Social Media Exposure and Muscle Dysmorphia Risk in Young German Athletes: A Cross-Sectional Survey with Machine-Learning Insights Using the MDDI-1
Abstract
1. Introduction
State of Research and Study Objective
- Determine the prevalence of clinically relevant MD symptoms in a nationwide sample of young German athletes engaged in strength, endurance, or hybrid training;
- Quantify the association between SM exposure (time and engagement metrics) and MDDI-1 scores;
- Identify via interpretable machine-learning models the digital predictors that most strongly increase MD risk.
2. Materials and Methods
2.1. Study Design
2.2. IP Classification and Verification of Origin
2.3. Operationalization of Social Media Variables
- Daily time spent on social media (five ordinal categories from “none” to “>120 min”);
- Perceived influence of social media on body image (Likert scale 1–5);
- Proportion of social media content related to fitness/sports (6-category scale);
- Degree of preoccupation with nutrition (6-category scale from “not at all” to “weigh and track everything”);
- Frequency of prioritizing sport over social obligations (Likert scale 1–6);
- Discomfort when eating spontaneously (Likert scale: “not acceptable” to “always acceptable”);
- Ability to accept body-related compliments (Likert scale 1–5);
- Visual ideal body selection (image-based response);
- Attention to others’ body appearance (Likert scale 1–6);
- History of bulking/cutting phases (yes/no).
2.4. Identification of Correlation and Classification and Regression Tree (CART) Analysis
2.5. Employing CatBoost and SHAP Values for Enhanced Predictive Analysis and Model Interpretability
3. Results
3.1. Survey Population
3.2. Identification of Correlations
3.2.1. The Use of Social Media Has Been Linked to Increased Pressure to Conform to a Specific Body Ideal, Which Is Often Portrayed as the Norm on These Platforms
3.2.2. The Influence of Social Media on Eating Behavior May Contribute to an Increased Prevalence of Eating Disorders
3.2.3. Social Media Has a Significant Influence on Our Body Perception
3.2.4. Social Media Time Deteriorate MDDI Scores
4. Discussion
4.1. Integration with the Existing Literature
4.2. Mechanistic Considerations
4.3. Practical Implications
4.4. Strengths and Limitations
4.5. Future Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
API | Application programming interfaces |
CART | Correlation and classification-and-regression tree |
MD | Muscle dysmorphia |
MDDI | Muscle dysmorphic disorder inventory |
MDS | Multidimensional scaling |
SM | Social media |
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Study | Sample | Focus | Instrument(s) | Method | Main Findings |
---|---|---|---|---|---|
Schoenenberg & Martin (2020) [15] | N = 203, German males | Instagram use and MD symptoms | MDDI (German) | Correlational | Instagram fitspiration linked to MD; limited to male bodybuilders |
Imperatori et al. (2022) [16] | N = 721, Italian young adults (non-athletic sample) | SM addiction, eating pathology, and MD symptoms | SMAS, EDI-2, MDDI | Mediation analysis | MD mediates link between SM addiction and disordered eating; not focused athletic samples |
Giordano et al. (2025) [17] | N = 2325, Italian students (non-athletic sample M/F) | Social support, self-efficacy, and MD | Custom scales + MDDI | Moderation models | Support buffers MD symptoms; SM time relevant; not focused athletic samples |
Schneider et al. (2017) [18] | N = 3149, high school students (non-athletic sample M/F) | MD, body schema, and social interaction | DSM-IV Priming task + self-report | Experimental | MD priming reduces social motivation; not focused athletic samples |
Present study | N = 540, German athletes (M/F) | SM behavior and MD in mixed-sport context | MDDI-1, custom SM items | Correlations + ML (CatBoost + SHAP) | Influencer comparison and SM time predict MD; novel use of explainable ML and MD symptoms in athletic individuals |
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Fueth, M.; Schmidt, S.V.; Reinkemeier, F.; Drysch, M.; Steubing, Y.; Bausen, S.; Puscz, F.; Lehnhardt, M.; Wallner, C. Social Media Exposure and Muscle Dysmorphia Risk in Young German Athletes: A Cross-Sectional Survey with Machine-Learning Insights Using the MDDI-1. Healthcare 2025, 13, 1695. https://doi.org/10.3390/healthcare13141695
Fueth M, Schmidt SV, Reinkemeier F, Drysch M, Steubing Y, Bausen S, Puscz F, Lehnhardt M, Wallner C. Social Media Exposure and Muscle Dysmorphia Risk in Young German Athletes: A Cross-Sectional Survey with Machine-Learning Insights Using the MDDI-1. Healthcare. 2025; 13(14):1695. https://doi.org/10.3390/healthcare13141695
Chicago/Turabian StyleFueth, Maria, Sonja Verena Schmidt, Felix Reinkemeier, Marius Drysch, Yonca Steubing, Simon Bausen, Flemming Puscz, Marcus Lehnhardt, and Christoph Wallner. 2025. "Social Media Exposure and Muscle Dysmorphia Risk in Young German Athletes: A Cross-Sectional Survey with Machine-Learning Insights Using the MDDI-1" Healthcare 13, no. 14: 1695. https://doi.org/10.3390/healthcare13141695
APA StyleFueth, M., Schmidt, S. V., Reinkemeier, F., Drysch, M., Steubing, Y., Bausen, S., Puscz, F., Lehnhardt, M., & Wallner, C. (2025). Social Media Exposure and Muscle Dysmorphia Risk in Young German Athletes: A Cross-Sectional Survey with Machine-Learning Insights Using the MDDI-1. Healthcare, 13(14), 1695. https://doi.org/10.3390/healthcare13141695