(PS1-75) Exploring the Impact of TikTok Videos on Self-Diagnosis - Exploring ADHD Perceptions Among College Students: An Exploratory Study

Abstract Text: Social media usage among adolescents and young adults has increased significantly, especially during the COVID-19 pandemic, with platforms like TikTok and Instagram playing a crucial role in shaping mental health discussions. While social media has helped destigmatize ADHD by providing relatable content, fostering community, and encouraging help-seeking behaviors, it also carries risks of misinformation and over-pathologization of normal behavior. This study examines the relationship between exposure to a 15-minute video comprised of ADHD content from TikTok and Instagram, and the likelihood of self-diagnosing ADHD among college students without a formal diagnosis. All Participants (n=128) will complete a screener along with the Adult ADHD Self-Report Scale (ASRS). Afterwards, participants will be randomly assigned to view either ADHD-related (n=64) or neutral content (n=64) for 15 minutes. After viewing the content, all participants will complete the Adult ADHD Self-Report Scale (ASRS) again, along with a Perceived Relevance Questionnaire, and a Likelihood of Self-Diagnosis Questionnaire. Since the Perceived Relevance and Likelihood of Self-Diagnosis questionnaires were created for the purposes of this study, it does not have established reliability or validity data hat was created for this study. It is hypothesized that participants exposed to ADHD-related content will report higher self-diagnosis likelihood post-exposure compared to the control group. Additionally, it is hypothesized that participants who view ADHD-related content will show an increase in self-reported ADHD symptoms on the Adult ADHD Self-Report Scale (ASRS) than those exposed to neutral content. The research will utilize a pre-test and post-test control group design, allowing for comparison between participants exposed to ADHD-related content and those exposed to neutral content. Once data collection is complete, data will be analyzed using paired and independent t-tests, as well as multiple regression analyses, to determine predictors of self-diagnosis tendencies. This study will contribute to understanding how social media influences self-perception of mental health conditions and the potential consequences of unverified online health information.