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Article(id=1240977216570454801, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240977214964036360, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202412224, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734019200000, receivedDateStr=2024-12-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773801624672, onlineDateStr=2026-03-18, pubDate=1748102400000, pubDateStr=2025-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773801624672, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773801624672, creator=13701087609, updateTime=1773801624672, updator=13701087609, issue=Issue{id=1240977214964036360, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='10', pageStart='1729', pageEnd='1920', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773801624289, creator=13701087609, updateTime=1773825591019, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241077738770068227, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240977214964036360, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241077738770068228, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240977214964036360, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1873, endPage=1878, ext={EN=ArticleExt(id=1240977217023439643, articleId=1240977216570454801, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Effect of multimorbidity on sleep duration among middle-aged and elderly individuals in China, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the impact of omorbidities on sleep duration in middle-aged and elderly individuals in China.

Methods

This study was a cross-sectional analysis using data from the 2020 China health and retirement longitudinal study database to examine the relationship between comorbidities and sleep duration. Directed acyclic graphs were used to identify potential confounding factors. Overlap weighting and linear regression with double confounding adjustment were applied to minimize group differences and make inter-group comparisons more akin to a randomized trial. The impact of comorbidities on sleep duration was explored through linear regression, and sensitivity analyses were conducted to verify the robustness of the results, with subgroup analyses performed.

Results

A total of 17 010 participants aged 45 years and older were included in the analysis. The results indicated that comorbidities affected individual sleep duration. Individuals with multimorbidity had a sleep duration that was approximately 0.12 hours shorter compared with those without multimorbidity (B=-0.121 4, P<0.05). Sensitivity analysis showed an E-value of 1.48. Furthermore, this impact was more pronounced in females (B=-0.159 4, P<0.05) and in the middle-aged group (45-60 years) (B=-0.225 8, P<0.05).

Conclusion

There is a difference in sleep duration between middle-aged and elderly individuals with chronic disease comorbidity, with a more pronounced effect observed in females and the middle-aged group. The study utilizes overlap weighting, demonstrating the significant impact of comorbidities on sleep duration, providing empirical evidence for improving sleep conditions in this population.

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目的

探讨中国中老年人群慢性病共病对睡眠时长的影响。

方法

本研究为横断面研究，以中国健康与养老追踪调查数据库中2020年的数据进行慢性病共病和睡眠时长的分析。有向无环图被使用来识别混杂因素，采用重叠加权和线性回归双重混杂处理，减少组间差异，使组间对比更接近随机试验，通过线性回归探讨慢性病共病对睡眠时长的影响，使用敏感性分析来验证结果的稳健性并进行亚组分析。

结果

共纳入17 010例45岁及以上的样本，分析发现慢性病共病对个体睡眠时长有影响，慢性病共病的个体相比于非慢性病共病的个体，睡眠时长减少了约0.12 h（B=-0.121 4, P<0.05）。敏感性分析结果显示E-values=1.48，且该影响在女性（B=-0.159 4, P<0.05）和中年群体（45~60岁）中（B=-0.225 8, P<0.05）中更为明显。

结论

中老年人慢性病共病与睡眠时长存在差异，且在女性和中年群体中影响尤为突出。

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孙丽华，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=Cz7Ac/50wxupkg3UQ+39EA==, magXml=a2V4u0TqKYeKR4t9PScvTg==, pdfUrl=null, pdf=gg9nIpEQEEUoKeTAgEgjNg==, pdfFileSize=697586, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=ubtiJq6pw0//4Omi8mnB2A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=1tkmSST2Y0iutnrXJfnm3w==, mapNumber=null, authorCompany=null, fund=null, authors=

潘春颖（1999—），女，硕士在读，护士，研究方向：急危重症、心理护理

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潘春颖（1999—），女，硕士在读，护士，研究方向：急危重症、心理护理

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潘春颖（1999—），女，硕士在读，护士，研究方向：急危重症、心理护理

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Journal of Abnormal Psychology, 2012, 121(1): 282-288., articleTitle=An invariant dimensional liability model of gender differences in mental disorder prevalence: evidence from a National sample, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1240996908777394360, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, xref=1., ext=[AuthorCompanyExt(id=1240996908806754492, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, companyId=1240996908777394360, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing, Dalian University，Dalian, Liaoning 116001, China), AuthorCompanyExt(id=1240996908840308926, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, companyId=1240996908777394360, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.大连大学护理学院,辽宁 大连 116001)]), AuthorCompany(id=1240996908936777932, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, xref=2., ext=[AuthorCompanyExt(id=1240996908940972237, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, companyId=1240996908936777932, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.大连大学附属新华医院，ICU)])], figs=[ArticleFig(id=1240996915765105243, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=EN, label=Fig.1, caption=Data Cleaning Flowchart, figureFileSmall=nAWdKkDwe7bOaIOme1mTKg==, figureFileBig=Boe85tdQiHEQ5LBO0573fQ==, tableContent=null), ArticleFig(id=1240996915890934372, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=CN, label=图1, caption=数据清洗流程图, figureFileSmall=nAWdKkDwe7bOaIOme1mTKg==, figureFileBig=Boe85tdQiHEQ5LBO0573fQ==, tableContent=null), ArticleFig(id=1240996916058706547, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=EN, label=Table 1, caption=

Baseline Characteristics After Overlap Weighting

, figureFileSmall=null, figureFileBig=null, tableContent=
未加权SMD加权dSMDa
慢性病共病非慢性病共病慢性病共病非慢性病共病
样本量2 08414 926
性别[n(%), 或 %]0.042<0.001b
女1 138(54.6)7 836(52.5)54.654.6
男946(45.4)7 090(47.5)45.445.4
年龄(岁，()69.35±9.8666.45±9.810.29569.35±9.8669.46±10.280.011b
吸烟[n(%), 或%]c0.1240.002b
是437(21.0)3 912(26.2)21.020.9
否1 647(79.0)11 014(73.8)79.079.1
饮酒[n(%), 或 %]c0.134<0.001b
是638(30.6)5 510(36.9)30.630.6
否1 446(69.4)9 416(63.1)69.469.4
社交活动[n(%), 或%]0.012<0.001b
不存在0(0.0)1(0.0)0.00.0
存在2 084(100.0)14 925(100.0)100.0100.0
疼痛[n(%), 或%]0.5240.004b
无509(24.4)6 589(44.2)24.424.4
轻中度839(40.3)5 932(39.7)40.340.1
中重736(35.3)2 405(16.1)35.335.5
婚姻状态[n(%), 或 %]0.1390.048b
已婚1 685(80.9)12 695(85.1)80.880.9
未婚9(0.4)82(0.5)0.40.6
离异24(1.2)244(1.6)1.21.6
丧偶366(17.5)1 905(12.8)17.616.9
教育水平[n(%), 或 %]0.0540.038b
初中及以下1 852(88.9)13 023(87.3)88.989.8
高中及中专193(9.3)1 604(10.7)9.28.7
大专及本科37(1.7)291(1.9)1.81.5
硕士及以上2(0.1)8(0.1)0.10.0
), ArticleFig(id=1240996916209701498, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=CN, label=表1, caption=

基线特征重叠加权结果

, figureFileSmall=null, figureFileBig=null, tableContent=
未加权SMD加权dSMDa
慢性病共病非慢性病共病慢性病共病非慢性病共病
样本量2 08414 926
性别[n(%), 或 %]0.042<0.001b
女1 138(54.6)7 836(52.5)54.654.6
男946(45.4)7 090(47.5)45.445.4
年龄(岁，()69.35±9.8666.45±9.810.29569.35±9.8669.46±10.280.011b
吸烟[n(%), 或%]c0.1240.002b
是437(21.0)3 912(26.2)21.020.9
否1 647(79.0)11 014(73.8)79.079.1
饮酒[n(%), 或 %]c0.134<0.001b
是638(30.6)5 510(36.9)30.630.6
否1 446(69.4)9 416(63.1)69.469.4
社交活动[n(%), 或%]0.012<0.001b
不存在0(0.0)1(0.0)0.00.0
存在2 084(100.0)14 925(100.0)100.0100.0
疼痛[n(%), 或%]0.5240.004b
无509(24.4)6 589(44.2)24.424.4
轻中度839(40.3)5 932(39.7)40.340.1
中重736(35.3)2 405(16.1)35.335.5
婚姻状态[n(%), 或 %]0.1390.048b
已婚1 685(80.9)12 695(85.1)80.880.9
未婚9(0.4)82(0.5)0.40.6
离异24(1.2)244(1.6)1.21.6
丧偶366(17.5)1 905(12.8)17.616.9
教育水平[n(%), 或 %]0.0540.038b
初中及以下1 852(88.9)13 023(87.3)88.989.8
高中及中专193(9.3)1 604(10.7)9.28.7
大专及本科37(1.7)291(1.9)1.81.5
硕士及以上2(0.1)8(0.1)0.10.0
), ArticleFig(id=1240996916339724931, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=EN, label=Table 2, caption=

Subgroup Analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
分组BP
年龄（岁）
<60-0.225 8<0.05
≥600.094 5>0.05
性别
男-0.074 0>0.05
女-0.159 4<0.05
), ArticleFig(id=1240996916452971148, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240977216570454801, language=CN, label=表2, caption=

亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分组BP
年龄（岁）
<60-0.225 8<0.05
≥600.094 5>0.05
性别
男-0.074 0>0.05
女-0.159 4<0.05
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慢性病共病对中国中老年人睡眠时长的影响
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潘春颖 1 , 祖碧琪 1 , 王婷 1 , 徐韶婕 1 , 刘梦 1 , 魏佳 1 , 张树勇 1 , 李瑶 1 , 孙丽华 2
现代预防医学 | 健康与社会行为 2025,52(10): 1873-1878
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现代预防医学 | 健康与社会行为 2025, 52(10): 1873-1878
慢性病共病对中国中老年人睡眠时长的影响
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潘春颖1, 祖碧琪1, 王婷1, 徐韶婕1, 刘梦1, 魏佳1, 张树勇1, 李瑶1, 孙丽华2
作者信息
  • 1.大连大学护理学院,辽宁 大连 116001
  • 2.大连大学附属新华医院，ICU

    • 潘春颖（1999—），女，硕士在读，护士，研究方向：急危重症、心理护理

通讯作者:

孙丽华，E-mail：
Effect of multimorbidity on sleep duration among middle-aged and elderly individuals in China
Chun-ying PAN1, Bi-qi ZU1, Ting WANG1, Shao-jie XU1, Meng LIU1, Jia WEI1, Shu-yong ZHANG1, Yao LI1, Li-hua SUN2
Affiliations
  • School of Nursing, Dalian University，Dalian, Liaoning 116001, China
出版时间: 2025-05-25 doi: 10.20043/j.cnki.MPM.202412224
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摘要
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目的

探讨中国中老年人群慢性病共病对睡眠时长的影响。

方法

本研究为横断面研究，以中国健康与养老追踪调查数据库中2020年的数据进行慢性病共病和睡眠时长的分析。有向无环图被使用来识别混杂因素，采用重叠加权和线性回归双重混杂处理，减少组间差异，使组间对比更接近随机试验，通过线性回归探讨慢性病共病对睡眠时长的影响，使用敏感性分析来验证结果的稳健性并进行亚组分析。

结果

共纳入17 010例45岁及以上的样本，分析发现慢性病共病对个体睡眠时长有影响，慢性病共病的个体相比于非慢性病共病的个体，睡眠时长减少了约0.12 h（B=-0.121 4, P<0.05）。敏感性分析结果显示E-values=1.48，且该影响在女性（B=-0.159 4, P<0.05）和中年群体（45~60岁）中（B=-0.225 8, P<0.05）中更为明显。

结论

中老年人慢性病共病与睡眠时长存在差异，且在女性和中年群体中影响尤为突出。

慢性病共病  /  睡眠时长  /  中老年人  /  中国健康与养老追踪调查
Objective

To investigate the impact of omorbidities on sleep duration in middle-aged and elderly individuals in China.

Methods

This study was a cross-sectional analysis using data from the 2020 China health and retirement longitudinal study database to examine the relationship between comorbidities and sleep duration. Directed acyclic graphs were used to identify potential confounding factors. Overlap weighting and linear regression with double confounding adjustment were applied to minimize group differences and make inter-group comparisons more akin to a randomized trial. The impact of comorbidities on sleep duration was explored through linear regression, and sensitivity analyses were conducted to verify the robustness of the results, with subgroup analyses performed.

Results

A total of 17 010 participants aged 45 years and older were included in the analysis. The results indicated that comorbidities affected individual sleep duration. Individuals with multimorbidity had a sleep duration that was approximately 0.12 hours shorter compared with those without multimorbidity (B=-0.121 4, P<0.05). Sensitivity analysis showed an E-value of 1.48. Furthermore, this impact was more pronounced in females (B=-0.159 4, P<0.05) and in the middle-aged group (45-60 years) (B=-0.225 8, P<0.05).

Conclusion

There is a difference in sleep duration between middle-aged and elderly individuals with chronic disease comorbidity, with a more pronounced effect observed in females and the middle-aged group. The study utilizes overlap weighting, demonstrating the significant impact of comorbidities on sleep duration, providing empirical evidence for improving sleep conditions in this population.

Multimorbidity  /  Sleep duration  /  Middle-aged and elderly individuals  /  CHARLS
潘春颖, 祖碧琪, 王婷, 徐韶婕, 刘梦, 魏佳, 张树勇, 李瑶, 孙丽华. 慢性病共病对中国中老年人睡眠时长的影响. 现代预防医学, 2025 , 52 (10) : 1873 -1878 . DOI: 10.20043/j.cnki.MPM.202412224
Chun-ying PAN, Bi-qi ZU, Ting WANG, Shao-jie XU, Meng LIU, Jia WEI, Shu-yong ZHANG, Yao LI, Li-hua SUN. Effect of multimorbidity on sleep duration among middle-aged and elderly individuals in China[J]. Modern Preventive Medicine, 2025 , 52 (10) : 1873 -1878 . DOI: 10.20043/j.cnki.MPM.202412224
正文
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全球人口老龄化趋势显著[1]，其造成的慢性疾病负担也随之增加，2019年，中国慢性病死亡人数达到950万人，占所有死亡人数的90.1%，高于全球74.3%的水平，预计未来30年这一数字将翻一番。慢性病共病（multimorbidity）是指同一患者同时患有两种或以上慢性疾病，其不良健康后果超出了任何单一疾病的风险[2]。慢性病共病在老年人群中最为常见[3]。而我国老年人慢性病共病率可高达92.24%，并呈持续增长趋势[4]。研究显示[5]，美国45~64岁、65岁及以上的人群中分别约有38.6%、28.1%的人存在睡眠不足。且多项研究表明[6-8]，睡眠不足会增加肥胖、糖尿病、高血压等的风险。这可能导致睡眠问题与健康状况之间的恶性循环，进而对中老年人的生命健康构成严重威胁。目前，已有研究发现睡眠行为紊乱与慢性病共病有关[9-12]。然而，这些研究在混杂因素的处理方法上存在差异，得到的效应量不能准确反映慢性病共病与睡眠时长的真实关系。因此，本研究利用中国健康与养老追踪调查（China health and retirement longitudinal study, CHARLS）数据，采用横断面研究设计，通过使用重叠加权法，最大程度平衡组间差异，旨在系统评估慢性病共病对中老年人睡眠时长的影响，为改善老年人睡眠健康及推进健康老龄化提供依据。
1　对象与方法
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CHARLS是一项对中国大陆45岁及以上代表性人群健康与养老状况的追踪调查[13]。本研究使用CHARLS的2020年数据，已获得了北京大学生物医学伦理委员会的批准（批号: IRB00001052-110155，在调查过程中，每位同意参加调查的受访者已签署知情同意书）。
1.1　研究纳入排除标准
本研究为横断面研究，数据为2020年随访截止时的数据，以比较慢性病共病对睡眠时长的影响。本次研究筛选年龄≥45岁且慢性病患病数量≥2的样本，排除睡眠时长及慢性病共病数据丢失的样本。数据清洗见图1。
1.2　变量选择与定义
本研究基于既往文献和研究经验，初步纳入了8个潜在变量。通过有向无环图（Directed Acyclic Graphs, DAGs）进行变量筛选，以识别潜在的混杂因素并评估共线性问题。最终，确定6个混杂因素，包括性别、年龄、吸烟、饮酒、社交活动和疼痛。以下是各变量的具体定义和分类标准：①年龄：在本研究中，年龄是根据2020年数据的发布日期（2023年11月6日）计算。②吸烟：如果样本中“仍然吸烟或已经戒烟为‘是’”或“曾经吸烟”为‘是’，则赋值为“是”；其他所有情况均分类为“否”。③饮酒：将“每月饮酒超过一次”以及“饮酒但少于每月一次”分类为“是”，将“以上均无”分类为“否”。④社交活动：在2020年数据中，共提到9项活动。如果样本能够正常完成其中任何一项活动，其社交活动将被视为“存在”。⑤疼痛：在本研究中，年龄与身体疼痛存在关联。将“无”归类为“无疼痛”，“有点”和“有些”归类为“轻度至中度疼痛”，“相当严重”和“非常严重”归类为“重度疼痛”。⑥婚姻状态：本研究提到配偶/伴侣的情况。将“已婚且配偶在身边”和“已婚但因工作等原因暂时未与配偶同住”归类为“已婚”，“分居且不再以配偶身份同住”和“离婚”归类为“离婚”，“从未结婚”归类为“未婚”，“丧偶”归类为“丧偶”。⑦教育水平：将“无正规教育（文盲）”“未完成小学”“私塾/在家上学”“小学”和“初中”归类为“初中及以下”，“高中”和“职业学校”归类为“高中及中职”，“两年制/三年制大学/副学士学位”和“本科/学士学位”归类为“大专及本科学历”，“硕士学位”和“博士学位/博士”归类为“硕士及以上”。
1.3　结局指标
1.3.1　睡眠时长
是指在CHARLS数据问卷中，调查被试者的“过去一个月实际睡眠时长”，其范围为0至24小时。
1.3.2　慢性病共病[2]
是指同一人群中存在两种或更多慢性疾病，以CHARLS数据问卷中“是否有医生曾诊断您有以下慢性病?”的答案为依据,共包含高血压病、血脂异常、糖尿病或血糖升高等15种慢性病。本研究依据这一条目，将数据分为两组进行分析，即“慢性病共病组”和“非慢性病共病组”
1.4　统计方法
1.4.1　统计描述
连续性变量结果以均数和标准差（）的形式对所有样本进行汇总。分类性变量结果以频率和百分比的形式报告，加权样本仅以百分比的形式报告。双侧检验水准α=0.05，被认为具有统计学差异。报告了估计回归系数及其E值，所有分析均使用Rstudio 4.4.2版本进行。
1.4.2　重叠加权和线性回归
重叠加权（Overlap weighting）是一种倾向得分方法，旨在模仿随机临床试验的关键特征，将数据转化为基线均衡可比的2组，减少混杂因素对结果的影响。使用重叠加权与线性回归相结合的双重稳健估计，可以将混杂因素的影响降至最低。本研究将这6个混杂因素用于重叠加权，利用新的权值将重叠加权的混杂因素和其他潜在混杂变量（婚姻状态，教育水平）纳入到线性回归模型，进行混杂校正，以评估慢性病共病对睡眠时长的影响。
1.4.3　敏感性分析
为了评估慢性病共病与睡眠时长之间的关联性对于潜在未测量混杂因素的稳健性，使用VanderWeele和Ding的方法计算了E值[14]。
1.4.4　亚组分析
分别对年龄、性别变量进行亚组分析，探讨不同亚组在慢性病共病状态下对睡眠时长的影响。
1.4.5　缺失值及处理方法
除吸烟缺少21例，饮酒缺失1例外，其余数据均无缺失值（数据缺失详情见表1）。使用多重插补法[15]处理指标和其他变量的缺失数据：以表1数据为基础构建5个插补数据集对缺失数据进行插补，以确保数据的完整性。
2　结果
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2.1　重叠加权结果比较
以慢性病共病为基础，对混杂因素进行重叠加权处理，保证组间的可比性。本研究共纳入17 010个样本，其中女性占比52.8%；年龄（）为66.80±9.86；吸烟者占比25.6%；饮酒者占比36.1%；存在社交活动者占比100.0%；无痛占比41.7%、轻中度疼痛占比39.8%、重度疼痛占比18.5%。通过对性别、年龄、吸烟、饮酒、社交活动以及疼痛6个变量进行重叠加权后，组间基线最大化达到平衡（见表1），混杂因素组间的标准均值差异值变化更为明显。
2.2　线性回归结果
线性回归结果显示：B=-0.121 4,P<0.05，慢性病共病的个体相比于非慢性病共病的个体，其睡眠时长减少了约0.12 h。
2.3　敏感性分析结果
敏感性分析结果显示：E-values=1.48，稳健性中等。
2.4　亚组分析
在年龄<60岁的亚组中分析，慢性病共病使睡眠时长减少约0.22 h；然而，在年龄≥60岁的亚组中，慢性病共病对睡眠时长的影响无统计学差异。在性别分组中，慢性病共病个体的睡眠时长对男性无明显影响，而减少女性的睡眠时长约0.16h。结果详见表2。
3　讨论
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3.1　中老年人群慢性病共病与睡眠时长明显相关
本研究发现慢性病共病影响了个体的睡眠时长。可能原因如下：
3.1.1　疾病症状及联合用药对睡眠时长的影响
慢性病患者的睡眠质量与疾病进程相互影响，疾病可能因睡眠质量差而加重，而差的睡眠质量又会加重这些疾病的症状[16]。并且，许多慢性疾病，如心脏病、糖尿病和慢性阻塞性肺病，在夜间症状加重，可能进一步干扰睡眠[17-18]。此外，睡眠障碍还可能加重肥胖、认知障碍和慢性疼痛等其他慢性病共病的严重性[18-20]，这种恶性循环会加重对睡眠的影响。
治疗慢性病使用的药物对睡眠的影响也不容忽视。慢性病共病治疗通常需要使用多种药物，而这些药物各自可能有不同的副作用。许多药物，如某些类固醇、减充血剂、止痛药以及用于治疗焦虑和抑郁的药物，可能干扰正常的睡眠-觉醒周期，导致失眠或白天嗜睡等症状。尤其是多药联合使用时，不仅增加了副作用的发生率，还可能引发药物间相互作用，进一步影响睡眠质量[21-22]。
3.1.2　心理因素的影响
慢性病共病患者疾病的持续性和多重治疗可能造成额外的心理负担，可能引发焦虑、抑郁、压力等情绪反应，这些心理因素与睡眠问题（如失眠、早醒等）密切相关。研究表明[23-24]，长期的心理困扰，如焦虑、抑郁和慢性压力，常常使患者处于持续的生理应激状态。应激激素（如皮质醇）在压力反应中扮演重要角色，其分泌水平的增加会干扰正常的睡眠周期。过高的皮质醇水平会减少深度睡眠的时间，并延长入睡时间，导致患者经历更浅的睡眠和更频繁的夜间觉醒。
3.1.3　生活方式和社会因素
中老年人群体中，慢性病共病患者常面临一系列由生活方式和社会因素引起的睡眠质量问题。首先，生活方式方面的改变，如缺乏足够的运动、久坐不动、饮食不规律等，可能导致睡眠问题[25]，不好的生活方式容易引发体内生物钟失调，进而影响昼夜节律和睡眠模式。此外，社会支持的缺乏、经济压力、社会隔离等都可能导致睡眠障碍的加重。慢性病共病患者可能面临更多的医疗资源使用、健康照护负担[26]，甚至可能在经济、家庭责任等方面遇到压力，这些外部压力可能导致一些心理问题的发生，从而对其睡眠产生负面影响[27]。
3.2　中老年人群慢性病共病对睡眠时长的影响与年龄有关
亚组分析结果显示，慢性病共病的中年人群对睡眠时长的影响比对老年人群的影响要大。与老年慢性病共病患者人群相比，中年慢性病共病患者虽然具有更强的生理代偿能力，但中年人常面临较高的工作压力和生活负担，长时间的工作、频繁的社交活动以及家庭责任，可能导致他们的睡眠时长减少。Wang等[25]研究表明，对于中年人群短睡眠（每天<6 h）和长睡眠（每天>9 h）都明显增加了慢性疾病的风险。但本研究结果显示，老年人群体中慢性病共病与睡眠时长之间未发现统计学差异，可能是因为本研究主要依赖于主观报告数据，这可能导致一定的误差，从而影响了本研究的结果。
3.3　中老年人群慢性病共病对睡眠时长的影响与性别有关
本研究结果显示女性慢性病共病患者睡眠时长明显减少，而男性慢性病共病患者不明显。这与Zou等[28]研究一致，短睡眠时长和睡眠不安普遍与慢性病共病风险增加相关，但其表现因性别不同而有所差异，即女性在睡眠不安和睡眠时长较短时，出现慢性病共病的风险较男性更高。女性慢性病共病患者睡眠时间较少的原因可能受多种因素影响，主要包括2个方面。在生理方面，女性在荷尔蒙水平波动的影响下，尤其在更年期或月经周期更可能出现睡眠问题[29-30]。同时，慢性腹泻症状和炎症性肠病与睡眠障碍的相关性在女性中更为明显[31]。在心理方面，女性往往比男性更容易出现焦虑和抑郁情绪，这些情绪问题与睡眠障碍密切相关[32]。女性面临的社会文化压力通常比男性更为复杂，她们常被期望在家庭、职业和社会中承担多重角色，如育儿、照顾家人以及职业成就等，这些都可能造成较大的情感负担[33-34]。此外，女性比男性更倾向于内化情绪[35]，特别是在面对压力时，她们更容易通过自责、过度担忧等方式表现负面情绪从而影响睡眠状况。
尽管本研究采用重叠加权法控制混杂因素，但未测量因素仍可能影响结果。睡眠时长数据基于自我报告，可能存在信息偏倚，且样本来源单一，限制了结果的普适性。因此，未来研究应进一步控制这些因素，探讨不同慢性病组合对睡眠的影响，并分析慢性病共病与睡眠时长的交互作用，以提供更科学的健康管理依据。
本研究证实，慢性病共病对睡眠时长有影响，且中年群体及女性受慢性病共病影响更大，这提示常规的健康管理不仅应包括慢性病相关的生理指标监测，还应重视睡眠问题的早期发现和干预，以降低慢性病共病对睡眠时长的不良影响，且针对中老年人群的健康干预需综合考虑个体的年龄和性别特征，以制定更精准的睡眠管理策略，从而减缓慢性病共病对睡眠的负面影响，改善整体健康水平。
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doi: 10.20043/j.cnki.MPM.202412224
  • 接收时间：2024-12-13
  • 首发时间：2026-03-18
  • 出版时间：2025-05-25
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    1.大连大学护理学院,辽宁 大连 116001
    2.大连大学附属新华医院，ICU

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