Advertisement

The dynamics of adolescents’ pornography use and psychological well-being: a six-wave latent growth and latent class modeling approach

  • Aleksandar Štulhofer
  • Azra Tafro
  • Taylor KohutEmail author
Original Contribution

Abstract

Despite increasing concerns that pornography decreases adolescents’ well-being, existing empirical support for this position is largely limited to cross-sectional studies. To explore possible links between adolescent pornography use and psychological well-being more systematically, this study focused on parallel dynamics in pornography use, self-esteem and symptoms of depression and anxiety. A sample of 775 female and 514 male Croatian high school students (Mage at baseline 15.9 years, SD 0.52) from 14 larger secondary schools, who were surveyed 6 times at approximately 5-month intervals, was used for the analyses. The longitudinal data were analyzed using latent growth curve and latent class growth modeling. We observed no significant correspondence between growth in pornography use and changes in the two indicators of psychological well-being over time in either female or male participants. However, a significant negative association was found between female adolescents’ pornography use and psychological well-being at baseline. Controlling for group-specific trajectories of pornography use (i.e., latent classes) confirmed the robustness of findings in the both female and male samples. This study’s findings do not corroborate the notion that pornography use in middle to late adolescence contributes to adverse psychological well-being, but do not rule out such a link during an earlier developmental phase—particularly in female adolescents. The findings have ramifications for educational and adolescent health specialists, but also for concerned parents.

Keywords

Adolescents Pornography use Depression and anxiety Self-esteem Psychological 

Notes

Author contributions

AŠ conceptualized and designed the study, collected data, carried out the analysis with AT and, together with TK, drafted the initial manuscript. All the authors revised and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Funding

The study has been funded by the Croatian Science foundation (Grant #9221 awarded to the first author). Additional funding was provided by Atlantic Grupa, d.d.

Compliance with ethical standards

Conflict of interest

The authors have no financial relationships relevant to this article.

References

  1. 1.
    Mesch GS (2009) Social bonds and Internet pornographic exposure among adolescents. J Adolesc 32(3):601–618PubMedCrossRefGoogle Scholar
  2. 2.
    Tylka TL (2015) No harm in looking, right? Men’s pornography consumption, body image, and well-being. Psychol Men Masculinity 16(1):97–107CrossRefGoogle Scholar
  3. 3.
    Peter J, Valkenburg PM (2011) The use of sexually explicit internet material and its antecedents: a longitudinal comparison of adolescents and adults. Arch Sex Behav 40(5):1015–1025PubMedCrossRefGoogle Scholar
  4. 4.
    Doornwaard SM, Bickham DS, Rich M, Vanwesenbeeck I, van den Eijnden RJJM, ter Bogt TFM (2014) Sex-related online behaviors and adolescents’ body and sexual self-perceptions. Pediatrics 134(6):1103–1110PubMedCrossRefGoogle Scholar
  5. 5.
    Kim YH (2001) Korean adolescents’ health risk behaviors and their relationships with the selected psychological constructs. J Adolesc Health 29(4):298–306PubMedCrossRefGoogle Scholar
  6. 6.
    Kim Y-H (2011) Adolescents ’ health behaviours and its associations with psychological variables. Cent Eur J Public Health 19(4):205–209PubMedCrossRefGoogle Scholar
  7. 7.
    Weaver JB, Weaver SS, Mays D, Hopkins GL, Kannenberg W, McBride D (2011) Mental- and physical-health indicators and sexually explicit media use behavior by adults. J Sex Med 8(3):764–772PubMedCrossRefGoogle Scholar
  8. 8.
    Ybarra ML, Mitchell KJ, Hamburger M, Diener-West M, Leaf PJ (2011) X-rated material and perpetration of sexually aggressive behavior among children and adolescents: is there a link? Aggress Behav 37(1):1–18PubMedCrossRefGoogle Scholar
  9. 9.
    Collins RL, Strasburger VC, Brown JD, Donnerstein E, Lenhart A, Ward LM (2017) Sexual media and childhood well-being and health. Pediatrics 140(Supplement 2):S162–S166PubMedCrossRefGoogle Scholar
  10. 10.
    Štulhofer A, Buško V, Landripet I (2010) Pornography, sexual socialization, and satisfaction among young men. Arch Sex Behav 39(1):168–178PubMedCrossRefGoogle Scholar
  11. 11.
    Zillmann D, Bryant J (1988) Effects of prolonged consumption of pornography on family values. J Fam 9(4):518–544CrossRefGoogle Scholar
  12. 12.
    Fredrickson BL, Roberts TA (1997) Toward understanding women’s lived experiences and mental health risks. Psychol Women Q 21(2):173–206CrossRefGoogle Scholar
  13. 13.
    Boynton PM (1999) ‘Is that supposed to be sexy?’ Women discuss women in ‘top shelf’ magazines. J Community Appl Soc Psychol 9(6):449–461CrossRefGoogle Scholar
  14. 14.
    Kohut T, Štulhofer A (2018) Is pornography use a risk for adolescent wellbeing? An examination of temporal relationships in two independent panel samples. PLoS One 13(8):1–20CrossRefGoogle Scholar
  15. 15.
    Valkenburg PM, Peter J (2013) The differential susceptibility to media effects model. J Commun 63(2):221–243CrossRefGoogle Scholar
  16. 16.
    Doornwaard SM, van den Eijnden RJJM, Overbeek G, ter Bogt TFM (2015) Differential developmental profiles of adolescents using sexually explicit internet material. J Sex Res 52(3):269–281PubMedCrossRefGoogle Scholar
  17. 17.
    Cranney S, Koletić G, Štulhofer A (2018) Varieties of religious and pornographic experience: latent growth in adolescents’ religiosity and pornography use. Int J Psychol Relig 28(3):174–186CrossRefGoogle Scholar
  18. 18.
    Kroenke K, Spitzer RL, Williams JBW, Lowe B (2009) An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics 50(6):613–621PubMedGoogle Scholar
  19. 19.
    Cénat JM, Hébert M, Blais M, Lavoie F, Guerrier M, Derivois D (2014) Cyberbullying, psychological distress and self-esteem among youth in Quebec schools. J Affect Disord 169:7–9PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Graham JW (2012) Missing data: analysis and design. Springer, New YorkCrossRefGoogle Scholar
  21. 21.
    Little TD (2013) Longitudinal structural equation modeling. Guilford Press, New YorkGoogle Scholar
  22. 22.
    Xitao F, Xiaotao F (2005) Power of latent growth modeling for detecting linear growth: number of measurements and comparison with other analytic approaches. J Exp Educ 73(2):121–139CrossRefGoogle Scholar
  23. 23.
    Preacher KJ, Wicham AL, MacCallum RC, Briggs NE (2008) Latent growth curve modeling. Sage, Thousand OaksCrossRefGoogle Scholar
  24. 24.
    Duncan TE, Duncan SC, Strycker LA (2006) An introduction to latent variable growth curve modeling. Psychology Press, New YorkGoogle Scholar
  25. 25.
    Nagin DS (1999) Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods 4(2):139–157CrossRefGoogle Scholar
  26. 26.
    Andruff H, Carraro N, Thompson A, Gaudreau P (2009) Latent class growth modelling: a tutorial. Tutor Quant Methods Psychol 5(1):11–24CrossRefGoogle Scholar
  27. 27.
    Jung T, Wickrama KAS (2008) An introduction to latent class growth analysis and growth mixture modeling. Soc Pers Psychol Compass 2(1):302–317CrossRefGoogle Scholar
  28. 28.
    Grimm KJ, Ram N, Estabrook R (2017) Growth modeling: structural equation and multilevel modeling approaches. Guilford Press, New YorkGoogle Scholar
  29. 29.
    Nagin DS (2005) Group-based modelling of development. Harvard University Press, CambridgeCrossRefGoogle Scholar
  30. 30.
    Berlin KS, Parra GR, Williams NA (2014) An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models. J Pediatr Psychol 39(2):188–203PubMedCrossRefGoogle Scholar
  31. 31.
    Nagin DS, Odgers CL (2010) Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol 6(1):109–138PubMedCrossRefGoogle Scholar
  32. 32.
    Nylund KL, Asparouhov T, Muthén BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a monte carlo simulation study. Struct Equ Model A Multidiscip J 14(4):535–569CrossRefGoogle Scholar
  33. 33.
    Proust-Lima C, Philipps V, Liquet B (2017) Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. J Stat Softw 78(2):1–56CrossRefGoogle Scholar
  34. 34.
    Tomić I, Burić J, Štulhofer A (2018) Associations between Croatian adolescents’ use of sexually explicit material and sexual behavior: does parental monitoring play a role? Arch Sex Behav 47(6):1881–1893PubMedCrossRefGoogle Scholar
  35. 35.
    Wetterneck CT, Burgess AJ, Short MB, Smith AH, Cervantes ME (2012) The role of sexual compulsivity, impulsivity, and experiential avoidance in internet pornography use. Psychol Rec 62(1):3–18CrossRefGoogle Scholar
  36. 36.
    Peter J, Valkenburg PM (2016) Adolescents and pornography: a review of 20 years of research. J Sex Res 53(4–5):509–531PubMedCrossRefGoogle Scholar
  37. 37.
    Harden KP (2014) A sex-positive framework for research on adolescent sexuality. Perspect Psychol Sci 9(5):455–469PubMedCrossRefGoogle Scholar
  38. 38.
    Mota NP, Cox BJ, Katz LY, Sareen J (2010) Relationship between mental disorders/suicidality and three sexual behaviors: results from the National Comorbidity Survey replication. Arch Sex Behav 39(3):724–734PubMedCrossRefGoogle Scholar
  39. 39.
    Hallfors DD, Waller MW, Bauer D, Ford CA, Halpern CT (2005) Which comes first in adolescence—sex and drugs or depression? Am J Prev Med 29(3):163–170PubMedCrossRefGoogle Scholar
  40. 40.
    Kastbom ÅA, Sydsjö G, Bladh M, Priebe G, Svedin C-G (2015) Sexual debut before the age of 14 leads to poorer psychosocial health and risky behaviour in later life. Acta Paediatr 104(1):91–100PubMedCrossRefGoogle Scholar
  41. 41.
    Principi N, Magnoni P, Grimoldi L, Carnevali D, Cavazzana L, Pellai A (2019) Consumption of sexually explicit internet material and its effects on minors’ health: latest evidence from the literature. Minerva Pediatr. https://www.minervamedica.it/en/journals/minervapediatrica/article.php?cod=R15Y9999N00A19021302
  42. 42.
    Baer JL, Kohut T, Fisher WA (2015) Is pornography use associated with anti-woman sexual aggression? Re-examining the confluence model with third variable considerations. Can J Hum Sex 24(2):160–173CrossRefGoogle Scholar
  43. 43.
    Campbell L, Kohut T (2017) The use and effects of pornography in romantic relationships. Curr Opin Psychol 13:6–10PubMedCrossRefGoogle Scholar
  44. 44.
    Kohut T, Balzarini RN, Fisher WA, Campbell L (2018) Pornography’s associations with open sexual communication and relationship closeness vary as a function of dyadic patterns of pornography use within heterosexual relationships. J Soc Pers Relat 35(4):655–676CrossRefGoogle Scholar
  45. 45.
    Rasmussen KR, Bierman A (2018) Risk or release? Porn use trajectories and the accumulation of sexual partners. Soc Curr 5(6):566–582CrossRefGoogle Scholar
  46. 46.
    Carvalheira A, Traeen B, Štulhofer A (2014) Correlates of men’s sexual interest: a cross-cultural study. J Sex Med 11(1):154–164PubMedCrossRefGoogle Scholar
  47. 47.
    Landripet I, Štulhofer A (2015) Is pornography use associated with sexual difficulties and dysfunctions among younger heterosexual men? J Sex Med 12(5):1136–1139PubMedCrossRefGoogle Scholar
  48. 48.
    Werner M, Štulhofer A, Waldorp L, Jurin T (2018) A network approach to hypersexuality: insights and clinical implications. J Sex Med 15(3):410–415CrossRefGoogle Scholar
  49. 49.
    Bergkvist L, Rossiter JR (2007) The predictive validity of multiple-item versus single-item measures of the same constructs. J Mark Res 44(2):175–184CrossRefGoogle Scholar
  50. 50.
    Diamantopoulos A, Sarstedt M, Fuchs C, Wilczynski P, Kaiser S (2012) Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. J Acad Mark Sci 40(3):434–449CrossRefGoogle Scholar
  51. 51.
    Suldo S, Thalji A, Ferron J (2011) Longitudinal academic outcomes predicted by early adolescents’ subjective well-being, psychopathology, and mental health status yielded from a dual factor model. J Posit Psychol 6(1):17–30CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Sociology, Faculty of Humanities and Social SciencesUniversity of ZagrebZagrebCroatia
  2. 2.Department of Electronics Systems and Information Processing, Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  3. 3.Department of Psychology, Faculty of Social Science, 7430 Social Science CentreUniversity of Western OntarioLondonCanada

Personalised recommendations