Social Media Use and Food Addiction on Depression and Body Mass Index in University Students: A Cross-Sectional Study

Social Media Use and Food Addiction on Depression and Body Mass Index in University Students: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Media Use and Food Addiction on Depression and Body Mass Index in University Students: A Cross-Sectional Study Murat ALTAN, Seda ÖNAL, Şemsi Gül YILMAZ, Hasan YILDIRIM This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7346078/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Social media use and food addiction have each been linked to depression and increased body mass index (BMI). University students, among the most active social media users, may be especially susceptible to these risks. Excessive social media use can contribute to depression through social comparison and isolation, while food addiction is associated with disordered eating, obesity, and depressive symptoms. Despite evidence for these individual relationships, few studies have examined their combined effects. This study aims to investigate the potential effects of social media use and food addiction on depression and body mass index (BMI) among university students. Methods This cross-sectional study, conducted in Elazığ, Turkey (Feb–Jul 2024), included 4,765 healthy university students. Data were collected via face-to-face surveys, assessing sociodemographics, social media use, the Modified Yale Food Addiction Scale version 2.0 (mYFAS 2.0), and the Beck Depression Inventory (BDI). BMI was calculated from measured anthropometrics. Statistical significance was set at p < 0.05. Results Female students had higher depression scores, while individuals with severe food addiction had the highest BDI and BMI scores (p < 0.001). Significant associations were found between social media use, food addiction, depression, and BMI. Higher depression and food addiction scores were observed among users of certain platforms, particularly TikTok. Increased food consumption and snacking while using social media were significantly associated with greater odds of depression. A linear trend showed that higher food addiction levels were moderately correlated with increased depression and BMI (p < 0.001). Conclusions This study reveals a significant relationship between food addiction, social media use, depression, and BMI among university students. Higher food addiction levels were associated with increased depression and BMI, while specific social media platforms, such as TikTok and Facebook, were linked to adverse outcomes. In contrast, YouTube use appeared to have a protective effect. Gender and BMI also influenced depression risk. These findings highlight the need for integrated interventions promoting healthy eating and responsible social media use during the critical university years. Body mass index Depression Food addiction Social media use Introduction Over the last decade, social media usage has increased significantly and has become a part of the social lives of many individuals, revolutionizing communication processes and becoming a part of everyday culture [ 1 ]. There are 4.8 billion social media users worldwide, which constitute 59.9% of the world’s population and 92.7% of internet users. It was recorded that there were 150 million new social media users between April 2022 and April 2023, an increase of 3.2% compared to the previous year [ 2 ]. Social media platforms are widely also used by university students [ 3 ]. Among social media users, social media use has positive effects on education, work life and health, but on the other hand, negative effects such as reduced human interaction, time loss and addiction have been identified [ 4 ]. In addition to these negative consequences, depression can also be an effect of social media [ 5 ]. Systematic reviews have identified a significant association between social media usage and depression [ 6 , 7 ]. Today, depression most commonly begins in young adulthood [ 8 ]. Many factors may contribute to depression, such as psychosocial stress, neurotransmitters, and circadian rhythm [ 9 ]. Frequent presence on social networking sites can give individuals the feeling that others are living happier, more social lives, which can cause individuals to feel more socially isolated in comparison and increase their depression levels [ 10 ]. Depression has the potential to functional impairment in several domains, including personal performance, behavioral patterns, and both peer and family relationships [ 11 ]. Individuals with relatively poor emotional regulation, such as depression, may instinctively avoid unpleasant emotions as a coping strategy, leading to the development of addictive behaviors such as problematic internet use or food addiction [ 12 ]. Studies have reported a strong relationship between depression and food addiction [ 13 , 14 ] but there is not enough evidence. One of the concepts that has received increasing attention in recent years is the concept of food addiction [ 15 ]. It has been suggested that food cravings or the desire to eat a certain food are associated with food addiction [ 16 ]. In other words, certain foods may have addictive potential, which can lead to certain eating disorders and obesity [ 17 ]. Earlier studies have found a correlation between increased severity of depressive symptoms and higher body mass index (BMI) values [ 18 – 21 ], and obese individuals who meet criteria for food addiction report more depressive symptoms compared to obese individuals who do not meet criteria for food addiction (FA)[ 19 ]. Furthermore, a major consequence of food addiction is weight gain, and evidence from a comprehensive meta-analysis suggests that overweight or obese adults are more than twice as likely to report symptoms of food addiction compared with adults at a healthy weight [ 22 ]. As a result of the literature research, there are limited studies addressing the relationship between social media, food addiction, depression and BMI. In this study, it is to investigate the possible effects of social media and food addiction, on depression and BMI, as well as the relationship between food addiction and depression in university students. Methods Participants and Collection of Data A study was conducted between February-July 2024 in Elazığ province with 4963 in healthy university students to evaluate the relationships between social media, food addiction and depression. The data from 198 participants were excluded from the study because they either took medication or did not complete the questionnaire. Consequently, the study concluded with a total of 4,765 participants. The study data were collected by the researchers using a face-to-face method during the students’ free time (recess, leisure time, etc.) with the questionnaire. The survey consisted of general informations, social media use, YFAS 2.0 and BDI. Participants with severe psychiatric disorders (e.g. psychotic disorder, post-traumatic, bipolar disorder, stress disorder, bulimia), those without any neurodegenerative diseases and those using antidepressants were not included in the study. Anthropometric Measurements Participants were asked to report their gender, age, disease status, body weight and height. The body weight of the participants was measured with the TANITA BC545N device in the morning while fasting, after defecation, with light/ thin clothes, without shoes and in accordance with the measurement technique. Height was measured using a portable stadiometer with the feet together and the head in the Frankfort plane. The measurement was performed with a sensitivity of 0.1 cm (1 mm). BMI values were obtained by dividing body weight (kg) by height squared (m 2 ) (BMI = kg/m 2 ). Modifed Yale Food Addiction Scale version 2.0 (mYFAS 2.0) The Yale Food Addiction Scale (YFAS) was initially developed in 2009 by Gearhardt et al.[ 23 ] to assess food addiction. The scale’s validity and reliability for the Turkish population were established by Tok in 2018. Scoring on the scale is based on the number of symptoms and diagnostic criteria. Using an eight-point Likert scale, the psychometric properties are evaluated based on the fulfillment of 11 criteria, with total scores ranging from 0 to 11. Additionally, responses to items 5 and 6 help determine diagnostic status and food addiction severity [ 24 ]. Beck Depression Inventory The Beck Depression Inventory (BDI) was developed by Beck, Ward, and Mendelson in 1961 to assess emotional, cognitive, somatic, and motivational aspects and to make self-assessment [ 25 ]. Hisli [ 26 ], carried out the validity and reliability studies for its adaptation into Turkish. The inventory’s goal is to objectively identify symptoms of depression rather than to diagnose depression. The inventory consists of 21 categories of symptoms: eleven questions cover cognitive symptoms, five relate to physical symptoms, two focus on emotions, two on behaviors, and one addresses interpersonal issues. Participants were asked to select the most appropriate option from four possible responses for each category. Each question was assigned a score ranging from 1 to 3, and the total score could range from 0 to 63. A score of 0 to 9 indicated minimal depressive symptoms, while scores from 10 to 16 represented mild symptoms. Scores from 17 to 29 were categorized as moderate depressive symptoms, and scores of 30 to 63 defined severe symptoms. Statistical Analysis In this section, the details of the descriptive statistics and comparative process performed in the statistical analysis process are presented. Statistical significance was set at p < 0.05. The descriptive statistics of the qualitative measurements are summarized with frequencies and percentages, whereas the quantitative measurements are expressed as mean and standard deviation. Regarding group-based comparisons, the independent samples t-test and Mann-Whitney U tests were utilized for comparisons at two levels, and one-way analysis of variance and Kruskal-Wallis H tests were employed for comparisons at more than two levels. The correlation analyses among numerical variables were investigated via Pearson, Spearman and Kendall Tau-b coefficients. Jonckheere-Terpstra Test was used to determine whether there is a linear trend in the levels of food addiction. The odds ratios for the likelihood estimation of the presence of food addiction and depression over various measurements were obtained. The assumptions of the parametric tests were tested for normality via Kolmogrov-Smirnov test, skewness/skewness and z-scores, the homogeneity of variance via Levene's test and the linearity assumption via scatter plot. The post-hoc tests were conducted by Games-Howell or Tukey test to evaluate the possible pairwise comparisons. The statistical significance value was set at 0.05 throughout the study. The whole analysis process was carried out via R (v4.3.2) and Medcalc (v.21) softwares. Results In this section, the results of the statistical analysis process are provided comprehensively. The descriptive statistics of the participants are summarized in Table 1. According to these findings, the mean age of the female participants was 23.47 (±3.17), male participants 22.60(±3.38) years, body mass index female participants was 22.12 (±3.68), male participants 24.03(±3.36) kg/m², YFAS Score female was 1.59 (±2.48), male 1.58(±2.31) and BDI Score female was 17.7 (±9.91), male 17.45(±9.87). Women and men use Instagram, YouTube and Twitter the most, and Tik Tok the least, respectively. Spending a long time on social media resulted in 53.8% more food consumption in women and 53.1% more food consumption in men. Among participants, 49.9% of women and 53.5% of men reported snacking while using social media, even when not hungry. Table 1. Participants’ characteristics (n:4765) Female (n:3659) Male (n:1106) Age (year) Mean (SD) 23.47 (3.17) 22.60 (3.38) BMI (kg/m2) Mean (SD) 22.12 (3.68) 24.03 (3.36) YFAS 1.59 (2.48) 1.58 (2.31) BDI 17.70 (9.91) 17.45 (9.87) n % n % Time spend on social media 0-30 mins 31-60 mins 61-120 mins 121-240 mins >360 mins 51 196 757 1734 648 1.5 5.8 22.4 51.2 19.1 25 110 263 422 162 2.5 11.2 26.8 43 16.5 Frequency social media? 0-8 9-30 31-57 >58 times 164 920 1100 1202 4.8 27.2 32.5 35.5 49 263 269 401 5 26.8 27.4 40.8 Social media platform Instagram Facebook Tik tok Youtube Twitter Pinterest Snapchat 3172 638 366 2830 1849 1025 1123 93.7 18.8 10.8 83.6 54.6 30.3 33.2 895 407 158 830 600 122 222 91.1 41.4 16.1 84.5 61.1 12.4 22.6 I consume more food on days when I use social media for a long time Yes No 1820 1566 53.8 46.2 521 461 53.1 46.9 Even if I'm full while using social media. I snack on something Yes No 1688 1698 49.9 50.1 525 457 53.5 46.5 BDI: Beck's Depression inventory; BMI: Body mass ındex; YFAS: Yale Food Addiction Scale. Numeric variables are presented as mean ± standard deviation Nominal variables are shown as percentage (frequency) The comparison results of BDI scores and BMI value in the food addiction groups are presented in Table 2. Participants average BDI score and BMI value had significant differences with all food addiction groups (p < 0.001), the trend was uniform. Additionally, severe food addiction group had the highest BDI score and BMI value. Table 2. The comparison of BDI scores and BMI value in FA groups Food Addiction Group n (%) BDI Score Mean (SD) κ p BMI Mean (SD) ψ p YFAS Mean (SD) δ p NFA Mild Moderate Severe 2987 (68.4) 628 (14.4) 372 (8.5) 381 (8.7) 15.11 a (8.32) 20.79 b (9.92) 23.73 c (10.60) 26.41 d (11.45) <0.001 22.22 a (3.49) 22.71 b (3.57) 23.43 c (4.06) 24.02 c (4.53) <0.001 0.249 a (0.432) 2.417 b (0.493) 4.463 c (0.499) 7.885 d (1.843) <0.001 Different letters represent a significant difference for corresponding group. κ : Welch Anova test with Games-Howell post hoc test ψ : Kruskall-Wallis test with DSCF (Dwass-Steel-Critchlow-Fligner) post hoc test δ : Welch Anova test with Games-Howell post hoc test BDI: Beck’s Depression inventory; BMI: Body mass ındex; NFA: Non-food addicted, YFAS: Yale Food Addiction Scale. Numeric variables are presented as mean (standard deviation) Table 3 displays the comparative findings of BDI, YFAS scores and BMI for each social media platform. Regarding BMI, a significant difference was found between users and non-users of Facebook, Twitter, Snapchat, Pinterest, and Tik Tok (p 0.05) was observed for other platforms (Instagram, YouTube). For BDI Score, those who use and those who do not use Youtube and Tik Tok there is a significant difference (p < 0.05) whereas no significant difference is observed for other platforms. In terms of the YFAS Score, users of Facebook, Youtube,Twitter, Snapchat, Tik Tok have higher scores than non-users (p 0.05). Table 3. The comparative results of BDI, YFAS and BMI measurements across social media platforms Social Media Platforms BMI BDI Score YFAS Score Mean SD p Mean SD p Mean SD p Instagram Yes 22.54 3.71 0.277 θ 17.57 9.85 0.123 υ 1.57 2.44 0.072 θ No 22.66 3.58 18.69 10.51 1.77 2.47 Facebook Yes 23.42 4.00 <0.001 θ 17.94 9.95 0.315 θ 1.87 2.63 <0.001 υ No 22.28 3.56 17.55 9.89 1.50 2.38 Youtube Yes 22.54 3.69 0.742 θ 17.39 9.68 0.002 υ 1.52 2.39 <0.001 υ No 22.61 3.75 18.97 10.88 1.93 2.68 Twitter Yes 22.43 3.68 0.005 θ 17.67 9.73 0.432 θ 1.50 2.40 0.002 υ No 22.71 3.72 17.62 10.12 1.69 2.49 Snapchat Yes 22.00 3.44 <0.001 θ 17.99 10.28 0.292 υ 1.71 2.47 0.002 υ No 22.80 3.79 17.49 9.73 1.53 2.43 Pinterest Yes 22.19 3.75 <0.001 θ 17.56 9.68 0.877 θ 1.62 2.52 0.368 θ No 22.68 3.68 17.68 9.98 1.57 2.41 Tik Tok Yes 23.18 3.73 <0.001 θ 19.67 10.70 <0.001 υ 2.24 2.56 <0.001 υ No 22.47 3.69 17.37 9.76 1.50 2.41 BDI: Beck's Depression inventory; BMI: Body mass ındex; YFAS: Yale Food Addiction Scale. θ : Independent samples t-test υ : Welch t-test The odds ratios between the presence of food addiction and depression along with demographic variables and social media platform preferences are given in Table 4. When examined in the whole study population BDI score, compared with in male had 21% significantly greater odds of depression in females (%95 CI: 1.048, 1.397 p=0.0091). Looking at the ratio for depression risk was 15.5% lower in participants aged 23-30 years than 18-22 years this ratio was statistically significant (%95 CI: 0.744, 0.959 p=0.0092). The odds ratio for the risk of having depression was 25.4% higher and statistically significant in Over 25 kg/m 2 compared to 25 kg/m 2 and lower statistically significant (%95 CI: 1.081, 1.455 p=0.0027). The odds of showing depression symptoms in those who consume more food on days when I use social media for a long time is 75.4%; similarly, in consumers who snack even if I’m full while using social media, it is 80.6% higher and statistically significant in consumers compared to non-consumers (%95 CI: respectively 1.553, 1.982 p<0.001; 1.599, 2.041 p<0.001). Concerning the presence of food addiction odds in Pinterest users is 19.0% (%95 CI: respectively 0.68, 0.964 p=0.0179); identicaly the odds of depression in youtube users is %15.5 lower than on the other hand TikTok users is %40.5 higher than in non-users and statistically significant (%95 CI: respectively 0.715, 0.997 p=0.046; 1.159, 1.702 p=0.0005). However, odds of other social media platforms was not found to be statistically significant Table 4. Odds ratios (95% CI) for the presence of food addiction and depression on a various variables Odds Ratio of being Addicted to Food Yes (95% CI; p) Odds Ratio of having Depression symptoms Yes (95% CI; p) Gender Male (Reference) Female 1.056 (0.876-1.274; p=0.5653) 1.210 (1.048-1.397; p=0.0091)* Age category 1. 18-22 years (Reference) 2. 23-30 years 3. >30 years (1 vs 2) 0.992 (0.840-1.171; p=0.7718) (1 vs 3) 1.219 (0.887-1.676; p=0.2227) (2 vs 3) 1.229 (0.888-1.701; p=0.2140) (1 vs 2) 0.845 (0.744-0.959; p=0.0092)* (1 vs 3) 0.899 (0.714-1.132; p=0.3653) (2 vs 3) 1.065 (0.842-1.347; p=0.6006) BMI 25 kg/m 2 and lower (Reference) Over 25 kg/m 2 0.948 (0.784-1.146; p=0.5862) 1.254 (1.081-1.455; p=0.0027)* I consume more food on days when I use social media for a long time No (Reference group) Yes 1.048 (0.894-1.227; p=0.5607) 1.754 (1.553-1.982; p<0.001) Even if I'm full while using social media. I snack on something No (Reference) Yes 0.958 (0.818-1.122; p=0.5968) 1.806 (1.599-2.041; p<0.001) Instagram No (Reference) Yes 0.872 (0.630-1.206; p=0.4087) 1.048 (0.826-1.329; p=0.6972) Facebook No (Reference) Yes 0.971 (0.808-1.168; p=0.7594) 0.976 (0.847-1.124; p=0.7359) Youtube No (Reference) Yes 0.846 (0.677-1.057; p=0.1425) 0.845 (0.715-0.997; p=0.046)* Twitter No (Reference) Yes 1.010 (0.861-1.184; p=0.8982) 1.051 (0.931-1.187; p=0.4160) Snapchat No (Reference) Yes 0.939 (0.793-1.113; p=0.4737) 1.021 (0.896-1.163; p=0.7531) Pinterest No (Reference) Yes 0.810 (0.680-0.964; p=0.0179) 1.024 (0.893-1.175; p=0.7297) TikTok No (Reference) Yes 0.876 (0.692-1.109; p=0.2720) 1.405 (1.159-1.702; p=0.0005)*** Note. The statistically significant odds are shown in bold. BMI: Body mass index *p<0.05, ***p<0.001 Table 5 includes the results of linear trend analysis of food addiction levels on Beck depression, and BMI scores. There was a statistically significant monotonically increasing trend in the median values of Beck depression scores as the level of food addiction increased (p<0.001). Kendall’s Tau-b association coefficient between food addiction level and Beck depression level was found to be 0.302 corresponding to moderate level. A similar interpretation can be drawn for the BMI (p<0.001). Kendall’s Tau-b association coefficient between food addiction level and BMI was found to be 0.109 at a low level. Table 5. The linear trend analysis results across the food addiction levels Food Addiction Level Normal Mild Moderate Severe Tau-b p * Median (IQR) Median (IQR) Median (IQR) Median (IQR) BDI Score 14 (9-19) 20 (13-27) 23 (15-32) 26 (18-34) 0.302 < .001 BMI 21.67 (19.72-24.22) 22.19 (20.33-24.55) 22.83 (20.73-25.56) 23.15 (20.83-26.83) 0.109 < .001 * Statistical significance results based on Jonckheere-Terpstra Test BDI: Beck's Depression inventory; BMI: Body mass ındex; IQR: Inter Quantile Range Discussion In this study, we conducted a cross sectional survey to investigate the impact of social media and FA, on depression and BMI. It was observed that as the severity of food addiction increased, BDI score and BMI (except for the moderate and severe food addiction groups) increased. This highlights that FA influences the depression and BMI of university students. In a previous study in Turkey, there was a positive relationship between food addiction and BDI scores, in addition to a positive relationship between food addiction and BMI[ 14 ]. Similarly, another study that was conducted on students reported that students with food addiction presented higher BMI values, worse depression problems [ 27 ]. However, despite these findings, there is still a lack of comprehensive research exploring the combined impact of social media use and food addiction on both depression and BMI among university students. To the best of our knowledge, this is the first study to evaluate the impact of social media and FA, on depression, and BMI in university students. According to Table 3 , individuals who used Facebook and TikTok had notably higher BMI levels compared to non-users, whereas those engaging with Twitter, Snapchat, and Pinterest tended to have lower BMI values. In terms of depression levels, YouTube users exhibited lower BDI scores than non-users, while TikTok users had higher BDI scores. Regarding food addiction, YouTube users again showed lower YFAS scores, whereas TikTok users had higher YFAS scores compared to non-users. Additionally, users of Facebook, Snapchat, TikTok, had elevated YFAS scores relative to non-users, whereas Twitter users showed lower YFAS scores. This finding suggests that the type of social media platform individuals engage with may be associated with differences in BMI, depression and food addiction potentially due to variations in content exposure, usage patterns, and lifestyle behaviors influenced by these platforms. A systematic review found that the diagnosis of FA was higher in women[ 28 ]. Parallel to this study, the risk of FA was higher in women than in men, although not statistically significant (Table 4 ). Although the difference was not statistically significant, the higher risk of food addiction observed in women compared to men may suggest underlying gender-related factors, such as emotional eating tendencies, hormonal influences, or societal pressures, that could contribute to variations in eating behaviors. As indicated in Table 4 , the odds ratio for the presence of food addiction in Pinterest users is 19% lower than in non-users. Pinterest is a social media platform for finding ideas like recipes, home and style inspiration, and more[ 29 ]. It has been noted that Pinterest can be a useful tool for disseminating information about dietary behaviors[ 30 ]. The lower odds of food addiction among Pinterest users compared to non-users may indicate that engagement with this platform is associated with healthier eating behaviors or more resistance to addictive eating patterns, potentially due to exposure to wellness and lifestyle content. Depression is a widely prevalent mental health condition affecting individuals globally[ 31 ]. Although Al-Qaisy et al.,[ 32 ] insisted that male students were more likely to experience depressive disorders than female students, most studies have confirmed that depression is more common in women than in men[ 31 , 33 ]. Roldan-Espínola et al.,[ 34 ] also showed that the female gender was significantly higher risk of developing major depressive disorder. Similarly, this study found that women had a statistically significant 21% higher odds of experiencing depression compared to men (Table 4 ). The results suggest that women are at a significantly greater risk of experiencing depression than men, which may be influenced by a combination of hormonal fluctuations, psychosocial stressors, and gender-related differences in coping mechanisms and help-seeking behaviors. The university experience can heighten psychological vulnerability due to factors such as academic demands, the transition to adulthood, and unstable relationships, all of which may negatively impact students’ mental health. Given that 62.5–75% of all mental disorders emerge by the age of 24, this period represents a critical window for mental health challenges[ 34 ]. Similarly, findings from this study suggest that advancing age is associated with a lower risk of depression (Table 4 ), indicating that as individuals grow older, they may develop greater resilience or coping mechanisms. Another factor in the etiology of depression, after age, is that being overweight and obesity are positively correlated with an increased risk of depression and depression-related symptoms[ 35 , 36 ]. The findings in this study indicate that certain lifestyle and behavioral factors are significantly associated with depression risk. Individuals with a BMI above 25 kg/m² were found to have a 25% higher likelihood of experiencing depression compared to those with a BMI of 25 kg/m² or lower (Table 4 ), suggesting a potential link between excess weight and mental health. Additionally, engaging in eating behaviors, such as snacking while using social media despite feeling full, was associated with an 80.6% increased risk of depression, highlighting the possible negative psychological effects of such habits. The increased snack consumption associated with social media use may paradoxically contribute to both elevated body weight and a heightened risk of depression. Interestingly, the study also found that YouTube users had a 15.5% lower risk of depression compared to non-users, which may indicate that certain types of online content consumption provide stress relief or social engagement that positively influences mental well-being. The different results found on different social media platforms suggest that the content may have different levels of impact in terms of the time spent and eating behaviors on individuals. Table 5 illustrates a statistically significant upward trend in the median BDI scores and BMI value as the severity of FA increases (p < 0.001). This suggests that individuals with more severe food addiction symptoms tend to exhibit higher levels of depressive symptoms and increased BMI. Supporting these results, Şanlıer et al.[ 14 ] also identified a positive association between food addiction, depression scores, and BMI. This reinforces the notion that disordered eating patterns linked to food addiction may contribute to both psychological distress and excessive weight gain. The connection between these variables may be explained by shared neurobiological mechanisms involving dopamine dysregulation, stress-related eating, and emotional dysregulation, all of which have been implicated in both mood disorders and compulsive eating behaviors[ 37 ]. Furthermore, Bartschi and Greenwood [ 38 ] emphasized food addiction symptoms as a significant behavioral risk factor for increased adiposity, particularly in individuals with greater depressive symptom severity. This relationship appears to be especially pronounced among those who experience heightened appetite during depressive episodes. Addressing the underlying emotional and behavioral factors contributing to food addiction could be crucial for mitigating the associated risks of obesity and depression. Future research should further explore the causal mechanisms underlying these associations and assess the effectiveness of multidisciplinary approaches in treatment and prevention. Conclusions This study provides valuable insights into the complex relationship between social media use, food addiction, depression, and BMI among university students. The results indicate that as food addiction severity increases, there is a corresponding rise in depression scores and BMI, reinforcing previous findings on the interplay between compulsive eating behaviors and mental health. Additionally, social media platform engagement appears to influence these associations, with specific platforms such as TikTok and Facebook being linked to higher BMI and food addiction scores, while YouTube users exhibited lower depression and food addiction levels. These variations suggest that the type of content consumed and user engagement patterns may contribute to differences in dietary behaviors and mental health outcomes. Gender differences were also observed, with women demonstrating a higher risk of both food addiction and depression, aligning with previous literature suggesting the influence of biological, psychological, and social factors. Additionally, being overweight or obese was significantly associated with a greater likelihood of experiencing depression, highlighting the need for interventions that address both physical and mental well-being. The findings emphasize the importance of a multidisciplinary approach to tackling food addiction and its associated health risks. Future research should explore the causal pathways linking type of social media and exposure time, eating behaviors, and mental health to develop targeted prevention and intervention strategies. Given the critical period of university life for mental health development, promoting healthy eating habits and responsible social media use could play a vital role in mitigating the risks of food addiction, obesity, and depression in young adults. Strenghts and Limitation This is the first known study to investigate the combined impact of social media use and food addiction (FA) on both depression and BMI among university students. This novel approach contributes important insights to the existing literature. It distinguishes between different social media platforms (e.g., TikTok, YouTube, Facebook, Pinterest), identifying platform-specific relationships with depression, food addiction, and BMI. This level of detail allows for more nuanced interpretations and targeted future interventions. The study’s cross-sectional nature limits the ability to draw causal inferences. It cannot determine whether social media use or food addiction causes depression or increased BMI, or vice versa. Data on social media use, depression symptoms, and food addiction were self-reported, which may introduce recall bias. The sample is limited to university students, which may not represent the general population. Age-related resilience and mental health patterns may differ in non-student populations. Declarations Ethics approval and consent to participate Written informed consent was obtained from all individuals participating in the study. The study was conducted according to the criteria set by the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Karamanoğlu Mehmetbey University (Approval no: E-75732670-050.04-181367). Consent for publication Not applicable. Competing interests The authors declare no competing interests Funding None. Author Contribution MA served in a supporting role for conceptualisation; methodology; investigation; supervision; writing and original draft. SÖ served in a supporting role for conceptualisation; investigation; supervision; writing and original draft. ŞGY served in a supporting role for conceptualisation; writing and original draft. HY served in a supporting role for data curation; formal analysis; supervision and writing and original draft. All authors have read and approved the final version submitted and take public responsibility for all aspects of the work. Acknowledgement The authors would like to thank the participants for their contribution to the study Data availability The corresponding author may provide you with the data that back up the study’s conclusions upon request. The data are not accessible to the general public due to privacy or ethical concerns. References Ahmed O, Siddiqua SJN, Alam N, Griffiths MD. The mediating role of problematic social media use in the relationship between social avoidance/distress and self-esteem. Technol Soc. 2021;64:101485. 10.1016/j.techsoc.2020.101485 . Undiscovered Maine. 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The associations between sedentary behaviour and mental health among adolescents: a systematic review. Int J Behav Nutr Phys Act. 2016;13:108. 10.1186/s12966-016-0432-4 . Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, et al. Association between social media use and depression among US young adults. Depress Anxiety. 2016;33(4):323–31. 10.1002/da.22466 . Hasler G. Pathophysiology of depression: do we have any solid evidence of interest to clinicians? World Psychiatry. 2010;9(3):155–61. 10.1002/j.2051-5545.2010.tb00298.x . Primack BA, Swanier B, Georgiopoulos AM, Land SR, Fine MJ. Association between media use in adolescence and depression in young adulthood: a longitudinal study. Arch Gen Psychiatry. 2009;66(2):181–8. 10.1001/archgenpsychiatry.2008.532 . Chou WP, Yen CF, Liu TL. Predicting effects of psychological inflexibility/experiential avoidance and stress coping strategies for internet addiction, significant depression, and suicidality in college students: a prospective study. Int J Environ Res Public Health. 2018;15(4):788. 10.3390/ijerph15040788 . Huang PC, Latner JD, O’Brien KS, Chang YL, Hung CH, Chen JS, et al. Associations between social media addiction, psychological distress, and food addiction among Taiwanese university students. J Eat Disord. 2023;11(1):43. 10.1186/s40337-023-00842-2 . da Silva Júnior AE, de Lima Macena M, de Oliveira ADS, Praxedes DRS, de Oliveira Maranhao Pureza IR, de Menezes Toledo Florencio TM, et al. Prevalence of food addiction and its association with anxiety, depression, and adherence to social distancing measures in Brazilian university students during the COVID-19 pandemic: a nationwide study. Eat Weight Disord. 2022;27(6):2027–35. 10.1007/s40519-021-01297-0 . Şanlier N, Türközü D, Toka O. Body image, food addiction, depression, and body mass index in university students. Ecol Food Nutr. 2016;55(6):491–507. 10.1080/03670244.2016.1189841 . Lennerz B, Lennerz JK. Food addiction, high-glycemic-index carbohydrates, and obesity. Clin Chem. 2018;64(1):64–71. 10.1373/clinchem.2017.273532 . Meule A. Food cravings in food addiction: exploring a potential cut-off value of the Food Cravings Questionnaire-Trait-reduced. Eat Weight Disord. 2018;23:39–43. 10.1007/s40519-017-0452-3 . Gearhardt AN, Boswell RG, White MA. The association of food addiction with disordered eating and body mass index. Eat Behav. 2014;15(3):427–33. 10.1016/j.eatbeh.2014.05.001 . Gibson-Smith D, Bot M, Snijder M, Nicolaou M, Derks EM, Stronks K, et al. The relation between obesity and depressed mood in a multi-ethnic population. Soc Psychiatry Psychiatr Epidemiol. 2018;53:629–38. 10.1007/s00127-018-1512-3 . Gearhardt AN, White MA, Masheb RM, Morgan PT, Crosby RD, Grilo CM. An examination of the food addiction construct in obese patients with binge eating disorder. Int J Eat Disord. 2012;45(5):657–63. 10.1002/eat.20957 . Usta E, Pehlivan M. Mediation effect of depression on the association between food addiction and body image in individuals with obesity. Konuralp Med J. 2021;13(3):576–84. 10.18521/ktd.897251 . Mills JG, Thomas SJ, Larkin TA, Pai NB, Deng C. Problematic eating behaviours, changes in appetite, and weight gain in major depressive disorder: the role of leptin. J Affect Disord. 2018;240:137–45. 10.1016/j.jad.2018.07.069 . Pursey KM, Stanwell P, Gearhardt AN, Collins CE, Burrows TL. The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review. Nutrients. 2014;6(10):4552–90. 10.3390/nu6104552 . Gearhardt AN, Corbin WR, Brownell KD. Preliminary validation of the Yale food addiction scale. Appetite. 2009;52(2):430–6. 10.1016/j.appet.2008.12.003 . Tok Ş. Modifiye edilmiş Yale Yeme Bağımlılığı Ölçeği sürüm 2.0'ın Türkçe uyarlanmasının geçerlilik ve güvenilirlik çalışması [Master’s thesis]. Sakarya University; 2018. Beck AT, Ward C, Mendelson M, Mock J, Erbaugh J. Beck Depression Inventory (BDI). Available from: https://books.google.com.tr/books?hl=tr&lr=&id=QQA4EQAAQBAJ . Accessed 2024 Jul 24. Hisli N. Beck depresyon envanterinin üniversite öğrencileri için geçerliliği, güvenilirliği. J Psychol. 1989;7:3–13. Romero-Blanco C, Hernández-Martínez A, Parra-Fernández ML, Onieva-Zafra MD, Prado-Laguna MDC, Rodríguez-Almagro J. Food addiction and lifestyle habits among university students. Nutrients. 2021;13(4):1352. 10.3390/nu13041352 . Pursey KM, Stanwell P, Gearhardt AN, Collins CE, Burrows TL. The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review. Nutrients. 2014;6(10):4552–90. 10.3390/nu6104552 . Pinterest Help Center. All about Pinterest. Available from: https://help.pinterest.com/en/guide/all-about-pinterest . Accessed 2025 Jul 15. Wilkinson JL, Strickling K, Payne HE, Jensen KC, West JH. Evaluation of diet-related infographics on Pinterest for use of behavior change theories: a content analysis. JMIR mHealth uHealth. 2016;4(4):e6367. 10.2196/mhealth.6367 . Sun XJ, Niu GF, You ZQ, Zhou ZK, Tang Y. Gender, negative life events and coping on different stages of depression severity: a cross-sectional study among Chinese university students. J Affect Disord. 2017;209:177–81. 10.1016/j.jad.2016.11.025 . Al-Qaisy LM. The relation of depression and anxiety in academic achievement among group of university students. Int J Psychol Couns. 2011;3(5):96–100. Shi P, Yang A, Zhao Q, Chen Z, Ren X, Dai Q. A hypothesis of gender differences in self-reporting symptom of depression: implications to solve under-diagnosis and under-treatment of depression in males. Front Psychiatry. 2021;12:589687. 10.3389/fpsyt.2021.589687 . Roldan-Espínola L, Riera-Serra P, Roca M, García-Toro M, Coronado-Simsic V, Castro A, et al. Depression and lifestyle among university students: a one-year follow-up study. Eur J Psychiatry. 2024;38(3):100250. 10.1016/j.ejpsy.2024.100250 . Badillo N, Khatib M, Kahar P, Khanna D. Correlation between body mass index and depression/depression-like symptoms among different genders and races. Cureus. 2022;14(2):e22014. 10.7759/cureus.21841 . Cui J, Sun X, Li X, Ke M, Sun J, Yasmeen N, et al. Association between different indicators of obesity and depression in adults in Qingdao, China: a cross-sectional study. Front Endocrinol (Lausanne). 2018;9:549. 10.3389/fendo.2018.00549 . Yau YH, Potenza MN. Stress and eating behaviors. Minerva Endocrinol. 2013;38(3):255–67. Bartschi JG, Greenwood LM. Food addiction as a mediator between depressive symptom severity and body mass index. Appetite. 2023;190:107008. 10.1016/j.appet.2023.107008 . Additional Declarations No competing interests reported. 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There are 4.8\u0026nbsp;billion social media users worldwide, which constitute 59.9% of the world\u0026rsquo;s population and 92.7% of internet users. It was recorded that there were 150\u0026nbsp;million new social media users between April 2022 and April 2023, an increase of 3.2% compared to the previous year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Social media platforms are widely also used by university students [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among social media users, social media use has positive effects on education, work life and health, but on the other hand, negative effects such as reduced human interaction, time loss and addiction have been identified [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to these negative consequences, depression can also be an effect of social media [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Systematic reviews have identified a significant association between social media usage and depression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eToday, depression most commonly begins in young adulthood [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Many factors may contribute to depression, such as psychosocial stress, neurotransmitters, and circadian rhythm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Frequent presence on social networking sites can give individuals the feeling that others are living happier, more social lives, which can cause individuals to feel more socially isolated in comparison and increase their depression levels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDepression has the potential to functional impairment in several domains, including personal performance, behavioral patterns, and both peer and family relationships [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Individuals with relatively poor emotional regulation, such as depression, may instinctively avoid unpleasant emotions as a coping strategy, leading to the development of addictive behaviors such as problematic internet use or food addiction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have reported a strong relationship between depression and food addiction [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] but there is not enough evidence.\u003c/p\u003e\u003cp\u003eOne of the concepts that has received increasing attention in recent years is the concept of food addiction [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It has been suggested that food cravings or the desire to eat a certain food are associated with food addiction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In other words, certain foods may have addictive potential, which can lead to certain eating disorders and obesity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEarlier studies have found a correlation between increased severity of depressive symptoms and higher body mass index (BMI) values [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and obese individuals who meet criteria for food addiction report more depressive symptoms compared to obese individuals who do not meet criteria for food addiction (FA)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, a major consequence of food addiction is weight gain, and evidence from a comprehensive meta-analysis suggests that overweight or obese adults are more than twice as likely to report symptoms of food addiction compared with adults at a healthy weight [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As a result of the literature research, there are limited studies addressing the relationship between social media, food addiction, depression and BMI. In this study, it is to investigate the possible effects of social media and food addiction, on depression and BMI, as well as the relationship between food addiction and depression in university students.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Collection of Data\u003c/h2\u003e\u003cp\u003eA study was conducted between February-July 2024 in Elazığ province with 4963 in healthy university students to evaluate the relationships between social media, food addiction and depression. The data from 198 participants were excluded from the study because they either took medication or did not complete the questionnaire. Consequently, the study concluded with a total of 4,765 participants. The study data were collected by the researchers using a face-to-face method during the students\u0026rsquo; free time (recess, leisure time, etc.) with the questionnaire. The survey consisted of general informations, social media use, YFAS 2.0 and BDI. Participants with severe psychiatric disorders (e.g. psychotic disorder, post-traumatic, bipolar disorder, stress disorder, bulimia), those without any neurodegenerative diseases and those using antidepressants were not included in the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnthropometric Measurements\u003c/h3\u003e\n\u003cp\u003eParticipants were asked to report their gender, age, disease status, body weight and height. The body weight of the participants was measured with the TANITA BC545N device in the morning while fasting, after defecation, with light/ thin clothes, without shoes and in accordance with the measurement technique. Height was measured using a portable stadiometer with the feet together and the head in the Frankfort plane. The measurement was performed with a sensitivity of 0.1 cm (1 mm). BMI values were obtained by dividing body weight (kg) by height squared (m\u003csup\u003e2\u003c/sup\u003e ) (BMI\u0026thinsp;=\u0026thinsp;kg/m\u003csup\u003e2\u003c/sup\u003e ).\u003c/p\u003e\n\u003ch3\u003eModifed Yale Food Addiction Scale version 2.0 (mYFAS 2.0)\u003c/h3\u003e\n\u003cp\u003eThe Yale Food Addiction Scale (YFAS) was initially developed in 2009 by Gearhardt et al.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to assess food addiction. The scale\u0026rsquo;s validity and reliability for the Turkish population were established by Tok in 2018. Scoring on the scale is based on the number of symptoms and diagnostic criteria. Using an eight-point Likert scale, the psychometric properties are evaluated based on the fulfillment of 11 criteria, with total scores ranging from 0 to 11. Additionally, responses to items 5 and 6 help determine diagnostic status and food addiction severity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBeck Depression Inventory\u003c/h3\u003e\n\u003cp\u003eThe Beck Depression Inventory (BDI) was developed by Beck, Ward, and Mendelson in 1961 to assess emotional, cognitive, somatic, and motivational aspects and to make self-assessment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hisli [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], carried out the validity and reliability studies for its adaptation into Turkish. The inventory\u0026rsquo;s goal is to objectively identify symptoms of depression rather than to diagnose depression. The inventory consists of 21 categories of symptoms: eleven questions cover cognitive symptoms, five relate to physical symptoms, two focus on emotions, two on behaviors, and one addresses interpersonal issues. Participants were asked to select the most appropriate option from four possible responses for each category. Each question was assigned a score ranging from 1 to 3, and the total score could range from 0 to 63. A score of 0 to 9 indicated minimal depressive symptoms, while scores from 10 to 16 represented mild symptoms. Scores from 17 to 29 were categorized as moderate depressive symptoms, and scores of 30 to 63 defined severe symptoms.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eIn this section, the details of the descriptive statistics and comparative process performed in the statistical analysis process are presented. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The descriptive statistics of the qualitative measurements are summarized with frequencies and percentages, whereas the quantitative measurements are expressed as mean and standard deviation. Regarding group-based comparisons, the independent samples t-test and Mann-Whitney U tests were utilized for comparisons at two levels, and one-way analysis of variance and Kruskal-Wallis H tests were employed for comparisons at more than two levels. The correlation analyses among numerical variables were investigated via Pearson, Spearman and Kendall Tau-b coefficients. Jonckheere-Terpstra Test was used to determine whether there is a linear trend in the levels of food addiction. The odds ratios for the likelihood estimation of the presence of food addiction and depression over various measurements were obtained. The assumptions of the parametric tests were tested for normality via Kolmogrov-Smirnov test, skewness/skewness and z-scores, the homogeneity of variance via Levene's test and the linearity assumption via scatter plot. The post-hoc tests were conducted by Games-Howell or Tukey test to evaluate the possible pairwise comparisons. The statistical significance value was set at 0.05 throughout the study. The whole analysis process was carried out via R (v4.3.2) and Medcalc (v.21) softwares.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this section, the results of the statistical analysis process are provided comprehensively. The descriptive statistics of the participants are summarized in Table 1. According to these findings, the mean age of the \u0026nbsp;female participants was 23.47 (\u0026plusmn;3.17), \u0026nbsp;male participants 22.60(\u0026plusmn;3.38) years, body mass index female participants was 22.12 (\u0026plusmn;3.68), male participants 24.03(\u0026plusmn;3.36) kg/m\u0026sup2;, YFAS Score female was \u0026nbsp; 1.59 (\u0026plusmn;2.48), male 1.58(\u0026plusmn;2.31) and BDI Score female was 17.7 (\u0026plusmn;9.91), male 17.45(\u0026plusmn;9.87).\u0026nbsp;Women and men use Instagram, YouTube and Twitter the most, and Tik Tok the least, respectively. Spending a long time on social media resulted in 53.8% more food consumption in women and 53.1% more food consumption in men. Among participants, 49.9% of women and 53.5% of men reported snacking while using social media, even when not hungry.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 682px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eParticipants\u0026rsquo; characteristics (n:4765)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eFemale (n:3659)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMale (n:1106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eAge (year) Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e23.47 (3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e22.60 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eBMI (kg/m2) Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e22.12 (3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e24.03 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eYFAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1.59 \u0026nbsp; (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e1.58 \u0026nbsp; (2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e17.70 (9.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e17.45 (9.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eTime spend on social media\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0-30 mins\u003c/p\u003e\n \u003cp\u003e31-60 mins\u003c/p\u003e\n \u003cp\u003e61-120 mins\u003c/p\u003e\n \u003cp\u003e121-240 mins\u003c/p\u003e\n \u003cp\u003e\u0026gt;360 mins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e757\u003c/p\u003e\n \u003cp\u003e1734\u003c/p\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eFrequency social media?\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0-8\u003c/p\u003e\n \u003cp\u003e9-30\u003c/p\u003e\n \u003cp\u003e31-57\u003c/p\u003e\n \u003cp\u003e\u0026gt;58 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003cp\u003e1100\u003c/p\u003e\n \u003cp\u003e1202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003cp\u003e40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eSocial media platform\u003c/p\u003e\n \u003cp\u003eInstagram\u003c/p\u003e\n \u003cp\u003eFacebook\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTik tok\u003c/p\u003e\n \u003cp\u003eYoutube\u003c/p\u003e\n \u003cp\u003eTwitter\u003c/p\u003e\n \u003cp\u003ePinterest\u003c/p\u003e\n \u003cp\u003eSnapchat\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3172\u003c/p\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003cp\u003e2830\u003c/p\u003e\n \u003cp\u003e1849\u003c/p\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003cp\u003e1123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003cp\u003e83.6\u003c/p\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e895\u003c/p\u003e\n \u003cp\u003e407\u003c/p\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003cp\u003e830\u003c/p\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.1\u003c/p\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eI consume more food on days when I use social media for a long time\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1820\u003c/p\u003e\n \u003cp\u003e1566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53.1\u003c/p\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eEven if I\u0026apos;m full while using social media. I snack on something\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1688\u003c/p\u003e\n \u003cp\u003e1698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.9\u003c/p\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003cp\u003e457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBDI:\u003c/strong\u003e Beck\u0026apos;s Depression inventory; \u003cstrong\u003eBMI:\u003c/strong\u003eBody mass ındex; \u003cstrong\u003eYFAS:\u0026nbsp;\u003c/strong\u003eYale Food Addiction Scale.\u003c/p\u003e\n\u003cp\u003eNumeric variables are presented as mean \u0026plusmn; standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNominal variables are shown as percentage (frequency)\u003c/p\u003e\n\u003cp\u003eThe comparison results of BDI scores and BMI value in the food addiction groups are presented in Table 2. Participants average BDI score and BMI value had significant differences with all food addiction groups (p \u0026lt; 0.001), the trend was uniform. Additionally, severe food addiction group had the highest BDI score and BMI value.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e The comparison of BDI scores and BMI value in FA groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFood Addiction Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBDI Score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003csup\u003e\u0026kappa;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003csup\u003e\u0026psi;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYFAS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003csup\u003e\u0026delta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNFA\u003c/p\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2987 (68.4)\u003c/p\u003e\n \u003cp\u003e628 \u0026nbsp; (14.4)\u003c/p\u003e\n \u003cp\u003e372 \u0026nbsp; (8.5)\u003c/p\u003e\n \u003cp\u003e381 \u0026nbsp; (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.11\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e (8.32)\u003c/p\u003e\n \u003cp\u003e20.79\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e (9.92)\u003c/p\u003e\n \u003cp\u003e23.73\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e (10.60)\u003c/p\u003e\n \u003cp\u003e26.41\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e (11.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.22\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e (3.49)\u003c/p\u003e\n \u003cp\u003e22.71\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e(3.57)\u003c/p\u003e\n \u003cp\u003e23.43\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e (4.06)\u003c/p\u003e\n \u003cp\u003e24.02\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e (4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.249\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e (0.432)\u003c/p\u003e\n \u003cp\u003e2.417\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e(0.493)\u003c/p\u003e\n \u003cp\u003e4.463\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e (0.499)\u003c/p\u003e\n \u003cp\u003e7.885\u003csup\u003ed\u003c/sup\u003e (1.843)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eDifferent letters represent a significant difference for corresponding group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026kappa;\u003c/sup\u003e\u003c/strong\u003e: Welch Anova test with Games-Howell post hoc test\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026psi;\u003c/sup\u003e\u003c/strong\u003e: Kruskall-Wallis test with DSCF (Dwass-Steel-Critchlow-Fligner) post hoc test\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026delta;\u003c/sup\u003e\u003c/strong\u003e: Welch Anova test with Games-Howell post hoc test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBDI:\u003c/strong\u003e Beck\u0026rsquo;s Depression inventory; \u003cstrong\u003eBMI:\u003c/strong\u003eBody mass ındex; \u003cstrong\u003eNFA:\u003c/strong\u003e Non-food addicted, \u003cstrong\u003eYFAS:\u0026nbsp;\u003c/strong\u003eYale Food Addiction Scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumeric variables are presented as mean (standard deviation)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 displays the comparative findings of BDI, YFAS scores and BMI for each social media platform. Regarding BMI, a significant difference was found between users and non-users of Facebook, Twitter, Snapchat, Pinterest, and Tik Tok (p \u0026lt; 0.05); however, no significant difference (p \u0026gt; 0.05) was observed for other platforms (Instagram, YouTube). For BDI Score, those who use and those who do not use Youtube and Tik Tok there is a significant difference (p \u0026lt; 0.05) whereas no significant difference is observed for other platforms. In terms of the YFAS Score, users of Facebook, Youtube,Twitter, Snapchat, Tik Tok have higher scores than non-users (p \u0026lt; 0.05), while no significant difference is detected for other platforms based on usage status (p \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"695\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e The comparative results of BDI, YFAS and BMI measurements across social media platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eSocial Media Platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBDI Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eYFAS Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eInstagram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.277\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.123\u003cstrong\u003e\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.072\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.315\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eYoutube\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.742\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTwitter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.432\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSnapchat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.292\u003cstrong\u003e\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ePinterest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.877\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.368\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTik Tok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBDI:\u003c/strong\u003e Beck\u0026apos;s Depression inventory; \u003cstrong\u003eBMI:\u0026nbsp;\u003c/strong\u003eBody mass ındex; \u003cstrong\u003eYFAS:\u0026nbsp;\u003c/strong\u003eYale Food Addiction Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026theta;\u003c/sup\u003e\u003c/strong\u003e: Independent samples t-test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026upsilon;\u003c/sup\u003e\u003c/strong\u003e: Welch t-test\u003c/p\u003e\n\u003cp\u003eThe odds ratios between the presence of food addiction and depression along with demographic variables and social media platform\u0026nbsp;preferences are given in Table 4. When examined in the whole study population BDI score, compared with in male had 21% significantly greater odds of depression in females (%95 CI: 1.048, 1.397\u0026nbsp;p=0.0091). Looking at the ratio for depression risk was 15.5% lower in participants aged 23-30 years than 18-22 years this ratio was statistically significant (%95 CI: \u0026nbsp;0.744, 0.959\u0026nbsp;p=0.0092). The odds ratio for the risk of having depression was 25.4% higher and statistically significant in Over 25 kg/m\u003csup\u003e2\u003c/sup\u003e compared to 25 kg/m\u003csup\u003e2\u003c/sup\u003e and lower statistically significant (%95 CI: 1.081, 1.455\u0026nbsp;p=0.0027). The odds of showing depression symptoms in those who consume more food on days when I use social media for a long time is 75.4%; similarly, in consumers who snack even if I\u0026rsquo;m full while using social media, it is 80.6% higher and statistically significant in consumers compared to non-consumers (%95 CI: respectively \u0026nbsp;1.553, 1.982\u0026nbsp;p\u0026lt;0.001; 1.599, 2.041\u0026nbsp;p\u0026lt;0.001). Concerning the presence of food addiction odds in Pinterest users is 19.0% (%95 CI: respectively \u0026nbsp;0.68, 0.964\u0026nbsp;p=0.0179); identicaly the odds of depression in youtube users is %15.5 lower than on the other hand TikTok \u0026nbsp;users is %40.5 higher than in non-users and statistically significant (%95 CI: respectively \u0026nbsp;0.715, 0.997\u0026nbsp;p=0.046; 1.159, 1.702\u0026nbsp;p=0.0005). However, odds of other social media platforms was not found to be statistically significant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 697px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Odds ratios\u0026nbsp;(95% CI) for the presence of food addiction and depression on a various variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eOdds Ratio of being Addicted to Food\u003c/p\u003e\n \u003cp\u003eYes (95% CI; p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eOdds Ratio of having Depression symptoms\u003c/p\u003e\n \u003cp\u003eYes (95% CI; p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale (Reference)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.056 (0.876-1.274; p=0.5653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.210 (1.048-1.397; p=0.0091)*\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eAge category\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1. 18-22 years (Reference)\u003c/p\u003e\n \u003cp\u003e2. 23-30 years\u003c/p\u003e\n \u003cp\u003e3. \u0026gt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1 vs 2) 0.992 (0.840-1.171; p=0.7718)\u003c/p\u003e\n \u003cp\u003e(1 vs 3) 1.219 (0.887-1.676; p=0.2227)\u003c/p\u003e\n \u003cp\u003e(2 vs 3) 1.229 (0.888-1.701; p=0.2140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(1 vs 2) 0.845 (0.744-0.959; p=0.0092)*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(1 vs 3) 0.899 (0.714-1.132; p=0.3653)\u003c/p\u003e\n \u003cp\u003e(2 vs 3) 1.065 (0.842-1.347; p=0.6006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e25 kg/m\u003csup\u003e2\u003c/sup\u003e and lower (Reference)\u003c/p\u003e\n \u003cp\u003eOver 25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.948 (0.784-1.146; p=0.5862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.254 (1.081-1.455; p=0.0027)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eI consume more food on days when I use social media for a long time\u003c/p\u003e\n \u003cp\u003eNo (Reference group)\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.048 (0.894-1.227; p=0.5607)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.754 (1.553-1.982; p\u0026lt;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eEven if I\u0026apos;m full while using social media. I snack on something\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.958 (0.818-1.122; p=0.5968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.806 (1.599-2.041; p\u0026lt;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eInstagram\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.872 (0.630-1.206; p=0.4087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.048 (0.826-1.329; p=0.6972)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.971 (0.808-1.168; p=0.7594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.976 (0.847-1.124; p=0.7359)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eYoutube\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.846 (0.677-1.057; p=0.1425)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.845 (0.715-0.997; p=0.046)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eTwitter\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.010 (0.861-1.184; p=0.8982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.051 (0.931-1.187; p=0.4160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSnapchat\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.939 (0.793-1.113; p=0.4737)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.021 (0.896-1.163; p=0.7531)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePinterest\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.810 (0.680-0.964; p=0.0179)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.024 (0.893-1.175; p=0.7297)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eTikTok\u003c/p\u003e\n \u003cp\u003eNo (Reference)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.876 (0.692-1.109; p=0.2720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.405 (1.159-1.702; p=0.0005)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 697px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e The statistically significant odds are shown in bold.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBMI:\u003c/strong\u003e Body mass index\u003c/p\u003e\n\u003cp\u003e*p\u0026lt;0.05, ***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 includes the results of linear trend analysis of food addiction levels on Beck depression, and BMI scores. There was a statistically significant monotonically increasing trend in the median values of Beck depression scores as the level of food addiction increased (p\u0026lt;0.001). Kendall\u0026rsquo;s Tau-b association coefficient between food addiction level and Beck depression level was found to be 0.302 corresponding to moderate level. A similar interpretation can be drawn for the BMI (p\u0026lt;0.001). Kendall\u0026rsquo;s Tau-b association coefficient between food addiction level and BMI was found to be 0.109 at a low level.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"734\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e The linear trend analysis results across the food addiction levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eFood Addiction Level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTau-b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ep\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBDI Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (9-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (13-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (15-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (18-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026nbsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.67 (19.72-24.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.19 (20.33-24.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.83 (20.73-25.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.15 (20.83-26.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026nbsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003eStatistical significance results based on Jonckheere-Terpstra Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBDI:\u003c/strong\u003e Beck\u0026apos;s Depression inventory; \u003cstrong\u003eBMI:\u003c/strong\u003eBody mass ındex; \u003cstrong\u003eIQR:\u003c/strong\u003eInter Quantile Range\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a cross sectional survey to investigate the impact of social media and FA, on depression and BMI. It was observed that as the severity of food addiction increased, BDI score and BMI (except for the moderate and severe food addiction groups) increased. This highlights that FA influences the depression and BMI of university students. In a previous study in Turkey, there was a positive relationship between food addiction and BDI scores, in addition to a positive relationship between food addiction and BMI[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, another study that was conducted on students reported that students with food addiction presented higher BMI values, worse depression problems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, despite these findings, there is still a lack of comprehensive research exploring the combined impact of social media use and food addiction on both depression and BMI among university students. To the best of our knowledge, this is the first study to evaluate the impact of social media and FA, on depression, and BMI in university students.\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, individuals who used Facebook and TikTok had notably higher BMI levels compared to non-users, whereas those engaging with Twitter, Snapchat, and Pinterest tended to have lower BMI values. In terms of depression levels, YouTube users exhibited lower BDI scores than non-users, while TikTok users had higher BDI scores. Regarding food addiction, YouTube users again showed lower YFAS scores, whereas TikTok users had higher YFAS scores compared to non-users. Additionally, users of Facebook, Snapchat, TikTok, had elevated YFAS scores relative to non-users, whereas Twitter users showed lower YFAS scores. This finding suggests that the type of social media platform individuals engage with may be associated with differences in BMI, depression and food addiction potentially due to variations in content exposure, usage patterns, and lifestyle behaviors influenced by these platforms.\u003c/p\u003e\u003cp\u003eA systematic review found that the diagnosis of FA was higher in women[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Parallel to this study, the risk of FA was higher in women than in men, although not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although the difference was not statistically significant, the higher risk of food addiction observed in women compared to men may suggest underlying gender-related factors, such as emotional eating tendencies, hormonal influences, or societal pressures, that could contribute to variations in eating behaviors.\u003c/p\u003e\u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the odds ratio for the presence of food addiction in Pinterest users is 19% lower than in non-users. Pinterest is a social media platform for finding ideas like recipes, home and style inspiration, and more[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It has been noted that Pinterest can be a useful tool for disseminating information about dietary behaviors[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lower odds of food addiction among Pinterest users compared to non-users may indicate that engagement with this platform is associated with healthier eating behaviors or more resistance to addictive eating patterns, potentially due to exposure to wellness and lifestyle content.\u003c/p\u003e\u003cp\u003eDepression is a widely prevalent mental health condition affecting individuals globally[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although Al-Qaisy et al.,[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] insisted that male students were more likely to experience depressive disorders than female students, most studies have confirmed that depression is more common in women than in men[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Roldan-Esp\u0026iacute;nola et al.,[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] also showed that the female gender was significantly higher risk of developing major depressive disorder. Similarly, this study found that women had a statistically significant 21% higher odds of experiencing depression compared to men (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results suggest that women are at a significantly greater risk of experiencing depression than men, which may be influenced by a combination of hormonal fluctuations, psychosocial stressors, and gender-related differences in coping mechanisms and help-seeking behaviors.\u003c/p\u003e\u003cp\u003eThe university experience can heighten psychological vulnerability due to factors such as academic demands, the transition to adulthood, and unstable relationships, all of which may negatively impact students\u0026rsquo; mental health. Given that 62.5\u0026ndash;75% of all mental disorders emerge by the age of 24, this period represents a critical window for mental health challenges[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, findings from this study suggest that advancing age is associated with a lower risk of depression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that as individuals grow older, they may develop greater resilience or coping mechanisms.\u003c/p\u003e\u003cp\u003eAnother factor in the etiology of depression, after age, is that being overweight and obesity are positively correlated with an increased risk of depression and depression-related symptoms[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The findings in this study indicate that certain lifestyle and behavioral factors are significantly associated with depression risk. Individuals with a BMI above 25 kg/m\u0026sup2; were found to have a 25% higher likelihood of experiencing depression compared to those with a BMI of 25 kg/m\u0026sup2; or lower (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a potential link between excess weight and mental health. Additionally, engaging in eating behaviors, such as snacking while using social media despite feeling full, was associated with an 80.6% increased risk of depression, highlighting the possible negative psychological effects of such habits. The increased snack consumption associated with social media use may paradoxically contribute to both elevated body weight and a heightened risk of depression.\u003c/p\u003e\u003cp\u003eInterestingly, the study also found that YouTube users had a 15.5% lower risk of depression compared to non-users, which may indicate that certain types of online content consumption provide stress relief or social engagement that positively influences mental well-being. The different results found on different social media platforms suggest that the content may have different levels of impact in terms of the time spent and eating behaviors on individuals.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates a statistically significant upward trend in the median BDI scores and BMI value as the severity of FA increases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that individuals with more severe food addiction symptoms tend to exhibit higher levels of depressive symptoms and increased BMI. Supporting these results, Şanlıer et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] also identified a positive association between food addiction, depression scores, and BMI. This reinforces the notion that disordered eating patterns linked to food addiction may contribute to both psychological distress and excessive weight gain. The connection between these variables may be explained by shared neurobiological mechanisms involving dopamine dysregulation, stress-related eating, and emotional dysregulation, all of which have been implicated in both mood disorders and compulsive eating behaviors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, Bartschi and Greenwood [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] emphasized food addiction symptoms as a significant behavioral risk factor for increased adiposity, particularly in individuals with greater depressive symptom severity. This relationship appears to be especially pronounced among those who experience heightened appetite during depressive episodes.\u003c/p\u003e\u003cp\u003eAddressing the underlying emotional and behavioral factors contributing to food addiction could be crucial for mitigating the associated risks of obesity and depression. Future research should further explore the causal mechanisms underlying these associations and assess the effectiveness of multidisciplinary approaches in treatment and prevention.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides valuable insights into the complex relationship between social media use, food addiction, depression, and BMI among university students. The results indicate that as food addiction severity increases, there is a corresponding rise in depression scores and BMI, reinforcing previous findings on the interplay between compulsive eating behaviors and mental health. Additionally, social media platform engagement appears to influence these associations, with specific platforms such as TikTok and Facebook being linked to higher BMI and food addiction scores, while YouTube users exhibited lower depression and food addiction levels. These variations suggest that the type of content consumed and user engagement patterns may contribute to differences in dietary behaviors and mental health outcomes.\u003c/p\u003e\u003cp\u003eGender differences were also observed, with women demonstrating a higher risk of both food addiction and depression, aligning with previous literature suggesting the influence of biological, psychological, and social factors. Additionally, being overweight or obese was significantly associated with a greater likelihood of experiencing depression, highlighting the need for interventions that address both physical and mental well-being.\u003c/p\u003e\u003cp\u003eThe findings emphasize the importance of a multidisciplinary approach to tackling food addiction and its associated health risks. Future research should explore the causal pathways linking type of social media and exposure time, eating behaviors, and mental health to develop targeted prevention and intervention strategies. Given the critical period of university life for mental health development, promoting healthy eating habits and responsible social media use could play a vital role in mitigating the risks of food addiction, obesity, and depression in young adults.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStrenghts and Limitation\u003c/h2\u003e\u003cp\u003eThis is the first known study to investigate the combined impact of social media use and food addiction (FA) on both depression and BMI among university students. This novel approach contributes important insights to the existing literature. It distinguishes between different social media platforms (e.g., TikTok, YouTube, Facebook, Pinterest), identifying platform-specific relationships with depression, food addiction, and BMI. This level of detail allows for more nuanced interpretations and targeted future interventions.\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s cross-sectional nature limits the ability to draw causal inferences. It cannot determine whether social media use or food addiction causes depression or increased BMI, or vice versa. Data on social media use, depression symptoms, and food addiction were self-reported, which may introduce recall bias. The sample is limited to university students, which may not represent the general population. Age-related resilience and mental health patterns may differ in non-student populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e Written informed consent was obtained from all individuals participating in the study. The study was conducted according to the criteria set by the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Karamanoğlu Mehmetbey University (Approval no: E-75732670-050.04-181367).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMA served in a supporting role for conceptualisation; methodology; investigation; supervision; writing and original draft. S\u0026Ouml; served in a supporting role for conceptualisation; investigation; supervision; writing and original draft. ŞGY served in a supporting role for conceptualisation; writing and original draft. HY served in a supporting role for data curation; formal analysis; supervision and writing and original draft. All authors have read and approved the final version submitted and take public responsibility for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the participants for their contribution to the study\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe corresponding author may provide you with the data that back up the study\u0026rsquo;s conclusions upon request. The data are not accessible to the general public due to privacy or ethical concerns.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed O, Siddiqua SJN, Alam N, Griffiths MD. The mediating role of problematic social media use in the relationship between social avoidance/distress and self-esteem. 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Minerva Endocrinol. 2013;38(3):255\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBartschi JG, Greenwood LM. Food addiction as a mediator between depressive symptom severity and body mass index. Appetite. 2023;190:107008. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.appet.2023.107008\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2023.107008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Body mass index, Depression, Food addiction, Social media use","lastPublishedDoi":"10.21203/rs.3.rs-7346078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7346078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSocial media use and food addiction have each been linked to depression and increased body mass index (BMI). University students, among the most active social media users, may be especially susceptible to these risks. Excessive social media use can contribute to depression through social comparison and isolation, while food addiction is associated with disordered eating, obesity, and depressive symptoms. Despite evidence for these individual relationships, few studies have examined their combined effects. This study aims to investigate the potential effects of social media use and food addiction on depression and body mass index (BMI) among university students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study, conducted in Elazığ, Turkey (Feb\u0026ndash;Jul 2024), included 4,765 healthy university students. Data were collected via face-to-face surveys, assessing sociodemographics, social media use, the Modified Yale Food Addiction Scale version 2.0 (mYFAS 2.0), and the Beck Depression Inventory (BDI). BMI was calculated from measured anthropometrics. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFemale students had higher depression scores, while individuals with severe food addiction had the highest BDI and BMI scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant associations were found between social media use, food addiction, depression, and BMI. Higher depression and food addiction scores were observed among users of certain platforms, particularly TikTok. Increased food consumption and snacking while using social media were significantly associated with greater odds of depression. A linear trend showed that higher food addiction levels were moderately correlated with increased depression and BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study reveals a significant relationship between food addiction, social media use, depression, and BMI among university students. Higher food addiction levels were associated with increased depression and BMI, while specific social media platforms, such as TikTok and Facebook, were linked to adverse outcomes. In contrast, YouTube use appeared to have a protective effect. Gender and BMI also influenced depression risk. These findings highlight the need for integrated interventions promoting healthy eating and responsible social media use during the critical university years.\u003c/p\u003e","manuscriptTitle":"Social Media Use and Food Addiction on Depression and Body Mass Index in University Students: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 13:16:52","doi":"10.21203/rs.3.rs-7346078/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcf5d216-fc41-4180-8bac-39118e709a47","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T09:23:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 13:16:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7346078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7346078","identity":"rs-7346078","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}