The Prevalence of Depression and Anxiety and Their Lifestyle Determinants in a Large Sample of Iranian Adults: Results from a Population Based Cross-Sectional Study
Bibliographic record
Abstract
Abstract Association of lifestyle-related factors and mental health has been less studied in Middle Eastern countries. This study aimed to examine the prevalence of two common mental health problems, i.e., depression and anxiety, and their lifestyle determinants in a large sample of Iranian population. This study was conducted within the framework of SEPAHAN population based cross-sectional study (N=4763(. The General Practice Physical Activity Questionnaire (GPPAQ) was used to assess physical activity and the Iranian-validated version of Hospital Anxiety and Depression Scale (HADS) was applied to screen for anxiety and depression. Logistic regression was used as the main statistical method for data analysis by SPSS version 16.0. A P-value <0.05 was considered to be statistically significant. The risk of anxiety and depression was 2.5 (OR=2.56,95% CI: 1.97-3.33) and 2.21(1.83-2.67) times higher in women than men, respectively. With every one-year increase in the age, the risk of anxiety decreased by 2% (OR=0.98,95% CI:0.97-0.99). Individuals with higher education had 56% lower risk of anxiety (OR=0.44,95% CI: 0.36-0.55) and 46% depression (OR=0.54,95% CI: 0.46-0.64) than the undergraduate group, and the risk of depression in the inactive (less than one hour of activity per week) group was 27% higher than the active group (OR=1.27,95% CI: 1.06-1.51). The risk of anxiety in the non-smoker group was 65% (OR=0.35,95% CI: 0.20-0.59) and depression was 64% lower than among smokers (OR=0.34,95% CI:0.22-0.53). In the ex-smoker group, the risk of anxiety was 60% (OR=0.40,95% CI:0.19-0.85) and depression was 59% lower than for the smoker group (OR=0.41,95% CI: 0.24-0.73). This current study’s results demonstrated significant associations between unhealthy lifestyle factors and increased risk of anxiety and depression. Hence, special attention must be paid to preventive intervention programmes aiming to enhance healthy lifestyle among at-risk populations.
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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 | 0.000 | 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) | 0.001 | 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".