Back

Association between TikTok use and the development of eating disorders in young Colombian adults between 18 and 25 years of age

Restrepo-Escudero, L.; McCormick, S.; Cuevas, M. I.; Mosquera, S.; Vasquez-Ponce, M.; Correa-Mendez, M. J.; Patino, M. C.; Reyes, P.; Gonzalez Ballesteros, L. M.

2024-10-23 psychiatry and clinical psychology
10.1101/2024.10.22.24315901 medRxiv
Show abstract

BackgroundVisual and appearance-based social media platforms like TikTok can establish unrealistic beauty standards and self-esteem issues, leading to the development of eating disorders (EDs). This study aims to evaluate the association between TikTok usage and the presence of EDs risk behaviors and body dissatisfaction among Colombian young adults aged between 18 and 25 years. MethodsA cross-sectional descriptive study was conducted via an online survey through snowball sampling with 171 participants. The survey assessed demographic variables, social media use, content consumption, EDs risk behaviors, and body dissatisfaction through validated tools. Non-parametric tests and regression models were used for the data analysis. ResultsTikTok users showed significantly higher scores in ED risk behaviors (M = 14.91) and body dissatisfaction (M = 21.9) compared to non-users. Contributory usage, particularly collaborative content creation, was the most associated with increased risk. The multivariate regression model for ED risk explained 3% of the variance, while TikTok use accounted for 30% of the variance in the model for body dissatisfaction. DiscussionOur study found a significant association between TikTok use and the development of ED risk behaviors and body dissatisfaction. These findings align with previous research and highlight the need for interventions that encourage mindful social media consumption.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.