，数字素养提升直接作用于生活便利与社交拓展，所以对抑郁的影响更大。此外，配偶可能成为数字技术的“共学者”或“督促者”，如一起学习视频通话、网购生活用品等，这种互动不仅提升了双方的数字素养，还增强了家庭内部的情感联结。配偶的存在能减少数字使用时的“孤独感”，避免独自面对技术故障的无助，放大数字素养的心理保护作用[25]。
基于以上结果，提出以下建议：在制定改善抑郁的干预措施时，应把提升老年人数字素养作为改善抑郁的一个重要手段，并根据不同老年群体的异质性制定具有针对性的数字素养提升计划。加大对老年数字素养教育的资金投入与政策支持，鼓励社会力量参与老年数字教育，开发适合老年人的数字学习平台和课程，打破时间与空间限制，满足老年人多样化学习需求，从而有针对性的提升不同老年群体的数字素养，进而缓解其抑郁。
本研究尚存在局限性，数字素养为基期数据，没有考虑随访期间数字素养的变化，未来可以建立双轨迹模型，动态分析数字素养与抑郁之间的关联。此外，未进一步探索数字素养与抑郁之间的影响机制。
基金
收起
  • 江苏省中医药科技发展计划专题研究项目(ZT202202)
  • 江苏省教育科学规划课题（重点项目）(B-b/2024/01/107)
  • 江苏高校“青蓝工程”
  • 南京中医药大学养老服务与管理学院（养老产业学院）专项研究项目(2024YLFWYGL016)
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2026年第53卷第2期
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doi: 10.20043/j.cnki.MPM.202509034
  • 接收时间：2025-09-02
  • 首发时间：2026-11-02
  • 出版时间：2026-01-25
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  • 收稿日期：2025-09-02
基金
江苏省中医药科技发展计划专题研究项目(ZT202202)
江苏省教育科学规划课题（重点项目）(B-b/2024/01/107)
江苏高校“青蓝工程”
南京中医药大学养老服务与管理学院（养老产业学院）专项研究项目(2024YLFWYGL016)
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    南京中医药大学养老服务与管理学院，江苏 南京 210023

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Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
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species		 占总种数比例
Percentage of total
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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
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