Computers in human behavior reports最新文献

The longitudinal effects of social media on sleep among youth: A scoping review 社交媒体对青少年睡眠的纵向影响：一项范围综述

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-16 DOI: 10.1016/j.chbr.2025.100908

Emma C. Sullivan , Bernadka Dubicka , Hannah Kirsop , Lisa-Marie Henderson

Social media has become an integral part of our everyday lives, with nearly 5 billion users worldwide. Its ubiquity has sparked concerns about the potential effects on sleep, particularly among young people. Previous research has generally shown that greater social media engagement is associated with poorer sleep outcomes, such as later bedtimes and longer sleep onset latency. However, most evidence is cross-sectional, limiting causal claims. This scoping review synthesises longitudinal studies published in the last five years examining the impact of social media use on subsequent sleep in young people. In accordance with cross-sectional findings, the majority of studies (∼70 %) provide evidence of a negative impact of social media use on bedtime and sleep onset latency, particularly in relation to problematic social media use, including at bedtime. However, methodological limitations, such as heavy reliance on self-reports and non-validated measures of social media use and sleep, restrict the strength of these conclusions. Future studies should employ validated measures and move beyond frequency-based metrics of social media to explore how content, patterns of problematic use, and the timing of use influence sleep longitudinally. It will also be important to consider bidirectional pathways and interactions with other key variables such as neurodiversity, socioeconomic status and mental health.

社交媒体已经成为我们日常生活中不可或缺的一部分，在全球拥有近50亿用户。它的普遍存在引发了人们对睡眠的潜在影响的担忧，尤其是在年轻人中。之前的研究普遍表明，社交媒体参与度越高，睡眠质量越差，比如就寝时间越晚，睡眠潜伏期越长。然而，大多数证据是横断面的，限制了因果关系的主张。这一范围审查综合了过去五年发表的纵向研究，研究了社交媒体使用对年轻人随后睡眠的影响。根据横断面研究结果，大多数研究（约70%）提供了社交媒体使用对就寝时间和睡眠发作潜伏期的负面影响的证据，特别是与有问题的社交媒体使用有关，包括就寝时间。然而，方法上的局限性，如严重依赖自我报告和未经验证的社交媒体使用和睡眠测量，限制了这些结论的强度。未来的研究应该采用有效的措施，超越基于社交媒体频率的指标，探索内容、有问题的使用模式和使用时间如何纵向影响睡眠。考虑双向途径和与其他关键变量（如神经多样性、社会经济地位和心理健康）的相互作用也很重要。

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引用次数: 0

Text-based emotion inference and empathetic response: Evaluating the capabilities of large language models relative to human counselors 基于文本的情感推理和共情反应：评估大型语言模型相对于人类辅导员的能力

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-15 DOI: 10.1016/j.chbr.2025.100904

Qianyi Zhou , Lintong Hu , Jiani Yan , Yaqi Cai , Ya Zhang

In the face of the growing mental health crisis, researchers are increasingly exploring how large language models (LLMs) can be integrated into psychological support and counseling. Empathy—a core therapeutic factor in human counseling—plays an equally vital role in LLM-based interactions. This study systematically evaluates the empathetic capabilities of LLMs across two key dimensions of human empathy: emotion inference and empathetic response. Using 50 human counselors (20 novices and 30 experienced practitioners) as a benchmark, we evaluated the performance of three advanced LLMs—DeepSeek-R1, Qwen-Max, and GPT-4o—across text-based counseling scenarios. To address imbalanced data and enhance statistical rigor, we conducted linear mixed-effects modeling (LMM) and reported effect sizes with 95 % confidence intervals. Results revealed a significant crossover interaction: human counselors, particularly novices, demonstrated higher accuracy in inferring negative emotions, whereas LLMs exhibited higher accuracy in identifying positive emotions. For empathetic responses, both humans and LLMs exhibited higher response quality in positive contexts than in negative ones, with no significant group differences overall. These findings refine earlier interpretations by revealing distinct but complementary performance profiles between human counselors and LLMs. Collectively, the results suggest that advanced LLMs can demonstrate comparable capabilities to humans in specific emotional contexts and generate standardized, appropriate empathetic responses. By situating LLM performance within the framework of counseling theory and mixed-effects analysis, this study offers theoretical and practical insights into the evolving role of LLMs in mental health support and digital counseling practice.

面对日益严重的心理健康危机，研究人员越来越多地探索如何将大型语言模型（LLMs）整合到心理支持和咨询中。同理心——人类咨询的核心治疗因素——在法学硕士的互动中发挥着同样重要的作用。本研究系统地评估了法学硕士的共情能力，涉及人类共情的两个关键维度：情感推理和共情反应。以50名人类咨询师（20名新手和30名经验丰富的从业人员）为基准，我们评估了三个高级法学硕士——deepseek - r1、Qwen-Max和gpt - 40——在基于文本的咨询场景中的表现。为了解决不平衡数据并提高统计严密性，我们进行了线性混合效应建模（LMM），并报告了95%置信区间的效应大小。结果显示了显著的跨界互动：人类咨询师，特别是新手，在推断负面情绪方面表现出更高的准确性，而法学硕士在识别积极情绪方面表现出更高的准确性。在共情反应方面，人类和法学硕士在积极情境下的反应质量高于消极情境，总体上没有显著的组间差异。这些发现通过揭示人类辅导员和法学硕士之间不同但互补的表现概况，完善了早期的解释。总的来说，研究结果表明，高级llm可以在特定的情感环境中表现出与人类相当的能力，并产生标准化的、适当的移情反应。通过将LLM的表现置于咨询理论和混合效应分析的框架内，本研究为LLM在心理健康支持和数字咨询实践中的作用演变提供了理论和实践见解。

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引用次数: 0

Decoding stress and coping ability levels through drawing features in the Person-In-The-Rain test: An exploratory study using a digital drawing device 利用数字绘图装置解读雨中人测试中绘图特征的压力和应对能力水平的探索性研究

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-13 DOI: 10.1016/j.chbr.2025.100902

Jeahong Kim , Minwoo Jo , Sooleen Nam , Yujin Lee , Yeonji Baik

Current study examined the validity of a digitalized Person-in-the-Rain test (dPITR) in assessing stress and coping abilities using traditional univariate and machine learning methods. By analyzing drawings and comparing results with established self-reported measures (Perceived Stress Scale and Ways of Coping Questionnaire), the study identifies significant relationships between drawing features and stress and coping scores. Machine learning models (K-Nearest Neighbor, Support Vector Machine, Random Forest) revealed complex, non-linear patterns, with stress-related features showing significant predictive power for perceived stress and coping styles. The findings support dPITR as a reliable, non-invasive tool for assessing psychological constructs and highlight the value of integrating advanced analytics in psychological assessments. This digital approach offers promising applications for scalable and precise mental health evaluations.

目前的研究检验了数字化雨中人测试（dPITR）在使用传统的单变量和机器学习方法评估压力和应对能力方面的有效性。通过对图画进行分析，并将结果与已建立的自我报告量表（感知压力量表和应对方式问卷）进行比较，研究发现，图画特征与压力和应对得分之间存在显著关系。机器学习模型（k近邻、支持向量机、随机森林）揭示了复杂的非线性模式，与压力相关的特征显示出对感知压力和应对方式的显著预测能力。研究结果支持dPITR作为一种可靠的、非侵入性的心理结构评估工具，并强调了在心理评估中整合高级分析的价值。这种数字方法为可扩展和精确的心理健康评估提供了有前途的应用。

引用次数: 0

Country-level differences in socio-economic development and cultural dimensions are associated with workers’ economic expectations of AI: Evidence from 31 countries 社会经济发展和文化层面的国家层面差异与工人对人工智能的经济期望有关：来自31个国家的证据

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-12 DOI: 10.1016/j.chbr.2025.100905

Leonhard Reiter , Robert Böhm , Christoph Fuchs

Although artificial intelligence (AI) affects many jobs and fundamentally changes labor, surprisingly little is known about workers' economic expectations of AI. Understanding workers' expectations is important, as it can inform the design of effective AI adoption strategies by firms and governments. Such expectations are likely co-shaped by the workers' economic and cultural realities. In the present research, we therefore examined how workers' economic expectations of AI differ between countries and explored the role of socio-economic and cultural dimensions in shaping these differences. Using data from 14,651 workers across 31 countries (M_age = 41.4, SD_age = 12.5, 46 % female), including a wide range of different economies and cultures, we find that (i) overall workers hold positive economic expectations of AI but (ii) there is substantial cross-country variance, and (iii) this variance is associated with a country's level of human development and cultural tightness–looseness. Specifically, we find that higher levels of human development are negatively associated with workers' expectations of AI, while cultural tightness is positively associated with their expectations of AI. Additionally, we find that workers' demographics, knowledge of AI, and perceived replacement likelihood are associated with their economic expectations. The findings remain robust when different model specifications and control variables are considered. Our research highlights that workers' economic expectations of AI are associated with both socio-economic development and cultural tightness–looseness, underscoring the importance of the country context when studying how workers anticipate technological change.

尽管人工智能（AI）影响了许多工作，并从根本上改变了劳动力，但令人惊讶的是，人们对人工智能的经济预期知之甚少。了解员工的期望很重要，因为它可以为企业和政府设计有效的人工智能采用策略提供信息。工人们的经济和文化现实很可能共同塑造了这种期望。因此，在本研究中，我们研究了各国工人对人工智能的经济期望如何不同，并探讨了社会经济和文化因素在形成这些差异中的作用。使用来自31个国家（Mage = 41.4, SDage = 12.5, 46%为女性）的14,651名工人的数据，包括各种不同的经济和文化，我们发现(i)总体上工人对人工智能持积极的经济期望，但（ii）存在很大的跨国差异，（iii）这种差异与一个国家的人类发展水平和文化的松紧度有关。具体来说，我们发现较高的人类发展水平与工人对人工智能的期望呈负相关，而文化紧密度与他们对人工智能的期望呈正相关。此外，我们发现工人的人口统计数据、人工智能知识和感知的替代可能性与他们的经济预期有关。当考虑不同的模型规格和控制变量时，结果仍然是稳健的。我们的研究强调，工人对人工智能的经济期望与社会经济发展和文化的松紧度有关，在研究工人如何预测技术变革时，强调了国家背景的重要性。

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引用次数: 0

Assessing the influence of mobile health platform ratings and sentiments on perceived treatment efficiency: A data-driven analysis 评估移动医疗平台评级和情绪对感知治疗效率的影响：数据驱动分析

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-09 DOI: 10.1016/j.chbr.2025.100903

Isaac Owusu Asante , Muhammad Ali , Feng Liu

This study examines the impact of user-generated ratings, sentiment analysis of reviews, reports of symptom improvement, and perceived satisfaction on treatment efficacy in mobile health (mHealth) platforms. Employing a mixed-methods approach, we examined text reviews and ratings from WeDoctor, a prominent Chinese website, integrating computational sentiment analysis with hierarchical regression modeling. The results indicated that comment scores exerted a more significant direct influence on treatment efficiency than sentiment scores; nevertheless, sentiment scores had a crucial indirect effect in enhancing satisfaction. The mention of symptom improvement considerably moderated the impact of comment scores, but not sentiment, indicating that clinical outcomes corroborate quantitative assessments more than emotional expressions. Using a BERT-based sentiment classification model pretrained on Chinese-language reviews, we analyzed the emotional tone of 15000 user-generated consultations. Results showed that 97 % of reviews were classified as positive, indicating a high degree of emotional satisfaction and trust in mHealth service experiences. This affective signal was then integrated with structured ratings and symptom improvement cues to model perceived treatment efficiency. The mixed-methods design leverages both structured and unstructured data, offering a multidimensional perspective that extends traditional Expectation Confirmation Theory to digital healthcare. By combining cognitive, affective, and clinical dimensions of user feedback, the study provides a multifaceted understanding of perceived efficiency. It informs the future design of emotionally intelligent and healthcare-grounded mHealth interfaces, aligning with key priorities in human-computer interaction research.

本研究考察了用户生成的评分、评论的情感分析、症状改善报告和感知满意度对移动健康（mHealth）平台治疗效果的影响。采用混合方法，我们检查了来自中国著名网站微医（WeDoctor）的文本评论和评分，将计算情感分析与层次回归模型相结合。结果表明，评价分数对治疗效率的直接影响显著高于情绪分数；然而，情绪得分在提高满意度方面有重要的间接影响。症状改善的提及显著地缓和了评论分数的影响，但没有缓和情绪，这表明临床结果比情绪表达更能证实定量评估。使用基于bert的情感分类模型对中文评论进行预训练，我们分析了15000条用户生成咨询的情感语气。结果显示，97%的评论被归类为积极的，表明对移动医疗服务体验的高度情感满意度和信任度。然后将这种情感信号与结构化评分和症状改善线索结合起来，建立感知治疗效率的模型。混合方法设计利用结构化和非结构化数据，提供多维视角，将传统的期望确认理论扩展到数字医疗保健。通过结合用户反馈的认知、情感和临床维度，本研究提供了对感知效率的多方面理解。它为未来设计情感智能和基于医疗保健的移动健康界面提供了信息，与人机交互研究的关键优先事项保持一致。

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引用次数: 0

A cross-lagged prospective network analysis of deviant peer affiliation and phubbing in adolescents 青少年异常同伴关系与低头症的交叉滞后前瞻性网络分析

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-06 DOI: 10.1016/j.chbr.2025.100897

Tingting Gao , Sihan Lyu , Fengtong Qian , Rui Li , Yimeng Lyu , Yingying Su

Background

Prior studies examining the association between deviant peer affiliation and phubbing have predominantly relied on latent variable modeling. However, the directionality, symptom-level pathways, and sex-specific patterns of their longitudinal associations remain largely unexplored within a network-theoretical framework. To address these gaps, the present study aims to clarify how specific symptoms of deviant peer affiliation and phubbing mutually influence one another over time, while further investigating whether these temporal dynamics differ by sex.

Methods

A total of 3296 adolescents who participated in two waves of a longitudinal survey were included. Cross-lagged panel network (CLPN) analysis was conducted to clarify the temporal associations between deviant peer affiliation symptoms and phubbing symptoms across a 1.5-year follow-up.

Results

Sex-stratified contemporaneous networks revealed distinct patterns in how deviant peer affiliation and phubbing were organized among male and female adolescents. Specific deviant peer behaviors such as friends steal things (DPA4) among males and friends skip class (DPA7) among females predict subsequent increases in phubbing. In addition, friend's internet addiction (DPA5) and relieving stress by focusing on phones (GSP12) were the most influential symptoms in the male network, while friend smoking (DPA1) and friend bullying others (DPA8) played similarly central roles in the female network.

Conclusion

These findings highlight the significance of sex-sensitive interventions and valuable perspectives for addressing problematic behaviors in adolescents. By identifying symptom-level pathways linking deviant peer affiliation and phubbing, this study provides novel insights and contributes to a better understanding of the mechanisms underlying these behaviors.

背景先前的研究主要依赖于潜在变量模型来检验异常同伴关系与低头症之间的关系。然而，在网络理论框架内，其纵向关联的方向性、症状水平通路和性别特异性模式在很大程度上仍未被探索。为了解决这些差距，本研究旨在阐明异常同伴关系和低头症的具体症状如何随着时间的推移相互影响，同时进一步调查这些时间动态是否因性别而异。方法对3296名青少年进行两期纵向调查。交叉滞后面板网络（CLPN）分析，以澄清在1.5年的随访中，异常同伴关系症状和低头症状之间的时间关联。结果性别分层的同期网络揭示了男性和女性青少年异常同伴关系和低头行为的不同组织模式。特定的越轨同伴行为，如男性的朋友偷东西（DPA4）和女性的朋友逃课（DPA7），预示着随后的低头症的增加。此外，朋友网瘾（DPA5）和专注于手机缓解压力（GSP12）是男性网络中影响最大的症状，而朋友吸烟（DPA1）和朋友欺凌他人（DPA8）在女性网络中也发挥了同样重要的作用。结论这些发现强调了性别敏感干预对解决青少年问题行为的重要性和有价值的观点。通过识别连接异常同伴关系和低头症的症状水平途径，本研究提供了新的见解，有助于更好地理解这些行为背后的机制。

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引用次数: 0

Identifying features that shape perceived consciousness in LLM-based AI: A quantitative study of human responses 在基于法学硕士的人工智能中识别塑造感知意识的特征：对人类反应的定量研究

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-05 DOI: 10.1016/j.chbr.2025.100901

Bongsu Kang , Jundong Kim , Taerim Yun , Hyojin Bae , Chang-Eop Kim

This study quantitatively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with AI and focusing on eight features—Metacognitive Self-reflection, Logical Reasoning, Empathy, Emotionality, Knowledge, Fluency, Unexpectedness, and Subjective Expressiveness—we surveyed with 123 participants. Using regression and clustering analyses, we investigated how these features influence participants' perceptions of AI consciousness. The results reveal that metacognitive self-reflection and the AI's expression of its own emotions significantly increased perceived consciousness, while a heavy emphasis on knowledge reduced it. Participants clustered into subgroups, each showing distinct feature-weighting patterns. Additionally, higher prior knowledge of LLMs and more frequent usage of LLM-based chatbots were associated with greater overall likelihood assessments of AI consciousness. This study underscores the multidimensional and individualized nature of perceived AI consciousness and provides a foundation for a better understanding of the psychosocial implications of human-AI interaction.

本研究定量考察了人工智能生成文本的哪些特征导致人类在基于大型语言模型（LLM）的人工智能系统中感知主观意识。我们从与人工智能的对话中提取了99个段落，重点关注8个特征——元认知自我反思、逻辑推理、移情、情感、知识、流畅性、意外性和主观表现力——我们对123名参与者进行了调查。使用回归和聚类分析，我们研究了这些特征如何影响参与者对人工智能意识的感知。结果表明，元认知自我反思和人工智能对自身情绪的表达显著增加了感知意识，而过分强调知识会降低感知意识。参与者被分成子组，每个子组都显示出不同的特征权重模式。此外，更高的法学硕士先验知识和更频繁地使用基于法学硕士的聊天机器人与更高的人工智能意识整体可能性评估相关。这项研究强调了感知人工智能意识的多维和个性化本质，并为更好地理解人类与人工智能互动的社会心理影响提供了基础。

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引用次数: 0

User-centered evaluation of an IntuNav in multi-browser virtual reality across diverse cognitive user profiles 以用户为中心的IntuNav在多浏览器虚拟现实中跨不同认知用户档案的评估

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-05 DOI: 10.1016/j.chbr.2025.100899

Mochammad Hannats Hanafi Ichsan , Cecilia Sik-Lanyi , Tibor Guzsvinecz , Aisshah Roesiana Dewi

This study examines the usability, user experience, and cognitive performance of an IntuNav that incorporates a Multi-Browser Virtual Environment (VE), a user-friendly desktop virtual reality (VR) system. The evaluation included three user groups that varied in characteristics: mainstream users, older adults, and students with neurodivergent conditions (Autistic Spectrum Disorders and Attention Deficit Hyperactivity Disorder). Fourteen hypotheses were developed to investigate differences in objective (5Q score, task time, error count, and perplexity) and subjective (SUS, IPQ, and NASA-TLX) metrics using a between-subjects experimental design. Statistical analyses indicated no significant differences in core performance metrics (5Q scores, error count) among groups, implying the system's overall usability. Significant variations in task time and perplexity were observed between older adults and neurodivergent users compared to mainstream users, highlighting the impact of cognitive and generational factors on navigational complexity. Older adults exhibited the highest subjective usability and presence scores, whereas cognitive load levels were elevated among older and neurodiverse users. The results indicate that the IntuNav navigation model and Multi-Browser VE provide inclusive and accessible desktop VR interaction for a diverse user base. This demonstrates the system's practical applicability in contexts necessitating multi-window VR interaction, including education, research, and digital productivity. Design recommendations are presented to enhance inclusivity, minimize cognitive demands, and improve adaptive navigation in future VR systems. An anonymized dataset and complete evaluation scripts are publicly accessible (OSF: 10.17605/OSF.IO/HU478), along with implementation resources (GitHub), which allows for reproducibility.

本研究考察了集成了多浏览器虚拟环境（VE）的IntuNav的可用性、用户体验和认知性能，这是一个用户友好的桌面虚拟现实（VR）系统。评估包括三个不同特征的用户组：主流用户、老年人和患有神经发散性疾病（自闭症谱系障碍和注意缺陷多动障碍）的学生。采用受试者间实验设计，提出了14个假设来研究客观（5Q得分、任务时间、错误计数和困惑度）和主观（SUS、IPQ和NASA-TLX）指标的差异。统计分析表明，各组之间的核心性能指标（5Q分数，错误计数）没有显着差异，这意味着系统的整体可用性。与主流用户相比，老年人和神经分化用户在任务时间和困惑度上存在显著差异，突出了认知和代际因素对导航复杂性的影响。老年人表现出最高的主观可用性和存在得分，而认知负荷水平在老年人和神经多样性用户中升高。结果表明，IntuNav导航模型和Multi-Browser VE为不同的用户群提供了包容性和可访问的桌面VR交互。这证明了该系统在需要多窗口VR交互的环境中的实际适用性，包括教育，研究和数字生产力。提出了设计建议，以增强包容性，最大限度地减少认知需求，并改善未来VR系统的自适应导航。一个匿名的数据集和完整的评估脚本是公开访问的（OSF: 10.17605/OSF）。IO/HU478)，以及实现资源（GitHub），这允许再现性。

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引用次数: 0

Hold me tight: Towards the detection of the flow experience using a commercial video game controller 抱紧我：利用商业电子游戏控制器检测心流体验

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-05 DOI: 10.1016/j.chbr.2025.100900

Lazaros Michailidis, Jesus Lucas Barcias

The prolific consumption of video games has inspired a rich body of research to explain their ability to induce satisfying experiences. The flow experience is often defined as a state that encapsulates the essential qualities of engaging gameplay. However, there is limited research addressing the detection of flow, and existing approaches often involve intricate equipment requirements that compromise commercial applicability. In this study, we review the challenges behind flow's conceptualization and discuss how they leak into approaches that employ machine learning techniques. We propose a novel, multidimensional representation of flow that combines subjective and objective data – game controller interaction and player performance on a secondary oddball task – whilst treating flow detection as a regression problem. Contrary to previous studies, the game difficulty is divorced from the model's training, with the aim of improving generalizability across game titles. The results indicate high predictive accuracy with an average composite loss rate of 0.0681 (±0.0036) that significantly outperformed two baseline models. This finding suggests an effective mapping of the objective data onto self-reported flow and reinforces the viability of game controllers as a means for flow detection. In addition, we identified that the main drivers behind the model's predictions were of inertial origin. We conclude that these insights offer practical considerations for the future of interactive applications.

电子游戏的大量消费激发了大量的研究来解释它们诱导满足体验的能力。流体验通常被定义为一种状态，它包含了引人入胜的游戏玩法的基本品质。然而，针对流量检测的研究有限，现有的方法往往涉及复杂的设备要求，影响了商业适用性。在本研究中，我们回顾了流概念背后的挑战，并讨论了它们如何渗透到使用机器学习技术的方法中。我们提出了一种新颖的多维流表示，结合了主观和客观数据-游戏控制器交互和玩家在次要古怪任务上的表现-同时将流检测视为回归问题。与之前的研究相反，游戏难度与模型的训练是分离的，目的是提高游戏名称的通用性。结果表明，该模型具有较高的预测精度，平均复合损失率为0.0681（±0.0036），显著优于两个基线模型。这一发现表明客观数据与自我报告的心流的有效映射，并强化了游戏控制器作为心流检测手段的可行性。此外，我们确定了模型预测背后的主要驱动因素是惯性起源。我们的结论是，这些见解为交互式应用程序的未来提供了实际考虑。

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引用次数: 0

The network and temporal changes among internet gaming disorder symptoms, suicidal ideation, and depressive symptoms among adolescents in Hong Kong 香港青少年网络游戏障碍症状、自杀意念及抑郁症状的网络及时间变化

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2025-12-04 DOI: 10.1016/j.chbr.2025.100898

Xue Yang , Xu Chen , Qian Li , Yilun Huang , Winnie W.S. Mak

Background

Adolescent Internet gaming disorder (IGD), depressive symptoms, and suicidal ideation are significant public health concerns. However, their temporal dynamics and symptom-level interactions in adolescents remain unclear. This study aims to explore the longitudinal relationships and network interactions between IGD symptoms, depressive symptoms, and suicidal ideation among Hong Kong adolescents.

Methods

We conducted a one-year, two-wave longitudinal study with 7581 Hong Kong adolescents. The participants completed an anonymous, self-administered questionnaire in their classrooms. Two trained research assistants administered the surveys, which took approximately 20 min to complete. Cross-lagged panel network analysis was used to examine contemporaneous and temporal associations, with subgroup analyses by gender.

Results

Both the contemporaneous and temporal network analyses demonstrated distinct patterns when stratified by gender. In contemporaneous networks, significant differences in the overall structures of male and female adolescents’ networks were observed (M = 0.10, p < 0.01). “Giving up other activities due to gaming” (IGD5) was a central symptom for males' network, whereas suicidal ideation was the most central symptom for females' network. Longitudinally, depressive symptoms were strongly correlated with subsequent suicidal ideation in both genders.

Conclusion

Our findings emphasize important gender-specific differences in these dynamic relationships and offer direction for developing tailored transdiagnostic interventions aimed at addressing the complex interplay between behavioral addictions and emotional disorders among adolescents.

青少年网络游戏障碍（IGD）、抑郁症状和自杀意念是重要的公共卫生问题。然而，它们在青少年中的时间动态和症状水平的相互作用尚不清楚。本研究旨在探讨香港青少年IGD症状、抑郁症状与自杀意念之间的纵向关系及网络相互作用。方法对香港7581名青少年进行为期一年的两波纵向研究。参与者在教室里完成了一份匿名的、自我管理的问卷。两名训练有素的研究助理负责调查，大约需要20分钟才能完成。交叉滞后面板网络分析用于检查同期和时间关联，并按性别进行亚组分析。结果在按性别分层时，同期和时间网络分析都显示出不同的模式。在同期网络中，男性和女性青少年网络的整体结构存在显著差异（M = 0.10, p < 0.01）。“因游戏而放弃其他活动”（IGD5）是男性网络的中心症状，而自杀意念是女性网络的中心症状。纵向上，抑郁症状与随后的自杀意念在两性中都有很强的相关性。我们的研究结果强调了这些动态关系中重要的性别差异，并为制定针对性的跨诊断干预措施提供了方向，旨在解决青少年行为成瘾和情绪障碍之间复杂的相互作用。

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引用次数: 0