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Article(id=1322924553189597192, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924415817757501, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.17.022, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760284800000, receivedDateStr=2025-10-13, revisedDate=1783872000000, revisedDateStr=2026-07-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339392229, onlineDateStr=2026-10-30, pubDate=1788969600000, pubDateStr=2026-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339392229, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339392229, creator=13701087609, updateTime=1793339392229, updator=13701087609, issue=Issue{id=1322924415817757501, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='17', pageStart='2961', pageEnd='3152', issueExtLink='null', onlineDate='null', pubDate='1788969600000', pubDateStr='2026-09-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339359478, creator='13701087609', updateTime=1793343089343, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940060072542250, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924415817757501, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940060072542251, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924415817757501, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3104, endPage=3109, ext={EN=ArticleExt(id=1322924554468859913, articleId=1322924553189597192, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Latent profile analysis of fall risk perception in patients with posterior circulation ischemia and its impact on exercise self-efficacy, columnId=1220689488284668633, journalTitle=Chinese Nursing Research, columnName=Comprehensive Study, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify latent classes of fall risk perception in patients with posterior circulation ischemia(PCI),and to analyze their influencing factors,and to explore the impact of different classes on exercise self-efficacy.

Methods

A total of 200 PCI patients admitted to our hospital from August 2023 to July 2025 were enrolled.Surveys included a general information questionnaire,the Fall Risk Perception Scale,Pittsburgh Sleep Quality Index(PSQI),Berg Balance Scale(BBS),Exercise Self-Efficacy Scale(MSES),and Activities of Daily Living(ADL) scale.Latent profile analysis(LPA) was used to identify latent classes of fall risk perception and their influencing factors.The impact of these classes on exercise self-efficacy was further examined.

Results

Fall risk perception in PCI patients fell into three latent classes:insufficient perception(78 cases,39.0%),moderate perception(92 cases,46.0%),and excessive perception(30 cases,15.0%).Multivariate logistic regression showed that PSQI score was a factor for the insufficient perception class(OR=1.553,P<0.001).BBS score,ADL score,MSES score,and fall history were factors for the excessive perception class(OR=0.840,0.882,0.933,and 2.945 respectively,P<0.05).Multiple linear regression showed that after controlling for confounders,the excessive perception class was a predictor of exercise self-efficacy score(P<0.05).

Conclusions

Fall risk perception in PCI patients showed population heterogeneity and it was influenced by sleep quality,balance function,and daily activity ability.The excessive perception class negatively predicted patients' exercise self-efficacy. Clinicians may consider identifying different patient classes and develope targeted intervention strategies.

, authors=Jinjin PAN, Shaolian XU, Juan ZHANG, Na ZHANG, Tingting ZHOU, Wei HUANG, authorsList=Jinjin PAN, Shaolian XU, Juan ZHANG, Na ZHANG, Tingting ZHOU, Wei HUANG, authorCompany=null, correspAuthors=Wei HUANG, 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=1322924555379023884, articleId=1322924553189597192, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=后循环缺血病人跌倒风险感知的潜在剖面分析及其对运动自我效能的影响, columnId=1220689488938980091, journalTitle=护理研究, columnName=综合研究, runingTitle=null, highlight=null, articleAbstract=
目的

探讨后循环缺血（PCI）病人跌倒风险感知的潜在类别，分析其影响因素，并探讨不同类别对运动自我效能的影响。

方法

选取2023年8月—2025年7月我院收治的200例PCI病人为研究对象。采用一般资料调查表、跌倒风险感知量表、匹兹堡睡眠质量指数（PSQI）、Berg平衡量表（BBS）、运动自我效能量表（MSES）及日常生活活动能力（ADL）量表进行调查。采用潜在剖面分析（LPA）识别跌倒风险感知的潜在类别，探讨潜在类别的影响因素，并进一步探讨跌倒风险感知潜在类别对运动自我效能的影响。

结果

PCI病人跌倒风险感知可分为感知不足型78例（39.0%）、中度感知型92例（46.0%）、过度感知型30例（15.0%）3个潜在类别。多因素Logistic回归分析显示，PSQI得分是感知不足型的影响因素（OR=1.553，P<0.001）；BBS得分、ADL得分、MSES得分及跌倒史是过度感知型的影响因素（OR值分别为0.840，0.882，0.933，2.945，P<0.05）。多重线性回归分析显示，在控制混杂因素后，过度感知型是运动自我效能得分的影响因素（P<0.05）。

结论

PCI病人跌倒风险感知存在群体异质性，并受到睡眠质量、平衡功能及日常活动能力等因素影响。过度感知型类别可负向预测病人的运动自我效能水平，临床工作者可考虑识别不同类别病人并探索针对性干预策略。

, authors=潘进进, 徐绍莲, 张娟, 张娜, 周婷婷, 黄维*, *, authorsList=潘进进, 徐绍莲, 张娟, 张娜, 周婷婷, 黄维, authorCompany=null, correspAuthors=黄维, authorNote=

潘进进，本科

, correspAuthorsNote=
黄维，E-mail：
HUANG Wei, E-mail:
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Fit indices for latent classes of fall risk perception in patients with posterior circulation ischemia

, figureFileSmall=null, figureFileBig=null, tableContent=
类别数AICBICaBICEntropyP类别概率
LMRTBLRT
1550.753566.384547.4351.00
2379.663405.714374.1320.989<0.001<0.0010.85/0.15
336.43472.90628.6900.9990.039<0.0010.46/0.39/0.15
417.34729.5477.3020.9970.128<0.0010.04/0.15/0.35/0.46
), ArticleFig(id=1322924562895216707, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=CN, label=表1, caption=

后循环缺血病人跌倒风险感知的潜在类别拟合结果

, figureFileSmall=null, figureFileBig=null, tableContent=
类别数AICBICaBICEntropyP类别概率
LMRTBLRT
1550.753566.384547.4351.00
2379.663405.714374.1320.989<0.001<0.0010.85/0.15
336.43472.90628.6900.9990.039<0.0010.46/0.39/0.15
417.34729.5477.3020.9970.128<0.0010.04/0.15/0.35/0.46
), ArticleFig(id=1322924562958131268, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=EN, label=Table 2, caption=

Scores of various dimensions of fall risk perception among patients with different types of posterior circulation ischemia（x±s）

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在类别例数跌倒行为易感性跌倒严重性跌倒环境易感性
中度感知型923.45±0.143.71±0.153.67±0.14
感知不足型782.81±0.192.87±0.192.91±0.21
过度感知型304.14±0.054.83±0.274.73±0.23
), ArticleFig(id=1322924563016851525, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=CN, label=表2, caption=

不同类别后循环缺血病人跌倒风险感知各维度得分（x±s）

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在类别例数跌倒行为易感性跌倒严重性跌倒环境易感性
中度感知型923.45±0.143.71±0.153.67±0.14
感知不足型782.81±0.192.87±0.192.91±0.21
过度感知型304.14±0.054.83±0.274.73±0.23
), ArticleFig(id=1322924563092348998, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=EN, label=Table 3, caption=

Univariate analysis of potential categories of fall risk perception in patients with posterior circulation ischemia

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类中度感知型（n=92）感知不足型（n=78）过度感知型（n=30）统计值P
性别[例（%）]男36(39.13)26(33.33)7(23.33)χ²=2.5750.276
女56(60.87)52(66.67)23(76.67)
年龄（岁）61.98±9.4360.85±9.3263.30±9.82F=0.7850.458
文化程度[例（%）]初中及以下32(34.78)28(35.90)12(40.00)
高中或专科43(46.74)32(41.03)13(43.33)χ²=1.1310.889
本科及以上17(18.48)18(23.08)5(16.67)
居住地[例（%）]农村36(39.13)32(41.03)14(46.67)χ²=0.5310.767
城镇56(60.87)46(58.97)16(53.33)
是否独居[例（%）]是29(31.52)22(28.21)11(36.67)χ²=0.7470.688
否63(68.48)56(71.79)19(63.33)
家庭人均月收入[例（%）]<3 000元27(29.35)20(25.64)11(36.67)
3 000~5 000元41(44.57)35(44.87)12(40.00)χ²=1.4010.844
>5 000元24(26.09)23(29.49)7(23.33)
婚姻状况[例（%）]已婚79(85.87)62(79.49)24(80.00)χ²=1.3440.511
未婚/离婚/丧偶13(14.13)16(20.51)6(20.00)
跌倒史[例（%）]有18(19.57)13(16.67)13(43.33)χ²=9.5670.008
无74(80.43)65(83.33)17(56.67)
吸烟史[例（%）]有26(28.26)23(29.49)8(26.67)χ²=0.0890.956
无66(71.74)55(70.51)22(73.33)
饮酒史[例（%）]有32(34.78)27(34.62)10(33.33)χ²=0.0220.989
无60(65.22)51(65.38)20(66.67)
合并高血压[例（%）]有49(53.26)41(52.56)17(56.67)χ²=0.1510.928
无43(46.74)37(47.44)13(43.33)
合并糖尿病[例（%）]有31(33.70)28(35.90)9(30.00)χ²=0.3430.843
无61(66.30)50(64.10)21(70.00)
助行器[例（%）]有38(41.30)31(39.74)13(43.33)χ²=0.1220.941
无54(58.70)47(60.26)17(56.67)
PSQI评分（分）5.32±1.276.24±1.695.73±1.41F=8.294<0.001
BBS评分（分）33.24±4.3132.56±4.1829.83±3.66F=7.601<0.001
MSES评分（分）65.32±7.2864.29±7.1160.53±6.84F=5.0860.007
ADL评分（分）68.54±6.5667.29±6.3863.87±5.43F=6.1540.003
), ArticleFig(id=1322924563264315463, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=CN, label=表3, caption=

后循环缺血病人跌倒风险感知的潜在类别的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类中度感知型（n=92）感知不足型（n=78）过度感知型（n=30）统计值P
性别[例（%）]男36(39.13)26(33.33)7(23.33)χ²=2.5750.276
女56(60.87)52(66.67)23(76.67)
年龄（岁）61.98±9.4360.85±9.3263.30±9.82F=0.7850.458
文化程度[例（%）]初中及以下32(34.78)28(35.90)12(40.00)
高中或专科43(46.74)32(41.03)13(43.33)χ²=1.1310.889
本科及以上17(18.48)18(23.08)5(16.67)
居住地[例（%）]农村36(39.13)32(41.03)14(46.67)χ²=0.5310.767
城镇56(60.87)46(58.97)16(53.33)
是否独居[例（%）]是29(31.52)22(28.21)11(36.67)χ²=0.7470.688
否63(68.48)56(71.79)19(63.33)
家庭人均月收入[例（%）]<3 000元27(29.35)20(25.64)11(36.67)
3 000~5 000元41(44.57)35(44.87)12(40.00)χ²=1.4010.844
>5 000元24(26.09)23(29.49)7(23.33)
婚姻状况[例（%）]已婚79(85.87)62(79.49)24(80.00)χ²=1.3440.511
未婚/离婚/丧偶13(14.13)16(20.51)6(20.00)
跌倒史[例（%）]有18(19.57)13(16.67)13(43.33)χ²=9.5670.008
无74(80.43)65(83.33)17(56.67)
吸烟史[例（%）]有26(28.26)23(29.49)8(26.67)χ²=0.0890.956
无66(71.74)55(70.51)22(73.33)
饮酒史[例（%）]有32(34.78)27(34.62)10(33.33)χ²=0.0220.989
无60(65.22)51(65.38)20(66.67)
合并高血压[例（%）]有49(53.26)41(52.56)17(56.67)χ²=0.1510.928
无43(46.74)37(47.44)13(43.33)
合并糖尿病[例（%）]有31(33.70)28(35.90)9(30.00)χ²=0.3430.843
无61(66.30)50(64.10)21(70.00)
助行器[例（%）]有38(41.30)31(39.74)13(43.33)χ²=0.1220.941
无54(58.70)47(60.26)17(56.67)
PSQI评分（分）5.32±1.276.24±1.695.73±1.41F=8.294<0.001
BBS评分（分）33.24±4.3132.56±4.1829.83±3.66F=7.601<0.001
MSES评分（分）65.32±7.2864.29±7.1160.53±6.84F=5.0860.007
ADL评分（分）68.54±6.5667.29±6.3863.87±5.43F=6.1540.003
), ArticleFig(id=1322924563352395848, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=EN, label=Table 4, caption=

Multivariate Logistic regression analysis of potential categories of fall risk perception in patients with posterior circulation ischemia

, figureFileSmall=null, figureFileBig=null, tableContent=
分类变量回归系数标准误Wald χ²值POR值95%CI
下限上限
感知不足型常量1.9322.5360.5810.446
PSQI评分0.4400.11514.748<0.0011.5531.2411.945
过度感知型常量15.1343.81015.779<0.001
BBS评分-0.1750.0579.3820.0020.8400.7510.939
MSES评分-0.0690.0344.0490.0440.9330.8720.998
ADL评分-0.1250.0428.7830.0030.8820.8120.958
有跌倒史1.0800.5124.4570.0352.9451.0808.026
), ArticleFig(id=1322924563427893321, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=CN, label=表4, caption=

后循环缺血病人跌倒风险感知潜在类别的多因素Logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分类变量回归系数标准误Wald χ²值POR值95%CI
下限上限
感知不足型常量1.9322.5360.5810.446
PSQI评分0.4400.11514.748<0.0011.5531.2411.945
过度感知型常量15.1343.81015.779<0.001
BBS评分-0.1750.0579.3820.0020.8400.7510.939
MSES评分-0.0690.0344.0490.0440.9330.8720.998
ADL评分-0.1250.0428.7830.0030.8820.8120.958
有跌倒史1.0800.5124.4570.0352.9451.0808.026
), ArticleFig(id=1322924563511779402, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=EN, label=Table 5, caption=

Multiple linear regression analysis of potential categories of fall risk perception and exercise self-efficacy in patients with posterior circulation ischemia

, figureFileSmall=null, figureFileBig=null, tableContent=
分类回归系数标准误t值P95%CI
下限上限
常量45.2426.6066.849<0.00132.21458.270
过度感知型-3.5111.639-2.1430.033-6.743-0.279
感知不足型-0.9631.155-0.8340.405-3.2401.315
), ArticleFig(id=1322924563608248395, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322924553189597192, language=CN, label=表5, caption=

后循环缺血病人跌倒风险感知的潜在类别与运动自我效能的多重线性回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分类回归系数标准误t值P95%CI
下限上限
常量45.2426.6066.849<0.00132.21458.270
过度感知型-3.5111.639-2.1430.033-6.743-0.279
感知不足型-0.9631.155-0.8340.405-3.2401.315
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后循环缺血病人跌倒风险感知的潜在剖面分析及其对运动自我效能的影响
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潘进进 , 徐绍莲 , 张娟 , 张娜 , 周婷婷 , 黄维 *, *
护理研究 | 综合研究 2026,40(17): 3104-3109
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护理研究 |综合研究 2026 , 40 (17) : 3104 -3109
后循环缺血病人跌倒风险感知的潜在剖面分析及其对运动自我效能的影响
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潘进进, 徐绍莲, 张娟, 张娜, 周婷婷, 黄维*, *
作者信息
  • 1皖西卫生职业学院附属医院，安徽 237000
通讯作者:
黄维，E-mail：
HUANG Wei, E-mail:
作者简介:

潘进进，本科

Latent profile analysis of fall risk perception in patients with posterior circulation ischemia and its impact on exercise self-efficacy
Jinjin PAN, Shaolian XU, Juan ZHANG, Na ZHANG, Tingting ZHOU, Wei HUANG
Affiliations
  • 1Affiliated Hospital of West Anhui Health Vocational College, Anhui 237000 China
出版时间: 2026-09-10 doi: 10.12102/j.issn.1009-6493.2026.17.022
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摘要
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目的

探讨后循环缺血（PCI）病人跌倒风险感知的潜在类别，分析其影响因素，并探讨不同类别对运动自我效能的影响。

方法

选取2023年8月—2025年7月我院收治的200例PCI病人为研究对象。采用一般资料调查表、跌倒风险感知量表、匹兹堡睡眠质量指数（PSQI）、Berg平衡量表（BBS）、运动自我效能量表（MSES）及日常生活活动能力（ADL）量表进行调查。采用潜在剖面分析（LPA）识别跌倒风险感知的潜在类别，探讨潜在类别的影响因素，并进一步探讨跌倒风险感知潜在类别对运动自我效能的影响。

结果

PCI病人跌倒风险感知可分为感知不足型78例（39.0%）、中度感知型92例（46.0%）、过度感知型30例（15.0%）3个潜在类别。多因素Logistic回归分析显示，PSQI得分是感知不足型的影响因素（OR=1.553，P<0.001）；BBS得分、ADL得分、MSES得分及跌倒史是过度感知型的影响因素（OR值分别为0.840，0.882，0.933，2.945，P<0.05）。多重线性回归分析显示，在控制混杂因素后，过度感知型是运动自我效能得分的影响因素（P<0.05）。

结论

PCI病人跌倒风险感知存在群体异质性，并受到睡眠质量、平衡功能及日常活动能力等因素影响。过度感知型类别可负向预测病人的运动自我效能水平，临床工作者可考虑识别不同类别病人并探索针对性干预策略。

后循环缺血  /  跌倒  /  风险感知  /  潜在剖面分析  /  运动自我效能  /  影响因素
Objective

To identify latent classes of fall risk perception in patients with posterior circulation ischemia(PCI),and to analyze their influencing factors,and to explore the impact of different classes on exercise self-efficacy.

Methods

A total of 200 PCI patients admitted to our hospital from August 2023 to July 2025 were enrolled.Surveys included a general information questionnaire,the Fall Risk Perception Scale,Pittsburgh Sleep Quality Index(PSQI),Berg Balance Scale(BBS),Exercise Self-Efficacy Scale(MSES),and Activities of Daily Living(ADL) scale.Latent profile analysis(LPA) was used to identify latent classes of fall risk perception and their influencing factors.The impact of these classes on exercise self-efficacy was further examined.

Results

Fall risk perception in PCI patients fell into three latent classes:insufficient perception(78 cases,39.0%),moderate perception(92 cases,46.0%),and excessive perception(30 cases,15.0%).Multivariate logistic regression showed that PSQI score was a factor for the insufficient perception class(OR=1.553,P<0.001).BBS score,ADL score,MSES score,and fall history were factors for the excessive perception class(OR=0.840,0.882,0.933,and 2.945 respectively,P<0.05).Multiple linear regression showed that after controlling for confounders,the excessive perception class was a predictor of exercise self-efficacy score(P<0.05).

Conclusions

Fall risk perception in PCI patients showed population heterogeneity and it was influenced by sleep quality,balance function,and daily activity ability.The excessive perception class negatively predicted patients' exercise self-efficacy. Clinicians may consider identifying different patient classes and develope targeted intervention strategies.

posterior circulation ischemia  /  fall  /  risk perception  /  latent profile analysis  /  exercise self-efficacy  /  influencing factors
潘进进, 徐绍莲, 张娟, 张娜, 周婷婷, 黄维. 后循环缺血病人跌倒风险感知的潜在剖面分析及其对运动自我效能的影响. 护理研究, 2026 , 40 (17) : 3104 -3109 . DOI: 10.12102/j.issn.1009-6493.2026.17.022
Jinjin PAN, Shaolian XU, Juan ZHANG, Na ZHANG, Tingting ZHOU, Wei HUANG. Latent profile analysis of fall risk perception in patients with posterior circulation ischemia and its impact on exercise self-efficacy[J]. Chinese Nursing Research, 2026 , 40 (17) : 3104 -3109 . DOI: 10.12102/j.issn.1009-6493.2026.17.022
正文
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后循环缺血是一种常见的急性脑缺血性疾病，病人常出现小脑和脑干的缺血性缺氧症状，表现为头晕、恶心、呕吐、平衡障碍和共济失调，从而增加了跌倒的风险[1]。当病人跌倒后，可能会引起软组织损伤、骨折甚至死亡等严重伤害，这会增加医疗开支并延长住院时长。跌倒风险感知是病人对跌倒发生的可能性和严重性的主观认识[2]。如果病人能够准确识别跌倒的风险，他们更有可能实施预防措施，进而减少实际跌倒事故的发生。个体对跌倒风险的感知程度不同，可能导致对风险的评估在不同群体中存在异质性。但当前多数研究将跌倒风险感知作为一个整体进行探讨，未能充分体现个体间的差异[3]。潜在剖面分析是一种以个体为中心的统计方法，用于探索潜在的群体结构，识别具有不同特征的群体，并依据变量的不同特征进行亚群分类[4]。运动自我效能是指个体对自己在运动或体育锻炼中成功实施某一特定行为过程能力的信心，可以正向预测运动锻炼行为，并对身体健康具有预测作用[5]。本研究应用潜在剖面分析，探究后循环缺血病人跌倒风险感知的不同潜在类别，通过单因素分析及多因素Logistic回归分析识别与跌倒风险感知类别相关的影响因素，并探究跌倒风险感知类别对运动自我效能的影响。
1　对象与方法
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1.1　研究对象
采用便利抽样法，于2023年8月—2025年7月于我院治疗的后循环缺血病人为研究对象。根据样本量计算方法[6]，样本量为自变量的5~10倍，本研究自变量包括性别、年龄、文化程度、居住地、是否独居、家庭人均月收入、婚姻状态、跌倒史、吸烟史、饮酒史、合并高血压、合并糖尿病、助行器、匹兹堡睡眠质量指数（PSQI）评分、Berg平衡量表（BBS）评分、多维度运动自我效能量表（MSES）、日常生活活动能力（ADL）评分，共17个变量。计算样本量为17×（5~10），考虑10%的样本流失率，即94~188例，最终纳入200例病人。纳入标准：1）符合《中国后循环缺血的专家共识》[7]中后循环缺血的诊断标准；2）年龄18~80岁；3）病人的生命体征稳定；4）病人及其家属已充分了解研究内容并签署同意书。排除标准：1）存在严重凝血功能障碍的病人；2）伴有恶性肿瘤的病人；3）长期卧床且无法行走的病人；4）患有精神疾病或认知功能受损的病人。本研究已经我院医学伦理委员会审批（伦理批号：院伦审LAEY-203-026）。
1.2　研究工具
1.2.1　一般资料调查表
研究者自行设计，包括性别、年龄、文化程度、居住地、是否独居、家庭人均月收入、婚姻状态、跌倒史、吸烟史、饮酒史、合并高血压、合并糖尿病、助行器。
1.2.2　跌倒风险感知量表
由鲍冠君等[8]开发，旨在评估老年人对跌倒风险的感知程度。该量表涵盖跌倒严重性（5个条目）、跌倒行为易感性（8个条目）和跌倒环境易感性（4个条目）3个维度。“完全不担心”计1分，“极度担心”计5分，总分17~85分，得分越高表示跌倒风险感知水平越高。量表总分17~54分为低风险感知（感知不足），55~69分为中度风险感知，70~85分为高风险感知（感知过度）。该量表的Cronbach's α系数为0.874。在本研究中该量表的Cronbach's α系数为0.901。
1.2.3　PSQI
由Buysse等[9]开发、刘贤臣等[10]进行汉化，用于衡量病人的睡眠质量。量表包含19个自我评估条目和5个他人评估条目，覆盖入睡时间、睡眠质量、睡眠障碍等方面，得分0~21分，得分越高表示睡眠质量越差。中文版PSQI的Cronbach's α系数为0.842。在本研究中该量表的Cronbach's α系数为0.812。
1.2.4　BBS
由Berg[11]编制，用于评价病人的平衡能力，包括站立到坐、坐到站、无靠背坐位、无支撑站立、闭眼站立、转移等14个任务，每项评分0~4分，最高分为56分。该量表的Cronbach's α系数为0.864。在本研究中该量表的Cronbach's α系数为0.879。
1.2.5　MSES
由Rodgers等[12]开发、董建秀等[13]汉化，中文版MSES涵盖时间安排效能、问题应对效能、任务完成效能3个维度，共9个条目。每个条目评分0~10分，0分表示“毫无信心”，10分表示“充满信心”。总分0~90分，得分越高表示运动自我效能水平越高。该量表的Cronbach's α系数为0.847。在本研究中该量表的Cronbach's α系数为0.862。
1.2.6　ADL
由巫嘉陵等[14]开发，用于衡量病人的日常生活自理能力，总分0~100分。得分越高表示病人的日常生活能力越强。该量表的Cronbach's α系数为0.966。在本研究中该量表的Cronbach's α系数为0.941。
1.3　统计学方法
采用Mplus 8.3进行潜剖面分析，建立1~4个潜在类别模型。Akaike信息准则（AIC）、贝叶斯信息准则（BIC）、样本校正的贝叶斯信息准则（aBIC）越小，代表模型拟合程度越好。熵（Entropy）越接近于1，代表模型分类越准确。而Lo-Mendell-Rubin似然比检验（LMRT）与基于Bootstrap的似然比检验（BLRT），P<0.05说明该类别数目的模型比类别数目-1的模型性能更优。采用SPSS 21.0软件进行统计学分析，定性资料采用例数、百分比（%）描述，采用χ2检验；符合正态分布的定量资料采用均数±标准差（x±s）描述，采用方差分析。采用Logistic回归分析不同潜在类别的影响因素。以P<0.05为差异有统计学意义。
2　结果
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2.1　后循环缺血病人跌倒风险感知现状
200例病人跌倒风险感知得分为（58.42±9.79）分，跌倒严重性条目均分为（3.56±0.69）分，跌倒行为易感性条目均分为（3.31±0.49）分，跌倒环境易感性条目均分为（3.54±0.64）分。
2.2　后循环缺血病人跌倒风险感知的潜在类别
以后循环缺血病人跌倒风险感知3个维度条目均分为外显指标，建立1~4个潜在类别模型，根据AIC、BIC、aBIC、LRT等检验指标选择最优潜在类别模型，模型拟合指标见表1。随着类别数量增加，AIC、BIC、aBIC逐渐减少，类别为3个时，Entropy>0.800，BLRT和LRT均P<0.05，较2类别模型性能提升明显。从简洁性与可解释性考量，3类别模型各潜在类别在跌倒风险感知3个维度上均值差异明显，故综合考虑选择3个类别模型为最优模型。
2.3　后循环缺血病人跌倒风险感知的潜在类别命名
基于3类别模型，以后循环缺血病人跌倒风险感知3个维度为横坐标，以各维度均分为纵坐标，作不同维度的分布特征图，潜在类别分布特征见图1。类别1共92例，各维度均分处于中等水平，因此命名为中度感知型；类别2共78例，各维度均分处于较低水平，提示病人的跌倒风险感知水平较低，因此命名为感知不足型；类别3共30例，各维度均分处于较高水平，提示病人的跌倒风险感知水平过高，因此命名为过度感知型，见表2。
2.4　后循环缺血病人跌倒风险感知潜在类别的单因素分析
单因素分析结果显示，3个类别病人的跌倒史、PSQI评分、BBS评分、MSES评分、ADL评分比较差异有统计学意义（P<0.05），见表3。
2.5　后循环缺血病人跌倒风险感知潜在类别的多因素Logistic回归分析
以后循环缺血病人跌倒风险感知的3个潜在类别作为因变量，以单因素分析有统计学意义的变量为自变量，以中度感知型为参照，进行多因素Logistic回归分析，结果显示，PSQI评分、BBS评分、MSES评分、ADL评分及跌倒史是后循环缺血病人跌倒风险感知的潜在类别的影响因素（P<0.05），见表4。对上述5项独立影响因素开展共线性诊断，方差膨胀因子依次为1.025，1.002，1.035，1.053，1.025，均小于10，表明其不存在严重多重共线性问题。
2.6　后循环缺血病人跌倒风险感知的潜在类别与运动自我效能的相关性
以MSES评分为因变量，在控制PSQI评分、BBS评分、ADL评分、跌倒史等混杂因素后，将后循环缺血病人跌倒风险感知的潜在类别设为自变量（以中度感知型为参照），进行多重线性回归分析，结果显示，调整后R²=0.043，能解释MSES评分总变异的4.3%，过度感知型与MSES评分负相关（P<0.05），见表5。
3　讨论
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3.1　后循环缺血病人跌倒风险感知现状
潜在剖面分析结果显示，后循环缺血病人跌倒风险感知可分为3个类别，其中感知不足型占39.0%，中度感知型占46.0%，过度感知型占15.0%，提示后循环缺血病人跌倒风险感知问题可能更突出，且部分后循环缺血病人存在感知不足。分析原因：感知不足型的形成是疾病特异性神经认知损害与睡眠障碍双重叠加的结果。后循环缺血直接影响脑干网状上行激活系统及小脑，导致空间定向、注意力分配等基础认知功能受损[15]。而过度感知型可能是因为部分后循环缺血病人因运动能力受限易发生跌倒，跌倒后的疼痛、骨折等负性体验会激活大脑杏仁核介导的恐惧反应，使其对跌倒产生条件反射式焦虑；为避免再次跌倒，病人会主动限制活动（如拒绝下床、减少行走），而长期活动减少又会导致下肢肌肉萎缩、平衡功能进一步下降，形成“越怕跌倒越不敢动，越不动平衡越差”的循环，最终加剧对跌倒风险的过度警觉[16]，本研究结果与张海燕等[17]研究结果相符。因此，保持适度的跌倒风险感知水平至关重要。基于此，3个类别病人偏差源于不同的病理心理机制，这决定了临床干预需遵循分型评估、分层施策的原则，实施精准化、差异化的管理策略。
3.2　后循环缺血病人跌倒风险感知的影响因素及干预
Logistic回归分析结果显示，PSQI评分是感知不足型的影响因素，而BBS评分、MSES评分、ADL评分及跌倒史是过度感知型的影响因素。2个类别病人的影响因素不同，2个类别病人感知偏差可能源于不同的病理心理机制。感知不足型病人主要表现为低估跌倒风险，其核心影响因素是睡眠质量差（PSQI评分高），可能机制在于睡眠不足导致日间疲劳、注意力涣散，使病人处于昏沉状态，难以有效识别环境中的危险因素，本质上是认知警觉性不足导致生理性地“无法”有效觉察环境风险[18-19]。过度感知型病人则表现为过度恐惧跌倒，其独立影响因素包括平衡功能差（BBS评分低）、日常生活能力下降（ADL评分低）以及既往跌倒史。这类病人因身体功能客观下降而自信心受损，陷入“害怕摔倒→不敢活动→功能进一步衰退”的恶性循环，其核心是恐惧回避心理与低自我效能[20-22]。
基于上述机制，本研究提出具有临床操作性的评估→分类→精准干预三步法。1）快速评估与分类，建议在病人入院初期，同步完成睡眠质量、平衡功能、日常生活能力及跌倒恐惧心理的快速筛查，并依据本研究的分类标准进行初步分型。2）实施靶向干预，对于感知不足型病人，干预重点在于唤醒与教育，首要任务是改善睡眠，可采用规律光照、睡眠限制等非药物方法提升日间警觉性；随后，在其精神最佳时段，利用虚拟现实模拟等沉浸式手段进行强效风险教育，重塑其安全认知。对于过度感知型病人，干预重点在于重建信心与功能。首先，通过简短沟通纠正“一动就摔”的灾难化思维；继而采用任务分解法，将日常活动（如行走、如厕）拆解为一系列从易到难的小步骤，确保病人在每个步骤都能获得成功体验，从而逐步提升其活动信心（自我效能），并配合奥塔戈运动等训练改善平衡功能。3）动态随访与调整，定期复查关键指标，根据病人变化灵活调整干预侧重点。
3.3　后循环缺血病人跌倒风险感知潜在类别与运动自我效能的相关性
运动自我效能是指个体在特定运动情境下，对自己能否持续并完成运动锻炼的评估，反映了个体的自信水平。运动自我效能较低的个体往往发现更难维持规律性体育活动[23]。本研究结果显示，过度感知型与MSES评分呈负相关，这一发现提示PCI病人康复过程中，心理障碍（过度恐惧）可能构成比躯体障碍更早出现且影响更深远的康复阻力。因此，MSES不应仅被视为一个结局指标，更应作为康复进程中的关键动态监测指标与干预效果敏感指标。当针对过度感知型病人实施干预时，MSES得分的提升可被视为恐惧循环开始被打破、康复行为得以启动的早期积极信号[24]，这为多学科康复团队提供了除躯体功能改善外的另一重要观察窗口。
4　小结
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后循环缺血病人跌倒风险感知存在群体异质性，可分为中度感知、感知不足、过度感知 3 类。睡眠质量差是感知不足型的独立危险因素；平衡功能差、日常生活能力低、运动自我效能低及有跌倒史是过度感知型的独立危险因素。同时，过度感知型可负向预测运动自我效能水平。因此，临床医护人员在评估PCI病人时，可结合其睡眠质量、平衡功能、日常活动能力及跌倒史，关注其跌倒风险感知的潜在类型。对于过度感知型病人，可关注其可能降低的运动自我效能，这为未来制定优化风险感知水平、提升运动信心的个性化干预方案提供参考。但本研究仍存在局限性：单中心便利抽样，样本可能存在选择偏倚，可能无法全面代表所有后循环缺血病人；研究虽控制了一些因素，但可能漏掉了一些重要的混杂因素；横断面调查分析发现过度感知型与较低的运动自我效能存在关联，但无法推断两者间的因果关系或时间顺序。未来的研究可以考虑与不同地区的医疗机构合作，开展大样本纵向队列研究，进行更广泛的后循环缺血病人跌倒风险感知分类研究，以提高研究结果的代表性、减少偏倚性；同时，纳入更多心理、社会及临床生物标志物，以深入探索各类别的形成机制。

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2026年第40卷第17期
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doi: 10.12102/j.issn.1009-6493.2026.17.022
  • 接收时间：2025-10-13
  • 首发时间：2026-10-30
  • 出版时间：2026-09-10
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  • 收稿日期：2025-10-13
  • 修回日期：2026-07-13
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    1皖西卫生职业学院附属医院，安徽 237000

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HUANG Wei, 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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