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Article(id=1325531641275507469, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325530655635039055, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025303, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746460800000, receivedDateStr=2025-05-06, revisedDate=1757260800000, revisedDateStr=2025-09-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1793960970466, onlineDateStr=2026-11-06, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793960970466, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793960970466, creator=13701087609, updateTime=1793960970466, updator=13701087609, issue=Issue{id=1325530655635039055, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='10', pageStart='1369', pageEnd='1520', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793960735472, creator='13701087609', updateTime=1793960735472, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1454, endPage=1458, ext={EN=ArticleExt(id=1325531645432062734, articleId=1325531641275507469, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Longitudinal study on the associations between sleep problems and non-suicidal self-injury behaviors in college students, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the longitudinal relationships between sleep problems and non-suicidal self-injury (NSSI) behaviors in college students, so as to provide a theoretical basis for the prevention and intervention of NSSI.

Methods

In October 2023 (T1), a total of 1 009 college students from two provincial colleges in Guangdong were selected by using a combination of convenience sampling and stratified cluster random sampling for a 1-year follow-up survey (October 2024, T2). The students were assessed by using the Insomnia Severity Index(ISI), Non-restorative Sleep Scale(NRSS), Epworth Sleepiness Scale (ESS), and Adolescent Non-suicidal Self-injury Assessment Questionnaire(ANSAQ). Data analysis was performed using McNemar's test and Logistic regression.

Results

The detection rate of NSSI behaviors was 18.83% at T1 and decreased to 7.73% at T2, with a statistically significant difference (χ2=66.24, P < 0.01). The trajectories of NSSI behaviors were divided into persistent group (4.06%), new-onset group (3.67%), remission group (14.77%), and resistance group (77.50%). Logistic regression analysis showed that sleep less than 7 hours per day at night, insomnia (mild, moderate-to-severe), non-restorative sleep (moderate, severe), daytime sleepiness, and weekly nightmare frequency (1-2, ≥3 times) were associated with NSSI behaviors at T1 (OR=5.02, 2.39, 5.51, 3.47, 9.70, 2.36, 2.63, 4.74, all P < 0.01). Insomnia (mild, moderate-to-severe), severe non-restorative sleep, daytime sleepiness, and weekly nightmares ≥3 times at T1 all increased the risk of NSSI behaviors at T2 (OR=1.76, 4.15, 4.05, 1.88, 5.63, all P < 0.05). Moderate-to-severe insomnia, severe non-restorative sleep, daytime sleepiness, and weekly nightmares ≥3 times at T1 increased the risk of belong to the new-onset NSSI group (OR=5.05, 3.37, 2.08, 5.86, all P < 0.05).

Conclusions

Insomnia, non-restorative sleep, daytime sleepiness, and frequent nightmares are important risk factors for NSSI behaviors among college students. Early assessment and intervention of sleep problems may be an important strategy for the prevention and treatment of NSSI.

, authors=Wenna LIAO1, Yongpeng SUN1, Fanxu KONG1, Xianghan LUO*, 2, authorsList=Wenna LIAO, Yongpeng SUN, Fanxu KONG, Xianghan LUO, authorCompany=null, correspAuthors=Xianghan LUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Chinese Journal of School Health. All rights reserved., 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=1325531650955961103, articleId=1325531641275507469, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=大学生睡眠问题与非自杀性自伤行为关系的追踪研究, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨大学生睡眠问题与非自杀性自伤(NSSI)行为的纵向关系，为预防和干预大学生NSSI提供参考依据。

方法

2023年10月(T1)，采用方便抽样与分层整群随机抽样相结合的方法选取广东2所省属高校1 009名大学生进行为期1年的追踪调查(2024年10月，T2)，采用失眠严重程度指数量表(ISI)、非恢复性睡眠量表(NRSS)、爱泼沃斯嗜睡量表(ESS)和青少年非自杀性自伤行为评定问卷(ANSAQ)等对大学生进行追踪测查，并采用McNemar's检验和Logistic回归进行数据分析。

结果

T1时大学生NSSI行为检出率为18.83%，T2时检出率下降为7.73%，差异有统计学意义(χ2=66.24，P < 0.01)。NSSI行为的变化轨迹分为持续组(4.06%)、新发组(3.67%)、消退组(14.77%)和免疫组(77.50%)。Logistic回归分析结果显示，T1夜间睡眠 < 7 h/d、失眠(轻度、中度及以上)、非恢复性睡眠(中度、重度)、嗜睡及每周噩梦次数(1~2，≥3次)为T1大学生NSSI行为的相关因素(OR值分别为5.02，2.39，5.51，3.47，9.70，2.36，2.63，4.74，P值均 < 0.01)；T1失眠(轻度、中度及以上)、重度非恢复性睡眠、嗜睡和每周≥3次噩梦均会增加T2时大学生NSSI行为的风险(OR值分别为1.76，4.15，4.05，1.88，5.63，P值均 < 0.05)；T1中度及以上失眠、重度非恢复性睡眠、嗜睡以及每周≥3次噩梦会增加个体进入NSSI行为新发组的风险(OR值分别为5.05，3.37，2.08，5.86，P值均 < 0.05)。

结论

失眠、非恢复性睡眠、嗜睡和频繁噩梦是大学生NSSI行为的重要风险因素，对睡眠问题进行早期评估和干预是防治NSSI的重要策略。

, authors=廖文娜1, 孙永鹏1, 孔繁旭1, 罗向晗*, 2, authorsList=廖文娜, 孙永鹏, 孔繁旭, 罗向晗, authorCompany=null, correspAuthors=罗向晗, authorNote=

廖文娜(1984-)，女，广东梅州人，硕士，副教授，主要研究方向为大学生心理危机筛查和干预

, correspAuthorsNote=
罗向晗，E-mail：
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Suicide Life Threat Behav, 2024, 54(2): 349-360., articleTitle=null, refAbstract=null), Reference(id=1325531734313558891, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=28, rfOrder=44, authorNames=null, journalName=null, refType=null, unstructuredReference=BENTLEY K H, NOCK M K, BARLOW D H. The four-function model of nonsuicidal self-injury: key directions for future research[J]. Clin Psychol Sci, 2014, 2(5): 638-656., articleTitle=null, refAbstract=null)], funds=[Fund(id=1325531682383881020, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, awardId=null, language=CN, fundingSource=广东省哲学社会科学规划项目(GD24CXL08)；广东省教育科学规划课题(高等教育专项)项目(2023GXJK655)；国家心理健康和精神卫生防治中心、中国教育发展基金会、教育部学生服务与素质发展中心“向阳花学生心理健康促进行动”支持项目(GX25B008), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1325531653774533393, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, xref=1, ext=[AuthorCompanyExt(id=1325531653799699218, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, companyId=1325531653774533393, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Public Teaching Department, Guangdong Open University, Guangzhou 510091, Guangdong Province, China), AuthorCompanyExt(id=1325531653812282131, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, companyId=1325531653774533393, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1广东开放大学公共课教学部，广州 510091)]), AuthorCompany(id=1325531654709863188, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, xref=2, ext=[AuthorCompanyExt(id=1325531654747611925, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, companyId=1325531654709863188, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2广州中医药大学第三附属医院党政办公室)])], figs=[ArticleFig(id=1325531676432163638, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=EN, label=Tab.1, caption=

Comparison of the detection rates of NSSI behaviors among college students with different demographic characteristics

, figureFileSmall=null, figureFileBig=null, tableContent=
人口学指标选项人数T1T2
检出人数χ2值P值检出人数χ2值P值
性别男43179(18.33)0.120.7346(10.67)9.13< 0.01
女578111(19.20)32(5.54)
年级大一40974(18.09)0.800.6729(7.09)1.040.59
大二32366(20.43)29(8.98)
大三27750(18.05)20(7.22)
专业文科38987(22.37)5.170.0226(6.68)0.970.32
理科620103(16.61)52(8.39)
家庭居住地农村705132(18.72)0.020.9058(8.23)0.810.37
城镇30458(19.08)20(6.58)
独生子女否868159(18.32)1.070.3067(7.72)< 0.010.97
是14131(21.99)11(7.80)
留守经历无716128(17.88)1.470.2348(6.70)3.640.06
有29362(21.16)30(10.24)
), ArticleFig(id=1325531676931285815, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=CN, label=表1, caption=

不同人口学特征大学生NSSI行为检出率比较

, figureFileSmall=null, figureFileBig=null, tableContent=
人口学指标选项人数T1T2
检出人数χ2值P值检出人数χ2值P值
性别男43179(18.33)0.120.7346(10.67)9.13< 0.01
女578111(19.20)32(5.54)
年级大一40974(18.09)0.800.6729(7.09)1.040.59
大二32366(20.43)29(8.98)
大三27750(18.05)20(7.22)
专业文科38987(22.37)5.170.0226(6.68)0.970.32
理科620103(16.61)52(8.39)
家庭居住地农村705132(18.72)0.020.9058(8.23)0.810.37
城镇30458(19.08)20(6.58)
独生子女否868159(18.32)1.070.3067(7.72)< 0.010.97
是14131(21.99)11(7.80)
留守经历无716128(17.88)1.470.2348(6.70)3.640.06
有29362(21.16)30(10.24)
), ArticleFig(id=1325531677371687736, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=EN, label=Tab.2, caption=

Logistic regression analysis of NSSI behaviors among college students[OR(95%CI), n=1 009]

, figureFileSmall=null, figureFileBig=null, tableContent=
T1睡眠状况T1时NSSIT2时NSSI
模型1模型2模型3模型4
每天夜间睡眠时长/h
  >81.001.001.001.00
  80.92(0.49~1.73)0.94(0.50~1.77)0.99(0.39~2.55)1.11(0.42~2.97)
  71.33(0.74~2.39)1.33(0.73~2.40)1.39(0.58~3.33)1.39(0.56~3.49)
   < 75.02(2.09~12.07)**4.93(2.03~11.99)**2.05(0.54~7.76)1.34(0.33~5.50)
失眠
  无1.001.001.001.00
  轻度2.39(1.71~3.34)**2.36(1.69~3.32)**1.76(1.08~2.88)*1.42(0.84~2.39)
  中度及以上5.51(2.92~10.39)**5.43(2.86~10.33)**4.15(1.85~9.32)**2.18(0.91~5.24)
非恢复性睡眠
  轻度1.001.001.001.00
  中度3.47(1.72~6.98)**3.56(1.76~7.22)**1.92(0.81~4.56)1.69(0.69~4.15)
  重度9.70(4.58~20.55)**9.76(4.57~20.82)**4.05(1.58~10.34)**2.60(0.96~7.05)
嗜睡
  否1.001.001.001.00
  是2.36(1.71~3.26)**2.38(1.71~3.32)**1.88(1.18~3.01)**1.53(0.93~2.54)
每周噩梦次数
  < 11.001.001.001.00
  1~22.63(1.37~5.06)**2.52(1.30~4.88)**1.77(0.68~4.67)1.50(0.54~4.18)
  ≥34.74(1.94~11.57)**5.08(2.06~12.53)**5.63(2.10~15.12)**3.57(1.17~10.93)*
), ArticleFig(id=1325531677761758009, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=CN, label=表2, caption=

大学生NSSI行为的Logistic回归分析[OR值(95%CI)，n=1 009]

, figureFileSmall=null, figureFileBig=null, tableContent=
T1睡眠状况T1时NSSIT2时NSSI
模型1模型2模型3模型4
每天夜间睡眠时长/h
  >81.001.001.001.00
  80.92(0.49~1.73)0.94(0.50~1.77)0.99(0.39~2.55)1.11(0.42~2.97)
  71.33(0.74~2.39)1.33(0.73~2.40)1.39(0.58~3.33)1.39(0.56~3.49)
   < 75.02(2.09~12.07)**4.93(2.03~11.99)**2.05(0.54~7.76)1.34(0.33~5.50)
失眠
  无1.001.001.001.00
  轻度2.39(1.71~3.34)**2.36(1.69~3.32)**1.76(1.08~2.88)*1.42(0.84~2.39)
  中度及以上5.51(2.92~10.39)**5.43(2.86~10.33)**4.15(1.85~9.32)**2.18(0.91~5.24)
非恢复性睡眠
  轻度1.001.001.001.00
  中度3.47(1.72~6.98)**3.56(1.76~7.22)**1.92(0.81~4.56)1.69(0.69~4.15)
  重度9.70(4.58~20.55)**9.76(4.57~20.82)**4.05(1.58~10.34)**2.60(0.96~7.05)
嗜睡
  否1.001.001.001.00
  是2.36(1.71~3.26)**2.38(1.71~3.32)**1.88(1.18~3.01)**1.53(0.93~2.54)
每周噩梦次数
  < 11.001.001.001.00
  1~22.63(1.37~5.06)**2.52(1.30~4.88)**1.77(0.68~4.67)1.50(0.54~4.18)
  ≥34.74(1.94~11.57)**5.08(2.06~12.53)**5.63(2.10~15.12)**3.57(1.17~10.93)*
), ArticleFig(id=1325531678164411194, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=EN, label=Tab.3, caption=

Predictive effects of sleep problems on changes in NSSI behaviors among college students[OR(95%CI), n=1 009]

, figureFileSmall=null, figureFileBig=null, tableContent=
T1睡眠状况新发组持续组
模型1模型2模型1模型2
每天夜间睡眠时长/h
  >81.001.001.001.00
  81.64(0.36~7.57)1.63(0.35~7.56)0.65(0.17~2.53)0.79(0.19~3.32)
  72.00(0.46~8.70)2.11(0.48~9.25)0.83(0.24~2.83)1.00(0.27~3.68)
   < 75.20(0.68~39.85)6.09(0.77~48.18)0.39(0.06~2.55)0.42(0.06~3.01)
失眠
  无1.001.001.001.00
  轻度1.45(0.71~2.93)1.56(0.76~3.18)1.17(0.56~2.46)1.15(0.52~2.54)
  中度及以上5.05(1.58~16.17)**5.10(1.54~16.89)**1.36(0.43~4.32)1.03(0.29~3.58)
非恢复性睡眠
  轻度1.001.001.001.00
  中度1.10(0.41~2.96)1.33(0.49~3.64)2.42(0.29~20.13)2.22(0.25~19.65)
  重度3.37(1.11~10.21)*4.03(1.31~12.43)*1.96(0.22~17.31)1.84(0.19~17.71)
嗜睡
  否1.001.001.001.00
  是2.08(1.06~4.05)*2.53(1.26~5.09)**0.95(0.47~1.90)0.98(0.47~2.03)
每周噩梦次数
   < 11.001.001.001.00
  1~22.93(0.84~10.24)3.24(0.91~11.61)0.57(0.12~2.65)0.72(0.15~3.48)
  ≥35.86(1.20~28.71)*6.86(1.33~35.37)*2.48(0.66~9.26)2.33(0.55~9.80)
), ArticleFig(id=1325531679905047355, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531641275507469, language=CN, label=表3, caption=

大学生睡眠问题对NSSI行为变化的预测作用[OR值(95%CI)，n=1 009]

, figureFileSmall=null, figureFileBig=null, tableContent=
T1睡眠状况新发组持续组
模型1模型2模型1模型2
每天夜间睡眠时长/h
  >81.001.001.001.00
  81.64(0.36~7.57)1.63(0.35~7.56)0.65(0.17~2.53)0.79(0.19~3.32)
  72.00(0.46~8.70)2.11(0.48~9.25)0.83(0.24~2.83)1.00(0.27~3.68)
   < 75.20(0.68~39.85)6.09(0.77~48.18)0.39(0.06~2.55)0.42(0.06~3.01)
失眠
  无1.001.001.001.00
  轻度1.45(0.71~2.93)1.56(0.76~3.18)1.17(0.56~2.46)1.15(0.52~2.54)
  中度及以上5.05(1.58~16.17)**5.10(1.54~16.89)**1.36(0.43~4.32)1.03(0.29~3.58)
非恢复性睡眠
  轻度1.001.001.001.00
  中度1.10(0.41~2.96)1.33(0.49~3.64)2.42(0.29~20.13)2.22(0.25~19.65)
  重度3.37(1.11~10.21)*4.03(1.31~12.43)*1.96(0.22~17.31)1.84(0.19~17.71)
嗜睡
  否1.001.001.001.00
  是2.08(1.06~4.05)*2.53(1.26~5.09)**0.95(0.47~1.90)0.98(0.47~2.03)
每周噩梦次数
   < 11.001.001.001.00
  1~22.93(0.84~10.24)3.24(0.91~11.61)0.57(0.12~2.65)0.72(0.15~3.48)
  ≥35.86(1.20~28.71)*6.86(1.33~35.37)*2.48(0.66~9.26)2.33(0.55~9.80)
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大学生睡眠问题与非自杀性自伤行为关系的追踪研究
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廖文娜 1 , 孙永鹏 1 , 孔繁旭 1 , 罗向晗 *, 2
中国学校卫生 | 心理卫生 2025,46(10): 1454-1458
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中国学校卫生 |心理卫生 2025 , 46 (10) : 1454 -1458
大学生睡眠问题与非自杀性自伤行为关系的追踪研究
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廖文娜1, 孙永鹏1, 孔繁旭1, 罗向晗*, 2
作者信息
  • 1广东开放大学公共课教学部，广州 510091
  • 2广州中医药大学第三附属医院党政办公室
通讯作者:
罗向晗，E-mail：
作者简介:

廖文娜(1984-)，女，广东梅州人，硕士，副教授，主要研究方向为大学生心理危机筛查和干预

Longitudinal study on the associations between sleep problems and non-suicidal self-injury behaviors in college students
Wenna LIAO1, Yongpeng SUN1, Fanxu KONG1, Xianghan LUO*, 2
Affiliations
  • 1Public Teaching Department, Guangdong Open University, Guangzhou 510091, Guangdong Province, China
出版时间: 2025-10-25 doi: 10.16835/j.cnki.1000-9817.2025303
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摘要
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目的

探讨大学生睡眠问题与非自杀性自伤(NSSI)行为的纵向关系，为预防和干预大学生NSSI提供参考依据。

方法

2023年10月(T1)，采用方便抽样与分层整群随机抽样相结合的方法选取广东2所省属高校1 009名大学生进行为期1年的追踪调查(2024年10月，T2)，采用失眠严重程度指数量表(ISI)、非恢复性睡眠量表(NRSS)、爱泼沃斯嗜睡量表(ESS)和青少年非自杀性自伤行为评定问卷(ANSAQ)等对大学生进行追踪测查，并采用McNemar's检验和Logistic回归进行数据分析。

结果

T1时大学生NSSI行为检出率为18.83%，T2时检出率下降为7.73%，差异有统计学意义(χ2=66.24，P < 0.01)。NSSI行为的变化轨迹分为持续组(4.06%)、新发组(3.67%)、消退组(14.77%)和免疫组(77.50%)。Logistic回归分析结果显示，T1夜间睡眠 < 7 h/d、失眠(轻度、中度及以上)、非恢复性睡眠(中度、重度)、嗜睡及每周噩梦次数(1~2，≥3次)为T1大学生NSSI行为的相关因素(OR值分别为5.02，2.39，5.51，3.47，9.70，2.36，2.63，4.74，P值均 < 0.01)；T1失眠(轻度、中度及以上)、重度非恢复性睡眠、嗜睡和每周≥3次噩梦均会增加T2时大学生NSSI行为的风险(OR值分别为1.76，4.15，4.05，1.88，5.63，P值均 < 0.05)；T1中度及以上失眠、重度非恢复性睡眠、嗜睡以及每周≥3次噩梦会增加个体进入NSSI行为新发组的风险(OR值分别为5.05，3.37，2.08，5.86，P值均 < 0.05)。

结论

失眠、非恢复性睡眠、嗜睡和频繁噩梦是大学生NSSI行为的重要风险因素，对睡眠问题进行早期评估和干预是防治NSSI的重要策略。

睡眠  /  自我伤害行为  /  精神卫生  /  回归分析  /  学生
Objective

To explore the longitudinal relationships between sleep problems and non-suicidal self-injury (NSSI) behaviors in college students, so as to provide a theoretical basis for the prevention and intervention of NSSI.

Methods

In October 2023 (T1), a total of 1 009 college students from two provincial colleges in Guangdong were selected by using a combination of convenience sampling and stratified cluster random sampling for a 1-year follow-up survey (October 2024, T2). The students were assessed by using the Insomnia Severity Index(ISI), Non-restorative Sleep Scale(NRSS), Epworth Sleepiness Scale (ESS), and Adolescent Non-suicidal Self-injury Assessment Questionnaire(ANSAQ). Data analysis was performed using McNemar's test and Logistic regression.

Results

The detection rate of NSSI behaviors was 18.83% at T1 and decreased to 7.73% at T2, with a statistically significant difference (χ2=66.24, P < 0.01). The trajectories of NSSI behaviors were divided into persistent group (4.06%), new-onset group (3.67%), remission group (14.77%), and resistance group (77.50%). Logistic regression analysis showed that sleep less than 7 hours per day at night, insomnia (mild, moderate-to-severe), non-restorative sleep (moderate, severe), daytime sleepiness, and weekly nightmare frequency (1-2, ≥3 times) were associated with NSSI behaviors at T1 (OR=5.02, 2.39, 5.51, 3.47, 9.70, 2.36, 2.63, 4.74, all P < 0.01). Insomnia (mild, moderate-to-severe), severe non-restorative sleep, daytime sleepiness, and weekly nightmares ≥3 times at T1 all increased the risk of NSSI behaviors at T2 (OR=1.76, 4.15, 4.05, 1.88, 5.63, all P < 0.05). Moderate-to-severe insomnia, severe non-restorative sleep, daytime sleepiness, and weekly nightmares ≥3 times at T1 increased the risk of belong to the new-onset NSSI group (OR=5.05, 3.37, 2.08, 5.86, all P < 0.05).

Conclusions

Insomnia, non-restorative sleep, daytime sleepiness, and frequent nightmares are important risk factors for NSSI behaviors among college students. Early assessment and intervention of sleep problems may be an important strategy for the prevention and treatment of NSSI.

Sleep  /  Self-injurious behavior  /  Mental health  /  Regression analysis  /  Students
廖文娜, 孙永鹏, 孔繁旭, 罗向晗. 大学生睡眠问题与非自杀性自伤行为关系的追踪研究. 中国学校卫生, 2025 , 46 (10) : 1454 -1458 . DOI: 10.16835/j.cnki.1000-9817.2025303
Wenna LIAO, Yongpeng SUN, Fanxu KONG, Xianghan LUO. Longitudinal study on the associations between sleep problems and non-suicidal self-injury behaviors in college students[J]. Chinese Journal of School Health, 2025 , 46 (10) : 1454 -1458 . DOI: 10.16835/j.cnki.1000-9817.2025303
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非自杀性自伤(non-suicidal self-injury, NSSI)行为是指个体在没有明确自杀意图的情形下，故意实施的自我伤害行为，包括切割、烧灼、撞击等形式[1]。近年的流行病学数据显示，中国大学生群体中NSSI行为的检出率为7.80%~23.23%，呈上升趋势[2-4]。NSSI行为是心理危机的重要表征，与抑郁、焦虑、自杀意念和自杀行为等密切相关[5-6]，已成为高校心理危机干预领域的焦点问题。
现有研究已识别出童年创伤经历、亲子疏离、情绪调节障碍、冲动性人格特质等社会心理因素对NSSI行为的影响[7-10]。睡眠问题可能削弱个体情绪调节能力，增加焦虑、抑郁等负性情绪[11-12]，进而影响个体应对方式及行为决策，使其更易采取NSSI等行为[13]。有研究发现，睡眠不足、失眠、噩梦等与NSSI行为存在关联[14]，为NSSI防治提供了一个有效的干预靶点。本研究采用追踪设计方式考察大学生常见睡眠问题与NSSI行为的关系，为NSSI防治提供科学依据。
1　对象与方法
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1.1　对象
2023年10月(T1)，采用方便抽样法选取广东省2所省属高校进行追踪调查，为避免大四学生毕业离校造成的样本流失，调查对象仅限大一至大三在校生。每所高校均按年级分层，使用随机数字表法在各年级抽取5个班级，以班级为单位集体施测。T1的调查回收有效问卷1 100份。12个月后(2024年10月，T2)，对T1有效被试进行第二阶段施测，回收有效问卷1 009份，有91名被试流失，流失率为8.3%。有效样本和流失样本在T1失眠、非恢复性睡眠、嗜睡、NSSI行为上得分差异均无统计学意义(Z值分别为-0.20，-1.40，-1.83，-0.74，P值均>0.05)。有效样本中，男生431名(42.72%)，女生578名(57.28%)；文科生389名(38.55%)，理科生620名(61.45%)；独生子女141名(13.97%)，非独生子女868名(86.03%)；来自农村705名(69.87%)，来自城镇304名(30.13%)；有留守经历293名(29.04%)，无留守经历716名(70.96%)；大一学生409名(40.54%)，大二323名(32.01%)，大三277名(27.45%)；年龄18~24岁，平均年龄(19.40±1.11)岁。研究已获得广州中医药大学第三附属医院医学伦理委员会批准(批准文号：PJ-KY-20230725-001)，调查对象均签订知情同意书。
1.2　方法
1.2.1　一般情况
采用自制问卷收集大学生性别、年龄、专业、年级、家庭居住地、是否为独生子女、留守经历、夜间实际睡眠时长、噩梦等信息。噩梦信息用“在过去一个月里，您平均每周会做几次噩梦？”来获取，选项包括“每周 < 1次、1~2次、≥3次”。
1.2.2　失眠情况
采用Yu等[15]翻译的失眠严重程度指数量表(Insomnia Severity Index, ISI)，共7个条目，采用0~4评分，总分0~28分。按分数划分为无(0~7分)、轻度(8~14分)、中度(15~21分)和重度(22~28分)4级失眠水平。本研究中该量表的Cronbach α系数为0.84。
1.2.3　非恢复性睡眠
采用洪莹莹等[16]修订的非恢复性睡眠量表(Non-restorative Sleep Scale, NRSS)，共12个条目，包含情感/情绪症状、白天功能状态、身体症状和主观睡眠后恢复感4个维度，采用1~5计分，总分12~60分，得分越低表示非恢复性睡眠越严重。本研究中该量表的Cronbach α系数为0.82。
1.2.4　嗜睡情况
采用彭莉莉等[17]修订的爱泼沃斯嗜睡量表(Epworth Sleepiness Scale, ESS)，共8个条目，采用0~3评分，总分0~24分，得分越高表明日间嗜睡越严重。总分>10分提示白天存在嗜睡。本研究中该量表的Cronbach α系数为0.82。
1.2.5　NSSI行为
采用万宇辉等[18]编制的青少年非自杀性自伤行为评定问卷(Adolescent Non-suicidal Self-injury Assessment Questionnaire, ANSAQ)，量表包含行为问卷和功能问卷，本研究使用行为问卷的12个条目，采用0~4评分(分别代表没有、偶尔、有时、经常、总是)，从无明显组织损伤(如用头撞硬物、拽头发等)和有明显组织损伤(如故意摩擦皮肤使其出血或淤血、咬伤自己等)2个维度评估自伤行为，总分0~48分，总分越高代表NSSI越频繁。其中，任何一种自伤行为发生次数≥1，界定为有NSSI行为发生[19]。本研究中该量表在T1和T2时期的Cronbach α系数分别为0.94，0.98。
1.3　质量控制
由课题组成员担任调查员，负责与调查高校联系，提前通知学生到指定课室参与调查；调查员先向学生说明调查目的、问卷填写要求及数据保密措施等。学生签署知情同意书后，用手机填答线上调查问卷；调查员全程在场答疑。问卷回收后，对平均每个条目作答时间少于2 s，前后作答出现逻辑矛盾及填答不完整的问卷予以剔除。
1.4　统计学分析
采用SPSS 29.0软件进行数据分析。用K-S检验对失眠、非恢复性睡眠、嗜睡和NSSI行为的评分进行正态检验，结果均为偏态分布，故以M(P25，P75)描述，组间比较采用Mann-Whitney U检验；计数资料以频数和百分率表示，组间比较采用χ2检验；用McNemar's检验分析NSSI行为检出率在T1和T2的差异。用Logistic回归分析睡眠指标与NSSI行为的关联。双侧检验水准α=0.05。
2　结果
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2.1　大学生睡眠问题和NSSI行为的检出情况
T1时大学生失眠、非恢复性睡眠、嗜睡和NSSI行为的得分分别为[7.00(4.00，9.00)，43.00(39.00，48.00)，8.00(5.00，11.00)，0.00(0.00，0.00)]分。有3.27%(33名)的学生夜间睡眠时长 < 7 h/d，失眠检出率为40.63%(410名)，嗜睡检出率为31.32%(316名)，有6.14%(62名)的学生至少每周有1次噩梦经历。
T1时大学生NSSI行为检出率为18.83%(190/1 009)，文科生高于理科生(P < 0.05)；T2时NSSI行为检出率下降为7.73%(78/1 009)，且男生高于女生(P < 0.01)；T1与T2时NSSI行为检出率差异有统计学意义(χ2=66.24，P < 0.01)，见表1。4.06%(41名)的学生在T1和T2时均有NSSI行为，定义为持续组；3.67%(37名)的学生仅在T2时才出现NSSI行为，定义为新发组；14.77%(149名)的学生在T1时有NSSI行为、T2时无NSSI行为，定义为消退组；77.50%(782名)的学生在T1和T2时均无NSSI行为，定义为免疫组。
2.2　大学生睡眠问题与NSSI行为的横向和纵向关联
如表2所示，模型1发现，T1时夜间睡眠时长 < 7 h、失眠、非恢复性睡眠、嗜睡和噩梦是T1时NSSI行为的相关因素(P值均 < 0.01)。调整性别、年龄、专业、家庭居住地、独生子女状况和留守经历等人口学指标后(模型2)，结果有统计学意义的因素未发生明显改变。
以T2时NSSI行为(无=0，有=1)为因变量进行Logistic回归分析(模型3)。结果显示，T1时轻度、中度及以上失眠，重度非恢复性睡眠，嗜睡和每周≥3次噩梦会增加T2时NSSI行为的发生风险(P值均 < 0.05)。调整T1时NSSI行为及人口学指标后(模型4)，仅T1时每周≥3次噩梦对T2时NSSI行为的预测作用有统计学意义(P < 0.05)。
2.3　大学生睡眠问题对NSSI行为变化的预测作用
以免疫组为参照，分析睡眠问题对大学生进入新发组的预测作用。结果如表3所示，模型1中，T1时中度及以上失眠、重度非恢复性睡眠、嗜睡以及每周≥3次噩梦会增加个体进入新发组的风险(P值均 < 0.05)。调整人口学变量后(模型2)，结果有统计学意义的因素未发生明显改变。
以消退组为参照，分析睡眠问题对大学生进入持续组的预测作用。模型1结果显示，所考察的睡眠变量对个体进入持续组的影响均无统计学意义(P值均>0.05)。调整人口学变量后(模型2)，结果无明显变化。见表3。
3　讨论
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本研究的基线调查发现大学生NSSI行为检出率为18.8%，与潘珍等[20]结果接近。大学生NSSI行为的检出率在1年后出现明显下降，与前人研究结果一致[21]。NSSI行为常是个体应对压力和调节情绪的不适应策略。随着时间的推移，学生逐渐适应了校园生活，建立新的社交网络，学习和生活节奏趋于稳定。这些因素可能对NSSI行为产生缓冲效应，从而减少NSSI行为的发生。
本研究中，77.50%的学生未报告有NSSI行为，属于“免疫组”，但持续组(4.06%)和新发组(3.67%)的情况需引起重视。持续组可能代表那些存在稳定风险因素(如慢性睡眠障碍、共病精神症状)的人群。而新发组则表明在追踪期间一些学生可能遭遇了应激事件(如学业挫折、人际冲突)或者睡眠问题进一步恶化，进而诱发NSSI行为。这一结果凸显了对NSSI行为进行动态监测的必要性。此外，消退组占比14.77%，说明大部分NSSI行为具有短暂性，可能由短期压力和睡眠问题所驱动，并且能够自发缓解。不过，对于这些个体的远期心理健康状况，仍需持续追踪。
与前人研究结果一致[22]，本研究发现睡眠问题是NSSI行为的风险因子。横断面分析结果表明，夜间睡眠时长 < 7 h/d、失眠、非恢复性睡眠、嗜睡及频繁噩梦均会增加NSSI行为的风险。纵向分析结果表明，失眠、重度非恢复性睡眠、嗜睡及高频噩梦仍可以预测远期NSSI行为。神经生理学方面的研究也证实，睡眠问题可能会使内侧前额叶皮质对杏仁核及其邻近中脑边缘结构自上而下的控制减弱，导致个体的情绪调节功能和冲动控制能力下降，进而增加NSSI行为的发生风险[23]。但值得注意的是，在控制人口学变量及基线NSSI行为后，仅高频噩梦对远期NSSI行为具有独立预测效应，与Liu等[24]针对中学生群体的追踪研究结果高度一致，表明噩梦对NSSI行为具有典型的独立预测效应，且该效应具有跨年龄阶段的稳定性。有研究提出噩梦是一种在做梦后破坏功能性情绪调节的过程，而功能性情绪调节的中断会导致个体在睡眠后出现消极情绪状态[25]。研究也证实，噩梦会通过情绪失调和消极情绪作用于NSSI行为[26]。提示噩梦可能是NSSI行为的特异性预警信号，高校可将其纳入心理健康动态监测体系，并重点关注有高频噩梦的学生，以阻断NSSI行为的发生或恶化。
本研究发现，中度及以上失眠、重度非恢复性睡眠、嗜睡及高频噩梦会增加个体进入NSSI行为新发组的风险。说明睡眠问题可能对NSSI行为的发生有“触发效应”[27]。然而，与预期不同，睡眠问题对个体进入NSSI行为持续组的影响均无统计学意义。说明睡眠问题可能是NSSI行为发生的“扳机点”，而非维持该行为的核心因素。NSSI的维持可能与其自我强化的属性有更为密切的关系[28]。NSSI主要通过正向和负向的自我强化过程得以维持，包括个体内部的正向强化(产生积极的情感或认知状态)和负向强化(减少负面的情感或认知状态)，以及人际间的正向强化(引发关注和寻求帮助)和负向强化(从令人不悦的社交情境中脱身或减少人际间的要求)[28]。因此，对于没有NSSI行为史但存在中度及以上失眠、重度非恢复性睡眠、嗜睡或高频噩梦的学生，应优先通过睡眠卫生教育以及针对噩梦的特异性干预等来阻断NSSI行为的发生。而对于已经有NSSI行为史的个体，则需要超越单纯的睡眠管理，整合认知重塑、情绪调节训练以及社会支持系统构建等方法，以阻止NSSI行为的持续发生。
本研究存在一些局限：第一，样本来源单一，后续研究可采用多中心点取样以提高样本的代表性；第二，睡眠和NSSI的数据均为自我报告，可能受回忆偏差或社会赞许的影响，未来需结合多种评估方法以提高研究结果的可靠性；第三，虽然在统计分析中纳入了一些关键的人口学变量和基线NSSI水平等作为控制变量，但其他未测量或未知混杂因素的影响仍需考虑，后续研究应进一步控制混杂变量的影响。
基金
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  • 广东省哲学社会科学规划项目(GD24CXL08)；广东省教育科学规划课题(高等教育专项)项目(2023GXJK655)；国家心理健康和精神卫生防治中心、中国教育发展基金会、教育部学生服务与素质发展中心“向阳花学生心理健康促进行动”支持项目(GX25B008)
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2025年第46卷第10期
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doi: 10.16835/j.cnki.1000-9817.2025303
  • 接收时间：2025-05-06
  • 首发时间：2026-11-06
  • 出版时间：2025-10-25
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  • 收稿日期：2025-05-06
  • 修回日期：2025-09-08
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广东省哲学社会科学规划项目(GD24CXL08)；广东省教育科学规划课题(高等教育专项)项目(2023GXJK655)；国家心理健康和精神卫生防治中心、中国教育发展基金会、教育部学生服务与素质发展中心“向阳花学生心理健康促进行动”支持项目(GX25B008)
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    1广东开放大学公共课教学部，广州 510091
    2广州中医药大学第三附属医院党政办公室

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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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