TY - JOUR
T1 - Uncovering the Most Robust Predictors of Problematic Pornography Use
T2 - A Large-Scale Machine Learning Study Across 16 Countries
AU - Problematic Pornography Use Machine Learning Study Consortium
AU - Bőthe, Beáta
AU - Vaillancourt-Morel, Marie Pier
AU - Bergeron, Sophie
AU - Hermann, Zsombor
AU - Ivaskevics, Krisztián
AU - Kraus, Shane W.
AU - Grubbs, Joshua B.
AU - Allen, Andrew
AU - Ballester-Arnal, Rafael
AU - Binnie, James
AU - van de Bongardt, Daphne
AU - Borgogna, Nicholas C.
AU - Cardos, Jorge
AU - Chen, Lijun
AU - Chiclana-Actis, Carlos
AU - Demetrovics, Zsolt
AU - Dion, Jacinthe
AU - Droubay, Brian A.
AU - Efrati, Yaniv
AU - Fernandez, David P.
AU - Fernández-Aranda, Fernando
AU - Floyd, Christopher G.
AU - Fuss, Johannes
AU - Gewirtz-Meydan, Ateret
AU - Griffiths, Mark D.
AU - Hashemi, Seyed Ghasem Seyed
AU - Hodgins, David C.
AU - Ince, Campbell
AU - Islam, Md Saiful
AU - Jiménez-Murcia, Susana
AU - Kannis-Dymand, Lee
AU - Khazaal, Yasser
AU - Koós, Mónika
AU - Kopcik, Kamil
AU - Kor, Ariel
AU - Kowalewska, Ewelina
AU - Leonhardt, Nathan D.
AU - Lev-Ran, Shaul
AU - Lewczuk, Karol
AU - Mestre-Bach, Gemma
AU - Noor, Syed
AU - Orosz, Gábor
AU - Savard, Claudia
AU - Schaub, Michael P.
AU - Sniewski, Luke
AU - Štulhofer, Aleksandar
AU - Volk, Fred
AU - Wizła, Magdalena
PY - 2024/6/17
Y1 - 2024/6/17
N2 - 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).
AB - 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).
UR - https://www.scopus.com/pages/publications/85197437940
U2 - 10.1037/abn0000913
DO - 10.1037/abn0000913
M3 - Article
C2 - 38884980
AN - SCOPUS:85197437940
SN - 2769-7541
VL - 133
SP - 489
EP - 502
JO - Journal of Psychopathology and Clinical Science
JF - Journal of Psychopathology and Clinical Science
IS - 6
ER -