Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in the past two decades, but most prior studies were characterized by several shortcomings (e.g., using homogenous, small samples), resulting in crucial knowledge gaps and a limited understanding concerning empirically based risk factors for PPU. This study aimed to identify the most robust risk factors for PPU using a preregistered study design. Independent laboratories' 74 preexisting self-report data sets (Nparticipants = 112,397; Ncountries = 16) were combined to identify which factors can best predict PPU using an artificial intelligence-based method (i.e., machine learning). We conducted random forest models on each data set to examine how different sociodemographic, psychological, and other characteristics predict PPU, and combined the results of all data sets using random-effects meta-analysis with meta-analytic moderators (e.g., community vs. treatment-seeking samples). Predictors explained 45.84% of the variance in PPU scores. Out of the 700+ potential predictors, 17 variables emerged as significant predictors across data sets, with the top five being (a) pornography use frequency, (b) emotional avoidance pornography use motivation, (c) stress reduction pornography use motivation, (d) moral incongruence toward pornography use, and (e) sexual shame. This study is the largest and most integrative data analytic effort in the field to date. Findings contribute to a better understanding of PPU's etiology and may provide deeper insights for developing more efficient, cost-effective, empirically based directions for future research as well as prevention and intervention programs targeting PPU. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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发现有问题的色情内容使用的最有力的预测因素：一项跨越 16 个国家的大规模机器学习研究。


有问题的色情内容使用（PPU）是国际疾病分类第11次修订中新引入的强迫性行为障碍诊断的最常见表现。过去二十年中，与 PPU 相关的研究激增，但大多数先前的研究都存在一些缺陷（例如，使用同质的小样本），导致严重的知识差距以及对基于经验的 PPU 风险因素的理解有限。本研究旨在使用预先注册的研究设计来确定 PPU 的最强有力的风险因素。独立实验室的 74 个预先存在的自我报告数据集（N 参与者 = 112,397；N 个国家 = 16）被合并起来，以确定哪些因素可以使用基于人工智能的方法（即机器学习）最好地预测 PPU。我们对每个数据集进行了随机森林模型，以研究不同的社会人口、心理和其他特征如何预测 PPU，并使用随机效应荟萃分析和荟萃分析调节因子（例如，社区与治疗）结合所有数据集的结果。 - 寻求样品）。预测因子解释了 PPU 分数中 45.84% 的方差。在 700 多个潜在预测变量中，有 17 个变量成为数据集中的重要预测变量，其中排名前五的变量是 (a) 色情内容使用频率，(b) 情绪回避色情内容使用动机，(c) 减压色情内容使用动机，(d) ) 对色情内容的道德不一致，以及 (e) 性羞耻。这项研究是迄今为止该领域规模最大、最综合的数据分析工作。 研究结果有助于更好地了解 PPU 的病因学，并可能为未来研究以及针对 PPU 的预防和干预计划制定更高效、更具成本效益、基于经验的方向提供更深入的见解。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。