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Article(id=1324077528964542946, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202508219, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755187200000, receivedDateStr=2025-08-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614283075, onlineDateStr=2026-11-02, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614283075, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614283075, creator=13701087609, updateTime=1793614283075, updator=13701087609, issue=Issue{id=1324077443828560770, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614262778, creator='13701087609', updateTime=1793614352085, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077818509918661, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077818509918662, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1270, endPage=1276, ext={EN=ArticleExt(id=1324077529207812579, articleId=1324077528964542946, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Relationship between activities of daily living and depressive symptoms in older adults with dual sensory impairment: a cross-lagged panel network analysis, columnId=1228016572892119056, journalTitle=Modern Preventive Medicine, columnName=Primary Health Services, runingTitle=null, highlight=null, articleAbstract=
Objective

To examine the bidirectional dynamic relationship between activities of daily living (ADL) and depressive symptoms among older adults with dual sensory impairment (DSI), identify key driving symptoms, and provide targets for precision interventions.

Methods

This study used longitudinal data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study (CHARLS), including 2 674 participants aged≥60 years with DSI. Depressive symptoms were assessed using the short form of the Center for Epidemiologic Studies Depression Scale, and ADL limitations were measured using the ADL scale. A cross-lagged panel network (CLPN) model was applied to examine time-lagged effects and symptom network characteristics.

Results

A significant cross-lagged pathway was identified from ADL item A11 “Managing money” to depressive symptom A1 “Being bothered by little things” (OR=1.19, 95% CI: 1.07-1.28), followed by D3 “Felt depressed” to A4 “Getting in, out of bed” (OR=1.16, 95% CI: 1.00-1.32), and D2 “Felt fearful” to A1 “Dressing” (OR=1.14, 95% CI: 1.00-1.35). Centrality analysis revealed that D3 “Felt depressed” (BEI=0.73) and A11 “Managing money” (BEI=0.59) were key bridge symptoms. D3 “Felt depressed” showed the highest out-expected influence (OEI=1.03), while A10 “Taking medication” was most susceptible to influence (IEI=0.77).

Conclusion

In older adults with DSI, ADL limitations and depressive symptoms may influence each other through specific pathways, and the CLPN indicates reciprocal prospective predictive associations between them, potentially forming a bidirectional vicious cycle via these pathways. Targeted interventions focusing on core bridge symptoms may help disrupt this cycle, thereby improving functional independence, enhancing psychological well-being, promoting healthy aging, and carrying important public health significance.

, authors=Ying HUANG, Jia-lin WANG, Qi AN, Ting LI, Li CHEN, authorsList=Ying HUANG, Jia-lin WANG, Qi AN, Ting LI, Li CHEN, authorCompany=null, correspAuthors=Jia-lin WANG, 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=1324077532760388084, articleId=1324077528964542946, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于交叉滞后网络分析的双重感觉障碍老年人日常生活活动能力与抑郁症状的关系, columnId=1228016573533847611, journalTitle=现代预防医学, columnName=基层卫生服务, runingTitle=null, highlight=null, articleAbstract=
目的

探究双重感觉障碍（dual sensory impairment，DSI）老年人日常生活活动能力（activities of daily living，ADL）与抑郁症状的双向动态关联机制，识别关键驱动症状，为精准干预提供靶点。

方法

基于中国健康与养老追踪调查2018 —2020年纵向数据，纳入2 674名≥60岁DSI老年人。采用简版流调中心抑郁量表评估抑郁症状，ADL量表评估ADL障碍，并运用交叉滞后网络模型（cross-lagged panel network，CLPN）分析变量间时滞效应与症状网络特性。

结果

ADL维度A11“管理钱财”→抑郁症状A1“因小事烦恼”是显著的交叉滞后边缘（OR=1.19，95%CI：1.07~1.28），其次是抑郁症状D3“情绪低落”→ADL维度A4“起床、下床”（OR=1.16，95%CI：1.00~1.32）、抑郁症状D2“感到害怕”→ADL维度A1“穿衣”（OR=1.14，95%CI：1.00~1.35）；中心性分析发现，D3“情绪低落”（BEI=0.73）、A11“管理钱财”（BEI=0.59）是关键的桥梁症状，其中D3“情绪低落”具有很强的影响能力（OEI=1.03），A10“服药”则最易受影响（IEI=0.77）。

结论

DSI老年人群ADL受限与抑郁症状通过特定路径相互影响，CLPN分析提示两者存在相互的前瞻性预测关系，可通过特定路径形成双向恶性循环。聚焦核心症状进行干预，可阻断循环进展，对提升功能独立性、促进心理健康及实现健康老龄化具有重要公共卫生意义。

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黄英（2000—），女，硕士在读，护士，研究方向：临床护理

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王佳琳，E-mail：
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黄英（2000—），女，硕士在读，护士，研究方向：临床护理

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注：绿色箭头代表正向预测；红色箭头代表负向预测；箭头越粗，表示效应越大。

, figureFileSmall=N/cemqugxNUo3gF9DxUFvg==, figureFileBig=VgYCiQrA3Ni/h7shs+4vxQ==, tableContent=null), ArticleFig(id=1324077537470591518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Figure 2, caption=OEI and IEI in CLPN, figureFileSmall=YjNf8Vp4YNdVCvf/X9gYZw==, figureFileBig=KjV7y422nJx0N9bTwK124g==, tableContent=null), ArticleFig(id=1324077537562866207, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=图2, caption=交叉滞后网络的预期外影响和预期内影响

注：图中以黑色圆点标示各节点预期影响的估计值；横轴坐标为经标准化转换的Z分数。

, figureFileSmall=YjNf8Vp4YNdVCvf/X9gYZw==, figureFileBig=KjV7y422nJx0N9bTwK124g==, tableContent=null), ArticleFig(id=1324077537650946592, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Figure 3, caption=Bridge expected influence estimates in CLPN, figureFileSmall=C9Gs4iP+vyby70OTkfga3g==, figureFileBig=NyxcaViF6KiRTQEvqNl6fQ==, tableContent=null), ArticleFig(id=1324077537734832673, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=图3, caption=交叉滞后网络中桥梁预期影响

注：图中黑色数据点表表示各节点桥接预期影响的估计值；横坐标为经标准化处理后的Z分数。

, figureFileSmall=C9Gs4iP+vyby70OTkfga3g==, figureFileBig=NyxcaViF6KiRTQEvqNl6fQ==, tableContent=null), ArticleFig(id=1324077537835495970, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Figure 4, caption=Bootstrap 95% CI for the weights of the edges in CLPN, figureFileSmall=Pjsf1Xr6pYUtmHzwmTbyNw==, figureFileBig=QFnmknIxgtx+elqAi6aJzQ==, tableContent=null), ArticleFig(id=1324077537923576355, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=图4, caption=交叉滞后网络分析中各边缘权重的自举95%CI

注：图中黑色实线及灰色区域表征基于bootstrap重抽样技术估计的平均边缘权重及其95% CI；红色实线则代表本研究实际观测样本计算所得的边缘权重值；横轴表示边缘权重的具体数值分布；纵轴依权重大小降序排列症状网络中各连边。

, figureFileSmall=Pjsf1Xr6pYUtmHzwmTbyNw==, figureFileBig=QFnmknIxgtx+elqAi6aJzQ==, tableContent=null), ArticleFig(id=1324077538003268132, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Figure 5, caption=Stability of CLPN centrality metrics, figureFileSmall=sM35HytJ7E2rihZLGU2wRg==, figureFileBig=nw8m9z3SrCUQQFl5Iv6nUQ==, tableContent=null), ArticleFig(id=1324077538070376997, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=图5, caption=CLPN中心性测度的稳健性分析, figureFileSmall=sM35HytJ7E2rihZLGU2wRg==, figureFileBig=nw8m9z3SrCUQQFl5Iv6nUQ==, tableContent=null), ArticleFig(id=1324077538162651686, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Table 1, caption=

Basic demographic characteristics of older adults with DSI

, figureFileSmall=null, figureFileBig=null, tableContent=
变量类别n（%）/（）
年龄（岁）68.54±5.68
性别男1 267（47.0）
女1 427（53.0）
婚姻在婚2 210（82.0）
非在婚484（18.0）
教育程度小学及以下2 046（75.9）
初中432（16.0）
高中及以上216（8.0）
年收入（元）<1 0001 005（37.3）
1 000~3 000907（33.7）
>3 000782（29.0）
2018年CES-D10得分20.24±6.80
2020年CES-D10得分20.71±6.66
2018年ADL得分13.02±3.61
2020年ADL得分13.13±3.95
), ArticleFig(id=1324077538246537767, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=表1, caption=

DSI老年人基本人群特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量类别n（%）/（）
年龄（岁）68.54±5.68
性别男1 267（47.0）
女1 427（53.0）
婚姻在婚2 210（82.0）
非在婚484（18.0）
教育程度小学及以下2 046（75.9）
初中432（16.0）
高中及以上216（8.0）
年收入（元）<1 0001 005（37.3）
1 000~3 000907（33.7）
>3 000782（29.0）
2018年CES-D10得分20.24±6.80
2020年CES-D10得分20.71±6.66
2018年ADL得分13.02±3.61
2020年ADL得分13.13±3.95
), ArticleFig(id=1324077538338812456, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=EN, label=Table 2, caption=

Descriptive statistical results of each item ()

, figureFileSmall=null, figureFileBig=null, tableContent=
题项标签题目描述2018年2020年
D1因小事烦恼2.04±1.142.11±1.56
D2难以集中精力2.05±1.132.18±1.17
D3情绪低落2.09±1.132.10±1.13
D4做事费尽2.20±1.202.26±1.22
D5对未来没有希望2.57±1.272.62±1.25
D6感到害怕1.48±0.931.51±0.94
D7睡眠障碍2.29±1.232.29±1.24
D8感到不愉快2.22±1.212.24±1.20
D9孤独1.74±1.101.76±1.11
D10继续生活1.56±0.991.63±1.03
A1穿衣1.12±0.411.15±0.46
A2洗澡1.17±0.521.20±0.56
A3吃饭1.04±0.211.06±0.28
A4起床、下床1.11±0.351.13±0.40
A5上厕所1.23±0.551.28±0.53
A6控制大小便1.09±0.371.10±0.36
A7做家务1.36±0.781.35±0.77
A8做饭1.27±0.751.28±0.73
A9购物1.23±0.701.22±0.68
A10服药1.10±0.401.11±0.43
A11管理钱财1.30±0.811.25±0.72
), ArticleFig(id=1324077538431087145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077528964542946, language=CN, label=表2, caption=

各题项的描述性统计结果（）

, figureFileSmall=null, figureFileBig=null, tableContent=
题项标签题目描述2018年2020年
D1因小事烦恼2.04±1.142.11±1.56
D2难以集中精力2.05±1.132.18±1.17
D3情绪低落2.09±1.132.10±1.13
D4做事费尽2.20±1.202.26±1.22
D5对未来没有希望2.57±1.272.62±1.25
D6感到害怕1.48±0.931.51±0.94
D7睡眠障碍2.29±1.232.29±1.24
D8感到不愉快2.22±1.212.24±1.20
D9孤独1.74±1.101.76±1.11
D10继续生活1.56±0.991.63±1.03
A1穿衣1.12±0.411.15±0.46
A2洗澡1.17±0.521.20±0.56
A3吃饭1.04±0.211.06±0.28
A4起床、下床1.11±0.351.13±0.40
A5上厕所1.23±0.551.28±0.53
A6控制大小便1.09±0.371.10±0.36
A7做家务1.36±0.781.35±0.77
A8做饭1.27±0.751.28±0.73
A9购物1.23±0.701.22±0.68
A10服药1.10±0.401.11±0.43
A11管理钱财1.30±0.811.25±0.72
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基于交叉滞后网络分析的双重感觉障碍老年人日常生活活动能力与抑郁症状的关系
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黄英 , 王佳琳 , 安琪 , 李婷 , 陈丽
现代预防医学 | 基层卫生服务 2026,53(7): 1270-1276
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现代预防医学 |基层卫生服务 2026 , 53 (7) : 1270 -1276
基于交叉滞后网络分析的双重感觉障碍老年人日常生活活动能力与抑郁症状的关系
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黄英（2000—），女，硕士在读，护士，研究方向：临床护理

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黄英, 王佳琳 , 安琪, 李婷, 陈丽
作者信息
  • 成都中医药大学护理学院，四川　成都　611137
通讯作者:
王佳琳，E-mail：
作者简介:

黄英（2000—），女，硕士在读，护士，研究方向：临床护理

Relationship between activities of daily living and depressive symptoms in older adults with dual sensory impairment: a cross-lagged panel network analysis
Ying HUANG, Jia-lin WANG , Qi AN, Ting LI, Li CHEN
Affiliations
  • College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
出版时间: 2026-04-10 doi: 10.20043/j.cnki.MPM.202508219
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摘要
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目的

探究双重感觉障碍（dual sensory impairment，DSI）老年人日常生活活动能力（activities of daily living，ADL）与抑郁症状的双向动态关联机制，识别关键驱动症状，为精准干预提供靶点。

方法

基于中国健康与养老追踪调查2018 —2020年纵向数据，纳入2 674名≥60岁DSI老年人。采用简版流调中心抑郁量表评估抑郁症状，ADL量表评估ADL障碍，并运用交叉滞后网络模型（cross-lagged panel network，CLPN）分析变量间时滞效应与症状网络特性。

结果

ADL维度A11“管理钱财”→抑郁症状A1“因小事烦恼”是显著的交叉滞后边缘（OR=1.19，95%CI：1.07~1.28），其次是抑郁症状D3“情绪低落”→ADL维度A4“起床、下床”（OR=1.16，95%CI：1.00~1.32）、抑郁症状D2“感到害怕”→ADL维度A1“穿衣”（OR=1.14，95%CI：1.00~1.35）；中心性分析发现，D3“情绪低落”（BEI=0.73）、A11“管理钱财”（BEI=0.59）是关键的桥梁症状，其中D3“情绪低落”具有很强的影响能力（OEI=1.03），A10“服药”则最易受影响（IEI=0.77）。

结论

DSI老年人群ADL受限与抑郁症状通过特定路径相互影响，CLPN分析提示两者存在相互的前瞻性预测关系，可通过特定路径形成双向恶性循环。聚焦核心症状进行干预，可阻断循环进展，对提升功能独立性、促进心理健康及实现健康老龄化具有重要公共卫生意义。

老年人  /  双重感觉障碍  /  日常生活活动能力  /  抑郁症状  /  交叉滞后网络
Objective

To examine the bidirectional dynamic relationship between activities of daily living (ADL) and depressive symptoms among older adults with dual sensory impairment (DSI), identify key driving symptoms, and provide targets for precision interventions.

Methods

This study used longitudinal data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study (CHARLS), including 2 674 participants aged≥60 years with DSI. Depressive symptoms were assessed using the short form of the Center for Epidemiologic Studies Depression Scale, and ADL limitations were measured using the ADL scale. A cross-lagged panel network (CLPN) model was applied to examine time-lagged effects and symptom network characteristics.

Results

A significant cross-lagged pathway was identified from ADL item A11 “Managing money” to depressive symptom A1 “Being bothered by little things” (OR=1.19, 95% CI: 1.07-1.28), followed by D3 “Felt depressed” to A4 “Getting in, out of bed” (OR=1.16, 95% CI: 1.00-1.32), and D2 “Felt fearful” to A1 “Dressing” (OR=1.14, 95% CI: 1.00-1.35). Centrality analysis revealed that D3 “Felt depressed” (BEI=0.73) and A11 “Managing money” (BEI=0.59) were key bridge symptoms. D3 “Felt depressed” showed the highest out-expected influence (OEI=1.03), while A10 “Taking medication” was most susceptible to influence (IEI=0.77).

Conclusion

In older adults with DSI, ADL limitations and depressive symptoms may influence each other through specific pathways, and the CLPN indicates reciprocal prospective predictive associations between them, potentially forming a bidirectional vicious cycle via these pathways. Targeted interventions focusing on core bridge symptoms may help disrupt this cycle, thereby improving functional independence, enhancing psychological well-being, promoting healthy aging, and carrying important public health significance.

Older adults  /  Dual sensory impairment  /  Activities of daily living  /  Depressive symptoms  /  Cross-lagged panel network
黄英, 王佳琳, 安琪, 李婷, 陈丽. 基于交叉滞后网络分析的双重感觉障碍老年人日常生活活动能力与抑郁症状的关系. 现代预防医学, 2026 , 53 (7) : 1270 -1276 . DOI: 10.20043/j.cnki.MPM.202508219
Ying HUANG, Jia-lin WANG, Qi AN, Ting LI, Li CHEN. Relationship between activities of daily living and depressive symptoms in older adults with dual sensory impairment: a cross-lagged panel network analysis[J]. Modern Preventive Medicine, 2026 , 53 (7) : 1270 -1276 . DOI: 10.20043/j.cnki.MPM.202508219
正文
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听觉与视觉障碍作为老年人常见的共患性年龄相关疾病，其共存状态被定义为双重感觉障碍（dual sensory impairment，DSI）[1-2]。全球老龄化进程加速推动DSI患病率持续攀升，流行病学数据显示全球约10%的老年人罹患DSI，社区患病率介于2.9%~38%[3-4]。研究证实，DSI显著增加全因死亡及心血管疾病死亡风险，其风险显著高于单一感觉障碍患者[5]。值得注意的是，DSI不仅直接损害个体的日常生活活动能力（activities of daily living，ADL）、增加抑郁风险[6]，还可导致其社交隔离、认知衰退及医疗负担加重，已成为亟待重视的重大公共卫生挑战[6-8]。
多项研究已证实，ADL功能受限与抑郁症状在老年人群中存在复杂的关联性[9-10]。然而，既往研究多局限于探讨单一感觉障碍对ADL或抑郁症状的独立、单向影响，未能充分考虑DSI的潜在协同效应，也缺乏利用纵向设计深入剖析ADL受限与抑郁症状在DSI老年人群中的动态交互作用机制及关键驱动因素。本研究基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）的纵向数据，旨在阐明DSI老年人群中ADL功能与抑郁症状的双向动态关系，识别关键驱动因素，为制定打破功能受限与心理障碍恶性循环的精准干预策略提供理论依据，进而提升该人群的功能独立性、心理健康及生活质量，为推进健康老龄化和优化照护资源配置提供支持。
1　对象与方法
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1.1　研究对象
本研究数据源于CHARLS项目2018和2020年的两期追踪数据。CHARLS是一项具有全国代表性的长期随访调查，其问卷内容广泛涉及受访者的社会人口学背景、身心健康状况、功能表现及医疗服务利用等信息。根据研究目的，排除存在关键变量缺失的样本后，最终纳入符合条件的老年人共2 694名。纳入标准：（1）2018年调查时参与者年龄≥60岁；（2）2018年调查时参与者同时报告视觉和听觉障碍；排除标准：（1）2018年有关年龄、性别、婚姻状况、教育水平、家庭收入、抑郁症状和ADL数据缺失者；（2）未参与2020年随访，且缺乏2020年抑郁和ADL相关信息者。
1.2　研究方法
1.2.1　DSI的界定
本研究将同时存在视觉与听觉损伤的状况定义为DSI。其中，视觉功能的评估依据受访者对“远距离认人”（如隔街识别朋友）和“近距离视物”（如阅读报纸）两项自评结果（均允许佩戴矫正眼镜）；听觉功能则通过自评听力水平（包含助听器使用情况）进行判断。上述评估均采用五级评分：极好、很好、好、一般和不好。若参与者在任一问题中选择一般或不好，则被判定为存在相应的视觉障碍或听觉障碍。当参与者同时存在视觉和听觉障碍时，则被界定为患有DSI[11]。
1.2.2　抑郁症状评估
抑郁症状采用抑郁自评量表（center for epidemiologic studies depresion scale 10，CES-D10）进行评估，用于测量参与者在过去一周内的情绪与行为状态。该量表由10个条目构成，采用Likert四点计分法，各选项及对应分值为：“极少（<1 d）”计1分、“偶尔（1~2 d）”计2分、“有时（3~4 d）”计3分、“多数（5~7 d）”计4分。其中，“我对未来充满希望”“我很愉快”这两个条目采用反向计分方式。本研究中2018和2020年CES-D10量表的Cronbach α系数分别为0.800、0.780。
1.2.3　ADL评估
本研究采用ADL量表进行评估ADL。将ADL分为基础性日常活动能力（basic activities of daily living，BADL）和工具性日常活动能力（instrumental activities of daily living，IADL）。其中，BADL包括如厕、进食、穿衣、控制大小便、上下床和洗澡等6项基本生活活动；IADL包括做家务、做饭、购物、服药和管理钱财等5项较为复杂的生活技能。每项活动的完成情况由参与者自评，共设4个等级：（1）无困难；（2）有困难但仍可完成；（3）有困难，需要帮助；（4）无法完成，分别赋值为1~4分。本研究中2018和2020年ADL量表的Cronbach α系数分别为0.804、0.856。
1.2.4　协变量
本研究协变量包括年龄、性别、婚姻状况、教育水平及经济水平。
1.3　统计学方法
本研究使用R 4.4.2软件中的glmnet、qgraph和bootnet软件包估计交叉滞后网络（cross-lagged panel metwork，CLPN）。其中，glmnet软件包采用LASSO回归方法用于拟合交叉滞后模型。图中箭头表示交叉滞后效应，箭头的粗细代表效应强度（越粗表示关系越强），颜色代表效应方向（蓝色表示正效应，红色表示负效应）。为识别网络中的核心症状，本研究计算了预期影响力（expected influence，EI），即一个节点在预测其他节点和被其他节点预测方面的重要性。本研究进一步区分了输出预期影响（out-expected influence，OEI）和输入预期影响（in-expected influence，IEI），分别指该节点向外连接的边的权重总和及连接到该节点的边的权重总和，这有助于揭示不同症状在因果关系中的方向性作用。同时，计算桥梁预期影响力（bridge expected influence，BEI）以识别在网络之间起桥梁作用的关键节点。本研究将边缘权重系数从对数比值转换为比值比（odds ratio，OR），以提升结果的可解释性。为评估网络中边权重的精确度与稳健性，本研究采用非参数bootstrap法计算各边缘权重95%的置信区间（confidence interval，CI），更窄的CI区间意味着估计结果更为稳定。此外，通过案例剔除bootstrap法计算相关性稳定性系数（coefficient of stability，CS），以检验节点中心性指标的可靠性。根据既有文献[12]，CS系数大于0.5表明中心性具有良好稳定性，0.25~0.5为可接受，小于0.25则表示稳定性较差。
2　结果
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2.1　一般人口学特征
本研究最终共纳入2 674名参与者，其中53.0%为女性，绝大多数为在婚状态，占82.0%，小学及以下文化程度占75.9%，家庭年收入<1 000元占37.3%，见表1。CES-D10与ADL量表各题项的缩写与描述性统计结果见表2。
2.2　网络分析
2.2.1　网络模型构建
图1所示，该网络包括21个节点，共有310条非零交叉滞后边，在省略自回归路径后，有289条非零交叉滞后边，其中272条边（94.1%）为正交叉滞后边。其中A11→D1（OR=1.19，95%CI：1.07~1.28）、D3→A4（OR=1.16，95%CI：1.00~1.32）、D2→A1（OR=1.14，95%CI：1.00~1.35）是联系最强的三条交叉滞后边缘。在ADL到抑郁的预测路径中，最强的边缘时是A11→D1（OR=1.19，95%CI：1.07~1.28），其次是A11→D3（OR=1.09，95%CI：1.02~1.16）、A11→D6（OR=1.07，95%CI：1.00~1.19）。在抑郁到ADL的预测路径中，最强的边缘时是D3→A4（OR=1.16，95%CI：1.00~1.32），其次D2→A1（OR=1.14，95%CI：1.00~1.35）、D2→A10（OR=1.13，95%CI：1.00~1.40）。
2.2.2　中心性指标分析
图2所示，输出预期影响最高的节点是D3（OEI=1.03），其次是D1（OEI=1.00）、D5（OEI=0.78），提示其有较强的预测其他节点的能力。输入预期影响最高的节点是A10（IEI=0.77），其次是A6（IEI=0.73）、A7（IEI=0.68），提示其较容易被其他节点预测。
2.2.3　桥接中心性指标分析
图3呈现了各节点的桥接中心性指标，其数值均已进行标准化（Z分数）处理。分析结果显示，D3是该网络中最核心的桥梁症状（BEI=0.73）。
2.2.4　网络准确性分析
如图4显示，采用bootstrap重抽样法计算得到的各边缘权重95%CI范围较窄，表明模型中边权重的估计精度较高，结果具有良好的统计稳健性。
2.2.5　网络稳定性分析
图5呈现了中心性指标及桥梁症状的稳定性检验结果。网络模型中，OEI和IEI的CS系数分别为0.28和0.59，而BEI的CS系数为0.36。所有系数均超过方法学建议的可接受阈值（0.25），表明本研究识别出的核心症状与桥梁症状均具备足够的稳定性。
3　讨论
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本研究首次采用交叉滞后网络分析方法，系统探讨了中国DSI老年人ADL（ADL）与抑郁症状之间的网络结构及症状间的潜在因果关系。结果显示，情绪低落、服药困难和管理钱财等问题在ADL功能与抑郁症状之间的关系中处于核心位置。此外，研究还揭示了DSI老年人ADL限制与抑郁症状之间存在复杂的双向关联。
在症状网络中，D3是最关键的节点，具有最高的预期外影响及桥梁作用，显示出其对其他症状的强预测性与影响力。既往研究指出，情绪低落是失能老年人抑郁症状的核心表现之一[13]。在感觉障碍人群中，尤其是同时存在视觉与听觉障碍的老年人抑郁风险显著升高[14]。这可能是因为感官障碍限制了个体的社交互动，削弱了与外界的联系，增加了对他人帮助的依赖，进而导致社会孤立、孤独感和社会支持缺失，从而诱发情绪低落。此外，已有研究表明，抑郁症状可预测老年人ADL的下降[15]，并可能通过加速脑部老化或增加认知障碍风险，进一步造成功能受限与残疾[16]。本研究也发现，抑郁症状显著预测DSI老年人的ADL限制。例如，D3可预测A4，D2可预测A1和A10。这与既往研究一致[17-18]，抑郁加速老年人功能退化，从而增加未来功能受限的严重程度。抑郁患者往往缺乏主动性和动机，导致身体活动减少[19]。在感觉障碍与抑郁情绪的双重作用下，老年人更容易出现对日常活动的兴趣减弱，进而出现如穿衣、起床等日常功能障碍。因此，应高度关注DSI老年人的心理健康问题，推动抑郁的早期筛查与干预，并提供有针对性的心理支持服务。特别是对“情绪低落”等核心症状的识别与干预，有助于预防其进一步影响ADL功能。
除情绪低落外，A11也被识别为重要的桥梁症状，并且本研究进一步发现，ADL功能在一定程度上可预测抑郁症状。例如，A11分别可预测D1、D3和D6，提示理财能力的下降可能对多种抑郁症状产生负面影响。作为IADL的一部分，理财能力反映了老年人独立生活的能力水平[20]。已有研究指出，感觉障碍人群普遍存在持续的财务处理障碍[4]。财务管理能力不仅关乎经济独立性，也与老年人的安全感、秩序感密切相关。ADL的丧失会导致生活混乱、生活质量下降，进而引发心理问题[21]。独立性和社会保障的丧失可能会对他们的心理健康产生不利影响。此外，有研究表明，IADL功能的受限对抑郁的长期影响较BADL更为显著[22]。相比BADL，IADL功能更复杂，其障碍可能阻碍老年人履行社会角色，减少其参与购物、出行等日常社会活动的机会，进而加重抑郁症状。因此，提升DSI老年人的IADL能力，尤其是财务管理能力，对于预防和干预抑郁具有重要意义。
本研究还发现，A10是网络中最易受其他症状影响的节点，具有最高的预期内影响，提示服药行为容易受到情绪波动的干扰。一项针对听力障碍老年人的研究发现，相比BADL，IADL功能更容易受到抑郁影响[23]。本研究进一步发现，D2和D5是影响A10的主要因素。已有研究也指出，抑郁是影响老年人服药依从性的主要风险因素之一[24]。注意力不集中可能导致漏服、误服、剂量错误等，增加健康风险，也会削弱对治疗方案的理解与执行能力。同时，负面情绪可能降低其服药动机和治疗信心，导致不遵医嘱，甚至放弃治疗。因此，在老年人精神健康干预中，应特别关注注意力缺陷与负面情绪对服药行为的影响。通过提供认知干预与心理支持，可有效提升服药依从性，进而改善健康结局与生活质量。
因此，本研究在两个方向均观察到显著的交叉滞后路径，一方面，抑郁症状前瞻性预测后续功能受限，另一方面，功能受限（尤其是IADL复杂活动，例如管理钱财）前瞻性预测后续抑郁症状加重。两者通过特定路径可能相互作用，推动负向循环的形成与维持。此外，本研究尚存在一定局限。首先，研究数据主要依赖自我报告，可能受到社会期望和回忆偏差的影响，从而对结论的准确性产生影响。其次，为确保样本量，本研究时间跨度较短，限制了对长期因果关系的推断。未来研究可考虑采用更长期的纵向设计，以更深入探讨ADL与抑郁之间的动态变化。最后，本研究聚焦于60岁及以上的中国老年人，研究结论的外推性有限，需谨慎应用于其他人群或文化背景。
综上所述，本研究发现，在DSI老年人群体中，D3是症状网络中最具影响力的节点，同时也是连接不同症状的关键桥梁，因而构成该人群抑郁干预的优先靶点。A10则被识别为最易受其他症状影响的节点，需特别关注其在功能退化过程中的作用机制。研究还揭示，ADL功能与抑郁症状之间存在显著的双向作用。这种交互强化可能形成一个负向反馈循环，一旦启动，抑郁与功能退化将相互作用并持续恶化。因此，识别症状网络中的核心驱动因素，优先干预关键ADL限制或抑郁症状，有望打破功能受限与情绪障碍之间的恶性循环。该策略不仅有助于推动健康老龄化，也为优化社会照护资源配置提供了理论依据与实践支持。
基金
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  • 2024年成都市哲学社会科学规划项目(2024BS015)
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2026年第53卷第7期
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doi: 10.20043/j.cnki.MPM.202508219
  • 接收时间：2025-08-15
  • 首发时间：2026-11-02
  • 出版时间：2026-04-10
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  • 收稿日期：2025-08-15
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2024年成都市哲学社会科学规划项目(2024BS015)
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    成都中医药大学护理学院，四川　成都　611137

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2种不同金属材料的力学参数
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total species (%)		 属
Genus		 种数
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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
栓菌属 Trametes 5 2.39
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