Unravelling the association between inhibitory control and loss of control over eating among adolescents

Appetite 125 (2018) 401e409

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Unravelling the association between inhibitory control and loss of control over eating among adolescents Eva Van Malderen a, *, Lien Goossens a, Sandra Verbeken a, Eva Kemps b a b

Ghent University, Department of Developmental, Personality and Social Psychology, Henri Dunantlaan 2, 9000 Ghent, Belgium School of Psychology, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 December 2017 Received in revised form 7 February 2018 Accepted 19 February 2018 Available online 22 February 2018

Objective: Loss of control over eating is common among adolescents and is associated with negative developmental outcomes. Recent evidence points to impaired self-regulation, and more specifically poor inhibitory control, as a contributing factor to loss of control over eating among adults; however evidence in adolescent samples is limited. Moreover, in line with dual-process models, researchers have recently started to investigate the moderating role of automatic processes in this relationship, but again studies in adolescents are lacking. Therefore, the aim of the current study was to: (1) investigate whether there is an association between poor inhibitory control and loss of control over eating also among adolescents, and (2) explore whether this relationship is moderated by automatic processing. Method: A community sample of 124 adolescents (10e17 years; 65.3% girls; Mage ¼ 14 years; SD ¼ 1.90) was divided into a ‘Loss of Control Group’ (n ¼ 30) and a ‘No Loss of Control Group’ (n ¼ 94) based on a clinical interview. Inhibitory control and automatic processing (general and food specific) were measured by self-report questionnaires. Results: Adolescents in the Loss of Control Group reported significantly more problems with overall selfregulation compared to the No Loss of Control Group; however, there was no group difference for inhibition specifically. Contrary to dual-process predictions, there was a trend significant interaction between poor inhibitory control and weaker food specific automatic processing in explaining loss of control over eating. Conclusions: Evidence was found for problems with overall self-regulation in adolescents with loss of control over eating. Concerning the specific role of inhibitory control, future studies should replicate whether automatic processing is indeed a crucial moderator. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Adolescents Loss of control over eating Self-regulation Inhibitory control Automatic processing

1. Introduction 1.1. Loss of control over eating Loss of control over eating, conceptualized as a subjective experience while eating, is characterized by a lack of control over eating once eating has started (Fairburn, Wilson, & Schleimer, 1993, pp. 317e360; Marcus & Kalarchian, 2003). Loss of control over eating is a common experience among youth in the general community, with prevalence rates of up to 28% (Kelly et al., 2016), and even higher estimates reported in female and overweight samples (Tanofsky-Kraff et al., 2011). In addition, a recent meta-analysis

* Corresponding author. E-mail address: [email protected] (E. Van Malderen). https://doi.org/10.1016/j.appet.2018.02.019 0195-6663/© 2018 Elsevier Ltd. All rights reserved.

across 36 studies found an overall loss of control prevalence rate of 31% among overweight or obese children and adolescents (aged 5e21 years) (He, Cai, & Fan, 2017). Importantly, this prevalence rate can be considered reliable, as it was obtained using the ‘golden standard’ for assessing loss of control over eating among adolescents, namely the Children's Eating Disorder Examination (Ch-EDE; Bryant-Waugh, Cooper, Taylor, & Lask, 1996). Youth who report loss of control over eating have higher levels of psychopathology and maladjustment, such as exacerbated eating pathology (e.g., restraint eating, body, shape and weight concerns), excessive weight/ fat gain, depressive symptomatology and poorer self-esteem (Rosenbaum & White, 2015; Tanofsky-Kraff et al., 2009, 2011). Loss of control over eating can also be a precursor to clinical eating disorders, such as Bulimia Nervosa or Binge Eating Disorder (Tanofsky-Kraff et al., 2011), and other psychiatric disorders such as depression or addiction (Herpertz-Dahlmann et al., 2015). Over the

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last decade, research on loss of control over eating in youth has expanded (Attia et al., 2013; Tanofsky-Kraff et al., 2013), but the mechanisms underlying this pathological eating behavior remain unclear (Tanofsky-Kraff et al., 2013). From a preventative perspective, and taking into account the negative physical and psychosocial outcomes associated with loss of control over eating, further research is important to gain insight into the mechanisms that underlie this pathological eating behavior in adolescents. 1.2. Loss of control over eating and inhibitory control Given the compulsive nature of loss of control over eating (Gearhardt, White, & Potenza, 2011), studies in this research domain emphasize the role of self-regulation, and more specifically inhibitory control. Inhibitory control can be defined as “the ability to inhibit a behavioral impulse in order to attain higher-order goals” (Barkley, 1997; Nigg, 2000). Previous research in adults has found an association between poor inhibitory control and increased food intake in the laboratory (e.g., Kakoschke, Kemps, & Tiggemann, 2015), as well as increased body mass index (e.g., Stice, Lawrence, Kemps, & Veling, 2016). However, the specific role of inhibitory control in loss of control over eating has been studied less. In adults, results demonstrate that impaired inhibitory control may contribute to the development and maintenance of loss of control over eating over and above its role in general obesity (e.g., Balodis et al., 2013; Manasse et al., 2016; Svaldi, Naumann, Trentowska, & Schmitz, 2014). In line with adult samples, some studies have found a relationship between impaired inhibitory control and body mass index in obese children (e.g., Smith, Hay, Campbell, & Trollor, 2011; Verbeken, Braet, Bosmans, & Goossens, 2014). Specifically, Goldschmidt et al. (2017) compared obese children (9e12 years old) with loss of control over eating with obese children without loss of control over eating and healthy controls on a broad range of self-regulation skills. Parents reported on their child's selfregulation using questionnaires, whereas the children completed several behavioral measures of self-regulation. The researchers concluded that each group had a unique pattern of self-regulatory dysfunctions: obese children with and without loss of control over eating had more difficulties with planning compared to normal weight controls (as assessed by the Tower of London task); obese children with loss of control over eating also performed more poorly than both obese children without loss of control over eating and normal weight controls on a working memory task (the List Sorting Task). However, there were no significant group differences for inhibitory control specifically (neither on the self-report questionnaires, nor on the behavioral measures of self-regulation). A possible explanation may be that Goldschmidt et al. (2017) only asked the parents to report on their child's self-regulation, and did not measure the children's own self-reported inhibitory control. Thus it remains unclear whether this aspect of self-regulation is an underlying mechanism of loss of control over eating in youth. Moreover, most of the research to date has assessed self-regulatory processing with the use of behavioral tasks. However, concerns regarding the limited ecological validity of such tasks (e.g., Chaytor & Schmitter-Edgecombe, 2003) emphasize the importance and advantages (e.g., measuring general executive functioning in daily life) of self-report questionnaires such as the Behavior Rating Inventory of Executive Function (i.e., BRIEF Gioia, Isquith, Guy, & Kenworthy, 2000). Despite the fact that loss of control over eating typically emerges during adolescence (Kessler et al., 2013), and that inhibitory control develops during that period with increasing maturation of the prefrontal cortex (Crone, 2009), there is a paucity of research that focuses on inhibitory control as a potential contributing factor to

loss of control over eating among adolescents. In addition, as adolescence is a time when individuals gain more autonomy, and thus assume greater responsibility for their own food choices, this is an important segment of the population in which to investigate the underlying self-regulatory mechanisms of loss of control over eating (Van Leijenhorst et al., 2010). To our knowledge, thus far only one study has investigated the specific role of inhibitory control in an adolescent sample with loss of control over eating. In particular, Kittel, Schmidt, and Hilbert (2017) compared inhibitory control capacities (using the Color-Word Interference Test) of obese adolescents with binge eating disorder (which encompasses loss of control over eating) with obese adolescents without binge eating disorder and normal-weight controls (all aged 12e20 years). The authors reported more inhibitory control problems among obese adolescents with binge eating disorder compared to normal-weight adolescents. However, no differences were found between obese adolescents with binge eating disorder compared to obese adolescents without binge eating disorder, making it difficult to ascertain whether inhibitory control makes a unique contribution to loss of control over eating in adolescents beyond its role in obesity. Moreover, this study specifically focused on a clinical (currently in treatment) sample of adolescents with binge eating disorder. Therefore, the results cannot be generalized to adolescents with loss of control over eating in the general community. In addition, and as acknowledged by the authors, again only behavioral measures were used to assess inhibitory control. As mentioned previously, it is important that future studies replicate and extend the findings with self-report questionnaires such as the Behavior Rating Inventory of Executive Function (i.e., BRIEF; Gioia et al., 2000). In addition, the limited number of studies that have been conducted in this area have focused on the association between loss of control over eating and inhibitory control without taking the role of automatic processing as a possible moderator into account. 1.3. Joint influences of inhibitory control and automatic processes Dual-process models (e.g., Strack & Deutsch, 2004) propose that eating behavior is governed not only by self-regulatory processes such as inhibitory control, but also by automatic processes (e.g., automatically driven attention towards salient stimuli in the environment). In contrast with self-regulatory processes, automatic processes are fast, implicit and effortless (Cauffman et al., 2010). Studies in adult samples conducted within the dual-process framework have recently begun to take the joint influences of self-regulatory and automatic processes into account, based on the premise that automatic processes play a moderating role in the relationship between inhibitory control and eating behavior. For example, Kakoschke et al. (2015) found a significant interaction between behavioral measures of automatic processing (i.e., attentional and approach avoidance bias) and inhibitory control on unhealthy snack food intake in adult women, such that participants with a strong automatic system combined with poor inhibitory control consumed the most snack foods. In contrast, and counter to dual-process predictions, Manasse et al. (2015) found that poorer behavioral inhibitory control distinguished obese adults with binge eating from those without binge eating, but only when automatic appetitive drives (as measured with the Power of Food Scale; Lowe et al., 2009) were low. Within the context of specifically loss of control among adults, this latter finding seems to be the first indication of the dynamic interplay between self-regulatory and automatic processes beyond their role in obesity more generally. During adolescence, development of automatic processes reaches its peak, whereas inhibitory control continues to evolve, at a moderate pace (e.g., Cauffman et al., 2010). Hence, the difference in

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maturation rates between automatic and self-regulatory processes is most striking in this developmental phase, and thus emphasizes the importance of taking the moderating role of automatic processes into account when studying the association between inhibitory control and disordered eating. Several studies have focused on the exclusive role of automatic processing in relation to eating behavior in children or adolescents. For example, Shank et al. (2015) found a positive association between attentional bias and body mass index, but only among youth who experience loss of control over eating. Previously, Braet and Crombez (2003) observed stronger attentional biases for food words using a food variant of the Stroop task among obese youth compared with normal weight controls. However, these studies did not take the interaction with self-regulatory processing into account. To the best of our knowledge, only two studies have investigated both the roles of automatic processes and inhibitory control in adolescent samples. First, Batterink, Yokum, and Stice (2010) found that adolescent girls (average age of 15 years) with lower inhibitory control and higher food reward neural activation had higher BMIs. Second, Matton, Goossens, Vervaet, and Braet (2017) obtained similar results in a community sample of adolescents. They found an interaction between self-reported sensitivity to reward (i.e., automatic process) and behavioral interference control (i.e., self-regulatory process such as inhibitory control) in explaining restrained eating, but only for girls. Specifically, girls who reported greater sensitivity to reward and showed poorer behavioral interference control exhibited higher levels of restrained eating. Unfortunately, the Batterink et al. (2010) and Matton et al. (2017) studies used different constructs and measures to capture inhibitory control and automatic processes, which makes it difficult to draw firm conclusions about their respective contributions to eating behavior. Moreover, neither study focused on loss of control over eating as the outcome measure. Therefore, more research is needed in adolescents who experience loss of control over eating to determine whether automatic processes are a contributing factor to the relationship between inhibitory control and this pathologic eating behavior, and to elucidate the direction of any such contribution. 1.4. Current study The aim of the current study was to gain insight into the mechanisms underlying loss of control over eating in adolescents by clarifying the role of self-regulatory processes, more specifically inhibitory control. The study sought to address two main research questions. First, we investigated whether, in line with research in adults (e.g., Manasse et al., 2016), impaired levels of self-reported inhibitory control (and overall self-regulation) can be detected in adolescents with loss of control over eating compared with those who do not experience loss of control over eating. Second, we explored whether the relationship between inhibitory control (and overall self-regulation) and loss of control over eating was moderated by self-reported automatic processing, and if so how. Based on previous research, the current study included measures of both general, temperamental based automatic processing and situational, food specific automatic processing. In line with several studies (e.g., Claes et al., 2010; Matton et al., 2017), general automatic processing was assessed by the Drive subscale of the Behavioral Activation Scale (i.e., BAS Drive; Carver & White, 1994). Following Manasse et al. (2015), food specific automatic processing was captured with the use of the Power of Food Scale (i.e., PFS; Dutch translation of Lowe et al., 2009). Because of previous mixed results, and the exploratory nature of our second research question, we made no a priori predictions about how levels of automatic processing (low or high) might interact with impaired inhibitory control to predict loss of control over eating in adolescents. To the

403

best of our knowledge, no study has yet addressed these research questions in an adolescent sample that experiences loss of control over eating.

2. Method 2.1. Participants and procedure A community sample of 133 adolescents aged between 10 and 17 years (65.3% female, Mage ¼ 13.51; SD ¼ 1.90) was recruited. Youngsters between 10 and 17 years were contacted by 3rd year psychology students (in the context of a practical course). They were informed about the present study, and if interested in participating, could provide the first author with their contact information. Those youngsters were then called by the researchers and an appointment was made for them to come to the laboratory. All participants filled out a written informed assent in which information about the study was provided. As participants were underage, parents also provided an active informed consent. Data collection occurred in two parts. First, online questionnaires were sent to the participants, for which they received a personal code to ensure anonymity. Second, participants were invited to the University's laboratory where a clinical interview was administered and some additional measurements (e.g., objective length and weight) were taken. Trained assistants or the primary researcher were present during the entire procedure to answer any questions. All participants received a cinema ticket as reimbursement for their time. Both parts of the study procedure were approved by Ghent University's Ethics Committee. The study is part of a larger PhDproject on the role of self-regulatory and automatic processing in eating behavior among adolescents.

2.2. Materials 2.2.1. Loss of control over eating To assess loss of control over eating, the Children's Eating Disorder Examination (Ch-EDE; Bryant-Waugh et al., 1996; Cooper &
& Braet, 1999) was Fairburn, 1987; translated by Decaluwe administered. This semi-structured clinical interview is a standardized instrument for assessing eating pathology. It is derived from the well-validated adult EDE (Cooper & Fairburn, 1987), but adapted for children and adolescents from the age of 8 years. The Ch-EDE consists of four underlying subscales (i.e., ‘Restraint’, ‘Eating Concern’, ‘Shape Concern’ and ‘Weight Concern’) and three types of eating behaviors (i.e. ‘Objective Binge Eating’, ‘Subjective Binge Eating’ and ‘Objective Overeating’). The current study only included an assessment of ‘Objective Binge Eating’ (i.e., eating an objectively large amount of food combined with experience of loss of control over eating) and ‘Subjective Binge Eating’ (i.e., eating a subjectively large amount of food combined with experience of loss of control over eating) (for a description of this procedure see Tanofsky-Kraff et al., 2004). The primary researcher trained the assistants to administer these measures of binge eating episodes. In addition, all interviews were audiotaped and independently reassessed by the primary researcher for interrater reliability. Adolescents were allocated to the ‘Loss of Control Group’ if they reported at least one loss of control over eating episode (i.e., one objective or subjective binge eating episode) over the past three months. The Ch-EDE has adequate psychometric characteristics
& Braet, 2004; Watkins, Frampton, Lask, & Bryant(e.g., Decaluwe Waugh, 2005). Previous studies have reported good discriminant validity and internal consistency of this interview (Frampton, 1996; Lask & Bryant-Waugh, 2000).

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2.2.2. Inhibitory control All participants completed the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000), a frequently used self-report questionnaire for measuring children's and adolescents' executive functioning (5e18 years). The BRIEF consists of two broad index categories with underlying subscales: ‘Behavioral Regulation’ index (with subscales ‘Inhibition’, ‘Emotional Control’ and ‘Shift’) and ‘Metacognition’ index (with subscales ‘Initiate’, ‘Working Memory’, ‘Plan/Organize’, ‘Organization of Materials’ and ‘Monitor’). The questionnaire comprises 68 items, scored from 1 (never) to 3 (often), with higher scores reflecting more problems regarding that specific scale or index. To examine the research questions of the present study, two BRIEF-scales were used. First, overall self-regulation was captured using the ‘Behavioral Regulation’ index (30 items; e.g., ‘I have trouble going from one activity to another’), which gives a broad indication of the flexibility in thinking and emotion regulation through the use of self-regulatory skills. Second, one specific subscale, namely ‘Inhibition’ (12 items; e.g., ‘I have trouble waiting for my turn’), was also included as a more in depth measurement of inhibitory control in particular. The BRIEF has adequate validity (Gioia, Isquith, Retzlaff, & Espy, 2002) and internal consistency (in the present study: behavioral regulation index: a ¼ .74; subscale inhibition a ¼ .88). 2.2.3. Automatic processing Participants completed a general, temperamental based automatic processing questionnaire as well as a situational, food specific automatic processing questionnaire (e.g., Claes et al., 2010; Manasse et al., 2015; Matton et al., 2017). First, temperamental based automatic processing was measured with the ‘Behavioral Inhibition/Activation Scales’ (BIS/BAS), a Dutch translation of the BIS/BAS scales from Carver and White (1994). This self-report questionnaire assesses dispositional inhibition/punishment (i.e., BIS scale) and activation/reward (i.e., BAS scale) sensitivities (Carver & White, 1994). The BAS scale contains 13 items scored from 1 (not at all true) to 4 (all true). Higher scores indicate higher levels of the specific sensitivity. The BAS scale is further divided into 3 lowerorder subscales, specifically ‘BAS Drive’ (4 items), ‘BAS Fun Seeking’ (4 items) and ‘BAS Reward Responsiveness’ (5 items). Previous research has shown that ‘BAS Drive’ (e.g., ‘If I want something, I usually do everything to get it’) has the most stable association with body mass index, food intake and pathological eating behavior (e.g., De Cock et al., 2016; Verbeken, Braet, Lammertyn, Goossens, & Moens, 2012; Voigt et al., 2009). In addition, Dawe, Gullo, and Loxton (2004) concluded that ‘BAS Drive’ was the best predictor of appetitive drives, as also confirmed by brain imaging research (e.g., Beaver et al., 2006). Hence, only this subscale was included in the present study. Psychometric characteristics for the ‘BAS Drive’ subscale are good (Cooper, Gomez, & Aucote, 2007), and internal consistency in this study was acceptable (a ¼ .55). Second, the ‘Power of Food Scale’ (PFS; Dutch translation of Lowe et al., 2009) was used to measure food specific automatic processing. This questionnaire captures the influence of highly palatable foods on how people think, feel and behave in the absence of hunger. The PFS contains 20 items (e.g., ‘I think I enjoy food a lot more than most other people’), scored from 1 (totally disagree) to 5 (totally agree), with higher scores reflecting a higher influence of palatable foods on participants in daily life. The questionnaire is widely used in eating behavior research and has been shown to have adequate psychometric characteristics (e.g., Appelhans et al., 2011). Internal consistency in this study was excellent (a ¼ .90). 2.2.4. Control variables All adolescents reported their age and gender. In addition,

adolescents' height and weight were recorded in the laboratory using calibrated instruments. From these, adjusted body mass index (kg/m2) was calculated using Flemish normative data taking into account participants’ age and gender (Roelants & Hauspie, 2004): [(actual body mass index (kg/m2)/percentile 50 of body mass index for age and gender) x 100]. Furthermore, socioeconomic status was computed with the well-validated Hollingshead Index formula (Hollingshead, 1975), which includes the level of education and profession of both parents or caregivers. Because of their known association with loss of control over eating (Kessler et al., 2013; Quon & McGrath, 2014; Tanofsky-Kraff et al., 2007, 2011), all of these variables (gender, age, adjusted body mass index and socio-economic status) were included as control variables. 2.3. Statistical analysis Before conducting the main analyses, missing data analysis was performed (Schafer, 1997), and descriptive characteristics of the sample were explored (i.e., distribution of gender, age, adjusted body mass index, socio-economic status and experience of loss of control over eating). Next, a series of analyses were performed to answer the two research questions of the present study. Potential confounding variables such as gender (0 ¼ male, 1 ¼ female), age (in years), adjusted body mass index and socio-economic status were included as control variables in each of the analyses. With regard to the first research question, to determine whether there were differences between the Loss of Control Group and the No Loss of Control Group on overall self-regulation and inhibitory control in particular, two separate ANOVAs were conducted in SPSS (version 24.0) with the BRIEF Behavioral Regulation Index and the BRIEF Inhibition Subscale entered as dependent variables, respectively. In both ANOVAs, group (Loss of Control versus No Loss of Control) was entered as a fixed factor and all remaining control variables (gender, age, adjusted body mass index and socioeconomic status) as covariates. With regard to the second research question, to test whether the interaction between selfregulatory skills (i.e., overall self-regulation and inhibitory control) and automatic processing (general and food specific) adds to the explanation of loss of control over eating, we performed binary logistic regressions because the main purpose was to predict loss of control (dependent variable) rather than to determine any between group differences. Thus, four separate binary logistic regressions were performed using PROCESS model 1 (within SPSS, version 24.0). Before running these regressions, all predictor variables were centered around their means (Aiken, West, & Reno, 1991). In each logistic regression, all variables were entered into the model together: group (Loss of Control versus No Loss of Control) as a categorical dependent variable, followed by one specific predictor (either BRIEF Behavioral Regulation Index or BRIEF Inhibition Subscale) and the moderator (either BAS Drive or PFS). All control variables (gender, age, adjusted body mass index and socioeconomic status) were included as covariates. Simple effects coefficients were computed for two values of the moderator (i.e., 1 SD below the mean and 1 SD above the mean). For all analyses, pvalues  .05 were considered statistically significant. Partial h2 was used as the effect size measure in all analyses. Benchmarks for partial h2 are .01, small; .06, medium; and .14, large. 3. Results 3.1. Missing values The initial sample consisted of 133 adolescents, 9 of whom did not report on experience of loss of control over eating. As this grouping variable (‘Loss of Control Group’ versus ‘No Loss of Control

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Group’) is the main variable in all analyses, these participants were excluded from further analyses. This resulted in a final sample of 124 adolescents (65.3% girls) aged between 10 and 17 years (Mage ¼ 13.51; SD ¼ 1.90). Next, the Missing Completely At Random test (MCAR; Little, 1988) was conducted for all control variables and questionnaire variables. The normed chi-square was nonsignificant (c2 ¼ 2.039/df ¼ 105, p ¼ 1.00), indicating that we could proceed with the assumption of randomly missing data (Bollen, 1989). As a consequence, all missing values were estimated with the Expectation Maximization method in SPSS (EM; Schafer, 1997). 3.2. Descriptive statistics Mean adjusted body mass index was 104.29 (SD ¼ 14.75), ranging from 80.57 to 150.55. In this sample, 5.6% of participants were classified as underweight (adjusted body mass index  85); 83.9% as normal weight (85 < adjusted body mass index < 120); 6.5% as overweight (120  adjusted body mass index < 140), and 4% as obese (adjusted body mass index  140) (Van Winckel & Van Mil, 2001). Socio-economic status ranged from 16.50 to 58.50, with a mean of 32.53 (SD ¼ 8.26). Most of the participants were classified as upper-middle class (50%; range 18e31) and middle class (42.7%; range 32e47), with few participants classified as lower-middle class (5.6%; range 48e63) or upper class (1.6%, range 11e17) (Hollingshead, 1975). Based on the Ch-EDE (Bryant-Waugh et al.,
& Braet, 1999), 24% of the adoles1996; translated by Decaluwe cents (n ¼ 30) were assigned to the Loss of Control Group (experiencing at least one loss of control over eating episode over the past three months). The number of loss of control over eating episodes over a three-month period ranged from 1 to 24 (M ¼ 1.79, SD ¼ 4.64). Specifically, the number of objective binge eating episodes ranged from 0 to 15 (M ¼ .50, SD ¼ 2.21), and the number of subjective binge eating episodes from 0 to 24 (M ¼ 1.31, SD ¼ 3.89). Table 1 gives an overview of all sample characteristics.

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self-reported scores on the BRIEF behavioral regulation index in the Loss of Control Group (M ¼ 50.12, SD ¼ 9.93) compared to the No Loss of Control Group (M ¼ 47.15, SD ¼ 7.92). However, for the BRIEF inhibition subscale in particular (second ANOVA), there was no significant difference between the Loss of Control (M ¼ 20.18, SD ¼ 4.42) and No Loss of Control (M ¼ 19.89, SD ¼ 3.66) groups, F(1,118) ¼ 2.67, p ¼ .655, partial h2 ¼ .002. Results of both ANOVAs can be found in Table 2. 3.4. Joint influences of inhibitory control and automatic processing There were no significant interactions between the BRIEF behavioral index and automatic processing in predicting loss of control over eating. Specifically, the BRIEF behavioral index did not significantly interact with the BAS (Wald c2 ¼ .04, b ¼ .008, SE ¼ .212, p ¼ .970, partial h2 ¼ .000), nor with the PFS (Wald c2 ¼ 1.23, b ¼ .299, SE ¼ .242, p ¼ .217, partial h2 ¼ .010). Regarding the link between the BRIEF inhibition subscale and loss of control over eating, there was again no significant moderating role of the BAS (Wald c2 ¼ .15, b ¼ .035, SE ¼ .234, p ¼ .882, partial h2 ¼ .000). However, there was a trend significant interaction between the BRIEF inhibition subscale and the PFS (Wald c2 ¼ 1.84, b ¼ .413, SE ¼ .225, p ¼ .066, partial h2 ¼ .010). More specifically, for adolescents with weaker automatic processing (i.e., lower scores on the PFS), higher scores on the BRIEF inhibition subscale were related to a greater probability of experiencing loss of control over eating, although this fell short of statistical significance, b ¼ .570, p ¼ .091. For adolescents with stronger automatic processing (i.e., higher scores on the PFS), higher scores on the BRIEF inhibition subscale were not significantly associated with experiencing loss of control over eating, b ¼ -.272, p ¼ .406. Fig. 1 graphs the interaction, showing the expected probability of experiencing loss of control over eating by the BRIEF inhibition subscale for the PFS at -1SD and þ1SD from the mean.

3.3. Loss of control over eating and inhibitory control

4. Discussion

Results of the first ANOVA revealed a significant difference between the Loss of Control Group and the No Loss of Control Group on the BRIEF behavioral regulation index, F(1,118) ¼ 3.84, p ¼ .052, partial h2 ¼ .032. Descriptive statistics show significantly higher

The aim of the current study was to gain insight into the mechanisms underlying loss of control over eating among adolescents. In particular, we sought to elucidate the role of selfregulatory processes, especially inhibitory control, in a community sample of adolescents who experience this pathological eating behavior. We also explored for the first time the possible moderating role of automatic processes in this relationship. In so doing, the present study is an important next step in this research domain in adolescents. Our first research question examined whether adolescents who report loss of control over eating differ from those who do not in terms of their overall level of self-regulation (assessed with the BRIEF behavioral regulation index) and inhibitory control in particular (assessed with the BRIEF inhibition subscale). Results showed that adolescents who experience loss of control over eating reported significantly more self-regulation difficulties overall, but not specifically poor inhibitory control. Our findings are in line with

Table 1 Sample characteristics.

Gender (M/F) Age AdjBMI SES

Total sample

LOC (24%)

No LOC (76%)

M(SD)

M(SD)

M(SD)

43/81 13.51 (1.90) 104.52 (14.43) 32.84 (8.39)

8/22 13.73 (1.20) 105.77 (15.69) 30.91 (8.16)

35/59 13.44 (1.87) 103.82 (15.50) 33.05 (8.26)

F/c

2

statistic

1.21 .56 .40 1.55

Note. M/F ¼ Male/Female; AdjBMI ¼ Adjusted Body Mass Index; SES ¼ Socioeconomic Status; LOC ¼ Loss of Control over Eating. F/c 2 values are given for the comparison of all control variables between the two groups.

Table 2 Differences between the Loss of Control Group and the No Loss of Control Group on the BRIEF behavioral regulation index and the BRIEF inhibition subscale.

BRIEF Behavioral Regulation Index BRIEF Inhibition Subscale

LOC

No LOC

M(SD)

M(SD)

50.12 (9.93) 20.18 (4.42)

47.15 (7.92) 19.89 (3.66)

FLOC

FAge

FAdjBMI

FSES

FGender

3.84* .201

7.58** 3.56

1.65 2.95

5.26* 4.92*

3.08 4.31*

Note. AdjBMI ¼ Adjusted Body Mass Index; SES ¼ Socio-economic Status; LOC ¼ Loss of Control over Eating. F-values represent the results of the univariate analyses in which age, adjBMI, SES and gender were included as covariates. *p  .05, **p  .01, ***p  .001.

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Fig. 1. Interaction of BRIEF inhibition subscale and PFS in explaining loss of control over eating. Note. LOC ¼ Loss of Control over Eating; BRIEF ¼ Behavior Rating Inventory of Executive Function; PFS ¼ Power of Food Scale.

those of Goldschmidt et al. (2017) who similarly found no impairment in inhibitory control (assessed with behavioral tasks by the children and with self-report questionnaires by the parents) among loss of control over eating in children; however, like us, they did find decrements in other aspects of self-regulation, such as planning and working memory. Collectively, the results point to an underlying role for self-regulation deficits more generally rather than inhibitory control difficulties in particular among children and adolescents with loss of control over eating. However, our findings are at odds with previous reports of impaired inhibitory control in adults with loss of control over eating (Balodis et al., 2013; Manasse et al., 2016) and in a clinical sample of adolescents with binge eating disorder (Kittel et al., 2017). There are several possible explanations for these contrasting results. First, the handful of studies conducted to date recruited very different samples. Balodis et al. (2013) and Manasse et al. (2016) used an adult community sample, whereas Kittel et al. (2017) used a clinical adolescent sample, and the current study used an adolescent community sample. Self-regulation, and in particular inhibitory control, are generally fully developed by adulthood, with a maturation peak visible during adolescence (Crone, 2009). Hence, while difficulties with inhibitory control may already be apparent in adolescence, their adverse effect on developmental outcomes such as eating behavior may only emerge when this aspect of self-regulation is fully developed in adulthood. Furthermore, problems with inhibitory control are likely more pronounced in a clinical sample than in a community sample, because of high comorbidity rates in clinical settings which may create additional vulnerabilities. A second explanation for the mixed results could be the use of different assessment methods. In contrast to the self-report measure used in the present study, previous studies assessed inhibitory control with fMRI (Balodis et al., 2013) and behavioral tasks (Kittel et al., 2017; Manasse et al., 2016). As argued also by other researchers, different assessment methods may measure different dimensions of the construct of inhibitory control (e.g., Claes, Nederkoorn, Vandereycken, Guerrieri, & Vertommen, 2006; Matton et al., 2017). Self-report measures such as the Behavior Rating Inventory of Executive Function (BRIEF) are known for measuring general executive

functioning in daily life (Gioia et al., 2000); however, they are susceptible to social desirability and recall biases. By contrast, behavioral tasks such as the Go/No-Go Task (Houben & Jansen, 2011) may target rather specific, implicit and impulsive parts of self-regulation. This can make them vulnerable to temporary fluctuations (Dougherty, Mathias, Marsh, & Jagar, 2005). In addition, self-report measures are typically used to capture stable, traitdependent aspects of self-regulation, whereas behavioral tasks are more appropriate for measuring state aspects of self-regulation (Dougherty et al., 2005). This highlights an important challenge for future research, in which multiple assessment methods are combined to provide the most comprehensive assessment of inhibitory control. Our second research question focused on a possible moderating role of automatic processing in the relationship between selfregulation/inhibitory control and loss of control over eating. Results revealed a trend significant interaction between inhibitory control (assessed with the BRIEF inhibition subscale) and food specific automatic processing (assessed with the PFS) in predicting loss of control over eating. This suggests that inhibitory control appears to have a somewhat different relationship with loss of control over eating depending on the individual's level of food specific automatic processing. Contrary to dual-process predictions, it was not the combination of strong automatic processing and impaired inhibitory control that was associated with a higher risk of experiencing loss of control over eating in adolescents. Instead, it was those adolescents who exhibited weaker food specific automatic processing together with inhibitory control difficulties who tended to have a slightly, albeit non-significantly, greater risk of experiencing loss of control over eating. These preliminary findings are consistent with Manasse et al.’s (2015) observation in adults with binge eating: it was particularly the combination of poor inhibitory control and weak food specific automatic processing that elevated the risk of binge eating. By contrast, overall self-regulation (assessed with the BRIEF behavioral index) did not interact with automatic processing (neither overall temperamental nor food specific, assessed with the BAS and the PFS, respectively) to predict loss of control over eating. This lack of moderation from general, temperamental based

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automatic processing, despite a trend significant moderation from food specific automatic processing fits with some previous research (e.g., Manasse et al., 2015); however, it is at odds with other evidence that has demonstrated a contribution from general, temperamental based automatic processing to eating behavior among adolescents (e.g., Batterink et al., 2010; Matton et al., 2017). Thus future research is warranted to further investigate the role of automatic processing in the relationship between self-regulation and loss of control over eating among adolescents. The present study has several strengths. First, it focused specifically on adolescents (10e17 year olds) recruited from the general community. Because pathological eating behavior often originates in adolescence (Kessler et al., 2013), studying its underlying mechanisms in this age group is an important extension of previous research in older (e.g., Manasse et al., 2016), younger (e.g., Goldschmidt et al., 2017) and clinical (e.g., Kittel et al., 2017) samples. Second, we specifically chose loss of control over eating as our outcome variable. Previous research that has examined the role of self-regulation in the context of eating behavior has used outcome measures such as body mass index, amount of food eaten or the presence of a clinical diagnosis (e.g., binge eating disorder). As loss of control over eating is common among adolescents (TanofskyKraff et al., 2011), and has been associated with several negative psychosocial outcomes (e.g., Rosenbaum & White, 2015), this particular eating behavior deserves exclusive attention in research. Importantly, we used the ‘golden standard’ for assessing loss of control over eating among adolescents, the Ch-EDE (Bryant-Waugh
& Braet, 1999). Based on this et al., 1996; translated by Decaluwe semi-structured clinical interview, 24% of our adolescent sample (n ¼ 30) was classified as having loss of control over eating (i.e., experienced at least one loss of control over eating episode over the past three months). This is comparable with prevalence rates in previous studies (e.g., Tanofsky-Kraff et al., 2011). Third, to the best of our knowledge, the present study was the first to investigate the independent and interactive contributions of self-regulation and automatic processes in the context of loss of control over eating among adolescents. In addition, with regard to self-regulation, the role of both overall self-regulatory skills and inhibitory control in particular were examined (e.g., based on Goldschmidt et al., 2017; Kittel et al., 2017). Thus we were able to make a distinction between these two regulatory constructs, an important consideration given the different conceptualizations and constructs used in previous studies. In addition, we examined both temperamental based and food specific automatic processing as possible moderators (e.g., based on Manasse et al., 2015; Matton et al., 2017) to provide further clarification on the nature of their dynamic interplay with self-regulation in the context of adolescents’ eating behavior. There are also some limitations that need to be acknowledged. First, the sample size was relatively small. Nevertheless, the power to detect our trend significant interaction between inhibitory control and food specific automatic processing was moderate (.56) (Cohen, 1992). Future research could usefully investigate whether this interaction would reach statistical significance with a larger sample of adolescents experiencing loss of control over eating. Second, the study was cross-sectional. Replication using a longitudinal design is necessary to draw causal inferences. Such a design would also make it possible to examine relationships between selfregulation/inhibitory control and loss of control over eating over time, and to determine whether certain ‘self-regulatory profiles’ could be detected that are predictive of loss of control over eating in adolescents. Despite these limitations, the present study has some noteworthy theoretical and clinical implications. First, the results may

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contribute to refining theoretical models of adolescents' pathological eating behavior in general, and loss of control over eating in particular. Current models focus largely on environmental factors (e.g., Mitchell, Farrow, Haycraft, & Meyer, 2013), emotion regulation (Haedt-Matt & Keel, 2011) and restraint eating (Goldfield et al., 2010). However, researchers have recently underscored the importance of adding cognitive elements to the models. Thus, the finding that overall self-regulation emerged as an important underlying mechanism in the explanation of loss of control over eating could be integrated into adolescents’ eating behavior models. Second, by investigating the role of underlying selfregulatory deficits in a community sample, this knowledge can be used in the future to screen youngsters who are at risk of developing pathological eating behavior such as loss of control over eating. Third, following replication of the current findings, early intervention strategies could be developed to improve selfregulatory skills. Such strategies have already shown proven benefit in enhancing self-regulation and weight control, as well as reducing unhealthy eating behavior in both adults (e.g., Allom & Mullan, 2015; Houben, 2011) and children (e.g., Verbeken, Braet, Goossens, & Van der Oord, 2013). In conclusion, the current study provides important preliminary evidence for generic self-regulation deficits in adolescents who experience loss of control over eating. Although no significant group differences were found for inhibitory control, findings further suggest that the relationship between poor inhibitory control in particular and loss of control over eating was moderated by food specific automatic processing. Specifically, counter to dualprocess predictions, adolescents with weaker food specific automatic processing together with poorer inhibitory control were at greater risk of experiencing loss of control over eating. Thus future research is warranted to determine whether automatic processing is indeed a crucial mediator of the relationship between selfregulation and loss of control over eating. Role of funding sources The research was supported by the Special Research Fund of Ghent University. The funding body had no role in the study design, collection, analysis or interpretation of the data, writing of the manuscript, or decision to submit the manuscript for publication. Contributors Lien Goossens and Eva Van Malderen designed the study and wrote the protocol. Eva Van Malderen conducted the statistical analyses and wrote the first draft of the manuscript. Lien Goossens, Eva Kemps and Sandra Verbeken edited subsequent drafts of the manuscript. All authors contributed to and have approved the final manuscript. Conflicts of interest All authors declare that they have no conflicts of interest. References Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple Regression: Testing and interpreting interactions. Sage. https://doi.org/10.2307/2583960. Allom, V., & Mullan, B. (2015). Two inhibitory control training interventions designed to improve eating behaviour and determine mechanisms of change. Appetite, 89, 282e290. https://doi.org/10.1016/j.appet.2015.02.022. Appelhans, B. M., Woolf, K., Pagoto, S. L., Schneider, K. L., Whited, M. C., & Liebman, R. (2011). Inhibiting food reward: Delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women. Obesity, 19(11), 2175e2182. https://doi.org/10.1038/oby.2011.57. Attia, E., Becker, A. E., Bryant-Waugh, R., Hoek, H. W., Kreipe, R. E., Marcus, M. D.,

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