Differential food intake and food choice by depression and body mass index levels following a mood manipulation in a buffet-style setting

Abstract

While eating in response to emotional cues is associated with intake of unhealthy foods, less is known about the extent to which obesity and depression may differentially influence food intake in a buffet-style setting where low- and high-calorie foods are available to choose from. Using a counterbalanced design, 154 participants were grouped by depression and obesity categories, then asked to read a series of vignettes that were sad (on 1 day) and neutral (on a different day), followed by a buffet to eat until full. Food intake (in grams and calories) and food choice (number of high- or low-calorie food options) were recorded. Results showed that participants who were obese and depressed had significantly greater energy intake following the sad versus happy vignette, largely due to increased intake of high-calorie foods. The results corroborate recent theories on emotional eating and extend the ecological validity of such effects in a buffet-style setting.

Keywords

affect diet eating eating behavior health behavior

Introduction

Emotional responsiveness and positive mood are indubitably linked (Dubé et al., 2005; Kampov-Polevoy et al., 2006), with comorbidities and prevalence of obesity and depression showing a marked increase in recent decades (Blazer, 2003; Djernes, 2006; Zamboni et al., 2005). Current theoretical frameworks to explain comorbidities and prevalence of obesity and depression focus on neurobiological data (Nestler, 2012), brain–environment interactions (Privitera et al., 2013), and cognitive-behavioral evidence, such as variations in self-control (Privitera et al., 2015), perceptions of body weight (Saules et al., 2009; Sousa et al., 2016), and homeostatic imbalance as part of a broader framework (DiClemente and Delahanty, 2016; Marks, 2015). In all, most theories predict that the increase in obesity and depression is not coincidental but instead is bidirectional or related.

Studies on depression and obesity often converge in research on emotional eating, which defines the tendency to eat in response to negative emotions (Heatherton et al., 1991; Macht, 2008) and particularly for foods that are high in fat and sugar, that is, comfort foods (Dubé et al., 2005). The psychosomatic theory of emotional eating views it as a consequence of not being able to distinguish hunger from other adverse internal states, which can be susceptible to learning (Kaplan and Kaplan, 1957) and is generally associated with increased consumption of high fat and sugary foods (Macht, 2008). From a neurobiological perspective, emotional eating may result from suppression of the hypothalamic–pituitary–adrenal axis response (Gibson, 2012), and brain signaling may engage both peripheral and central nervous system signaling pathways in a bidirectional manner to drive appetite and preference for sweet-tasting and energy-dense foods (Singh, 2014).

While emotional eating is observed among people with and without psychopathology, evidence points to dopaminergic pathways to explain increased intake of “comfort foods” in response to emotions (Frank et al., 2010; Tindell et al., 2009), with greater neural activity evident in brain “reward” regions in response to high-calorie (HC) versus low-calorie (LC) foods in general (Frank et al., 2010). Diagnostic criteria for depression are further linked to increases in the intake of comfort foods (Privitera et al., 2013, 2015; Smith and Ditschun, 2009), although the extent to which emotional eating is specifically related to depression is less understood.

One increasingly common symptom of depression is weight gain and increased appetite (Blanco et al., 2012; Privitera et al., 2013). Consistent with the role of emotion in the etiology of obesity, obese individuals, compared to those who are normal weight, score higher on emotional eating scales (De Lauzon-Guillain et al., 2006; Van Strien et al., 2009). Overall, studies looking at the relationship between depression and intake show further evidence that depression is associated with consumption of an overall less healthy diet (Sarlio-Lähteenkorva et al., 2004), reduced intake and choices for fruits and vegetables (Cohen et al., 2002; Privitera et al., 2015), increased intake and choices of more energy-dense and sweet-tasting foods (Jeffery et al., 2009; Privitera et al., 2015), and more disordered eating patterns (Goldschmidt et al., 2013; Hay and Katsikitis, 2014).

The data on emotional eating and food intake, however, are mixed, with some studies showing an association between emotional eating and intake of energy-dense foods (De Lauzon et al., 2004; Elfhag et al., 2008), whereas other studies fail to show such an association (Anschutz et al., 2009; Lluch et al., 2000). To explain these conflicting findings, consider that many studies that fail to show an association tend to assess overall macronutrient intake, whereas studies that show an association tend to specifically assess intake of energy-dense foods (Elfhag et al., 2008), which are often overeaten among clinical and nonclinical populations (Jeffery et al., 2009; Privitera et al., 2015). Thus, when measuring intake of energy-dense foods, the evidence generally shows an association between emotional eating and amount consumed.

While some studies suggest that depression and obesity are not associated with emotional responsiveness to energy-dense foods (Hay et al., 2014; Masheb and Grilo, 2006; Wiedemann and Saules, 2013), such studies tend to measure responsiveness to food images (Hay et al., 2014), or self-reports of food intake (Wiedemann et al., 2013), and not actual food intake. This is problematic because self-reports or estimates of intake often do not align with actual food intake (Smith and Ditschun, 2009; Wansink, 2010), with the accuracy of estimates largely affected by factors related to visual cues in the food environment (Scheibehenne et al., 2010).

Overall, the “weight” of evidence suggests that emotional eating does contribute to unhealthy food choices (Konttinen et al., 2010), with inconsistencies largely driven by studies failing to measure actual intake and failing to account for intake specifically of energy-dense foods. Therefore, in this study, we examined the ecological validity of previous findings, where emotions prior to eating were experimentally manipulated with food choice and actual food intake of low- and high-energy-dense foods in a buffet-style setting measured as the dependent variables. In this study design, we predicted that intake following a manipulation to induce sadness would be moderated by depression and body mass index (BMI), with greater intake and food choice for energy-dense foods evident for obese individuals with diagnostic symptoms of depression.

Method

Participants

All participants were adults who signed a written informed consent. The St. Bonaventure University Institutional Review Board (IRB) approved the protocol for this study. A total of 154 university undergraduate students (56 men, 98 women; less than 5% of the participants identified as a race other than White) were recruited through university classroom visits and sign-up sheets. In an initial screening phase, participants were weighed on a scale and height measures taken from which BMI scores could be computed. The following participant variables are summarized by sex and depression category in Table 1: height, weight, age, and BMI. They further reported that they were in general good health with no significant physical or doctor diagnosed food allergies, pregnancy, or dietary restrictions. Participants with and without current depressive symptoms were permitted to enter the study. Only 10 percent (N = 15) of the sample had seen a physician for depression. None of the participants sampled were on any type of psychiatric medications, and no participants had been diagnosed at any time with anorexia nervosa or bulimia nervosa. Participants were told during the initial screening phase not to eat within 2 hours of the study and were given a list of foods to be served in the study. Only participants who agreed that they were willing to eat all the foods served were included in the study, and all participants gave preference ratings for each food listed on a scale from 1 = dislike very much to 7 = like very much. As described in the “Results” section, participants did not show differences in preference ratings for these foods between groups. Because hunger states can influence food choice and intake (Fedorchak and Bolles, 1987; Yeomans, 2006), participants who ate within 2 hours of the study were excluded from data analyses; hunger was further measured and evaluated statistically, as described in the “Statistical analysis” section. All participants identified that they were familiar with, had consumed, and generally liked the foods that would be served in the buffet.

Table 1.

Participant characteristics by sex and depression diagnosis criteria.

HAMD-17	Sex	N	Height (cm)		Weight (kg)		Age (years)		BMI (kg/m²)
HAMD-17	Sex	N	M	SD	M	SD	M	SD	M	SD
Normal (0–7)	Male	26	178.1	8.4	81.5	10.9	19.3	1.0	25.7	3.2
Normal (0–7)	Female	54	162.8	5.6	68.9	11.5	19.8	1.1	25.9	4.1
Depression (8–18)	Male	30	175.5	9.7	80.1	13.0	19.9	2.1	26.1	3.9
Depression (8–18)	Female	44	162.1	7.6	70.2	12.7	19.6	1.2	26.8	4.8

BMI: body mass index; HAMD: Hamilton Depression Rating Scale; SD: standard deviation.

Vignettes

Participants read a series of short vignettes that were either sad or neutral based on vignettes described by Mayer et al. (1995). The vignettes chosen for this study had a Cronbach’s alpha equal to .97. An example of a sad vignette was “Your best friend just got married and is moving far away from you” (p. 140) and “No one remembers your birthday” (p. 140). Neutral moods were induced using vignettes developed and matched for length. An example of a neutral vignette was “You get up in the morning, get dressed, and have your usual breakfast” (Marzillier and Davey, 2005: 733). In all, there were seven vignettes for each mood induction procedure. The vignettes were presented one at a time at 15-second intervals in a Microsoft PowerPoint presentation to match the time required to read the vignettes. Thus, vignettes were completed within 110 and 120 seconds when time to finish reading the final vignette was accounted for. The computer was located on the table in front of each participant.

Participants reported changes in mood using an adapted version of the Affect Grid (Russell et al., 1989), a valid and reliable single-item scale used to assess mood (pleasant feelings vs unpleasant feelings). The scale was completed immediately before and following the reading of the vignette; the difference in ratings from time 1 to time 2 was recorded. Negative difference scores indicate a decrease in mood; positive difference scores indicate an increase in mood.

Procedures

Participants were observed one at a time on each of the two consecutive days from 1100 to 1300 hours (i.e. “lunchtime”). On each day, participants were seated one at a time at a table with a personal computer (PC) on it in a quiet room where a buffet of foods, identified in Table 2, were visible on another table about 2 m in front of them. This setting was the same on both days with the same buffet of foods offered each day. On day 1, participants were asked to complete demographic surveys. Specifically, they completed the Hamilton Depression Rating Scale (HAMD) in which scores of 0–7 are in the normal range and 8–18 met criteria for depression. All participants included in this study scored between 0 and 18. This measure has an internal consistency alpha coefficient of .83 (Rush et al., 2003) and is widely used in clinical diagnosis for depression. Participants then gave hunger ratings on a self-report scale from 1 (very full) to 7 (very hungry) and completed their initial pre-vignette rating on the Affect Grid. For the Affect Grid, participants were asked to rate “their current state of mood” on the scale. Once completed, participants were asked to read a PowerPoint presentation of vignettes. For half the participants, the vignettes were sad on day 1 and neutral on day 2; the other half were shown the neutral vignettes on day 1 and the sad vignettes on day 2. So, the presentation of vignettes was fully counterbalanced across groups, thereby making any possible order effects equal across the vignettes used. Also, it is unlikely that the buffet itself significantly affected intake differently between groups because all participants were equally aware of being observed on each day, and the buffet given was the same for all groups with foods presented in the same way and displayed in identical locations on each day.

Table 2.

Macronutrients and total calories for each snack-sized food option.

Food type	Macronutrients by serving			Total calories
Food type	Fat (g)	CHO (g)	Protein (g)	Total calories
Pak^® Flavorz Apple Slices	0	7	0	30
Mott’s^® Natural Applesauce	0	13	0	50
Berryquick^® blueberries	0	6	0	25
Bolthouse Farms^® baby cut carrots	0	8	1	35
Del Monte^® mixed fruit cups	0	11	0	40
Marketside^® French green beans	0	6	2	25
Frito-Lay Cheetos^®	10	13	2	150
Nabisco Chips Ahoy!^® cookies	9	27	2	190
Frito-Lay Doritos^®	8	18	2	150
Hostess^® cupcakes	6	26	1	160
Keebler Chips^® Deluxe Mini Rainbow cookies	10	27	2	200
Pop Tarts^® chocolate fudge	4	38	2	200

CHO: carbohydrate.

The top six foods listed were in the “healthy” category; the bottom six foods were in the “less healthy” category. This list is exhaustive of all foods made available in the buffet.

After each vignette, participants again rated the vignettes using the Affect Grid and were then invited to eat the buffet as a lunch. Participants were allowed to choose any number of foods and asked to “eat until full.” The amount consumed in the buffet (in calories and grams) and the number of LC and HC food options chosen were recorded on each day. Water was provided as a beverage with utensils, 102-mm paper plates, and 8-ounce paper cups. Water was the beverage because when consumed with food it has little effect on food intake (DellaValle et al., 2005; Rolls et al., 1999). On day 2, the procedures were the same as day 1, except that demographic and depression measures were not recorded again. At the end of day 1, participants were asked to return the next day at the same time for a “follow-up.” All participants returned for a follow-up. At the end of day 2, participants were thanked for their time, debriefed, and dismissed. All participants signed the consent after day 2 that they did not take any foods “to go.” No food was found on the ground. The amount (grams) of each food consumed was recorded as the pre-weight minus post-weight of each pre-portioned plate of food and also converted to calories for statistical analysis.

Statistical analysis

To evaluate whether depression and BMI moderate food intake following a brief mood manipulation, a 3 × 2 multivariate analysis of variance (ANOVA) was computed with depression (normal and depression) and BMI (lean (<25.0 kg/m²), overweight (25.0–30.0 kg/m²), and obese (>30.0 kg/m²)) as the between-subjects factors. Sex (male and female) was initially included as a factor but removed when it showed no significance with the results reported here. The dependent variables were the difference in intake (grams and calories) between sad and neutral day, the difference in the number of LC food options chosen from the sad–neutral day, and the difference in the number of HC food options chosen from the sad–neutral day. A Tukey’s honestly significant difference (HSD) was computed for each post hoc analysis, and planned comparison two-independent sample t tests were computed to analyze significant interactions for the HAMD factor at each level of BMI. Due to the restriction of range resulting from scores for depression not being observed across the full range of clinical categories, a regression analysis was not computed on raw scores for depression and BMI.

Pre-vignette mood ratings were used as baseline measures on each day and compared between groups using a related samples t test to check that baseline mood was similar between depression groups prior to reading the vignettes. To check that the mood manipulation was effective, the difference between pre- and post-mood ratings was compared across the sad and neutral days using a related samples t test. Two-way ANOVAs were also computed with depression and BMI categories as factors and hunger ratings at the start of each day as the dependent variable to check that self-reported hunger ratings did not vary between the groups. All hypothesis tests were computed at a .05 experimentwise level of significance.

Results

Of the 154 participants, BMI scores ranged from 16.1 to 42.2 kg/m² with M ± SD of 26.2 ± 4.1 kg/m². Because only two participants fell below the 18.5 bottom cutoff for the “lean” BMI range (one in each depression category), these participants were placed in the “lean” BMI category to not further reduce sample size. HAMD-17 scores ranged from 3 to 18 with M ± SD score of 8.6 ± 4.1. Affect ratings (M ± SD) for the vignettes were −1.2 ± 1.6 (sad vignette) and 0.1 ± 1.3 (neutral vignette). The t test confirmed that baseline mood was statistically similar between depression groups prior to reading the sad, t(153) = −0.630, p = .53, d = .05, and the neutral vignettes, t(153) = 0.338, p = .74, d = .03. A t test also confirmed that the sad vignette made participants feel significantly sadder compared to the neutral vignette, t(153) = −8.54, p < .001, d = .69. An ANOVA test including the groups as factors showed that post-vignette affect ratings following the sad vignette (p > .20 for both main effects and p > .18 for the interaction) and the neutral vignette (p > .26 for both main effects and p > .16 for the interaction) were the same across depression and obesity groups. Table 3 summarizes the M ± SD for post-vignette affect ratings. Hunger ratings (M ± SD) prior to the mood manipulation were similar between groups on each day (sad vignette day: hunger ratings = 3.1 ± 0.9, p > .32; neutral vignette day: hunger ratings = 3.3 ± 1.1, p > .45). With preference ratings for each food as the dependent variable, there were no differences in food preferences across the BMI and depression categories for each food type (p > .09 for all comparisons). Food intake and food choice (M ± SD) by depression and BMI categories are given in Table 4.

Table 3.

Differences in affect ratings following the sad and neutral vignettes.

HAMD-17	BMI	N	Affect score (post–pre)
HAMD-17	BMI	N	Sad vignette	Neutral vignette
Normal	Lean	30	−1.2 ± 1.4	−0.3 ± 1.2
	Overweight	34	−1.4 ± 1.7	0 ± 1.5
	Obese	16	−0.8 ± 1.2	0.6 ± 1.4
Depression	Lean	30	−1.5 ± 1.7	0.1 ± 1.3
	Overweight	24	−1.1 ± 1.8	0.2 ± 1.0
	Obese	20	−1.3 ± 1.5	0 ± 1.1

HAMD: Hamilton Depression Rating Scale; BMI: body mass index; SD: standard deviation.

While ratings following the sad vignette were significantly lower compared to the neutral vignette, ratings did not differ between groups. Values are given as M ± SD.

Table 4.

Differences in intake and food choice for LC and HC food items.

HAMD-17	BMI	N	Intake		Food choice
HAMD-17	BMI	N	Calories	Grams	LC	HC
Normal	Lean	30	9.0 ± 92.3	19.3 ± 87.3	0.4 ± 14	0.7 ± 0.7
	Overweight	34	2.2 ± 82.7	−9.1 ± 82.1	0.2 ± 1.3	0 ± 0.8
	Obese	16	90.0 ± 162.5	38.4 ± 89.8	0 ± 1.3	0.6 ± 1.2
Depression	Lean	30	7.0 ± 89.7	−0.8 ± 99,4	0.2 ± 1.3	0.3 ± 0.9
	Overweight	24	52.9 ± 115.0	1.4 ± 91.7	−0.4 ± 1.3	0.8 ± 0.9*
	Obese	20	201.5 ± 109.5*	32.3 ± 76.3	−0.5 ± 1.4	1.8 ± 1.2*

HAMD: Hamilton Depression Rating Scale; BMI: body mass index; SD: standard deviation; LC: low calorie; HC: high calorie.

Values are given as M ± SD for the difference between the negative–neutral mood manipulation days.

A significant difference compared to the control (normal depression group) for the respective BMI category at p < .05.

Dependent variable: intake

With intake as the dependent variable, groups showed no significant difference in amount consumed in grams between the sad–neutral mood manipulations (p > .12 for each main effect and p = .65 for the interaction). When intake was converted to calories, however, a significant main effect was evident for depression, F(1, 148) = 9.29, p = .003, R² = .06. While intake was greater for those with depression following the sad versus neutral vignette, this effect was moderated by BMI, as evident by the interaction, F(2, 148) = 3.30, p = .04, R² = .04. Simple main effect tests showed that depression levels moderated food intake for participants who were obese, t(34) = 2.45, p = .02, d = .84, but not for those who were lean or overweight (p = .93). Participants who were obese with depression consumed significantly greater calories following the sad versus neutral vignette compared to participants in the normal category. A significant main effect of BMI was also evident, F(2, 148) = 20.79, p < .001, R² = .22, with participants who were obese consuming greater calories following the sad versus neutral vignette compared to lean and overweight participants (Tukey’s HSD, p < .001).

Dependent variable: food choice

Results generally show that increased caloric intake was largely due to increased choices for HC foods, not decreased choices for LC foods. There was no significant difference in the number of LC foods chosen between the sad–neutral mood manipulations (p > .06 for the main effects and p = .60 for the interaction). The number of HC foods chosen, however, did significantly vary following the sad versus neutral vignette. For HC food choices, a significant main effect of depression was evident, F(1, 148) = 22.83, p < .001, R² = .13. While those with depression chose a greater number of HC foods following the sad versus neutral vignette, this effect was further moderated by BMI, as evident by the interaction, F(2, 148) = 3.01, p = .03, R² = .05. Simple main effect tests showed that depression levels moderated HC food choices for participants who were overweight, t(56) = 3.69, p = .001, d = .99, and obese, t(34) = 2.63, p = .01, d = .88, but not for those who were lean (p = .35). Participants who were overweight or obese with depression chose a significantly greater number of HC foods from the buffet following the sad versus neutral vignette compared to normal participants. A significant main effect of BMI was also evident, F(2, 148) = 13.29, p < .001, R² = .15, with participants who were obese choosing significantly more HC foods following the sad versus neutral vignette compared to lean and overweight participants (Tukey’s HSD, p < .001).

Discussion

The results in this study show an interesting pattern that when taken together, they show evidence to support the prediction that food intake following a manipulation to induce sadness would be moderated by depression and BMI. Obese participants with depression showed significant increases in food intake (in calories) and choices for HC foods following a manipulation to induce sadness. Thus, greater intake and choice for energy-dense foods was evident for obese participants with diagnostic symptoms of depression compared to nondepression controls.

With regard to food choice, emotional eating is related to food choice and intake of sweet-tasting (Konttinen et al., 2010) and energy-dense foods (Dubé et al., 2005; Macht, 2008; Privitera et al., 2015). Food choice and intake of LC foods, however, such as fruits and vegetables, is likely moderated by factors other than emotional eating (Konttinen et al., 2010). This pattern in the literature is consistent with the data reported here using an experimental manipulation, where inducing sadness increased intake of HC foods but did not affect choices for LC foods. Additionally, when food intake was measured in grams, no differences were observed in this study. Thus, “sad” participants did not eat more food per se; instead, they specifically ate more energy-dense foods, thereby increasing the calories but not the weight of foods consumed. Estimating calorie intake is challenging, and often, these estimates do not align with actual food intake (Smith and Ditschun, 2009; Wansink, 2010), meaning that conclusions from studies reporting on emotional eating based solely on self-reports of food intake should be taken with caution. Instead, the results reported here suggest that measuring actual food intake, specifically in calories, is necessary to detect differences in food intake related to emotional eating, although studies that directly compare estimates along with actual intake can best delineate the outcome validity across measures.

Many controls were included in this study to eliminate alternative explanations for the data reported here. First, the sad or neutral manipulation was counterbalanced. Thus, any effects due to the order of the manipulation or due to getting tired of eating certain foods on the second day (i.e. sensory-specific satiety; Rolls, 1986; Rolls et al., 1981) were the same across groups and cannot explain differences observed between groups. Similarly, the same buffet of foods was served on each day, such that each buffet was identical in presentation. Thus, the dimensions and the size of each portion in the buffet, which can affect intake (Privitera et al., 2012; Rolls et al., 2002), were also held constant and cannot explain differences between groups. In addition, manipulation checks were included to ensure that the sad or neutral manipulation was effective and that hunger levels were the same across groups on each day.

Several limitations can be further identified. First, we did not measure emotional eating and instead manipulated this factor using a valid or reliable procedure with checks to ensure it was effective. Whether emotional eating may have further moderated the effects reported here cannot be determined, although as a point of caution some reports suggest that the extent to which emotional eating will be related to these findings will depend on the type of measure used to evaluate emotional eating because multiple survey items can be used to assess emotional eating (Konttinen et al., 2010). Also, in this study, scores on the HAMD were not observed across the full range of scores on this diagnostic measure, and thus, regression analyses could not be reliably performed, which could have lent further insights into the nature of the relationship between depression and obesity on emotional eating. In addition, the latency to initiate food intake and the rate of food intake was not measured. Dietary histories of healthy eating were also not recorded; instead, BMI scores were used to infer levels of healthy eating prior to the study. Such additional variables can be recorded in future studies and may lend further insights into the nature of the effects reported here.

At present, the results in our study show evidence to support the prediction that food intake and food choice when an individual is “sad” is moderated by depression and BMI, with obese participants with depression showing increased food intake (in calories) and greater number of choices for HC foods compared to nondepression controls. The results corroborate recent theories suggesting that depression and obesity are related to emotional eating (Nestler, 2012; Privitera et al., 2013) and highlight the need to identify depression as a possible risk factor for increased intake and choices for energy-dense foods in response to daily, short-term fluctuations in sad emotion, specifically among those individuals who are obese.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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