The present study explored associations between the five personality traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) and dietary patterns of apparently healthy individuals. Demographic, anthropometric, dietary, and personality data were obtained from 480 adults, up to 45 years of age, through self-completed questionnaires. Dietary habits were assessed via a validated for the population Food Frequency Questionnaire (FFQ), whereas personality was evaluated with the validated Greek version of the 50-item International Personality Item Pool (IPIP). Five dietary patterns were derived from the principal component analysis: the processed foods, the plant-based, the Western-type, the healthy, and the alcohol-coffee pattern. Regression analysis revealed significant findings for various dietary patterns. For the alcohol-coffee pattern, neuroticism showed a positive correlation in the 18-24 age group. In the healthy dietary pattern, BMI and openness were positive predictors, with age predicting healthy eating in participants with under- and normal weight. For the plant-based pattern, male gender was a negative predictor, while openness was positive. For the western-type pattern, male gender was a positive predictor, whereas age and neuroticism negatively predicted adherence in females. Age and conscientiousness negatively predicted the processed foods dietary pattern. More research is needed in this field.

Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study was to compare the quality assessment of online dietary weight loss information conducted by an AI assistant (ChatGPT 4.5) to that of health professionals. Thus, 177 webpages publishing dietary advice on weight loss were retrieved from the web and assessed by ChatGPT-4.5 and by dietitians through (1) a validated instrument (DISCERN) and (2) a self-made scale based on official guidelines for weight management. Also, webpages were assessed by a ChatGPT custom scoring system. Analysis revealed no significant differences in quantitative quality scores between human raters, ChatGPT-4.5, and the AI-derived system (p = 0.528). On the contrary, statistically significant differences were found between the three content accuracy scores (p < 0.001), with scores assigned by ChatGPT-4.5 being higher than those assigned by humans (all p < 0.001). Our findings suggest that ChatGPT-4.5 could complement human experts in evaluating online weight loss information, when using a validated instrument like DISCERN. However, more relevant research is needed before forming any suggestions.

Background: Given the limited evidence in the field, the present study aimed to explore the association of UPF intake with food literacy levels in an adult Mediterranean-based population. Methods: Self-reported demographic and anthropometric data were collected from 317 apparently healthy adults (52.5% males) Food literacy and dietary intake of this population were also assessed. Foods were classified as ultra-processed according to the NOVA system, and their contribution to total daily energy intake (%) was calculated. Then, participants were grouped into the (1) higher UPF intake (HUPFI), and (2) lower UPF intake (LUPFI) groups, based on the median value of this population as a cut-off. Results: Between-group analysis revealed that LUPFI scored statistically significantly higher than the HUPFI group in total food literacy (93.5 [84.0–104.0] vs. 86.0 [78.0–99.0], p < 0.001) and in three out of five food literacy sub-dimensions. Sub-analysis revealed no differences between LUPFI and HUPFI groups of individuals with normal weight. In participants with overweight or obesity, the LUPFI group scored lower than the HUPFI in the total food literacy score (95.0 [87.0–104.0] vs. 81.0 [70.0–94.0], p < 0.001) and in each sub-dimension. Conclusions: Higher UPF intake was associated, in adults with overweight or obesity, with lower levels of food literacy.

Background and objectives: Numerous studies link ultra-processed food (UPF) intake to adverse health outcomes. However, data on the relationship between UPF intake and overall dietary quality are scarce. Therefore, the present analysis aimed to explore possible differences in dietary intake between people with a high UPF intake and those with a low one. Methods: A cross-sectional study was conducted between 12/2022 and 4/2023, in which 113 adults (49.56% females, from 18 to 65 years of age) participated. Dietary habits were evaluated in terms of energy, macronutrients, food variety, and UPF intake using 24 h recalls. Food classification as UPFs was based on their processing using the NOVA system. Self-reported demographic and anthropometric characteristics of the participants were also noted. For the present analysis, participants were grouped into those with a lower (LUPFI) and those with a higher UPF intake (HUPFI), using the median (Mdn) value as a cut-off. Differences between groups were examined, using the chi-square test for qualitative variables, and the independent samples t and Mann–Whitney tests for quantitative parametric and non-parametric variables, respectively. The level of statistical significance was set at 5%. Results: The two groups did not statistically significantly differ in age (p = 0.649) and BMI (p = 0.252). Regarding dietary intake, the LUPFΙ group consumed less energy (Mdn 1686 vs. 2117 kcal, p = 0.009), more protein (18.1 ± 4.2 vs. 15.7 ± 3.9%, p < 0.001), fewer carbohydrates from UPF (11.3 ± 6.9 vs. 26.3 ± 10.0%, p < 0.001), less fat from UPFs (Mdn 6.4 vs. 24.2%, p < 0.001), more food variety (Mdn 11.0 vs. 9.0 foods, p = 0.009), less variety of UPFs (Mdn 3.0 vs. 4.0 foods, p < 0.001), less total and UPF sweet desserts (Mdn 0.0 vs. 1.0, p = 0.022 and p = 0.033, respectively), and less UPF cheese (Mdn 0.0 vs. 0.5, p < 0.001). Discussion: These preliminary results show that higher UPF consumption is associated with a higher energy intake and poorer diet quality, although it does not necessarily translate into worse choices across all food groups. Further research is needed to verify the present findings and to explore the relationship between UPFs and overall dietary intake more thoroughly, as well as to identify those UPFs that actually contribute to a poorer dietary quality.

Whether the association between UPF intake and body weight persists after accounting for dietary quality remains unclear. This analysis examined whether UPF intake is associated with BMI after adjustment for diet quality and whether a threshold exists above which diet quality deteriorates. Demographic and anthropometric data were collected from 307 adults in Greece (December 2022 to January 2024). Dietary intake was assessed using 24 h recalls. UPF intake was expressed as a percentage of energy, and diet quality was evaluated using the Healthy Diet Indicator 2020 (HDI-2020). Multivariable regression examined UPF intake and HDI associations with BMI, adjusting for age, gender, energy intake, smoking, and education. Linear, quadratic, and segmented models examined UPF–HDI associations and thresholds. Higher UPF intake was associated with higher BMI after adjusting for HDI and covariates (B = 0.022 kg/m² per 1% of energy from UPF, 95% CI 0.005–0.040, p = 0.013), whereas HDI was not associated with BMI (p = 0.081). No significant UPF × HDI interaction emerged (p = 0.267). UPF was inversely associated with HDI (p < 0.001), with no threshold within the observed exposure range. Findings suggest an association of UPF intake with BMI after diet-quality adjustment, while its inverse association with HDI appears linear.