Gender-specific insights into adherence to Mediterranean diet and lifestyle: analysis of 4,000 responses from the MEDIET4ALL project
Bibliographic record
Abstract
Background The Mediterranean Diet (MedDiet) is widely recognized for its health benefits, though adherence varies across populations and is influenced by multiple lifestyle and demographic factors. This study examined MedDiet adherence patterns and their associations with lifestyle behaviors, with particular attention to gender differences in a large, multinational cohort. Methods Data were obtained via the MEDIET4ALL survey, an international cross-sectional study that included 4,010 participants (mean age: 36.04 ± 15.06 years; 59.5% female) across 10 countries. The evaluation of adherence to the MedDiet was conducted using the MedLife Index, a validated tool that assesses adherence to MedDiet patterns and lifestyle behaviors through three blocks: Mediterranean food consumption, MedDiet habits, and lifestyle behaviors. Additionally, validated instruments were used to measure associated factors, including perceived barriers to adherence, physical activity, sleep quality and disturbances, mental health, life satisfaction, social participation, and technology use behaviors. Results While total Mediterranean lifestyle (MedLife) scores showed no significant gender differences, women demonstrated better adherence to food consumption components (p < 0.001), while men showed greater physical activity and social participation. Women reported poorer sleep metrics (efficiency, latency, duration) and higher insomnia severity (all p < 0.05). Psychological distress was more prevalent among women, who also expressed greater needs for psychosocial and nutritional support (p < 0.001). The MedLifeMEDLIFE Index showed significant positive correlations with physical activity (r = 0.298), social participation (r = 0.227), and sleep satisfaction (r = 0.181), and negative correlations with mental health measures (insomnia: r = –0.137; depression: r = –0.115; stress: r = –0.089; anxiety: r = –0.076; all p < 0.001). Conclusion Our findings reveal distinct gender-specific patterns in MedDiet adherence and associated lifestyle factors. These results underscore the need for differentiated public health approaches that address the unique behavioral and psychosocial needs of men and women to promote MedLife adoption.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".