Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Dec 1.
Published in final edited form as: Behav Res Ther. 2020 Oct 8;135:103753. doi: 10.1016/j.brat.2020.103753

Internalizing Symptoms Modulate Real-World Affective Response to Sweet Food and Drinks in Children

Tyler B Mason 1, Christine H Naya 1, Susan M Schembre 2, Kathryn E Smith 3, Genevieve F Dunton 1,4
PMCID: PMC7793613  NIHMSID: NIHMS1636651  PMID: 33049549

Abstract

The purpose of the current study was to examine affective response to sweet foods and drinks as a function of children’s internalizing symptoms using ecological momentary assessment (EMA). A sample of 192 8–12-year-old children completed a self-report measure of internalizing symptoms and EMA prompts of affect and food intake for eight days, excluding time at school. There was an interaction between sweet food intake and internalizing symptoms for positive affect and for sweet drink intake and internalizing symptoms for negative affect. Those low in internalizing symptoms had significantly lower positive affect after consumption of sweet foods compared to when they did not consume sweet foods whereas those higher in internalizing symptoms had slightly, but not significantly, higher positive affect after consumption of sweet foods. Those low in internalizing symptoms had significantly higher negtive affect after consumption of sweet foods compared to when they did not consume sweet foods whereas those higher in internalizing symptoms had slightly, but not significantly, lower negative affect after consumption of sweet foods. Findings highlight the ways in which internalizing symptoms may modulate affective response to sweet foods and drinks.

Keywords: affective response, internalizing symptoms, children, eating behavior, obesity prevention

Obesity and internalizing symptoms (e.g., anxiety and depressive symptoms) are highly prevalent in children (Bor et al., 2014; Ludwig, 2018; Patel et al., 2007). Further, research has shown positive links between internalizing symptoms and obesity in children and adults (Bodenlos et al., 2011; Reeves et al., 2008). Yet, more research is needed to study the mechanisms that underlie the association between internalizing symptoms and obesity among children, which will serve to elucidate new prevention and intervention targets. Diet is of critical importance to weight maintenance and obesity prevention (Sothern, 2004). Specifically, excess intake of sugar-sweetened foods and drinks is particularly relevant given they are typically higher in calories, associated with lower satiety, and can lead to insulin resistance (Drewnowski, 1998; Ma et al., 2016; Poti et al., 2014). Consistently, studies have found elevated caloric and sugar intake in children with obesity (Dehghan et al., 2005; Garipağaoğlu et al., 2008; Pei et al., 2014) as well as in children with greater negative affectivity (Mason et al., 2019; O’Reilly et al., 2015). This evidence suggests that intake of sweet foods and drinks are key modifiable behaviors that may serve as a link between internalizing symptoms and obesity in children (Reeves et al., 2018).

A long history of research has examined how intake of food and drinks is related to momentary mood states with the primary focus on how mood may precipitate intake and how intake affects subsequent mood (Booth 1994; Macht, 2008). Although results are inconsistent, negative moods tend to precipitate unhealthy food and drink consumption and positive moods tend to precipitate healthy food and drink consumption (e.g., fruit and vegetable intake; Mason, Do, et al., 2019). A recent theoretical framework suggests that behaviors, such as food intake, may serve as acute-mood-altering agents that can reinforce or repel subsequent engagement in the behavior (Dunton et al., 2019). That is, one’s affective response following engaging in a behavior may lead to future engagement in or avoidance of the behavior. Specifically, sugar-sweetened food and drinks may have mood-altering effects as sugar has an observable effect on the brain (DiNicolantonio et al., 2018); although, research has been somewhat inconclusive as to whether sugar improves or worsens mood (Mantanzis et al., 2019).

Affective response to sweet food and drink intake has received less attention in empirical studies, and limited research has been conducted in children. A laboratory study of adult women with normal weight and overweight found elevated negative affect and lower positive affect following consumption of more energy-dense foods; women with overweight displayed even greater negative affective states compared to women with normal weight (Macht, Gerer, & Ellgring, 2003). Further, in studies of adults using naturalistic methodology, Liao et al. (2018) found no associations between high sugar foods and subsequent mood states while Jeffers et al. (2019) found increased negative mood following consumption of high sugar foods. Research has yet to examine differences in affective response to food in children and how affective response may differ as a function of individual difference variables.

Individuals with mood and anxiety disorders typically have blunted reward functioning and reward learning (Morris et al., 2015; Tremblay et al., 2005). In addition, in a large sample of adolescents, higher internalizing symptoms were positively associated with baseline hedonic hunger (i.e., extreme responsiveness and pleasure towards food) as well as greater hedonic hunger across 1-year (Mason, Dunton, et al., 2019). Reward deficiency syndrome suggests that, for people who have deficits in reward, palatable foods and drinks can activate reward areas in the brain in order to serve a self-medication function (Blum & Braverman, 2001; Davis & Fox, 2008). It is possible that children with greater internalizing symptoms may have a different affective response to sweet food and drinks compared to children with fewer internalizing symptoms, and this may be explained by reward deficiency syndrome. Specifically, children with internalizing symptoms may have a more rewarding affective response to sweet food and drinks (i.e., increased positive affect and reduced negative affect), which may reinforce sweet food and drink consumption. Because internalizing symptoms begin developing and increasing in middle childhood (Shanahan et al., 2014), this is a critical time to study how internalizing symptoms may modulate affective response to sweet food and drinks.

Ecological momentary assessment (EMA) is a methodology that allows for studying micro-temporal processes, such as affective response, across the day in individuals’ daily lives (Shiffman et al., 2008). To do this, participants complete surveys of behaviors, experiences, and context multiple times across the day for a short period of time (e.g., a week). Primary advantages of EMA include limiting self-report biases and ability to examine momentary, temporal relations among variables across the day in individuals’ natural environments (Shiffman et al., 2008). Further, EMA allows for the studying of real-world affective response to food intake behaviors.

There is a paucity of studies examining affective response to sweet food and drink consumption in children. Furthermore, no studies have examined how internalizing symptoms modulate affective response to sweet food and drink consumption. Therefore, the goals of the secondary data analysis were to use EMA to examine affective response to sweet foods and drinks in children and to assess internalizing symptoms as a moderator of affective response. It was hypothesized that children with greater internalizing symptoms would respond positively to consuming sweet foods and drinks compared with children lower in internalizing symptoms. Previous manuscripts from this data have examined affect as a predictor or consequence of food intake (Liao et al., 2018, Mason et al., 2019), but this is the first to study internalizing symptoms as a moderator of affective response to sweet food and drink intake in children

Method

Participants

There were 202 children recruited into the Mothers and Their Children’s Health (MATCH) study (Dunton et al., 2015), and 192 provided data for the current paper. The MATCH study is a longitudinal study of mothers and their children’s eating, physical activity, and obesity risk. The current study used baseline data from the study. Participants were recruited from elementary schools and after-school programs in the greater Los Angeles area through informational flyers and in-person research staff visits. Fifty-one percent of children were female, and the mean age of children was 9.60 (SD=0.92; Range=8–12). Approximately 20% of children were in the overweight range and 16% were in the obese range, and the mean BMI-z of children was 0.49 (SD=1.06).

Inclusion criteria consisted of the following: (a) child currently in 3rd – 6th grade, (b) child resides with mother at least 50% of time, and (c) ability of both mother and child to speak and read in English or Spanish. Exclusion criteria included: (a) use of medication for thyroid or psychological condition, (b) health condition limiting ability to be physically active, (c) child enrolled in a special education program due to concerns about reduced understanding of assent, (d) currently using oral or inhalant corticosteroids for asthma, (e) pregnancy, (f) child underweight (BMI < 5th% for age and sex) due to concerns about these children having a different set of health concerns, and (g) mother works more than two evenings (between 5–9pm) during the week, or more than one 8-hour weekend shift.

Procedures

The study was approved by the appropriate institutional review boards. Parental consent and child assent were obtained prior to participation. Children completed an in-person study visit with their mothers where mothers and children completed paper-and-pencil questionnaires. In addition, anthropometric measures were taken by trained staff using a portable stadiometer and electronically calibrated digital scale. Body mass index (BMI; kg/m2) and CDC age- and gender-specific BMI z-scores were determined using Epi Info, (CDC, Atlanta, GA). Children also received instruction for the EMA protocol and completed EMA surveys for the next eight days, with the first day starting immediately following the study visit.

The EMA application consisted of a customized Java application for Android Operating System (Google, Inc). Participants were able to download the application on their personal Android device or could borrow a study phone (MotoG). Through home Wi-Fi, EMA data was securely transferred from the device to a secure cloud server managed by Google for safe storage utilizing precautions such as encryption, network restrictions, and security protection (e.g., firewall, antivirus). Participants who were not responding consistently received reminder calls from study staff to increase response rates.

Children received EMA survey prompts for eight days, excluding time at school. Prompts were generated in stratified random sampling windows, with one prompt randomly occurring during each window (e.g., 3–4PM, 5–6PM). Children received three prompts on weekdays between 3PM to 8PM and seven prompts on weekends between 7AM to 8PM. Each survey question was presented on a separate screen. Sleep and wake times were customized for each participant. Each survey took about 2–3 minutes to complete. Participants had the option to delay surveys if prompted during incompatible times. Up to two reminder prompts would be delivered within 10 minutes of the initial prompt, and, if there was no response after the reminder prompts, then the survey was closed.

Measures

Revised Children’s Anxiety and Depression Scale (RCADS; Chorpita et al., 2000).

The RCADS was used to assess children’s internalizing symptoms. Thirty-two items from the Major Depression, Generalized Anxiety, Separation Anxiety, and Panic Disorder subscales were used. Children use a response scale ranging from 1 (never) to 4 (always) to answer each item. A mean score with the four subscales averaged together was calculated, and higher scores indicating greater internalizing symptoms. A systematic review and meta-analysis showed that the RCADS has adequate psychometric properties across various assessment settings, languages, and locations (Piqueras et al., 2017). The internal consistency of the scale in the current study was α=.92.

EMA Affective State.

A subset of items from the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) were chosen to measure affect in order to keep the burden of EMA surveys low. Negative affect was measured with three items: “Right before the phone went off, how (1) mad, (2) sad, and (3) stressed were you feeling?”, and positive affect was measured using two items: “Right before the phone went off, how (1) happy and (2) joyful were you feeling?” Affect items were each presented to the participant on a unique screen, and the response options consisted of a scale ranging from 1 (not at all) to 4 (extremely). Items were averaged to create a negative and positive affect score, respectively.

EMA Sweet food and drink intake.

Children reported on recent dietary intake of sweet food and drinks by asking them: “Over the last 2 hours, which of these things have you done?” Response options included a checklist of several food items including “eaten pastries or sweets” and “drank soda or energy drinks (not counting diet). Food and drink intake responses in EMA have shown good concordance with 24-hour recall dietary assessments in children (O’Connor et al., 2018).

Statistical Analyses

Three-level multilevel linear models, accounting for clustering of prompts within days within persons, were used with past two-hour sweet food or drink intake as predictors of affect. Bootstrapping with 1000 resamples was used to account or non-normality of negative affect (Pek et al., 2018). Four models were run; models examined sweet food consumption as a predictor of negative affect and positive affect and sweet drink consumption as a predictor of positive affect and negative affect. Models included sweet food or drink intake, total internalizing symptoms, and the cross-level interaction between sweet food or drink intake and internalizing symptoms as predictors of affect. All models included baseline BMI-z score, baseline age, and ethnicity (non-Hispanic versus Hispanics) as covariates. All available data was used. Significance values based on bootstrapped 95% bias-corrected confidence intervals (BC CIs) were used for significance testing. Interactions were plotted at 1SD below the mean and 1SD above the mean of internalizing symptoms, representing low and high internalizing symptoms respectively. Simple slopes were investigated using recommended methods for probing two-way cross-level multilevel interactions (Bauer & Curran, 2005; Curran et al., 2006; Preacher et al., 2006).

Results

Children responded to 8,122 of 10,639 total prompts. Sweet food intake was reported in 3.2% of prompts. Sweet drink intake was reported in 2.1% of prompts. Within-subjects, internal consistency reliabilities (ωs) were .90 for positive affect and .86 for negative affect, and between-subjects ωs were .97 for positive affect and .89 for negative affect. Children generally exhibited low levels of internalizing symptoms (RCADS M=4.28, SD=3.17, Range:0–17.50).

Multilevel models examining sweet food or drink intake, internalizing symptoms, and the interaction between sweet food or drink intake and internalizing symptoms are reported in Table 1. Higher BMI-z scores were associated with lower positive affect. Consistent with hypotheses, there were interactions between sweet food intake and internalizing symptoms predicting positive affect and a trend for negative affect. See Figure 1 for a plot of the interaction for positive affect. Simple slopes analyses showed that those lower in internalizing symptoms had lower positive affect after consumption of sweet foods compared to when they did not consume sweet foods (Estimate=−0.17, z=−2.94, p=0.003). While the plot showed those higher in internalizing symptoms had higher positive affect after consumption of sweet foods compared to when they did not consume sweet foods, the simple slope did not reach significance (Estimate=0.09, z=1.52, p=0.13). A plot of the interaction for negative affect (see Figure 2) showed an opposite pattern such that those higher in internalizing symptoms had lower negative affect when sweet food intake was reported and those lower in internalizing symptoms had higher negative affect when sweet food intake was reported; simple slopes were not run since this was a trend.

Table 1.

Three-level multilevel Models of Sugar Intake and Internalizing Symptoms Predicting Affect

Positive Affect Negative Affect
B SE pa 95% BC CI B SE pa 95% BC CI
Sweet Food Intake
Hispanic 0.03 0.10 .77 [−0.17, 0.23] −0.04 0.04 .32 [−0.11, 0.04]
BMI-z −0.010 0.05 .04 [−0.20, −0.01] 0.02 0.02 .28 [−0.02, 0.05]
Age 0.001 0.06 .99 [−0.11, 0.11] 0.03 0.02 .15 [−0.01, 0.07]
Sweet food intake −0.21 0.07 .002 [−0.34, −0.08] 0.11 0.04 .02 [0.02, 0.19]
Internalizing symptoms −0.03 0.02 .08 [−0.06, 0.003] 0.02 0.01 .002 [0.01, 0.03]
Sweet food intake X internalizing symptoms 0.04 0.01 .002 [0.01, 0.07] −0.02 0.01 .06 [−0.03, 0.001]
Sweet Drink Intake
Hispanic 0.03 0.10 .78 [−0.17, 0.23] −0.04 −0.04 .30 [−0.11, 0.04]
BMI-z −0.10 0.05 .04 [−0.20, −0.003] 0.02 0.02 .29 [−0.02, 0.05]
Age 0.00 0.06 .99 [−0.11, 0.11] 0.03 0.02 .15 [−0.01, 0.07]
Sweet drink intake 0.01 0.08 .90 [−0.15, 0.17] 0.15 0.05 .002 [0.06, 0.26]
Internalizing symptoms −0.03 0.02 .12 [−0.06, 0.01] 0.02 0.01 .001 [0.01, 0.03]
Sweet drink intake X internalizing symptoms 0.01 0.08 .54 [−0.02, 0.04] −0.03 0.01 .005 [−0.04, −0.01]
Open in a new tab

Note.

a

Significance values based off 95% BC CIs. BMI=body mass index; BC CI= bias-corrected confidence interval.

Figure 1.

Figure 1.

Open in a new tab

Interaction of sweet food consumption and individual differences in internalizing symptoms predicting positive affect. Low internalizing symptoms=1SD below the mean of internalizing symptoms. High internalizing symptoms=1SD above the mean of internalizing symptoms.

Figure 2.

Figure 2.

Open in a new tab

Interaction of sweet food consumption and individual differences in internalizing symptoms predicting negative affect. Low internalizing symptoms=1SD below the mean of internalizing symptoms. High internalizing symptoms=1SD above the mean of internalizing symptoms.

There was also an interaction between sweet drink intake and internalizing symptoms predicting negative affect, but not positive affect. See Figure 3 for a plot of the interaction for negative affect. Simple slopes analyses showed that those lower in internalizing symptoms had higher negative affect after consumption of sweet drinks compared to when they did not consume sweet drinks (Estimate=0.13, z=−2.92, p=0.004). While the plot showed those higher in internalizing symptoms had lower negative affect after consumptions of sweet drinks compared to when they did not consume sweet drinks, the simple slope did not reach significance (Estimate=−0.04, z=−0.95, p=0.35).

Figure 3.

Figure 3.

Open in a new tab

Interaction of sweet drink consumption and individual differences in internalizing symptoms predicting negative affect. Low internalizing symptoms=1SD below the mean of internalizing symptoms. High internalizing symptoms=1SD above the mean of internalizing symptoms.

Discussion

This study set out to examine the differences in affective response to sweet food and drink intake as a function of internalizing symptoms in children. Using EMA surveys, we found interactions between sweet food intake and internalizing symptoms for positive affect, such that those with low internalizing symptoms showed lower positive affect after consuming sweet food compared to when they did not, while those with higher internalizing symptoms showed slightly, but not significantly, higher positive affect after consuming sweet food compared to when they did not. There was also an interaction between sweet drink intake and internalizing symptoms for negative affect, such that those with low internalizing symptoms showed higher negative affect after consuming sweet drinks compared to when they did not, while those with higher internalizing symptoms showed slightly, but not significantly, lower negative affect after consuming sweet drinks compared to when they did not. A similar interaction was found between sweet food intake and internalizing symptoms for negative affect, but it did not reach significance.

Our findings suggest that children with higher internalizing symptoms show similar or slightly higher positive or slightly lower negative affect after sweet food or drink intake compared to no sweet food or drink intake whereas children with lower internalizing symptoms have an adverse affective response after sweet food or drink intake compared to no sweet food or drink intake. This may help explain why internalizing symptoms and obesity co-occur in children. The results of our study seem to suggest that children with lower internalizing symptoms may become deterred from consuming sweet food and drinks given the adverse affective response that they experience, which could explain lower obesity risk in this group.

The preliminary finding of slightly rewarding affect after sweet consumption in children higher in internalizing symptoms is consistent with reward deficiency syndrome. If children with higher internalizing symptoms are continuously experiencing a rewarding affective response after sugar consumption, they may come to learn to self-soothe via sugary food/drinks. While this finding in our study was small, and non-significant, it is important to consider that this was a community-based sample of children who overall experienced relatively low levels of internalizing symptoms. It is possible that rewarding affective response to sweet food and drinks may be magnified in children with clinical levels of internalizing symptoms, and this should be studied in future research.

Further, findings of internalizing symptoms as a moderator of affective responses to sweet food and drink intake may explain the mixed findings in the literature. For example, reviews of the effect of sugar on behavior and cognition have found no significant associations between sugar consumption and mood (Mantantzis et al., 2019; Wolraich et al., 1995). Also, in a recent EMA study, there was no association between consumption of sweet food over the past two hours and subsequent affect in adult mothers (Liao et al., 2018). Yet, a separate EMA study of young adults found sugary food to be related to higher subsequent negative affect (Jeffers et al., 2019). This body of research has not considered trait-level internalizing symptoms as a modulator of affective response to food intake. Therefore, this could explain some mixed findings in the literature.

This study had several strengths. The sample was diverse with regards to gender, race, and ethnicity and comprised children from late middle-to-late childhood. In addition, using EMA surveys allowed for repeated assessment of sweet food and drink intake and affect throughout the day in individuals’ naturalistic setting and extends the limited literature on use of EMA to study eating in children (Mason, Do, et al., 2019). By use of EMA, this study maximized ecological validity, which allows findings to be extended to children’s natural environment where context and situational factors are highly variable.

However, although this study identified important findings regarding affective response to sweet food and drink intake, there are limitations that should be described. As stated above, given this was a community sample of children, there was a restricted range of internalizing symptoms, such that the majority of children had low symptom levels. Future research on affective response to sweet food and drink intake is needed in clinical samples. Extrapolating from the findings of the current study, children with clinically significant symptom elevations may have an even more rewarding affective response to sweets. Only a subset of items were used to measure affect in order to reduce the burden of EMA surveys; as such, a wider range of affective states may be useful to examine in future EMA studies.

Although EMA was used to assess sweet food and drink consumption, other factors that can change affective response such as type of carbohydrate, individual glucose regulation, meal composition, and portion size (Benton & Donohoe, 1999; Mantantzis et al., 2019; Reid & Hammersley, 1998; Smith et al.,1988) were not measured, as they were beyond the scope of this study. Future studies with EMA assessment of portion size may be especially informative, as it would allow for examination of dose-response relationships. In addition, children reported relatively low levels of sweet food and drink consumption, and thus, each child’s affective response was characterized by only a small number of sweet food and drink episodes. Also, it is possible that there was under-reporting of sweet food and drink intake, and in addition to potential underreporting, the lower percentage may be a function of signal- vs. event-contingent reports of eating. These limit ability to generalize results across all sweet food and drink occurrences.”

Further, since EMA surveys simultaneously assessed current affect and past two-hour sweet food and drink intake, the exact time lapse between sweet food/drink consumption and assessment of affect could vary from prompt to prompt. Research suggests that carbohydrate consumption can influence mood in as short as 15 minutes to as long as two hours after consumption (Denson et al., 2010; Smit et al., 2004). Thus, future studies that are designed to capture more precise micro-temporal associations could help elucidate the dynamic relationship between eating and affective response. This could be accomplished through use of event-contingent signals in EMA to capture all eating and drinking episodes. Finally, although standard in EMA studies with children (Mason, Do, et al., 2019), children did not complete EMA recordings during the day on weekdays due to school, which resulted in missing part of the day on weekdays.

This study investigated affective response to consumption of sweet food and drinks in children and internalizing symptoms as a moderator using EMA. We identified differential affective response to sweet food and drink consumption as a function of internalizing symptoms in children, such that children with higher internalizing symptoms show slightly rewarding affective response to sweet food and drink intake, and children with lower internalizing symptoms show an adverse affective response to sweet food and drink intake. Results from this study suggest the need to expand the current framework around children’s obesity prevention efforts beyond just promoting healthy eating or physical activity and incorporate skills on awareness of one’s affective responses to eating. Mindfulness-based programs that have incorporated this kind of instruction have been proven to be successful in behavioral interventions for youth obesity and disordered eating (Atkinson & Wade 2015; Dalen et al., 2015). Future research can expand on these findings by examining whether rewarding affect responses to eating predicts future weight gain among those with internalizing symptoms, particularly as children become more autonomous.

Highlights.

  • Internalizing symptoms and obesity co-occur in children.

  • Internalizing symptoms modulated affect response to sweet food and drinks.

  • Children low in internalizing symptoms had a worsening affective response.

  • Children high in internalizing symptoms had a slightly rewarding response.

Acknowledgments

This work was supported by the National Heart Lung and Blood Institute (R01HL119255). The authors have no conflicts of interest to disclose.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest

The authors have no conflicts of interest.

References

  1. Atkinson MJ, & Wade TD (2015). Mindfulness‐based prevention for eating disorders: A school‐based cluster randomized controlled study. International Journal of Eating Disorders, 48, 1024–1037. 10.1002/eat.22416 [DOI] [PubMed] [Google Scholar]
  2. Bauer DJ, & Curran PJ (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40, 373–400. 10.1207/s15327906mbr4003_5 [DOI] [PubMed] [Google Scholar]
  3. Benton D, & Donohoe RT (1999). The effects of nutrients on mood. Public Health Nutrition, 2, 403–409. doi: 10.1017/S1368980099000555 [DOI] [PubMed] [Google Scholar]
  4. Blum K, & Braverman ER (2001). “Reward deficiency syndrome”: An emerging concept. Molecular Psychiatry, 6, S3. [Google Scholar]
  5. Bodenlos JS, Lemon SC, Schneider KL, August MA, & Pagoto SL (2011). Associations of mood and anxiety disorders with obesity: Comparisons by ethnicity. Journal of Psychosomatic Research, 71, 319–324. 10.1016/j.jpsychores.2011.03.004 [DOI] [PubMed] [Google Scholar]
  6. Booth DA (1994). Psychology of nutrition. London: Taylor & Francis. [Google Scholar]
  7. Bor W, Dean AJ, Najman J, & Hayatbakhsh R (2014) Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Australian and New Zealand Journal of Psychiatry, 48, 606–616. doi: 10.1177/0004867414533834 [DOI] [PubMed] [Google Scholar]
  8. Chorpita BF, Yim L, Moffitt C, Umemoto LA, & Francis SE (2000). Assessment of symptoms of DSM-IV anxiety and depression in children: A revised child anxiety and depression scale. Behaviour Research and Therapy, 38, 835–855. [DOI] [PubMed] [Google Scholar]
  9. Curran PJ, Bauer DJ, & Willoughby MT (2006). Testing and probing interactions in hierarchical linear growth models In Bergeman CS & Boker SM (Eds.), The Notre Dame Series on Quantitative Methodology, Volume 1: Methodological issues in aging research (pp. 99–129). Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]
  10. Dalen J, Brody JL, Staples JK, & Sedillo D (2015). A conceptual framework for the expansion of behavioral interventions for youth obesity: A family-based mindful eating approach. Childhood Obesity, 11, 577–584. 10.1089/chi.2014.0150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Davis C, & Fox J (2008). Sensitivity to reward and body mass index (BMI): evidence for a non-linear relationship. Appetite, 50, 43–49. 10.1016/j.appet.2007.05.007 [DOI] [PubMed] [Google Scholar]
  12. Dehghan M, Akhtar-Danesh N, & Merchant AT (2005). Childhood obesity, prevalence and prevention. Nutrition Journal, 4, 24 10.1186/1475-2891-4-24 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Denson TF, von Hippel W, Kemp RI, & Teo LS (2010). Glucose consumption decreases impulsive aggression in response to provocation in aggressive individuals. Journal of Experimental Social Psychology, 46, 1023–1028. 10.1016/j.jesp.2010.05.023 [DOI] [Google Scholar]
  14. Drewnowski A (1998). Energy density, palatability, and satiety: implications for weight control. Nutrition Reviews, 56, 347–353. 10.1111/j.1753-4887.1998.tb01677.x [DOI] [PubMed] [Google Scholar]
  15. Dunton GF, Kaplan J, Monterosso J, Pang R, Mason TB, Kirkpatrick M, … & Leventhal AM (2019). Conceptualizing health behaviors as acute mood-altering agents: Implications for cancer control. Manuscript submitted for publication. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Garipağaoğlu M, Sahip Y, Budak N, Akdikmen Ö, Altan T, & Baban M (2008). Food types in the diet and the nutrient intake of obese and non−obese children. Journal of Clinical Research in Pediatric Endocrinology, 1, 21. doi: 10.4008/jcrpe.v1i1.5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jeffers AJ, Mason TB, & Benotsch EG (2019). Psychological eating factors, affect, and ecological momentary assessed diet quality. Eating and Weight Disorders, 1–9. 10.1007/s40519-019-00743-3 [DOI] [PubMed] [Google Scholar]
  18. Lengua LJ (2003). Associations among emotionality, self-regulation, adjustment problems, and positive adjustment in middle childhood. Journal of Applied Developmental Psychology, 24, 595–618. 10.1016/j.appdev.2003.08.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Liao Y, Schembre SM, O’Connor SG, Belcher BR, Maher JP, Dzubur E, & Dunton GF (2018). An electronic ecological momentary assessment study to examine the consumption of high-fat/high-sugar foods, fruits/vegetables, and affective states among women. Journal of Nutrition Education and Behavior, 50, 626–631. 10.1016/j.jneb.2018.02.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ludwig DS (2018) Epidemic childhood obesity: Not yet the end of the beginning, Pediatrics, 141, e20174078. doi: 10.1542/peds.2017-4078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ma J, Jacques PF, Meigs JB, Fox CS, Rogers GT, Smith CE, … & McKeown NM (2016). Sugar-sweetened beverage but not diet soda consumption is positively associated with progression of insulin resistance and prediabetes. The Journal of Nutrition, 146, 2544–2550. 10.3945/jn.116.234047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Macht M (2008). How emotions affect eating: A five-way model. Appetite, 50, 1–11. 10.1016/j.appet.2007.07.002 [DOI] [PubMed] [Google Scholar]
  23. Mantantzis K, Schlaghecken F, Sünram-Lea SI, & Maylor EA (2019). Sugar rush or sugar crash? A meta-analysis of carbohydrate effects on mood. Neuroscience & Biobehavioral Reviews, 101, 45–67. 10.1016/j.neubiorev.2019.03.016 [DOI] [PubMed] [Google Scholar]
  24. Mason TB, Do B, Wang S, & Dunton GF (2019). Ecological momentary assessment of eating and dietary intake behaviors in children and adolescents: A systematic review of the literature. Appetite, 104465. 10.1016/j.appet.2019.104465 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Michels N, Sioen I, Braet C, Eiben G, Hebestreit A, Huybrechts I, … & De Henauw S (2012). Stress, emotional eating behaviour and dietary patterns in children. Appetite, 59, 762–769. 10.1016/j.appet.2012.08.010 [DOI] [PubMed] [Google Scholar]
  26. Morris BH, Bylsma LM, Yaroslavsky I, Kovacs M, & Rottenberg J (2015). Reward learning in pediatric depression and anxiety: Preliminary findings in a high‐risk sample. Depression and Anxiety, 32, 373–381. 10.1002/da.22358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. O’Connor SG, Ke W, Dzubur E, Schembre S, & Dunton GF (2018). Concordance and predictors of concordance of children’s dietary intake as reported via ecological momentary assessment and 24 h recall. Public Health Nutrition, 21, 1019–1027. 10.1017/S1368980017003780 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. O’Reilly GA, Huh J, Schembre SM, Tate EB, Pentz MA, & Dunton G (2015). Association of usual self-reported dietary intake with ecological momentary measures of affective and physical feeling states in children. Appetite, 92, 314–321. 10.1016/j.appet.2015.05.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Patel V, Flisher AJ, Hetrick S, and Mcgorry P (2007) Mental health of young people: A global public-health challenge, Lancet 369, 1302–1313. doi: 10.1016/s0140-6736(07)60368-7 [DOI] [PubMed] [Google Scholar]
  30. Pek J, Wong O, & Wong A (2018). How to address non-normality: A taxonomy of approaches, reviewed, and illustrated. Frontiers in Psychology, 9, 2104 10.3389/fpsyg.2018.02104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pei Z, Flexeder C, Fuertes E, Standl M, Buyken A, Berdel D, … & GINIplus and LISAplus Study Group. (2014). Food intake and overweight in school-aged children in Germany: results of the GINIplus and LISAplus studies. Annals of Nutrition and Metabolism, 64, 60–70. 10.1159/000362694 [DOI] [PubMed] [Google Scholar]
  32. Piqueras JA, Martin-Vivar M, Sandin B, San Luis C, & Pineda D (2017). The Revised Child Anxiety and Depression Scale: A systematic review and reliability generalization meta-analysis. Journal of Affective Disorders, 218, 153–169. 10.1016/j.jad.2017.04.022 [DOI] [PubMed] [Google Scholar]
  33. Poti JM, Slining MM, & Popkin BM (2014). Where are kids getting their empty calories? Stores, schools, and fast-food restaurants each played an important role in empty calorie intake among US children during 2009–2010. Journal of the Academy of Nutrition and Dietetics, 114, 908–917. 10.1016/j.jand.2013.08.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Preacher KJ, Curran PJ, & Bauer DJ (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437–448. 10.3102/10769986031004437 [DOI] [Google Scholar]
  35. Reeves GM, Postolache TT, & Snitker S (2008). Childhood obesity and depression: Connection between these growing problems in growing children. International Journal of Child Health and Human Development, 1, 103–114. [PMC free article] [PubMed] [Google Scholar]
  36. Reid M, & Hammersley R (1998). The effects of sugar on subsequent eating and mood in obese and non-obese women. Psychology, Health & Medicine, 3, 299–313. 10.1080/13548509808400604 [DOI] [Google Scholar]
  37. Smit HJ, Cotton JR, Hughes SC, & Rogers PJ (2004). Mood and cognitive performance effects of” energy” drink constituents: caffeine, glucose and carbonation. Nutritional Neuroscience, 7, 127–139. 10.1080/10284150400003041 [DOI] [PubMed] [Google Scholar]
  38. Smith A, Leekam S, Ralph A, & McNeill G (1988). The influence of meal composition on post-lunch changes in performance efficiency and mood. Appetite, 10, 195–203. 10.1016/0195-6663(88)90012-8 [DOI] [PubMed] [Google Scholar]
  39. Sothern MS (2004). Obesity prevention in children: Physical activity and nutrition. Nutrition, 20, 704–708. 10.1016/j.nut.2004.04.007 [DOI] [PubMed] [Google Scholar]
  40. Tremblay LK, Naranjo CA, Graham SJ, Herrmann N, Mayberg HS, Hevenor S, & Busto UE (2005). Functional neuroanatomical substrates of altered reward processing in major depressive disorder revealed by a dopaminergic probe. Archives of General Psychiatry, 62, 1228–1236. doi: 10.1001/archpsyc.62.11.1228 [DOI] [PubMed] [Google Scholar]
  41. Tryon MS, Stanhope KL, Epel ES, Mason AE, Brown R, Medici V, … & Laugero KD (2015). Excessive sugar consumption may be a difficult habit to break: a view from the brain and body. The Journal of Clinical Endocrinology & Metabolism, 100, 2239–2247. 10.1210/jc.2014-4353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Watson D, Clark LA, & Tellegen A (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. [DOI] [PubMed] [Google Scholar]
  43. Wolraich ML, Wilson DB, & White JW (1995). The effect of sugar on behavior or cognition in children: A meta-analysis. JAMA, 274, 1617–1621. doi: 10.1001/jama.1995.03530200053037 [DOI] [PubMed] [Google Scholar]

RESOURCES