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Article(id=1325531135933173921, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325530655635039055, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025305, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753891200000, receivedDateStr=2025-07-31, revisedDate=1757779200000, revisedDateStr=2025-09-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1793960849983, onlineDateStr=2026-11-06, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793960849983, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793960849983, creator=13701087609, updateTime=1793960849983, 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=1427, endPage=1431, ext={EN=ArticleExt(id=1325531140911812770, articleId=1325531135933173921, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Cross-lagged analysis of academic procrastination, negative emotions and self-control, and smartphone addiction among college students, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the causal relationship between academic procrastination, negative emotions, self-control, and smartphone addiction among college students, so as to provide theoretical reference for promoting their mental health and academic achievement.

Methods

A multi-stage cluster random sampling method was used to select 452 first-year students from a university in Guangzhou as the research subjects. Procrastination Assessment Scale- Student (PASS), The Depression Anxiety Stress Scale (DASS-21), Self Control Scale, and Short Version of the Smartphone Addiction Scale (SAS-SV) were used as measurement tools. Three questionnaire surveys were completed in October 2024 (T1), February 2025 (T2), and May 2025 (T3). Coss-lagged models were utilized to analyze the causal relationship between academic procrastination, negative emotions, self-control, and smartphone addiction.

Results

The SAS-SV scale for college students showed an average score of (40.52±1.96), with 43.1% of freshmen exhibiting smartphone addiction.Positive correlations were observed between academic procrastination, negative emotions, and smartphone addiction across different time points, while self-control exhibited negative correlations with these three variables (r=0.30-0.62, -0.72 to -0.34, all P < 0.05). Cross-lagged model results indicated that academic procrastination and negative emotions at T1 and T2 positively predicted smartphone addiction at T2 and T3 (T1→T2, β=0.22, 0.35; T2→T3, β=0.21, 0.24; all P < 0.05). Self-control negatively predicted smartphone addiction (T1→T2, β=-0.32; T2→T3, β=-0.26; both P < 0.05). In reverse-causality regression models, smartphone addiction at T1 and T2 positively predicted academic procrastination and negative emotions at T2 and T3 (T1→T2, β=0.09, 0.24; T2→T3, β=0.10, 0.35; all P < 0.05), but no statistically significant predictions were found for self-control (T1→T2, β=-0.04; T2→T3, β=-0.03; both P>0.05).

Conclusion

Academic procrastination and negative emotions exhibit bidirectional causality with smartphone addiction among college students, while self-control unidirectionally predicts smartphone addiction.

, authors=Wenting WANG1, Jinqiu SONG*, 2, Zhonglou ZHANG3, authorsList=Wenting WANG, Jinqiu SONG, Zhonglou ZHANG, authorCompany=null, correspAuthors=Jinqiu SONG, 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=1325531154685907112, articleId=1325531135933173921, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=大学生学业拖延负性情绪和自我控制与智能手机成瘾的交叉滞后分析, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探索大学生学业拖延、负性情绪、自我控制与智能手机成瘾之间的因果关系, 为促进大学生心理健康和学业成就提供参考。

方法

选用多阶段整群随机抽样方法, 抽取广州市某高校452名大一年级学生为研究对象, 使用学业拖延量表(PASS)、抑郁-焦虑-压力量表(DASS-21)、自我控制量表和智能手机成瘾量表(SAS-SV)作为测量工具, 于2024年10月(T1)、2025年2月(T2)和2025年5月(T3)完成3次问卷调查。运用交叉滞后模型分析大学生学业拖延、负性情绪、自我控制与智能手机成瘾的因果关系。

结果

大学生SAS-SV量表平均分为(40.52±1.96), 大一新生智能手机成瘾率为43.1%。大学生学业拖延、负性情绪与智能手机成瘾在不同时间段两两之间呈正相关(r=0.30~0.62), 自我控制与其他3个变量间呈负相关(r=-0.72~-0.34)(P值均 < 0.05)。交叉滞后模型结果显示, T1和T2学业拖延、负性情绪均可正向预测T2和T3智能手机成瘾(T1→T2, β值分别为0.22, 0.35;T2→T3, β值分别为0.21, 0.24), 自我控制负向预测智能手机成瘾(T1→T2, β=-0.32;T2→T3, β=-0.26)(P值均 < 0.05);反向因果回归模型中, T1和T2智能手机成瘾均可正向预测T2和T3学业拖延及负性情绪(T1→T2, β值分别为0.09, 0.24;T2→T3, β值分别为0.10, 0.35, P值均 < 0.05), 但对自我控制预测作用无统计学意义(T1→T2, β=-0.04;T2→T3, β=-0.03, P值均>0.05)。

结论

大学生学业拖延、负性情绪与智能手机成瘾互为因果关系, 自我控制单向预测智能手机成瘾。

, authors=王文婷1, 宋金秋*, 2, 张忠楼3, authorsList=王文婷, 宋金秋, 张忠楼, authorCompany=null, correspAuthors=宋金秋, authorNote=

王文婷(1986-), 女, 江苏沛县人, 博士, 讲师, 主要研究方向为大学生心理健康、思想政治教育

, correspAuthorsNote=
宋金秋，E-mail:
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注: *P < 0.05，**P < 0.01。

, figureFileSmall=S0kxliAmFnw5+Y2BXZP0tA==, figureFileBig=FKThDicLDVfJbpCtefQX2A==, tableContent=null), ArticleFig(id=1325531195458736332, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=EN, label=Fig.2, caption=Cross-lagged analysis of academic procrastination, self-control, and smartphone addiction among college students, figureFileSmall=c+ITT4kHt6NVm/DEdCncdw==, figureFileBig=xrIoA3OA/9an5yFhY95WBw==, tableContent=null), ArticleFig(id=1325531196649918669, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=CN, label=图2, caption=大学生学业拖延、自我控制和智能手机成瘾的交叉滞后分析

注：*P < 0.05，**P < 0.01。

, figureFileSmall=c+ITT4kHt6NVm/DEdCncdw==, figureFileBig=xrIoA3OA/9an5yFhY95WBw==, tableContent=null), ArticleFig(id=1325531197190983886, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=EN, label=Tab.1, caption=

Scores of smartphone addiction among college students with different demographic characteristics(x±s)

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人口学指标选项人数得分t值
性别男16329.63±0.720.14
女28931.24±1.32
专业理科10430.08±1.52-1.09
文科34831.97±0.86
居住地城镇28429.81±0.920.05
农村16829.75±0.68
是否独生子女是2933.48±1.922.08*
否31329.43±0.57
是否有留守经历是8831.09±1.171.19
否36429.45±0.61
是否为班干部是9028.71±1.36-0.98
否36230.04±0.59
), ArticleFig(id=1325531199682400463, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=CN, label=表1, caption=

不同人口学特征大学生智能手机成瘾得分(x±s)

, figureFileSmall=null, figureFileBig=null, tableContent=
人口学指标选项人数得分t值
性别男16329.63±0.720.14
女28931.24±1.32
专业理科10430.08±1.52-1.09
文科34831.97±0.86
居住地城镇28429.81±0.920.05
农村16829.75±0.68
是否独生子女是2933.48±1.922.08*
否31329.43±0.57
是否有留守经历是8831.09±1.171.19
否36429.45±0.61
是否为班干部是9028.71±1.36-0.98
否36230.04±0.59
), ArticleFig(id=1325531200491901136, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=EN, label=Tab.2, caption=

Correlation analysis of main research variables at different time periods among college students(r, n=452)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量T1学业拖延T1负性情绪T1自我控制T1智能手机成瘾T2学业拖延T2负性情绪T2自我控制T2智能手机成瘾T3学业拖延T3负性情绪T3自我控制
T1负性情绪0.35**
T1自我控制-0.44**-0.53**
T1智能手机成瘾0.60**0.62**-0.72**
T2学业拖延0.38**0.53**-0.38**0.48**
T2负性情绪0.58**0.62**-0.40**0.54**0.56**
T2自我控制-0.53**-0.40**0.46**-0.34**-0.38**-0.46**
T2智能手机成瘾0.58**0.42**-0.42**0.46**0.39**0.41**-0.43**
T3学业拖延0.35**0.37**-0.41**0.40**0.44**0.41**-0.37**0.37**
T3负性情绪0.40**0.47**-0.48**0.35**0.46**0.56**-0.39**0.38**0.41**
T3自我控制-0.53**-0.53**0.48**-0.46**-0.63**-0.36**0.30**-0.37**-0.41**-0.56**
T3智能手机成瘾0.57**0.52**-0.42**0.48**0.42**0.53**-0.60**0.40*0.53**0.49**-0.40*
), ArticleFig(id=1325531201355927761, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531135933173921, language=CN, label=表2, caption=

不同时间阶段大学生学业拖延、负性情绪、自我控制与智能手机成瘾间的相关性分析(r值，n=452)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量T1学业拖延T1负性情绪T1自我控制T1智能手机成瘾T2学业拖延T2负性情绪T2自我控制T2智能手机成瘾T3学业拖延T3负性情绪T3自我控制
T1负性情绪0.35**
T1自我控制-0.44**-0.53**
T1智能手机成瘾0.60**0.62**-0.72**
T2学业拖延0.38**0.53**-0.38**0.48**
T2负性情绪0.58**0.62**-0.40**0.54**0.56**
T2自我控制-0.53**-0.40**0.46**-0.34**-0.38**-0.46**
T2智能手机成瘾0.58**0.42**-0.42**0.46**0.39**0.41**-0.43**
T3学业拖延0.35**0.37**-0.41**0.40**0.44**0.41**-0.37**0.37**
T3负性情绪0.40**0.47**-0.48**0.35**0.46**0.56**-0.39**0.38**0.41**
T3自我控制-0.53**-0.53**0.48**-0.46**-0.63**-0.36**0.30**-0.37**-0.41**-0.56**
T3智能手机成瘾0.57**0.52**-0.42**0.48**0.42**0.53**-0.60**0.40*0.53**0.49**-0.40*
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大学生学业拖延负性情绪和自我控制与智能手机成瘾的交叉滞后分析
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王文婷 1 , 宋金秋 *, 2 , 张忠楼 3
中国学校卫生 | 心理卫生 2025,46(10): 1427-1431
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中国学校卫生 |心理卫生 2025 , 46 (10) : 1427 -1431
大学生学业拖延负性情绪和自我控制与智能手机成瘾的交叉滞后分析
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王文婷1, 宋金秋*, 2 , 张忠楼3
作者信息
  • 1广州城市职业学院数字商贸学院, 广东 510000
  • 2哈尔滨师范大学体育科学学院
  • 3哈尔滨体育学院民族传统体育学院
通讯作者:
宋金秋，E-mail:
作者简介:

王文婷(1986-), 女, 江苏沛县人, 博士, 讲师, 主要研究方向为大学生心理健康、思想政治教育

Cross-lagged analysis of academic procrastination, negative emotions and self-control, and smartphone addiction among college students
Wenting WANG1, Jinqiu SONG*, 2 , Zhonglou ZHANG3
Affiliations
  • 1Digital Business College of Guangzhou City Polytechnic, Guangzhou 510000, Guangdong Province, China
出版时间: 2025-10-25 doi: 10.16835/j.cnki.1000-9817.2025305
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摘要
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目的

探索大学生学业拖延、负性情绪、自我控制与智能手机成瘾之间的因果关系, 为促进大学生心理健康和学业成就提供参考。

方法

选用多阶段整群随机抽样方法, 抽取广州市某高校452名大一年级学生为研究对象, 使用学业拖延量表(PASS)、抑郁-焦虑-压力量表(DASS-21)、自我控制量表和智能手机成瘾量表(SAS-SV)作为测量工具, 于2024年10月(T1)、2025年2月(T2)和2025年5月(T3)完成3次问卷调查。运用交叉滞后模型分析大学生学业拖延、负性情绪、自我控制与智能手机成瘾的因果关系。

结果

大学生SAS-SV量表平均分为(40.52±1.96), 大一新生智能手机成瘾率为43.1%。大学生学业拖延、负性情绪与智能手机成瘾在不同时间段两两之间呈正相关(r=0.30~0.62), 自我控制与其他3个变量间呈负相关(r=-0.72~-0.34)(P值均 < 0.05)。交叉滞后模型结果显示, T1和T2学业拖延、负性情绪均可正向预测T2和T3智能手机成瘾(T1→T2, β值分别为0.22, 0.35;T2→T3, β值分别为0.21, 0.24), 自我控制负向预测智能手机成瘾(T1→T2, β=-0.32;T2→T3, β=-0.26)(P值均 < 0.05);反向因果回归模型中, T1和T2智能手机成瘾均可正向预测T2和T3学业拖延及负性情绪(T1→T2, β值分别为0.09, 0.24;T2→T3, β值分别为0.10, 0.35, P值均 < 0.05), 但对自我控制预测作用无统计学意义(T1→T2, β=-0.04;T2→T3, β=-0.03, P值均>0.05)。

结论

大学生学业拖延、负性情绪与智能手机成瘾互为因果关系, 自我控制单向预测智能手机成瘾。

情绪  /  自我  /  行为, 成瘾  /  精神卫生  /  学生
Objective

To explore the causal relationship between academic procrastination, negative emotions, self-control, and smartphone addiction among college students, so as to provide theoretical reference for promoting their mental health and academic achievement.

Methods

A multi-stage cluster random sampling method was used to select 452 first-year students from a university in Guangzhou as the research subjects. Procrastination Assessment Scale- Student (PASS), The Depression Anxiety Stress Scale (DASS-21), Self Control Scale, and Short Version of the Smartphone Addiction Scale (SAS-SV) were used as measurement tools. Three questionnaire surveys were completed in October 2024 (T1), February 2025 (T2), and May 2025 (T3). Coss-lagged models were utilized to analyze the causal relationship between academic procrastination, negative emotions, self-control, and smartphone addiction.

Results

The SAS-SV scale for college students showed an average score of (40.52±1.96), with 43.1% of freshmen exhibiting smartphone addiction.Positive correlations were observed between academic procrastination, negative emotions, and smartphone addiction across different time points, while self-control exhibited negative correlations with these three variables (r=0.30-0.62, -0.72 to -0.34, all P < 0.05). Cross-lagged model results indicated that academic procrastination and negative emotions at T1 and T2 positively predicted smartphone addiction at T2 and T3 (T1→T2, β=0.22, 0.35; T2→T3, β=0.21, 0.24; all P < 0.05). Self-control negatively predicted smartphone addiction (T1→T2, β=-0.32; T2→T3, β=-0.26; both P < 0.05). In reverse-causality regression models, smartphone addiction at T1 and T2 positively predicted academic procrastination and negative emotions at T2 and T3 (T1→T2, β=0.09, 0.24; T2→T3, β=0.10, 0.35; all P < 0.05), but no statistically significant predictions were found for self-control (T1→T2, β=-0.04; T2→T3, β=-0.03; both P>0.05).

Conclusion

Academic procrastination and negative emotions exhibit bidirectional causality with smartphone addiction among college students, while self-control unidirectionally predicts smartphone addiction.

Emotions  /  Ego  /  Behavior, addictive  /  Mental health  /  Students
王文婷, 宋金秋, 张忠楼. 大学生学业拖延负性情绪和自我控制与智能手机成瘾的交叉滞后分析. 中国学校卫生, 2025 , 46 (10) : 1427 -1431 . DOI: 10.16835/j.cnki.1000-9817.2025305
Wenting WANG, Jinqiu SONG, Zhonglou ZHANG. Cross-lagged analysis of academic procrastination, negative emotions and self-control, and smartphone addiction among college students[J]. Chinese Journal of School Health, 2025 , 46 (10) : 1427 -1431 . DOI: 10.16835/j.cnki.1000-9817.2025305
正文
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大学生手机成瘾(依赖)问题凸显，一项Meta分析结果显示，我国大中专学生手机依赖率为23%[1]；广州某医学高校大学生的手机依赖比例为60.2%[2]，吉林省5所高校大学生手机依赖倾向率高达66.1%[3]。智能手机成瘾会造成个体心理、生理以及社会功能等明显受损，对学生的学业发展、生活方式、行为习惯养成以及心理健康产生不利影响[4]。
学业拖延作为大学生常见问题，被认为是个体因逃避学习任务引发的负面情绪的策略性行为[5]。同时，自我控制的有限资源模型指出，智能手机的高频刺激会持续消耗认知资源，削弱个体对学业任务的专注力[6]。拖延倾向、执行功能、抑制控制和决策能力的降低，压力事件刺激的不良适应等均被认为是手机成瘾发生的重要促进因素[4]。学业拖延能够预测网络成瘾[7]，且其不仅直接影响手机依赖，还可通过消极认知情绪调节与焦虑情绪的链式中介作用间接影响手机依赖[8]；手机成瘾也会引发学业拖延[9]，还可能通过影响时间管理和引发负性情绪导致拖延行为[10-12]。本研究拟采用三时点交叉滞后模型分析大学生学业拖延、负性情绪、自我控制与智能手机成瘾间的纵向因果关系，为针对性提供有效的心理干预提供证据支撑。
1　对象与方法
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1.1　对象
采用多阶段整群随机抽样方法，在广州市某高校8个学院中采用抽签法抽取4个学院，在抽中的学院中采用抽签法选取2个专业，整群抽取以上专业大一年级的班级作为研究对象。本研究进行3次分阶段的前瞻性研究，时间间隔参考了同类大学生研究中常见的3个月追踪周期[13]。于2024年10月进行基线调查(T1)，此时新生已完成军训，专业学习正式开展，其心理与行为模式尚未受到大学生活的深度影响，有利于获取反映新生初始状态的可靠数据；第2次调查(T2)于2025年2月实施，此时学生已完整经历第一学期并度过寒假，有助于捕捉大学适应早期阶段的心理行为变化；第3次调查(T3)于2025年5月进行，处于第二学期末但避开期末考试周，可用于评估大学生活相对稳定的状态。
T1共发放调查问卷1 061份，获得有效问卷1 037份，有效回收率97.7%，其中男生352名，女生685名。T2剔除T1研究对象中未计划接受下一轮随访的样本289人，对剩余748名初始样本进行追踪，回收有效问卷719份，其中男生251名，女生468名。T3再次剔除T2研究对象中不接受第2轮随访的样本135人，对584名参与者再次进行追踪，得到有效问卷452份，其中男生163名，女生289名。本研究已通过广州城市职业学院学术委员会的伦理审查(编号：20240826)，所有调查对象均知情同意。
本研究对完成全部3次测量(452名)与中途脱落(585名)的研究对象在基线(T1)时的人口学特征进行比较，检验结果显示两组在性别、专业、居住地、独生状况、是否有留守经历、是否班干部分布上差异均无统计学意义(χ2值分别为1.60，0.12，0.10，0.12，0.41，0.00，P值均> 0.05)。
1.2　方法
1.2.1　一般人口学资料
包括性别、专业、家庭居住地、是否独生子女、是否有留守经历、是否为班干部等。
1.2.2　学业拖延量表(Procrastination Assessment Scale-Student, PASS)
采用关雪菁[14]修订的中文版，使用量表的第一部分考察大学生的学业拖延行为。该量表共18题，涵盖6个学习情境。每个情境包含3个项目，所有项目均按5点量表评分(赋1~5分)。个体拖延程度为各情境下前2个项目的总分，总分范围为12~60分，得分越高表明拖延行为越严重。本研究中该量表的Cronbach α系数为0.93。
1.2.2　抑郁-焦虑-压力量表(Depression Anxiety Stress Scale-21，DASS-21)
采用龚栩等[15]修订的简体中文版，共21题，分为抑郁、焦虑和压力3个分量表。采用4点计分(赋0~3分)，总分范围为0~63分，总分越高反映个体的负性情绪体验越明显。本研究中该量表的Cronbach α系数为0.95。
1.2.3　自我控制量表
采用谭树华等[16]修订的量表，共19题，采用5点计分(赋1~5分)，总分19~95分，总分越高表示自我控制能力越强。本研究中该量表的Cronbach α系数为0.89。
1.2.4　智能手机成瘾量表(Short Version of the Smartphone Addiction Scale，SAS-SV)
使用项明强等[17]修订的中文版，共10题，要求被试根据过去一个月的手机使用情况作答。采用6点计分(赋1~6分)，总分范围6~60分，分数越高表明智能手机成瘾倾向越严重。总分男性≥31分或女性≥33分界定为智能手机成瘾。本研究中该量表的Cronbach α系数为0.92。
1.3　质量控制
针对调查员开展统一培训，使其熟练掌握问卷指导语及填写注意事项解读。调查过程充分尊重调查对象并保护其隐私。研究采用问卷星收集数据，数据收集后进行统一检查，根据答题时间、答题内容逻辑关系剔除问卷；剔除标准：(1)总答题时间少于3 min；(2)在任何一份量表中连续题目出现超过80%的答案选择同一选项；(3)在整个问卷中出现有规律的作答模式；(4)在问卷中设置的语义相反或相关联的检验题目上答案出现明显且不合理的问题。
1.4　统计学分析
采用SPSS 26.0软件进行描述统计分析，计数资料采用频数或百分率描述。正态性检验采用Kolmogorov-Smirnov法进行评估，计量资料用(x±s)描述，单因素分析采用t检验，采用Harman单因素检验方法进行共同方法偏差检验，Pearson相关分析各变量之间的相关性；采用Mplus 8.3软件构建交叉滞后模型。双侧检验水准α=0.05。
2　结果
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2.1　共同方法偏差检验
在未旋转情况下的结果显示，共有6个因子的特征根大于1，其中第2个因子的累计方差解释率为28.46%，低于40%，说明本研究不存在明显的共同方法偏差，各变量之间的关系可靠。
2.2　大学生智能手机成瘾的单因素分析
T3时大学生SAS-SV量表平均分为(30.52±1.96)分，智能手机成瘾率为43.1% (195/452)。智能手机成瘾得分在是否独生子女间差异有统计学意义(P < 0.05)，其他社会人口学特征组间差异均无统计学意义(P值均>0.05)。见表1。
2.3　大学生学业拖延、负性情绪、自我控制与智能手机成瘾的相关性分析
T1、T2、T3大学生学业拖延得分分别为(50.24±10.01)(50.78±9.62)(51.01±9.88)分；负性情绪得分分别为(36.34±7.01)(35.96±6.43)(36.21±6.84)分；自我控制得分分别为(67.38±9.02)(67.52±9.52)(68.05±11.73)分；智能手机成瘾得分分别为(30.76±1.54)(31.02±1.21)(30.52±1.96)分。Pearson相关分析结果显示，大学生学业拖延、负性情绪与智能手机成瘾得分在不同时间段均呈正相关，自我控制与其他3个变量间均呈负相关(P值均 < 0.05)。见表2。
2.4　不同时间阶段主要研究变量的交叉滞后分析
2.4.1　不同时间段大学生学业拖延、负性情绪和智能手机成瘾交叉滞后分析
结果发现模型识别良好，学业拖延、负性情绪和智能手机成瘾3个变量在不同时段自回归系数均有统计学意义。因果回归模型显示，T1和T2学业拖延与T2和T3智能手机成瘾、负性情绪，T1和T2负性情绪与T2和T3智能手机成瘾均存在正向预测的因果关系(P值均 < 0.05)。反向因果回归模型中，T1和T2智能手机成瘾均可正向预测T2和T3学业拖延及负性情绪；T1和T2负性情绪均可正向预测T2和T3学业拖延(P值均 < 0.05)。据此可知，学业拖延与智能手机成瘾、负性情绪，负性情绪与智能手机成瘾间互为因果关系。见图1。
2.4.2　不同时间段大学生学业拖延、自我控制和智能手机成瘾的交叉滞后分析
结果发现模型识别良好，从自回归系数来看，T1和T2学业拖延均可负向预测T2和T3自我控制、正向预测T2和T3智能手机成瘾；T1和T2自我控制负向预测T2和T3智能手机成瘾(P值均 < 0.05)。反向因果回归模型中，T1和T2智能手机成瘾均可正向预测T2和T3学业拖延；T1和T2自我控制均可负向预测T2和T3学业拖延(P值均 < 0.05)。而T1和T2智能手机成瘾对T2和T3自我控制预测作用均无统计学意义(P值均>0.05)。表明学业拖延与智能手机成瘾之间、自我控制与学业拖延之间均存在双向因果关系，自我控制仅对智能手机成瘾具有单向预测作用，未发现反向的影响路径。见图2。
3　讨论
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大学生学业拖延与智能手机成瘾存在互为因果关系。学业拖延受个人特质、自我调节及环境等多种因素影响[7]，根据自我防御理论，当个体处于压力性情景中，学业拖延是缓解焦虑以及压力的非适应性防御行为[18-19]，具有学业拖延的大学生很可能通过使用智能手机逃避学习压力，进而形成“拖延-逃避-成瘾”的行为强化循环。因此，具有拖延行为的大学生是智能手机成瘾的高危人群，以往有研究指出学业拖延会直接预测网络成瘾[7-8]。相反，一方面手机成瘾会占用大学生学习时间，另一方面可导致大学生认知执行功能损伤，逐渐削弱目标管理能力，从而导致学业拖延行为的发生，最终诱发学业拖延行为渐进性加重，与国内外多项研究结果一致[9-12]。本研究通过三时点交叉滞后设计证实二者存在动态因果关系。
大学生负性情绪与智能手机成瘾存在互为因果关系。当个体经历负性情绪时，智能手机提供了便捷的逃避出口和即时满足感。负性情绪状态常伴随自我调节资源耗竭、冲动性增加和决策能力下降，使得个体更难抵抗手机的诱惑，更容易在情绪低落时无意识地、反复地使用手机，导致耐受性依赖行为的形成，发生智能手机成瘾，与以往的研究结果一致[20-21]，与国外提出的“情感-认知-行为强化循环”相一致[4]。反之，过度使用手机可导致任务拖延、效率下降、身体健康等问题，这些负面后果又成为新的、强烈的压力源引发或加剧焦虑、抑郁情绪。此外，成瘾行为因戒断症状产生的生理心理不适也会引发负性情绪[22]。这种互为因果的关系形成一个不断升级的闭环，即情绪越差，越想用手机逃避；手机使用越多，现实问题越严重，情绪越差。
本研究结果表明，自我控制可负向预测大学生智能手机成瘾，即自我控制水平越高，手机成瘾倾向越低，与既往研究一致[23]。然而，智能手机成瘾并不能预测大学生自我控制水平。可能是因为自我控制是一种形成于早期发展阶段、在成年期趋于稳定的个人特质或核心调节能力[4]。因此，自我控制更可能是智能手机成瘾的前因变量。在本研究中，智能手机成瘾更可能被视为低自我控制能力在特定领域的一种表现或结果；在本研究观察时段内未发现其造成特质性自我控制显著下降，该效应可能较弱、需更长时间显现，或受其他中介变量掩盖。
大学生学业拖延与负性情绪呈相互预测的因果关系，形成双向影响的恶性循环。拖延行为可预测负性情绪，这在多项研究中得到证实[5, 24]。学生因意识到任务未完成、预期表现不佳或失败，会激活压力反应系统，引发心率加快、肌肉紧张等生理反应，进而可能发展为焦虑[25]。情绪上则直接引发内疚、沮丧，若拖延导致学业成绩差等实际失败，还可诱发抑郁。反之，负性情绪也会导致学业拖延，与既往研究一致[26]。
本研究发现，大学生学业拖延、负性情绪与智能手机成瘾构成双向强化的恶性循环。学业拖延可能通过引发学业落后预期激活压力反应，滋生负性情绪；负性情绪促使个体借助手机逃避现实，诱发成瘾行为，并反过来加剧拖延；手机成瘾不仅直接引发负性情绪，也间接导致拖延。因此，应早期识别学生的焦虑、抑郁或执行功能困难，警惕其与拖延行为的相互诱发；对拖延学生需评估情绪状态，理解其背后的情绪和认知机制，减少简单归因。综合干预应同步聚焦行为管理、拖延改善、手机使用规划与情绪调节，以切断三者互扰路径，预防或减轻成瘾与拖延行为，促进学生心理健康与学业发展。
本研究亦存在一定的局限性：一方面是研究对象来自同一所学校，样本的数量和代表性不足，同时调查中存在一定样本脱落；另一方面是研究采用问卷调查法，可能存在一定程度的共同方法偏差和社会赞许效应，致调查结果出现偏差。未来可以通过扩大样本量和多样性、采用更具激励性的追踪策略以减少脱落及采用多种数据收集方法来克服局限。
基金
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  • 广州市教育局2024年高校科研项目(2024312469);广州城市职业学院2025年校级科研课题(KYSZ2025007)
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2025年第46卷第10期
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doi: 10.16835/j.cnki.1000-9817.2025305
  • 接收时间：2025-07-31
  • 首发时间：2026-11-06
  • 出版时间：2025-10-25
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  • 收稿日期：2025-07-31
  • 修回日期：2025-09-14
基金
广州市教育局2024年高校科研项目(2024312469);广州城市职业学院2025年校级科研课题(KYSZ2025007)
作者信息
    1广州城市职业学院数字商贸学院, 广东 510000
    2哈尔滨师范大学体育科学学院
    3哈尔滨体育学院民族传统体育学院

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