Behavioral addictions and their reciprocal associations with each other, substance use disorders, and mental health problems: Findings from a longitudinal cohort study of young Swiss men
Bibliographic record
Abstract
Background and Aims: The co-occurrence of behavioral addictions (BAs) and substance use disorders (SUDs) or other mental health problems (MHPs) is well documented. However, there is limited evidence on associations between changes in the severity of BAs, SUDs, and MHPs, or their directions of influence or causation. Methods: A non-self-selecting sample of 5,611 young Swiss men (mean age 25.5 at baseline and 28.3 at follow-up) completed a self-reporting questionnaire on various BAs (gambling, gaming, internet, internet pornography, smartphone, work), SUDs (alcohol, cannabis) and MHPs (major depressive disorder, ADHD, borderline personality disorder, social anxiety disorder). Latent change score models were used to evaluate pairwise, bidirectional associations in symptom severity among different BAs, and between BAs and SUDs or MHPs. Results: Overall, changes in each BA's symptom severity were significantly and positively correlated with changes in the symptom severity of other BAs, alcohol use disorder, and MHPs; for cannabis use disorder, such correlations were only found with gaming and work. Significant bidirectional cross-lagged associations were found between the severity of BAs and MHPs, and between the severity of internet and smartphone addiction and other BAs. For SUDs, cross-lagged pathways were often not significant (e.g., with gambling or pornography) or even negative (between cannabis use disorder and work). Discussion and Conclusions: This study provides strong evidence that BAs and MHPs mutually reinforce each other over time. While this interplay can develop and maintain dysfunction, it may also enable positive change, highlighting the need for a comprehensive theoretical framework and integrated intervention approaches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".