Article(id=1322168994052821616, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168583854080893, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.08.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753372800000, receivedDateStr=2025-07-25, revisedDate=1775059200000, revisedDateStr=2026-04-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159252893, onlineDateStr=2026-10-28, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159252893, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159252893, creator=13701087609, updateTime=1793159252893, updator=13701087609, issue=Issue{id=1322168583854080893, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='8', pageStart='1241', pageEnd='1424', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159155095, creator='13701087609', updateTime=1793159155095, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1257, endPage=1264, ext={EN=ArticleExt(id=1322168994312868465, articleId=1322168994052821616, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction and validation of the social frailty risk prediction model for elderly people with chronic diseases in communities, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct the social frailty risk prediction model for elderly people with chronic diseases in communities,and to explore the predictive value of intrinsic capacity for social frailty among them.

Methods

A total of 785 elderly patients with chronic diseases in the communities,who were surveyed in Lu'nan district of Tangshan city from July 2024 to May 2025,were selected as research subjects.LASSO regression was used to screen the variables.Logistic regression analysis was conducted to analyze the influencing factors.A column chart was drawn,and a baseline model without intrinsic ability variables and a prediction model including intrinsic capacity variables were constructed.

Results

The detection rate of social frailty among community-dwelling elderly with chronic diseases was 23.69%.The results of Logistic regression analysis showed that age,educational level,duration of chronic disease,level of e-health literacy,satisfaction with community public services,family function,and intrinsic capacity were the influencing factors of social frailty in community-dwelling elderly with chronic diseases(all P<0.05).Two models were established,one without the intrinsic ability variable and the other with the intrinsic ability variable.The results showed that the prediction model had significantly improved reclassification ability and discriminative ability compared to the baseline model.The area under the ROC curve of the prediction model training set and validation set was 0.828 and 0.856 respectively.The DCA curve indicated that the model had good clinical practicability.

Conclusions

The social frailty risk prediction model for elderly people with chronic diseases in the communities had certain predictive efficacy.It could be used to identify the risk of social frailty in elderly people with chronic diseases in the communities at an early stage.It could provide basis for taking timely preventive and intervention measures.

, authors=Zhaoying HAN, Kejin SHI, Yaning ZHAO, Jianmin LI, Yao LIU, authorsList=Zhaoying HAN, Kejin SHI, Yaning ZHAO, Jianmin LI, Yao LIU, authorCompany=null, correspAuthors=Yaning ZHAO, authorNote=null, correspAuthorsNote=
ZHAO Yaning, E-mail:
, 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=1322168997286630018, articleId=1322168994052821616, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=社区慢性病老年人社会衰弱风险预测模型的构建及验证, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

构建社区慢性病老年人社会衰弱风险预测模型,探讨内在能力对社区慢性病老年人发生社会衰弱的预测价值。

方法

选取2024年7月—2025年5月在唐山市路南区参与调查的785例社区慢性病老年病人为研究对象。采用LASSO回归筛选变量,Logistic回归分析影响因素,并绘制列线图,构建不包含内在能力变量的基准模型和包含内在能力变量的预测模型。

结果

社区慢性病老年人的社会衰弱检出率为23.69%。Logistic回归分析结果显示,社区慢性病老年人年龄、文化程度、慢性病病程、电子健康素养水平、社区公共服务满意度、家庭功能、内在能力是其社会衰弱的影响因素(均P<0.05)。建立不包含内在能力变量的基准模型和包含内在能力变量的预测模型,结果显示预测模型在重分类能力和判别能力上较基准模型具有明显提升。预测模型训练集和验证集的受试者工作特征曲线下面积分别为0.828和0.856。决策曲线显示模型具有较好的临床实用性。

结论

基于内在能力构建的社区慢性病老年人社会衰弱预测模型有一定预测效能,可用于早期识别社区慢性病老年人的社会衰弱发生风险,为及时采取预防和干预措施提供依据。

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韩朝颖,护师,硕士研究生在读

, correspAuthorsNote=
赵雅宁,E-mail:
ZHAO Yaning, E-mail:
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Comparison of relevant information between elderly patients with and without social frailty

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项目分类例数无社会衰弱(n=599)社会衰弱(n=186)χ2值P
性别男3812811002.6680.102
女40431886
年龄60~70岁4003584278.531<0.001
>70岁385241144
婚姻状况有配偶3413083365.515<0.001
无配偶444291153
居住方式非独居52742010710.1960.001
独居25817979
文化程度初中及以下45030414644.655<0.001
高中/中专及以上33529540
个人月收入<3 000元40427912524.175<0.001
≥3 000元38132061
子女数量≤1个4963961009.3010.002
>1个28920386
子女探望频率低频率32919313697.510<0.001
高频率45640650
服药种类≤1种2552163933.221<0.001
2种18415331
3种及以上346230116
慢性病病程5年以下1651511469.323<0.001
5~10年21618927
10年以上404259145
自评健康状况较好4623996362.824<0.001
较差323200123
电子健康素养低水平39626213445.482<0.001
高水平38933752
社区公共服务满意度不满意33221212049.325<0.001
满意45338766
社会支持低水平33721212558.625<0.001
高水平44838761
家庭功能家庭功能障碍34421712759.229<0.001
家庭功能良好44138259
内在能力较差38923815197.547<0.001
较好39636135
), ArticleFig(id=1322530524682367726, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168994052821616, language=CN, label=表1, caption=

无社会衰弱老年人与社会衰弱老年人相关资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类例数无社会衰弱(n=599)社会衰弱(n=186)χ2值P
性别男3812811002.6680.102
女40431886
年龄60~70岁4003584278.531<0.001
>70岁385241144
婚姻状况有配偶3413083365.515<0.001
无配偶444291153
居住方式非独居52742010710.1960.001
独居25817979
文化程度初中及以下45030414644.655<0.001
高中/中专及以上33529540
个人月收入<3 000元40427912524.175<0.001
≥3 000元38132061
子女数量≤1个4963961009.3010.002
>1个28920386
子女探望频率低频率32919313697.510<0.001
高频率45640650
服药种类≤1种2552163933.221<0.001
2种18415331
3种及以上346230116
慢性病病程5年以下1651511469.323<0.001
5~10年21618927
10年以上404259145
自评健康状况较好4623996362.824<0.001
较差323200123
电子健康素养低水平39626213445.482<0.001
高水平38933752
社区公共服务满意度不满意33221212049.325<0.001
满意45338766
社会支持低水平33721212558.625<0.001
高水平44838761
家庭功能家庭功能障碍34421712759.229<0.001
家庭功能良好44138259
内在能力较差38923815197.547<0.001
较好39636135
), ArticleFig(id=1322530524774642415, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168994052821616, language=EN, label=Table 2, caption=

Assignment of variables

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变量赋值方式
年龄60~70岁=0,>70岁=1
婚姻状况有配偶=0,无配偶=1
文化程度高中/中专及以上=0,初中及以下=1
子女探望频率高频率=0,低频率=1
慢性病病程5年以下=(0,0),5~10年=(1,0),10年以上=(0,1)
电子健康素养高水平=0,低水平=1
社区公共服务满意度满意=0,不满意=1
家庭功能家庭功能良好=0,家庭功能障碍=1
内在能力较好=0,较差=1
), ArticleFig(id=1322530524845945584, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168994052821616, language=CN, label=表2, caption=

变量赋值情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值方式
年龄60~70岁=0,>70岁=1
婚姻状况有配偶=0,无配偶=1
文化程度高中/中专及以上=0,初中及以下=1
子女探望频率高频率=0,低频率=1
慢性病病程5年以下=(0,0),5~10年=(1,0),10年以上=(0,1)
电子健康素养高水平=0,低水平=1
社区公共服务满意度满意=0,不满意=1
家庭功能家庭功能良好=0,家庭功能障碍=1
内在能力较好=0,较差=1
), ArticleFig(id=1322530524917248753, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168994052821616, language=EN, label=Table 3, caption=

Logistic regression analysis of influencing factors of social frailty in older adults with chronic diseases

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变量回归系数标准误Wald χ2值POR值95%CI
常量-4.5940.412124.467<0.001
年龄0.7210.2518.2480.0042.057[1.257,3.366]
文化程度0.8280.22813.141<0.0012.288[1.463,3.580]
慢性病病程0.7100.3524.0680.0442.033[1.020,4.053]
电子健康素养0.6370.3243.8750.0491.891[1.003,3.567]
社区公共服务满意度0.7220.20512.410<0.0012.059[1.378,3.078]
家庭功能0.7000.3144.9780.0262.014[1.089,3.725]
内在能力0.7840.2658.7760.0032.190[1.304,3.679]
), ArticleFig(id=1322530525005329138, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168994052821616, language=CN, label=表3, caption=

慢性病老年人社会衰弱影响因素的Logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Wald χ2值POR值95%CI
常量-4.5940.412124.467<0.001
年龄0.7210.2518.2480.0042.057[1.257,3.366]
文化程度0.8280.22813.141<0.0012.288[1.463,3.580]
慢性病病程0.7100.3524.0680.0442.033[1.020,4.053]
电子健康素养0.6370.3243.8750.0491.891[1.003,3.567]
社区公共服务满意度0.7220.20512.410<0.0012.059[1.378,3.078]
家庭功能0.7000.3144.9780.0262.014[1.089,3.725]
内在能力0.7840.2658.7760.0032.190[1.304,3.679]
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社区慢性病老年人社会衰弱风险预测模型的构建及验证
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韩朝颖 , 石珂金 , 赵雅宁 *, * , 李建民 , 刘瑶
护理研究 | 科研论著 2026,40(8): 1257-1264
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护理研究 |科研论著 2026 , 40 (8) : 1257 -1264
社区慢性病老年人社会衰弱风险预测模型的构建及验证
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韩朝颖, 石珂金, 赵雅宁*, * , 李建民, 刘瑶
作者信息
  • 1华北理工大学护理与康复学院,河北 063210
通讯作者:
赵雅宁,E-mail:
ZHAO Yaning, E-mail:
作者简介:

韩朝颖,护师,硕士研究生在读

Construction and validation of the social frailty risk prediction model for elderly people with chronic diseases in communities
Zhaoying HAN, Kejin SHI, Yaning ZHAO , Jianmin LI, Yao LIU
Affiliations
  • 1College of Nursing and Rehabilitation, North China University of Science and Technology, Hebei 063210 China
  • Corresponding Author:
    ZHAO Yaning, E-mail:
出版时间: 2026-04-25 doi: 10.12102/j.issn.1009-6493.2026.08.003
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目的

构建社区慢性病老年人社会衰弱风险预测模型,探讨内在能力对社区慢性病老年人发生社会衰弱的预测价值。

方法

选取2024年7月—2025年5月在唐山市路南区参与调查的785例社区慢性病老年病人为研究对象。采用LASSO回归筛选变量,Logistic回归分析影响因素,并绘制列线图,构建不包含内在能力变量的基准模型和包含内在能力变量的预测模型。

结果

社区慢性病老年人的社会衰弱检出率为23.69%。Logistic回归分析结果显示,社区慢性病老年人年龄、文化程度、慢性病病程、电子健康素养水平、社区公共服务满意度、家庭功能、内在能力是其社会衰弱的影响因素(均P<0.05)。建立不包含内在能力变量的基准模型和包含内在能力变量的预测模型,结果显示预测模型在重分类能力和判别能力上较基准模型具有明显提升。预测模型训练集和验证集的受试者工作特征曲线下面积分别为0.828和0.856。决策曲线显示模型具有较好的临床实用性。

结论

基于内在能力构建的社区慢性病老年人社会衰弱预测模型有一定预测效能,可用于早期识别社区慢性病老年人的社会衰弱发生风险,为及时采取预防和干预措施提供依据。

社区  /  慢性病  /  社会衰弱  /  预测模型  /  内在能力
Objective

To construct the social frailty risk prediction model for elderly people with chronic diseases in communities,and to explore the predictive value of intrinsic capacity for social frailty among them.

Methods

A total of 785 elderly patients with chronic diseases in the communities,who were surveyed in Lu'nan district of Tangshan city from July 2024 to May 2025,were selected as research subjects.LASSO regression was used to screen the variables.Logistic regression analysis was conducted to analyze the influencing factors.A column chart was drawn,and a baseline model without intrinsic ability variables and a prediction model including intrinsic capacity variables were constructed.

Results

The detection rate of social frailty among community-dwelling elderly with chronic diseases was 23.69%.The results of Logistic regression analysis showed that age,educational level,duration of chronic disease,level of e-health literacy,satisfaction with community public services,family function,and intrinsic capacity were the influencing factors of social frailty in community-dwelling elderly with chronic diseases(all P<0.05).Two models were established,one without the intrinsic ability variable and the other with the intrinsic ability variable.The results showed that the prediction model had significantly improved reclassification ability and discriminative ability compared to the baseline model.The area under the ROC curve of the prediction model training set and validation set was 0.828 and 0.856 respectively.The DCA curve indicated that the model had good clinical practicability.

Conclusions

The social frailty risk prediction model for elderly people with chronic diseases in the communities had certain predictive efficacy.It could be used to identify the risk of social frailty in elderly people with chronic diseases in the communities at an early stage.It could provide basis for taking timely preventive and intervention measures.

communities  /  chronic disease  /  social frailty  /  prediction model  /  intrinsic capacity
韩朝颖, 石珂金, 赵雅宁, 李建民, 刘瑶. 社区慢性病老年人社会衰弱风险预测模型的构建及验证. 护理研究, 2026 , 40 (8) : 1257 -1264 . DOI: 10.12102/j.issn.1009-6493.2026.08.003
Zhaoying HAN, Kejin SHI, Yaning ZHAO, Jianmin LI, Yao LIU. Construction and validation of the social frailty risk prediction model for elderly people with chronic diseases in communities[J]. Chinese Nursing Research, 2026 , 40 (8) : 1257 -1264 . DOI: 10.12102/j.issn.1009-6493.2026.08.003
我国老龄化趋势日益加重,慢性病已成为制约老年人健康、导致老年人残疾和死亡的重要因素[1],不仅影响健康状态和生活质量,还会增加经济和心理负担。身心双重受限一定程度上影响老年人的社会参与和资源利用,最终诱发社会衰弱(social frailty)[2],即个体持续处于失去社会重要资源、社会行为及活动缺失,以及自我管理能力不足的高风险状态之中[3]。研究证实,社会衰弱与老年人发生不良健康结局存在显著关联,增加慢性病老年人的死亡率[4]。内在能力(intrinsic capacity)作为个体持续可动用的综合资源,涉及认知、运动、心理、感官以及活力五大关键领域[5],是个体能力的综合体现,与老年人社会衰弱密切相关[6]。目前,尚未检索到针对社区慢性病老年人社会衰弱预测风险的研究。因此,本研究以内在能力为切入点建立预测模型,旨在探讨内在能力对社区慢性病老年人社会衰弱的影响,为社区慢性病老年人社会衰弱的识别和干预提供思路和依据。
本研究为横断面研究,采用便利抽样法,于2024年7月—2025年5月,选取唐山市路南区的社区慢性病老年人作为调查对象。慢性病符合相关疾病诊断标准[7],包括高血压、糖尿病、冠心病、脑卒中、恶性肿瘤、呼吸系统疾病等。纳入标准:年龄≥60岁;至少患有1种由二级甲等及以上医院确诊的慢性病;意识清楚,自愿参与本研究。排除病情危重、患有严重精神疾病者。根据Logistic回归样本量估算方法,样本量为自变量个数的5~10倍,本研究纳入16个自变量,社区老年人社会衰弱发生率为25.91%[8],再考虑10%的失访率,所需样本量至少为343例。使用R软件按照7∶3的比例将调查对象随机分为训练集与验证集。本研究已获得我校伦理委员会审批(编号:2024271)。
由研究者自行研制调查表收集。一般资料包括性别、年龄、婚姻状况、居住方式、文化程度、个人月收入、子女数量、子女探望频率,子女探望频率将“每天”“每周至少1次”划分为高频率,将“每个月少于2次”“几个月1次”“从不”划分为低频率。疾病资料包括慢性病病程、服药种类、自评健康状况,慢性病病程根据本研究实际情况划分为5年以下、5~10年、10年以上;自评健康状况以条目“您觉得您的健康状况怎么样?”判断,选项包括“较好”“较差”。
采用Ma等[9]研发的社会衰弱量表(Help,Participation,Loneliness,Financial,Talk,HALFT),包含社会角色、社交活动、社会支持、孤独感4个方面,共5个条目,回答“是”计0分,“否”计1分,总分为0~5分,其中0~2分定义为非社会衰弱,≥3分定义为社会衰弱。
采用2013年由我国学者郭帅军等[10]汉化的电子健康素养量表(eHealth Literacy Scale,eHEALS)进行评估,包括应用能力、评价能力、决策能力3个维度,共8个条目,各条目均采用Likert 5级评分法,从“完全不同意”到“完全同意”分别计1~5分。得分<26分为低水平电子健康素养,≥26分为高水平电子健康素养。
参考邹薇[11]对城市社区公共服务居民满意度的研究,由社区老年人对所在社区所提供的公共服务满意度进行衡量,社区公共服务主要包括社区安全保障、社区基本社会保障、社区文体服务、社区基础生活设施,以条目“您对所在社区提供的公共服务是否满意?”判断,将选项的“非常满意”“比较满意”归为“满意”,将“不太满意”“非常不满意”归为“不满意”。
采用由肖水源[12]修订的社会支持评定量表(Social Support Rating Scale,SSRS)进行评估,包含客观支持、主观支持和对支持的利用度3个维度,共10个条目,总分12~66分。本研究以得分中位数进行分组,将<39分划分为低水平社会支持,≥39分划分为高水平社会支持。
采用由国内学者吕繁等[13]汉化的家庭关怀度指数问卷(Adaption,Partnership,Growth,Affection,and Resolve,APGAR),共5个条目,由适应、合作、成长、情感和亲密度5个维度组成。采用3级(0~2分)计分,得分为0~10分,本研究将≥7分划分为家庭功能良好,<7分划分为家庭功能障碍。
采用WHO提出的内在能力五维度评估工具。1)运动:采用简易躯体能力测试[14](the Short Physical Performance Battery,SPPB),总分12分,将SPPB<10分判定为运动领域水平下降,包括平衡能力测试、下肢力量测试、4 m步速测试。2)认知:采用简易智力状态评估量表(Mini-Cog)[15]评定,总分5分,其中总分≥3分代表认知能力正常,包括画钟试验和词语回忆测试2部分。3)活力:采用微型营养评估量表(MNA-SF)[16]评定,包括饮食及体重变化、精神心理、体质指数等6个条目,总分0~14分,≥12分为活力正常。4)心理:采用老年抑郁量表(GDS-15)[17]评定,共15个条目,总分为15分,得分越高表示抑郁症状越严重,<5分为正常。5)感觉:能看清正常距离书上的文字,且双耳可听清一臂距离内耳语词汇为正常。内在能力得分为各维度得分总和,各维度正常计1分,异常计0分,总分0~5分,本研究将≥4分划分为内在能力较好,<4分划分为内在能力较差。
在正式开展调查之前,调查员详细向研究对象阐述本次调研的意图和内容,并对填写的内容进行全面审查,在数据录入阶段采用双人录入,一旦发现录入的数据存在差异,将依据问卷编码迅速核对原始资料并进行修正,以确保数据录入的准确无误。本研究共发放问卷810份,回收有效问卷785份,有效回收率为96.9%。
采用SPSS 26.0、R4.5.0软件进行数据分析。定性资料采用频数、百分比(%)表示,单因素分析采用χ2检验。采用LASSO回归分析筛选相关变量,Logistic回归分析慢性病老年人社会衰弱的影响因素,构建预测模型并绘制列线图。采用净重新分类改善指数(NRI)和综合判别改善指数(IDI)评估内在能力对社会衰弱的预测效能。在训练集进行模型构建,在验证集进行模型性能评估。采用Bootstrap进行模型内部验证,训练集及验证集模型分别通过受试者工作特征(ROC)曲线、拟合优度检验、校准曲线及决策(DCA)曲线进行评估。以P<0.05为差异有统计学意义。
共纳入785例慢性病老年人,社会衰弱检出率为23.69%(186/785)。训练集549例,其中社会衰弱者124例(22.59%);验证集236例,其中社会衰弱者62例(26.27%)。无社会衰弱老年人与社会衰弱老年人年龄、婚姻状况、居住方式、文化程度、个人月收入、子女数量、子女探望频率、服药种类、慢性病病程、自评健康状况、电子健康素养、社区公共服务满意度、社会支持、家庭功能、内在能力比较,差异均有统计学意义(P<0.05),见表1。
将单因素分析有统计学意义的15个变量进行LASSO回归分析,根据10折交叉验证进行变量筛选,系数非0时具有意义,在λ=0.045时所筛选变量结果最佳,此时纳入的变量为年龄、婚姻状况、文化程度、子女探望频率、慢性病病程、电子健康素养、社区公共服务满意度、家庭功能、内在能力,见图1、图2。
以社会衰弱(否=0,是=1)为因变量,将LASSO回归分析筛选出的9个变量为自变量进行Logistic回归分析,变量赋值见表2,结果见表3。
根据Logistic回归分析筛选出的预测因素,构建社区慢性病老年人社会衰弱发生风险列线图,见图3。建立不包含内在能力变量的基准模型和包含内在能力变量的预测模型,通过NRI以及IDI评估预测模型相较于基准模型的性能。发现预测模型在重分类能力和判别能力上较基准模型具有明显提升(NRI=0.117,P<0.05;IDI=0.016,P<0.05)。采用Bootstrap重抽样法重复抽样1 000次对模型进行内部验证,模型准确率为80.16%,Hosmer-Lemeshow拟合优度检验显示,χ2=6.376,P=0.605,提示模型拟合度良好。绘制校准曲线,预测值和实际值较为接近,提示模型的校准能力较好,见图4、图5。绘制训练集和验证集的ROC曲线并计算ROC曲线下面积(AUC),在训练集和验证集中AUC分别为0.828(0.789,0.866)和0.856(0.805,0.907),模型在训练集和验证集均具有良好区分度,见图6、图7。进一步对训练集和验证集进行临床适用度分析,模型临床实用性较好,见图8。
本研究结果显示,社区慢性病老年人社会衰弱检出率为23.69%,与陈瑛玮等[8]研究结果相近。年龄越大的慢性病老年人发生社会衰弱的风险越高,与吴和梅等[18]研究结果一致。随着年龄增长,老年人身体机能逐渐衰退,影响身体健康,限制其社会参与能力。文化程度较低的慢性病老年人社会衰弱发生的风险较高,与Qi等[19]的研究结果一致。低文化程度老年人受限于自身知识储备与认知能力,自我保健意识较弱,难以获取健康信息,影响其疾病自我管理与健康促进。慢性病病程长越容易导致社会衰弱,病程越长,老年人患并发症的概率和经济负担越大,不仅损害其生理健康状态,还可能通过神经内分泌机制诱发抑郁情绪[20],限制老年人社交活动。电子素养水平高的老年人社会衰弱发生风险较低。既往研究发现,老年人电子健康素养越高,生活质量越高[21]。电子健康素养水平较高的老年慢性病病人,在健康管理过程中能够通过有效甄别、利用数字化健康资源[22],降低疾病相关心理负担,提升健康状态,减小社会衰弱风险。社区公共服务与社区老年人生活密切相关,低满意度可能由于老年人对社区缺乏归属感和信任感[11],参与社区事务与社交活动的积极性不高。已有研究证实,社区服务的提供能有效防止老年人发生社会隔离等问题[23]。慢性病病人对社区医疗服务的需求较高,有效的健康服务也能帮助其进行疾病管理[24]。家庭功能障碍的慢性病老年人发生社会衰弱的风险更高,与王婕等[25]的研究结果一致。慢性病老年人患病时间较长,治疗负担较重,家庭成员无法提供支持与援助会致使老年人陷入经济保障缺失、健康状况恶化等多重困境[26],增加社会衰弱风险。
本研究结果显示,内在能力较差的社区慢性病老年人发生社会衰弱的风险更高。内在能力作为多维度评估老年人健康状态的指标,已被证实与社会衰弱密切相关[6]。慢性病老年人受到疾病影响,可能存在疼痛、日常生活活动能力减弱等情况[27],严重影响其运动功能,进而影响个体的移动范围与社交活动,削弱其原有的社会网络,增加社会衰弱风险。在慢性病进程中,多因素病理机制相互作用可引发营养不良,肌肉合成减少,增加衰弱风险[18],影响日常活动。既往研究发现,患有高血压、心脏病、脑卒中等慢性病的老年人认知障碍发生率显著升高[28]。当慢性病老年人出现认知障碍后,一方面会影响其神经功能,另一方面导致老年人信息处理效率降低、社会资源整合障碍及行为执行能力受损,致使病人社会参与能力的全面衰退[18]。抑郁症状越严重,慢性病老年人越容易感到孤独[20],社交活动受到严重影响,发生社会衰弱的可能性越大。感知觉下降会影响老年人与外界沟通,造成老年人社交频率减少,影响社会参与能力[29],最终造成社会衰弱。
本研究结果显示,内在能力是社区慢性病老年人社会衰弱的预测因子,与由年龄、文化程度、慢性病病程、电子健康素养、社区公共服务满意度、家庭功能构成的基准模型相比,在添加了内在能力后可显著提高基准模型的重分类和判别能力,证明内在能力对慢性病老年人社会衰弱的预测价值。预测模型训练集AUC为0.828,验证集AUC为0.856,Hosmer-Lemeshow拟合优度P值为0.605,说明预测模型拟合度良好。社区医护人员可以使用该预测模型早期、快速识别社区慢性病老年人社会衰弱的发生风险,从而实施预防和精准干预。
社区慢性病老年人社会衰弱的发生与其年龄、文化程度、慢性病病程、电子健康素养、社区公共服务满意度、家庭功能和内在能力有关。基于内在能力构建的社区慢性病老年人社会衰弱风险预测模型,可为社区医护人员早期识别社会衰弱高风险老年人提供参考。但本研究也存在一定局限性,由于采用横断面研究设计,无法获取个体发展变化的数据,可能存在信息偏倚,未来可考虑采用前瞻性队列研究以提升模型精度。

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doi: 10.12102/j.issn.1009-6493.2026.08.003
  • 接收时间:2025-07-25
  • 首发时间:2026-10-28
  • 出版时间:2026-04-25
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  • 收稿日期:2025-07-25
  • 修回日期:2026-04-02
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    1华北理工大学护理与康复学院,河北 063210

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赵雅宁,E-mail:
ZHAO Yaning, 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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