The Association of Social Media Use and Psychiatric Diagnoses Among Missouri College Students
Bibliographic record
Abstract
Introduction: “Many college students experience significant mental health difficulties, with the COVID-19 pandemic exacerbating these concerns (Sonet al., 2020). Mental health is significantly impacted by social media use, and this relationship has become increasingly complex after the COVID-19 pandemic (Haddad et al., 2021).” Research shows that depression, eating disorders, and anxiety are associated with more time spent on social media (Alam et al., 2021; Khan et al., 2019; Reighm et al., 2019; Sansha et al., 2017; Sidani et al., 2016). To follow-up on this research and assess the relationship between social media usage and psychiatric diagnoses among Missouri college students, we hypothesized that undergraduate students diagnosed with psychiatric disorders within the past year are spending more hours on social media per week. Methods: Cross-sectional data from 2022 were obtained from the Missouri College Health Behavior Survey (MACHB), a retrospective, self-report survey administered to undergraduate students at several universities in Missouri. Descriptive statistics were examined for social media use and the frequency of reported past-year diagnoses. A multiple regression model in SPSS was conducted to examine the effect of past-year diagnoses of anxiety, depression, PTSD, OCD/ADHD/ODD, sleep issues, and eating disorders on the average hours spent on social media per week. Results: The diagnosis of anxiety within the past year significantly predicted greater weekly use of social media (M = 19.30, SD = 17.90), compared to those without anxiety (M = 17.32, SD = 15.89), B = 2.06, p = .003. Students with PTSD spent significantly less time (M = 17.16, SD = 18.07) using social media than those not diagnosed (M = 18.09, SD = 16.54), B = -2.32, p = .029.nSimilarly, those with OCD/ADD/ADHD spent less time on social media (M = 17.28, SD = 16.68) than students not diagnosed (n = 3,264, M = 18.15, SD = 16.65), B = -1.68, p = .031. Past-year diagnoses of depression, eating disorders, and chronic sleep issues were not associated with social media use. Discussion: Students diagnosed with anxiety in the past year spend more time on social media than those who were not, consistent with previous research (Alam et al., 2021; Bettman et al., 2020; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017; Vannucci et al., 2017). Contrary to hypotheses and prior research (Alam et al., 2021; Bettman et al., 2020; Khan et al., 2019; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017), past-year OCD/ADD/ADHD and PTSD diagnoses were associated with less time spent on social media. However, viewing sensitive content on social media and experiencing cyber bullying has been found to elicit PTSD symptoms (McHugh et al. (2018), which can potentially help explain less time spent on social media among those diagnosed with PTSD. No associations were found between social media usage and eating disorders, major depressive disorder, or chronic sleep issues, despite some research highlighting associations with these disorders (Alam et al., 2021; Bettman et al., 2020; Khan et al., 2019; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017; Sidani et al., 2016). Future research may include investigating the impact of social media usage on symptoms of anxiety disorders to determine directionality of the association. Additionally, Faulhaber et al. (2023) found that limiting social media use was associated with statistically significantly decreased anxiety compared to pretreatment. Further research testing various treatment approaches to decrease time spent on social media among individuals diagnosed with anxiety. References: Alam, M. K., Ali, F. B., Banik, R., Yasmin, S., & Salma, N. (2022). Assessing the mental health condition of home-confined university level students of Bangladesh due to the COVID-19 pandemic. Journal of Public Health, 30(7), 1685–1692. https://doi.org/10.1007/s10389-021-01542-w Bettmann, J.E., Anstadt, G., Casselman, B. et al. Young Adult Depression and Anxiety Linked to Social Media Use: Assessment and Treatment. Clin Soc Work J 49, 368–379 (2021). https://doi-org.ezproxy.umsl.edu/10.1007/s10615-020-00752-1. Faulhaber, M. E., Lee, J. E., & Gentile, D. A. (2023). The Effect of Self-Monitoring Limited Social Media Use on Psychological Well-Being. Technology, Mind, and Behavior, 4(2: Summer 2023). https://doi.org/10.1037/tmb0000111. Haddad, J. M., Macenski, C., Mosier-Mills, A., Hibara, A., Kester, K., Schneider, M.,... & Liu, C. H. (2021). The impact of social media on college mental health during the COVID-19 pandemic: a multinational review of the existing literature. Current psychiatry reports, 23, 1-12. Khan, A., Uddin, R., & Lee, E.-Y. (2021). Excessive recreational Internet use was associated with poor mental health in adolescents. Acta Paediatrica, 110(2), 571–573. https://doi.org/10.1111/apa.15528 Maras, D., Flament, M. F., Murray, M., Buchholz, A., Henderson, K. A., Obeid, N., & Goldfield, G. S. (2015). Screen time is associated with depression and anxiety in Canadian youth. Preventive medicine, 73, 133–138. https://doi-.org.ezproxy.umsl.edu/10.1016/j.ypmed.2015.01.029. McHugh, B.C., Wisniewski, P., Rosson, M.B. and Carroll, J.M. (2018), "When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress", Internet Research, Vol. 28 No. 5, pp. 1169-1188. https://doi.org/10.1108/IntR-02-2017-0077. Riehm, K. E., Feder, K. A., Tormohlen, K. N., Crum, R. M., Young, A. S., Green, K. M., Pacek, L. R., La Flair, L. N., & Mojtabai, R. (2019). Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. JAMA Psychiatry, 76(12), 1266. https://doi.org/10.1001/jamapsychiatry.2019.2325 Shensa, A., Escobar-Viera, C. G., Sidani, J. E., Bowman, N. D., Marshal, M. P., & Primack, B. A. (2017). Problematic social media use and depressive symptoms among U.S. young adults: A nationally-representative study. Social Science & Medicine, 182, 150–157. https://doi.org/10.1016/j.socscimed.2017.03.061 Sidani, J. E., Shensa, A., Hoffman, B., Hanmer, J., & Primack, B. A. (2016). The Association between Social Media Use and Eating Concerns among U.S. Young Adults. Journal of the Academy of Nutrition and Dietetics, 116(9), 1465–1472. https://doi.org/10.1016/j.jand.2016.03.021 Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students' mental health in the United States:Interview survey study. Journal of medical internet research, 22(9), e21279. Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207, 163–166. https://doi.org/10.1016/j.jad.2016.08.040
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".