Associations between sexting behaviours and muscle dysmorphia symptomatology among a Canadian sample of young adults
Bibliographic record
Abstract
Sexting is increasingly prevalent among young adults and has been linked to body dissatisfaction and disordered eating. However, its relationship with muscle dysmorphia symptoms remains unexplored. This study investigates the association between sexting behaviours and muscle dysmorphia symptomatology in a Canadian sample of young adults aged 18-30 (N = 878). Participants reported their sexting activities over the past 12 months and completed the Muscle Dysmorphic Disorder Inventory (MDDI) to assess muscle dysmorphia symptomatology. Multiple linear regression analyses revealed significant associations between receiving unsolicited photo and video sexts and greater overall muscle dysmorphia symptomatology. Sending photo sexts was also associated with greater total muscle dysmorphia symptoms, Drive for Size, and Functional Impairment. Sending video sexts, however, was associated with greater Drive for Size subscale scores only. Asking for photo sexts was associated with greater total muscle dysmorphia symptoms and Drive for Size, while asking for video sexts did not show any significant associations. None of the sexting behaviours studied were significantly associated with the Appearance Intolerance subscale. These findings suggest that sexting may be associated with muscularity-related body image concerns among young adults, potentially through sociocultural processes prevalent in digital contexts, such as exposure to idealized body norms, appearance-based social comparisons, and the pursuit of external validation via image-based body portrayals. This study contributes to the growing literature on the relationships between online engagement and body image, emphasizing the need for future research and targeted interventions to address the unique societal experiences of young adults in an increasingly technological world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".