Problematic Social Media Use and Lifestyle Behaviors in Adolescents: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The use of social media by adolescents has increased considerably in the past decade. With this increase of social media use in our daily lives, there has been a rapidly expanding awareness of the potential unhealthy lifestyle-related health effects arising from excessive, mal-adaptive or addictive social media use. OBJECTIVE: The aim of this study is to assess the association between adolescents' social media use and health-related behaviours. METHODS: We employed a cross-sectional research approach and analysed data from 96,919 adolescents at high schools throughout the Netherlands. A structured 43-item questionnaire was used to gather data on sociodemographic data, dietary and lifestyle factors and the degree of social media use based on the Compulsive Internet Scale (CIUS). Logistic regression analysis were performed to assess the association between problematic social media use and lifestyle behaviours, while adjusting for sociodemographic factors. RESULTS: From the 96,919 included adolescents, 7.4% (7022) were identified as at-risk for problematic social media users (PSMUs). Furthermore, logistic regression results showed that adolescents who are at-risk for PSMU were more likely to report alcohol consumption and smoking, while simultaneously having significant lower levels of health-promoting behaviour such as healthy eating habits (eating fruits, vegetables and breakfast regularly) and physical activity. CONCLUSIONS: This study confirms that adolescents at-risk of PSMU were more likely to exhibit an unhealthy lifestyle. Being at-risk for PSMU was determinant of soft drugs use, alcohol consumption, smoking, poor eating habits and lower physical activity independent of the additional adjusted covariates including demographic variables and remaining lifestyle variables. Future research is needed to confirm this observation in an experimental setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".