Skip to main content
NCBI home page
Food Science & Nutrition logoLink to Food Science & Nutrition
. 2025 May 7;13(5):e70248. doi: 10.1002/fsn3.70248

Factors Affecting Ultra‐Processed Food Consumption: Hedonic Hunger, Food Addiction, and Mood

Özge Mengi Çelik 1,✉, Ümmügülsüm Güler 2, Emine Merve Ekici 3
PMCID: PMC12058449  PMID: 40342528

ABSTRACT

Ultra‐processed foods (UPF) play a central role in modern diets but pose a serious threat to public health. The aim of this study was to investigate the relationship between UPF consumption and hedonic hunger, food addiction, and mood and to explain the impact of these factors on dietary habits. This cross‐sectional study included 3997 adults (2517 female, 1480 male), aged 18–65 years, recruited through snowball sampling in Ankara, Turkey. Data were collected via a web‐based survey that included demographic information, self‐reported anthropometric measurements, and validated scales: Screening Questionnaire of Highly Processed Food Consumption, The Power of Food Scale, Yale Food Addiction Scale, and Depression Anxiety Stress Scale‐21. The mean age of the participants was 31.7 ± 12.82 years. The Screening Questionnaire of Highly Processed Food Consumption was positively correlated with the Yale Food Addiction Scale score and the subdimension scores of the Depression Anxiety Stress Scale‐21 (p < 0.05). According to linear regression analysis, it was determined that gender, age, Yale Food Addiction Scale score, and the scores of depression, anxiety, and stress affected the Screening Questionnaire of Highly Processed Food Consumption score (p < 0.05). There was a difference between the groups with low and high levels of UPF consumption in terms of age, gender, marital and working status, Yale Food Addiction Scale score, and the subdimension scores of the Depression Anxiety Stress Scale‐21 (p < 0.05). In conclusion, considering the influence of food addiction and mood on UPF consumption, policies and interventions targeting the psychosocial processes of individuals should be developed and further research conducted in this area to limit the UPF consumption and promote healthy eating habits along with conscious eating behaviors.

Keywords: anxiety, depression, food addiction, hedonic hunger, stress, ultra‐processed food consumption

According to linear regression analysis, it was determined that gender, age, Yale Food Addiction Scale score, and the scores of the depression, anxiety, and stress affected the Screening Questionnaire of Highly Processed Food Consumption score (p < 0.05). There was a difference between the groups with low and high levels of UPF consumption in terms of age, gender, marital and working status, Yale Food Addiction Scale score, and the subdimension scores of the Depression Anxiety Stress Scale‐21 (p < 0.05).

graphic file with name FSN3-13-e70248-g002.jpg

1. Introduction

Since prehistoric times, people have struggled with many environmental challenges to survive, and nutrition has become a fundamental element of survival in this process (Yılmaz 2023). While the primary purpose of nutrition in ancient times was only to fill the stomach, these habits have transformed over time under the influence of environmental, cultural, and technological factors and have formed the basis of today's understanding of nutrition (Pekcan et al. 2008). Food processing methods, which have become widespread, especially with the development of technology (Yılmaz 2023), have caused industrially processed foods to play a central role in modern nutritional habits (Bonaccio et al. 2021).

The degree of processing of foods and the purpose of this process have become an important determinant of population health by affecting the nutrient profile of foods, and therefore, the quality of diet. In this context, the NOVA classification system offers an internationally accepted method by grouping foods according to the nature and scope of industrial processing (Ülker and Çamli 2024). According to the NOVA classification, foods are divided into four main groups according to their degree of processing and intended use (Monteiro et al. 2018a). Ultra‐processed foods (UPF), one of these categories, are defined as foods whose natural structures have deteriorated, contain many additives, and have undergone intensive industrial processing (Günay 2024). Ingredients such as anti‐humectants, emulsifiers, and flavor enhancers are commonly used in the production of UPFs, enabling these products to achieve characteristics such as extended shelf life, low production cost, and intensified flavor (Monteiro et al. 2018a).

The advantages of UPFs such as practicality and attractiveness, combined with the acceleration of urbanization, increased participation of women in business life, and aggressive advertising campaigns of food companies, have caused the consumption of these foods to become widespread (Oruçoğlu et al. 2024). However, the high energy and saturated fat content of UPFs, low nutritional value (Monteiro et al. 2019), and addictive properties (Prescott et al. 2024) pose a serious threat to public health by causing health problems such as obesity, diabetes, and cancer (Oruçoğlu et al. 2024; Okyar et al. 2023). It is also stated that UPF consumption does not only cause physical health problems but also affect individuals' psychosocial processes, such as hedonic hunger, food addiction, and mood (Monteiro et al. 2019; Prescott et al. 2024; Okyar et al. 2023; Doğan 2024).

Food addiction is defined as an adaptation to certain foods that individuals are hypersensitive to and regularly consume and is included in the DSM‐V (Gearhardt et al. 2011a; Randolph 1956). This addiction is usually seen in foods rich in carbohydrates and fats (Cope and Gould 2017; Akten and Noyan 2019). A limited number of studies have shown that UPFs may be associated with food addiction and may cause addiction symptoms such as deprivation, excessive desire, and uncontrolled consumption in individuals due to their refined carbohydrate and fat content (Schulte et al. 2015; Wiss and LaFata 2024). In addition to the addictive effect of these foods, it has been seen that they may also be related to the concept of hedonic hunger by affecting the brain's reward system (Armutçu 2021). However, there is no study examining the effect of hedonic hunger on UPF consumption.

Hedonic hunger is characterized by increased reward sensitivity, pleasure, and food drive in the absence of physiological hunger (Mason et al. 2020). In the development of hedonic hunger, neural systems play an important role (Lee and Dixon 2017). In particular, brain regions such as the amygdala, hippocampus, and orbitofrontal cortex, where cannabinoid receptor and opioid signals are linked, play a critical function in the neural processes associated with hedonic hunger (Takeuchi 2021). UPFs can cause functional changes in the reward regions of the brain and increase endogenous opioid levels, thereby increasing food cravings (Armutçu 2021). The few studies in the literature that have examined the effect of hedonic hunger on UPF consumption suggest that there is a positive relationship between UPF consumption and hedonic hunger, and that individuals with hedonic hunger may be more likely to gravitate toward UPFs made palatable by high fat, sugar, and salt content (Doğan 2024; Gupta et al. 2020; Horwath et al. 2020).

Other changes in individuals' stress levels and mood can cause changes in appetite (Şarahman Kahraman and Akçil 2022). It is known that eating behavior, especially in individuals with high levels of stress, affects neuroendocrine mechanisms such as the hypothalamic–pituitary–adrenal axis, making the brain's reward center more sensitive and can create a positive feedback loop with the opioid stimulation provided by UPFs (Adam and Epel 2007; Wei et al. 2019). This shows that individuals with mood disorders such as depression, anxiety, and stress may generally prefer UPFs (Ülker and Çamli 2024).

In this context, examining the food addiction, hedonic hunger, and mood factors that may affect UPFs consumption will provide a deeper understanding of individuals' eating behaviors and contribute to obtaining comprehensive information about the psychosocial factors that shape these behaviors. Since the number of studies on psychosocial factors affecting UPFs consumption is limited in the literature, it is thought that this study will contribute to the literature. The main purpose of the study is to examine the effects of hedonic hunger, food addiction, and mood on UPF consumption.

2. Materials and Methods

This cross‐sectional study was conducted among 3997 adults (2517 female, 1480 male) aged 18–65 years between September 2024 and January 2025. The research data were collected via a web‐based survey form (Google Forms) created by the researchers and taken from the Ankara province of Türkiye using the snowball sampling method. Snowball sampling, although a non‐probability method, has been reported as a practical approach in online health behavior studies to reach large and diverse samples in a cost‐effective manner (Naderifar et al. 2017). The survey data were collected via the social media tools Twitter, Facebook, WhatsApp, and Instagram. The inclusion criteria of the study were as follows: Individuals aged between 18 and 65, with internet access, who checked the “I agree to participate in this study voluntarily” tab at the beginning of the online survey and who completed the survey completely were included in the study. Participants with incomplete survey responses (n = 101), reports of eating disorders (n = 19), or history of psychiatric disorders (n = 10) were excluded from the study. A total of 4127 people were reached through the survey form; however, in line with the determined exclusion criteria, 130 participants were excluded from the study and the final analysis was carried out with a sample of 3997 participants (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Study flowchart.

Before starting the study, ethical approval with decision number 2024/541 from the University of Health Sciences Gulhane Scientific Research Ethics Committee was obtained. All study procedures were performed in compliance with the principles of the Declaration of Helsinki. The demographic characteristics (gender, age, education level, marital status, income status, and working status) and anthropometric measurements (body weight and height) of the individuals were questioned. Previous studies have validated the applicability of all scales measuring UPF consumption, hedonic hunger, food addiction, and mood within online research settings (Erdoğan Gövez et al. 2024; Varnado et al. 2024; Wittayapun et al. 2023; Howard et al. 2021).

2.1. Anthropometric Measurements

Anthropometric measurements, including body weight and height, were obtained through self‐report provided by the participants. Detailed written instructions were added to the anthropometric measurements section to minimize measurement error and ensure consistency. Participants were instructed to weigh themselves using a calibrated scale, after a fast of at least 4 h, without shoes and in light clothing. For the measurement of height, it was suggested that they stand upright with their backs against the wall without shoes and measure using a flat object (such as a book) aligned with the top of their head and with the help of another person. A visual was added on how to provide the Frankfurt plane. In order to standardize the process, explanatory pictures of all measurements were included in the questionnaire form and step‐by‐step guidance was provided. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (m2). A BMI of < 18.50 kg/m2 was classified as underweight, 18.50–24.99 kg/m2 as individuals with normal weight, 25.00–29.99 kg/m2 as individuals with overweight, and 30.00 kg/m2 or higher as individuals with obesity (Madden and Smith 2016).

2.2. Ultra‐Processed Food Consumption

The Screening Questionnaire of Highly Processed Food Consumption was used to assess food consumption. The Turkish validity and reliability study of the scale was conducted by Erdoğan Gövez et al. (2024). The Cronbach's alpha coefficient of the scale is 0.65. The scale consists of 11 food items. If the participant answers “yes” to consumption, each food item is given 1 point, while if the participant answers “no,” each food item is given 0 points. The total score varies between 0 and 11. Scores of 6 and above on the scale are considered as high consumption. As the score increases, UPF consumption also increases.

2.3. Hedonic Hunger

The Power of Food Scale was used to assess hedonic hunger status. The reliability and validity of the Turkish version of the scale were established by Ulker et al. (2021). The Cronbach's alpha coefficient of the scale is 0.922. This 5‐point Likert‐type scale consists of 13 items. The total score is obtained by summing the item scores and dividing them by the number of items. An increase in the score indicates a greater tendency to hedonic hunger.

2.4. Food Addiction

The food addiction status of the individuals was evaluated with the Yale Food Addiction Scale. The Yale Food Addiction Scale was developed by Gearhardt et al. (2009). The Turkish validity and reliability study of the scale was conducted by Sevinçer et al. (2014). The Cronbach alpha coefficient value of the scale is 0.93. The scale consists of 27 items and is used to measure addiction‐like eating behaviors toward certain food types over the last 12 months. The scale was created by adapting the substance addiction criteria in the DSM‐IV to food addiction to determine the addiction to certain foods. The scale consists of eight symptoms. The scale score is calculated on the total score of the questions belonging to each symptom. Individuals with a symptom score ≥ 3 are diagnosed with food addiction, provided they score 1 point from one of the 15th and 16th questions of the scale.

2.5. Mood

The emotional states of the individuals were evaluated with the Depression Anxiety Stress Scale‐21. This scale was developed by Lovibond and Lovibond. The Turkish validity and reliability study of the scale were conducted by Yılmaz et al. (2017). The scale consists of three subdimensions: depression, anxiety, and stress.

The Cronbach alpha coefficient of the scale was found to be 0.94, 0.87, and 0.91 for the subdimensions, respectively. Each dimension contains seven items. The scale is a 4‐point Likert type, and individuals are asked to give a score between 1 and 4 for each item of the scale questions. As the score received from the subscales increases, the severity of the symptoms increases.

2.6. Statistical Analysis

The G*Power software (Version 3.1.9.6) was used to analyze the sample's size. The effect size of the study was aimed to be a poor or medium correlation between UPF consumption and food addiction. Based on an effect size of |ρ| = 0.20, correlation: point biserial model, according to the with two tail, alpha of 5%, power (1 − β) = 0.95, the sample size was calculated 726. The post doc analysis was performed after the study; based on the analysis (r = 0.225), the statistical power (1 − β) was found to be 98% for the statistical significance of a two‐sided alpha of 5%. The G*Power software is widely used in health research for a priori and post hoc power analysis to determine adequate sample sizes for detecting hypothesized effects (Faul et al. 2007).

All analyses were conducted using the Statistical Package for the Social Sciences software (version 22.0). Descriptive statistics, including mean, standard deviation, frequency, and percentage, were used to evaluate the data. Data distribution was assessed using histograms, the coefficient of variation, skewness, kurtosis, and Kolmogorov–Smirnov tests. Relationships between variables were determined using the Pearson correlation coefficient. Differences in mean values between groups were evaluated with the independent t‐test. Chi‐squared analysis was used to compare qualitative data between groups. Linear regression analysis was performed to predict UPF consumption. Results were interpreted at a 95% confidence interval with a significance level of p < 0.05.

3. Results

The general characteristics of the participants are shown in Table 1. Most of the participants were female (63.0%). The mean age was 31.7 ± 12.82 years, and the mean BMI was 24.5 ± 4.53 kg/m2. According to the BMI classification, 5.6% of the participants was classified as individuals with underweight, 55.2% as individuals with normal weight, 27.5% as individuals with overweight, and 11.7% as individuals with obesity. Most of the participants (52.9%) were unemployed. The mean score obtained from the Screening Questionnaire of Highly Processed Food Consumption was 5.1 ± 2.73. The mean score of the Power of Food Scale was 2.8 ± 1.03, and the mean score of the Yale Food Addiction Scale was 2.9 ± 1.65. The mean scores for depression, anxiety, and stress on the Depression Anxiety Stress Scale‐21 were 5.6 ± 4.76, 5.0 ± 4.38, and 5.9 ± 4.47, respectively. While 44.8% of the participants had a high UPF consumption, 55.2% had a low UPF consumption. While 86.7% of the participants were found to have food addiction, 13.3% were found not to have food addiction.

TABLE 1.

General characteristics of individuals.

Variables X¯ ± SD
Age (years) 31.7 ± 12.82
BMI (kg/m2) 24.5 ± 4.53
Number of main meals 2.4 ± 0.53
Number of snacks 1.5 ± 1.00
Screening Questionnaire of Highly Processed Food Consumption score 5.1 ± 2.73
Power of Food Scale score 2.8 ± 1.03
Yale Food Addiction Scale score 2.9 ± 1.65
Depression Anxiety Stress Scale‐21 score
Depression score 5.6 ± 4.76
Anxiety score 5.0 ± 4.38
Stress score 5.9 ± 4.47
Number (%)
Gender
Female 2517 (63.0)
Male 1480 (37.0)
Education level
Primary school 158 (4.0)
Middle school 199 (5.0)
High school 900 (22.5)
University 2541 (63.6)
Master's degree/Doctorate 199 (5.0)
Marital status
Single 2391 (59.8)
Married 1606 (40.2)
Income status
Income less than expenses 1060 (26.5)
Income equal to expenses 1798 (45.0)
Income more than expenses 1139 (28.5)
BMI classification
Underweight 224 (5.6)
Normal 2207 (55.2)
Overweight 1098 (27.5)
Obese 468 (11.7)
Working status
Yes 1881 (47.1)
No 2116 (52.9)
Ultra‐processed food consumption
High consumption 1789 (44.8)
Low consumption 2208 (55.2)
Food addiction
Yes 3465 (86.7)
No 532 (13.3)
Open in a new tab

The relationship between the Screening Questionnaire of Highly Processed Food Consumption, Power of Food Scale, Yale Food Addiction Scale, and Depression Anxiety Stress Scale‐21 is shown in Table 2. A positive significant relationship was found between the Screening Questionnaire of Highly Processed Food Consumption score and the Yale Food Addiction Scale score (r = 0.225, p < 0.001). Similarly, the Screening Questionnaire of Highly Processed Food Consumption score showed positive significant relationships with the subdimension scores of the Depression Anxiety Stress Scale‐21 (r = 0.218; p < 0.00, r = 0.223; p < 0.00, r = 0.223; p < 0.001, respectively).

TABLE 2.

The relationship between Screening Questionnaire of Highly Processed Food Consumption, Power of Food Scale, Yale Food Addiction Scale, and Depression Anxiety Stress Scale‐21.

Screening Questionnaire of Highly Processed Food Consumption score
Power of Food Scale score

r = 0.019

p = 0.240
Yale Food Addiction Scale score

r = 0.225

p < 0.001*

Depression Anxiety Stress Scale‐21
Depression score

r = 0.218

p < 0.001*

Anxiety score

r = 0.223

p < 0.001*

Stress score

r = 0.223

p < 0.001*

Open in a new tab
*

p < 0.05.

Evaluation of individuals' characteristics according to UPF consumption is shown in Table 3. The mean age of individuals with high UPF consumption was lower (28.8 ± 11.75 years), whereas the mean age of participants with low UPF consumption was higher (34.1 ± 13.17 years). A statistically significant difference was found between low and high consumption in terms of gender distribution (p < 0.001). 59.1% of female participants and 40.9% of male participants had high UPF consumption. A statistically significant difference was found between low and high consumption in terms of marital status (p < 0.001). High UPF consumption was found to be higher in single participants (68.6%) than married participants (31.4%). When working status was examined, it was seen that unemployed individuals (55.4%) had higher UPF consumption (p = 0.002). There was no significant difference between the groups in terms of the number of main meals and snacks, BMI classification, and the Power of Food Scale score. A significant difference was observed in the Yale Food Addiction Scale score between the groups (p < 0.001). The mean score of the Yale Food Addiction Scale was higher in participants with high UPF consumption (3.3 ± 1.79). In addition, the scores of depression, anxiety, and stress were found to be significantly higher in participants with high UPF consumption (p < 0.001).

TABLE 3.

Evaluation of individuals' characteristics according to ultra‐processed foods consumption.

Variables Highly processed food consumption p
High consumption (n = 1789) Low consumption (n = 2208)
Age (years) 28.8 ± 11.75 34.1 ± 13.17 < 0.001*
Gender
Female 1057 (59.1) 1460 (66.1) < 0.001*
Male 732 (40.9) 748 (33.9)
Education level
Primary school 50 (2.8) 108 (4.9) 0.056
Middle school 78 (4.4) 121 (5.5)
High school 398 (22.2) 502 (22.7)
University 1197 (66.9) 1344 (60.9)
Master's degree/Doctorate 66 (3.7) 133 (6.0)
Marital status
Single 1228 (68.6) 1163 (52.7) < 0.001*
Married 561 (31.4) 1045 (47.3)
Income status
Income less than expenses 506 (28.3) 554 (25.1) 0.067
Income equal to expenses 779 (43.5) 1019 (46.2)
Income more than expenses 504 (28.2) 635 (28.8)
Working status
Yes 797 (44.6) 1084 (49.1) 0.002*
No 992 (55.4) 1124 (50.9)
Number of main meals 2.5 ± 0.53 2.5 ± 0.53 0.996
Number of snacks 1.6 ± 1.02 1.4 ± 0.97 0.146
BMI (kg/m2) 24.5 ± 4.72 24.4 ± 4.37 0.699
BMI classification
Underweight 110 (6.1) 114 (5.2) 0.250
Normal 970 (54.2) 1237 (56.0)
Overweight 470 (26.3) 628 (28.4)
Obese 239 (13.4) 229 (10.4)
Power of Food Scale score 2.8 ± 1.05 2.8 ± 1.02 0.427
Yale Food Addiction Scale score 3.3 ± 1.79 2.7 ± 1.48 < 0.001*
Depression Anxiety Stress Scale‐21 score
Depression score 6.5 ± 4.96 4.9 ± 4.47 < 0.001*
Anxiety score 5.8 ± 4.53 4.3 ± 4.12 < 0.001*
Stress score 6.8 ± 4.62 5.2 ± 4.21 < 0.001*
Food addiction
Yes 322 (18.0) 210 (9.5) < 0.001*
No 1467 (82.0) 1998 (90.5)
Open in a new tab
*

p < 0.05.

Linear regression analysis was performed to evaluate the factors that could affect the Screening Questionnaire of Highly Processed Food Consumption score, and the model was found to be significant (R 2 = 0.353; p < 0.001). It was determined that gender, age, Yale Food Addiction Scale score, and the scores of the depression, anxiety, and stress affected the model (p < 0.05) (Table 4).

TABLE 4.

Linear regression model for highly processed food consumption prediction.

Model Screening Questionnaire of Highly Processed Food Consumption score
Beta t p
Gender −0.199 −13.227 < 0.001*
Age (years) −0.101 −6.758 < 0.001*
Yale Food Addiction Scale score 0.165 10.447 < 0.001*
Depression score 0.139 8.769 < 0.001*
Anxiety score 0.151 9.336 < 0.001*
Stress score 0.150 9.482 < 0.001*
R 2 = 0.353; p < 0.001*
Open in a new tab

Note: Variable values: Gender (Male = 1, Female = 0).

*

p < 0.05.

4. Discussion

In this study, hedonic hunger, food addiction, and mood factors affecting UPF consumption were examined. The main findings showed that individuals with higher UPF consumption had significantly higher food addiction and negative mood scores. Furthermore, UPF intake was more common among young adults, women, single individuals, and unemployed individuals, and food addiction and mood symptoms were more intense in individuals with higher UPF consumption. Regression models showed that UPF consumption was inversely associated with age and gender, while it was positively associated with food addiction and mood symptoms.

Global consumption of UPFs and the percentage of energy coming from UPF consumption are increasing significantly (Zhang and Giovannucci 2023; Martini et al. 2021). Many factors, such as income growth, urbanization, changing workforce structures, and demographic characteristics in developed and developing countries, play a role in this increase in UPF consumption (Baker et al. 2020). However, UPFs, which stand out in their practicality, high palatability, and low cost, play an important role in shaping the psychological state of individuals and causing changes in their eating behavior (Monteiro et al. 2018a; Prescott et al. 2024; Wiss and LaFata 2024; Sen et al. 2024; Melekoğlu 2021).

The findings of the study that individuals with high UPF consumption are mostly women and that UPFs consumption is reported more in the Screening Questionnaire of Highly Processed Food Consumption scores than in men underline the gender differences in UPF consumption. In parallel with the results of this study, a study examining UPF consumption and socio‐demographic factors found that UPF consumption by women was statistically significant compared to men (Khandpur et al. 2020). A different study conducted in 2024 found that the subjects with high UPF consumption were women (Hosseinpour‐Niazi et al. 2024). Gender differences, along with the increasing participation of women in the workforce, have contributed to the growing appeal and suitability of ready‐to‐eat and ready‐to‐heat food options (Monteiro et al. 2013). In addition, nutrition is now seen by many people as a method of coping with a negative situation or emotion, or as a rewarding method (Bilici et al. 2020). Accordingly, the tendency toward foods with high sugar content increases in stressful situations (Melekoğlu 2021), and this situation is also associated with the symptoms of craving for sweet, tasty foods in women (Lustig 2020). The absence of preparation and cooking stages in UPFs has also been identified as a contributing factor to this outcome (Melekoğlu 2021). It has also been thought that these gender differences may be because men are more likely to describe unhealthy foods as healthy than women (Nelson and Fleming 2019).

When the relationship between marital status and UPFs was examined, it was seen that the relationship between single status and high UPF consumption was significant. Similar results were observed in a study based on the Turkey Nutrition and Health Survey data, and it was found that marital status affected UPF consumption, with single individuals consuming more UPFs than married individuals (Aciduman Subaşiay 2022). Similar results were observed in another study conducted in Spain, where single individuals consumed more UPFs (Gómez‐Donoso et al. 2020). This relationship has been attributed to the tendency of these foods to meet nutritional needs quickly (Aciduman Subaşiay 2022). It is thought that this may also be due to the different lifestyles and eating habits of single individuals.

In studies examining the relationship between age and UPF consumption, data from different countries are noteworthy. In Portugal, the National Survey on Food, Nutrition and Physical Activity, conducted in 2015–2016, examined the sociodemographic characteristics of 5005 participants. In this study, it was determined that younger age groups, especially adolescents, consumed more UPFs. Data from the internet‐based NutriNet‐Santé study, launched in France from May 2009 to June 2014, also showed similar results. This study assessed the socio‐demographic characteristics of 74,470 participants, together with 24‐h dietary records. As a result of the study, it was determined that younger age groups consumed more UPFs (Magalhães et al. 2021). In a systematic review evaluating studies conducted up to 2021 and including a total of 99 studies, the mean age of participants was 38.9 years and 58.8% were female. In this review, UPF consumption generally decreased with age and was most common in children and adolescents and least common in the elderly (Marino et al. 2021). Our results are consistent with these findings, showing that the mean age was lower in high UPF consumption than low UPF consumption. According to our linear regression model, the negative association of age with UPF consumption suggests generational differences in dietary habits (Casini et al. 2015). The negative association of UPF intake with age may be influenced by differences in age‐specific food preferences (e.g., younger children consumed more fruit juice/beverages and cakes, biscuits and other pastries than other age groups, while adolescents and younger adults consumed more fast food, frozen meals, and soft drinks). These preferences may be explained by the increased dietary requirements of younger generations such as Generation Z, peer social pressure, limited cooking skills, and the affordability of UPFs (Monteiro et al. 2018b; Luszczynska et al. 2013; Janssen et al. 2018). In addition, the presence of UIG advertising in school meals and commercials may also explain this association (Aciduman Subaşiay 2022). In this context, practices such as intergenerational nutrition interventions, school feeding programs, and advertising media may also be considered by policymakers to increase the effectiveness of public health initiatives (Aciduman Subaşiay 2022; Casini et al. 2015).

Another important finding of the study is the positive relationship between UPF consumption and food addiction. Participants with high UPF consumption were also found to have a high level of food addiction. This relationship, measured with the Yale Food Addiction Scale, supports studies in the literature suggesting that the high refined carbohydrate and fat content of UPFs may lead to changes in the brain's reward‐punishment system, leading to the emergence of addictive eating behaviors (Schulte et al. 2015; Gordon et al. 2018; Canhada et al. 2020). The association of UPFs with food addiction is also associated with the increasing prevalence of noncommunicable diseases (Askari et al. 2020; Capra et al. 2022) In addition, the high addictive potential of UPFs has been attributed to the individual or combined effects of sugar (Lustig 2020) and fat (Schulte et al. 2015). Given the association of UPFs with food addiction, it is of great importance to promote healthy eating habits, identify a potentially addictive profile of certain foods, and provide public health education (Gearhardt et al. 2013; Gearhardt and Brownell 2013; Gearhardt et al. 2011b).

Furthermore, the significant association between UPF consumption and mood factors such as depression, anxiety, and stress highlights the complex interaction between diet and emotional health. In the study, depression, anxiety, and stress scores were higher in individuals with higher UPF consumption. This significant association highlights the bidirectional relationship between emotional well‐being and diet (Adjibade et al. 2019). It also suggests that UPFs may be used as a coping mechanism for negative emotional states (Hecht et al. 2022). However, it should be noted that they may exacerbate mental and physical health problems in the long term (Sen et al. 2024). Studies in the literature have shown similar results to the present study and have found an association with increased stress levels; increased consumption of sugary foods, fast food, and snacks; and decreased consumption of meat, fish, and vegetables, and fresh fruit at main meals (Kandiah et al. 2006; Oliver and Wardle 1999). Two prospective cohort studies, including 183,439 individuals, found an increased risk of depression with higher UPF consumption (Adjibade et al. 2019). Another study from 2024, which assessed UPFs and emotional states, observed significant associations between anxiety and UPF consumption (Ülker and Çamli 2024). As a result, individuals with mood swings were reported to be more likely to consume UPFs, which are often made palatable by their high fat, sugar, and salt content (Lowe and Butryn 2007; Gearhardt and Schulte 2021). Taken together, UPF consumption is positively associated with the risk of developing depressive symptoms, suggesting that addressing this non‐nutritive aspect of the diet may be necessary to improve mental health (Adjibade et al. 2019).

In general, high UPF consumption is associated with increased food addiction and mood. In this study, unlike other studies, no relationship was found between UPF consumption and the hedonic hunger of the participants. It stated in the literature that the hedonic properties of UPFs, which are associated with high fat, sugar and salt content, may cause consumers to continue eating these foods even when they are full because the “pleasure of eating” will dominate the feeling of satiety (Fardet and Rock 2019). Similarly, most studies have observed significant relationships between UPFs and hedonic hunger. However, possible reasons for these inconsistencies in our study may be the observational nature of the study and its limited duration, the physiological complexity, and the interactions of various factors affecting food intake and metabolism (Doğan 2024). In addition, there is no information about the participants' level of awareness or knowledge about nutrition in this study. Therefore, participants with low UPFs consumption may have preferred a diet rich in UPFs.

The main strength of this study is to jointly evaluate the impact of food addiction, hedonic hunger, and mood on UPFs consumption. Furthermore, the large sample size strengthens the statistically significant results of the study. The study has some limitations. One of the most important limitations of this study is the use of a non‐probabilistic snowball sampling method, which may have introduced selection bias and affected the generalizability of the findings. Although it enabled access to a large and diverse sample via online platforms, it does not guarantee that every individual in the target population had an equal chance of being included, which limits the representativeness of the results. Second, the cross‐sectional nature of the study may prevent causal inferences between the variables. Therefore, longitudinal studies are needed to determine the direction of the observed relationships. Third, participant‐reported data may lead to reporting bias and produce unreliable results. Fourth, this study conducted in Türkiye may not fully reflect socioeconomic or cultural differences in UPFs consumption patterns. Finally, although the primary aim of this study was to identify factors affecting UPFs consumption, future studies may consider constructing separate regression models in which UPF intake is treated as an independent variable and psychological scales (such as food addiction, depression, anxiety, and stress) as outcomes. This approach would allow for a more comprehensive evaluation of bidirectional associations between diet and mental health.

5. Conclusion

Tthe current study highlights the impact of food addiction and mood on UPF consumption. In addition, age, gender, marital, and working status were found to be factors associated with UPF consumption. This study provided a better understanding of the effects of eating behaviors and moods on UPF consumption. The results suggest that UPF consumption is used as a negative coping mechanism. UPF consumption should be addressed not only in terms of individual health but also in terms of public health policies and interventions. In this context, it is important to promote healthy eating habits; limit access to high‐calorie, low‐nutrient foods; and increase awareness programs to support conscious eating behavior.

Author Contributions

Özge Mengi Çelik: conceptualization, data curation, formal analysis, supervision, validation, writing – original draft, writing – review and editing; Ümmügülsüm Güler: conceptualization, data curation, writing – original draft; Emine Merve Ekici: conceptualization, data curation, writing – original draft.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

We are grateful to all the study respondents for giving their time to participate in the surveys.

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Aciduman Subaşiay, G. 2022. Türkiye Beslenme ve Sağlık Araştırması Verilerine Göre Ultra İşlenmiş Gıda Tüketim Durumu ve Tüketim ile İlişkili Bazı Sosyodemografik Özellikler.
  2. Adam, T. C. , and Epel E. S.. 2007. “Stress, Eating and the Reward System.” Physiology & Behavior 91, no. 4: 449–458. [DOI] [PubMed] [Google Scholar]
  3. Adjibade, M. , Julia C., Allès B., et al. 2019. “Prospective Association Between Ultra‐Processed Food Consumption and Incident Depressive Symptoms in the French NutriNet‐Santé Cohort.” BMC Medicine 17: 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Akten, E. , and Noyan C. O.. 2019. “Yeme eylemi; fizyolojik bir ihtiyaç mı? Psikolojik bir davranış mı? üniversite öğrencilerinde bağlanma stilleri ile yeme bağımlılığı ilişkisi.” Kıbrıs Türk Psikiyatr Ve Psikol Derg 1, no. Özel Sayı 1: 30–32. [Google Scholar]
  5. Armutçu, A. 2021. Yetişkin bireylerin hedonik açlık durumları ile dürtüsellik ve aşırı besin isteği durumları arasındaki ilişki. İstanbul Gelişim Üniversitesi Lisansüstü Eğitim Enstitüsü. [Google Scholar]
  6. Askari, M. , Heshmati J., Shahinfar H., Tripathi N., and Daneshzad E.. 2020. “Ultra‐Processed Food and the Risk of Overweight and Obesity: A Systematic Review and Meta‐Analysis of Observational Studies.” International Journal of Obesity 44, no. 10: 2080–2091. [DOI] [PubMed] [Google Scholar]
  7. Baker, P. , Machado P., Santos T., et al. 2020. “Ultra‐Processed Foods and the Nutrition Transition: Global, Regional and National Trends, Food Systems Transformations and Political Economy Drivers.” Obesity Reviews 21, no. 12: e13126. [DOI] [PubMed] [Google Scholar]
  8. Bilici, S. , Ayhan B., Karabudak E., and Koksal E.. 2020. “Factors Affecting Emotional Eating and Eating Palatable Food in Adults.” Nutrition Research and Practice 14, no. 1: 70–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bonaccio, M. , Di Castelnuovo A., Costanzo S., et al. 2021. “Ultra‐Processed Food Consumption Is Associated With Increased Risk of All‐Cause and Cardiovascular Mortality in the Moli‐Sani Study.” American Journal of Clinical Nutrition 113, no. 2: 446–455. [DOI] [PubMed] [Google Scholar]
  10. Canhada, S. L. , Luft V. C., Giatti L., et al. 2020. “Ultra‐Processed Foods, Incident Overweight and Obesity, and Longitudinal Changes in Weight and Waist Circumference: The Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil).” Public Health Nutrition 23, no. 6: 1076–1086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Capra, B. , Matthews E., Kerrihard A., and Hosick P.. 2022. “Associations Between Ultra‐Processed Food Consumption and BMI, Sedentary Time, and Adverse Dietary Habits in Active Young Adults.” Journal of Exercise Nutrition 5, no. 1. https://journalofexerciseandnutrition.com/index.php/JEN/article/view/121. [Google Scholar]
  12. Casini, L. , Contini C., Romano C., and Scozzafava G.. 2015. “Trends in Food Consumptions: What Is Happening to Generation X?” British Food Journal 117, no. 2: 705–718. [Google Scholar]
  13. Cope, E. C. , and Gould E.. 2017. “New Evidence Linking Obesity and Food Addiction.” Biological Psychiatry 81, no. 9: 734–736. [DOI] [PubMed] [Google Scholar]
  14. Doğan, Z. 2024. Yetişkin bireylerde hedonik hiperfaji ve vücut ağırlığı bakımından kendini damgalama durumu ile ultra işlenmiş besin tüketimi arasındaki ilişkinin belirlenmesi. Gazi Üniversitesi. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp. [Google Scholar]
  15. Erdoğan Gövez, N. , Köksal E., Martinez‐Perez C., and Daimiel L.. 2024. “Validity and Reliability of the Turkish Version of the Screening Questionnaire of Highly Processed Food Consumption (sQ‐HPF).” Nutrients 16, no. 15: 2552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fardet, A. , and Rock E.. 2019. “Ultra‐Processed Foods: A New Holistic Paradigm?” Trends in Food Science and Technology 93: 174–184. [Google Scholar]
  17. Faul, F. , Erdfelder E., Lang A. G., and Buchner A.. 2007. “G* Power 3: A Flexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences.” Behavior Research Methods 39, no. 2: 175–191. [DOI] [PubMed] [Google Scholar]
  18. Gearhardt, A. , Roberts M., and Ashe M.. 2013. “If Sugar Is Addictive… What Does It Mean for the Law?” Journal of Law, Medicine & Ethics 41, no. S1: 46–49. [DOI] [PubMed] [Google Scholar]
  19. Gearhardt, A. N. , and Brownell K. D.. 2013. “Can Food and Addiction Change the Game?” Biological Psychiatry 73, no. 9: 802–803. [DOI] [PubMed] [Google Scholar]
  20. Gearhardt, A. N. , Corbin W. R., and Brownell K. D.. 2009. “Preliminary Validation of the Yale Food Addiction Scale.” Appetite 52, no. 2: 430–436. [DOI] [PubMed] [Google Scholar]
  21. Gearhardt, A. N. , Grilo C. M., DiLeone R. J., Brownell K. D., and Potenza M. N.. 2011b. “Can Food Be Addictive? Public Health and Policy Implications.” Addiction 106, no. 7: 1208–1212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gearhardt, A. N. , and Schulte E. M.. 2021. “Is Food Addictive? A Review of the Science.” Annual Review of Nutrition 41, no. 1: 387–410. [DOI] [PubMed] [Google Scholar]
  23. Gearhardt, N. , Davis C., Kuschner R., and Brownell D.. 2011a. “The Addiction Potential of Hyperpalatable Foods.” Current Drug Abuse Reviews 4, no. 3: 140–145. [DOI] [PubMed] [Google Scholar]
  24. Gómez‐Donoso, C. , Sánchez‐Villegas A., Martínez‐González M. A., et al. 2020. “Ultra‐Processed Food Consumption and the Incidence of Depression in a Mediterranean Cohort: The SUN Project.” European Journal of Nutrition 59: 1093–1103. [DOI] [PubMed] [Google Scholar]
  25. Gordon, E. L. , Ariel‐Donges A. H., Bauman V., and Merlo L. J.. 2018. “What Is the Evidence for “Food Addiction?” A Systematic Review.” Nutrients 10, no. 4: 477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Günay, E. 2024. Polikistik over sendromlu kadınların ultra işlenmiş gıda tüketiminin biyokimyasal parametreler ve semptomlar ile ilişkisinin araştırılması.
  27. Gupta, A. , Osadchiy V., and Mayer E. A.. 2020. “Brain–Gut–Microbiome Interactions in Obesity and Food Addiction.” Nature Reviews. Gastroenterology & Hepatology 17, no. 11: 655–672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hecht, E. M. , Rabil A., Steele E. M., et al. 2022. “Cross‐Sectional Examination of Ultra‐Processed Food Consumption and Adverse Mental Health Symptoms.” Public Health Nutrition 25, no. 11: 3225–3234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Horwath, C. C. , Hagmann D., and Hartmann C.. 2020. “The Power of Food: Self‐Control Moderates the Association of Hedonic Hunger With Overeating, Snacking Frequency and Palatable Food Intake.” Eating Behaviors 38: 101393. [DOI] [PubMed] [Google Scholar]
  30. Hosseinpour‐Niazi, S. , Niknam M., Amiri P., et al. 2024. “The Association Between Ultra‐Processed Food Consumption and Health‐Related Quality of Life Differs Across Lifestyle and Socioeconomic Strata.” BMC Public Health 24, no. 1: 1955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Howard, L. M. , Heron K. E., Smith K. E., et al. 2021. “Examining the Ecological Validity of the Power of Food Scale.” Eat Weight Disord Anorexia 26: 717–721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Janssen, H. G. , Davies I. G., Richardson L. D., and Stevenson L.. 2018. “Determinants of Takeaway and Fast Food Consumption: A Narrative Review.” Nutrition Research Reviews 31, no. 1: 16–34. [DOI] [PubMed] [Google Scholar]
  33. Kandiah, J. , Yake M., Jones J., and Meyer M.. 2006. “Stress Influences Appetite and Comfort Food Preferences in College Women.” Nutrition Research 26, no. 3: 118–123. [Google Scholar]
  34. Khandpur, N. , Cediel G., Obando D. A., Jaime P. C., and Parra D. C.. 2020. “Sociodemographic Factors Associated With the Consumption of Ultra‐Processed Foods in Colombia.” Revista de Saúde Pública 54: 19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lee, P. C. , and Dixon J. B.. 2017. “Food for Thought: Reward Mechanisms and Hedonic Overeating in Obesity.” Current Obesity Reports 6: 353–361. [DOI] [PubMed] [Google Scholar]
  36. Lowe, M. R. , and Butryn M. L.. 2007. “Hedonic Hunger: A New Dimension of Appetite?” Physiology & Behavior 91, no. 4: 432–439. [DOI] [PubMed] [Google Scholar]
  37. Lustig, R. H. 2020. “Ultraprocessed Food: Addictive, Toxic, and Ready for Regulation.” Nutrients 12, no. 11: 3401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Luszczynska, A. , de Wit J. B. F., de Vet E., et al. 2013. “At‐Home Environment, Out‐Of‐Home Environment, Snacks and Sweetened Beverages Intake in Preadolescence, Early and Mid‐Adolescence: The Interplay Between Environment and Self‐Regulation.” Journal of Youth and Adolescence 42: 1873–1883. [DOI] [PubMed] [Google Scholar]
  39. Madden, A. M. , and Smith S.. 2016. “Body Composition and Morphological Assessment of Nutritional Status in Adults: A Review of Anthropometric Variables.” Journal of Human Nutrition and Dietetics 29, no. 1: 7–25. [DOI] [PubMed] [Google Scholar]
  40. Magalhães, V. , Severo M., Correia D., et al. 2021. “Associated Factors to the Consumption of Ultra‐Processed Foods and Its Relation With Dietary Sources in Portugal.” Journal of Nutritional Science 10: e89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Marino, M. , Puppo F., Del Bo' C., et al. 2021. “A Systematic Review of Worldwide Consumption of Ultra‐Processed Foods: Findings and Criticisms.” Nutrients 13, no. 8: 2778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Martini, D. , Godos J., Bonaccio M., Vitaglione P., and Grosso G.. 2021. “Ultra‐Processed Foods and Nutritional Dietary Profile: A Meta‐Analysis of Nationally Representative Samples.” Nutrients 13, no. 10: 3390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Mason, T. B. , Dunton G. F., Gearhardt A. N., and Leventhal A. M.. 2020. “Emotional Disorder Symptoms, Anhedonia, and Negative Urgency as Predictors of Hedonic Hunger in Adolescents.” Eating Behaviors 36: 101343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Melekoğlu, E. 2021. “Üniversite Öğrencilerinin Stres Durumları ile Fruktoz ve İşlenmiş Besin Tüketimi Arasındaki İlişki.” Çukurova Tarım Ve Gıda Bilim Derg 36, no. 2: 263–274. [Google Scholar]
  45. Monteiro, C. A. , Cannon G., Levy R. B., et al. 2019. “Ultra‐Processed Foods: What They Are and How to Identify Them.” Public Health Nutrition 22, no. 5: 936–941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Monteiro, C. A. , Cannon G., Moubarac J. C., Levy R. B., Louzada M. L. C., and Jaime P. C.. 2018a. “The UN Decade of Nutrition, the NOVA Food Classification and the Trouble With Ultra‐Processing.” Public Health Nutrition 21, no. 1: 5–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Monteiro, C. A. , Moubarac J., Cannon G., Ng S. W., and Popkin B.. 2013. “Ultra‐Processed Products Are Becoming Dominant in the Global Food System.” Obesity Reviews 14: 21–28. [DOI] [PubMed] [Google Scholar]
  48. Monteiro, C. A. , Moubarac J. C., Levy R. B., Canella D. S., da Costa Louza M. L., and Cannon G.. 2018b. “Household Availability of Ultra‐Processed Foods and Obesity in Nineteen European Countries.” Public Health Nutrition 21, no. 1: 18–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Naderifar, M. , Goli H., and Ghaljaie F.. 2017. “Snowball Sampling: A Purposeful Method of Sampling in Qualitative Research.” Strides Dev Medical Education 14, no. 3. https://sdme.kmu.ac.ir/article_90598.html. [Google Scholar]
  50. Nelson, A. M. , and Fleming R.. 2019. “Gender Differences in Diet and Social Media: An Explorative Study.” Appetite 142: 104383. [DOI] [PubMed] [Google Scholar]
  51. Okyar, S. , Tosun Ö., Bezdegümeli E., et al. 2023. “Ultra İşlenmiş Gıdaların Yaygın Etkileri.” Arşiv Kaynak Tarama Derg 32, no. 2: 68–82. [Google Scholar]
  52. Oliver, G. , and Wardle J.. 1999. “Perceived Effects of Stress on Food Choice.” Physiology & Behavior 66, no. 3: 511–515. [DOI] [PubMed] [Google Scholar]
  53. Oruçoğlu, B. , Durmuş E., and Kemaloğlu M.. 2024. “Ultra İşlenmiş Besinler.” Gümüşhane Üniversitesi Sağlık Bilim Derg 13, no. 1: 466–474. [Google Scholar]
  54. Pekcan, G. , Alphan E., Köksal E., et al. 2008. Yetişkinlerde ağırlık yönetimi. Ekspress Baskı. [Google Scholar]
  55. Prescott, S. L. , Holton K. F., Lowry C. A., Nicholson J. J., and Logan A. C.. 2024. “The Intersection of Ultra‐Processed Foods, Neuropsychiatric Disorders, and Neurolaw: Implications for Criminal Justice.” NeuroScience 5, no. 3: 354–377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Randolph, T. G. 1956. “The Descriptive Features of Food Addiction. Addictive Eating and Drinking.” Quarterly Journal of Studies on Alcohol 17, no. 2: 198–224. [PubMed] [Google Scholar]
  57. Şarahman Kahraman, C. , and Akçil O. M.. 2022. “Hedonic Hunger Status and Related Factors in Adults.” Perspectives in Psychiatric Care 58, no. 4: 2099–2106. [DOI] [PubMed] [Google Scholar]
  58. Schulte, E. M. , Avena N. M., and Gearhardt A. N.. 2015. “Which Foods May Be Addictive? The Roles of Processing, Fat Content, and Glycemic Load.” PLoS One 10, no. 2: e0117959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sen, A. , Brazeau A. S., Deschênes S., Ramiro Melgar‐Quiñonez H., and Schmitz N.. 2024. “Ultra‐Processed Foods Consumption, Depression, and the Risk of Diabetes Complications in the CARTaGENE Project: A Prospective Cohort Study in Quebec, Canada.” Frontiers Endocrinology 14: 1273433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sevinçer, G. M. , Konuk N., Bozkurt S., Saraçlı Ö., and Coşkun H.. 2014. “Psychometric Properties of the Turkish Version of the Yale Food Addiction Scale Among Bariatric Surgery Patients.” Anadolu Psikiyatr Derg 16, no. 1: 44–53. [Google Scholar]
  61. Takeuchi, S. 2021. “Agouti‐Related Protein.” In Handbook of Hormones, 115–116. Elsevier. [Google Scholar]
  62. Ulker, I. , Ayyildiz F., and Yildiran H.. 2021. “Validation of the Turkish Version of the Power of Food Scale in Adult Population.” Eat Weight Disorders 26: 1179–1186. [DOI] [PubMed] [Google Scholar]
  63. Ülker, İ. , and Çamli A.. 2024. “Ultra İşlenmiş Besin Tüketimi Hedonik Açlığı Etkiler Mi?” Beslenme Ve Diyet Derg 52, no. 1: 68–76. [Google Scholar]
  64. Varnado, A. , Smith A., Mason T. B., and Smith K. E.. 2024. “The Ecological Validity of the Yale Food Addiction Scale 2.0 and Momentary Food Addiction Symptoms.” Psychology of Addictive Behaviors 38, no. 5: 628–636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wei, N. L. , Quan Z. F., Zhao T., et al. 2019. “Chronic Stress Increases Susceptibility to Food Addiction by Increasing the Levels of DR2 and MOR in the Nucleus Accumbens.” Neuropsychiatric Disease and Treatment 15: 1211–1229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wiss, D. A. , and LaFata E. M.. 2024. “Ultra‐Processed Foods and Mental Health: Where Do Eating Disorders Fit Into the Puzzle?” Nutrients 16, no. 12: 1955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wittayapun, Y. , Summart U., Polpanadham P., et al. 2023. “Validation of Depression, Anxiety, and Stress Scales (DASS‐21) Among Thai Nursing Students in an Online Learning Environment During the COVID‐19 Outbreak: A Multi‐Center Study.” PLoS One 18, no. 6: e0288041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Yılmaz, M. S. 2023. “Gıda Ürünlerinin İşlenme Seviyelerine Göre Sınıflandırılması: NOVA Sistemi ve Ultra İşlenmiş Gıdaların İnsan Sağlığı Üzerine Etkileri.” Recep Tayyip Erdoğan Üniversitesi Fen Ve Mühendislik Bilim Derg 4, no. 2: 270–287. [Google Scholar]
  69. Yılmaz, Ö. , Boz H., and Arslan A.. 2017. “Depresyon Anksiyete Stres Ölçeğinin (DASS 21) Türkçe kisa formunun geçerlilik‐güvenilirlik çalişmasi.” Finans Ekon Ve Sos Araştırmalar Derg 2, no. 2: 78–91. [Google Scholar]
  70. Zhang, Y. , and Giovannucci E. L.. 2023. “Ultra‐Processed Foods and Health: A Comprehensive Review.” Critical Reviews in Food Science and Nutrition 63, no. 31: 10836–10848. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Food Science & Nutrition are provided here courtesy of Wiley

RESOURCES