1 前言
随着时代的发展,青少年常常面临许多不确定与压力,如社交焦虑[1]、学业焦虑[2]、分离焦虑[3,4],这往往导致他们产生相当多的焦虑。焦虑是对真实是或想象的刺激或对威胁情况产生的一种正常的情绪反应[5]。焦虑给青少年带来一系列的负面影响,包括记忆与注意[6],同伴关系[7]。在最近的研究发现,焦虑在我国青少年群体中的检出率高达22.34%[8],在COVID-19期间甚至达到了36.7%[9]。因此,关注青少年焦虑及其带来的一系列影响是社会各界应重视的问题。
研究发现,焦虑通常与青少年较高的网络成瘾有关。网络成瘾是指个体过度的、有问题的、强迫性的卷入到与网络有关的成瘾行为[10,11]。网络成瘾青少年的焦虑检出率相当高,而且女生普遍高于男生[12]。焦虑与青少年网络成瘾之间具有显著的正相关关系[13,14],并且焦虑是青少年网络成瘾的重要危险因素[15]。此外,研究发现焦虑在多个因素与青少年网络成瘾之间具有显著的中介效应[16]。焦虑可显著正向预测青少年网络成瘾[14,17],而这种关系在纵向研究中也得到了证实[15,18,19]。基于这些证据,本研究假设焦虑可显著预测青少年网络成瘾。
除焦虑外,抑制控制也与青少年网络成瘾有关。研究发现,网络成瘾与中学生抑制控制有强烈的关联性[20]。网络成瘾分数与默认模式网络(Default Mode Network,DMN)较低的连通性有关,而这种低连通性与较低的抑制控制有关[21,22]。在实验研究中发现网络成瘾个体的抑制控制任务的表现水平显著低于非网络成瘾者[23]。因此,抑制控制较低的青少年,其网络成瘾水平可能较高。而焦虑对青少年的抑制控制水平可能有负面影响。研究发现,在高焦虑个体中,其抑制控制任务表现较差[24,25],影响了个体在进行抑制控制任务时神经募集的水平[25]。同样,在实验室中诱发的焦虑,可削弱个体的抑制控制水平[26,27]。而且基于自我控制力量模型(The Strength Model of Self-Control)的解释[28],焦虑会占用并不断消耗个体的认知资源,当认知资源被不断消耗时,其进行抑制网络使用的行为则可能失败。因此,焦虑水平越高,对青少年的抑制控制水平影响越大,进而发展更高水平的网络成瘾。基于以上回顾,我们假设焦虑可预测青少年的抑制控制,而抑制控制也可预测青少年的网络成瘾。
焦虑与青少年抑制控制的关系可能受到其他因素的调节。研究发现,体力活动可降低青少年的焦虑与抑郁[29],并可促进海马体结构发育,血清素的分泌[30],进而调节个体的情绪与认知发展水平[31]。同样,体力活动可促进个体抑制控制的发展[32],并且得到了大量元分析研究证据的支持[33-37]。结合之前的回顾,本研究假设体力活动可削弱焦虑对青少年抑制控制的影响。
结合以上回顾,本研究将进一步通过抑制控制探讨焦虑与青少年网络成瘾之间的关系。并且考虑了体力活动在焦虑与青少年抑制控制之间的调节作用。因此,本研究构建了如图1所示的假设模型。
2 方法
2.1 参与者
2024年2月,通过便利抽样方法,从山东省W市两所中学选取了1677名中学生作为研究对象。通过班级群线上发放电子问卷的形式进行调查,向参与者及其监护人明确说明了研究内容、数据的匿名性、保密性及使用目的,并取得了在线知情同意。参与者基本能在15分钟内完成问卷。在数据收集后,排除了回答时间过短或回答模式过于规律的样本,最终分析的样本数为1607个,其中男生664名,女生943名,平均年龄为15.86岁(SD = 0.73)。
2.2 测量工具
2.2.1 体力活动评估
通过一道题来评估青少年的体力活动水平:“在过去的7天里,你有多少次锻炼或做了至少20分钟的身体活动,让你出汗或呼吸困难?”选项范围从0天到7天[38]。这项测量体力活动的工具已在前期研究中进行使用[39]。
2.2.2 网络成瘾量表
采用由魏祺进行修订并验证的网络成瘾量表采集青少年的网络成瘾水平[40]。该量表包含8个题目,采用李克特5级评分,范围为1(非常不同意)到5(非常同意)。各题目得分之和代表青少年网络成瘾程度,分数范围在8-40分之间。得分越高,表示青少年网络成瘾水平越高。本研究样本的Cronbach 's α为0.884。
2.2.3 焦虑量表
采用由龚栩等人进行修订并验证的抑郁-焦虑-压力自评量表中的焦虑分量表采集青少年的焦虑水平[41]。焦虑分量表共7个题目,采用李克特4级评分,范围为1(完全不符合)到4(完全符合)。各题目得分之和代表青少年的焦虑水平,分数范围在7-28分之间,得分越高,则代表青少年的焦虑程度越高。本研究样本的Cronbach's α为0.869。
2.2.4 抑制控制量表
采用由黄春晖等人编制的执行功能量表中的抑制控制分量表[42]。抑制控制分量表包括6个道题,每个选项按1(从不)到3(经常)进行评估,各题目得分之和为青少年的抑制控制分数,分数范围在6-18分之间,得分越高代表青少年的抑制控制水平越低。因此,为使得研究结果符合一般预期,本研究将此量表得分进行反向计分。即得分越高代表青少年的抑制控制水平越高。本研究样本的Cronbach's α为0.844。
2.3 统计分析
在本研究中,我们运用SPSS 26.0软件进行统计分析,步骤如下:首先,通过Harman的单因素检验来检测共同方法偏差,若该检验的阈值低于40%,则可认为本研究未发现显著的共同方法偏差[43]。其次,对主要的分析变量和人口统计学变量进行了相关性分析,在此过程中,对关键变量进行了标准化处理。最终,为了验证假设,本研究采用了SPSS的PROCESS宏插件(模型4和模型7),来分析抑制控制在焦虑与青少年网络成瘾之间的中介作用,以及体力活动在焦虑与抑制控制之间的调节作用[44]。该PROCESS宏插件通过5000次的bootstrap重采样迭代,来对模型进行检验和估计95%的置信区间(95% Confidence Interval,95%CI)。在这一过程中,如果95%CI不包含0,则认为该关系具有统计学意义。此外,分析时控制了性别与年龄。
3 结果
3.1 描述性结果
本研究的共同方法偏差检验结果发现存在两个特征值大于1的因子,第一个因子占总方差的29.95%,小于40%的阈值,表明本研究不存在明显的共同方法偏差风险。
3.2 相关性分析
表1结果显示,青少年焦虑与体力活动(r = -0.160,p<0.001)和抑制控制(r = -0.423,p<0.001)呈显著负相关,与网络成瘾呈显著正相关(r = 0.413,p<0.001)。抑制控制与体力活动(r = 0.143,p<0.001)呈显著正相关,与网络成瘾(r = -0.368,p<0.001)呈显著负相关。
表 1 相关性分析 | ||||
变量 | 1 | 2 | 3 | 4 |
1 年龄 | - | |||
2 焦虑 | 0.032 | - | ||
3 体力活动 | 0.012 | -0.160*** | - | |
4 抑制控制 | -0.048 | -0.423*** | 0.143*** | - |
5 网络成瘾 | 0.087*** | 0.413*** | -0.124*** | -0.368*** |
注:***:p<0.001 |
3.3 调节中介模型检验
表2显示了调节中介模型检验的结果。在对人口学变量进行控制后,加入体力活动后焦虑依然可显著负向预测青少年抑制控制(β= -0.410,SE = 0.023, p<0.001)。体力活动显著正向预测青少年抑制控制(β= 0.064,SE = 0.023, p<0.01),并且体力活动可显著降低焦虑对青少年抑制控制的预测作用(β= -0.092,SE = 0.022, p<0.001)。详见图2。
表 2 调节中介模型检验 | ||||||
结果变量 | 预测变量 | β | SE | t | R² | F |
抑制控制 | 性别 | -0.070 | 0.046 | -1.505 | 0.196 | 77.888*** |
年龄 | -0.052 | 0.031 | -1.699 | |||
焦虑 | -0.410 | 0.023 | -18.006*** | |||
体力活动 | 0.064 | 0.023 | 2.760** | |||
焦虑X体力活动 | -0.092 | 0.022 | -4.175*** | |||
网络成瘾 | 性别 | 0.048 | 0.045 | 1.066 | 0.221 | 113.418*** |
年龄 | 0.090 | 0.030 | 2.972** | |||
焦虑 | 0.311 | 0.024 | 12.757*** | |||
抑制控制 | -0.231 | 0.024 | -9.470*** | |||
注:*:p<0.05;**:p<0.01;***:p<0.001 |
4 讨论
本研究讨论了焦虑与青少年网络成瘾之间的关系,以及抑制控制在二者之间的中介作用,并且体力活动调节了焦虑与抑制控制之间的关系。研究发现,焦虑与青少年网络成瘾有显著的正相关关系,而与青少年抑制控制有显著的负相关关系。体力活动与青少年焦虑有显著的负相关关系,而与青少年抑制控制有显著的正相关关系。抑制控制在焦虑与青少年网络成瘾之间的起到中介作用。最后,体力活动降低了焦虑对青少年抑制控制的预测作用。
本研究发现,高焦虑水平预测了青少年较高的网络成瘾水平,这点与前人的研究类似[14,18]。研究发现,焦虑通常是网络成瘾的危险因素[45,46]。青少年通常面临各种焦虑[3],他们通常寻求娱乐活动来缓解这种情绪带来的压力。在我们的研究中发现了这一现象,这符合情绪增强假说(mood enhancement hypothesis)的观点[47]。同时,由于互联网的匿名性与便捷性[48],进一步加剧了青少年在高焦虑水平下选择卷入互联网行为的程度,进而逐渐形成网络成瘾。因此,结合以上证据,我们假设焦虑预测青少年网络成瘾得到了成立。
焦虑除了可预测青少年网络成瘾外,还可通过负面影响青少年抑制控制水平,从而间接影响青少年网络成瘾。研究发现,当个体处于高焦虑水平下,其抑制控制受到了显著的负面影响[24,26,49],这符合先前的理论[50]。而焦虑对青少年抑制控制的影响程度并不总是恒定的。本研究发现体力活动可缓解焦虑对青少年抑制控制的负面影响。这不仅源于体力活动对焦虑的影响[29,30,51-53],以及与情绪调节有关的脑区的影响[31]。还源于体力活动对抑制控制的促进作用[32,33,35-37,54,55]。因此,体力活动可削弱焦虑对青少年抑制控制的影响。
本研究的重要贡献在于深入探讨了焦虑与青少年网络成瘾之间的联系,并分析了抑制控制在这一关系之间的中介作用,同时还考察了体力活动对焦虑与抑制控制之间关系的调节效应。然而,研究也存在一些局限性。首先,由于研究基于横断面数据,因此在解释变量间的因果关系时存在局限。其次,主要变量的测量依赖于主观报告,可能缺乏客观性。最后,样本主要来自一所学校,这可能限制了研究结果在不同文化背景下的普遍适用性。
5 结论
研究结果显示,焦虑能够显著正向预测青少年的网络成瘾,而这种预测作用可以通过抑制控制的中介效应得到部分解释。此外,焦虑对青少年抑制控制能力的影响可以被体力活动所调节。因此,建议学校与家庭关注青少年的情绪状态,不额外增加不合时宜的学习任务以减少青少年焦虑的发展。同时,学校与家庭应鼓励青少年参与体力活动,以缓解焦虑带来的负面影响。
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