Next Article in Journal
Association Between the Korean Healthy Eating Index (KHEI) and Healthcare Costs Among Adults: The Korea National Health and Nutrition Examination Survey (KNHANES) 2016 and 2021
Previous Article in Journal
Associations Between Dietary Amino Acid Intake and Elevated High-Sensitivity C-Reactive Protein in Children: Insights from a Cross-Sectional Machine Learning Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 17
Issue 13
10.3390/nu17132236
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial

by
Ludmila de Melo Barros
,
Vanessa Amorim Peixoto
,
Guilherme César Oliveira de Carvalho
,
Micnéias Róberth Pereira
,
Rodrigo Tenório Lins Carnaúba
and
Nassib Bezerra Bueno
*
Laboratório de Nutrição e Metabolismo (LANUM), Programa de Pós-Graduação em Nutrição (PPGNUT), Faculdade de Nutrição, Universidade Federal de Alagoas, Campus AC Simões, Av. Lourival Melo Mota, s/n, Cidade Universitária, Maceió 57072-900, AL, Brazil
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(13), 2236; https://doi.org/10.3390/nu17132236
Submission received: 13 June 2025 / Revised: 1 July 2025 / Accepted: 4 July 2025 / Published: 5 July 2025
(This article belongs to the Special Issue Mechanisms of Ultra-Processed Foods and Health Outcomes)
Browse Figures
Review Reports Versions Notes

Abstract

Background/Objectives: A faster eating rate is associated with increased energy intake and risk of obesity. High consumption of ultra-processed foods (UPFs) is associated with a faster eating rate. Psychobehavioral aspects, such as body image self-perception, eating disorders, and anxiety, may modulate this eating behavior. Therefore, this study examined the moderating role of psychobehavioral factors in the association between meal type (UPF vs non-UPF) and eating rate among individuals with obesity. Methods: It is a secondary analysis of a randomized, parallel clinical trial conducted with 39 adults who have obesity. Participants were assigned to consume either a UPF-only composed meal or a UPF-free meal, both of which were isoenergetic (~550 kcal). Psychobehavioral variables (food addiction—mYFAS 2.0, body image perception and satisfaction—Silhouette Rating Scale, eating disorders—EAT-26, and anxiety—GAD-7) were assessed. Eating rate was measured in kcal/min. Results: Body image perception and satisfaction significantly interacted with the type of meal. In the UPF group, lower body image dissatisfaction was associated with a higher eating rate (β = 4.79 kcal/min; 95% CI: 1.40; 8.19; p = 0.007), while a higher body image perception score was associated with a lower eating rate (β = −4.61 kcal/min; 95% CI: −8.57; −0.65; p = 0.024). No significant associations were observed for food addiction scores, eating disorders or anxiety. Conclusions: Body image modulates the eating rate in the context of UPF consumption. These findings suggest that interventions against obesity should consider individual psychobehavioral characteristics.

Graphical Abstract

1. Introduction

Obesity imposes an estimated economic burden of USD 2 trillion annually [1]. Globally, approximately 890 million adults have excess body fat that adversely affects their quality of life [2] and is associated with increased risks of morbidity and all-cause mortality [3,4]. Despite advances in identifying the factors underlying the energy imbalance that contribute to its etiology, the limited effectiveness of current obesity treatments suggests an incomplete understanding of the mechanisms driving excessive food intake [5,6].
The eating rate is a key aspect of food microstructure that influences energy intake [7]. A systematic review with meta-analysis of 22 clinical trials demonstrated that faster eating leads to higher energy intake, with the effect size sensitive to the magnitude of the change in eating rate [8]. Shorter oral processing time and limited chewing activity also tend to increase post-meal hunger and desire to eat [9], factors positively associated with excess body weight [10,11]. Furthermore, individuals with obesity exhibit faster eating rates, correlated with larger bite sizes, shorter oral exposure times per bite, and higher energy intake, suggesting a typically obesogenic eating style [12,13].
This pattern of behavior is influenced by the rising availability of ultra-processed foods (UPFs) in the food system [14]. These industrialized formulations, which are generally composed of food additives to enhance sensory characteristics and are even absent from whole foods [15], undergo degradation of the food matrix, resulting in a softer texture that is easier to chew [16,17]. As a result, UPF consumption exhibits a significantly higher eating rate compared with other processing categories [18], even in individuals with obesity [19,20]. This may partially explain the sustained increases in energy intake and body weight attributed to the consumption of these products [18,20,21].
In addition to environmental influences, psychobehavioral factors also explain individual differences in susceptibility to overeating [22]. For example, compulsive eating disorders impose a higher risk of losing control over appetite [23], leading to higher energy intake and lower satiety in overweight individuals [24]. Patients with bulimia nervosa also eat twice as fast as those without the condition [25]. Meanwhile, generalized anxiety disorder has been positively associated with hunger and disinhibition behaviors, which lead to overeating [26]. In the specific context of UPF consumption, body image perception and dissatisfaction are psychosocial drivers that promote greater intake of these products [27]. In turn, food addiction, a phenomenon recognized for its similarities to substance use disorders, also has an impact on the consumption of UPF [28,29].
Hence, it is essential to deepen our understanding of the synergistic effect between individual and environmental determinants in disruptive eating behavior [30]. Therefore, the present study aims to identify psychobehavioral factors that interact with the increased UPF eating rate, previously identified in a randomized clinical trial with individuals with obesity [19]. This secondary analysis offers insights into more integrated approaches to treating overeating and obesity [31].

2. Materials and Methods

2.1. Experimental Design

This is a secondary analysis of a randomized, parallel clinical trial with two research arms [19]. Due to the nature of the intervention, it was not possible to conduct a double-blind study. The present study is reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines [32]. The trial was approved by the Research Ethics Committee of the Federal University of Alagoas (UFAL) (CAAE registration number: 69062123.2.0000.5013) and submitted to the Registry of Brazilian Clinical Trials (RBR-56nsh92). Eligible candidates who satisfied all inclusion criteria provided written informed consent prior to enrollment, thereby formalizing their study participation.

2.2. Location, Sample, and Sampling

The investigation was carried out at Nutrition and Metabolism Laboratory (LANUM), affiliated with the Faculty of Nutrition (FANUT) at the Federal University of Alagoas (UFAL). The non-probabilistic convenience sampling method was used between July and August 2023. Recruitment strategies included announcements on campus and social media accounts of the Laboratory and UFAL, as well as the university’s official website. The study enrolled adult participants aged 19–60 years with obesity, requiring fulfillment of at least two diagnostic criteria: (1) BMI ranging from 25.0 to 40.0 kg/m2; (2) gender-specific abdominal circumference thresholds (≥88 cm for female participants; ≥102 cm for males); or (3) elevated adiposity levels (≥35% body fat for women; ≥25% for men) as measured by bioelectrical impedance analysis [33]. Participants were not included if they were taking chronic medications (e.g., antibiotics, glucose-lowering agents, antiretrovirals, immunomodulators, or psychotropic drugs), had food hypersensitivities or dietary restrictions that could interfere with test meal consumption, presented any clinical conditions preventing anthropometric or metabolic assessments, were menopausal, pregnant, or lactating women, or had undergone bariatric procedures. Additionally, individuals who consumed less than 95% of the test meal were excluded.

2.3. Randomization and Allocation

A computerized randomization scheme using R statistical software (v4.3.1) was implemented. The algorithm generated 100 pseudorandom values (0–1 range), subsequently rounded to integers (0 = control; 1 = UPF group). To ensure allocation concealment, the randomization sequence was securely maintained by an independent researcher not involved in participant screening or enrollment. Group assignments were only determined after completion of all baseline measurements, with the allocation information subsequently revealed by a blinded study team member prior to each participant’s test meal session.

2.4. Procedures

On the scheduled testing day, participants reported to the research center between 7:00 and 9:00 AM following a 12 h overnight fast and 24 h abstinence from vigorous physical activity, caffeine, and energy drinks. Upon arrival, researchers performed baseline evaluations including resting energy expenditure measurements and heart rate variability assessments, along with other biological parameter analyses. Between 8:00 and 10:00 AM, participants were escorted to a private, climate-controlled (22–24 °C) testing room where they received their assigned test meal (breakfast) with instructions to consume it completely. Additional methodological details are available in Galdino-Silva et al. [19]

2.5. Test Meals

Two isocaloric test meals matched for macronutrient composition, fiber content, sodium levels, and energy density (1.4 kcal/g), differing solely in their degree of food processing, were compared. Both meals provided approximately 550 kcal, containing 70 g carbohydrates, 22 g lipids, 18 g protein, 9 g dietary fiber, and 1300 mg sodium (Table 1). The ultra-processed food (UPF) condition featured exclusively industrial formulations: packaged toast with processed cheese and ham, commercial strawberry jam, margarine, and fiber-fortified guava juice. In contrast, the control meal comprised only fresh or minimally processed ingredients: artisanal bread with extra-virgin olive oil, pan-fried chicken eggs (using soybean oil and salt), and freshly prepared guava juice sweetened with honey (Figure 1). All meals were prepared under controlled conditions in the Technical and Dietetics Laboratory of FANUT/UFAL one hour before serving, following standardized food safety protocols for research-grade meal preparation.

2.6. Food Addiction

Food addiction was measured using a modified Yale Food Addiction Scale 2.0 (mYFAS 2.0) applied at baseline [34]. This scale was translated and cross-culturally validated into Brazilian Portuguese, demonstrating internal consistency and an adequate factor structure [35,36]. It consists of 13 questions, of which 11 represent symptoms related to the individual’s eating behavior that refer to aspects of substance use disorders in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [37], and two refer to clinical impairment or distress. Each question is answered according to the frequency with which the symptom occurs, ranging from “never” to “every day,” and there is a threshold for each of them so that the criteria for the symptom are met. Finally, the 11 symptoms were summed up to create a symptom score. Individuals who presented two or more symptoms and met the thresholds for any of the clinical impairment or distress questions were classified as having food addiction [34].

2.7. Body Image Self-Perception

To assess body image self-perception, the Silhouette Rating Scale, a validated and adapted instrument for the Brazilian population [38], was employed. The scale consists of 15 white silhouette figures on a black background, with corresponding BMI averages ranging from 12.5 kg/m2 at the first silhouette to 47.5 kg/m2 at the last, with an interval of 2.5 kg/m2 between each figure. At baseline, participants were presented with 15 silhouette figures in an ascending orderly series and asked the following questions: “Which figure best represents your current size?”, “Which figure would you consider to be the ideal size for your gender in general?” and “Which figure represents the size you would like to have?”. The discrepancy between the ideal self-image and the current self-image at the time of the questionnaire was calculated by subtracting the numbers of the chosen silhouettes. Thus, the closer the subtraction result is to zero, the lower the participant’s dissatisfaction with body image. Furthermore, the ability to estimate the self-perception body image was also verified by comparing the self-image chosen as “current” and the individual’s BMI.

2.8. Generalized Anxiety Disorder Scale (GAD-7)

The GAD-7 scale, translated and validated for Brazilian Portuguese, was applied at baseline to identify potential cases of generalized anxiety disorder [39]. It consists of seven items, arranged on a four-point Likert scale ranging from 0 to 3, where 0 represents “not at all” and 3 “almost every day”; the score is summed and varies from 0 to 21 points. A probable case of generalized anxiety disorder was considered when the score obtained was equal to or greater than 10 [40].

2.9. Eating Attitudes

To assess eating behaviors and attitudes that may be associated with eating disorders, the Eating Attitudes Test (EAT-26) [41] was applied at baseline. The EAT-26 was translated and validated into Portuguese [42] and consists of 26 items, which are divided into three factors: “diet”, “bulimia and preoccupation with food”, and “oral control”. The questions are arranged on a Likert-type scale, which varies between “always = 3 points”, “often = 2 points”, “sometimes = 1 point”, “rarely = 0 points”, “almost never = 0 points”, and “never = 0 points”. After summing up the scores assigned to each item, individuals who obtained a score higher than 21 [41,42] were considered at risk for eating disorders.

2.10. Eating Rate

The entire meal consumption process was video-recorded for subsequent analysis of eating rate parameters. Participants were informed about the recording procedure exclusively after meal completion. Meal duration was operationally defined as the interval between the first bite and final swallow. Based on these recordings, calories consumed per minute (kcal/min) were calculated for both solid and liquid components using established methodologies [18,43].

2.11. Additional Data

Demographic, socioeconomic, clinical, and lifestyle data, including sex, age, educational attainment, alcohol and tobacco use patterns, chronic medication regimens, pre-existing medical conditions, and socioeconomic status (classified according to Brazilian Economic Classification Criteria [44]), were obtained through a validated research questionnaire. Anthropometric measurements included the following: (1) body weight, assessed to the nearest 100 g using a calibrated portable digital scale (capacity: 150 kg) with participants wearing light clothing and no footwear; (2) standing height, measured with 1 mm precision using a portable stadiometer (range: 0–2.2 m); (3) waist circumference, determined at the midpoint between the lowest rib and iliac crest using a non-stretch tape measure with participants in standardized anatomical position; and (4) body composition parameters (fat mass percentage, fat-free mass, and total body water) evaluated via tetrapolar bioelectrical impedance analysis (RJL Quantum IV, RJL Systems). Subjective appetite sensations were quantified after 12 h fasting state using a 100 mm visual analog scale (VAS) assessing four domains: hunger (“How hungry do you feel now?”), satiety (“How full do you feel right now?”), desire to eat (“How much do you want to eat now?”), and capacity to eat (“How much do you think you can eat now?”), with anchors ranging from “nothing/none” (0 mm) to “excessive” (100 mm) [18,45].

2.12. Sample Size Calculation

Estimating a standardized effect size (Cohen’s d) of 0.9, indicating a “large” effect size, considering the post-prandial measure of hunger as the primary outcome, with a statistical power of 80% and a significance level of 5%, 21 patients per group were required to achieve significant results. Although studies investigating effects on eating rate may vary greatly in sample size [46], other clinical trials have similar sample sizes [47,48].

2.13. Statistical Analysis

Multivariable linear regression was performed to evaluate the effect of psychobehavioral factors (the symptom score of FA, EAT-26, GAD-7, as well as body perception and satisfaction), used as independent variables in the different models, and the eating rate, used as outcome in various models. Initially, we conducted separate analyses by group, and thereafter, we ran a complete model, calculating the interaction term between the independent variable and the intervention group (UPF meal or control meal). All analyses were adjusted for age and sex. The results were reported as beta coefficients, along with their corresponding 95% confidence intervals and p-values. Pearson’s correlation coefficient was calculated between body perception and satisfaction to explore the relationship between complementary aspects of body image.
Continuous variables were expressed as mean (standard deviation), while categorical variables were reported as frequency counts and percentages. A t-test and Fisher’s exact test were also used to compare means between groups. All analyses were conducted using JAMOVI software, version 2.3.28 (Sidney, Australia), with an alpha value of 0.05 for statistical significance.

3. Results

Of the 94 individuals assessed for eligibility, 49 were excluded (see Figure 2 for details), resulting in 43 participants with obesity being allocated to the intervention or control group. However, four participants were excluded because they failed to consume at least 95% of the test meal (Figure 2). Consequently, the final sample consisted of 39 individuals (control group, n = 20; UPF group, n = 19), predominantly female, who self-identified as Black, non-alcoholic, sedentary, and with a high school education level. None of the participants were smokers, had diagnosed noncommunicable chronic diseases, or exhibited signs of fever and tachycardia. Baseline characteristics are shown in Table 2, with no statistically significant differences between groups.
Table 3 presents the results of the linear regression analysis examining the associations between psychobehavioral variables and intervention groups, including interaction and subgroup analyses. A significant interaction was found between body image satisfaction and meal type on eating rate (β = 4.79 kcal/min; 95% CI: 1.40; 8.19; p-value×group = 0.007), in contrast to the model without adjustment for sex and age (β = 2.80 kcal/min; 95% CI: −2.07; 7.68; p-value×group = 0.251). Specifically, in the UPF group, higher body image satisfaction, reflected by scores closer to zero, was associated with an increased eating rate, suggesting that individuals with less dissatisfaction with their bodies consumed food more quickly when exposed to UPF meals (Figure 3), which contrasted with the control group.
Regarding body image perception, the results also revealed a significant interaction with meal type, with (β = −4.61 kcal/min; 95% CI: −8.57; −0.65; p-value×group = 0.024) and without adjustment (β = −6.11 kcal/min; 95% CI: −11.37; −0.85; p-value×group = 0.024). In comparison with the control group, the UPF group’s higher body image perception scores were associated with a decreased eating rate, as shown in Figure 4. A significant negative association was also revealed between body image perception and satisfaction (r = −0.522, p < 0.001), indicating that as perception scores increase, satisfaction tends to decrease (Figure A1).
No significant associations were found for food addiction symptoms, EAT-26, or GAD-7 scores in either group or the overall sample (Table 3).

4. Discussion

This study’s results suggest that body image satisfaction and perception affect the eating rate according to the meal’s processing level. In the UPF meal group, decreased body image dissatisfaction was associated with a faster eating rate, whereas in the control meal group, it was associated with a slower eating rate. In addition, participants in the UPF meal group with higher body image perception values tended to eat more slowly, in contrast to the increased eating rate in the control meal group. The eating rate did not show a significant association with the screening of risk behaviors for eating disorders, anxiety levels, and food addiction symptom scores, which highlights the specific influence of body image on this eating behavior in the present study.
Although clinical trials have consistently reported faster eating rates with UPF meals due to their physical properties, such as soft texture and high energy density, which encourage larger bites, less chewing, and shorter meal duration [18,20,49,50], our results suggest that psychobehavioral variables, particularly body image satisfaction and perception may influence counteract this trend. The contrast with the pattern observed by Galdino-Silva et al. (2024) [19], who reported significantly faster eating rates among individuals with obesity consuming the same UPF meals, suggests that the effect of UPFs is not uniform across populations. Instead, it may be modulated by individual psychological factors, which influence how people engage with these foods.
Body image is a multidimensional psychological construct that reflects how individuals perceive, think, and feel about their own bodies. Its perceptual component refers to the accuracy with which this perception aligns with actual body size [51]. In our study, higher scores of self-body perception, either as a smaller discrepancy between actual and perceived BMI or as an overestimation of body size, were associated with a reduced eating rate during the consumption of the UPF meal. The attitudinal component of self-body image, which encompasses emotional and cognitive responses to one’s own body, such as satisfaction or dissatisfaction with appearance, also plays a role in eating behavior. These attitudes often translate into efforts to alter body weight, typically shaped by internalized ideals of appearance [52,53]. Interestingly, our findings indicate that higher levels of body image satisfaction, even when still within the negative range, were associated with increased eating rates during UPF consumption. Given the established connection between self-image and emotional responses [51], individuals who identify with a higher body weight tend to show greater body image perception scores and lower levels of satisfaction, reinforcing the idea that how one perceives and evaluates their body can shape patterns of eating behavior, particularly eating rate.
It is known that greater dissatisfaction with body image tends to lead to maladaptive eating patterns, such as increased compensatory restriction [54]. Such behavior involves the intentional and continuous limitation of food intake, often guided by self-imposed dietary rules that restrict the consumption of foods considered bad [55]. This stigmatization of “good” or “bad” foods may be rooted in the adverse health outcomes linked to the consumption of UPF [56,57], leading to discouragement of its consumption at the public health level, and thus contributing to the stigmatization of these products [15,58].
Given this context, our finding that individuals in the UPF group who reported greater body image dissatisfaction due to the desire to be smaller than their perceived body size tended to eat more slowly becomes particularly noteworthy. This pattern suggests that when confronted with a stigmatized food category like UPF, individuals with heightened body image concerns adopt a slower eating rate, either consciously or unconsciously, as a form of self-regulation. Such behavior may reflect an internal conflict between the appeal of consuming the food and concerns about its perceived negative impact on their body image [54,59]. These observations underscore the need to explore further the synergistic effect between UPF consumption and body image, in order to clarify the mechanisms underlying this relationship that may explain the reduction in eating rate.
The relationship between UPF consumption and psychobehavioral factors has already been explored in the literature. In a systematic review, Pereira et al. [60] investigated the associations between UPF consumption and eating disorders, food addiction, and body image dissatisfaction. Their findings revealed a significant association between food addiction, bulimia, binge eating, and unspecified eating disorders with UPF consumption. Considering that eating rate tends to be higher when consuming UPF, these psychological traits might also be associated with the eating rate. However, our findings did not show any significant interaction between UPF consumption and the risk of eating disorders in terms of eating rate. This may be partially explained by the limitations of the assessment tool used (EAT-26), which predominantly targets behaviors linked to anorexia nervosa and bulimia nervosa, disorders less prevalent among individuals with overweight and obesity compared with binge eating disorder [61,62]. Moreover, the similarity between the intervention and control meals in terms of texture, macronutrient composition, and energy density, factors that influence eating rate [63], may have attenuated the potential effects related to food addiction, which is more likely to emerge with the consumption of hyperpalatable and energy-dense items [64].
It is worth noting that body perception among individuals with excess weight is frequently distorted, often involving underestimation or overestimation of body size [65,66,67]. A systematic review and meta-analysis reported that individuals with obesity exhibit higher levels of body dissatisfaction compared with those with an adequate weight [68]. This dissatisfaction tends to be more pronounced among women, as consistently documented in the literature [69,70], underscoring the importance of considering gender in our analytical model. However, in our sample, body perception presented a more uniform pattern between male and female participants.
Some limitations should be acknowledged when interpreting these results. This is a secondary analysis of a randomized clinical trial, which means that causal inference cannot be drawn because the hypotheses tested here were not the primary objective of the original design. In addition, sample losses resulting from the exclusion of participants who did not consume the entire meal offered reduced statistical power. Although video recording may have influenced participants’ eating behavior due to the tendency to alter behavior when being observed, it remains the least intrusive method for capturing eating episodes [71,72,73]. About body image assessment, comparisons with the literature are limited by the diversity of tools used to measure this construct [68]. Despite this, body silhouette scales demonstrate predictive validity by using standardized images that reduce the diversity of individual abstractions [74,75]. In particular, the scale proposed by Kakeshita et al. [38] was developed for the Brazilian population, reflecting its particular cultural and socioeconomic context [38]. In this sense, the findings provide relevant evidence on the potential synergy between the environmental impact of UPF consumption and individual psychological traits. Given the limited effectiveness of traditional information-based campaigns in improving dietary habits, acknowledging the interaction between body image and UPF intake may enable more strategic allocation of public health resources and enhance the impact of interventions targeting obesity. These insights highlight the importance of tailoring policy strategies to the specific needs of psychologically vulnerable subgroups [30,31,76].

5. Conclusions

This study revealed a significant interaction between body image and the food processing level on the eating rate of individuals with obesity. Higher body perception scores, as well as higher levels of dissatisfaction related to the desire for a smaller body than perceived, were associated with a slower eating rate during the consumption of UPF, contrary to the pattern generally expected for this type of food. These findings suggest that psychological factors, particularly the relationship with one’s own body image, can modulate the behavioral effects commonly attributed to UPF consumption. This highlights the relevance of future research focusing on populations with body image distortions and dissatisfaction, in order to better understand the nuanced impacts of UPF consumption in these groups.

Author Contributions

N.B.B. and L.d.M.B.: conceptualization. N.B.B., G.C.O.d.C., and M.R.P.: methodology and investigation. N.B.B. and M.R.P.: data curation. L.d.M.B., V.A.P., G.C.O.d.C., R.T.L.C., and M.R.P.: writing—original draft and formal analysis. N.B.B.: validation, formal analysis, writing—review and editing, visualization, supervision, and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Council for Scientific and Technological Development (CNPq), process number 409166/2021-9, and by the Alagoas State Research Support Foundation (FAPEAL), number 002/2022. L.d.M.B., G.C.O.d.C., and M.R.P. received scholarships (numbers 88887.955577/2024-00, 88887.135780/2025-00, and 88887.135788/2025-00, respectively) from the Coordination for the Improvement of Higher Education Personnel (CAPES), and V.A.P. received a scholarship (number E:60030.0000000350/2023) from Alagoas State Research Support Foundation (FAPEAL). N.B.B. is supported by a research productivity grant from the National Council for Scientific and Technological Development (CNPq) (number 311401/2022-8). R.T.L.C received a scholarship from the Programa de Incentivos e Bolsas para a Iniciação Científica (PIBIC) from UFAL, number 01/2024.

Institutional Review Board Statement

The present study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Federal University of Alagoas (UFAL) according to resolution No. 466/12 of the National Health Council/Ministry of Health, number CAAE 69062123.2.0000.5013 (2 June 2023), and registered in the Brazilian Registry of Clinical Trials, RBR-56nsh92.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Acknowledgments

The authors would like to acknowledge the Laboratory of Nutrition and Metabolism for providing essential support that made this study feasible. We also thank the faculty of nutrition at the Federal University of Alagoas for offering the infrastructure necessary for the execution of this research.

Conflicts of Interest

The authors declare no conflicts of interest. Furthermore, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Nutrients 17 02236 g0a1
Figure A1. Scatter plot from correlation between body image satisfaction and perception.
Figure A1. Scatter plot from correlation between body image satisfaction and perception.
Nutrients 17 02236 g0a1

References

  1. Dobbs, R.; Sawers, C.; Thompson, F.; Manyika, J.; Woetzel, J.R.; Child, P.; Spatharou, A. Overcoming Obesity: An Initial Economic Analysis; McKinsey Global Institute: New York, NY, USA, 2014. [Google Scholar]
  2. World Health Organization. Obesity and Overweight. 2024. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 1 March 2025).
  3. Aune, D.; Sen, A.; Prasad, M.; Norat, T.; Janszky, I.; Tonstad, S.; Vatten, L.J. BMI and all cause mortality: Systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ 2016, 353, i2156. [Google Scholar] [CrossRef]
  4. Taylor, V.H.; Forhan, M.; Vigod, S.N.; McIntyre, R.S.; Morrison, K.M. The impact of obesity on quality of life. Best Pract. Res. Clin. Endocrinol. Metab. 2013, 27, 139–146. [Google Scholar] [CrossRef] [PubMed]
  5. Dulloo, A.G. Explaining the failures of obesity therapy: Willpower attenuation, target miscalculation or metabolic compensation? Int. J. Obes. 2012, 36, 1418–1420. [Google Scholar] [CrossRef]
  6. Mauro, M.; Taylor, V.; Wharton, S.; Sharma, A.M. Barriers to obesity treatment. Eur. J. Intern. Med. 2008, 19, 173–180. [Google Scholar] [CrossRef] [PubMed]
  7. Bellisle, F. Edograms: Recording the microstructure of meal intake in humans—A window on appetite mechanisms. Int. J. Obes. 2020, 44, 2347–2357. [Google Scholar] [CrossRef]
  8. Robinson, E.; Almiron-Roig, E.; Rutters, F.; De Graaf, C.; Forde, C.G.; Smith, C.T.; Nolan, S.J.; Jebb, S.A. A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger. Am. J. Clin. Nutr. 2014, 100, 123–151. [Google Scholar] [CrossRef] [PubMed]
  9. Krop, E.M.; Hetherington, M.M.; Nekitsing, C.; Miquel, S.; Postelnicu, L.; Sarkar, A. Influence of oral processing on appetite and food intake–a systematic review and meta-analysis. Appetite 2018, 125, 253–269. [Google Scholar] [CrossRef]
  10. Fang, K.; Liu, H.; Zhai, B.; Wang, L.; Zhao, L.; Hao, L.; Huang, L.; Zhang, X. Association between eating rate and childhood overweight/obesity: A systematic review and meta-analysis. J. Trop. Pediatr. 2024, 70, fmae040. [Google Scholar] [CrossRef]
  11. Ohkuma, T.; Hirakawa, Y.; Nakamura, U.; Kiyohara, Y.; Kitazono, T.; Ninomiya, T. Association between eating rate and obesity: A systematic review and meta-analysis. Int. J. Obes. 2015, 39, 1589–1596. [Google Scholar] [CrossRef]
  12. Fogel, A.; Goh, A.T.; Fries, L.R.; Sadananthan, S.A.; Velan, S.S.; Michael, N.; Tint, M.T.; Fortier, M.V.; Chan, M.J.; Toh, J.Y.; et al. A description of an ‘obesogenic’ eating style that promotes higher energy intake and is associated with greater adiposity in 4.5 year-old children: Results from the GUSTO cohort. Physiol. Behav. 2017, 176, 107–116. [Google Scholar] [CrossRef]
  13. Berkowitz, R.I.; Moore, R.H.; Faith, M.S.; Stallings, V.A.; Kral, T.V.; Stunkard, A.J. Identification of an obese eating style in 4-year-old children born at high and low risk for obesity. Obesity 2010, 18, 505–512. [Google Scholar] [CrossRef] [PubMed]
  14. Hall, K.D. From dearth to excess: The rise of obesity in an ultra-processed food system. Philos. Trans. R. Soc. B 2023, 378, 20220214. [Google Scholar] [CrossRef]
  15. Monteiro, C.A.; Cannon, G.; Levy, R.B.; Moubarac, J.C.; Louzada, M.L.; Rauber, F.; Khandpur, N.; Cediel, G.; Neri, D.; Martinez-Steele, E.; et al. Ultra-processed foods: What they are and how to identify them. Public Health Nutr. 2019, 22, 936–941. [Google Scholar] [CrossRef] [PubMed]
  16. Forde, C.G.; Bolhuis, D. Interrelations Between Food Form, Texture, and Matrix Influence Energy Intake and Metabolic Responses. Curr. Nutr. Rep. 2022, 11, 124–132. [Google Scholar] [CrossRef]
  17. Forde, C.G.; Mars, M.; De Graaf, K. Ultra-processing or oral processing? A role for energy density and eating rate in moderating energy intake from processed foods. Curr. Dev. Nutr. 2020, 4, nzaa019. [Google Scholar] [CrossRef] [PubMed]
  18. Hall, K.D.; Ayuketah, A.; Brychta, R.; Cai, H.; Cassimatis, T.; Chen, K.Y.; Chung, S.T.; Costa, E.; Courville, A.; Darcey, V.; et al. Ultra-processed diets cause excess calorie intake and weight gain: An inpatient randomized controlled trial of ad libitum food intake. Cell Metab. 2019, 30, 67–77. [Google Scholar] [CrossRef] [PubMed]
  19. Galdino-Silva, M.B.; Almeida, K.M.M.; Oliveira, A.D.S.D.; Santos, J.V.L.D.; Macena, M.D.L.; Silva, D.R.; Pereira, M.R.; Silva-Júnior, A.E.; Ferro, D.C.; Paula, D.T.d.C.; et al. A Meal with Ultra-Processed Foods Leads to a Faster Rate of Intake and to a Lesser Decrease in the Capacity to Eat When Compared to a Similar, Matched Meal Without Ultra-Processed Foods. Nutrients 2024, 16, 4398. [Google Scholar] [CrossRef]
  20. Hamano, S.; Sawada, M.; Aihara, M.; Sakurai, Y.; Sekine, R.; Usami, S.; Kubota, N. Ultra-processed foods cause weight gain and increased energy intake associated with reduced chewing frequency: A randomized, open-label, crossover study. Diabetes Obes. Metab. 2024, 26, 5431–5443. [Google Scholar] [CrossRef]
  21. Dicken, S.J.; Batterham, R.L. Ultra-processed food and obesity: What is the evidence? Curr. Nutr. Rep. 2024, 13, 23–38. [Google Scholar] [CrossRef]
  22. Hetherington, M.M. Cues to overeat: Psychological factors influencing overconsumption. Proc. Nutr. Soc. 2007, 66, 113–123. [Google Scholar] [CrossRef]
  23. Hilbert, A. Binge-eating disorder. Psychiatr. Clin. 2019, 42, 33–43. [Google Scholar] [CrossRef] [PubMed]
  24. Mirch, M.C.; McDuffie, J.R.; Yanovski, S.Z.; Schollnberger, M.; Tanofsky-Kraff, M.; Theim, K.R.; Krakoff, J.; Yanovski, J.A. Effects of binge eating on satiation, satiety, and energy intake of overweight children. Am. J. Clin. Nutr. 2006, 84, 732–738. [Google Scholar] [CrossRef] [PubMed]
  25. Zimmerli, E.J.; Devlin, M.J.; Kissileff, H.R.; Walsh, B.T. The development of satiation in bulimia nervosa. Physiol. Behav. 2010, 100, 346–349. [Google Scholar] [CrossRef] [PubMed]
  26. Hussenoeder, F.S.; Conrad, I.; Engel, C.; Zachariae, S.; Zeynalova, S.; Glaesmer, H.; Hinz, A.; Witte, V.; Tönjes, A.; Löffler, M.; et al. Analyzing the link between anxiety and eating behavior as a potential pathway to eating-related health outcomes. Sci. Rep. 2021, 11, 14717. [Google Scholar] [CrossRef]
  27. Oliveira, N.; de Oliveira Coelho, G.M.; Cabral, M.C.; Bezerra, F.F.; Faerstein, E.; Canella, D.S. Association of body image (dis) satisfaction and perception with food consumption according to the NOVA classification: Pró-Saúde Study. Appetite 2020, 144, 104464. [Google Scholar] [CrossRef]
  28. da Silva Júnior, A.E.; Gearhardt, A.N.; Bueno, N.B. Association between food addiction with ultra-processed food consumption and eating patterns in a Brazilian sample. Appetite 2023, 186, 106572. [Google Scholar] [CrossRef]
  29. Whatnall, M.; Clarke, E.; Collins, C.E.; Pursey, K.; Burrows, T. Ultra-processed food intakes associated with ‘food addiction’ in young adults. Appetite 2022, 178, 106260. [Google Scholar] [CrossRef]
  30. Vogel, C.; Abbott, G.; Ntani, G.; Barker, M.; Cooper, C.; Moon, G.; Ball, K.; Baird, J. Examination of how food environment and psychological factors interact in their relationship with dietary behaviours: Test of a cross-sectional model. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 12. [Google Scholar] [CrossRef]
  31. Heeren, F.A.N.; Darcey, V.L.; Deemer, S.E.; Menon, S.; Tobias, D.; Cardel, M.I. Breaking down silos: The multifaceted nature of obesity and the future of weight management. Philos. Trans. R. Soc. B 2023, 378, 20220215. [Google Scholar] [CrossRef]
  32. Schulz, K.F.; Altman, D.G.; Moher, D. CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. J. Pharmacol. Pharmacother. 2010, 1, 100–107. [Google Scholar] [CrossRef]
  33. Rubino, F.; Cummings, D.E.; Eckel, R.H.; Cohen, R.V.; Wilding, J.P.H.; Brown, W.A.; Stanford, F.C.; Batterham, R.L.; Farooqi, I.S.; Farpour-Lambert, N.J.; et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025, 13, 221–262. [Google Scholar] [CrossRef]
  34. Schulte, E.M.; Gearhardt, A.N. Development of the modified Yale food addiction scale version 2.0. Eur. Eat. Disord. Rev. 2017, 25, 302–308. [Google Scholar] [CrossRef] [PubMed]
  35. Nunes-Neto, P.R.; Köhler, C.A.; Schuch, F.B.; Quevedo, J.; Solmi, M.; Murru, A.; Vieta, E.; Maes, M.; Stubbs, B.; Carvalho, A.F. Psychometric properties of the modified Yale Food Addiction Scale 2.0 in a large Brazilian sample. Rev. Bras. Psiquiatr. 2018, 40, 444–448. [Google Scholar] [CrossRef]
  36. Silva-Junior, A.E.; Bueno, N.B. Comments on the translated version of the modified Yale Food Addiction Scale 2.0 into Brazilian Portuguese. Braz. J. Psychiatry 2023, 45, 198–199. [Google Scholar] [CrossRef] [PubMed]
  37. American Psychiatric Association. Substance-Related and Addictive Disorders. In DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2014. [Google Scholar]
  38. Kakeshita, I.S.; Silva, A.I.P.; Zanatta, D.P.; Almeida, S.S. Construção e fidedignidade teste-reteste de escalas de silhuetas brasileiras para adultos e crianças. Psicologia 2009, 25, 263–270. [Google Scholar] [CrossRef]
  39. Sousa, T.V.; Viveiros, V.; Chai, M.V.; Vicente, F.L.; Jesus, G.; Carnot, M.J.; Gordo, A.C.; Ferreira, P.L. Reliability and validity of the Portuguese version of the Generalized Anxiety Disorder (GAD-7) scale. Health Qual. Life Outcomes 2015, 13, 50. [Google Scholar] [CrossRef]
  40. Spitzer, R.L.; Kroenke, K.; Williams, J.B.W.; Löwe, B. A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Arch. Intern. Med. 2006, 166, 1092–1097. [Google Scholar] [CrossRef]
  41. Garner, D.M.; Olmsted, M.P.; Bohr, Y.; Garfinkel, P.E. The eating attitudes test: Psychometric features and clinical correlates. Psychol. Med. 1982, 12, 871–878. [Google Scholar] [CrossRef]
  42. Bighetti, F. Tradução e Validação do Eating Attitudes Test (EAT-26) em Adolescentes do Sexo Feminino na Cidade de Ribeirão Preto—SP. Master’s Thesis, Universidade de São Paulo, Ribeirão Preto, Brazil, 2003. [Google Scholar]
  43. Hamada, Y.; Kashima, H.; Hayashi, N. The number of chews and meal duration affect diet-induced thermogenesis and splanchnic circulation. Obesity 2014, 22, E62–E69. [Google Scholar] [CrossRef]
  44. Associação Brasileira de Empresas de Pesquisa. Critérios de Classificação Econômica Brasil. 2021. Available online: https://abep.org/criterio-brasil/ (accessed on 1 February 2024).
  45. Dioneda, B.; Healy, M.; Paul, M.; Sheridan, C.; Mohr, A.E.; Arciero, P.J. A gluten-free meal produces a lower postprandial thermogenic response compared to an iso-energetic/macronutrient whole food or processed food meal in young women: A single-blind randomized cross-over trial. Nutrients 2020, 12, 2035. [Google Scholar] [CrossRef]
  46. Chen, Y.; Fogel, A.; Bi, Y.; Yen, C.C. Factors associated with eating rate: A systematic review and narrative synthesis informed by socio-ecological model. Nutr. Res. Rev. 2023, 37, 376–395. [Google Scholar] [CrossRef] [PubMed]
  47. Teo, P.S.; Lim, A.J.; Goh, A.T.; Janani, R.; Choy, J.Y.M.; McCrickerd, K.; Forde, C.G. Texture-based differences in eating rate influence energy intake for minimally processed and ultra-processed meals. Am. J. Clin. Nutr. 2022, 116, 244–254. [Google Scholar] [CrossRef] [PubMed]
  48. Cunningham, P.M.; Roe, L.S.; Keller, K.L.; Hendriks-Hartensveld, A.E.; Rolls, B.J. Eating rate and bite size were related to food intake across meals varying in portion size: A randomized crossover trial in adults. Appetite 2023, 180, 106330. [Google Scholar] [CrossRef]
  49. Slyper, A. Oral processing, satiation and obesity: Overview and hypotheses. Diabetes Metab. Syndr. Obes. 2021, 14, 3399–3415. [Google Scholar] [CrossRef]
  50. Juul, F.; Vaidean, G.; Parekh, N. Ultra-processed foods and cardiovascular diseases: Potential mechanisms of action. Adv. Nutr. 2021, 12, 1673–1680. [Google Scholar] [CrossRef] [PubMed]
  51. Schilder, P. The somato-psyche in psychiatry and social psychology. J. Abnorm. Soc. Psychol. 1934, 29, 314. [Google Scholar] [CrossRef]
  52. Cash, T.F. (Ed.) Cognitive-behavioral perspectives on body image. In Encyclopedia of Body Image and Human Appearance; Elsevier Academic Press: London, UK, 2012; pp. 334–342. [Google Scholar]
  53. Neves, A.N.; Morgado, F.F.D.R.; Tavares, M.D.C.G.C.F. Body image assessment: Essential notes for a good research practice. Psicologia 2015, 31, 375–380. [Google Scholar]
  54. Eck, K.M.; Quick, V.; Byrd-Bredbenner, C. Body dissatisfaction, eating styles, weight-related behaviors, and health among young women in the United States. Nutrients 2022, 14, 3876. [Google Scholar] [CrossRef]
  55. Ruderman, A.J. Dietary restraint: A theoretical and empirical review. Psychol. Bull. 1986, 99, 247. [Google Scholar] [CrossRef]
  56. Moradi, S.; Hojjati Kermani, M.A.; Bagheri, R.; Mohammadi, H.; Jayedi, A.; Lane, M.M.; Asbaghi, O.; Mehrabani, S.; Suzuki, K. Ultra-processed food consumption and adult diabetes risk: A systematic review and dose-response meta-analysis. Nutrients 2021, 13, 4410. [Google Scholar] [CrossRef]
  57. Wang, M.; Du, X.; Huang, W.; Xu, Y. Ultra-processed foods consumption increases the risk of hypertension in adults: A systematic review and meta-analysis. Am. J. Hypertens. 2022, 35, 892–901. [Google Scholar] [CrossRef] [PubMed]
  58. Lockyer, S.; Spiro, A.; Berry, S.; He, J.; Loth, S.; Martinez-Inchausti, A.; Mellor, D.; Raats, M.; Sokolović, M.; Vijaykumar, S.; et al. How do we differentiate not demonise—Is there a role for healthier processed foods in an age of food insecurity? Proceedings of a roundtable event. Nutr. Bull. 2023, 48, 278–295. [Google Scholar] [CrossRef] [PubMed]
  59. Vocks, S.; Legenbauer, T.; Heil, A. Food intake affects state body image: Impact of restrained eating patterns and concerns about eating, weight and shape. Appetite 2007, 49, 467–475. [Google Scholar] [CrossRef]
  60. Pereira, T.; Mocellin, M.C.; Curioni, C. Association between ultraprocessed foods consumption, eating disorders, food addiction and body image: A systematic review. BMJ Open 2024, 14, e091223. [Google Scholar] [CrossRef]
  61. Papini, N.M.; Jung, M.; Cook, A.; Lopez, N.V.; Ptomey, L.T.; Herrmann, S.D.; Kang, M. Psychometric properties of the 26-item eating attitudes test (EAT-26): An application of rasch analysis. J. Eat. Disord. 2022, 10, 62. [Google Scholar] [CrossRef]
  62. Agüera, Z.; Lozano-Madrid, M.; Mallorquí-Bagué, N.; Jiménez-Murcia, S.; Menchón, J.M.; Fernández-Aranda, F. A review of binge eating disorder and obesity. Neuropsychiatrie 2021, 35, 57–67. [Google Scholar] [CrossRef]
  63. Wee, M.S.M.; Goh, A.T.; Stieger, M.; Forde, C.G. Correlation of instrumental texture properties from textural profile analysis (TPA) with eating behaviours and macronutrient composition for a wide range of solid foods. Food Funct. 2018, 9, 5301–5312. [Google Scholar] [CrossRef] [PubMed]
  64. Gearhardt, A.N.; Corbin, W.R.; Brownell, K.D. Food addiction: An examination of the diagnostic criteria for dependence. J. Addict. Med. 2009, 3, 1–7. [Google Scholar] [CrossRef]
  65. Kakeshita, I.S.; Almeida, S.D.S. Relação entre índice de massa corporal e a percepção da auto-imagem em universitários. Rev. Saúde Pública 2006, 40, 497–504. [Google Scholar] [CrossRef]
  66. Cabral, M.C.; Coelho, G.M.; Oliveira, N.; Canella, D.S.; Brasil, R.L.; Campos, T.A.; Faerstein, E.; Bezerra, F.F.; Antonio-Villa, N.E. Association of body image perception and (dis) satisfaction with adiposity in adults: The Pró-Saúde study. PLoS ONE 2024, 19, e0304987. [Google Scholar] [CrossRef]
  67. Makara-Studzińska, M.; Zaborska, A. Obesity and body image. Psychiatr. Pol. 2009, 43, 109–114. [Google Scholar] [PubMed]
  68. Weinberger, N.A.; Kersting, A.; Riedel-Heller, S.G.; Luck-Sikorski, C. Body dissatisfaction in individuals with obesity compared to normal-weight individuals: A systematic review and meta-analysis. Obes. Facts 2017, 9, 424–441. [Google Scholar] [CrossRef]
  69. Kakeshita, I.S.; Almeida, S.S. The relationship between body mass index and body image in Brazilian adults. Psychol. Neurosci. 2008, 1, 103–107. [Google Scholar] [CrossRef]
  70. Albuquerque, L.D.S. Fatores Associados com a Insatisfação da Imagem Corporal: Resultados da Linha de Base do ELSA-Brasil. Master’s Thesis, Escola Nacional de Saúde Pública, Rio de Janeiro, Brazil, 2014. [Google Scholar]
  71. Sedgwick, P.; Greenwood, N. Understanding the Hawthorne effect. BMJ 2015, 351, h4672. [Google Scholar] [CrossRef] [PubMed]
  72. Robinson, E.; Hardman, C.A.; Halford, J.C.; Jones, A. Eating under observation: A systematic review and meta-analysis of the effect that heightened awareness of observation has on laboratory measured energy intake. Am. J. Clin. Nutr. 2015, 102, 324–337. [Google Scholar] [CrossRef] [PubMed]
  73. Tufano, M.; Lasschuijt, M.; Chauhan, A.; Feskens, E.J.; Camps, G. Capturing eating behavior from video analysis: A systematic review. Nutrients 2022, 14, 4847. [Google Scholar] [CrossRef]
  74. Truby, H.; Paxton, S.J. Development of the children’s body image scale. Br. J. Clin. Psychol. 2002, 41, 185–203. [Google Scholar] [CrossRef]
  75. Williamson, S.; Delin, C. Young children’s figural selections: Accuracy of reporting and body size dissatisfaction. Int. J. Eat. Disord. 2001, 29, 80–84. [Google Scholar] [CrossRef]
  76. Vanderweele, T.J.; Knol, M.J. A tutorial on interaction. Epidemiol. Methods 2014, 3, 33–72. [Google Scholar] [CrossRef]
Nutrients 17 02236 g001
Figure 1. (a) Test meal rich in ultra-processed foods offered to the group of participants allocated to the UPF Meal group on the day of the clinical trial. (b) Test meal without ultra-processed foods offered to the group of participants allocated to the Control Meal group on the day of the clinical trial. Reproduced from Galdino-Silva et al. [19].
Figure 1. (a) Test meal rich in ultra-processed foods offered to the group of participants allocated to the UPF Meal group on the day of the clinical trial. (b) Test meal without ultra-processed foods offered to the group of participants allocated to the Control Meal group on the day of the clinical trial. Reproduced from Galdino-Silva et al. [19].
Nutrients 17 02236 g001
Nutrients 17 02236 g002
Figure 2. CONSORT 2025 flow diagram of participant progress through the trial.
Figure 2. CONSORT 2025 flow diagram of participant progress through the trial.
Nutrients 17 02236 g002
Nutrients 17 02236 g003
Figure 3. Interaction plot from the linear regression between body image satisfaction and eating rate (kcal/min) across groups, adjusted for sex and age.
Figure 3. Interaction plot from the linear regression between body image satisfaction and eating rate (kcal/min) across groups, adjusted for sex and age.
Nutrients 17 02236 g003
Nutrients 17 02236 g004
Figure 4. Interaction plot from the linear regression between body image perception and eating rate (kcal/min) across groups, adjusted for sex and age.
Figure 4. Interaction plot from the linear regression between body image perception and eating rate (kcal/min) across groups, adjusted for sex and age.
Nutrients 17 02236 g004
Table 1. Composition of UPF and non-UPF meals in the randomized clinical trial.
Table 1. Composition of UPF and non-UPF meals in the randomized clinical trial.
UPF MealControl Meal
Bauducco® Traditional Toast (04 units)—30 g
Polenghi® dish cheese (03 fts)—54 g
Perdigão® Ham (2.5 fts)—50 g
Purifruti® Strawberry Jelly (02 tablespoons)—40 g
Primor® Margarine (01 dessert spoon)—15 g
Maratá® guava juice (01 glass)—200 mL
Nestlé® FiberMais fiber module (1 tablespoon)—9 g		Regular bakery bread toast (01 unit)—50 g
Chicken egg (02 units)—100 g
Soybean vegetable oil (01 teaspoon)—5 mL
Refined salt (01 coffee spoon)—2 g
Extra virgin olive oil (01 tablespoon)—8 mL
Guava pulp juice (1 glass)—200 mL
Bee honey (01 tablespoon)—20 g
MealKCALPTN (g)PTN (%)CHO (g)CHO (%)LIP (g)LIP (%)Fibers (g)Na (mg)
UPF557.318.413.271.351.221.935.48.51493.3
No UPF559.718.713.470.650.522.135.58.51267.5
UPF: ultra-processed food; g: grams; fts: slices; mL: milliliters; KCAL: kilocalories; PTN: proteins; CHO: carbohydrates; LIP: lipids; Na: sodium; mg: milligrams; %: percentage. Reproduced from Galdino et al. [19].
Table 2. Baseline descriptive characteristics of the study sample.
Table 2. Baseline descriptive characteristics of the study sample.
VariablesControl Group
(n = 20)		UPF Group
(n = 19)		p-Value a
n%n%
Sex 0.17
Female1680.01157.9
Male420.0842.1
Skin color 0.79
White525.0631.6
Brown315.015.3
Black1260.01263.2
Alcohol consumer 0.75
Yes945.01052.6
No1155.0947.4
Physically inactive 0.75
Yes1155.0947.4
No945.01052.6
Brazilian economic class system 0.29
A15.015.3
B115.000.0
B2420.0631.6
C1735.0631.6
C2735.0315.8
D/E00.0315.8
Educational level 0.74
Completed high school1470.01263.2
Completed higher education630.0736.8
MeanSDMeanSDp-Value b
Age (years)29.97.9128.06.570.42
Height (m)1.640.101.680.090.16
Weight (kg)82.0915.4191.3917.100.08
BMI (kg/m2)30.403.4432.023.910.17
Waist circumference (cm)92.9012.8698.1510.630.17
Body fat percentage (%)41.386.4539.416.550.35
Fasting hunger44.526.545.530.60.91
Fasting fullness25.318.620.418.10.41
Fasting satisfaction54.927.949.930.10.60
Fasting prospective food consumption57.822.661.326.30.65
UPF: ultraprocessed food; SD: standard deviation; m: meters; kg: kilograms; kg/m2: kilograms per square meter; cm: centimeters; a p-Values obtained using Fisher’s exact test; b p-Values obtained using the independent samples t-test.
Table 3. Association between psychobehavioral variables and eating rate (kcal/min) in a meal with or without UPF.
Table 3. Association between psychobehavioral variables and eating rate (kcal/min) in a meal with or without UPF.
Control Group
(n = 20)		UPF Group
(n = 19)		Overall Sample
(n = 39)
Beta95% CIp-Value aBeta95% CIp-Value aBeta95% CIp-Value bp-Value × Group c
Number of FA symptoms1.68−0.73; 4.100.150.85−5.62; 7.320.781.70−1.32; 4.720.260.89
EAT-26 score0.30−0.79; 1.410.56−0.14−2.18; 1.890.880.08−1.01; 1.180.870.49
GAD-7 score0.41−0.77; 1.610.46−0.50−3.50; 2.490.720.29−1.11; 1.710.670.23
Body satisfaction−1.40−3.20; 0.390.112.71−0.14; 5.570.060.59−1.17; 2.350.490.007
Body image perception0.72−0.56; 2.010.25−3.10−8.02; 1.800.190.01−1.83; 1.850.980.024
UPF: ultra-processed food; 95% CI: 95% confidence interval; FA: food addiction; EAT-26: Eating Attitudes Test-26; GAD-7: Generalized Anxiety Disorder-7; a p-values refer to linear regression adjusted for sex, and age; b p-Values refer to linear regression adjusted for group, sex, and age; c p-Value for the interaction coefficient between group and each independent variables, adjusted by sex and age.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Barros, L.d.M.; Peixoto, V.A.; Carvalho, G.C.O.d.; Pereira, M.R.; Carnaúba, R.T.L.; Bueno, N.B. Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial. Nutrients 2025, 17, 2236. https://doi.org/10.3390/nu17132236

AMA Style

Barros LdM, Peixoto VA, Carvalho GCOd, Pereira MR, Carnaúba RTL, Bueno NB. Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial. Nutrients. 2025; 17(13):2236. https://doi.org/10.3390/nu17132236

Chicago/Turabian Style

Barros, Ludmila de Melo, Vanessa Amorim Peixoto, Guilherme César Oliveira de Carvalho, Micnéias Róberth Pereira, Rodrigo Tenório Lins Carnaúba, and Nassib Bezerra Bueno. 2025. "Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial" Nutrients 17, no. 13: 2236. https://doi.org/10.3390/nu17132236

APA Style

Barros, L. d. M., Peixoto, V. A., Carvalho, G. C. O. d., Pereira, M. R., Carnaúba, R. T. L., & Bueno, N. B. (2025). Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial. Nutrients, 17(13), 2236. https://doi.org/10.3390/nu17132236

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop