Fast Food, Slower Control? Exploring the Relationship Between Fast Food Consumption and Self-Regulatory Abilities in Early Adolescents

Fast Food, Slower Control? 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Exploring the Relationship Between Fast Food Consumption and Self-Regulatory Abilities in Early Adolescents Meagan Ashley Belflower This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8197318/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 Fast food consumption has been linked to poorer health indicators and behavioral dysregulation in adolescents, yet little research has examined its association with self-regulation. Regulation of emotions, cognitions, and behaviors is critical for the transition into adolescence. Objective This study explored whether frequency of fast food consumption was associated with the three domains of self-regulation in early adolescents while controlling for sociodemographic factors. Methods Participants were 2,164 early adolescents ( M = 12.53 years) drawn from a large, school-based dataset. Adolescents reported their weekly frequency of fast food consumption and completed a 13-item self-regulation measure with domain specific subscales. Covariates included age, grade, gender, subjective socioeconomic status, and parental monitoring. Multiple regression was utilized to test whether fast food consumption was uniquely associated with self-regulation after adjusting for covariates. Results Greater fast food consumption was associated with lower overall self-regulation scores (β = –.05, p = .01). When subscales were examined, fast food consumption remained significantly associated with poorer emotional self-regulation (β = –.06, p = .003) but not cognitive self-regulation (β = –.03, p = .12). Conclusions Fast food consumption showed modest negative associations with emotional and behavioral self-regulation in early adolescence, even after adjusting for demographic factors. These findings cautiously emphasize the importance of considering everyday health behaviors when examining self-regulatory abilities in early adolescence. Further research is necessary to replicate findings and to determine whether practical implications exist. early adolescence self-regulation dietary habits fast food emotional regulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Adolescence is a period of rapid physical, cognitive, psychological, and social transformation. As adolescence progresses, teenagers gradually separate from their childhood microcosm and enter a production and reproduction-based society (Lerner & Steinberg, 2004, p. 85). During transitional periods, an individual relies on existing coping skills to navigate novel challenges, or they must cultivate new self-regulatory strategies to thrive if they lack the necessary skills. The ability to utilize these skills significantly predicts the ontogenetic trajectory of an individual (Schulz & Heckhausen, 1999; Skinner, 1999). Self-regulation capabilities measured in young adolescents can predict later positive and negative developmental outcomes (Gestsdóttir & Lerner, 2007). Self-regulation, defined by Barkley (2004) as "a deliberate attempt to modulate, modify, or inhibit actions or reactions toward a more adaptive end," is a critical neuropsychological skill and an element of executive function (Anderson, 2002). The development of executive function skills such as impulse/inhibitory control, emotional control, behavioral control, and attentional control begins in early childhood, but these skills undergo a vigorous maturational process during adolescence (Anderson, 2002; Diamond, 2013). For typically developing individuals in optimal environments, the preference for attaining long-term goals over pursuing short-term hedonistic desires increases with age. Self-regulation requires explicit cognitive processing and is crucial for planning and goal setting. This distinct style of processing enables individuals to envision hypothetical futures when calculating the risks versus rewards of their behaviors (Barkley, 2001). Adolescents with high self-regulatory abilities perform better in school and are better able to resist negative peer pressure (King et al., 2018; Tetering et al., 2018). Consequently, poor regulatory control predicts and results in numerous negative and maladaptive outcomes such as: Depression (d’Acremont & Van der Linden, 2007), committing crime (Vohs & Baumeister, 2004), nicotine addiction (Baumeister, 2017), substance abuse (Maranges & Baumeister, 2016), gambling (Blaszczynski et al., 1997), risky sexual behavior (Gailliot & Baumeister, 2007), overspending (Gailliot et al., 2008), and poor academic performance (Vohs & Baumeister, 2016). Extensive research has revealed that the brain undergoes protracted yet significant morphological and physiological changes throughout adolescence. These transformations continue to mature until the brain reaches full development around 24–25 years of age. (Giedd et al., 1999; Huttenlocher & Dabholkar, 1997; Perrin et al., 2008; Wahlstrom et al., 2010). Regressive and progressive alterations, such as the thinning of grey matter and an increase in myelination, facilitate greater activity in the frontoparietal and frontopolar regions of the brain. These cortical regions are dedicated to cognitive, behavioral, and impulsive control (Corrigan et al., 2021; Marek & Dosenbach, 2018; O’Donnell et al., 2005; Tamnes et al., 2017). The brain also undergoes dopaminergic transformations in early adolescence at the neurobiological and neurochemical levels. Dopamine-signaling axons in the striatal regions that project to the medial prefrontal cortex increase in density and augment transmission efficiency (Naneix et al., 2012). Dopamine receptors are abundant during this developmental stage. However, the mesocortical pathway, which aids self-regulation and decision-making, is underdeveloped. For these reasons, young adolescents are prone to pursue pleasure, sensation-seeking, and reward instead of planning or inhibiting actions (Naneix et al., 2012; Reynolds & Flores, 2021). A young teenager’s brain is highly plastic but is also undergoing critical maturational processes. This makes the young adolescent brain highly vulnerable to a myriad of environmental influences that might hinder proper self-regulation development– including their diet (Lowe et al., 2020; Reynolds & Flores, 2021). America and other Western societies can provide consumers with instant or on-demand goods and services. American consumers consider fast food to be more convenient than incorporating whole foods into their diet (Glanz et al., 1998). Convenience-based obesogenic food options in Western cultures and the Western diet overall lack nutritional value but are calorically dense. This diet is characterized as being high in fat and sugar (Cordain et al., 2005). In a laboratory study, young adolescent mice were fed a diet high in fat and sugar throughout this developmental period. Male rats fed this diet during their young adolescent period experienced permanent hippocampal-related memory impairments into adulthood. When fed a healthy diet in late adolescence, female rats were able to recover from those deficits, but male rats were not (Hayes et al., 2024). The hippocampus, along with the medial prefrontal cortex, plays a role in emotional regulation by assigning context to episodic and biographical memories (Jin & Maren, 2015). Adolescents with reduced hippocampal and amygdala volume exhibit heightened reactivity to stressors, which can lead to psychopathology (Weissman et al., 2020). In an MRI study, children aged 5–9 who frequently consumed a high-fat, high-sugar Western diet, as reported by parents using the Youth/Adolescent Food Frequency Questionnaire, showed reduced hippocampal volume in the left hemisphere (Stadterman, 2020). The left hemisphere of the hippocampus is particularly involved in forming new episodic memories by associating environmental cues with context (Miller et al., 2018). In a complementary resting-state fMRI study, Stadterman (2019) found that children who self-reported frequent consumption of Western diet items also exhibited reduced intrinsic functional connectivity between the left and right hippocampi. Dietary sugar was identified as the probable culprit for the disruption, and they posited that chronic rather than acute sugar intake may heighten behavioral issues such as hyperactivity or ADHD symptoms (Stadterman, 2019). The researchers cited Bast & Feldon (2003), who discovered that the right hemisphere of the hippocampus has a lesser-known role in regulating sensory motor input. Similarly, Reichelt & Rank (2017) found that high fat and high sugar intake can overstimulate the mesocorticolimbic dopamine system and alter the signaling of dopamine and γ-aminobutyric acid (GABA) in adolescents, which can contribute to impulsive behavior. Additionally, in a study examining mental health and dietary choices, young adolescents whose diets mainly consisted of junk food showed greater externalizing issues and were more aggressive than their peers who consumed more fruits and leafy green vegetables (Oddy et al., 2009). Junk food was also discovered to disrupt prefrontal cortex maturation and alter limbic-prefrontal interactions, which can affect emotional volatility (Reichelt & Rank, 2017). However, many of these studies used broad age ranges in their samples (e.g., 5–17 years), limiting developmental specificity. This approach may obscure age-specific effects, particularly during early adolescence when the brain is undergoing substantial restructuring. Moreover, American fast food is a specific form of the Western diet that can be calorically dense in fat and sugar. Yet, these previous studies did not reveal the frequency of fast-food consumption. Due to its high accessibility and potential to deliver a more potent "dose" of Western dietary components, isolating fast food as a discrete variable may offer new insights into eating patterns and regulatory abilities. Also, self-regulation is distinct from internalizing and externalizing symptoms, as it is a transdiagnostic process that underlies a wide range of psychological outcomes and is critical for adaptive functioning. As mentioned above in the preceding paragraphs, each of these listed regions and their circuitry undergo major maturation during adolescence. Because of the lack of studies using fast food consumption as a culturally relevant solo construct and the growing scientific understanding that self-regulatory processes undergo rapid maturation in young adolescents, it is essential to begin an exploration of this understudied relationship. The Current Study The present study explores whether varying levels of fast food consumption are associated with differences in early adolescent self-regulation using a large, regionally representative sample in the southeast. Given prior evidence that consumption of a Western diet has been linked to cognitive functioning, externalizing behaviors (emotional dysregulation), and hyperactivity (behavioral impulsivity). The current study explores how fast food frequency is related to the distinguished subdomains of self-regulation. Emotional, cognitive, and behavioral self-regulation is important to study, because each of these facets reflect distinct neurocognitive processes: cognitive self-regulation which relies on prefrontal cortex function, emotional self-regulation which involves limbic-prefrontal interactions, and behavioral self-regulation which depends on frontoparietal networks (Diamond, 2013 ). Few studies, if any, have specifically assessed the relationship between fast food consumption frequency and distinct facets of self-regulation in early adolescence. To the researcher's knowledge, this is the first study to explore how fast food intake may relate to these three domains of self-regulation within a single framework. Additionally, most adolescent self-regulation studies have focused on inherent demographic factors such as gender, age, grade level, and socioeconomic status, or non-dietary environmental influences like parenting style and parental monitoring (Barry et al., 2022 ; Purdie et al., 2004 ; Tetering et al., 2020 ; Theurel & Gentaz, 2018 ). In contrast, fast food consumption, an easily accessible, potent component of the Western diet, remains an underexplored dietary correlate of cognitive, emotional, and behavioral self-regulation in early adolescence. The study will also explore whether certain socioeconomic groups consume fast food more frequently. In past research, lower socioeconomic statuses were associated with greater fast food consumption due to its low cost, high-energy density, and easy accessibility (Drewnowski & Specter, 2004 ). The current study does not examine any mediating effects between SES, fast food, and self-regulation. Instead, it represents a first launch into the investigation of possible disparities in self-regulatory abilities by examining if any differential patterns of fast-food consumption exist between SES groups in a large sample. Since early adolescence is a critical developmental window for self-regulatory abilities, understanding even modest associations between eating behaviors and self-regulation may inform future longitudinal or intervention studies. This study is an exploratory first step, so findings should be interpreted with appropriate caution. The cross-sectional design does not allow for causal inference, nor does it explain the direction of the relationship. However, this study offers insight into a critical developmental window in early adolescence. Since fast food is connected to highly marketed and recognizable food chains, adolescents may be more reliable in recalling the frequency of its consumption compared to listing the individual frequencies of singular food items like glasses of milk, as seen in the Youth /Adolescent Food Questionnaire (Rockett et al., 1997 ) that has been utilized in past research. However, the limited precision of the single fast food measure in this particular study constrains dose–response interpretations and reflects one component of the Western diet. Nonetheless, this work represents the initial phase in investigating the ways fast food consumption may be associated with early adolescent self-regulatory functioning. Due to the exploratory nature of this work, no directional hypotheses were formulated. Method Participants Participants were drawn from children enrolled in grades 5–9 within the North Carolina public school system during the 2014–2015 school year ( N = 2104). The sample consisted of 1096 females (52.1%) and 1008 males (47.9%). Students were aged between 10 and 14 years ( M = 12.43, SD = 1.08). The majority of the sample identified as White (65.3%). This was followed by Black (28.5%) and Hispanic (14.5%). The students self-reported their socioeconomic status. Based on their subjective responses, most students lived in upper-middle-class (45.9%) and middle-class (28.4%) families. Students also reported living in an upper-class family (21.6%) and a lower-class family (2%). These participants represent time wave one of a longitudinal study. Procedure Data for this analysis was derived from a publicly available dataset, specifically administrative data from the North Carolina Department of Public Instruction in collaboration with the Research on Adaptive Interests, Skills, and Environments (RAISE) study conducted by Duke University (Hoyle, 2024). Potential participants were contacted by phone. A large majority of parents (97%) provided informed consent for their child's participation and the linkage of survey responses with NCDPI records. Adolescent participants provided verbal assent to complete the Baseline Adolescent Survey and agreed to future contact and future surveys. Both parental consent and adolescent assent procedures were approved by the institutional review board and adhered to ethical standards for research with minors. The Baseline Adolescent Survey was administered via telephone between April and August of 2015. Although the complete survey lasted approximately 90 minutes, participants did not complete it in a single sitting to reduce the chances of response fatigue. Parents were also instructed to ensure that their child completed the survey in private. The survey included questions related to demographics, home environment, self-regulatory abilities, physical and mental health, and problem behaviors. Participants received $ 30 upon completion of the survey. Measures Subjective Socioeconomic Status Subjective socioeconomic status (SES) was assessed with a single-item question: In your opinion, how is your family doing financially? Participants selected from four responses: (1) We do not have enough money to meet our basic needs (low SES), (2) Money is tight, but we are able to meet our basic needs (lower-middle SES), (3) We have all the money we need to live a comfortable life (middle SES), and (4) We have enough money to do most anything we want (upper-middle to high SES). Single-item subjective SES measures are commonly used in adolescent health research to capture perceived family financial status. Davisson et al. ( 2025 ) found that an adolescent’s answer on a subjective SES measure moderately correlated with their parent’s reported SES. SES was included as a control variable in regression analyses. Parental Monitoring Parental monitoring was operationalized using the 5-item parental monitoring scale developed by Fletcher et al. ( 2004 ). Participants reported how much their parents attempted to gain information about their whereabouts, how they spent their time, their friendships, how they spent money, and what they were studying in school. Items such as “How much do your parents try to know how you spend your free time?” were rated on a 3-point scale (0 = They don’t try , 1 = They try a little , 2 = They try a lot ). This scale has demonstrated acceptable internal consistency in prior research, with Cronbach’s alpha of α = .72 (Fletcher et al., 2004 ). Total scores were created by averaging the five items, with higher scores reflecting greater parental monitoring. This variable was included as a control in regression analyses. Fast Food Consumption Fast food consumption was assessed by asking participants how many days they had eaten fast food in the past week, with response options ranging from 0 to 7. Rather than using this as a continuous variable, responses were grouped into four consumption categories: 0 days ( rarely eats fast food ), 1–2 days ( occasionally ), 3–4 days ( moderately ), and 5 or more days ( frequently ). This decision was made to address the non-normal distribution of responses across days and the small number of participants reporting fast food consumption for all 7 days. Categorization improved interpretability and reduced the risk of influential outliers skewing the regression results. While categorizing continuous data can reduce statistical power, this approach was deemed appropriate for this exploratory analysis and the distributional characteristics of the sample. The resulting categorical variable was used as the independent variable in regression analyses. Self-Regulation Self-regulation was assessed using a 13-item survey with three subscales: emotional, cognitive, and behavioral self-regulation. Participants responded on a scale from 1 ( Not at all like me ) to 5 ( Very much like me ). The emotional regulation subscale contained five statements, such as I have a hard time controlling my temper and I slam doors when I am mad . The cognitive regulation subscale contained five statements, such as Once I have a goal, I make a plan to reach it , and I get distracted by little things . The behavioral subscale contained three statements, such as I get fidgety after a few minutes if I am supposed to sit still and I have a hard time sitting still during important tasks . This survey is a condensed version of Novak and Clayton's (2001) self-regulation measure, which includes the same three subscales. The original scale demonstrated strong internal consistency: Cronbach's α = .95 for emotional regulation, α = .96 for cognitive regulation, and α = .94 for behavioral regulation. Cronbach's alpha was computed for the condensed version of the measure within the current sample. Internal consistency was acceptable for the total scale α = .80, for the emotional subscale α = .79, and the behavioral subscale α = .74, The cognitive subscale demonstrated lower internal consistency than the emotional and behavioral subscales with a reliability score of α = .64. Prior research supports the validity of self-reports in this age group. Fine et al. ( 2016 ) found that self-reported self-control in 15-year-olds significantly predicted delinquency, aligning with real-world outcomes. The use of a single measure was necessitated by the secondary nature of the RAISE data (Hoyle, 2024). Covariates Participants provided demographic information during a 90-minute phone interview. The key control variables included gender, age, grade level, SES, and parental monitoring, as these factors have previously been associated with adolescent self-regulation (Barry et al., 2022 ; Purdie et al., 2004 ; Tetering et al., 2020 ; Theurel & Gentaz, 2018 ). Gender was coded as 0 = male and 1 = female, whereas age and grade level were treated as continuous variables. SES was measured using a self-reported financial status scale, in which participants rated their family's financial situation. Parental monitoring was assessed using the abovementioned measure, which evaluated the extent to which parents were aware of their child’s daily activities, with higher scores indicating greater parental involvement. These covariates were included to account for potential individual and contextual differences in self-regulation, ensuring that any observed associations between fast food consumption and self-regulatory abilities were not driven by these factors. Data Analytic plan Standard multiple regression analyses were conducted to examine the associations between fast food consumption and self-regulation. Due to the exploratory nature of this study and lack of directional hypotheses, a standard multiple regression was deemed the most appropriate statistical measure. The dependent variables included overall self-regulation scores as well as subscale scores measuring emotional, cognitive, and behavioral self-regulation. The key independent variable was fast food consumption frequency, which was dummy coded into three groups: Occasional (1–2 days per week), Moderate (3–4 days per week), and Frequent (5 + days per week), with Rarely (0 days per week) serving as the reference group. Age, gender, SES, grade level, and parental monitoring were included as covariates to control for potential confounding factors. All predictors were entered simultaneously into the regression model to assess their distinct contributions to overall self-regulation outcome scores and subscale outcome scores. Standardized beta coefficients (β), significance values (p), and R² values were reported to evaluate effect sizes and model fit. A Kruskal-Wallis test was conducted to examine differences in fast food consumption across socioeconomic status (SES) groups. This nonparametric test was chosen because the SES variable was ordinal and unequally distributed, with the low SES group comprising only 2% of the sample. The data did not meet the normality requirement for conducting a one-way ANOVA. The independent variable was self-reported SES, categorized into four levels: lower, middle, upper-middle, and upper classes. The dependent variable was fast food consumption frequency, measured as the total number of days participants reported eating fast food in the prior week. To further explore group differences, a series of Mann-Whitney U tests were conducted as post hoc analyses, comparing fast food consumption between SES groups. Mean rank values were examined to assess patterns of fast food intake across socioeconomic levels. Statistical significance was set at p < .05 for all analyses. Results Descriptive Statistics Descriptive statistics for overall self-regulation and its subscales (emotional, cognitive, behavioral) across fast food consumption groups are presented in Table 1 . Participants who reported frequent fast food consumption (5 + days per week) exhibited the lowest self-regulation scores overall ( M = 3.24, SD = 0.72), whereas those who rarely consumed fast food (0 days per week) demonstrated the highest overall self-regulation scores ( M = 3.68, SD = 0.71). Table 1 Overall Emotional Cognitive Behavioral Group M SD M SD M SD M SD Rarely 3.68 0.72 3.89 0.95 3.67 0.81 3.37 1.19 Occasionally 3.59 0.72 3.75 0.99 3.64 0.78 3.26 1.20 Moderately 3.53 0.71 3.64 1.00 3.60 0.67 3.21 1.22 Frequently 3.24 0.73 3.19 1.17 3.53 0.77 2.84 1.17 Descriptive characteristics of self-regulation scores per fast food consumption group Note M and SD represent mean and standard deviation, respectively Preliminary Analyses Because several variables, including fast food frequency across 7 days and socioeconomic status, violated the assumptions of normality, Spearman’s rank-order correlations were computed to assess the strength and direction of associations among variables used in the regression analyses. Fast food consumption was negatively correlated with overall self-regulation (ρ = –.105, p < .001), emotional regulation (ρ = –.120, p < .001), behavioral regulation (ρ = –.073, p < .001), and cognitive regulation (ρ = –.055, p = .043). Fast food consumption was also negatively correlated with age (ρ = –.083, p < .001), with younger teens reporting more fast food intake, and positively correlated with gender (ρ = .043, p = .043), with males reporting higher fast food consumption. Fast food consumption was not significantly correlated with grade level or subjective SES. All self-regulation subscales were significantly positively correlated with one another and with the total self-regulation score (ρ = .443 to .756, p < .001). Age was positively correlated with cognitive regulation (ρ = .065, p = .007) and behavioral regulation (ρ = .060, p = .012) and negatively correlated with fast food consumption and gender. Subjective SES was positively associated with cognitive regulation (ρ = .053, p = .035), but not with fast food consumption. All bivariate correlations are represented in the correlation matrix shown in Table 2 . Table 2 Spearman Correlations Among Study Variables (N = 2,084–2,104) Variable 1 2 3 4 5 6 7 8 9 10 1. Parental monitoring — 2. Subjective SES .08** — 3. Total self-regulation .16** .14** — 4. Emotional regulation .12** .13** .81** — 5. Cognitive regulation .19** .08** 68** .33** — 6. Behavioral regulation .09** .10** .74** .43** .31** — 7. Fast food consumption .01 − .03 − .11** − .12** –.04* − .07** — 8. Child Grade level − .05* 0.03 .05* 0.03 .05* 0.02 0.02 — 9. Child age − .06** 0.02 .04* 0.02 0.03 -0.02 .05* .83** — 10. Child Gender 0.02 0.01 − .05* − .04* -0.02 0.03 .07** 0 0 — Note . *p < .05. * p < .01 Collinearity Diagnostics To assess multicollinearity among the predictors, the variance inflation factor (VIF) and tolerance statistics were examined. All VIF values ranged from 1.01 to 1.09, and tolerance values ranged from .92 to .99, indicating that multicollinearity was not a concern (VIF .20; Cohen, 1988 ). Overall Self-Regulation A standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and overall self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, F (8, 2025) = 13.191, p < .001, accounting for 5.0% of the variance in overall self-regulation ( R² = .050, Adjusted R² = .046). Among the variables, frequent fast food consumption (5 + days per week) had the strongest negative association with self-regulation ( β = -0.110, p < .001), followed by moderate consumption (3–4 days per week; β = -0.068, p = .008) and occasional consumption (1–2 days per week; β = -0.054, p = .037). When examining covariates, parental monitoring ( β = 0.142, p < .001) and SES ( β = 0.118, p < .001) were both positively associated with self-regulation, indicating that greater parental involvement and higher perceived financial well-being were linked to higher self-regulation scores. Age, gender, and grade level were not significantly associated with self-regulation ( p > .05). A complete summary of regression coefficients is presented in Table 3 , and the group means are presented visually in Fig. 1. Table 3 Multiple Regression Associations Between Fast Food Consumption and Overall Self-Regulation Scores 95% CI Variables B SE β t p Lower Upper Intercept 3.048 0.223 - - < .001 - - Child's Age -0.019 0.025 -0.029 -0.765 .444 -0.067 0.029 SES 0.118 0.021 0.118 5.408 < .001 0.077 0.159 Gender -0.005 0.032 -0.003 -0.153 .878 -0.068 0.058 Grade 0.016 0.027 0.023 0.599 .549 -0.036 0.068 Parental Monitoring 0.142 0.049 0.142 6.506 < .001 0.085 0.199 Fast Food: Occasionally -0.081 0.039 -0.054 -2.082 .037 -0.158 -0.003 Fast Food: Moderately -0.147 0.055 -0.068 -2.672 .008 -0.247 -0.047 Fast Food: Frequently -0.434 0.091 -0.110 -4.785 < .001 -0.612 -0.256 Note . *B* = Unstandardized coefficient; SE = Standard error; *β* = Standardized coefficient; CI = Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group. /table> Overall self-regulation scores by fast food consumption group Note This displays the average score on the self-regulation survey overall by fast food consumption group Emotional Self-Regulation A standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and emotional self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, F (8, 2025) = 11.467, p < .001, accounting for 4.3% of the variance in emotional self-regulation ( R² = .043, Adjusted R² = .040). The full regression results are presented in Table 4 . Among the predictors, frequent fast food consumption (5 + days per week) had the strongest negative association with emotional self-regulation ( β = -0.123, p < .001), followed by moderate fast food consumption (3–4 days per week; β = -0.081, p = .002) and occasional consumption (1–2 days per week; β = -0.062, p = .018). In contrast, parental monitoring ( β = 0.096, p < .001) and SES ( β = 0.116, p < .001) were positively associated with emotional self-regulation, indicating that greater parental involvement and higher perceived financial well-being were related to higher emotional self-regulation scores. Age, gender, and grade level were not significantly associated with emotional self-regulation ( p > .05). Figure 2 provides a visual representation of the mean emotional self-regulation scores across fast food consumption groups. Table 4 Multiple Regression Associations Between Fast Food Consumption and Emotional Self-Regulation Subscale Scores 95% CI Variables B SE β t p Lower Upper Intercept 2.993 0.307 - 9.740 < .001 - - Child’s Age -0.018 0.035 -0.02 -0.523 0.601 -0.087 0.051 SES 0.152 0.029 0.116 5.284 < .001 0.095 0.209 Gender -0.071 0.044 -0.035 -1.618 0.106 -0.158 0.016 Grade 0.052 0.037 0.052 1.386 0.166 -0.02 0.124 Parental Monitoring 0.300 0.068 0.096 4.397 < .001 0.166 0.434 Fast Food: Occasionally -0.127 0.053 -0.062 -2.372 0.018 -0.231 -0.023 Fast Food: Moderately -0.241 0.076 -0.081 -3.168 0.002 -0.39 -0.092 Fast Food: Frequently -0.671 0.125 -0.123 -5.353 0.001 -0.918 -0.424 Note . *B* = Unstandardized coefficient; SE = Standard error; *β* = Standardized coefficient; CI = Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group. Note This displays the average score on the emotional subscale of the self-regulation survey by fast food consumption group Note This displays the average score on the emotional self-regulation survey by fast food consumption group Cognitive Self-Regulation A standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and cognitive self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, F (8, 2025) = 8.343, p < .001, accounting for 3.2% of the variance in cognitive self-regulation ( R² = .032, Adjusted R² = .028). Among the variables, parental monitoring had the strongest positive association with cognitive self-regulation ( β = 0.159, p < .001). SES was also positively associated with cognitive self-regulation ( β = 0.054, p = .015). However, none of the fast food consumption groups were significantly associated with cognitive self-regulation ( p > .05). Age, gender, and grade level were also not significantly associated with cognitive self-regulation ( p > .05). A full summary of regression coefficients is presented in Table 5 . Group means are displayed visually in Fig. 3 . Table 5 Multiple Regression Associations Between Fast Food Consumption and Cognitive Self-Regulation Subscale Scores 95% CI Variables B SE β t p Lower Upper Intercept 3.004 0.238 - - < .001 2.537 3.471 Child’s Age -0.015 0.027 -0.021 -0.55 0.582 -0.068 0.038 SES 0.054 0.022 0.054 2.439 0.015 0.01 0.098 Gender 0.038 0.034 0.024 1.104 0.270 -0.029 0.105 Grade 0.011 0.029 0.015 0.396 0.692 -0.046 0.068 Parental Monitoring 0.382 0.053 0.159 7.242 < .001 0.278 0.486 Fast Food Occasionally -0.03 0.041 -0.019 -0.719 0.472 -0.11 0.05 Fast Food Moderately -0.064 0.059 -0.028 -1.084 0.279 -0.179 0.051 Fast Food Frequently -0.135 0.097 -0.032 -1.395 0.163 -0.325 0.055 Note . B = Unstandardized coefficient; SE = Standard error; β = Standardized coefficient; CI = Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group. Note This displays the average score on the cognitive self-regulation survey by fast food consumption group Behavioral Self-regulation A standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and behavioral self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, F (8, 2025) = 6.081, p < .001, accounting for 2.3% of the variance in behavioral self-regulation ( R² = .023, Adjusted R² = .020). Among the fast food groups, frequent fast food consumption (5 + days per week) had a significant negative association with behavioral self-regulation ( β = -0.082, p < .001), while occasional ( β = -0.036, p = .168) and moderate consumption ( β = -0.037, p = .156) were not significantly associated. Among the covariates, SES ( β = 0.092, p < .001) and parental monitoring ( β = 0.067, p = .002) were both positively associated with behavioral self-regulation, indicating that greater parental involvement and higher perceived financial well-being were linked to better behavioral self-regulation scores. Age, gender, and grade level were not significantly associated with behavioral self-regulation ( p > .05). A full summary of regression coefficients is presented in Table 6 , and the group means are presented visually in Fig. 4 . Table 6 Multiple Regression Associations Between Fast Food Consumption and Behavioral Self-Regulation Subscale Scores 95% CI Variables B SE β t p Lower Upper Intercept 3.222 0.373 - 8.636 < .001 2.49 3.954 Child’s Age -0.029 0.043 -0.026 -0.681 .496 -0.113 0.055 SES 0.145 0.035 0.092 4.16 < .001 0.077 0.213 Gender 0.034 0.053 0.014 0.639 .523 -0.07 0.138 Grade -0.035 0.045 -0.03 -0.778 .436 -0.123 0.053 Parental Monitoring 0.252 0.083 0.067 3.046 .002 0.089 0.415 Fast Food: Occasionally -0.089 0.065 -0.036 -1.378 .168 -0.216 0.038 Fast Food: Moderately -0.131 0.092 -0.037 -1.419 .156 -0.311 0.049 Fast Food: Frequently -0.538 0.152 -0.082 -3.537 < .001 -0.837 -0.239 Note . B = Unstandardized coefficient; SE = Standard error; β = Standardized coefficient; CI = Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group. Figure 4 Behavioral Self-Regulation Scores by Fast Food Consumption Group Note This displays the average score on the behavioral self-regulation survey by fast food consumption group Socioeconomic Status and Fast Food Consumption A Kruskal-Wallis H test was conducted to examine differences in fast food consumption across socioeconomic status (SES) groups. The test was significant, H (3) = 16.571, p < .001, indicating that fast food consumption differed by SES. Follow-up Mann-Whitney U tests were performed to compare specific SES groups. Results showed that lower-class individuals reported the highest fast food consumption, with significantly greater intake compared to middle-class individuals, U = 257,143.50, Z = -3.137, p = .002, upper-middle-class individuals, U = 14,573.00, Z = -2.646, p = .008, and upper-class individuals, U = 10,152.00, Z = -1.586, p = .113 (non-significant). Additionally, middle-class individuals consumed significantly more fast food than upper-middle-class individuals, U = 257,143.50, Z = -3.137, p = .002. Notably, upper-class individuals reported significantly greater fast food consumption than the upper-middle class, U = 200,137.00, Z = -2.467, p = .014, revealing that fast food intake generally decreased with increasing SES, but this pattern was not entirely linear. Discussion The present study explored how fast food consumption relates to adolescents’ self-regulation abilities across emotional, cognitive, and behavioral domains. While the observed associations were modest, they emerged within a large sample. As noted by researchers such as McCartney and Rosenthal ( 2000 ), in large population-based studies, even small effects can be meaningful, especially when examining widespread behavioral trends or informing public health strategies. In fields like developmental psychology and nutritional science, where human behavior is shaped by a wide array of environmental, biological, and psychosocial influences, even small effects can provide meaningful insight, particularly when studied at scale. In nutritional studies, particularly, small effects can also be meaningful over time due to an accumulative effect. Preliminary correlational findings showed that early adolescents at the younger range of this developmental stage (10–11 years old) consumed fast food more frequently than those at the upper range (12–14 years old). Fast food slightly decreased as age increased. Findings also revealed that males consumed fast food more frequently than females. Interestingly, in the preliminary correlations, fast food was associated with poorer cognitive self-regulation, but not in the main regression analyses. The cognitive subscale had the lowest reliability out of the three subscales tested. It is therefore unknown whether there was a negative relationship between fast food and cognitive self-regulation since it is unknown if the condensed subscale was truly a valid measure of cognitive self-regulation. It would be prudent for future researchers to use the full self-regulation scale rather than the condensed one. The main findings revealed that frequent fast food consumption was associated with the lowest overall self-regulation scores, indicating poorer self-regulation abilities and an inverse relationship between variables. Unexpectedly, even adolescents who consumed fast food occasionally (one to two days per week) exhibited significantly poorer self-regulation scores than adolescents who rarely ate fast food, implying that fast food intake might have meaningful implications even in moderation. These results are particularly notable given the consistency of negative associations between fast food intake and self-regulation across varying consumption levels, even after controlling for key demographic factors such as gender, age, grade level, socioeconomic status, and parental monitoring. Similarly to cognitive self-regulation, the demographic factors of gender and age revealed weak, yet significant, associations with self-regulation scores in the preliminary analyses. However, they showed no such associations in the main analyses. Both older participants and girls demonstrated slightly higher self-regulation abilities, but only in preliminary bivariate associative testing. This might suggest that their associations may be partially explained by shared variance with other predictors, such as SES or fast food frequency. This pattern is typical in multivariate analyses, where the unique predictive value of each variable is assessed while controlling for others. These findings demonstrate the importance of evaluating predictors within a full model, as bivariate associations may overstate the contribution of individual factors when contextual influences are not considered. That said, the weak association between gender and self-regulation and the lack of gender-related correlations in the main analyses contrast with prior findings. Past studies suggest gender differences in adolescent self-regulatory development. Studies have found that males in early adolescence reach peak brain volume four years after females, on average, and their self-regulatory abilities develop more gradually than those of females (Lenroot & Giedd, 2010 ; Tetering et al., 2020 ). However, a key consideration is that the current study included a large representative sample and utilized self-reported self-regulation as the key outcome measure. The difference in methodology and scope of study might explain these contradictions, rendering these studies incomparable to the present one. Nonetheless, these findings may emphasize that certain dietary factors are important and a previously overlooked correlate in self-regulatory abilities in early adolescent research. The emotional self-regulation subscale assessed participant volatility and their ability to remain composed when agitated or angry. Frequent fast food consumption (5 or more days per week) yielded the most significant negative association with self-regulation and the greatest effect size among all the variables. Similar to overall self-regulation, gender, age, and grade level showed no significant associations with emotional self-regulation. Frequent fast food consumption surpassed the critical environmental factors of socioeconomic status and parental monitoring in effect size, signifying its potential scientific and practical relevance in understanding emotional self-regulation in early adolescence. These results mirror those of Oddy et al. ( 2009 ), who found that young adolescents who consumed a Westernized diet high in fat and sugar struggled with poorer mental health than peers who consumed diets richer in leafy green vegetables and fruits. Specifically, this study found that consumption of high-sugar confectionary desserts and fatty meats were associated with externalizing symptoms (aggression and delinquency) rather than internalizing symptoms (anxiety, depression, somatization, and withdrawal). The cognitive self-regulation subscale assessed goal-motivated behavior and the ability to remain persistent through challenges. In the primary regression analyses, none of the fast food consumption groups were significantly associated with the cognitive self-regulation subscale. Parental monitoring demonstrated the strongest positive effect size, followed by self-reported socioeconomic status. No other variables were significantly associated with this subscale score. These results suggest that parents who are highly knowledgeable about their children’s daily experiences may augment their children's motivation and goal persistence, regardless of dietary habits. This finding aligns with that of Kristjansson and Sigfusdottir ( 2009 ), who found that greater parental monitoring was positively correlated with adolescents’ academic achievement and the effort they invested in schoolwork, even when controlling for parental education and family structure. For the behavioral subscale, only the frequent fast food group exhibited significantly poorer self-regulation. The subscale measured the ability to sit still and how often the participant struggled with fidgeting. Socioeconomic status showed the greatest effect size, suggesting that participants in the higher-status groups were more likely to be able to sit still during tasks. However, frequent fast food consumption demonstrated a similar effect size, though in a negative direction. This finding suggests that high fast food consumption is associated with a reduced ability to sit still during tasks requiring sustained attention. This also implies that frequent fast food intake is associated with more fidgeting. The finding supports prior research that discovered high sugar diets reduce communication between the left and right hippocampi (Stadterman, 2019). This disruption is meaningful due to the right hemisphere’s lesser-known role in regulating sensory motor input (Bast & Feldon, 2003). Consequently, these students may have more difficulty sitting in a classroom. Participants in the lowest socioeconomic status group had the highest frequency of fast food intake. These students may be particularly vulnerable to the aforementioned academic obstacles. Prior research demonstrates that fast food consumption is particularly high in disadvantaged African American communities due to the ease of access to fast food restaurants, revealing nutritional disparities between their communities and predominantly white communities (James et al., 2014 ). Limitations and Future Directions Although the study was novel in its exploration of the relationship between fast food consumption and self-regulation, there are several limitations to address. The current study utilized a single self-reported self-regulation measure. This study did not include alternative measures, such as effortful control, which may have provided complementary or unique perspectives. Future research should also gather both parental and teacher evaluations of self-regulation to prevent biased reporting. An important caveat is that the present study used a cross-sectional design. However, this design was intentional in order to examine potential dietary correlations, specifically during early adolescence. Still, whether these results would exhibit stability over time or predict future self-regulatory issues remains unknown. Furthermore, the direction of the relationship between fast food and self-regulation was also unknown due to its cross-sectional design. Young teenagers with poor self-regulation may provoke their parents to purchase fast food for them. In addition, it is also possible that the relationship is bidirectional and cyclical. Fast food may be addictive and lower self-regulation simultaneously. That drop in regulation may impair an individual's ability to further resist eating fast food. The examples presented above reveal that no causal relationship can be drawn from this study. Future studies should examine if fast food intake remains stable across adolescence and if these relationships persist longitudinally. Furthermore, participants were asked how many days they had eaten fast food, but not specifically what they had eaten. Given the wide variety of fast food and quick-serve restaurants, some chains offer healthier choices. Knowing what items participants ordered and from which fast food chains might have provided greater understanding of these correlational findings. Future research should include a fast food measure that asks which restaurants participants typically eat at and what they typically order. The measure should also span more than just the previous week, as fast food consumption may vary depending on life circumstances. Since fast food may be more memorable to children and adolescents than individual food items, fast food may serve as a reliable proxy for the Western diet in future studies. That said, not everyone who consumes high-fat and high-sugar items is purchasing them from restaurants, so both the YAQ and fast food measures should be used in tandem in the future. Overall, the model also yielded a small prediction percentage, which could be the result of the large sample size or the presence of other covariates or confounders, such as sleep or exercise. Future research should add more covariates to the model presented in this study. The data used in this study were collected in 2015, which may limit the applicability of the findings to today’s rapidly evolving media and dietary environments. However, this timeframe may also offer a unique advantage since it precedes the widespread popularity of short-form content and TikTok, thereby removing a potential confound. Finally, the regional sample was large, but demonstrates low generalizability since a majority of the sample was white and lived in middle to upper-middle-class households. Future research should recruit a more representative sample. Conclusion In summary, greater fast food intake was associated with lower self-regulation overall and was the most significant predictor of lower emotional self-regulation skills within the sample, even when considering factors such as age, gender, grade level, SES, and parental monitoring. Though this exploratory study yielded interesting results, more research is needed to justify any causal claims or practical implications. This work represents the first step in the exploration of fast food's potential impact on self-regulation. It would be prudent to replicate this study with enhanced measurements, a more diverse sample, and utilize a longitudinal design. 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This displays the average score on the self-regulation survey overall by fast food consumption group\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8197318/v1/a92fa61cea7d42e8c9dae18d.png"},{"id":97689507,"identity":"230e2107-f83a-4741-babb-29e43f1e5ea4","added_by":"auto","created_at":"2025-12-08 10:47:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29236,"visible":true,"origin":"","legend":"\u003cp\u003eEmotional\u003cem\u003e Self-Regulation Scores by Fast Food Consumption Group\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8197318/v1/39a1c21199edd788e7ed5052.png"},{"id":97893835,"identity":"323a56ec-4eda-4247-8bee-e155616db9ae","added_by":"auto","created_at":"2025-12-10 15:31:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22753,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive \u003cem\u003eSelf-Regulation Scores by Fast Food Consumption Group Note\u003c/em\u003e. This displays the average score on the cognitive self-regulation survey by fast food consumption group\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8197318/v1/c6e2689ea7f2ba0a0b57f63b.png"},{"id":97689509,"identity":"7cd072a6-b66c-4d2e-ac66-94b055bd7b3a","added_by":"auto","created_at":"2025-12-08 10:47:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBehavioral\u003c/em\u003e \u003cem\u003eSelf-Regulation Scores by Fast Food Consumption Group Note\u003c/em\u003e. This displays the average score on the behavioral self-regulation survey by fast food consumption group\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8197318/v1/a520e1ba55a9004ec16a7179.png"},{"id":97902531,"identity":"a9f08675-ef8f-42ae-af91-fc37f3014826","added_by":"auto","created_at":"2025-12-10 15:52:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8197318/v1/c1a70dba-882c-4d29-9cad-18df76e5dcc6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fast Food, Slower Control? Exploring the Relationship Between Fast Food Consumption and Self-Regulatory Abilities in Early Adolescents","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence is a period of rapid physical, cognitive, psychological, and social transformation. As adolescence progresses, teenagers gradually separate from their childhood microcosm and enter a production and reproduction-based society (Lerner \u0026amp; Steinberg, 2004, p. 85). During transitional periods, an individual relies on existing coping skills to navigate novel challenges, or they must cultivate new self-regulatory strategies to thrive if they lack the necessary skills. The ability to utilize these skills significantly predicts the ontogenetic trajectory of an individual (Schulz \u0026amp; Heckhausen, 1999; Skinner, 1999). Self-regulation capabilities measured in young adolescents can predict later positive and negative developmental outcomes (Gestsd\u0026oacute;ttir \u0026amp; Lerner, 2007). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSelf-regulation, defined by Barkley (2004) as \u0026quot;a deliberate attempt to modulate, modify, or inhibit actions or reactions toward a more adaptive end,\u0026quot; is a critical neuropsychological skill and an element of executive function (Anderson, 2002). The development of executive function skills such as impulse/inhibitory control, emotional control, behavioral control, and attentional control begins in early childhood, but these skills undergo a vigorous maturational process during adolescence (Anderson, 2002; Diamond, 2013). For typically developing individuals in optimal environments, the preference for attaining long-term goals over pursuing short-term hedonistic desires increases with age. Self-regulation requires explicit cognitive processing and is crucial for planning and goal setting. This distinct style of processing enables individuals to envision hypothetical futures when calculating the risks versus rewards of their behaviors (Barkley, 2001). Adolescents with high self-regulatory abilities perform better in school and are better able to resist negative peer pressure (King et al., 2018; Tetering et al., 2018). Consequently, poor regulatory control predicts and results in numerous negative and maladaptive outcomes such as: Depression (d\u0026rsquo;Acremont \u0026amp; Van der Linden, 2007), committing crime (Vohs \u0026amp; Baumeister, 2004), nicotine addiction (Baumeister, 2017), substance abuse (Maranges \u0026amp; Baumeister, 2016), gambling (Blaszczynski et al., 1997), risky sexual behavior (Gailliot \u0026amp; Baumeister, 2007), overspending (Gailliot et al., 2008), and poor academic performance (Vohs \u0026amp; Baumeister, 2016).\u003c/p\u003e\n\u003cp\u003eExtensive research has revealed that the brain undergoes protracted yet significant morphological and physiological changes throughout adolescence. These transformations continue to mature until the brain reaches full development around 24\u0026ndash;25 years of age. (Giedd et al., 1999; Huttenlocher \u0026amp; Dabholkar, 1997; Perrin et al., 2008; Wahlstrom et al., 2010). Regressive and progressive alterations, such as the thinning of grey matter and an increase in myelination, facilitate greater activity in the frontoparietal and frontopolar regions of the brain. These cortical regions are dedicated to cognitive, behavioral, and impulsive control (Corrigan et al., 2021; Marek \u0026amp; Dosenbach, 2018; O\u0026rsquo;Donnell et al., 2005; Tamnes et al., 2017). The brain also undergoes dopaminergic transformations in early adolescence at the neurobiological and neurochemical levels. Dopamine-signaling axons in the striatal regions that project to the medial prefrontal cortex increase in density and augment transmission efficiency\u0026nbsp;(Naneix et al., 2012). Dopamine receptors are abundant during this developmental stage. However, the mesocortical pathway, which aids self-regulation and decision-making, is underdeveloped. For these reasons, young adolescents are prone to pursue pleasure, sensation-seeking, and reward instead of planning or inhibiting actions\u0026nbsp;(Naneix et al., 2012; Reynolds \u0026amp; Flores, 2021). A young teenager\u0026rsquo;s brain is highly plastic but is also undergoing critical maturational processes. This makes the young adolescent brain highly vulnerable to a myriad of environmental influences that might hinder proper self-regulation development\u0026ndash; including their diet\u0026nbsp;(Lowe et al., 2020; Reynolds \u0026amp; Flores, 2021). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmerica and other Western societies can provide consumers with instant or on-demand goods and services. American consumers consider fast food to be more convenient than incorporating whole foods into their diet (Glanz et al., 1998). Convenience-based obesogenic food options in Western cultures and the Western diet overall lack nutritional value but are calorically dense. This diet is characterized as being high in fat and sugar (Cordain et al., 2005). In a laboratory study, young adolescent mice were fed a diet high in fat and sugar throughout this developmental period. Male rats fed this diet during their young adolescent period experienced permanent hippocampal-related memory impairments into adulthood. When fed a healthy diet in late adolescence, female rats were able to recover from those deficits, but male rats were not (Hayes et al., 2024). The hippocampus, along with the medial prefrontal cortex, plays a role in emotional regulation by assigning context to episodic and biographical memories (Jin \u0026amp; Maren, 2015). Adolescents with reduced hippocampal and amygdala volume exhibit heightened reactivity to stressors, which can lead to psychopathology (Weissman et al., 2020). In an MRI study, children aged 5\u0026ndash;9 who frequently consumed a high-fat, high-sugar Western diet, as reported by parents using the Youth/Adolescent Food Frequency Questionnaire, showed reduced hippocampal volume in the left hemisphere (Stadterman, 2020). The left hemisphere of the hippocampus is particularly involved in forming new episodic memories by associating environmental cues with context (Miller et al., 2018). In a complementary resting-state fMRI study, Stadterman (2019) found that children who self-reported frequent consumption of Western diet items also exhibited reduced intrinsic functional connectivity between the left and right hippocampi. Dietary sugar was identified as the probable culprit for the disruption, and they posited that chronic rather than acute sugar intake may heighten behavioral issues such as hyperactivity or ADHD symptoms (Stadterman, 2019). The researchers cited Bast \u0026amp; Feldon (2003), who discovered that the right hemisphere of the hippocampus has a lesser-known role in regulating sensory motor input. Similarly, Reichelt \u0026amp; Rank (2017) found that high fat and high sugar intake can overstimulate the mesocorticolimbic dopamine system and\u0026nbsp;alter the signaling of dopamine and \u0026gamma;-aminobutyric acid (GABA) in adolescents, which can contribute to impulsive behavior. Additionally, in a study examining mental health and dietary choices, young adolescents whose diets mainly consisted of junk food showed greater externalizing issues and were more aggressive than their peers who consumed more fruits and leafy green vegetables\u0026nbsp;(Oddy et al., 2009). Junk food was also discovered to disrupt prefrontal cortex maturation and alter limbic-prefrontal interactions, which can affect emotional volatility (Reichelt \u0026amp; Rank, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, many of these studies used broad age ranges in their samples (e.g., 5\u0026ndash;17 years), limiting developmental specificity. This approach may obscure age-specific effects, particularly during early adolescence when the brain is undergoing substantial restructuring. Moreover, American fast food is a specific form of the Western diet that can be calorically dense in fat and sugar. Yet, these previous studies did not reveal the frequency of fast-food consumption. Due to its high accessibility and potential to deliver a more potent \u0026quot;dose\u0026quot; of Western dietary components, isolating fast food as a discrete variable may offer new insights into eating patterns and regulatory abilities. Also, self-regulation is distinct from internalizing and externalizing symptoms, as it is a transdiagnostic process that underlies a wide range of psychological outcomes and is critical for adaptive functioning. As mentioned above in the preceding paragraphs, each of these listed regions and their circuitry undergo major maturation during adolescence. Because of the lack of studies using fast food consumption as a culturally relevant solo construct and the growing scientific understanding that self-regulatory processes undergo rapid maturation in young adolescents, it is essential to begin an exploration of this understudied relationship.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eThe present study explores whether varying levels of fast food consumption are associated with differences in early adolescent self-regulation using a large, regionally representative sample in the southeast. Given prior evidence that consumption of a Western diet has been linked to cognitive functioning, externalizing behaviors (emotional dysregulation), and hyperactivity (behavioral impulsivity). The current study explores how fast food frequency is related to the distinguished subdomains of self-regulation. Emotional, cognitive, and behavioral self-regulation is important to study, because each of these facets reflect distinct neurocognitive processes: cognitive self-regulation which relies on prefrontal cortex function, emotional self-regulation which involves limbic-prefrontal interactions, and behavioral self-regulation which depends on frontoparietal networks (Diamond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Few studies, if any, have specifically assessed the relationship between fast food consumption frequency and distinct facets of self-regulation in early adolescence. To the researcher's knowledge, this is the first study to explore how fast food intake may relate to these three domains of self-regulation within a single framework. Additionally, most adolescent self-regulation studies have focused on inherent demographic factors such as gender, age, grade level, and socioeconomic status, or non-dietary environmental influences like parenting style and parental monitoring (Barry et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Purdie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tetering et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Theurel \u0026amp; Gentaz, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, fast food consumption, an easily accessible, potent component of the Western diet, remains an underexplored dietary correlate of cognitive, emotional, and behavioral self-regulation in early adolescence. The study will also explore whether certain socioeconomic groups consume fast food more frequently. In past research, lower socioeconomic statuses were associated with greater fast food consumption due to its low cost, high-energy density, and easy accessibility (Drewnowski \u0026amp; Specter, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The current study does not examine any mediating effects between SES, fast food, and self-regulation. Instead, it represents a first launch into the investigation of possible disparities in self-regulatory abilities by examining if any differential patterns of fast-food consumption exist between SES groups in a large sample. Since early adolescence is a critical developmental window for self-regulatory abilities, understanding even modest associations between eating behaviors and self-regulation may inform future longitudinal or intervention studies.\u003c/p\u003e\u003cp\u003eThis study is an exploratory first step, so findings should be interpreted with appropriate caution. The cross-sectional design does not allow for causal inference, nor does it explain the direction of the relationship. However, this study offers insight into a critical developmental window in early adolescence. Since fast food is connected to highly marketed and recognizable food chains, adolescents may be more reliable in recalling the frequency of its consumption compared to listing the individual frequencies of singular food items like glasses of milk, as seen in the Youth /Adolescent Food Questionnaire (Rockett et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) that has been utilized in past research. However, the limited precision of the single fast food measure in this particular study constrains dose\u0026ndash;response interpretations and reflects one component of the Western diet. Nonetheless, this work represents the initial phase in investigating the ways fast food consumption may be associated with early adolescent self-regulatory functioning. Due to the exploratory nature of this work, no directional hypotheses were formulated.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eParticipants were drawn from children enrolled in grades 5\u0026ndash;9 within the North Carolina public school system during the 2014\u0026ndash;2015 school year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2104). The sample consisted of 1096 females (52.1%) and 1008 males (47.9%). Students were aged between 10 and 14 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.43, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.08). The majority of the sample identified as White (65.3%). This was followed by Black (28.5%) and Hispanic (14.5%). The students self-reported their socioeconomic status. Based on their subjective responses, most students lived in upper-middle-class (45.9%) and middle-class (28.4%) families. Students also reported living in an upper-class family (21.6%) and a lower-class family (2%). These participants represent time wave one of a longitudinal study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eData for this analysis was derived from a publicly available dataset, specifically administrative data from the North Carolina Department of Public Instruction in collaboration with the Research on Adaptive Interests, Skills, and Environments (RAISE) study conducted by Duke University (Hoyle, 2024). Potential participants were contacted by phone. A large majority of parents (97%) provided informed consent for their child's participation and the linkage of survey responses with NCDPI records. Adolescent participants provided verbal assent to complete the Baseline Adolescent Survey and agreed to future contact and future surveys. Both parental consent and adolescent assent procedures were approved by the institutional review board and adhered to ethical standards for research with minors. The Baseline Adolescent Survey was administered via telephone between April and August of 2015. Although the complete survey lasted approximately 90 minutes, participants did not complete it in a single sitting to reduce the chances of response fatigue. Parents were also instructed to ensure that their child completed the survey in private. The survey included questions related to demographics, home environment, self-regulatory abilities, physical and mental health, and problem behaviors. Participants received \u003cspan\u003e$\u003c/span\u003e30 upon completion of the survey.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSubjective Socioeconomic Status\u003c/h2\u003e\u003cp\u003eSubjective socioeconomic status (SES) was assessed with a single-item question: \u003cem\u003eIn your opinion, how is your family doing financially?\u003c/em\u003e Participants selected from four responses: (1) \u003cem\u003eWe do not have enough money to meet our basic needs\u003c/em\u003e (low SES), (2) \u003cem\u003eMoney is tight, but we are able to meet our basic needs\u003c/em\u003e (lower-middle SES), (3) \u003cem\u003eWe have all the money we need to live a comfortable life\u003c/em\u003e (middle SES), and (4) \u003cem\u003eWe have enough money to do most anything we want\u003c/em\u003e (upper-middle to high SES). Single-item subjective SES measures are commonly used in adolescent health research to capture perceived family financial status. Davisson et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that an adolescent\u0026rsquo;s answer on a subjective SES measure moderately correlated with their parent\u0026rsquo;s reported SES. SES was included as a control variable in regression analyses.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParental Monitoring\u003c/h3\u003e\n\u003cp\u003eParental monitoring was operationalized using the 5-item parental monitoring scale developed by Fletcher et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Participants reported how much their parents attempted to gain information about their whereabouts, how they spent their time, their friendships, how they spent money, and what they were studying in school. Items such as \u003cem\u003e\u0026ldquo;How much do your parents try to know how you spend your free time?\u0026rdquo;\u003c/em\u003e were rated on a 3-point scale (0\u0026thinsp;=\u0026thinsp;\u003cem\u003eThey don\u0026rsquo;t try\u003c/em\u003e, 1\u0026thinsp;=\u0026thinsp;\u003cem\u003eThey try a little\u003c/em\u003e, 2\u0026thinsp;=\u0026thinsp;\u003cem\u003eThey try a lot\u003c/em\u003e). This scale has demonstrated acceptable internal consistency in prior research, with Cronbach\u0026rsquo;s alpha of α\u0026thinsp;=\u0026thinsp;.72 (Fletcher et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Total scores were created by averaging the five items, with higher scores reflecting greater parental monitoring. This variable was included as a control in regression analyses.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFast Food Consumption\u003c/h2\u003e\u003cp\u003eFast food consumption was assessed by asking participants how many days they had eaten fast food in the past week, with response options ranging from 0 to 7. Rather than using this as a continuous variable, responses were grouped into four consumption categories: 0 days (\u003cem\u003erarely eats fast food\u003c/em\u003e), 1\u0026ndash;2 days (\u003cem\u003eoccasionally\u003c/em\u003e), 3\u0026ndash;4 days (\u003cem\u003emoderately\u003c/em\u003e), and 5 or more days (\u003cem\u003efrequently\u003c/em\u003e). This decision was made to address the non-normal distribution of responses across days and the small number of participants reporting fast food consumption for all 7 days. Categorization improved interpretability and reduced the risk of influential outliers skewing the regression results. While categorizing continuous data can reduce statistical power, this approach was deemed appropriate for this exploratory analysis and the distributional characteristics of the sample. The resulting categorical variable was used as the independent variable in regression analyses.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSelf-Regulation\u003c/h3\u003e\n\u003cp\u003eSelf-regulation was assessed using a 13-item survey with three subscales: emotional, cognitive, and behavioral self-regulation. Participants responded on a scale from 1 (\u003cem\u003eNot at all like me\u003c/em\u003e) to 5 (\u003cem\u003eVery much like me\u003c/em\u003e). The emotional regulation subscale contained five statements, such as \u003cem\u003eI have a hard time controlling my temper\u003c/em\u003e and \u003cem\u003eI slam doors when I am mad\u003c/em\u003e. The cognitive regulation subscale contained five statements, such as \u003cem\u003eOnce I have a goal, I make a plan to reach it\u003c/em\u003e, and \u003cem\u003eI get distracted by little things\u003c/em\u003e. The behavioral subscale contained three statements, such as \u003cem\u003eI get fidgety after a few minutes if I am supposed to sit still\u003c/em\u003e and \u003cem\u003eI have a hard time sitting still during important tasks\u003c/em\u003e. This survey is a condensed version of Novak and Clayton's (2001) self-regulation measure, which includes the same three subscales. The original scale demonstrated strong internal consistency: Cronbach's α\u0026thinsp;=\u0026thinsp;.95 for emotional regulation, α\u0026thinsp;=\u0026thinsp;.96 for cognitive regulation, and α\u0026thinsp;=\u0026thinsp;.94 for behavioral regulation. Cronbach's alpha was computed for the condensed version of the measure within the current sample. Internal consistency was acceptable for the total scale α\u0026thinsp;=\u0026thinsp;.80, for the emotional subscale α\u0026thinsp;=\u0026thinsp;.79, and the behavioral subscale α\u0026thinsp;=\u0026thinsp;.74, The cognitive subscale demonstrated lower internal consistency than the emotional and behavioral subscales with a reliability score of α\u0026thinsp;=\u0026thinsp;.64. Prior research supports the validity of self-reports in this age group. Fine et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that self-reported self-control in 15-year-olds significantly predicted delinquency, aligning with real-world outcomes. The use of a single measure was necessitated by the secondary nature of the RAISE data (Hoyle, 2024).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003e Participants provided demographic information during a 90-minute phone interview. The key control variables included gender, age, grade level, SES, and parental monitoring, as these factors have previously been associated with adolescent self-regulation (Barry et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Purdie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tetering et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Theurel \u0026amp; Gentaz, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Gender was coded as 0\u0026thinsp;=\u0026thinsp;male and 1\u0026thinsp;=\u0026thinsp;female, whereas age and grade level were treated as continuous variables. SES was measured using a self-reported financial status scale, in which participants rated their family's financial situation. Parental monitoring was assessed using the abovementioned measure, which evaluated the extent to which parents were aware of their child\u0026rsquo;s daily activities, with higher scores indicating greater parental involvement. These covariates were included to account for potential individual and contextual differences in self-regulation, ensuring that any observed associations between fast food consumption and self-regulatory abilities were not driven by these factors.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData Analytic plan\u003c/h2\u003e\u003cp\u003eStandard multiple regression analyses were conducted to examine the associations between fast food consumption and self-regulation. Due to the exploratory nature of this study and lack of directional hypotheses, a standard multiple regression was deemed the most appropriate statistical measure. The dependent variables included overall self-regulation scores as well as subscale scores measuring emotional, cognitive, and behavioral self-regulation. The key independent variable was fast food consumption frequency, which was dummy coded into three groups: Occasional (1\u0026ndash;2 days per week), Moderate (3\u0026ndash;4 days per week), and Frequent (5\u0026thinsp;+\u0026thinsp;days per week), with Rarely (0 days per week) serving as the reference group. Age, gender, SES, grade level, and parental monitoring were included as covariates to control for potential confounding factors. All predictors were entered simultaneously into the regression model to assess their distinct contributions to overall self-regulation outcome scores and subscale outcome scores. Standardized beta coefficients (β), significance values (p), and R\u0026sup2; values were reported to evaluate effect sizes and model fit.\u003c/p\u003e\u003cp\u003eA Kruskal-Wallis test was conducted to examine differences in fast food consumption across socioeconomic status (SES) groups. This nonparametric test was chosen because the SES variable was ordinal and unequally distributed, with the low SES group comprising only 2% of the sample. The data did not meet the normality requirement for conducting a one-way ANOVA. The independent variable was self-reported SES, categorized into four levels: lower, middle, upper-middle, and upper classes. The dependent variable was fast food consumption frequency, measured as the total number of days participants reported eating fast food in the prior week. To further explore group differences, a series of Mann-Whitney \u003cem\u003eU\u003c/em\u003e tests were conducted as post hoc analyses, comparing fast food consumption between SES groups. Mean rank values were examined to assess patterns of fast food intake across socioeconomic levels. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\u003cp\u003eDescriptive statistics for overall self-regulation and its subscales (emotional, cognitive, behavioral) across fast food consumption groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants who reported frequent fast food consumption (5\u0026thinsp;+\u0026thinsp;days per week) exhibited the lowest self-regulation scores overall (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.24, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72), whereas those who rarely consumed fast food (0 days per week) demonstrated the highest overall self-regulation scores (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.68, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEmotional\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCognitive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBehavioral\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRarely\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive characteristics of self-regulation scores per fast food consumption group\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e and \u003cem\u003eSD\u003c/em\u003e represent mean and standard deviation, respectively\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePreliminary Analyses\u003c/h2\u003e\u003cp\u003eBecause several variables, including fast food frequency across 7 days and socioeconomic status, violated the assumptions of normality, Spearman\u0026rsquo;s rank-order correlations were computed to assess the strength and direction of associations among variables used in the regression analyses. Fast food consumption was negatively correlated with overall self-regulation (ρ = \u0026ndash;.105, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), emotional regulation (ρ = \u0026ndash;.120, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), behavioral regulation (ρ = \u0026ndash;.073, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and cognitive regulation (ρ = \u0026ndash;.055, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.043). Fast food consumption was also negatively correlated with age (ρ = \u0026ndash;.083, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with younger teens reporting more fast food intake, and positively correlated with gender (ρ\u0026thinsp;=\u0026thinsp;.043, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.043), with males reporting higher fast food consumption. Fast food consumption was not significantly correlated with grade level or subjective SES. All self-regulation subscales were significantly positively correlated with one another and with the total self-regulation score (ρ\u0026thinsp;=\u0026thinsp;.443 to .756, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Age was positively correlated with cognitive regulation (ρ\u0026thinsp;=\u0026thinsp;.065, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007) and behavioral regulation (ρ\u0026thinsp;=\u0026thinsp;.060, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.012) and negatively correlated with fast food consumption and gender. Subjective SES was positively associated with cognitive regulation (ρ\u0026thinsp;=\u0026thinsp;.053, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.035), but not with fast food consumption. All bivariate correlations are represented in the correlation matrix shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman Correlations Among Study Variables (N\u0026thinsp;=\u0026thinsp;2,084\u0026ndash;2,104)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Parental monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Subjective SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.08**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Total self-regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.14**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Emotional regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.13**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.81**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Cognitive regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.19**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.08**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.33**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Behavioral regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.09**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.10**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.74**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.31**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. Fast food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. Child Grade level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9. Child age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.06**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.83**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10. Child Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote\u003c/em\u003e. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCollinearity Diagnostics\u003c/h2\u003e\u003cp\u003eTo assess multicollinearity among the predictors, the variance inflation factor (VIF) and tolerance statistics were examined. All VIF values ranged from 1.01 to 1.09, and tolerance values ranged from .92 to .99, indicating that multicollinearity was not a concern (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10; Tolerance\u0026thinsp;\u0026gt;\u0026thinsp;.20; Cohen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eOverall Self-Regulation\u003c/h2\u003e\u003cp\u003eA standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and overall self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, \u003cem\u003eF\u003c/em\u003e(8, 2025)\u0026thinsp;=\u0026thinsp;13.191, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, accounting for 5.0% of the variance in overall self-regulation (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .050, Adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .046). Among the variables, frequent fast food consumption (5\u0026thinsp;+\u0026thinsp;days per week) had the strongest negative association with self-regulation (\u003cem\u003eβ\u003c/em\u003e = -0.110, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), followed by moderate consumption (3\u0026ndash;4 days per week; \u003cem\u003eβ\u003c/em\u003e = -0.068, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008) and occasional consumption (1\u0026ndash;2 days per week; \u003cem\u003eβ\u003c/em\u003e = -0.054, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.037). When examining covariates, parental monitoring (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.142, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and SES (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.118, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were both positively associated with self-regulation, indicating that greater parental involvement and higher perceived financial well-being were linked to higher self-regulation scores. Age, gender, and grade level were not significantly associated with self-regulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). A complete summary of regression coefficients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the group means are presented visually in \u003cb\u003eFig.\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Regression Associations Between Fast Food Consumption and Overall Self-Regulation Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild's Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Occasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Moderately\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Frequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. *B* = Unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;Standard error; *β* = Standardized coefficient; CI\u0026thinsp;=\u0026thinsp;Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group.\u003c/td\u003e\u003c/tr\u003e/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eOverall self-regulation scores by fast food consumption group\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis displays the average score on the self-regulation survey overall by fast food consumption group\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEmotional Self-Regulation\u003c/h2\u003e\u003cp\u003eA standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and emotional self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, \u003cem\u003eF\u003c/em\u003e(8, 2025)\u0026thinsp;=\u0026thinsp;11.467, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, accounting for 4.3% of the variance in emotional self-regulation (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .043, Adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .040). The full regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among the predictors, frequent fast food consumption (5\u0026thinsp;+\u0026thinsp;days per week) had the strongest negative association with emotional self-regulation (\u003cem\u003eβ\u003c/em\u003e = -0.123, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), followed by moderate fast food consumption (3\u0026ndash;4 days per week; \u003cem\u003eβ\u003c/em\u003e = -0.081, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) and occasional consumption (1\u0026ndash;2 days per week; \u003cem\u003eβ\u003c/em\u003e = -0.062, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.018). In contrast, parental monitoring (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and SES (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.116, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were positively associated with emotional self-regulation, indicating that greater parental involvement and higher perceived financial well-being were related to higher emotional self-regulation scores. Age, gender, and grade level were not significantly associated with emotional self-regulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a visual representation of the mean emotional self-regulation scores across fast food consumption groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Regression Associations Between Fast Food Consumption and Emotional Self-Regulation Subscale Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Occasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Moderately\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Frequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. *B* = Unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;Standard error; *β* = Standardized coefficient; CI\u0026thinsp;=\u0026thinsp;Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis displays the average score on the emotional subscale of the self-regulation survey by fast food consumption group\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis displays the average score on the emotional self-regulation survey by fast food consumption group\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCognitive Self-Regulation\u003c/h2\u003e\u003cp\u003eA standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and cognitive self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, \u003cem\u003eF\u003c/em\u003e(8, 2025)\u0026thinsp;=\u0026thinsp;8.343, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, accounting for 3.2% of the variance in cognitive self-regulation (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .032, Adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .028). Among the variables, parental monitoring had the strongest positive association with cognitive self-regulation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.159, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). SES was also positively associated with cognitive self-regulation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.015). However, none of the fast food consumption groups were significantly associated with cognitive self-regulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). Age, gender, and grade level were also not significantly associated with cognitive self-regulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). A full summary of regression coefficients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Group means are displayed visually in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Regression Associations Between Fast Food Consumption and Cognitive Self-Regulation Subscale Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food\u003c/p\u003e\u003cp\u003eOccasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food\u003c/p\u003e\u003cp\u003eModerately\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food Frequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. B\u0026thinsp;=\u0026thinsp;Unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;Standard error; β\u0026thinsp;=\u0026thinsp;Standardized coefficient; CI\u0026thinsp;=\u0026thinsp;Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis displays the average score on the cognitive self-regulation survey by fast food consumption group\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eBehavioral Self-regulation\u003c/h2\u003e\u003cp\u003eA standard multiple regression analysis was conducted to examine the associations between fast food consumption frequency and behavioral self-regulation while controlling for SES, parental monitoring, age, gender, and grade level. The overall regression model was statistically significant, \u003cem\u003eF\u003c/em\u003e(8, 2025)\u0026thinsp;=\u0026thinsp;6.081, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, accounting for 2.3% of the variance in behavioral self-regulation (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .023, Adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .020). Among the fast food groups, frequent fast food consumption (5\u0026thinsp;+\u0026thinsp;days per week) had a significant negative association with behavioral self-regulation (\u003cem\u003eβ\u003c/em\u003e = -0.082, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), while occasional (\u003cem\u003eβ\u003c/em\u003e = -0.036, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.168) and moderate consumption (\u003cem\u003eβ\u003c/em\u003e = -0.037, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.156) were not significantly associated. Among the covariates, SES (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.092, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and parental monitoring (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.067, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) were both positively associated with behavioral self-regulation, indicating that greater parental involvement and higher perceived financial well-being were linked to better behavioral self-regulation scores. Age, gender, and grade level were not significantly associated with behavioral self-regulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). A full summary of regression coefficients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and the group means are presented visually in \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Regression Associations Between Fast Food Consumption and Behavioral Self-Regulation Subscale Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Occasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Moderately\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast Food: Frequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. B\u0026thinsp;=\u0026thinsp;Unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;Standard error; β\u0026thinsp;=\u0026thinsp;Standardized coefficient; CI\u0026thinsp;=\u0026thinsp;Confidence interval. Fast food consumption groups were dummy-coded, with the rarely consumed (0 days) serving as the reference group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 4\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003eBehavioral Self-Regulation Scores by Fast Food Consumption Group\u003c/em\u003e\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis displays the average score on the behavioral self-regulation survey by fast food consumption group\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eSocioeconomic Status and Fast Food Consumption\u003c/h2\u003e\u003cp\u003eA Kruskal-Wallis H test was conducted to examine differences in fast food consumption across socioeconomic status (SES) groups. The test was significant, \u003cem\u003eH\u003c/em\u003e(3)\u0026thinsp;=\u0026thinsp;16.571, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, indicating that fast food consumption differed by SES. Follow-up Mann-Whitney \u003cem\u003eU\u003c/em\u003e tests were performed to compare specific SES groups. Results showed that lower-class individuals reported the highest fast food consumption, with significantly greater intake compared to middle-class individuals, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;257,143.50, \u003cem\u003eZ\u003c/em\u003e = -3.137, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, upper-middle-class individuals, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14,573.00, \u003cem\u003eZ\u003c/em\u003e = -2.646, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008, and upper-class individuals, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10,152.00, \u003cem\u003eZ\u003c/em\u003e = -1.586, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.113 (non-significant). Additionally, middle-class individuals consumed significantly more fast food than upper-middle-class individuals, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;257,143.50, \u003cem\u003eZ\u003c/em\u003e = -3.137, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002. Notably, upper-class individuals reported significantly greater fast food consumption than the upper-middle class, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200,137.00, \u003cem\u003eZ\u003c/em\u003e = -2.467, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.014, revealing that fast food intake generally decreased with increasing SES, but this pattern was not entirely linear.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study explored how fast food consumption relates to adolescents\u0026rsquo; self-regulation abilities across emotional, cognitive, and behavioral domains. While the observed associations were modest, they emerged within a large sample. As noted by researchers such as McCartney and Rosenthal (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), in large population-based studies, even small effects can be meaningful, especially when examining widespread behavioral trends or informing public health strategies. In fields like developmental psychology and nutritional science, where human behavior is shaped by a wide array of environmental, biological, and psychosocial influences, even small effects can provide meaningful insight, particularly when studied at scale. In nutritional studies, particularly, small effects can also be meaningful over time due to an accumulative effect.\u003c/p\u003e\u003cp\u003ePreliminary correlational findings showed that early adolescents at the younger range of this developmental stage (10\u0026ndash;11 years old) consumed fast food more frequently than those at the upper range (12\u0026ndash;14 years old). Fast food slightly decreased as age increased. Findings also revealed that males consumed fast food more frequently than females. Interestingly, in the preliminary correlations, fast food was associated with poorer cognitive self-regulation, but not in the main regression analyses. The cognitive subscale had the lowest reliability out of the three subscales tested. It is therefore unknown whether there was a negative relationship between fast food and cognitive self-regulation since it is unknown if the condensed subscale was truly a valid measure of cognitive self-regulation. It would be prudent for future researchers to use the full self-regulation scale rather than the condensed one.\u003c/p\u003e\u003cp\u003eThe main findings revealed that frequent fast food consumption was associated with the lowest overall self-regulation scores, indicating poorer self-regulation abilities and an inverse relationship between variables. Unexpectedly, even adolescents who consumed fast food occasionally (one to two days per week) exhibited significantly poorer self-regulation scores than adolescents who rarely ate fast food, implying that fast food intake might have meaningful implications even in moderation. These results are particularly notable given the consistency of negative associations between fast food intake and self-regulation across varying consumption levels, even after controlling for key demographic factors such as gender, age, grade level, socioeconomic status, and parental monitoring. Similarly to cognitive self-regulation, the demographic factors of gender and age revealed weak, yet significant, associations with self-regulation scores in the preliminary analyses. However, they showed no such associations in the main analyses. Both older participants and girls demonstrated slightly higher self-regulation abilities, but only in preliminary bivariate associative testing. This might suggest that their associations may be partially explained by shared variance with other predictors, such as SES or fast food frequency. This pattern is typical in multivariate analyses, where the unique predictive value of each variable is assessed while controlling for others. These findings demonstrate the importance of evaluating predictors within a full model, as bivariate associations may overstate the contribution of individual factors when contextual influences are not considered. That said, the weak association between gender and self-regulation and the lack of gender-related correlations in the main analyses contrast with prior findings. Past studies suggest gender differences in adolescent self-regulatory development. Studies have found that males in early adolescence reach peak brain volume four years after females, on average, and their self-regulatory abilities develop more gradually than those of females (Lenroot \u0026amp; Giedd, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tetering et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, a key consideration is that the current study included a large representative sample and utilized self-reported self-regulation as the key outcome measure. The difference in methodology and scope of study might explain these contradictions, rendering these studies incomparable to the present one. Nonetheless, these findings may emphasize that certain dietary factors are important and a previously overlooked correlate in self-regulatory abilities in early adolescent research.\u003c/p\u003e\u003cp\u003e The emotional self-regulation subscale assessed participant volatility and their ability to remain composed when agitated or angry. Frequent fast food consumption (5 or more days per week) yielded the most significant negative association with self-regulation and the greatest effect size among all the variables. Similar to overall self-regulation, gender, age, and grade level showed no significant associations with emotional self-regulation. Frequent fast food consumption surpassed the critical environmental factors of socioeconomic status and parental monitoring in effect size, signifying its potential scientific and practical relevance in understanding emotional self-regulation in early adolescence. These results mirror those of Oddy et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), who found that young adolescents who consumed a Westernized diet high in fat and sugar struggled with poorer mental health than peers who consumed diets richer in leafy green vegetables and fruits. Specifically, this study found that consumption of high-sugar confectionary desserts and fatty meats were associated with externalizing symptoms (aggression and delinquency) rather than internalizing symptoms (anxiety, depression, somatization, and withdrawal).\u003c/p\u003e\u003cp\u003eThe cognitive self-regulation subscale assessed goal-motivated behavior and the ability to remain persistent through challenges. In the primary regression analyses, none of the fast food consumption groups were significantly associated with the cognitive self-regulation subscale. Parental monitoring demonstrated the strongest positive effect size, followed by self-reported socioeconomic status. No other variables were significantly associated with this subscale score. These results suggest that parents who are highly knowledgeable about their children\u0026rsquo;s daily experiences may augment their children's motivation and goal persistence, regardless of dietary habits. This finding aligns with that of Kristjansson and Sigfusdottir (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), who found that greater parental monitoring was positively correlated with adolescents\u0026rsquo; academic achievement and the effort they invested in schoolwork, even when controlling for parental education and family structure.\u003c/p\u003e\u003cp\u003eFor the behavioral subscale, only the frequent fast food group exhibited significantly poorer self-regulation. The subscale measured the ability to sit still and how often the participant struggled with fidgeting. Socioeconomic status showed the greatest effect size, suggesting that participants in the higher-status groups were more likely to be able to sit still during tasks. However, frequent fast food consumption demonstrated a similar effect size, though in a negative direction. This finding suggests that high fast food consumption is associated with a reduced ability to sit still during tasks requiring sustained attention. This also implies that frequent fast food intake is associated with more fidgeting. The finding supports prior research that discovered high sugar diets reduce communication between the left and right hippocampi (Stadterman, 2019). This disruption is meaningful due to the right hemisphere\u0026rsquo;s lesser-known role in regulating sensory motor input (Bast \u0026amp; Feldon, 2003). Consequently, these students may have more difficulty sitting in a classroom.\u003c/p\u003e\u003cp\u003eParticipants in the lowest socioeconomic status group had the highest frequency of fast food intake. These students may be particularly vulnerable to the aforementioned academic obstacles. Prior research demonstrates that fast food consumption is particularly high in disadvantaged African American communities due to the ease of access to fast food restaurants, revealing nutritional disparities between their communities and predominantly white communities (James et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eAlthough the study was novel in its exploration of the relationship between fast food consumption and self-regulation, there are several limitations to address. The current study utilized a single self-reported self-regulation measure. This study did not include alternative measures, such as effortful control, which may have provided complementary or unique perspectives. Future research should also gather both parental and teacher evaluations of self-regulation to prevent biased reporting. An important caveat is that the present study used a cross-sectional design. However, this design was intentional in order to examine potential dietary correlations, specifically during early adolescence. Still, whether these results would exhibit stability over time or predict future self-regulatory issues remains unknown. Furthermore, the direction of the relationship between fast food and self-regulation was also unknown due to its cross-sectional design. Young teenagers with poor self-regulation may provoke their parents to purchase fast food for them. In addition, it is also possible that the relationship is bidirectional and cyclical. Fast food may be addictive and lower self-regulation simultaneously. That drop in regulation may impair an individual's ability to further resist eating fast food. The examples presented above reveal that no causal relationship can be drawn from this study. Future studies should examine if fast food intake remains stable across adolescence and if these relationships persist longitudinally. Furthermore, participants were asked how many days they had eaten fast food, but not specifically what they had eaten. Given the wide variety of fast food and quick-serve restaurants, some chains offer healthier choices. Knowing what items participants ordered and from which fast food chains might have provided greater understanding of these correlational findings. Future research should include a fast food measure that asks which restaurants participants typically eat at and what they typically order. The measure should also span more than just the previous week, as fast food consumption may vary depending on life circumstances. Since fast food may be more memorable to children and adolescents than individual food items, fast food may serve as a reliable proxy for the Western diet in future studies. That said, not everyone who consumes high-fat and high-sugar items is purchasing them from restaurants, so both the YAQ and fast food measures should be used in tandem in the future. Overall, the model also yielded a small prediction percentage, which could be the result of the large sample size or the presence of other covariates or confounders, such as sleep or exercise. Future research should add more covariates to the model presented in this study. The data used in this study were collected in 2015, which may limit the applicability of the findings to today\u0026rsquo;s rapidly evolving media and dietary environments. However, this timeframe may also offer a unique advantage since it precedes the widespread popularity of short-form content and TikTok, thereby removing a potential confound. Finally, the regional sample was large, but demonstrates low generalizability since a majority of the sample was white and lived in middle to upper-middle-class households. Future research should recruit a more representative sample.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, greater fast food intake was associated with lower self-regulation overall and was the most significant predictor of lower emotional self-regulation skills within the sample, even when considering factors such as age, gender, grade level, SES, and parental monitoring. Though this exploratory study yielded interesting results, more research is needed to justify any causal claims or practical implications. This work represents the first step in the exploration of fast food's potential impact on self-regulation. It would be prudent to replicate this study with enhanced measurements, a more diverse sample, and utilize a longitudinal design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData is openly available on ICPSR at (https://www.icpsr.umich.edu/web/ICPSR/studies/36850).\u003c/p\u003e\n\u003cp\u003eThe author has no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eThe study was not funded.\u003c/p\u003e\n\u003cp\u003eThis article was conducted using secondary data from Duke University\u0026apos;s RAISE study.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The author would like to thank Duke University\u0026rsquo;s RAISE study team for making the dataset publicly available. No other individuals contributed to the writing of this article.\u003c/p\u003e\n\u003cp\u003eAll procedures performed in that study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards, including both verbal consent given by the parents of the minors and the participants themselves.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson, P. (2002). 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Reduced hippocampal and amygdala volume as a mechanism underlying stress sensitization to depression following childhood trauma. \u003cem\u003eDepression and anxiety\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(9), 916\u0026ndash;925. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/da.23062\u003c/span\u003e\u003cspan address=\"10.1002/da.23062\" 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":true,"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":"early adolescence, self-regulation, dietary habits, fast food, emotional regulation","lastPublishedDoi":"10.21203/rs.3.rs-8197318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8197318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFast food consumption has been linked to poorer health indicators and behavioral dysregulation in adolescents, yet little research has examined its association with self-regulation. Regulation of emotions, cognitions, and behaviors is critical for the transition into adolescence.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study explored whether frequency of fast food consumption was associated with the three domains of self-regulation in early adolescents while controlling for sociodemographic factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eParticipants were 2,164 early adolescents (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.53 years) drawn from a large, school-based dataset. Adolescents reported their weekly frequency of fast food consumption and completed a 13-item self-regulation measure with domain specific subscales. Covariates included age, grade, gender, subjective socioeconomic status, and parental monitoring. Multiple regression was utilized to test whether fast food consumption was uniquely associated with self-regulation after adjusting for covariates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGreater fast food consumption was associated with lower overall self-regulation scores (β = \u0026ndash;.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01). When subscales were examined, fast food consumption remained significantly associated with poorer emotional self-regulation (β = \u0026ndash;.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) but not cognitive self-regulation (β = \u0026ndash;.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.12).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eFast food consumption showed modest negative associations with emotional and behavioral self-regulation in early adolescence, even after adjusting for demographic factors. These findings cautiously emphasize the importance of considering everyday health behaviors when examining self-regulatory abilities in early adolescence. Further research is necessary to replicate findings and to determine whether practical implications exist.\u003c/p\u003e","manuscriptTitle":"Fast Food, Slower Control? Exploring the Relationship Between Fast Food Consumption and Self-Regulatory Abilities in Early Adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:46:57","doi":"10.21203/rs.3.rs-8197318/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":"2fe6015a-b6a5-4105-9ed2-b0b416b85065","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:25:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:46:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8197318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8197318","identity":"rs-8197318","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}