Exploring Determinants of Mediterranean Lifestyle Adherence: Findings from the Multinational MEDIET4ALL e-Survey Across Ten Mediterranean and Neighboring Countries
Notice bibliographique
Résumé
Background/Objectives: Despite its well-established health benefits, adherence to the Mediterranean lifestyle (MedLife) has declined globally, including in its region of origin, alongside a significant shift toward ultra-processed food consumption. Understanding the factors associated with MedLife adherence is essential for developing targeted interventions and tailored policy recommendations. As part of the MEDIET4ALL PRIMA project, this cross-sectional study aimed to comprehensively examine geo-demographic, socio-economic, psychological, behavioral, and barrier-related factors associated with and potentially contributing to MedLife adherence. Methods: Data were collected from 4010 participants aged 18 years and above across ten Mediterranean and neighboring countries using the multinational MEDIET4ALL e-survey, which included the validated MedLife index, along with various other questionnaires. Results: Results indicate that only 22% of respondents demonstrated high adherence to the Mediterranean lifestyle (MedLife), with significant variability observed across countries, age groups, education levels, and health statuses. Spain had the highest proportion of participants with high adherence (38%). Factors associated with significantly higher adherence rates include older age, living in the Mediterranean region, higher education levels, a greater awareness of MedLife principles, lower perceived barriers, normal BMI, better health status, and stable economic and marital conditions (p-values ranging from 0.04 to <0.001). Additionally, individuals with high MedLife adherence exhibited more socially and physically active lifestyles and experienced less psychological strain (p < 0.001). Regression analyses identified MedLife awareness as the strongest positive predictor of adherence (β = 0.206), followed by social participation (β = 0.194) and physical activity (β = 0.096). Additional positive contributors include life satisfaction, sleep quality, living in the Mediterranean region, age, and education (β ranging from 0.049 to 0.093). Conversely, factors that are negatively associated with adherence include sedentary behavior, living environment, and barriers such as low motivation, taste dislike, price unaffordability, limited availability, and the time-consuming nature of preparing Mediterranean food (MedFood; β ranging from −0.036 to −0.067). Conclusions: These findings indicate that fewer than one in four adults across Mediterranean and neighboring countries demonstrate high adherence to MedLife, supporting prior evidence of suboptimal adherence even within Mediterranean regions. This study identified a range of behavioral, socio-demographic, and environmental factors—both positive and negative predictors—that can help guide the design of targeted, culturally adapted interventions to promote MedLife behavior. Future research should incorporate objective measurements and longitudinal monitoring to better understand underlying mechanisms, establish causality, and develop sustainable strategies for enhancing MedLife adherence in diverse populations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».