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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: Int J Eat Disord. 2023 Oct 27;57(1):93–103. doi: 10.1002/eat.24087

State-level working memory and dysregulated eating in children and adolescents: An exploratory ecological momentary assessment study

Andrea B Goldschmidt 1, Stephanie P Goldstein 2, Florian Schmiedek 3, Erin Stalvey 4, Bailey Irizarry 2, J Graham Thomas 2
PMCID: PMC10872824  NIHMSID: NIHMS1940527  PMID: 37888341

Abstract

Background:

Children with loss of control (LOC) eating and overweight/obesity have relative deficiencies in trait-level working memory (WM), which may limit adaptive responding to intra- and extra-personal cues related to eating. Understanding of how WM performance relates to eating behavior in real-time is currently limited.

Methods:

We studied 32 youth (ages 10–17y) with LOC eating and overweight/obesity (LOC-OW; n=9), overweight/obesity only (OW; n=16), and non-overweight status (NW; n=7). Youth completed spatial and numerical WM tasks requiring varying degrees of cognitive effort and reported on their eating behavior daily for 14 days via smartphone-based ecological momentary assessment. Linear mixed effects models estimated group-level differences in WM performance, as well as associations between contemporaneously completed measures of WM and dysregulated eating.

Results:

LOC-OW were less accurate on numerical WM tasks compared to OW and NW (ps<.01); groups did not differ on spatial task accuracy (p=.41). Adjusting for between-subject effects (reflecting differences between individuals in their mean WM performance and its association with eating behavior), within-subject effects (reflecting variations in moment-to-moment associations) revealed that more accurate responding on the less demanding numerical WM task, compared to one’s own average, was associated with greater overeating severity across the full sample (p=.013). There were no associations between WM performance and LOC eating severity (ps>.05).

Conclusions:

Youth with LOC eating and overweight/obesity demonstrated difficulties mentally retaining and manipulating numerical information in daily life, replicating prior laboratory-based research. Overeating may be related to improved WM, regardless of LOC status, but temporality and causality should be further explored.

Keywords: Loss of control eating, overeating, overweight, obesity, child, adolescent, working memory, executive functioning, neurocognitive functioning, ecological momentary assessment

Pediatric overweight and obesity affect >30% of youth in the United States (Ogden et al., 2016) and nearly 20% of youth worldwide (Di Cesare et al., 2019), and are associated with deleterious health consequences, reduced quality of life, increased healthcare spending (Finkelstein et al., 2014; Hoelscher et al., 2013), and elevated risk for tracking excess weight status into adulthood (Guo et al., 2002). Therefore, prevention and early intervention are key public health priorities. Dysregulated eating behaviors are major determinants of overweight/obesity onset and maintenance in youth (French et al., 2012). Two behaviors robustly linked to weight gain and obesity include loss of control (LOC) eating, involving a sense that one cannot control what or how much one is eating, and overeating, involving consumption of an unusually large amount of food in a discrete time period (American Psychiatric Association, 2013). These behaviors collectively affect 10–15% of young people in population-based samples (Goldschmidt et al., 2015) and >30% of youth with overweight/obesity (He et al., 2016). In addition to being associated with weight gain, adiposity, and overweight/obesity in cross-sectional and prospective studies, LOC eating and overeating may undermine obesity treatment outcome (Moustafa et al., 2021) via associations with multiple forms of psychosocial dysfunction, including full-syndrome eating disorders (Goldschmidt, 2017). Therefore, these behaviors are amenable targets for preventing excess weight gain in youth. A clearer understanding of mechanisms maintaining dysregulated eating is needed to optimize prevention and early intervention.

Existing models of mechanisms underlying dysregulated eating have primarily focused on negative affect (Haedt-Matt & Keel, 2011) and dietary restraint (Schaumberg et al., 2016), while neurocognitive factors (i.e., functions associated with specific brain regions or pathways) have largely been overlooked (with some exceptions; Steegers et al., 2021). It is critical to understand how neurocognitive processes relate to dysregulated eating because they inform how one identifies, experiences, and respond to emotions (Nigg, 2016), as well as one’s capacity to engage in healthy weight regulation behaviors (Liang et al., 2014). An additional limitation of etiological research on dysregulated eating in youth is that it has largely focused on trait-level group differences in onset and maintenance factors. These approaches fail to inform an understanding of processes that relate to eating behavior on a moment-to-moment basis, thus limiting development of interventions that can be delivered in real-time when individuals are most at risk for engaging in dysregulated eating behaviors.

Poorer executive functioning, characterized by diminished ability to adaptively engage in ongoing, goal-oriented behavior (Dohle et al., 2018), is a neurocognitive process implicated in the onset and maintenance of overweight/obesity and dysregulated eating (Eichen et al., 2017). Working memory (WM) is a core component of executive functioning that is associated with eating- and weight-related problems (Calvo et al., 2014; Duchesne et al., 2010; Voon, 2015). Our group found that children with LOC eating and overweight/obesity had poorer performance on a laboratory-based WM task than their overweight/obese and non-overweight peers (Goldschmidt, O’Brien, et al., 2018), and more recent work has highlighted WM as a prospective predictor of adiposity during the transition from childhood to adolescence (Shields et al., 2021). WM refers to the ability to retain goal-relevant information when faced with distracting or irrelevant information. Thus, WM may influence one’s ability to forego immediate rewards (e.g., palatable foods) in pursuit of longer-term goals (e.g., weight regulation; see Wesley & Bickel, 2014 for a conceptual review of the relationship between WM and delay discounting). Although WM training has been proposed as an intervention for both overweight/obesity (Jansen et al., 2015) and dysregulated eating (Juarascio et al., 2015), such interventions have not been tested (and minimal transfer effects of such training interventions may limit their clinical impact; Melby-Lervåg et al., 2016), underscoring the importance of mechanistic research that can aid in intervention development.

While WM is typically conceptualized as a trait-level factor, it may be influenced at the state-level by intra- and extra-personal factors (e.g., cognitive load, mood, sleep, time of day; Brose et al., 2014; Brose et al., 2012; Könen et al., 2015; Schmidt et al., 2015; Weigelt et al., 2009). Within-person changes in WM may be an important determinant of dysregulated eating behavior, independent of one’s trait-level WM. Alternatively, certain aspects of eating behavior (e.g., nutritional composition of foods consumed) and related factors (e.g., affective or environmental antecedents/consequences) may predict within-person changes in WM (Blasiman & Was, 2018). Yet, associations between state-level WM among youth with excess weight and/or dysregulated eating and real-world, real-time eating behavior are currently unknown. Ecological momentary assessment (EMA), which involves repeated measurement of behavioral, psychosocial, and/or environmental constructs in near real-time in the natural environment, can clarify momentary fluctuations and associations among constructs of interest, and thus is optimal for assessing inter- and intra-personal factors related to dysregulated eating (Smith et al., 2019). Very few studies have utilized EMA to assess neurocognitive factors in relation to eating behavior. In a series of EMA studies among transdiagnostic adults with binge eating, Smith and colleagues (Smith, Mason, Juarascio, et al., 2020; Smith, Mason, Schaefer, et al., 2020) found that poorer state-level inhibitory control and greater state-level attention bias to palatable foods may increase risk of binge eating (although these associations may be moderated by momentary affective processes). There have been no EMA studies investigating real-time associations between WM (or any other neurocognitive factors) and dysregulated eating in youth, thus inhibiting understanding of how WM operates at the momentary level in relation to risk for eating pathology and excess weight gain throughout development.

The current exploratory pilot study investigated children and adolescents’ performance on a novel momentary WM task embedded within a smartphone-based EMA protocol, and to assessed relations between contemporaneously measured (i.e., measured at the same EMA recording) state-level WM and dysregulated eating behavior. We hypothesized that youth with overweight/obesity and LOC eating would have poorer WM performance relative to those with overweight/obesity and non-overweight controls, and that poorer state-level WM would be associated with elevated levels of LOC and overeating. Overall, results were designed to inform scientific and clinical understanding of momentary mechanisms related to dysregulated eating in youth and ultimately facilitate development of interventions to alleviate their cumulative personal and societal burden.

METHODS

Participants and Procedures

Participants were youth between ages 10–17 years who (along with at least one adult caregiver) were recruited between 2018–2020 from the Greater Providence, Rhode Island area for a study involving 14 days of smartphone-based monitoring of eating behavior and WM. Recruitment methods included community advertisements, referrals from pediatric weight management and community healthcare providers, and contact lists from previous studies where participants had agreed to be contacted for future studies. Participants were excluded if they were taking medication known to affect appetite or weight; had current or past medical or psychiatric conditions affecting appetite, weight, or executive functioning (e.g., recent concussion, history of traumatic brain injury), with the exception of binge-eating disorder; scored in the borderline range or lower on the Wechsler Abbreviated Scale of Intelligence (WASI; The Psychological Corporation, 1999); or were actively receiving treatment for overweight/obesity.

Caregivers of interested youth were screened by phone for initial eligibility, then invited to attend a baseline evaluation during which they provided written informed consent/assent, completed baseline measures, and were trained to complete EMA survey items and WM tasks. Participants carried a personal smartphone or were loaned a phone, as needed. Families were contacted every 1–2 days by phone by a member of the study team throughout the 14-day period to discuss compliance and address any questions/concerns regarding assessment procedures. In total, 98 youth were screened via phone, 60 of whom presented to the research site for a baseline evaluation. Based on this evaluation, 49 youth were eligible and initiated the 14-day protocol.

The 14-day assessment period involved daily, repeated measurement of eating behavior and WM in the natural environment. We employed interval- and signal-contingent recordings (Wheeler & Reis, 1991). Event-contingent recordings were not included based on our prior research showing very low compliance with self-initiated reports of eating episodes in youth of similar ages (Goldschmidt, Smith, et al., 2018). Interval-contingent prompts occurred daily around bedtime (9:30pm). Signal-contingent prompts arrived at semi-random intervals 4–5 times per day: within +/− 30 minutes of 8:00am, 2:30pm, 5:30pm, and 7:30pm on weekdays (to avoid interfering with the school day); and within +/− 30 minutes of 8:00am, 11:30am, 2:30pm, 5:30pm, and 7:30pm on weekend days, to arrive between typical mealtimes. During all recordings, participants were instructed to report on any recent eating episode that had not been previously recorded, along with relevant psychological, physiological, and environmental factors. The WM tasks were administered at only three of the signal-contingent recordings each day (i.e., 8:00am, 2:30pm, and 7:30pm) to limit participant burden. Therefore, while eating episodes and WM task completion did not occur contemporaneously, measures of these constructs were completed contemporaneously. We henceforth refer to these constructs as “contemporaneously assessed,” “contemporaneously measured,” or “contemporaneously completed.”

Upon completing the 14-day protocol, participants returned to the research center to complete final assessments of weight and eating behavior, return any study-loaned smartphone, and receive a cash honorarium. Participants earned $50 for the baseline evaluation; $50 for completion of the 2-week protocol; and additional monetary incentives for daily assessments prorated according to degree of response to random signals. Study procedures were approved by the Lifespan Institutional Review Board (protocol #1288337).

Measures

Trait-level measures:

Height and weight were measured by trained research staff using a stadiometer and calibrated digital scale, respectively. Children’s standardized body mass index (kg/m2; z-BMI) was calculated using CDC growth charts and procedures (Kuczmarski et al., 2000). Demographic data were reported by children and parents, and included children’s age, gender, race (White, Black/African-American, Asian, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, multi-racial, or other), ethnicity (Hispanic/Latino or non-Hispanic/Latino), current medications, and medical problems.

Diagnostic items from the Child Eating Disorder Examination (Child EDE; Bryant-Waugh et al., 1996) were administered to assess overeating and/or LOC eating over the past 3 months, and rule out other eating disorder diagnoses. The Child EDE is a semi-structured, interviewer-based instrument based on the adult EDE, with language modifications for a younger audience. It has adequate reliability and validity (Bryant-Waugh et al., 1996; Decaluwe & Braet, 2004; Watkins et al., 2005).

State-level measures:

Overeating (“To what extent do you feel that you overate?”) and LOC (“While you were eating, did you… feel a sense of loss of control?”; “…feel that you could not stop eating once you had started?”; “…feel like you could not resist eating?”; “…feel like a car without brakes, you just kept eating and eating?”) were rated via 5-point Likert-type scale (1=“no, not at all,” and 5=“yes, extremely”) each time an eating episode was reported. The four items assessing LOC were summed to form a composite score (range=4–20) based on their high internal consistency (α=.96).

Based on prior work (Dirk & Schmiedek, 2016; Galeano-Keiner et al., 2022), WM was assessed using two momentary tasks developed for children, both of which involved remembering and rapidly mentally updating visual information. Tasks tapped into spatial and numerical domains of WM functioning. In the spatial task, participants were presented with two different colored and shaped cartoon creatures (“germs that can get you sick”) arranged within a 4×4 grid for 3000ms, to which sequential positioning updates were applied by moving the images around the grid as indicated by an arrow matching each cartoon’s color scheme (at a rate of 2500ms per updating operation). In the numerical task, participants were simultaneously presented with three single digit numerals (“cars moving in and out of a parking lot”; range=0–9; 3000ms), to which sequential updating operations were applied (additions or subtractions of numbers ranging from −2 to +2). The numerical task was delivered in two iterations designed to vary with respect to difficulty by modifying the speed with which sequential updates were presented (slower task=2500ms; faster task=1500ms). A total of three WM tasks were presented in standardized order during daily signal-contingent recordings (with a single trial of each task at each recording). For all three tasks, participants were given 30000ms to enter a response before the screen automatically advanced. Instructions and visuals for both tasks were designed to be engaging for children as young as 10 (see Fig. 1).

Figure 1.

Figure 1.

Figure 1.

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Visual depiction of numerical (a) and spatial (b) working memory (WM) tasks.

Statistical Analysis

All analyses were conducted using SPSS Version 27.0 (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp). Descriptive statistics were calculated for participant characteristics, using means with standard deviations and counts with percentages, as appropriate. The intraclass correlation (ICC) was calculated for each task (separately for response time and accuracy) to inform the degree to which WM variability could be attributed to differences between subjects (i.e., the degree to which an individual consistently demonstrated better or worse WM, relative to other participants) versus to within-subjects fluctuations (i.e., moment-to-moment variation in WM occurring within a given individual).

In the first step of inferential analysis, linear mixed effects models (LMM) were fitted separately for each performance metric (i.e., accuracy and response time) of each task (i.e., faster numerical task, slower numerical task, spatial task). Performance on an individual task served as the unit of analysis (i.e., a single accuracy score or response time; data were not aggregated). The effect of subject was included as a random effect. Fixed effects were specified for group (i.e., OW+LOC, OW, or NW) and covariates included day of protocol (to adjust for potential learning effects), gender, age, and race/ethnicity (given that WM may be related to these sociodemographic features, e.g., Cowan et al., 2011; Ibbotson & Roque-Gutierrez, 2023).

A second step of inferential analysis tested associations between WM performance and eating behavior using the same LMM approach. Dependent variables (evaluated in separate models) were degree of LOC and overeating from individual EMA ratings, and predictors were WM accuracy and response time from WM tasks completed concurrently with the EMA ratings and the abovementioned covariates. Grand-mean centering and person-mean centering were used to disaggregate between- and within-subjects variance, respectively. Between-subjects variance represented variance accounted for by individual or trait-level differences (i.e., differences in mean WM performance from one participant to the next) and within-subjects variance represented variance accounted for by moment-to-moment or contextual factors (e.g., environmental influences on WM at a given time point). Because of the design (i.e., not all EMA surveys included WM assessment) and modest levels of LOC and overeating reported across surveys, we did not undertake prospective analyses to understand temporality more definitively in the associations between WM performance and eating behavior. Instead, we completed secondary data analyses, using the same LMM approach, examining day-level associations between WM performance and eating behavior. These secondary analyses were designed to understand whether days on which WM was poorer, on average, were also characterized by greater average severity of LOC or overeating across eating episodes, in line with hypotheses.

A priori power analyses were based on multi-level Monte Carlo simulations in Mplus (Muthén & Muthén, 1998–2013) assuming a standardized medium effect size of .55 for WM as a predictor of likelihood of LOC eating, given 14 days of EMA recordings with an estimated 4 responses per day, an EMA compliance rate of 85%, an intraclass correlation (ICC) of .30, and a two-tailed alpha of .05. A sample of 50 participants was estimated to provide statistical power of >.80.

RESULTS

Descriptive Characteristics

Sample characteristics.

Participants were 49 youth (M age=15.3; SD=2.0; M z-BMI=1.67; SD=1.01), of whom 17 (34.7%) had overweight/obesity and reported at least one episode of LOC eating in the past 3 months according to the Child EDE (OW+LOC); 22 (44.9%) had overweight/obesity but denied any recent LOC eating (OW); and 10 (20.4%) fell in the normal-weight range and denied any recent LOC eating (NW). Participants were mostly female (n=28; 57.1%) and self-identified as White (n=32; 65.3%), multi-racial (n=7; 14.3%), Hispanic or Latino (n=3; 6.1%), Native Hawaiian or Pacific Islander (n=2; 4.1%), Black or African American (n=1; 2.0%), Asian (n=1; 2.0%) or other (n=3; 6.1%). Participants were included in analyses if they responded to at least 35% of EMA signals during the 14-day protocol (based on average compliance rates required for inclusion in prior EMA studies of children and adolescents; Hiekkaranta et al., 2021; Michel et al., 2022; Müller et al., 2021; Parker et al., 2022; van Roekel et al., 2019; Woods et al., 2020), and had accuracy above 0% on at least 50% of completed WM tasks, indicating that responses reflected at least some level of effort. A total of 17 participants were removed from analyses due to low compliance (n=14) or attrition (n=3). Thus, the final sample comprised 32 youth (n=14 OW+LOC; n=18 OW; and n=10 NW). These participants did not differ significantly from the 17 who were excluded on age, gender, race/ethnicity, or eating/weight status (all ps<.07). Compared to both OW and NW controls, OW+LOC participants reported significantly higher LOC (coefficient=5.00; SE=1.38; p<.001) and overeating severity (coefficient=1.20; SE=0.30; p<.001) via EMA. OW and NW did not differ from one another on severity scores for either construct (all ps>.08).

EMA compliance.

The 32 participants included in the analytic sample were retained in the EMA protocol for an average of 13.69 days (SD=0.59) and completed an average of 42.78 signal-contingent recordings (SD=9.92) and 9.88 bedtime recordings (SD=3.06), for an average overall EMA adherence rate of 71.16% (SD=16.44). Compliance with signal-contingent recordings with (69.91%; SD=16.23) and without (72.82%; SD=18.43) WM tasks did not differ significantly (t=1.569; p=.127).

WM task performance.

Final analyses were limited to numerical WM tasks trials with responses ≤20 seconds and spatial WM task trials ≤30 seconds. Average response time on the faster numerical WM task was 7.39 seconds (SE=0.30; range=1.00–19.30), and average response accuracy was 69.04% (SE=4.37; range=0–100). The ICC, reflecting the portion of the variance attributable to between-subjects factors, was .25 for response time, and .40 for accuracy. Average response time on the slower numerical WM task was 6.96 seconds (SE=0.30; range=0.54–18.99), and average response accuracy was 69.85% (SE=4.20; range=0–100). The ICC was .26 for response time, and .36 for accuracy. Finally, average response time on the spatial WM task was 2.56 seconds (SE=0.13; range=0.31–28.06), and average response accuracy was 70.77% (SE=4.33; range=0–100). The ICC was .07 for response time, and .35 for accuracy.

OW+LOC had significantly lower accuracy than OW and NW controls on both the faster (coefficient=−43.256; SE=10.8603; p<.001) and slower numerical WM task (coefficient=−37.907; SE=11.5153; p=.001). Groups did not differ on accuracy for the spatial WM task (p=.41), or on response time for any of the three tasks (all ps<.05).

Effects of Time on WM Performance

Day of protocol.

Day of protocol was positively associated with faster response time for both numerical WM tasks (all ps<.01), but not for the spatial WM task (p=.16). Task accuracy did not differ by day of protocol for any of the three tasks (all p>.05). None of the tasks showed significant differences for response time or accuracy when comparing weekdays and weekend days (all ps>.05).

Time of day.

Adjusting for relevant baseline characteristics and day of protocol, response time on the slower numerical WM task was slower after 3:00pm (coefficient=−.4276; SE=0.1849; p=0.02). There were no significant differences by time of day for accuracy on this task (p=.41), and response time and accuracy did not differ by time of day for any of the other tasks (all ps>.05).

Associations Between WM Performance and Eating Behavior

Neither response time nor accuracy was significantly associated with contemporaneously reported LOC severity for any of the three WM tasks (all ps>.05; see Table 1). Response time was not significantly associated with contemporaneously reported overeating severity for any of the three WM tasks (all ps>.05). Accuracy was not significantly associated with contemporaneously reported overeating severity for the faster numerical WM task or the spatial WM task (all ps>.05). However, the within-subjects variance components for accuracy on the slower numerical WM task was associated with contemporaneously reported overeating severity (coefficient=.004; SE=.0014; p=.013), such that on occasions when an individual had higher accuracy than their own average, they tended to also report greater overeating severity. Between-subjects effects for accuracy on this latter task were non-significant (p=.38).

Table 1.

Associations between contemporaneously assessed working memory and dysregulated eating

Loss of control eating severity
 Overeating severity
95% CI 95% CI
Independent variable Effect level Coefficient t SE Lower Upper p Coefficient t SE Lower Upper p
Slow numerical task n=492 records analyzed n=493 records analyzed
Response time BS .000 −1.230 .0003 −.001 .000 .219 −849.50 −1.400 606.64 .000 342.50 .162
WS −4,439.00 −.082 543.89 .000 .000 .935 −404.80 −.242 167.53 −369.70 288.70 .809
Accuracy BS −.020 −.754 .0271 −.074 .033 .451 −.005 −.876 .0060 −.017 .007 .382
WS .008 1.786 .0046 −.001 .017 .075 .004 2.499 .0014 .001 .006 .013
Group* OW 2.178 1.626 1.3393 −4.54 4.809 .105 .494 1.676 .2947 −.085 1.073 .094
OW+LOC 4.882 2.780 1.7559 1.432 8.332 .006 1.158 2.992 .3871 .398 1.919 .003
Protocol day .000 .004 .0325 −.064 .064 .997 −.017 −1.673 .0100 −.037 .003 .095
Age .422 1.356 .3112 −.189 1.034 .176 .081 1.189 .0685 −.053 .216 .235
Gender† −.030 −.028 1.0473 −2.087 2.028 .977 .115 .497 .2309 −.339 .568 .620
Race/
ethnicity§ 		Multi-racial 2.266 .763 2.9689 −3.568 8.100 .446 .645 .974 .6624 −.656 1.947 .330
White .969 .363 2.6700 −4.277 6.215 .717 .442 .710 .5952 −.747 1.592 .478
Hawaiian −.045 −.013 3.5890 −7.097 7.007 .990 −.046 −.058 .8050 −1.628 1.535 .954
Latino/a/x −.724 −.230 3.1434 −6.900 5.453 .818 .084 .121 .6979 −1.287 1.455 .904
Fast numerical task n=486 records analyzed n=487 records analyzed
Response time BS −182.50 −.068 .0003 −.001 .001 .946 −424.50 −.710 598.22 .000 751.00 .478
WS 257.50 .497 517.67 −759.70 .000 .619 −177.90 −.110 161.66 −335.50 299.90 .912
Accuracy BS −.007 −.266 .0281 −.063 .048 .790 −.005 −.832 .0061 −.017 .007 .406
WS −.004 −.931 .0048 −.014 .005 .352 .001 .969 .0015 −.001 .004 .333
Group* OW 1.727 1.317 1.3112 −.849 4.304 .188 .405 1.425 .2843 −.154 .964 .155
OW+LOC 4.508 2.601 1.7336 1.102 7.915 .010 .985 2.615 .3768 .245 1.726 .009
Protocol day .016 .505 .0320 −.047 .079 .614 −.013 −1.264 .0100 −.032 .007 .207
Age .528 1.673 .3154 −.092 1.147 .095 .090 1.308 .0686 −.045 .224 .191
Gender† −.265 −.248 1.070 −2.368- 1.838 .804 .062 .266 .2331 −.396 .520 .790
Race/
ethnicity§ 		Multi-racial 1.394 .465 3.0002 −4.501 7.290 .642 .451 .681 .6622 −.850 1.752 .496
White 1.157 .429 2.6938 −4.137 6.450 .668 .442 .745 .5931 −.724 1.607 .457
Hawaiian .556 .154 3.6090 −6.535 7.648 .878 .125 .156 .8006 −1.449 1.698 .876
Latino/a/x −.761 −.240 3.1669 −6.984 5.462 .810 .035 .051 .6941 −1.329 1.399 .960
Spatial task n=503 records analyzed n=504 records analyzed
Response time BS .000 −1.110 .0004 −.001 .000 .268 −777.30 −.780 996.96 .000 .000 .436
WS .000 1.874 892.62 −810.30 .000 .062 341.90 1.218 280.79 −209.80 893.60 .224
Accuracy BS .020 1.152 .0172 −.014 .054 .250 .005 1.355 .0039 −.002 .013 .176
WS −.001 −.151 .0043 −.009 .008 .880 .000 .315 .0014 −.002 .003 .753
Group* OW 1.901 1.823 1.0428 −.148 3.950 .069 .411 1.746 .2354 −.052 .874 .081
OW+LOC 5.677 4.535 1.2519 3.217 8.137 .000 1.318 4.671 .2822 .764 1.872 .000
Protocol day .011 .343 .0319 −.052 .074 .732 −.013 −1.339 .0100 −.033 .006 .181
Age .443 1.667 .2660 −.079 .966 .096 .090 1.498 .0603 −.028 .209 .135
Gender† .070 .079 .8860 −1.671 1.811 .937 .152 .753 .2012 −.244 .547 .452
Race/
ethnicity§ 		Multi-racial 2.646 1.030 2.5686 −2.401 7.693 .303 .731 1.237 .5914 −.430 1.893 .217
White 1.517 .653 2.3217 −3.045 6.078 .514 .540 1.011 .5341 −.509 1.590 .312
Hawaiian 1.236 .397 3.1155 −4.886 7.357 .692 .221 .306 .7240 −1.201 1.644 .760
Latino/a/x −.090 −.033 2.7271 −5.449 5.268 .974 .224 .359 .6246 −1.003 1.452 .720
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Note: BS=between-subjects; WS=within-subjects. OW=overweight/obesity with no recent loss of control eating; OW+LOC=overweight/ obesity and repored at least one episode of loss of control eating in the past 3 months. Bold font indicates statistically significant effect (p<.05). Response time is reflected in seconds.

*

Reference group is non-overweight children with no recent loss of control eating.

†

Reference category is males.

§

Reference category is Asian.

To address limitations arising from concurrent administration of EMA surveys and WM tasks, mean WM performance (i.e., average accuracy and response time for each task), mean LOC, and mean overeating scores were generated for each participant, for each day of their participation. Secondary analyses examining day-level effects found no associations between average WM performance on a given day and average LOC or overeating severity on the same day (ps>.075).

DISCUSSION

This study aimed to understand state-level WM performance of youth across the eating and weight spectrum, and how this relates to real world eating behavior, using smartphone-based EMA. We found that WM response time varied by day of protocol and time of day, while accuracy varied by group (with respect to eating pathology and weight status). Indeed, similar to our prior laboratory-based research (Goldschmidt, O’Brien, et al., 2018), youth with LOC eating and overweight/obesity demonstrated poorer WM performance than controls with and without overweight/obesity. Use of a novel task assessing state-level WM in the natural environment within day-to-day contexts appears to replicate prior findings related to trait-level WM performance among youth with overweight/obesity and LOC eating assessed in laboratory-based settings.

Contrary to expectations, most markers of WM performance were unrelated to momentary ratings of LOC or overeating. One exception was that, at the within-subjects level, higher accuracy on a numerical WM task was associated with greater contemporaneously reported overeating severity. These findings must be considered in light of the design, whereby self-reports of eating behavior were based on retrospective recall of the most recent eating episode since the last prompt (and participants were not required to report on the time of the eating episode). Thus, although WM performance and overeating severity were captured at the same recording, it is likely that the corresponding eating episodes occurred before completion of the task. Understood in this context, energy intake and/or satiation resulting from overeating may be related to improvements in numerical WM, although temporality can only be inferred rather than definitively established. Further research is needed to understand why overeating, relative to more “normative” eating, may impact WM in youth, for example, whether this is a function of the types or macronutrient content of foods consumed during episodes involving overeating (e.g., Blasiman & Was, 2018).

Overall, the minimal observed associations between momentary WM performance and eating behavior may indicate that relations between these constructs are reflected in more trait-level than state-level processes. That is, an individual’s overall capacity for WM may contribute to the development and/or maintenance of problematic eating on a moment-to-moment basis to a greater degree than periodic fluctuations they may experience within that overall capacity (although further research is needed to understand how reliable these fluctuations are). Indeed, future studies should consider ways to validate trait-level measures of WM administered within naturalistic settings against those collected in laboratory-based settings to further explore this question, and better understand the impact of environmental factors (e.g., ambient noise) on WM and potential associations with eating. It is also possible that our study captured too few episodes of problematic eating to elucidate momentary associations between WM performance and eating behavior. Indeed, because of the design (in which WM was assessed at only some time points and not others to minimize participant burden), we had inadequate power to rigorously examine prospective associations between state-level WM and later reports of eating behavior (i.e., inability to adjust for eating behavior at the prior time point due to low likelihood of reporting an eating episode at the same time point as a WM assessment). Finally, it is possible that momentary associations between WM and eating behavior are only evident in the presence of other related processes (e.g., increasing negative affect; Smith, Mason, Juarascio, et al., 2020; Smith, Mason, Schaefer, et al., 2020). Indeed, given that WM and affect influence one another in real-time (Brose et al., 2012), it is possible that their interaction modifies vulnerability to maladaptive eating.

This study had several limitations which should be considered when interpreting the findings. First, the number of trials of the WM tasks was relatively low. With only one trial of each task per recording, the reliability with which systematic fluctuations in WM performance can be captured could not be determined, but may be limited. Finding an optimal number of task trials that balances positive (i.e., increased reliability) and negative (i.e., participant burden) aspects of test length is an important challenge for future studies. Furthermore, the analytic sample was relatively small and included mostly White youth (although, of note, the sample was more racially and ethnically diverse than expected given the demographic region, according to recent census data). A sizeable proportion of youth were excluded from the analyses due to low compliance and/or implausible performance on the WM tasks, which likely reflects a lack of effort given that participation required at least low average general intellectual functioning. Given that prior EMA studies of youth in this age range have also reported modest compliance (Goldschmidt, Smith, et al., 2018; Ranzenhofer et al., 2014), modifications to the incentive structure may be needed to improve response rate and/or effort level. These findings should also be considered in the context of growing efforts to integrate neurocognitive training components into interventions for maladaptive eating and/or weight regulation (Eichen et al., 2017; Juarascio et al., 2015), as compliance with those tools may be similarly problematic in some populations, especially when completed multiple times per day. Other limitations include the inability to assess participants during the school day, and the reliance on self-report to assess eating-related constructs. Finally, we were unable to examine temporal associations between WM and eating behavior due to limitations of the design and number of reported eating episodes involving LOC and/or overeating, which may reflect children’s difficulties adhering to a relatively frequent sampling schedule; future studies may wish to oversample youth with more frequent maladaptive eating episodes to assure adequate power to capture temporally dynamic relationships (while being mindful of effects of sample bias on generalizability).

Nevertheless, there were several important strengths, such as the community-based sample which included both male- and female-identifying participants, the large age range covering middle/late childhood and adolescence, and the inclusion of control groups with both overweight/obesity and normal-weight status. Importantly, this was the first study, to our knowledge, to assess WM (or neurocognitive performance more broadly) in a sample of youth with LOC eating and overweight/obesity using naturalistic, real-time assessment methods.

Taken together, the current study suggests that youth with LOC eating and higher BMI may have difficulties mentally retaining and manipulating information in their daily lives and natural environments. Overeating may be associated with improved WM performance according to some metrics, although further research is needed to understand temporality (i.e., using time-lagged analytic approaches) and causality (i.e., using experimental study designs). Future studies should investigate other aspects of neurocognitive functioning that may be related to real-time, real-world eating behavior in youth to inform etiologic and/or mechanistic models and identify modifiable targets for interventions focused on healthy regulation of eating and body weight.

Public Significance Statement:

Our findings suggest that youth with loss of control eating and overweight/obesity may experience difficulties mentally retaining and manipulating numerical information in daily life relative to their peers with overweight/obesity and normal-weight status, which may contribute to the maintenance of dysregulated eating and/or elevated body weight. However, it is unclear whether these individual differences are related to eating behavior on a moment-to-moment basis.

Acknowledgements:

This project was supported by grant R03-DK117198 awarded to Dr. Goldschmidt.

Footnotes

Conflict of interest: Drs. Goldschmidt consults with Thrive Behavioral Health and receives royalties from Routledge. Dr. Thomas reports board membership, consultancy, and stock ownership for Lumme Health, Inc., and scientific advisory board participation for Medifast, Inc.

IRB Statement: This project was conducted in accordance with the Belmont Report and was approved by the Lifespan IRB (protocol #014018).

Data availability statement:

Data are available upon reasonable request from the corresponding author.

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Associated Data

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Data Availability Statement

Data are available upon reasonable request from the corresponding author.

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