三波RI-CLPM研究：中国高中生运动、学习倦怠与问题性短视频使用的纵向关联

摘要

背景：

问题性短视频使用已成为青少年日益严重的行为健康问题，但其与体育锻炼和学习倦怠的纵向关联仍未被充分理解。本研究考察了体育锻炼、学习倦怠和问题性短视频使用之间的个体内双向关联，检验了学习倦怠是否作为连接体育锻炼与后续问题性短视频使用的纵向路径，并探讨了这些变量对后续问题性短视频使用的预测价值。

方法：

对中国云南省五所高中的1,327名16–18岁学生进行了间隔6个月的三波纵向调查。采用体育活动等级量表-3（PARS-3）、青少年学生倦怠量表及短视频成瘾量表评估体育锻炼、学习倦怠和问题性短视频使用。使用随机截距交叉滞后面板模型，在控制人口学和教育协变量的同时，将稳定的个体间差异与个体内波动分离。纵向间接关联采用5,000次偏差校正Bootstrap样本进行估计。补充的岭回归和极端梯度提升模型采用嵌套交叉验证和留出测试集，考察体育锻炼和学习倦怠对后续问题性短视频使用的增量预测价值。

结果：

在个体间水平上，较高的长期体育锻炼与较低的学习倦怠和问题性短视频使用相关，而较高的学习倦怠与较高的问题性短视频使用相关。在个体内水平上，高于平常的体育锻炼预测了随后较低的学习倦怠和问题性短视频使用。学习倦怠和问题性短视频使用随时间表现出正向相互关联，且二者均预测了随后较低的体育锻炼。从体育锻炼经学习倦怠到后续问题性短视频使用的纵向间接关联具有统计学显著性，但效应较小。在补充的机器学习分析中，最佳留出R²为0.311，但在先前问题性短视频使用之外加入体育锻炼和学习倦怠并未改善预测。因此，扩展XGBoost模型的SHAP结果被解释为探索性特征归因。

结论：

在中国高中生中，体育锻炼、学习倦怠和问题性短视频使用表现出相互关联的纵向关系。学习倦怠可能代表连接体育锻炼与后续问题性短视频使用的一条较小的纵向间接路径，而近期问题性短视频使用为未来风险提供了最具信息量的信号。这些发现可为开发和评估支持体育锻炼、应对学习倦怠并回应近期问题性使用症状的学校健康促进策略提供参考。

1 引言

1.1 问题性短视频使用作为青少年公共卫生关切

在数字时代，短视频使用已深度融入日常生活，重塑了个体获取信息、调节情绪、进行社交互动以及安排闲暇时间的方式（Qi, 2024）。与传统媒体相比，短视频平台通过算法推荐、无限滚动、自动播放和即时反馈机制，在捕捉并维持用户注意力方面尤为有效，从而促使用户频繁且习惯性地使用（Radesky et al., 2024；West et al., 2024）。对于自我调节能力仍在发展中的青少年而言，这种媒介环境可能增加其失控性或补偿性使用模式的风险（Montag et al., 2024；Shannon et al., 2022）。许多用户在 TikTok、YouTube Shorts 和 Instagram Reels 等平台上经历长时间沉浸和时间感减弱，并可能在情绪调节、社交娱乐和学业回避等情境中逐渐形成难以控制的使用模式（Huang et al., 2022）。

问题性短视频使用（PSVU）是指一种类似成瘾的短视频参与模式，其特征包括控制受损、持续沉浸、类戒断体验以及日常功能受损，并日益成为重要的青少年公共卫生问题（Li et al., 2025）。据国际电信联盟（International Telecommunication Union, 2025）统计，2025年全球约有60亿人使用互联网，占全球人口近四分之三。一项发表于 JAMA、涉及4,285名美国青少年的队列研究进一步显示，在4年随访期内，31.3%的青少年呈现成瘾性社交媒体使用轨迹上升，24.6%呈现成瘾性手机使用轨迹上升；高或上升的成瘾性屏幕使用轨迹与较差的心理健康、自杀意念和自杀行为相关（Xiao et al., 2025）。PSVU 还可能对青少年的学习、睡眠、情绪功能和日常适应构成风险（Jiang and Yoo, 2024；Zhan and Zhu, 2025）。鉴于世界卫生组织（2020）已将减少久坐性屏幕行为纳入儿童和青少年健康促进建议，阐明 PSVU 与学业适应及健康相关行为的纵向关联具有重要的公共卫生和教育意义。

在更广泛的青少年群体中，高中生是一个具有独特教育和发展特征的群体，值得重点关注研究。他们不仅面临持续的学业要求和高利害的教育评价，而且在数字媒体使用方面拥有日益增加的自主权。在这一阶段，学业适应、体育锻炼和问题性数字媒体使用可能变得尤为交织。因此，本研究聚焦中国高中生，考察 PSVU 与其学业及健康相关行为之间的纵向关联。

1.2 学习倦怠与问题性短视频使用

学习倦怠（LB）是指在持续的学习压力和学业要求下出现的一种情绪耗竭、学业疏离和效能感降低的状态，它是青少年学业适应受损的重要表现（Wu et al., 2010）。在一项针对2216名中国青少年的研究中，Li et al. (2014)表明，学业疏离、身心耗竭和自我效能感降低能够可靠地反映青少年的学习倦怠体验。与资源保存（COR）理论的基本框架一致，持续的学业要求会消耗认知、情感和社会资源；当这些要求超过可用资源时，学习倦怠就更可能出现（Hobfoll, 1989；Salmela-Aro and Upadyaya, 2014）。对于高中生而言，持续的课业负担、频繁的学业评价以及有限的恢复时间，可能使学习要求与可用心理资源之间的失衡尤为突出。

当学业压力不断累积而得不到充分恢复时，高中生可能会经历情绪耗竭、对学习的疏离以及学业效能感下降。COR理论认为，持续的资源损失可能会增加对所需努力较少且能提供即时缓解的应对方式的依赖（Hobfoll, 1989）。因此，对于学习倦怠水平较高的学生而言，短视频可能作为一种易于获取、以逃避为导向的调节方式发挥作用（Ye et al., 2025）。反之，问题性短视频使用（PSVU）也可能通过挤占睡眠或学业恢复时间、使可用于持续学习的资源减少，从而先于更高的学习倦怠出现（Dewald et al., 2010；Zhao and Kou, 2024）。

横断面研究已将问题性短视频使用与学习倦怠、焦虑、睡眠受损和日常功能受损联系起来（Jiang and Yoo, 2024；Mao and Liao, 2025），但无法确定时间先后顺序。因此，二者的相互关联与可能的COR资源损失螺旋相一致，尽管睡眠紊乱、时间挤占、注意力耗竭和应对动机并未被测量，仍只是可能的解释。需要纵向分析来确定是其中一个方向还是两个方向随时间发挥作用。这一问题也具有更广泛的教育意义，因为学习倦怠与较差的学业成绩、逃学或辍学以及适应困难相关（Bask and Salmela-Aro, 2013；Wang et al., 2015）。

1.3 体育锻炼作为一种保护性行为资源

体育锻炼（PE）作为一种低成本且可持续的健康行为，可能在LB和PSVU的发展中作为一种保护性行为资源发挥作用（Biddle et al., 2019；Caspersen et al., 1985；Liu et al., 2026）。资源守恒理论提出，资源投入可以产生额外资源，并增强个体应对后续压力源的能力（Hobfoll, 1989）。在高中情境中，PE可能支持身体恢复、情绪调节和同伴互动，从而增加可用于应对学业要求的资源（Aguayo et al., 2019；Khosravi, 2021）。既往研究同样通过更高的心理韧性、更低的压力和改善的情绪功能，将PE与更低的LB联系起来（Deng et al., 2025；Fu et al., 2023）。因此，当学生比平时进行更多PE时，随后的LB可能更低。

PE也可能与随后的PSVU直接相关。时间自我调节理论表明，行为选择反映了自我调节能力、启动成本以及即时奖励与延迟奖励之间的平衡（Hall, 2013；Hall and Fong, 2015）。PE通常需要计划和努力，并提供延迟收益，而短视频则以相对较低的成本提供即时反馈。因此，PE可能通过强化自我调节体验、提供有回报的线下活动以及减少对即时数字奖励的依赖，与随后更低的PSVU相关（Li et al., 2021；Zhao et al., 2024）。

这些关联也可能以相反方向发挥作用。更高的LB可能与启动和维持PE的能力降低相关，而PSVU可能通过可能挤占课后时间和恢复，先于更低的PE出现（Zink et al., 2024）。因此，在COR框架内，PE、LB和PSVU可以被理解为一种相互关联的资源获得与损失模式（Zhao et al., 2024）。由于睡眠、注意力、时间使用和自我调节资源的变化未被直接测量，这些是理论解释，而非数据所确立的机制。需要纵向模型来检验这些关联的方向和时间一致性。

1.4 学习倦怠作为纵向间接路径

在上述所构建的COR框架内，LB可能代表一条具有情境意义的学习适应路径，将PE与随后的PSVU联系起来（Chen et al., 2015；Hobfoll, 1989）。规律PE与儿童和青少年的认知及元认知功能相关（Álvarez-Bueno et al., 2017），这可能与恢复和学习调节有关。更低的LB反过来可能与更少依赖以逃避为导向的短视频使用相关。由于认知、恢复、学习调节和应对动机未被直接评估，这一解释是作为一种理论上合理的路径提出，而非已证明的机制。

既往横断面研究发现，PE、自我控制、问题性数字媒体使用与LB之间存在相互关联（Du et al., 2025）。然而，同时性关联无法确定这些变量的时间顺序。因此，本研究考察了中国高中生中从T1 PE经由T2 LB到T3 PSVU的纵向间接路径。

1.5 当前研究

关于PE、LB和PSVU的现有研究在很大程度上依赖横断面设计，难以确定这三个构念之间的时间顺序和动态方向性。在高中阶段，持续的学业要求、有限的恢复时间以及数字媒体使用自主性的增加，可能形成PE、LB和PSVU之间具有教育情境特异性的动态联系。然而，针对高中生的纵向证据仍然有限，这些构念在个体内水平上是否随时间相互预测仍不清楚。

此外，传统的交叉滞后面板模型（CLPM）通常无法区分稳定的个体间差异与个体内动态变化（Hamaker et al., 2015；Sorjonen and Melin, 2024）。RI-CLPM引入随机截距，将观测分数分解为稳定的个体间差异和个体内波动，因此适合检验某一波次上个体偏离其通常水平的程度是否与下一波次的后续偏离相关（Mulder and Hamaker, 2021）。COR理论作为H1–H4的主要框架，将PE概念化为一种潜在的资源获取行为，将LB和PSVU概念化为与资源损失相关的状况。时间自我调节理论仅作为PE–PSVU直接关联的补充视角。

因此，我们使用中国高中生的三波纵向数据，采用RI-CLPM考察PE、LB和PSVU之间稳定的个体间关联和个体内双向交叉滞后关系。我们进一步检验了LB在PE与后续PSVU关联中的纵向间接作用。为补充RI-CLPM的解释性证据，我们还使用岭回归和XGBoost对T3 PSVU进行了探索性样本外预测。我们考察了PE和LB在先前PSVU之外的增量预测价值，并使用SHAP解释特征贡献（Chen and Guestrin, 2016；Hoerl and Kennard, 1970；Lundberg and Lee, 2017）。基于上述理论框架和实证证据，提出以下假设：

H1：在个体间水平上，PE与LB和PSVU呈负相关，而LB与PSVU呈正相关。

H2：在个体内水平上，PE负向预测后续LB和PSVU。

H3：在个体内水平上，LB和PSVU表现出正向相互预测关联，且二者均负向预测后续PE。

H4：在个体内水平上，LB在PE与后续PSVU的关联中发挥纵向间接作用。

2 方法

2.1 参与者与程序

本研究采用三波纵向设计。数据在三个时间点收集：2025年3月（T1）、2025年9月（T2）和2026年3月（T3），相邻两波之间间隔6个月。参与者为中国云南省五所学校的高中生。问卷调查在班级层面进行。在每一波数据收集前，经过培训的研究助理介绍了研究目的、问卷说明、参与的自愿性质，以及保密、假名化纵向匹配和数据保护的程序。学生在课堂上完成问卷，完成的问卷当场回收。为实现跨波次的纵向匹配，参与者提供了学号。这些标识符仅用于纵向匹配，并在匹配过程中保密处理。纵向匹配完成后，标识符从分析数据集中移除，后续分析使用去标识化数据进行。纳入标准如下：（1）为参与学校之一注册在读的高中生；（2）能够独立理解并完成问卷；（3）自愿同意参与，且已获得学生本人及其法定监护人的知情同意；（4）提供了用于跨波次纵向匹配的身份信息。排除标准如下：（1）一个或多个核心变量存在严重缺失数据；（2）几乎所有条目均选择同一应答选项；（3）有明显证据表明存在随机或规律性作答；（4）纵向身份信息缺失或无法匹配；（5）三波之间的人口学信息存在逻辑不一致。在T1，经过初步实地筛查后，1,664份问卷构成基线队列。在T2未参与的152名学生中，97名是来自三个班级的十二年级学生，他们在T1至T2之间毕业，而55名因转学、缺席、退学或其他随访失访而无法参与。另有114名学生完成了T2但未参与T3，最终获得1,398份完成的T3问卷。在数据清理和纵向匹配过程中，28份问卷因标识符缺失或不一致而无法可靠关联，29名参与者因条目层面缺失而至少有一个核心变量得分不可用，14名因规律性作答或人口学信息不一致而被排除。因此，最终完整三波分析样本包括1,327名学生（占有效T1样本的79.75%）。在T1，PE、LB和PSVU得分不可用数分别为2、3和2；在T2分别为3、4和3；在T3分别为4、5和6。这些变量层面的缺失值与波次层面的未参与和纵向匹配失败相区分。来自学校1–5的参与者在T1分别为327、451、382、276和228人；在T2分别为300、415、363、246和188人；在T3分别为267、390、339、228和174人；在最终分析样本中分别为246、380、324、219和158人。相应的T1至最终保留率分别为75.23%、84.26%、84.82%、79.35%和69.30%。基线样本包括800名十年级、767名十一年级和97名十二年级学生；相应的最终人数为680、647和0。基线十二年级组的完全流失反映了T1至T2之间的毕业，而非条目层面缺失。主要分析基于1,327名具有有效三波链接和完整核心变量得分的参与者。基线T1数据仍可用于比较保留在最终分析样本中的参与者与被排除的参与者。然而，在去标识化后，被排除参与者的有效跨波链接未被保留，其不完整的纵向记录无法在个体层面重建。因此，关于波次未参与、链接失败和核心变量得分不可用的信息来自汇总的实地工作和数据清理摘要。 consequently，没有可用的个体链接部分纵向数据集用于FIML重新估计，因此未应用FIML。

2.2 测量指标

2.2.1 体育锻炼

PE采用体育活动等级量表-3（PARS-3；Liang, 1994）进行评估。尽管该工具的既定英文名称包含“Physical Activity”一词，但PARS-3通过三个成分对锻炼行为进行操作化：锻炼强度、每次锻炼时长和锻炼频率。因此，在本研究中，PARS-3所评估的构念统称为体育锻炼（PE），而较宽泛的术语“身体活动”仅在指代所引文献中更宽泛的构念或官方工具及来源名称时保留。

该量表包含三个条目，分别评估锻炼强度、每次锻炼时长和锻炼频率。示例条目包括“您通常进行的体育锻炼强度如何？”、“您每次通常进行上述锻炼多长时间？”以及“您每月进行上述锻炼多少次？”每个条目采用5点计分。PE总分计算公式为：锻炼强度 ×（锻炼时长−1）× 锻炼频率。总分范围为0至100分，得分越高表示PE水平越高。得分≤19、20–42和≥43分别代表低、中、高锻炼水平。

PARS-3是一种基于公式的行为复合指标，而非传统的反映性量表：锻炼强度、时长和频率代表锻炼行为的不同成分，并非单一潜在因子的可互换指标。因此，未评估Cronbach's alpha和反映性因子测量不变性。纵向可比性改为基于T1、T2和T3时使用相同的条目措辞、作答选项、施测程序和计分规则。

2.2.2 学习倦怠

LB采用青少年学生倦怠量表（ASBI；Wu et al., 2010）进行评估。该量表包含16个条目，涵盖三个维度：学业疏离、身心耗竭和自我效能感降低。代表性条目包括：学业疏离维度的“我认为学习没有意义”，身心耗竭维度的“学习一天后，我感到极度疲惫”，以及自我效能感降低维度的“我在学习中没有体验到成就感”。条目采用5点Likert量表计分，从1（“非常不同意”）到5（“非常同意”）。对正向措辞条目进行反向计分后，总分通过将所有条目相加计算，可能得分范围为16至80分。得分越高表示LB水平越高。在本研究中，T1、T2和T3时的Cronbach's α值分别为0.847、0.845和0.841，表明三个波次均具有良好的内部一致性。

2.2.3 问题性短视频使用

PSVU采用短视频成瘾量表进行评估（Chen et al., 2025）。该量表包含17个条目，涵盖四个维度：时间扭曲、现实逃避、戒断症状和注意冲突。前三个维度各包含四个条目，而注意冲突维度包含五个条目。代表性条目包括：时间扭曲维度的“我发现我观看短视频的时间总是比我预期的要长”，现实逃避维度的“当我遇到无法解决的问题时，我会观看短视频”，戒断症状维度的“当我无法观看短视频时，我会变得烦躁或不安，但一旦恢复使用这种感觉就会消失”，以及注意冲突维度的“观看短视频使我在学习或工作时容易分心”。条目采用5点Likert量表评分，从1（“与我完全不符”）到5（“与我完全相符”）。总分范围为17至85分，分数越高表明PSVU水平越高。在本研究中，Cronbach's α值在T1、T2和T3分别为0.857、0.862和0.858，表明各波次内部一致性良好。

2.3 协变量

鉴于PE、LB和PSVU可能因高中生的人口学和教育背景特征而有所不同，性别、学业成绩、独生子女状况、父母受教育程度和居住状况被纳入作为协变量。这些协变量用于预测PE、LB和PSVU的随机截距，从而控制稳定的个体间人口学差异。性别编码为1 = 男生，2 = 女生；独生子女状况编码为1 = 是，2 = 否；居住状况编码为1 = 寄宿，2 = 非寄宿。学业成绩被视为有序变量，编码为1 = 差，2 = 中下，3 = 中等，4 = 中上，5 = 优秀。父母受教育程度也被视为有序变量，编码为1 = 小学或以下，2 = 初中，3 = 高中，4 = 大专/职业学校，5 = 本科或以上。

2.4 统计分析

数据使用SPSS 29.0、Mplus 8.3和Python 3.12.13进行分析。通过比较最终三波样本（n = 1,327）与被排除参与者（n = 337）的T1特征来评估流失偏差，连续变量采用Welch t检验及标准化均值差，分类变量采用Pearson's χ²检验及Cramér's V。主要纵向分析使用1,327个完整三波匹配案例。尽管基线T1数据允许对保留和排除的参与者进行比较，但排除案例在去标识化后未保留有效的跨波链接。因此，这些被排除的记录无法重建为可个体链接的部分纵向数据集以进行FIML重新估计。计算了描述性统计量和Pearson相关。在每个波次分别进行了Harman单因素检验作为初步诊断，但被认为不足以排除共同方法偏差。还估计了个体层面的无条件随机截距模型，以量化稳定的个体间和个体内方差。

在结构建模之前，使用项目包层面的CFA分别对LB和PSVU检验了纵向测量不变性。在每个波次中，LB条目被分配到三个固定的、互不重叠的项目包中，分别包含条目1–6、7–11和12–16，PSVU条目被分配到四个项目包中，分别包含条目1–5、6–8、9–11和12–17；没有条目被遗漏或重复。项目包按条目均值计算，各波次分配相同。使用项目包层面的指标来评估总分可比性，同时避免条目层面三波次模型（包含48个LB和51个PSVU指标）所需的繁重参数化。依次检验了形态、度量和标量不变性，ΔCFI ≤0.010和ΔRMSEA ≤0.015表示不变性（Chen, 2007）。这些分析支持项目包层面的纵向可比性，但并未确立每个条目的不变性。

首先估计了传统的CLPM作为参照，随后估计了RI-CLPM以检验PE、LB和PSVU之间的纵向关联。RI-CLPM通过随机截距分离稳定的个体间差异，并在个体内水平估计自回归、交叉滞后和同波次关联（Hamaker et al., 2015；Mulder and Hamaker, 2021）。核心变量以连续观测分数输入，模型使用最大似然法估计。该设定保留了既定的量表计分程序，并对三个变量在三个波次上提供了简约的表示。前述不变性分析评估了分数可比性，但未将潜测量模型引入RI-CLPM；因此测量误差未与个体内波动分离。人口学和教育协变量预测随机截距，在学校调整的敏感性模型中加入了四个学校指标。报告了标准化路径估计值。对于最终的RI-CLPM，使用5,000个Bootstrap样本获得了标准化个体间、自回归和交叉滞后估计值的95%偏差校正Bootstrap置信区间。

使用五个嵌套RI-CLPM评估了时间不变性：无约束模型（M1）；将相应的交叉滞后路径（M2）、自回归路径（M3）或T2–T3同波次残差协方差（M4）约束为相等的模型；以及纳入全部三组约束的完全约束模型（M5）。约束施加于非标准化参数；因此由于波次特定方差，标准化系数在各时间间隔间可能略有差异。时间不变性主要根据可接受的绝对拟合、ΔCFI ≤0.010和ΔRMSEA ≤0.015来评估，χ²差异检验因其对大样本中微小差异的敏感性而被视为补充性指标（Chen, 2007）。在保留的模型中，报告了主要自回归和交叉滞后路径以及纵向间接关联的STDYX标准化估计值和基于5,000个Bootstrap样本的95%偏差校正置信区间；后者还以非标准化形式报告。可接受的拟合定义为CFI >0.90、RMSEA <0.08和SRMR <0.08（Hu and Bentler, 1999）。所有检验均为双尾，α = 0.05。

由于仅有五所学校可用，探索性的学校聚类MLR模型产生了不稳定的三明治标准误，因此未用于推断。学校层面的稳健性改为使用学校调整模型进行评估，并通过依次排除每所学校后重新估计M5来检验。由于无法获得个体班级标识符，无法考察班级层面的聚类。

补充预测分析使用岭回归和XGBoost来预测T3 PSVU（Chen and Guestrin, 2016；Hoerl and Kennard, 1970）。这些模型为探索性分析，旨在补充RI-CLPM分析，而非验证其纵向关联的因果解释。对于仅T1和T1 + T2预测变量集，基线模型包括人口学变量和先前PSVU，而扩展模型还额外包括PE和LB。分析样本中无缺失的预测变量或结果变量。二分类变量保留其两级编码，学业成绩和父母教育程度保留其有序的1–5编码，无需进行独热编码。预测变量在岭回归流程内进行标准化；XGBoost使用其原始数值尺度。

参与者按T3 PSVU的经验十分位数进行分层，并随机分配到开发集（80%，n = 1,061）和留出集（20%，n = 266）。嵌套交叉验证使用五个打乱的外层折和四个打乱的内层折，通过最小化交叉验证RMSE来选择超参数。岭回归α从10⁻³到10³之间25个对数间隔值中选择。对于XGBoost，对n_estimators（100、200、350、500、700）、max_depth（2、3、4）、learning_rate（0.02、0.04、0.06、0.10）、subsample和colsample_bytree（0.70、0.85、1.00）、min_child_weight（1、3、5、8）、reg_alpha（0、0.05、0.20、0.80）以及reg_lambda（1、3、8、15）随机抽样了24种组合。所有预处理和调参均限制在相关训练折内进行。最终超参数通过完整开发集内的五折交叉验证选择，之后模型在该集上重新拟合，并在未触碰的留出集上进行评估。主种子20,260,704控制分配、折打乱和调参，折特定种子由该种子确定性派生；自助重采样使用种子20,260,803。

留出集性能使用R²、RMSE和MAE进行评估。模型差异通过2,000次配对自助重采样估计，对两个模型应用相同的266个重采样索引，并得出基于百分位数的95%置信区间。对于留一学校验证，每所学校各作为一次测试集，而模型拟合和四折分组调参仅使用其余学校。SHAP值在留出样本中针对T1 + T2扩展XGBoost模型计算，仅解释为探索性的、模型特定的特征归因——而非增量预测价值或因果效应的证据（Lundberg and Lee, 2017）。

3 结果

3.1 共同方法偏差

鉴于本研究采用三波纵向设计，我们对T1、T2和T3的所有自我报告条目分别进行了未旋转主成分分析，作为潜在共同方法偏差的初步诊断。每一波均提取出多个特征值大于1的因子，第一个未旋转因子在T1、T2和T3分别解释了17.55%、19.08%和18.53%的方差。这些结果表明，在任何一波中都没有单一因子主导协方差结构。然而，Harman单因子检验是一种有限的诊断方法，无法确定共同方法偏差的不存在或实际不显著（Podsakoff et al., 2003）。由于PE、LB和PSVU均由同一批参与者自我报告评估，共享方法方差和特定场合的测量误差仍可能对观察到的关联有所贡献，因此在解释研究结果时应予以考虑。

3.2 样本流失、样本特征与初步分析

流失分析显示，最终三波匹配样本（n = 1,327）与未纳入最终分析样本的参与者（n = 337）在年龄、性别、其他人口学特征或T1基线PE、LB和PSVU水平上均无显著差异（p = 0.116–0.741）。所有组间效应量均较小，最大绝对标准化均数差为0.073，最大Cramér's V为0.059。这些发现表明，基于所测量的T1基线特征，没有明确证据显示存在系统性流失偏差。

表1呈现了统一分析样本（n = 1,327）的人口学特征和描述性统计。Panel A报告人口学特征，Panel B报告PE、LB和PSVU在三波中的均值和标准差。样本包括629名男生（47.40%）和698名女生（52.60%）；777名参与者（58.55%）为独生子女，1,043名参与者（78.60%）为寄宿生。学业成绩主要集中在平均水平（43.48%），父母受教育程度最常见为高中（29.92%）或初中（24.87%）。在三波中，PE的均值水平变化相对较小。相比之下，LB表现出明显的非均匀时间模式，从T1的47.67上升至T2的50.24，随后在T3下降至47.36。PSVU在T1为47.32，T2为48.11，T3为45.56。这些描述性模式表明，三个构念在研究期间并未表现出均匀稳定的均值水平。

表1

统一分析样本研究变量的人口学特征与描述性统计。

N = 1,327。PE，体育锻炼；LB，学习倦怠；PSVU，问题性短视频使用；M，均值；SD，标准差。

如图1所示，PE与LB和PSVU在波内及跨波均呈显著负相关。波内相关系数方面，PE–LB为r = −0.217至−0.279，PE–PSVU为r = −0.143至−0.264。相比之下，LB与PSVU在所有三个波次中均呈显著正相关，波内相关系数为r = 0.247至0.343。各构念在相邻波次间也表现出中到强的正向稳定性。跨波稳定性相关系数方面，PE为r = 0.532至0.627，LB为r = 0.380至0.535，PSVU为r = 0.434至0.573。总体而言，相关热图表明PE主要与LB和PSVU呈负相关，而LB主要与PSVU呈正相关。这些零阶相关为进一步区分稳定的个体间差异与个体内动态过程提供了初步支持。

图1

此外，基于三次重复测量，PE、LB和PSVU的个体水平ICC分别为0.592、0.483和0.511。具体而言，PE总方差的59.2%可归因于稳定的个体间差异，而40.8%可归因于个体内随时间的变化。对于LB，总方差的48.3%可归因于个体间差异，51.7%可归因于个体内波动。对于PSVU，总方差的51.1%可归因于个体间差异，48.9%可归因于个体内波动。这些结果表明，三个变量均既包含显著的稳定个体间差异，也包含有意义的个体内动态变异，为使用RI-CLPM分离个体间效应和个体内效应提供了实证支持。

3.3 测量模型与纵向不变性

在检验结构关系之前，对LB和PSVU进行了题目包水平的纵向测量不变性检验（表2）。形态模型对两个构念均显示出可接受的拟合，表明各波次保留了相同的题目包水平测量结构。对相应题目包载荷施加等式约束仅引起拟合的微小变化，支持了弱等值性。进一步约束相应题目包截距同样未使模型拟合显著恶化，支持了题目包水平的强等值性。这些结果表明，聚合题目包指标在三个波次间显示出充分的纵向可比性。然而，由于分析是使用题目包进行的，不应将其解释为证明了每个单独题项的纵向不变性，也不应将其视为在后续观测分数RI-CLPM中考虑了测量误差。

表2

LB和PSVU的纵向测量不变性。

CFI，比较拟合指数；RMSEA，近似误差均方根。ΔCFI 和 ΔRMSEA 是相对于前一个约束较少的模型计算的。形态等值性检验各波次是否保留了相同的题目包层面结构；度量等值性约束相应题目包的载荷相等，而标量等值性还额外约束相应题目包的截距相等。LB 和 PSVU 的模型拟合变化均在预设范围内，支持题目包指标在三个波次上的形态、度量和标量等值性。这些结果涉及题目包层面的总体可比性，并不确立每个单独题目的等值性。

3.4 模型比较

为检验 RI-CLPM 相对于传统 CLPM 的适用性，首先估计了包含协变量的传统 CLPM。结果显示，传统 CLPM 具有可接受的拟合，χ² (24) = 174.09，CFI = 0.960，RMSEA = 0.069，SRMR = 0.036。路径结果初步表明，PE、LB 和 PSVU 均表现出显著的跨时间稳定性和双向滞后关联。然而，由于 CLPM 无法区分稳定的个体间差异与个体内动态波动，它可能将类特质差异误读为个体内滞后效应。因此，CLPM 结果仅作为参考模型进行解释。相应的传统 CLPM 见 图 2。

图 2

随后，检验了 RI-CLPM 参数的时间等值性（表 3）。未约束模型显示出良好的拟合，χ² (33) = 38.49，p = 0.235，CFI = 0.989，RMSEA = 0.011，SRMR = 0.013。约束跨滞后路径（M2）或自回归路径（M3）对近似拟合产生的变化可忽略不计。尽管 M4 与 M1 之间的 χ² 差异显著，Δχ² (3) = 9.64，p = 0.022，但 CFI 和 RMSEA 的变化很小（ΔCFI = −0.002；ΔRMSEA = 0.005），且 M4 仍保持良好的绝对拟合。因此，该结果被解释为有限的实践性恶化，而非反对时间等值性的充分证据。完全约束的 M5 也显示出良好的拟合，χ² (45) = 53.60，p = 0.178，CFI = 0.988，RMSEA = 0.012，SRMR = 0.016，且与 M1 无显著差异，Δχ² (12) = 15.11，p = 0.235，ΔCFI = −0.001，ΔRMSEA = 0.001。据此，M5 被保留为更简约的主要模型。所有等值约束均施加于非标准化参数；因此，下文报告的标准化估计值在不同时间间隔之间可能略有差异。

表 3

随机截距交叉滞后面板模型的模型拟合与比较。

M1，无约束基线模型；M2，将对应的交叉滞后路径约束为跨时间间隔相等的模型；M3，将对应的自回归路径约束为相等的模型；M4，将对应的T2和T3波内残差协方差约束为相等的模型；M5，同时纳入全部三组等式约束的模型。等式约束施加于非标准化参数。ΔCFI和ΔRMSEA相对于M1计算。时间不变性主要依据可接受的绝对拟合、ΔCFI ≤0.010和ΔRMSEA ≤0.015进行评估；χ²差异检验被视为补充证据。

3.5 最终RI-CLPM结果

STDYX标准化估计值及其95%偏差校正自助法置信区间报告于表4，而图3提供了主要路径的图形化总结。M5中的等式约束施加于对应的非标准化参数；因此，所报告的标准化系数在不同时间间隔之间可能略有差异，因为它们纳入了各波次特有的方差。在个体间水平上，RI-PE与RI-LB（β = −0.440，p = 0.001）和RI-PSVU（β = −0.240，p = 0.002）均呈显著负相关，而RI-LB与RI-PSVU呈显著正相关（β = 0.461，p = 0.001）。这些发现表明，长期PE水平较高的青少年通常具有较低的LB和PSVU水平，而长期LB水平较高的青少年往往表现出较高的PSVU水平。

表4

最终时间不变RI-CLPM的标准化估计值。

β，STDYX标准化估计值；BC，偏差校正。置信区间基于5,000个自助法样本。个体间关联不特定于时间间隔，因此仅报告一次。等式约束施加于对应的非标准化参数；由于各波次特有的方差，标准化估计值在不同时间间隔之间可能略有差异。

图3

在个体内水平上，PE、LB 和 PSVU 在 M5 中均显示出显著的正向自回归效应。约束交叉滞后估计表明，高于通常水平的 PE 与随后较低的 LB 和 PSVU 相关；高于通常水平的 LB 与随后较低的 PE 和较高的 PSVU 相关；高于通常水平的 PSVU 与随后较低的 PE 和较高的 LB 相关。如补充表 S1所示，在未约束的 M1 中，所有九条自回归和交叉滞后路径均保持相同方向，其中七条在两个时间间隔内均具有统计学显著性。两条相对较弱的路径显示出时间间隔特异的不确定性：PE → PSVU 从 T1 到 T2 不显著（β = −0.041，p = 0.382），但从 T2 到 T3 显著（β = −0.132，p = 0.004），而 LB → PE 从 T1 到 T2 显著（β = −0.103，p = 0.016），但从 T2 到 T3 不显著（β = −0.054，p = 0.302）。重要的是，不同时间间隔之间统计学显著性的差异本身并不能确立相应系数之间存在显著差异，并且联合约束交叉滞后路径并未显著恶化模型拟合。因此，M5 被保留为总体时间模式的简约表示，而 PE → PSVU 和 LB → PE 的时间间隔特异性证据应谨慎解读。这些发现代表个体内纵向关联，而非因果效应。

学校层面的敏感性分析广泛支持了主要的 RI-CLPM。经学校调整的 M5 显示出极佳的拟合，χ² (69) = 87.04，CFI = 0.995，TLI = 0.992，RMSEA = 0.014，SRMR = 0.017。所有九条自回归和交叉滞后路径均保持其方向和统计学显著性，其基于模型的标准化 SE 与主要模型的差异不超过 0.001。在逐一排除一所学校的分析中，所有路径方向保持稳定，九条路径中有七条在每个重新估计的模型中均保持显著。两个例外是排除学校 3 时相对较小的 LB → PE 路径（p = 0.064）以及排除学校 1 时的 PE → PSVU 路径（p = 0.051）。

3.6 通过学习倦怠的纵向间接关联

在最终的时间不变 RI-CLPM 中检验了纵向间接关联。T1 PE 与 T2 LB 呈负相关（B = −0.007，β = −0.147，β 的 95% 偏差校正 bootstrap CI [−0.216, −0.080]），而 T2 LB 与 T3 PSVU 呈正相关（B = 0.222，β = 0.240，95% CI [0.171, 0.307]）。间接关联具有统计学显著性但较小（B = −0.002，95% CI [−0.003, −0.001]，p < 0.001；β = −0.035，95% CI [−0.057, −0.018]；表 5）。在调整学校固定效应后，该关联仍然显著（B = −0.002，95% CI [−0.002, −0.001]，p < 0.001）。这些发现支持一个小的、按时间顺序排列的纵向间接关联，而非因果中介。

表 5

最终时间不变 RI-CLPM 中的成分路径和纵向间接关联。

B = 非标准化估计值；β，STDYX标准化估计值；BC bootstrap CI，偏差校正bootstrap置信区间。置信区间基于5,000个bootstrap样本。

3.7 探索性机器学习结果

探索性机器学习分析从学生层面的样本外预测角度，考察了PE和LB对T3 PSVU的增量预测价值。如表6所示，当仅纳入T1信息时，模型的留出测试集R²值为0.142至0.155。加入T2信息后，留出测试集R²增至0.288–0.311。在原始的学生层面留出模型中，T1 + T2基线XGBoost模型表现出最佳的预测性能，R²为0.311，RMSE为7.502，MAE为6.021。

表6

预测T3 PSVU的补充机器学习模型的交叉验证和学生层面留出预测性能。

CV结果以嵌套交叉验证五个外层折的均值 ± 标准差表示。所有预处理和超参数选择均限于相应的训练数据，留出集仅在最终模型重新拟合后使用。开发样本和最终留出样本分别包括1,061名和266名参与者。T1基线模型包括人口学协变量和T1 PSVU；T1扩展模型额外包括T1 PE和T1 LB。T1 + T2基线模型包括人口学协变量以及在T1和T2测量的PSVU；相应的扩展模型额外包括在T1和T2测量的PE和LB。较高的R²和较低的RMSE和MAE表明预测性能更好。PE，体育锻炼；LB，学习倦怠；PSVU，问题性短视频使用；CV，交叉验证；RMSE，均方根误差；MAE，平均绝对误差。

然而，在已经包含先前PSVU和人口学变量的模型中添加PE和LB，并未在留出测试集中一致地改善预测性能。对于T1模型，从基线模型到扩展模型的R²增幅为：Ridge回归0.008，95% CI [−0.043, 0.059]，XGBoost约为0，95% CI [−0.057, 0.051]。对于T1 + T2模型，相应的增幅分别为−0.001，95% CI [−0.044, 0.039]和−0.016，95% CI [−0.062, 0.026]。在T1 + T2扩展模型中，XGBoost相对于Ridge回归的R²增幅为0.006，95% CI [−0.014, 0.028]。由于所有置信区间均包含零，这些结果表明，在先前PSVU和人口学变量之外，添加PE和LB并未提供稳定的增量预测价值。它们还表明，XGBoost在该数据集中并未显示出相对于Ridge回归的明显预测优势。

为评估模型开发与测试之间不存在同校重叠时的预测性能，进行了留一学校法敏感性分析。所有学校特定测试折均得到正的R²值，在不同模型设定下范围为0.116至0.409。对于仅含T1预测变量的集合，合并R²值范围为0.177至0.231；纳入T2预测变量后，范围为0.337至0.382。T1 + T2扩展Ridge模型表现出最佳的合并学校分组性能（R² = 0.382，RMSE = 7.236，MAE = 5.746），其次是T1 + T2扩展XGBoost模型（R² = 0.365，RMSE = 7.335，MAE = 5.850）。尽管扩展模型产生的点估计值高于相应的基线模型，但由于仅有五个学校折可用，未进行推断性比较。因此，这些差异不应被解释为稳定增量预测价值的证据。

对T1 + T2扩展XGBoost模型的探索性SHAP分析显示，T2 PSVU占总绝对SHAP贡献的45.5%，其次是T2 LB、T1 PSVU和T2 PE，分别占17.5%、17.2%和11.2%（图4）。T1 LB、T1 PE和人口学协变量的贡献较小。这些数值描述了拟合扩展模型内预测归因的分配。由于添加PE和LB并未可靠地改善相对于相应基线模型的留出性能，其SHAP贡献不应被解释为超出先前PSVU和人口学变量的增量预测价值的证据。

图4

4 讨论

Based on three-wave longitudinal data from Chinese high school students, this study used an RI-CLPM to examine the longitudinal associations among PE, LB, and PSVU at both the between- and within-person levels. The constrained M5 supported an overall pattern in which higher-than-usual PE was associated with lower subsequent LB and PSVU, LB and PSVU showed positive reciprocal associations, and higher-than-usual LB and PSVU were associated with lower subsequent PE. The unconstrained M1 retained the same directional pattern but revealed interval-specific uncertainty in the comparatively weak PE → PSVU and LB → PE paths. A significant but small longitudinal indirect association from T1 PE to T3 PSVU through T2 LB was also observed. These findings extend previous cross-sectional evidence by showing that fluctuations in PE, LB, and PSVU within the same individual are prospectively related across waves. Consistent with prior evidence that LB predicts later compulsive Internet use and academic impairment (Liou et al., 2022), the present results further suggest that LB may represent one longitudinal pathway linking PE to subsequent PSVU. Importantly, however, although the RI-CLPM separates stable between-person differences from within-person fluctuations, it does not eliminate confounding from unmeasured factors that vary over time (Mund et al., 2021). Accordingly, the within-person estimates should be interpreted as temporally ordered longitudinal associations rather than causal effects. Compared with the traditional CLPM, the principal value of the RI-CLPM lies in examining whether deviations from an individual’s own usual level are associated with subsequent deviations in related outcomes (Hamaker et al., 2015).

4.1 Longitudinal relationship between physical exercise and problematic short-video use

This study found that PE negatively predicted subsequent PSVU after stable between-person differences were controlled, supporting H2. This finding is important not because students with higher overall PE tend to report lower PSVU, but because it indicates a within-person association. When the same student engaged in more PE than their usual level at a given wave, the severity of subsequent PSVU tended to be lower. This within-person association is less susceptible to confounding by stable personality traits, family background, or long-term lifestyle differences, but may still be influenced by unmeasured time-varying factors. Therefore, the finding should be interpreted as evidence that higher-than-usual PE is associated with lower subsequent problematic use rather than as proof of a causal effect (Li et al., 2025).

Consistent with the COR account and the complementary temporal self-regulation perspective outlined above, the PE–PSVU association may reflect differences in resource demands, initiation costs, and the relative value of immediate and delayed outcomes (Hobfoll, 1989; Hall, 2013; Hall and Fong, 2015). Short-video use provides immediate feedback at relatively little initiation cost, whereas PE requires planning, physical effort, and acceptance of more delayed benefits. Adolescents may be particularly responsive to novelty, immediate feedback, and social rewards (Chein et al., 2011; Galván, 2010; Somerville et al., 2010). Peer and platform feedback may further increase the reinforcing value of short-video content (Albert et al., 2013; Sherman et al., 2018). This combination may make short videos especially attractive during periods of academic fatigue or reduced self-regulatory resources, although reward sensitivity and self-regulatory capacity were not directly assessed in the present study.

From the same perspective, PE may provide a resource-enhancing alternative through physical recovery, offline reward, competence experiences, and social interaction. Previous longitudinal evidence has similarly linked PE and self-control with lower subsequent problematic mobile-phone use (Zhao et al., 2024), while other research has associated PE with lower psychological distress and problematic mobile-phone use (Li et al., 2021). These findings provide indirect support for a resource-based interpretation of the PE–PSVU association. However, because the present study did not directly measure executive control, reward processing, offline reward, or actual short-video viewing duration, these processes should be regarded as plausible explanations rather than mechanisms demonstrated by the data.

This study also found that PSVU negatively predicted subsequent PE, supporting H3. The measured characteristics of PSVU offer several possible explanations for this direction of association. Time distortion may interfere with students’ planning of after-school activities, attention conflict may weaken the execution of exercise plans, and reality escape or withdrawal discomfort may increase reliance on readily accessible digital activities. Previous studies have associated problematic digital-media use with sleep disturbance and less favorable health-behavior patterns (Chen and Wu, 2021; Zhang et al., 2023). Research using broader physical-activity measures has also linked higher screen exposure with lower activity participation and poorer sleep recovery (Stiglic and Viner, 2019; Zablotsky et al., 2025). These findings provide relevant contextual evidence, but they should not be treated as construct-equivalent evidence for PARS-3-assessed PE. Moreover, sleep disruption, time displacement, attentional depletion, and effort valuation were not directly measured; they remain possible explanations rather than established pathways.

Taken together, M5 supported an overall reciprocal association between PE and PSVU, but M1 provided stronger interval-specific evidence for PSVU → PE, which was significant in both intervals, than for PE → PSVU, which was significant only from T2 to T3. The findings therefore support a focused resource-based account in which PE and PSVU may be longitudinally interrelated, while not establishing a temporally uniform or causal feedback cycle. They may inform the identification of candidate behavioral targets for future intervention research, but they do not demonstrate that increasing PE will reduce PSVU or that reducing PSVU will increase PE. Whether changing either behavior produces subsequent changes in the other requires direct experimental or quasi-experimental evidence.

4.2 Dynamic longitudinal associations and the longitudinal indirect role of learning burnout

The RI-CLPM results indicated that PE, LB, and PSVU were not independent of one another. At the between-person level, high school students with higher long-term levels of PE generally showed lower levels of LB and PSVU, whereas those with higher long-term levels of LB tended to report higher levels of PSVU, supporting H1. The within-person results further showed that when the same student’s PE was higher than their usual level at one wave, their LB was relatively lower at the next wave. Conversely, when LB was higher than usual, subsequent PSVU was relatively higher. In addition, PSVU positively predicted subsequent LB, and both LB and PSVU negatively predicted later PE. Taken together, the constrained M5 supported H2 and H3 at the overall time-invariant level and suggested a reciprocal pattern of temporally ordered within-person associations rather than a simple unidirectional sequence. However, the unconstrained M1 retained the same path directions but showed interval-specific uncertainty for the comparatively weak PE → PSVU and LB → PE paths. Because unmeasured time-varying confounding cannot be excluded, these reciprocal paths should not be interpreted as evidence of a closed or causal feedback system. The nonuniform mean-level pattern of LB across waves further underscores that wave-specific contextual influences cannot be excluded when interpreting these within-person associations.

Within the primary conservation of resources (COR) framework, the association between PE and subsequent LB can be understood as a possible resource-gain process. From this perspective, the relevance of PE may extend beyond temporarily interrupting academic tasks. In the high school context, PE may provide opportunities for physical recovery, emotional regulation, competence experiences, and peer interaction. These experiences may help students preserve or replenish the resources needed to cope with coursework, frequent examinations, and high-stakes academic evaluation. Accordingly, when the same student engaged in more PE than their usual level, greater access to these resources may have been associated with lower subsequent emotional exhaustion, academic alienation, and reduced efficacy. This interpretation is consistent with previous evidence linking PE with lower adolescent LB and with psychological resources relevant to academic adaptation (Hobfoll, 1989; Salmela-Aro and Upadyaya, 2014; Deng et al., 2025; Fu et al., 2023). However, because resource gain, coping capacity, and academic recovery were not directly measured, they should be regarded as plausible explanations rather than mechanisms demonstrated by the present data.

The association from LB to subsequent PSVU can be interpreted from the resource-loss side of the same framework. Emotional exhaustion, academic alienation, and reduced efficacy may indicate that students have fewer psychological resources available for sustained learning and effortful self-regulation. Under these conditions, short videos may become particularly attractive because they are easily accessible, require little initiation effort, and provide immediate feedback. Their use may therefore serve as a temporary form of escape-oriented regulation when students feel unable to cope effectively with academic demands, which is conceptually consistent with the reality-escape dimension of PSVU. Previous studies have similarly linked LB and academic stress with problematic forms of digital-media use and avoidance-oriented coping (Hao et al., 2022; Tomaszek and Muchacka-Cymerman, 2020; Wang et al., 2020). Nevertheless, the present study did not directly assess coping motives, emotional relief, or reward-seeking processes. These explanations therefore remain theoretically plausible interpretations of the observed longitudinal association.

The reverse association from PSVU to subsequent LB suggests that this resource-loss process may also operate in the opposite direction. Time distortion and attention conflict may interfere with learning plans and sustained academic engagement, while withdrawal discomfort may add to students’ emotional burden. Repeated disruption of learning time, attention, and recovery may consequently be associated with further depletion of learning-related resources and higher subsequent LB. Importantly, however, the RI-CLPM identified temporally ordered within-person associations rather than the intervening processes themselves. Sleep disruption, attentional depletion, time displacement, and coping motives were not directly measured, and unmeasured time-varying factors may also have contributed to the reciprocal pattern. The resource-loss interpretation should therefore be understood as a focused and parsimonious theoretical account of the findings, rather than as evidence of a demonstrated causal feedback mechanism.

Notably, the longitudinal indirect association from T1 PE to T3 PSVU through T2 LB was statistically significant but small, supporting the longitudinal indirect role of LB and H4. This finding indicates that when the same student’s PE was higher than their usual level, subsequent LB tended to be lower, and lower LB was further associated with lower subsequent PSVU severity. However, the direct PE → PSVU association was significant in the constrained M5 but showed interval-specific uncertainty in M1. LB should therefore be understood as one possible longitudinal pathway linking PE to subsequent PSVU rather than as the sole explanatory mechanism. The key value of the RI-CLPM is that this longitudinal indirect association is based on deviations from individuals’ own usual levels rather than stable differences between students. Nevertheless, the pathway may still be influenced by unmeasured time-varying factors. Accordingly, the findings support a longitudinal indirect association with temporal ordering but do not establish a causal mediation mechanism.

4.3 Bridging longitudinal explanation and prediction: proximal state dependence of PSVU

The machine-learning analysis was not intended to replicate the RI-CLPM findings, but rather to provide an additional layer of evidence from a predictive perspective. Whereas the RI-CLPM examined within-person longitudinal associations among PE, LB, and PSVU, the machine-learning models addressed a different question: whether these variables predicted subsequent PSVU among students who were not involved in model training. The models also tested whether PE and LB provided incremental predictive information beyond prior PSVU. Because statistical associations do not necessarily translate into out-of-sample prediction, combining explanatory longitudinal modeling with cross-validated prediction can help evaluate the generalizability of theoretically relevant variables and constrain overinterpretation (Shmueli, 2010; Yarkoni and Westfall, 2017). Compared with previous studies that mainly identified high-risk groups using concurrent digital-use characteristics, the present study used three-wave data to predict T3 PSVU, thereby further distinguishing longitudinal association from incremental predictive value (Kim et al., 2024; Lee and Kim, 2021).

The results revealed clear proximal state dependence. When only T1 information was used, the holdout test-set R² ranged from 0.142 to 0.155. After T2 information was added, R² increased to 0.288–0.311, with the T1 + T2 baseline XGBoost model showing the best performance (R² = 0.311). In the extended model, T2 PSVU made the largest SHAP contribution, accounting for 45.5% of the total absolute SHAP value, followed by T2 LB, T1 PSVU, and T2 PE. This pattern indicates that problematic use states closer to the outcome wave contained more predictive information. For high school students embedded in intensive coursework, frequent examinations, and relatively fixed daily routines, once short-video use develops into time distortion, reality escape, withdrawal discomfort, and attention conflict, it may show a certain degree of behavioral continuity. Therefore, the prediction of PSVU should not be reduced to the prediction of viewing duration alone; the key issue is whether problematic patterns of use have already emerged (Marciano and Camerini, 2022).

Adding PE and LB to models that already included prior PSVU did not yield reliable improvements in holdout performance. This pattern should not be regarded as inconsistent with the RI-CLPM findings, because explanatory longitudinal modeling and out-of-sample prediction address different questions. The RI-CLPM examines whether within-person deviations in PE and LB precede subsequent changes in PSVU, whereas the predictive models assess whether these variables improve prediction once prior PSVU is already known. Proximal PSVU may summarize part of the accumulated behavioral and contextual information shared with PE and LB, thereby limiting their unique predictive contribution. Accordingly, their SHAP contributions reflect how predictive information is allocated within the fitted model, rather than independent incremental value or causal effects (Lundberg and Lee, 2017). The comparable performance of Ridge regression and XGBoost likewise suggests limited benefit from additional nonlinear complexity. Leave-one-school-out validation indicated that the predictive pattern was not dependent on same-school overlap between the development and test sets. Nevertheless, with only five schools from one regional sampling frame, this finding supports cross-school robustness within the participating sample rather than external transportability. Overall, recent PSVU appears most informative for near-term risk identification, whereas PE and LB may be more relevant to understanding the broader developmental process than to serving as independent screening indicators.

4.4 Implications for future school-based prevention research

Taken together, the RI-CLPM, longitudinal indirect-association, and machine-learning findings identify several candidate targets for future school-based prevention research. Recent PSVU symptoms—including time distortion, reality escape, withdrawal discomfort, and attention conflict—may warrant attention in future risk-identification research, while opportunities for PE and support for LB may be examined as complementary upstream components. However, because the study was observational, the longitudinal indirect association was small, and PE and LB did not provide stable incremental predictive value beyond prior PSVU, these findings do not establish an effective stratified intervention or demonstrate that modifying PE or LB will reduce PSVU.

The study did not measure objective short-video viewing duration and therefore cannot identify a safe-use threshold or determine the effectiveness of reducing viewing time. Future controlled or quasi-experimental studies should evaluate whether interventions addressing exercise opportunities, learning burnout, and digital self-regulation produce meaningful changes in PSVU before these approaches are recommended for routine school practice.

5 Limitations and future directions

First, the sample was drawn from five selected high schools in Yunnan Province, China, limiting generalizability to other regions and educational contexts. Participants were nested within five schools and 36 classrooms. Anonymous school codes enabled school-fixed-effect and leave-one-school-out RI-CLPM sensitivity analyses, which broadly supported the overall pattern but indicated some sensitivity in two comparatively small paths. However, only five schools were available, precluding reliable use of conventional school-clustered sandwich standard errors. Individual-level classroom identifiers and classroom-specific analytic sample sizes were also unavailable; therefore, classroom-level ICCs, clustered standard errors, and multilevel RI-CLPMs could not be estimated. Unmodeled within-classroom dependence may have produced underestimated standard errors and overly narrow confidence intervals, particularly for smaller effects. The primary analyses were also restricted to 1,327 participants with valid three-wave linkage and complete core-variable scores. Although baseline T1 data permitted comparison between retained and excluded participants, valid cross-wave linkage for excluded cases was not retained after de-identification. Their incomplete longitudinal records could therefore not be reconstructed as an individually linked partial dataset for FIML sensitivity analysis. Selection associated with graduation or other loss to follow-up may consequently have influenced the findings. Future studies should recruit more diverse schools and retain non-identifying school, classroom, and longitudinal linkage codes for all eligible partial records, permitting multilevel or small-cluster-robust inference and missing-data sensitivity analyses.

Second, this study used a three-wave longitudinal design with 6-month intervals. Although this design allowed the temporal associations among PE, LB, and PSVU to be examined while separating stable between-person differences from within-person fluctuations, three waves may be insufficient to characterize longer-term developmental trajectories or associations operating over shorter time scales. Future studies should incorporate additional measurement waves, longer follow-up periods, and, where appropriate, shorter assessment intervals to examine whether these associations remain consistent across developmental stages and academic periods.

Third, the study relied primarily on self-report measures, and several measurement-related limitations warrant consideration. Although longitudinal measurement invariance was supported for LB and PSVU at the parcel level, the primary RI-CLPM used observed scale scores rather than fully latent measurement models. Measurement error was therefore not explicitly separated from within-person fluctuations and may have influenced the estimated autoregressive and cross-lagged associations. In addition, parcel-level invariance may mask item-specific non-invariance and should not be interpreted as evidence that every individual item functioned identically across waves. PARS-3 represents a different measurement structure because exercise intensity, duration, and frequency form a formula-based behavioral composite rather than interchangeable reflective indicators; its longitudinal comparability was therefore based on identical administration, response options, and scoring procedures across waves rather than reflective-factor measurement invariance. Because PE, LB, and PSVU were all assessed by self-report from the same participants, the observed associations may also remain susceptible to recall bias, social desirability, shared-method variance, and occasion-specific measurement error. Harman’s single-factor test provides only a limited diagnostic of whether a single factor dominates the covariance structure and cannot establish the absence or practical insignificance of common method bias. Importantly, although the three-wave longitudinal design provides temporal ordering and the RI-CLPM separates stable between-person differences from within-person fluctuations, neither approach eliminates shared-method variance or occasion-specific measurement error. Future studies should consider item-level longitudinal measurement models and multiple-indicator latent RI-CLPMs that explicitly account for measurement error, together with objective or device-assisted measures of exercise intensity, duration, and frequency, digital-use records, and multi-informant assessments.

Fourth, although the RI-CLPM separates stable between-person differences from within-person temporal fluctuations, it does not eliminate unmeasured time-varying confounding. Examination pressure, short-term academic stress, sleep, school schedules, peer relationships, and changes in digital-media exposure may vary across waves and simultaneously influence PE, LB, and PSVU. This issue is particularly relevant because assessments were conducted in March 2025, September 2025, and March 2026, and LB showed a nonuniform descriptive pattern across these waves, increasing from 47.67 at T1 to 50.24 at T2 before decreasing to 47.36 at T3. Because these time-varying contextual factors were not repeatedly measured, their contribution to the observed cross-lagged and longitudinal indirect associations cannot be excluded. Accordingly, these estimates should be interpreted as within-person longitudinal associations after accounting for stable between-person differences, rather than as causal effects or causal mediation. Future studies should repeatedly assess major time-varying academic, behavioral, and contextual factors and incorporate them as time-varying covariates; where feasible, experimental or quasi-experimental designs would provide stronger evidence regarding causal processes.

Fifth, the present study focused on the core longitudinal associations among PE, LB, and PSVU and therefore did not capture the broader multilevel context in which adolescent digital behavior develops. Individual, family, peer, school, and platform-level factors may jointly shape these processes, and their effects may vary across developmental and educational contexts. Future research could integrate variables such as sleep quality, peer relationships, family environment, school climate, examination periods, and platform algorithmic exposure to develop a more comprehensive account of the factors associated with PSVU.

Finally, the machine-learning analyses should be regarded as supplementary and exploratory rather than as causal validation of the RI-CLPM findings. The original development and holdout sets were divided at the student level, allowing students from the same schools to appear in both datasets; these results therefore represent internal prediction in new students from the same source population. The additional leave-one-school-out analysis provided a more stringent assessment and showed no marked deterioration in predictive performance. Nevertheless, because it included only five schools from the same regional sampling frame, it should be interpreted as a school-grouped sensitivity analysis rather than definitive external validation. Classroom-grouped validation could not be conducted because individual classroom identifiers were unavailable. Although the extended models yielded higher point estimates under leave-one-school-out validation, five school folds were insufficient for reliable inferential comparisons and did not establish stable incremental predictive value for PE and LB beyond prior PSVU. The current models should not be used for individual screening or school-level decision-making, and SHAP values should be interpreted only as model-specific attributions within the extended model, not as evidence of causal effects or incremental predictive value beyond the baseline model. Future studies should use larger numbers of schools, classroom-grouped resampling, independent external samples, and richer behavioral and contextual predictors.

6 Conclusion

This study identified stable between-person associations and an overall pattern of temporally ordered within-person associations among PE, LB, and PSVU in Chinese high school students. The constrained M5 indicated that higher-than-usual PE was associated with lower subsequent LB and PSVU, whereas higher-than-usual LB and PSVU were associated with lower subsequent PE. The unconstrained M1 retained the same path directions but showed interval-specific uncertainty in the comparatively weak PE → PSVU and LB → PE associations. By contrast, PE → LB, the reciprocal associations between LB and PSVU, and PSVU → PE were supported across both unconstrained intervals. T1 PE was also weakly associated with T3 PSVU through T2 LB, suggesting that LB may represent one longitudinal pathway linking PE to later PSVU.

The machine-learning analysis further showed that recent PSVU was the most informative predictor of subsequent PSVU, whereas PE and LB did not provide clear incremental predictive improvement beyond prior PSVU. This distinction underscores that longitudinal association modeling and out-of-sample prediction address different questions. Overall, the findings may help identify candidate targets for future school-based intervention research, but they do not demonstrate that changing PE or LB will reduce PSVU. Multisource measurement, independent-sample validation, and experimental or quasi-experimental intervention studies are needed to determine whether these associations can be translated into effective prevention approaches.

Statements

Data availability statement

The datasets presented in this article are not readily available because they contain sensitive information from adolescent participants, and public sharing is restricted by the conditions of the ethics approval and informed consent. De-identified data may be made available by the corresponding author upon reasonable request and subject to applicable ethical approval. Requests to access the datasets should be directed to Zeng Gao, wwzgaozeng@163.com.

Ethics statement

The studies involving humans were approved by the Biomedical Research Ethics Committee of Yunnan Normal University before data collection (Ethics approval number: ynnuethic2025-071). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was obtained from all participating students and their legal guardians before data collection.

Author contributions

TY: Conceptualization, Investigation, Funding acquisition, Writing – original draft, Data curation, Methodology. YP: Writing – review & editing, Project administration, Formal analysis, Software, Supervision. LC: Investigation, Data curation, Writing – review & editing. ZW: Data curation, Supervision, Software, Writing – review & editing, Investigation. XL: Writing – review & editing, Supervision, Data curation, Formal analysis. CX: Writing – original draft, Supervision, Visualization, Project administration. ZG: Supervision, Project administration, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the 2025 Scientific Research Project of the Hunan Provincial Department of Education (Outstanding Young Scholars Project; Grant No. 25B0132).

Acknowledgments

The authors thank all participants and the researchers who assisted with data collection and processing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2026.1929181/full#supplementary-material

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Keywords

high school students, learning burnout, machine learning, physical exercise, problematic short-video use, random-intercept cross-lagged panel model

Citation

Yin T, Peng Y, Chen L, Wang Z, Liu X, Xie C and Gao Z (2026) Does learning burnout link physical exercise and problematic short-video use? Evidence from a three-wave random-intercept cross-lagged panel model and supplementary machine-learning analysis among Chinese high school students. Front. Psychol. 17:1929181. doi: 10.3389/fpsyg.2026.1929181

Received

05 July 2026

Revised

30 August 2026

Accepted

18 September 2026

Published

01 October 2026

Volume

17 - 2026

Edited by

Jing Chen, The Affiliated Hospital of Southwest Medical University, China

Updates

Copyright

© 2026 Yin, Peng, Chen, Wang, Liu, Xie and Gao.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zeng Gao, wwzgaozeng@163.com

† These authors have contributed equally to this work and share first authorship

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.