Investigating the Associations of ADHD Symptoms, Impulsivity, Physical Exercise, and Problematic Pornography Use in a Chinese Sample
Bibliographic record
Abstract
Investigating the relationship between attention-deficit/hyperactivity disorder (ADHD) symptoms and impulsivity will benefit our understanding of the concept of problematic pornography use (PPU), and revealing predisposing and malleable moderators of PPU will be beneficial for its prevention and intervention. The current study not only aimed to observe these relationships, but also explored the potential moderating role of physical exercise in the general population. A total of 600 Chinese adults (Mage = 32.31, SDage = 12.40, 39.8% women) were recruited and completed an online survey. The results showed that participants with regular exercise scored lower than those without exercise on ADHD, impulsivity, and PPU (all ps < 0.001). Using latent moderated structural equations (LMS), the findings revealed that the relationship between ADHD symptoms and PPU was mediated by impulsivity, and physical exercise moderated this relationship (B = −0.14, p = 0.048). Specifically, when individuals’ physical exercise was higher than 0.84 standard deviations above the mean, the positive predictive effect of impulsivity on PPU was not significant. These findings indicate the important role of impulsivity in the relationship between ADHD and PPU, and physical exercise could be a meaningful component of interventions among individuals experiencing PPU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".