Relationship Between Fear of Missing Out and Social Media Fatigue: Cross-Lagged Panel Design

• Published in: Journal of medical Internet research Vol. 27; no. 1; p. e75701
• Main Authors: Yao, Xue, Zhao, Junzhe, Chao, Wenfan, Gao, Dongdong, Wang, Minghui, Zhao, Guoxiang
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 18.07.2025; JMIR Publications
• Subjects: Adult; Analysis; Anxiety and Stress Disorders; Care and treatment; Data entry; Diagnosis; Fatigue; Fear - psychology; Female; Humans; Longitudinal Studies; Male; Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine; Original Paper; Problematic Internet Use, Online Gambling and Game Addiction; Psychological aspects; Social Media; Social Media in Adolescence; Social phobia; Students - psychology; Surveys and Questionnaires; Young Adult; United States; FoMO; Fear of Missing Out scale; negative cycle; college students; longitudinal design; social media fatigue
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/75701

In today's digital landscape, social media proliferation offers easier access to others' information and social activities but also introduces challenges such as social media fatigue (SMF). Previous studies have linked the fear of missing out (FoMO) to SMF; however, the directionality of this relationship remains unclear. This study aimed to explore the relationship between FoMO and SMF among college students and examine whether a mutually predictive relationship exists between them. This study adopted a longitudinal research design, administering questionnaires at two distinct time points (ie, T1 and T2) separated by a two-month interval. At T1, the questionnaire included demographic variables of the research subjects (student ID, name, gender, etc.), the Fear of Missing Out Scale, and the Social Media Fatigue Scale. At T2, the questionnaire consisted only of collecting demographic information (student ID and name) for matching, along with the same two scales. Following data collection, the datasets from the two time points were matched based on the demographic information; only successfully matched data were included in the final analyses. Subsequently, descriptive statistics and correlation analyses of FoMO and SMF at T1 and T2 were conducted using SPSS (version 26.0). Finally, a cross-lagged panel analysis was conducted using the FoMO and SMF at T1 and T2 to examine the autoregressive and cross-lagged relationships between the variables over time. A total of 862 valid questionnaires were matched across the two data collection steps. Correlation analysis showed that FoMO at T1 was positively correlated with SMF at T1 (r=0.340; P<.001) and FoMO at T2 (r=0.332; P<.001) and SMF at T2 (r=0.229; P<.001). FoMO at T2 was positively correlated with SMF at T1 (r=0.217; P<.001) and T2 (r=0.417; P<.001). SMF at T1 and T2 were also positively correlated (r=0.425; P<.001). The cross-lagged regression results indicated that using the autoregressive path, FoMO at T1 positively predicted FoMO at T2 (β=0.300; P<.001), and SMF at T1 positively predicted SMF at T2 (β=0.351; P<.001). Additionally, FoMO at T1 positively predicted SMF at T2 (β=0.067; P=.003), and SMF at T1 positively predicted FoMO at T2 (β=0.156; P<.001). There is a bidirectional relationship between FoMO and SMF among college students, suggesting a mutual influence over each other and that this relationship perpetuates a negative cycle. These findings further extend existing research and provide insights for developing mental health programs for college students.