张量,与智能手机使用相关的普遍社交行为,涉及用户忽略他们的对话伙伴与他们的手机互动。尽管不断探索其与错过恐惧概念(FOMO)的联系,现有文献中的研究结果表现出明显的不一致性。为了解决这个差距,这项研究采用了系统综述和荟萃分析,以审查phubbing行为与FOMO之间的复杂关系.全面系统的审查,时间跨度至2023年12月10日,包括PubMed等数据库,Scopus,WebofScience,ProQuest,谷歌学者。由此产生的数据集包括27项符合条件的研究,纳入来自15个国家的20415名参与者的见解。使用纽卡斯尔渥太华量表对研究质量进行了严格的评估,而统计分析是使用RStudio精心进行的。显示出强烈的正关联,吐痰行为与FOMO显著相关(效应大小[ES]=0.43,95%CI:0.36,0.49,I2:97.5%,τ2:0.05)。使用修剪和填充方法校正检测到的出版偏差,纳入了另外16项研究,加强研究结果的稳健性。适度分析揭示了位置的显著影响(p<0.01),收入水平(p<0.01),抽样法(p<0.01),phubbing秤(p<0.01),与FOMO量表和类型有关(p<0.01)的估计关系。单变量荟萃回归强调了样本量的实质性影响(R2=11.81%,p<0.01),而多元元回归说明了出版年份的综合影响,学习质量评分,样本量,平均年龄,和女性比例在估计关系上(k=19,R2=52.85%,I2=93.78%,p<0.05)。此外,事后影响力分析,通过留一法进行,为考试提供了更多的深度。
Phubbing, a pervasive social behavior linked to smartphone usage, involves users neglecting their conversation partners to engage with their phones. Despite consistent exploration of its association with the concept of fear of missing out (FOMO), findings in the existing literature exhibit notable inconsistency. To address this gap, this study employs a systematic
review and meta-analysis to scrutinize the intricate relationship between phubbing behavior and FOMO. A comprehensive systematic
review, spanning up to December 10, 2023, encompassed databases such as PubMed, Scopus, Web of Science, ProQuest, and Google Scholar. The resulting dataset comprised 27 eligible studies, incorporating insights from 20,415 participants across 15 countries. Rigorous evaluation of study quality was executed using the Newcastle Ottawa Scale, while statistical analyses were meticulously conducted using R Studio. Revealing a robust positive association, phubbing behavior was significantly linked to FOMO (effect size[ES] = 0.43, 95% CI: 0.36, 0.49, I2: 97.5%, τ2: 0.05). Correcting for detected publication bias using the Trim and Fill method, an additional 16 studies were included, fortifying the robustness of the findings. Moderation analysis uncovered significant influences of location (p < 0.01), income level (p < 0.01), sampling method (p < 0.01), phubbing scale (p < 0.01), and FOMO scale and type (p < 0.01) on the estimated relationship. Univariate meta-regression highlighted the substantial impact of sample size (R2 = 11.81%, p < 0.01), while multivariate meta-regression illuminated the combined effects of publication year, study quality score, sample size, mean age, and female proportion on the estimated relationship (k = 19, R2 = 52.85%, I2 = 93.78%, p < 0.05). Furthermore, post hoc influential analysis, conducted through the leave-one-out method, offered additional depth to the examination.