Digital technology use and <scp>muscle‐building</scp> behaviors in young adults
Bibliographic record
Abstract
OBJECTIVE: Digital technology use and muscle-building behaviors reflect a wide range of behaviors with associated health risks. However, links between digital technology use and muscle-building behaviors remain unknown and this study aimed to address this gap. METHOD: Data were collected from a diverse sample of 1,483 young adults (mean age 22.2 ± 2.0 years) participating in the population-based Eating and Activity over Time 2018 study. Gender-stratified-modified Poisson regression models were used to determine cross-sectional associations between three types of digital technology use (screen time, social media, weight-related self-monitoring apps) and five types of muscle-building behaviors (changing eating, exercise, protein powders/shakes, pre-workout drinks, steroids/growth hormone/creatine/amino acids) in young adulthood, adjusted for sociodemographic characteristics and body mass index. RESULTS: Screen time and social media were either not found to be associated with muscle-building behaviors or in a few instances, associated with less use of these behaviors (e.g., screen time and pre-workout drinks in men). In contrast, the use of weight-related self-monitoring apps was positively associated with all muscle-building behaviors, including steroids/growth hormone/creatine/amino acids in men (prevalence ratio [PR] = 1.83; 95% confidence interval [CI]: 1.13-2.97) and women (PR = 4.43; 95% CI: 1.68-11.68). DISCUSSION: While most recreational screen time may represent sedentary behaviors not related to muscle-building behaviors, weight-related self-monitoring apps are highly associated with more muscle-building behaviors and could be a future target for interventions to discourage the use of steroids and other harmful muscle-building substances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".