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Frontiers in Psychology· Ana María Ruiz-Ruano García·· 2 小时前精选AI 评分62

Frontiers in Psychology：高屏幕时间儿童的语言发育预警指标网络连接更密集

Dense connectivity in networks of red-flag language milestones among high screen-time children

AI 导读

一项发表于 Frontiers in Psychology 的研究基于2023年美国全国儿童健康调查中16914名1至5岁儿童的数据，用网络分析比较不同屏幕时间下语言发育预警指标之间的关联结构。

推荐理由

基于16914名儿童的数据，用网络分析呈现高屏幕时间与语言发育预警指标更密集关联的模式。

正文 · AI 翻译

译文尚不完整，完整内容请切换到原文。

摘要

引言：

屏幕时间可能影响幼儿早期的语言发展，尽管其影响可能因情境因素而异。在成人引导下，屏幕使用可以支持发展，但过度接触与较不利的语言结果相关。

方法：

数据来自2023年全国儿童健康调查，共纳入16,914名儿童（51%为女孩，78%为白人；M = 3.03岁，SD = 1.37）。分别对1-2岁和3-5岁儿童估计了预警性语言里程碑的网络。参与者根据世界卫生组织（WHO）的屏幕时间建议（<1小时/天 vs. ≥1小时/天）进行分类。

结果：

部分正则化网络模型显示，屏幕暴露水平较高的儿童中，语言发展指标之间的连接更为密集。这一模式反映为更多的网络边和完全连接的子集群，并且在3-5岁儿童中尤为明显。

讨论：

研究结果表明，较高的屏幕时间暴露与幼儿期语言发展困难之间更为相互关联的模式有关。这些结果有助于更深入地理解儿童屏幕使用背景下的语言发展，并可能为家庭、教育专业人员和政策制定者提供有用的见解，以促进健康的发展环境并就屏幕暴露做出明智决策。

1 引言

屏幕使用和数字媒体暴露对儿童语言习得的影响在近期研究中受到越来越多的关注（Boe Rayce et al., 2024；Sundqvist et al., 2024a；Sundqvist et al., 2025）。虽然数字通信技术是语言发展方面的一项当代挑战，但关于媒体影响儿童言语的担忧并不新鲜。早期关于电视暴露的研究同样发现了对语言结果的潜在负面影响（Christakis et al., 2009；Duch et al., 2013；Mendelsohn et al., 2008），这表明尽管媒体类型随时间发生了变化，但关于儿童语言发展潜在风险的担忧仍然相关。这些担忧促使世界卫生组织（2019）和美国儿科学会传播与媒体委员会执行委员会（2016）就儿童媒体使用向儿科医生、家长和媒体行业发布了指导意见。

这些建议针对儿童健康媒介互动发展的不同方面。在这方面，对于18个月以下的儿童，建议避免使用媒介（视频通话除外）；对于24个月以下的儿童，建议选择高质量内容；对于2–5岁的儿童，建议每天屏幕时间不超过1小时。然而，尽管有这些建议，McArthur et al. (2022a) 指出，75.3%的2岁以下儿童和64.4%的2–5岁儿童超过了各组建议的时间。近期研究也观察到了类似的使用模式（Boe Rayce et al., 2024；Brushe et al., 2024；Gago-Galvagno et al., 2025；Okenwa-Vincent et al., 2025；Przybylski and Weinstein, 2019；Sundqvist et al., 2024a；Sundqvist et al., 2025；Vanderloo et al., 2022）。

1.1 儿童数字媒介暴露与语言

尽管存在这些担忧，屏幕时间或数字媒体暴露是否是言语问题的直接原因仍不清楚（Jing et al., 2023；Madigan et al., 2020）。这种不确定性凸显了进一步研究屏幕暴露与语言延迟之间关系的必要性。一些先前的研究报告了数字媒体使用与语言发展之间可能存在的负面关联。当前该领域的研究主要集中在三个方面：情境、内容和时间。关于情境，已观察到当儿童参与共享式数字媒体使用时，他们往往表现出更有利的语言结果。相比之下，独自观看通常与负面效应相关（Almohammadi et al., 2025；Kucker et al., 2024；Sundqvist et al., 2024b）。在家中营造丰富的读写环境也很重要，一些证据表明，缺乏此类支持的儿童更有可能出现语言延迟（Almohammadi et al., 2025）。Madigan et al. (2020) 观察到，共同观看或选择高质量资源对语言发展有积极结果。父母参与，例如花时间一起读书、教学互动或加入媒体参与，似乎对屏幕暴露在语言发展中的结果具有缓解作用（Gago-Galvagno et al., 2025；Medawar et al., 2023；Sundqvist et al., 2024b）。共享阅读提供了一个宝贵的机会，让儿童接触新的或熟悉的词汇并进行对话。Boe Rayce et al. (2024) 观察到，共享阅读能提高儿童的综合技能。同样，Rosslund et al. (2025) 和 Paoletti et al. (2025) 指出，当父母和儿童共享阅读书籍时，表达性和接受性词汇量会增加。虽然某些积极因素可能抵消屏幕暴露的潜在有害影响，但一些情境因素仍值得关注。例如，考虑父母的数字媒体使用很重要，因为已观察到它会挤占亲子互动，并可能干扰儿童的学习过程（Mustonen et al., 2022）。关于母亲的数字媒体使用，也已被指出对儿童语言发展中的产出技能（手势）和理解能力有负面影响（Mustonen et al., 2022；Paoletti et al., 2025）。

聚焦媒体内容分析的研究探讨儿童的语言能力如何受到教育或娱乐资源的影响。在这个意义上，人们通常认为教育性内容有助于儿童更好的语言发展（Madigan et al., 2020；Paoletti et al., 2025；Sundqvist et al., 2024b）。然而，正如Jing et al. (2023)所指出的，接触所产生效应的性质取决于所采用的方法学取向，因此难以确定这些效应最终是积极的还是消极的。例如，有研究观察到教育性内容与更长的句子产出相关，而用于安抚儿童的内容则与更短的句子相关（Kucker et al., 2024）。相反，Medawar et al. (2023)观察到教育性内容对儿童的句子使用具有负面影响。无论如何，Sundqvist et al. (2025)强调需要仔细考虑儿童所接触的内容。正如Sundqvist et al. (2024b)所观察到的，一些家长并不了解自己的孩子接触屏幕的内容，这往往是由于内容描述含糊或不精确。这种认知的缺乏不仅引发了关于儿童在做什么的重要关切，也引发了对此类接触可能对其认知、情感和语言发展产生潜在影响的重要关切。

关于时间，已有研究观察到儿童超过推荐屏幕时间与较差的语言发展结果相关（Boe Rayce et al., 2024；McArthur et al., 2022b；Sugiyama et al., 2023）。同样，屏幕时间与语言和沟通困难之间也被指出存在正相关（Paoletti et al., 2025；Slobodin et al., 2024）。例如，Sugiyama et al. (2023)观察到，接触屏幕时间超过推荐时长的儿童在4岁时表现出更大的沟通、日常生活技能和社交方面的困难。研究还观察到，更多的屏幕接触与较低的整体语言表现水平相关（Madigan et al., 2020；Mustonen et al., 2022）。一些研究发现，屏幕时间与语言发展的多个方面（包括词汇量、语法和话语长度）之间存在负相关（Asikainen et al., 2021；Gago-Galvagno et al., 2025；Kucker et al., 2024；Rosslund et al., 2025；Sundqvist et al., 2022，2025；Sundqvist et al., 2024b）。此外，Sundqvist et al. (2024a)指出，屏幕时间对词汇量的影响超出了即时结果，并会随时间持续存在。

1.2 早期沟通的复杂性

语言习得对儿童的全面发展至关重要。存在语言习得困难的儿童也更有可能出现社交（Chow et al., 2021）、情绪（Fields-Olivieri et al., 2024）和认知发展问题（Cadime et al., 2025）。语言习得延迟或语言困难与入学准备和学业表现相关（Okenwa-Vincent et al., 2025；Vanderloo et al., 2022）。正如Conti-Ramsden and Durkin (2012)所强调的，语言困难与记忆技能、非言语能力、社交互动、行为和情绪方面的问题有关，这些问题会显著影响儿童的学业成就。鉴于已有报道指出屏幕使用、数字媒体暴露与语言习得之间的关联，与屏幕相关的行为也可能与儿童发展的更广泛方面相关。因此，持续研究其与语言技能的关系对于增进我们对儿童发展的理解至关重要。

沟通是一种复杂的现象，涉及多个相互作用的要素，这些要素在整个发展过程中不断变化（Ladyman et al., 2013）。从经典和神经生物学的角度来看（Conti-Ramsden and Durkin, 2012），语言技能通常被分为两个基本领域：表达——恰当说出或写出词语的能力——和理解——理解口语或书面语言的能力。虽然传统分类区分了表达性语言和接受性语言，但Bloom and Lahey (1978)提出了一个有用的框架来描述语言发展和刻画语言困难。根据这一框架，可以通过考虑三个核心维度来评估语言：使用（指语言语用）、内容（指意义）和形式（指语言结构和规则）。而更精细的语言评估分类则考虑词汇、语篇、语用、音系和语义评估（Conti-Ramsden and Durkin, 2012）。本研究旨在通过应用网络分析来探索屏幕时间与语言发展之间的复杂关联，网络分析能够对这一现象提供细致入微的理解。尽管这些框架仍被广泛用于描述语言发展，但它们应被理解为概念性指南，而非对当代语言习得所有观点的详尽呈现。

1.3 本研究

本研究旨在将网络分析的应用范围拓展至语言发展领域，从而有助于更深入地理解幼儿和儿童的屏幕时间与语言技能之间的关系。心理障碍网络理论的提出，为精神病理学的结构与动力学提供了一种全新的概念化框架（Borsboom et al., 2011；Borsboom, 2017；Borsboom and Cramer, 2013；Schmittmann et al., 2013）。该框架是作为认知障碍共同原因模型的一种替代方案而提出的，后者认为某一特定障碍的症状是单一潜在原因——即疾病本身——所产生的结果。相比之下，网络框架将障碍概念化为症状之间相互作用模式所涌现的产物。该框架并非将症状归因于某一单一潜在原因，而是认为症状本身在其他症状的产生中发挥着因果作用。因此，例如，连接紧密的症状网络被解释为反映了更高程度的症状相互依赖性（Borsboom, 2017）。从治疗的角度来看，网络方法也被认为具有价值，因为识别核心症状被认为可能有助于理解症状的组织结构以及打破适应不良的症状动力学（Cramer et al., 2010）。

尽管网络分析最初是为应用于心理障碍——如抑郁或焦虑——而设计的（见 Borsboom and Cramer, 2013），但其底层方法论可以很容易地迁移到语言发展研究中。虽然语言发展研究不涉及临床意义上的症状观察，但诸如错误、延迟或未能达到预期里程碑等偏差可以类比地进行解读。这些偏差可以作为潜在发展困难的指标——或“症状”——为语言习得模式提供有价值的见解。因此，早期语言发展中的危险信号相互作用模式可以被编码在一个网络结构之中，这反过来又使得识别显著的发展指标并刻画它们在更广泛网络结构中的关系成为可能。例如，语言习得延迟的核心标志物在被表征于网络结构中时，可能有助于识别特别突出的发展指标，并阐明它们如何嵌入更广泛的关联模式之中。此外，将语言习得延迟的标志物表征于网络框架之中，可能有助于更全面地理解语言相关困难的组织结构，并为未来研究生成假设。

本研究并不假设一个与精神障碍网络理论严格一致的视角。事实上，症状网络中中心性指数的解释近来一直是方法论争论的主题，若干作者质疑中心节点在多大程度上可以被解释为因果驱动因素或优先干预目标（例如，Spiller et al., 2020）。因此，本研究不对网络结构或中心性测量采取因果解释。此外，本研究采用的网络模型并非旨在近似所建模变量之间关系的因果解释，这与专门为因果推断设计的其他方法（如贝叶斯网络）不同（例如，参见 López Puga et al., 2015）。相反，所估计的网络应被理解为红旗语言发展指标之间条件关联模式的表征。据此，所观察到的连接和中心性指数被解释为网络结构的描述性特征，而非因果影响或发展机制的证据。

本研究的目的是基于儿科医生和卫生组织提供的建议，探索儿童早期语言发展中红旗指标相互作用的网络。如果较高的屏幕时间与较不利的语言发展结果相关，那么可以预期，在较高的屏幕暴露水平下会观察到更密集的红旗指标相互作用网络。如果情况如此，识别这些网络中最中心的节点可能有助于识别值得进一步研究的突出发展指标。这些见解可能有助于更好地理解与不同屏幕暴露水平相关的语言发展模式。尽管网络分析已在其他领域得到应用，但其在语言发展中的应用在很大程度上仍未被探索。因此，本研究通过引入网络分析作为一种考察屏幕时间与语言发展之间关系的新方法，为文献做出了贡献。

2 材料与方法

2.1 参与者

数据来自2023年全国儿童健康调查（美国人口普查局，人口项目副主任，全国儿童健康调查，2023年），该调查于2023年6月至2024年1月期间通过在线、纸质和电话调查方式收集信息。该调查关注0–17岁儿童的健康与福祉，由其照护者完成。该调查获得的测量结果对目标人群具有代表性，但不包括居住在机构环境中的儿童。在本研究中，选取了1–5岁儿童的数据（M = 3.03，SD = 1.37）（n = 16,914）。排除了患有某种精神障碍或残疾的儿童（注意缺陷/多动障碍、自闭症谱系障碍、发育迟缓、言语障碍、智力障碍、脑瘫或唐氏综合征）。排除被诊断患有神经发育障碍或残疾的儿童，是为了降低人群异质性，并将分析聚焦于一般人群中的语言发展模式。排除1岁以下儿童，是因为未对该年龄组实施与语言里程碑相关的问题。样本中女孩（8,687，51%）和男孩（8,227，49%）的比例相当，且超过四分之三的儿童在其家庭中是第一胎（2,756，26%）或第二胎（5,624，54%）。大多数儿童为白人（77.83%，13,165），9.67%（1,635）为两个或更多种族，6.05%（1,023）为亚裔，5.34%（904）为黑人或非裔美国人，0.78%（132）为美洲印第安人或阿拉斯加原住民，0.33%（55）为夏威夷原住民或其他太平洋岛民。大多数儿童要么是独生子女（6,425，38%），要么与一个兄弟姐妹同住（7,303，43%），而与两个（2,367，14%）或三个以上（819，4.8%）兄弟姐妹同住则较少见。

样本中的照护者在高等教育（6,761，40%）和中等教育（6,702，40%）水平上分布相当，仅完成义务教育或短期教育的比例较低（3,451，20%）。照护者还报告了较高的心理健康水平，样本中超过91%（10,460）表示两位照护者的健康状况均为良好、很好或极好。大多数家庭要么两位照护者都工作（10,460，70%），要么只有一人就业（4,258，28%），而没有照护者工作的家庭较少见（233，1.6%）。第三代个体占样本的四分之三以上（13,015，78%），表明以本土出生人口为主。有关样本特征的更详细描述，请参见补充表S2、S3。

2.2 测量指标

2.2.1 语言发展指标

关于语言里程碑的照护者回答来自调查的“学习与活动”部分。共选取了11个二分项目，用于探究1–2岁儿童的语言发展。对于3–5岁儿童，这些项目被保留，并补充了7个额外的有序多分类项目，以捕捉语言发展的其他维度（见补充表S1）。二分项目被重新编码，1表示该语言里程碑未达成，0表示已达成。在多分类项目原本的编码方式未能体现语言里程碑难度越高得分越高的情况下，对其进行了重新编码以表达这种关系。因此，所有项目均按反映达成语言里程碑难度的水平进行编码。这种编码方式使这些项目可以被解释为潜在的危险信号或非典型语言发展的早期指标。换言之，本研究考察的危险信号是指在评估时尚未达成、尽管在相应发育阶段通常应已具备的语言技能。因此，它们可被视为语言发展困难的潜在指标。重要的是，危险信号一词并非在精神病理学意义上使用，而是指预期发展轨迹中可能的延迟或薄弱之处。

这些项目根据Bloom and Lahey（1978）提出的理论框架以及Conti-Ramsden and Durkin（2012）建议的细粒度分类进行了归类。这一分类为网络表示中节点的编码提供了依据，确保每个节点都反映语言评估中具有概念基础的维度。据此，每个项目被赋予三个不同的编码：一个表明其属于理解还是表达；一个表明其属于使用、形式还是内容；一个表明其属于语篇、词汇、音系（不适用于1–2岁儿童）、语用还是语义。

2.2.2 屏幕时间

屏幕时间测量仅涉及工作日，回答选项包括“少于1小时”“1小时”“2小时”“3小时”和“4小时或以上”。根据世界卫生组织（2019）的建议，对该变量应用了截断值，将儿童分为两组。具体而言，如果照护者报告屏幕时间少于1小时，则儿童被归入一组；如果报告为1小时或以上，则归入第二组。这一分类既基于公共卫生指南，也与本研究的主要目标高度一致。

2.2.3 人口学及儿童相关变量

除语言发展项目外，还纳入了社会人口学变量和与儿童相关的指标，主要用于描述样本特征并提供与结果解读相关的背景信息。共考虑了三类变量。第一类包括儿童特有的特征，如年龄、性别和出生顺序。第二类涵盖照护者和家庭相关因素，包括教育程度、就业状况、心理健康状况、出生地以及同住儿童数量。第三类关注儿童的典型活动，如共同阅读、讲故事、唱歌，以及平日和周末户外玩耍的时间。

2.3 数据分析

采用蒙特卡洛方法，使用 powerly（版本 1.8.6）R 包估算最佳样本量（Constantin et al., 2026）。由于各年龄组的节点数不同——1–2 岁组为 11 个，3–5 岁组为 18 个——样本量估算分别针对 1–2 岁和 3–5 岁儿童进行。在样本量估算中，将边密度阈值设为 0.3 或更低，并将敏感度和统计功效均固定为 0.8。在网络估计之前，语言发展指标通过均值中心化和缩放至单位方差进行标准化，并控制儿童性别、年龄和照护者教育水平。加权正则化网络模型使用默认的 EBICglasso 算法进行估计，该算法依赖多分格相关和多序列相关来处理有序和混合类型数据，如 bootnet（版本 1.6）R 包中所实现（Epskamp et al., 2018）。鉴于 EBICglasso 算法倾向于生成相对密集的正则化网络，应用了包含 1,000 次随机重抽样的非参数自举程序，以识别 95% 置信区间不包含零的边。这些边被保留下来，用于后续对每个年龄组最终网络结构的可视化和统计分析。

在网络分析框架内，对网络中每个节点计算了常见的中心性指标，包括接近中心性、中介中心性和期望影响（Borsboom and Cramer, 2013）。接近中心性基于平均最短路径长度（SPLs），量化一个节点与网络中所有其他节点的接近程度。它被定义为给定节点到所有其他节点的平均距离的倒数，反映其高效影响或被网络其余部分影响的潜力。在早期语言发展的红旗网络中，接近中心性可以解释为语言困难表现之间紧密相互关联程度的度量。具有高接近中心性的节点可能代表与许多其他症状或指标密切相关的症状或指标，表明它们有可能影响或被网络中广泛的发展问题所影响。中介中心性也量化节点在网络中的重要性，但它通过考虑一个节点位于其他节点对之间最短路径上的次数来实现。换句话说，具有高中介中心性的节点充当关键的中介或“桥梁”，促进网络中的连接。在语言发展迟缓的红旗网络中，较高的中介中心性可以解释为节点在调节其他语言习得困难的共现或进展中的相关性的指标。最后，从系统层面的角度来看，期望影响可能是最相关的中心性指标，因为它建立在节点度——一个节点与其他节点的直接连接数量——的基础上，同时结合了这些连接的强度。在语言发展的红旗网络中，期望影响识别出发展困难的核心表现，这些表现可能作为教育、家庭或临床干预的关键目标。中心性稳定性通过模拟随机删除案例来评估，对每个删除案例比例进行1,000次重复。对于每次模拟，计算完整样本的中心性估计与缩减样本的中心性估计之间的相关性。然后估计稳定性系数CS(cor = 0.7)，表明在保持原始和缩减中心性值之间相关性至少为0.7的情况下，可以删除的最大案例比例（Epskamp et al., 2018）。

每个节点都计算了 Barrat 等（2004） 提出的局部聚类系数。该指标是 Watts 和 Strogatz（1998） 提出的聚类系数的加权扩展，允许在评估局部连通性时考虑边的权重。聚类系数评估一个节点的邻居之间相互连接的程度，即形成紧密联系群体的程度。聚类系数越高，表明该节点嵌入在一个连接密集的子网络中。在语言发展的预警信号网络中，这些系数有助于识别某一特定指标是否属于与不典型语言习得相关的紧密关联的症状或表现集群。网络使用 Fruchterman 和 Reingold（1991） 算法进行可视化，该算法在 igraph（版本 2.1.4）R 包中实现（Kolaczyk 和 Csárdi，2020）。原始数据集和处理后的数据集、源代码、表格、图表以及支持性元数据均可通过 Open Science Framework（OSF）项目公开获取，以供验证和重用，网址为 https://doi.org/rp4g。源代码也可通过 GitHub 获取（https://github.com/dr46/LaDeNetST），该项目的永久存档版本已存入 Zenodo（https://doi.org/rp4h）。

3 结果

3.1 网络的结构比较

如 图 1 所示，在两个年龄组中，连接数量均随屏幕时间水平的增加而增加。对于 1–2 岁儿童，节点数量保持恒定为 11，而边数从 26（低屏幕时间）增加到 30（高屏幕时间）。相比之下，3–5 岁儿童表现出网络复杂性的显著增加：节点数量从 12 增加到 17，边数从 16 急剧增加到 40（增加了一倍以上），表明在较高屏幕时间条件下，预警信号语言里程碑之间的关联模式具有更强的相互连接性。在两个年龄组中，边密度（节点之间实际连接数与完全互联网络中所有可能连接数的比例）均随屏幕时间增加：1–2 岁儿童从 0.47 增加到 0.55，3–5 岁儿童从 0.24 增加到 0.29。值得注意的是，这种增加甚至发生在年龄较大组节点数量上升的情况下，表明在较高屏幕时间条件下网络结构按比例更加密集。

图 1

估计网络揭示了不同条件下边缘重叠和独特性的结构差异。对于1–2岁儿童，低屏幕时间与高屏幕时间条件之间有22条边缘共享，而对于3–5岁儿童，仅有15条边缘是共同的。边缘独特性进一步说明了网络之间的结构差异。对于1–2岁儿童，有4条边缘是低屏幕时间条件所独有的，而高屏幕时间条件下出现了8条新边缘。相比之下，3–5岁儿童的网络显示出更为显著的转变：低屏幕时间条件下仅有1条独特边缘，而高屏幕时间条件下出现了25条独特边缘（见补充图S1、S2）。这种新连接的大幅增加表明，高屏幕时间条件下的儿童在红旗语言里程碑之间表现出一种独特的关联模式，尤其是在年龄较大的组别中。

3.2 聚类系数

在两个年龄组中，每日屏幕时间超过1小时的儿童网络内的聚类程度明显更高。这表明该组中红旗语言发育里程碑之间的相互联系更为紧密，与屏幕时间较少的儿童相比，语言发育指标的关联模式更为紧密。在低屏幕时间的1–2岁儿童网络中，有三个节点显示出最高的聚类系数值（见图2；节点描述缩写见表1）。值得注意的是，其中两个节点——ak2，问“为什么”和“如何”；以及tel，讲故事的能力——属于一个由四个节点紧密互联的群体，均与语言产生相关。这意味着这些节点中的每一个都直接连接到群体内的其他节点，形成一个完全连接的聚类。第三个节点un1（理解“不”的含义）也形成了一个类似的完全连接的四个节点群体，表明一个由密切相关的语言困难组成的独特聚类。相比之下，每日使用屏幕超过1小时的儿童网络也包括三个具有最高可能聚类系数的节点（wo1，说至少一个词；un1，理解“不”的含义；以及tel，讲故事的能力）。然而，与低屏幕时间组不同，这些节点各自嵌入不同的、完全连接的聚类中，表明网络结构更为密集和多样化聚类。这种模式表明，该组中的语言发育困难可能分布在多个紧密互联的子群体中。

图2

表1

节点语言技能ST < 1小时/天ST > 1小时/天
BCEIBCEI
1–2岁
ak1问‘谁’、‘什么’、‘何时’、‘何地’160.750.82120.740.84
ak2问‘为什么’和‘如何’90.670.7890.660.89
di1遵循口头指令80.720.7730.650.64
di2遵循两步指令00.720.7810.730.79
poi指向物品20.770.7300.740.66
tel讲故事00.540.3700.550.40
un1理解‘不’的含义00.560.3100.430.17
un2理解‘在……里’、‘在……上’和‘在……下’40.740.750.750.82
wo1说至少一个词00.540.2600.720.37
wo2将两个词一起使用200.910.81170.860.86
wo3将三个词一起使用230.910.79180.860.9
3–5岁
ak1问‘谁’、‘什么’、‘何时’、‘何地’180.50.49190.310.65
ak2问‘为什么’和‘如何’240.610.37640.360.63
cou数物体00.430.3100.210.38
di1遵循口头指令00.230.29
di2遵循两步指令00.230.32
emo命名情绪100.610.4700.280.57
exp解释看到或做过的事情360.750.6640.330.89
poi指向物品190.280.61
rbe单词的起始音00.590.700.270.85
rhy押韵词80.540.54150.250.62
sso两个以相同音开头的词240.651.08370.291.11
tel讲故事280.690.4300.340.55
tur等待轮流00.380.1000.20.2
un2理解‘在……里’、‘在……上’和‘在……下’00.210.33
wo1说至少一个词00.180.18
wo2将两个词一起使用00.410.3740.190.22
wo3将三个词连在一起使用00.360.3170.270.40

估计网络的中心性统计量。

ST，屏幕时间；B，中介中心性；C，接近中心性；EI，期望影响。接近中心性值按100缩放。

在3–5岁儿童估计的网络中，也观察到聚类增强的类似趋势。如图2所示，聚类系数最高的四个节点在不同组间存在显著差异：对于每日屏幕时间少于1小时的儿童，这些节点主要与语言产生相关，而对于每日屏幕时间超过1小时的儿童，它们则与语言理解相关。这表明，在屏幕使用水平较高的儿童所表现出的语言发展困难中，与理解相关的困难在估计的网络结构中占据更突出的位置。此外，在低屏幕时间组中，聚类系数最高的四个节点嵌入在两个全连接聚类中，每个聚类由三个节点组成。相比之下，在高屏幕时间组中，同样数量的排名最高节点涉及七个不同的全连接子网络，每个子网络包含三到五个节点。这一模式反映出一种更为碎片化但密集聚类的结构，表明该组的语言困难可能分布在多个紧密互连的领域中。

3.3 中心性测量

在1–2岁儿童中，比较每日屏幕时间少于1小时与超过1小时的儿童，其期望影响、接近中心性和中介中心性均无实质性差异（见表1）。中介中心性和期望影响中心性最高的四个节点——均与表达性语言产生相关——在各组间相同，尽管其排名因所考虑的指标而异。在两组中，中介中心性最高的两个节点均对应形式相关项目（wo2，将两个词连在一起使用；以及wo3，将三个词连在一起使用），随后是两个与语言使用相关的节点（ak1，问“谁”、“什么”、“何时”、“哪里”；以及ak2，问“为什么”和“如何”）。此外，在两组中，考虑接近中心性指标时，wo3和wo2也是最具中心性的节点。这表明两组中最具中心性的指标均与在单个话语中组合两个或三个词，以及使用疑问句提问有关。然而，在低屏幕组中，就期望影响而言，最具中心性的节点与提问相关，而在高屏幕时间组中，期望影响最高的节点则与在单个话语中组合三个词有关。

对于3–5岁儿童，预期影响最高的三个节点——sso，两个以相同音开头的词；rbe，词的首音；以及exp，解释所见或所做之事——在两组（低屏幕时间与高屏幕时间）中均保持一致。然而，它们的相对排名有所不同。值得注意的是，sso对应一个与音系相关的项目，在两个网络中均成为预期影响最高的节点。尽管在考虑中介中心性时，节点sso在网络中并不占据中心位置，但在此指标下它与其他节点相比仍相对边缘。其高预期影响得分主要归因于它与rbe的强连接，这构成了两组整个网络中变量之间最强的关系。尽管在考虑中介中心性时，sso和rbe在网络中占据相对边缘的位置——表明它们在其他节点之间的连接中介中并不扮演突出角色——但由于它们与其他节点的强直接连接，它们仍然施加了相当大的影响。这些高权重边对其预期影响得分贡献显著。这两个项目均属于音系类别，表明这可能代表了两组中观察到的一个突出困难领域。

在每日屏幕时间超过1小时的3–5岁儿童所估计的网络中，使用“为什么”和“如何”提问（ak2）以及提供解释的技能（exp）在考虑中介中心性时成为最中心的节点。节点ak2和exp在每日屏幕时间少于1小时的儿童网络中同样处于中心位置。然而，在每日屏幕时间超过1小时的组中，通过这些节点的最短路径数量显著更多（各64条路径），而屏幕时间较少的组则分别为24条和36条。这两个危险信号语言里程碑在估计网络中占据了其他语言发展困难的桥接位置，尤其是在每日屏幕时间超过1小时的组中。与屏幕时间较少的组相比，它们在该组中连接其他变量的作用更为明显。此外，ak2在网络中还表现出最高的接近中心性，表明它是与所有其他节点最邻近连接的节点。这表明ak2可能代表了危险信号语言里程碑网络中一个高度中心的指标，充当了各种语言相关困难相互连接的枢纽——尤其是在每日屏幕时间较高的组中。

3.4 屏幕时间、人口统计学特征及儿童相关变量

样本中儿童长时间屏幕使用（即每天一小时或以上）的观察患病率为72.2%（16,677人中的12,033人，95% CI[71.4, 72.8]），表明其中大多数儿童每天有大量屏幕使用时间。若干变量被发现与屏幕使用时间存在统计学关联，而其他变量则未显示出显著关系（见补充表S2、S3）。平均而言，每天使用屏幕少于1小时的儿童（M = 2.53，SD = 1.37）显著比每天使用屏幕超过1小时的儿童（M = 3.23，SD = 1.31）年龄更小，t(8097) = −29.98，p < 0.001，r = 0.32，双侧对比。照护者的教育水平与屏幕使用时间显著相关（χ2(2) = 512.53，p < 0.001，V = 0.18），低屏幕使用时间组中超过50%的照护者拥有长周期教育背景，而中等周期教育在高屏幕使用时间组的照护者中最为常见；此外，低周期教育在高屏幕使用时间组中比在低屏幕使用时间组中更为普遍。照护者的心理健康状况（χ2(1) = 45.13，p < 0.001，V = 0.06）、照护者的就业状况（χ2(2) = 26.15，p < 0.001，V = 0.04）、父母出生地（χ2(3) = 20.16，p < 0.001，V = 0.04）以及性别（χ2(1) = 4.57，p < 0.033，V = 0.02）均与屏幕使用时间显示出统计学显著关联，但效应量相对较小。在考虑出生顺序（χ2(3) = 5.08，p = 0.2，V = 0.02）或家庭中儿童数量（χ2(3) = 1.65，p = 0.6，V = 0.01）时，屏幕使用时间未发现统计学显著差异。

关于儿童相关变量，组间也观察到屏幕使用时间的差异。给孩子读书与屏幕使用时间的关联最强，低屏幕使用时间组中超过50%的儿童每天被读书，而高屏幕使用时间组中这一比例不到50%（χ2(3) = 602.18，p < 0.001，V = 0.19）。给孩子讲故事或唱歌的活动也观察到类似趋势，低屏幕使用时间组中每天接受此类互动的儿童比例高于高屏幕使用时间组（χ2(3) = 300.89，p < 0.001，V = 0.13）。

3–5 岁儿童中长时间屏幕时间的流行率（80.8%）显著高于 1–2 岁儿童（61.2%），χ2(1) = 748.47，p < 0.001，V = 0.21，95% CI [20.8, 17.9%]。在各组内（见 补充表 S4、S5），每日屏幕时间超过 1 小时的儿童平均年龄大于屏幕时间较少的同龄儿童：这一模式在 1–2 岁组（t(5,311) = −17.77，p < 0.001，r = 0.24，双侧对比）和 3–5 岁组（t(3,038) = −4.74，p < 0.001，r = 0.09，双侧对比）中均可见。在全样本中观察到的照护者教育水平与儿童屏幕时间之间的关联在各年龄组中保持一致。这一模式在 1–2 岁组（χ2(2) = 203.82，p < 0.001，V = 0.17）和 3–5 岁组（χ2(2) = 323.11，p < 0.001，V = 0.18）中均很明显。与全样本的发现一致，照护者的心理健康状况和就业状况在两个年龄组中均与儿童屏幕时间相关。对于照护者的心理健康状况，这一关联在 1–2 岁儿童（χ2(1) = 20.17，p < 0.001，V = 0.06）和 3–5 岁儿童（χ2(1) = 22.25，p < 0.001，V = 0.05）中均可见。同样，对于照护者的就业状况，在 1–2 岁组（χ2(2) = 15.47，p < 0.001，V = 0.05）和 3–5 岁组（χ2(2) = 9.81，p = 0.007，V = 0.03）中均发现显著关联。相比之下，儿童性别、出生顺序和父母出生地仅在两个年龄组中的一个与屏幕时间存在显著关联，而家庭中儿童数量在两个年龄组中均未观察到显著差异（见 补充表 S4、S5）。

儿童相关变量呈现出与全样本相似的趋势。其中，给孩子读书在两个年龄组中均显示出与屏幕时间最强的关联——1–2 岁（χ2(3) = 178.27，p < 0.001，V = 0.16）和 3–5 岁（χ2(3) = 385.69，p < 0.001，V = 0.2）。给孩子讲故事或唱歌也显示出在两个年龄组中一致的关联强度，1–2 岁儿童（χ2(3) = 66.83，p < 0.001，V = 0.01）和 3–5 岁儿童（χ2(3) = 108.89，p < 0.001，V = 0.11）的结果均显著。对于 3–5 岁儿童，工作日户外玩耍（χ2(4) = 21.49，p < 0.001，V = 0.05）和周末户外玩耍（χ2(4) = 16.26，p < 0.003，V = 0.04）也与屏幕时间显著相关（见 补充表 S6）。

4 讨论

当前研究表明，较高的屏幕时间水平与更相互关联的红旗语言里程碑相关，尤其是在3–5岁儿童中。这一模式与近期先前研究一致，表明屏幕时间增加可能与早期儿童较差的语言结果相关（Boe Rayce et al., 2024；McArthur et al., 2022b；Paoletti et al., 2025；Slobodin et al., 2024；Sugiyama et al., 2023）。该研究表明，暴露于超过推荐水平的屏幕时间的儿童在语言获得延迟标志物之间表现出更强的连通性，无论是在网络边还是次级聚类结构方面。最近一项研究报告了关于网络连通性的类似结果，特别是在考察幼儿屏幕时间与问题行为相互关联模式之间的关系时（Yang et al., 2025）。

这种密集相互连接的网络拓扑结构在3–5岁儿童高屏幕时间组估计的网络中尤为明显。在高屏幕时间组中，识别出七个完全连接的次级聚类，它们彼此部分相互连接。相比之下，在低屏幕时间组中仅观察到两个完全独立的聚类（见图2底部面板）。在低屏幕时间组中，具有较高聚类系数的节点主要与产出性语言技能相关，而在高屏幕时间组中，聚类最多的节点与语言理解相关。这一发现尤其值得注意，因为在早期发展中，理解技能通常先于产出性语言（Bloom and Lahey, 1978；Conti-Ramsden and Durkin, 2012）。儿童必须先理解词语，然后才能在言语中有意义地使用它们。理解相关延迟在高屏幕时间组中更为突出这一事实尤其值得注意，因为3–5岁儿童通常被期望在语言发展的这一关键阶段已经习得基础理解技能。因此，这些发现可能值得在未来针对该年龄组儿童的研究中给予额外关注，以帮助尽量减少可能因语言技能发展不良而产生的潜在发展挑战，特别是在社交、情感和认知领域（Cadime et al., 2025；Chow et al., 2021；Conti-Ramsden and Durkin, 2012；Fields-Olivieri et al., 2024；Okenwa-Vincent et al., 2025；Vanderloo et al., 2022）。

In terms of centrality, the ability to produce words beginning with specific sounds (node sso) exhibited the highest expected influence in the network of children aged 3–5 years. Although this variable was positioned peripherally, it remained connected to other phonological items. The pattern differed slightly between children exposed to low and high screen time: in the high screen time group, the number of connections was greater, and these variables were more tightly clustered with non-phonological speech and language benchmarks. These phonological variables are closely related to phonological awareness, a skill that is critical for both speech development and early reading acquisition. Phonological awareness also appears to be associated with vocabulary growth and the integrated development of broader language abilities (Noiray et al., 2019). Previous studies have reported that higher screen time is associated with lower vocabulary levels (Asikainen et al., 2021; Brushe et al., 2024; Kucker et al., 2024; Rosslund et al., 2025; Sundqvist et al., 2022, 2025; Sundqvist et al., 2024a,b). Future research should examine whether phonological awareness is associated with the patterns observed in the present study. The network structure estimated for the high screen time group also revealed that the ability to ask “why” and “how” questions (ak2 node), along with the skill to explain things (exp node), occupied prominent positions within the estimated network structure. Both nodes occupied bridging positions within the estimated networks–given their high betweenness and closeness centrality–linking various markers of language development difficulties. These nodes are related to the use dimension of language, which encompasses pragmatic skills such as initiating conversations, asking questions, and explaining ideas. This result aligns with findings by Sugiyama et al. (2023), who reported that extended screen time is linked to diminished daily living skills and socialization.

Among children aged 1–2 years, the differences between high and low screen time groups appear to be minimal, as the number of nodes in the networks remains constant and the increase in new edges is less pronounced compared to children aged 3–5 years. This limited differentiation may be due to the fact that children in this age group are still too young to exhibit pronounced language-development difficulties. Another possible explanation is that the effects of screen time on early vocabulary acquisition and other components of language development may not manifest until later stages, suggesting that associations involving screen exposure may become more apparent at later developmental stages (Brushe et al., 2024; Kucker et al., 2024; Sugiyama et al., 2023; Sundqvist et al., 2024a). However, edge density increases, and the number of fully connected clusters rises–from two independent subnetworks to three partially interconnected ones–suggesting a subtle shift in network organization even at this early developmental stage. This pattern further suggests that red-flag language milestones in the high screen time group are more densely interconnected, potentially reflecting a broader and more interconnected pattern of language-development difficulties among very young children.

While screen time is correlated with differences in the structure of the estimated networks associated with language developmental delays, it is important to consider other child-related and contextual factors that may also contribute to the observed patterns. For example, previous research has shown that reading to the child is an important factor that can moderate the negative impact high screen time may have on language development (Boe Rayce et al., 2024; Medawar et al., 2023; Paoletti et al., 2025; Rosslund et al., 2025). Results from this study align with previous findings, as children in the low screen time group are also more frequently read to. Among the variables analyzed, reading to the child showed the largest estimated effect size, suggesting that it may represent an important factor associated with the network patterns observed in the present study of markers associated with delayed language acquisition. As noted by Sundqvist et al. (2024b), engaging in high-quality reading experiences with the child may be associated with more favorable language-development outcomes.

In the same vein, storytelling and singing to the child were more frequently reported among participants in the low screen time groups in this study. These activities have also been reported to alleviate or reduce the negative impact of screen time on children’s language development. Accordingly, child-directed parental talk may help explain the patterns of connectivity observed in the estimated networks (Asikainen et al., 2021; Brushe et al., 2024; Sundqvist et al., 2022). In particular, singing may exert a twofold positive effect in preventing delays in language skills: first, by enhancing phonological awareness, and second, by making the interaction more enjoyable and engaging for the child. Additionally, singing may activate imitation schemas in children, when they attempt to reproduce familiar songs, which could in turn support the development of expressive language skills.

Finally, weekday and weekend outdoor play also differed between the low and the high screen time groups. As reported by Sugiyama et al. (2023), higher screen time is associated with reduced outdoor play. It is possible that outdoor play partially contributes to the lower connectivity observed in the networks estimated for the low screen time group. Outdoor play may provide opportunities for peer interaction, which in turn can support language development–particularly in dimensions related to social communication. In sum, these practices may act as protective and enriching experiences that buffer potential negative effects of screen use. Therefore, the differences observed in networks structures and language-related outcomes might reflect a broader constellation of developmental influences, rather than screen time alone.

The prevalence of screen time in this study was calculated using the threshold defined by the World Health Organization (2019), which recommends no more than 1 h per day of sedentary screen time for children under 5 years of age. Based on this criterion, 72.2% of the children in the sample exceeded the recommended screen time. Specifically, 61.2% of children aged 1–2 years and 80.8% of children aged 3–5 years were classified as having excessive screen time. These findings are consistent with those reported in a recent systematic review by McArthur et al. (2022a), which analyzed data from 1999 to 2020 (excluding studies related to the COVID-19 pandemic) and found a prevalence of 75.3% for children under 2 years and 64.4% for children aged 2–5 years, using the same cutoff point. On the one hand, the data suggest that screen time tends to increase with age, as previously reported by Przybylski and Weinstein (2019). On the other hand, the figures also indicate a slight generational increase in the prevalence of excessive screen time highlighting the growing relevance of this issue for researchers, practitioners, and public health stakeholders.

4.1 Limitations and strengths

The findings of the present study should be interpreted in light of several limitations. First, the analysis was based exclusively on screen time during weekdays. Including both weekday and weekend screen time would likely yield a more comprehensive and accurate estimate of children’s overall screen exposure. Second, screen time was measured using a categorical variable with only five levels, which may introduce measurement error and limit the granularity of the data. A more precise approach–such as using open-ended questions that allow caregivers to report the exact amount of time their children spend on screens–could provide richer data and enable a more nuanced analysis of the quantitative impact of screen time on language development. Third, the screen time measure used in this study was a composite score that did not differentiate between types of devices (e.g., PC, smartphone, tablet, or television). A segmented measure by device type could help identify specific risks associated with particular forms of screen use. Additionally, screen time was self-reported by caregivers, a method known to be susceptible to recall bias, imprecision, and underreporting due to social desirability. Finally, the cross-sectional design of the study limits the ability to draw causal inferences regarding the relationship between screen time and language development. Longitudinal or sequential designs would be better suited to explore the directionality and long-term effects of screen exposure. Nevertheless, the relatively large sample size and the use of random sampling procedures strengthen the reliability and generalizability of the findings. Moreover, the application of network analysis—an underutilized methodology in language development research—offers a novel perspective for understanding the complex interrelation among developmental markers. By modeling these relationships as interconnected systems, this approach provides a useful framework for characterizing patterns of association among developmental indicators. In addition to the limitations for causal inference imposed by the cross-sectional study design, causal interpretations cannot be derived from the statistical network models employed in the present study. Future research could apply alternative methodological and statistical approaches specifically designed to investigate causal processes and thereby provide further insight into the relationship between screen time and language development.

In the present study, screen time was examined using the cut-off values proposed by the World Health Organization, with a 1-hour threshold used to classify children into low and high screen-time groups. Although this approach may be meaningful from a public health perspective, it may also have introduced potential biases affecting statistical power and the observed group differences. Consequently, future studies should investigate the relationship between screen time and language development using analytical approaches that preserve the full variability of the data, including continuous or dose–response models, thereby providing a more comprehensive understanding of these associations. Furthermore, it should be noted that the findings reported in the present study are strictly descriptive in nature and should not be interpreted as evidence of statistically significant differences between networks. Such conclusions would require the application of robust network comparison procedures specifically designed to evaluate differences in network structure. Consequently, future research should address this question using formal network comparison methodologies, such as the Network Comparison Test, to determine whether the observed structural differences are statistically reliable. Additionally, alternative network estimation methods could have been employed, potentially yielding different structural patterns and connectivity profiles. Consequently, the findings reported here should be interpreted in light of the specific methodological choices adopted in the present study. Future research should examine the robustness and stability of these results across different network estimation approaches and validation procedures to determine the extent to which the observed patterns are replicable. An additional limitation is that the final networks contained different numbers of nodes across conditions, particularly in the 3–5 year age group. As a consequence, global network metrics such as density should be interpreted with caution, since their values may be partly influenced by differences in network size. Future research should examine the robustness of the present findings using approaches that facilitate comparisons across networks with equivalent node structures.

The language indicators examined in this study were derived from caregiver reports obtained through a large-scale population survey and may therefore be affected by recall bias, reporting bias, and social desirability effects. Furthermore, the red-flag language milestones were not originally designed as a psychometric scale, and consequently some observed network associations may reflect measurement characteristics in addition to developmental processes. Future studies should evaluate the extent to which the present findings can be replicated using direct assessments of language development and alternative measurement approaches. Although the analyses controlled for child age, sex, and caregiver education level, other potentially relevant factors were not included in the adjustment process. Variables such as broader socioeconomic conditions, parent–child interaction patterns, childcare experiences, and pre-existing developmental vulnerabilities may also influence both screen time and language development. Consequently, residual confounding cannot be ruled out, and future studies should investigate the robustness of the present findings using more comprehensive adjustment strategies.

These findings contribute to a better understanding of the organization of language-related difficulties within the observed networks but should not be interpreted as identifying causal mechanisms or intervention targets. The network analysis highlights that these markers do not operate in isolation but rather form tightly linked patterns of associations. These findings contribute to a better understanding of how language-related difficulties are organized within the observed networks and may help identify particularly salient indicators for future investigation. In this context, the most central nodes represent language milestones that occupy prominent positions within the estimated network structures and may therefore warrant additional attention in future research. Although centrality measures provide useful information regarding the structural organization of the estimated networks, their interpretation should be approached with caution. In cross-sectional association networks, highly central nodes do not necessarily represent causal mechanisms, developmental drivers, or optimal targets for intervention. Consequently, the centrality results reported in this study should be interpreted as descriptive indicators of the relative prominence of language milestones within the observed network structures.

An additional limitation of the present study is that children diagnosed with neurodevelopmental disorders or disabilities were excluded from the analyses. Although this decision was made to reduce population heterogeneity and to facilitate the examination of language-development patterns within the general population, it also limits the generalizability of the findings. Consequently, the network structures reported here cannot be assumed to extend to clinical or neurodevelopmental populations, whose developmental trajectories may differ substantially from those observed in typically developing children. Future studies should investigate whether the patterns identified in the present work are replicated among children with neurodevelopmental disorders and disabilities, or whether distinct configurations of language-development indicators emerge in these populations.

4.2 Conclusion

The findings reported in this study suggest that the pattern of relationships among language-development milestones between the ages of 1 and 5 is complex, evolves across developmental stages, and is associated with children’s screen-time exposure. Consequently, understanding these phenomena requires a systemic approach that is sensitive to the interconnected nature of language development. From a public health perspective, the present findings support the continued exploration of strategies aimed at promoting healthy and developmentally appropriate screen-use practices during early childhood. Likewise, speech-language pathologists, psychologists, educators, and other professionals working with young children may wish to consider how screen-use habits relate to broader patterns of language development when evaluating individual cases.

An interesting finding of the present study is that indicators related to the use dimension of language appeared to occupy particularly prominent positions within the estimated network structures. This observation raises important questions for future developmental research and suggests that the role of pragmatic language skills in the context of children’s screen use deserves closer examination. Further studies are needed to determine whether these patterns can be replicated and to clarify their implications for language development. Although the present study does not allow specific intervention recommendations to be derived, we hope that the findings reported here may contribute to future research seeking to identify effective strategies for supporting language development and addressing potential difficulties associated with different patterns of screen use during early childhood.

Children under the age of five are in a critical period for overall language development, and language is arguably not merely an “additional cognitive skill”. In a striking statement, Winograd and Flores (1987) asserted that “nothing exists except through language” (p. 68), emphasizing that our cognitive universe–and our very identity as human beings–is shaped and sustained by language. Given this perspective it is essential to maximize every context in which children can engage with language, whether screens are involved or not. Families play a crucial role by monitoring both the duration and the content of screen exposure–and even more importantly, by sharing screen time with their children to foster interaction and dialog. Yet, perhaps the most vital contribution caregivers can make is simply to provide children with opportunities to speak–and to truly listen to them.

Statements

Ethics statement

This study is based on a secondary analysis of publicly available data collected from human participants as part of the 2023 National Survey of Children’s Health (NSCH). Ethical oversight, participant consent procedures, and data collection protocols were managed by the organizations responsible for the original survey. The analyses were conducted in accordance with applicable journal guidelines for research using publicly available secondary datasets. The present study involved the use of anonymized secondary data and did not involve direct contact with participants. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

Author contributions

AMR-RG: Conceptualization, Data curation, Writing – review & editing, Formal analysis, Software, Writing – original draft, Funding acquisition, Methodology. TF-M: Writing – review & editing, Writing – original draft. JLP: Data curation, Methodology, Conceptualization, Funding acquisition, Software, Writing – review & editing, Writing – original draft, Formal analysis.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was not supported by any specific research grant from public, commercial, or non-profit funding agencies. However, Ana María Ruiz-Ruano García and Jorge López Puga received financial support from the University of Granada through the 2025 Research Plan program “Ayudas para Realizar Estancias Breves en Centros de Investigación Nacionales y Extranjeros”. This support enabled a research stay at the National Institute of Public Health, University of Southern Denmark (Copenhagen, Denmark), from June 16 to September 16, 2025, during which scientific collaboration related to this work was facilitated.

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 used in the creation of this manuscript. Microsoft Copilot was used to assist with language editing, including the identification and correction of grammatical and typographical errors. All outputs were reviewed and validated by the authors, who assume full responsibility for the manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2026.1754404/full#supplementary-material

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Keywords

children, digital media exposure, language development, network analysis, screen time

Citation

Ruiz-Ruano García AM, Flensborg-Madsen T and López Puga J (2026) Dense connectivity in networks of red-flag language milestones among high screen-time children. Front. Psychol. 17:1754404. doi: 10.3389/fpsyg.2026.1754404

Received

25 November 2025

Revised

09 September 2026

Accepted

15 September 2026

Published

05 October 2026

Volume

17 - 2026

Updates

Copyright

© 2026 Ruiz-Ruano García, Flensborg-Madsen and López Puga.

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: Jorge López Puga, jlpuga@ugr.es

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.

来源：Frontiers in Psychology · frontiersin.org

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