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. Author manuscript; available in PMC: 2025 Oct 27.
Published in final edited form as: Appetite. 2025 Aug 27;216:108277. doi: 10.1016/j.appet.2025.108277

Examining Longitudinal Associations Between Early Environmental Deprivation and Unpredictability and Dietary Quality and Eating Behaviors a Decade Later

Emily L Goldberg 1, Rebecca L Brock 1, Amy Lazarus Yaroch 2, Jennie L Hill 3, W Alex Mason 4, Jennifer Mize Nelson 1,5, Kimberly Andrews Espy 6, Timothy D Nelson 1
PMCID: PMC12554352  NIHMSID: NIHMS2117416  PMID: 40882821

Abstract

Purpose:

Although emerging evidence suggests that deprivation and unpredictability, two unique dimensions of early adversity, may be associated with eating, this association has not been examined across key developmental periods with robust measurement of dietary quality and eating behaviors. This study aims to examine the unique effect that experience of early deprivation and unpredictability may have on later eating across adolescence.

Methods:

Participants in this longitudinal study were 337 children (51% female) initially recruited between ages 3 and 6. Deprivation and unpredictability were measured upon study entry during preschool by observation and primary caregiver self-report, respectively. Eating across adolescence was measured at three time points by 24-hour dietary recalls via the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24), assessing for dietary quality, and the Three-Factor Eating Questionnaire R-18 (TFEQ-R18), assessing for eating behaviors. This study utilized structural equation modeling to examine longitudinal associations between deprivation, unpredictability, and dietary quality (Model #1) and between deprivation, unpredictability, and eating behaviors (Model #2), while controlling for sex, socioeconomic status, and maternal education (both models).

Results:

Greater experience of early deprivation was uniquely associated with higher caloric intake and greater emotional eating across adolescence. Alternatively, greater experience of early unpredictability was uniquely associated with lower caloric intake across adolescence.

Conclusion:

Experience of early deprivation and unpredictability emerged as significant predictors of caloric intake and emotional eating (deprivation only) across adolescence. These results have potential implications for interventions and prevention efforts aimed at reducing less healthful eating across adolescence by identifying early modifiable targets.

Keywords: Dietary Quality, Eating Behaviors, Deprivation, Unpredictability, Early Adversity

1. Introduction

During adolescence, dietary quality and eating behaviors tend to become worse, increasing risk for a range of health concerns (Moreno et al., 2010; Doom, et al., 2024a; Doom et al., 2023). Therefore, research to identify factors that contribute to these worsening trends during development is essential (Moreno et al., 2010). There has been much agreement within existing literature that experiencing adverse childhood experiences (ACEs) can increase the likelihood of poor health behaviors (Hemmingsson, 2018; Aquilina et al., 2021), physical health (Abrahamyan et al., 2023; Felitti et al., 1998; Hadwen et al., 2022; Secginli et al., 2022; Trossman et al., 2021) and mental health (Hadwen et al., 2022; McLaughlin & Sheridan, 2016). More recently, conceptual frameworks have been developed that support related, yet distinct, dimensions that underlie ACEs, including environmental deprivation (lack of expected environmental inputs and neglect) and unpredictability (changes or variations in one’s immediate environment) (McLaughlin & Sheridan, 2016; Usacheva et al., 2022). These conceptual models were developed and have been examined primarily within the context of psychopathology, however, more recently, these dimensional approaches have been applied to health behavior research and preliminary evidence suggests that unpredictability and deprivation may be uniquely associated with unhealthy eating patterns (e.g., Doom et al., 2023). This study aims to extend the literature by examining unique longitudinal associations between deprivation and unpredictability during preschool and eating (i.e., dietary quality and eating behaviors) across adolescence.

Adolescence is a unique period in development that marks a critical time to examine dietary quality and eating behaviors (Nelson et al., 2020). During this period, while family influence still plays a role, adolescents gain more autonomy over their diet quality and eating behaviors (Story et al., 2002). In conjunction with increased autonomy and independence, the developmental period of adolescence presents a mismatch between regulatory abilities and reward response centers within the brain. Specifically, the prefrontal cortex (PFC), which underpins regulatory abilities, is still developing during adolescence, which is paired with increased amygdala hyperactivity, which underpins responsivity to reward and emotion reactivity (Guyer et al., 2008; Nelson et al., 2019). When the amygdala reacts to highly motivating environmental stimuli, the PFC is involved in regulating and controlling this reaction (Duffy et al., 2018). Imbalance between regulation and reward, which can be exacerbated by a range of factors, including early adversity, particularly within the context of emerging autonomy to make healthy choices, may increase susceptibility to poor dietary quality and eating behaviors due to inability to regulate food intake and decisions (Doom et al., 2023; Nelson & Stice, 2023).

Early adversity is associated with later poor dietary quality (Hemmingsson, 2018; Kappes et al., 2023; Kazmierski et al., 2022). More specifically, experiencing a higher number of ACEs is associated with decreased fruit and vegetable intake (Horino & Yang, 2021; Mendoza et al., 2023; Yanagi et al., 2020), increased consumption of fried potatoes and other potatoes (Mendoza et al., 2023), and poor dietary quality in general (Aquilina et al., 2021) among adults. Experiencing ACEs also is associated with poorer eating behaviors, such as increased emotional eating among adults (Çakır et al., 2024) and increased risk for the development of an eating disorder among adolescents (Kovács-Tóth et al., 2022), consistent with findings among adults (Guney et al., 2025; Mares et al., 2023). Despite well documented associations between early adversity as a total and later dietary quality and eating behaviors, examinations between dimensions of early environment adversity and later dietary quality and eating behaviors during adolescence are limited.

Previous conceptual frameworks have identified specific dimensions of early adversity as relevant to later child development and health. Specifically, two dimensional models of early adversity have emerged, including the harshness-unpredictability model and the threat-deprivation model (Dimensional Model of Adversity and Psychopathology) (McLaughlin & Sheridan, 2016; Usacheva et al., 2022). Dimensional modeling approaches differ from traditional ACEs research, as it captures the shared variance across various types of ACEs, organizing them into distinct, but related, environmental experiences (McLaughlin & Sheridan, 2016). Traditionally, studies have either examined individual ACEs in isolation or used a cumulative risk approach. However, both methods of conceptualizing ACEs fall short in revealing specific patterns of co-occurrence and how these patterns uniquely influence the type and severity of outcomes later in life (McLaughlin & Sheridan, 2016). Dimensional models of early adversity propose that the occurrence of greater environmental adversity, particularly during periods of high developmental plasticity, such as during preschool, are linked to worse mental and physical health outcomes (McLaughlin et al., 2014; Doom et al., 2023).

In the present study, these two models (i.e., the harshness-unpredictability model and the threat-deprivation model) were combined, and deprivation and unpredictability were examined as two distinct dimensions of early adversity that may uniquely predict later dietary quality and eating behaviors (Doom et al., 2023). Deprivation has been defined as a lack of expected environmental inputs (e.g., access to cognitively stimulating materials and experiences) and neglect (both physical and emotional) (McLaughlin et al., 2014; McLaughlin & Sheridan, 2016). Consistent with the “fast life strategy” hypothesis, unpredictability is defined as changes or stressful variations in one’s immediate environment (e.g., residential changes, family disruptions, parental job changes; (Frankenhuis et al., 2021; Doom et al., 2024b)), instability, inconsistency, and chaos or disorganization (Doom et al., 2024b; Ugarte & Hastings, 2024). Within this framework, engaging in behaviors that result in immediate benefits may be more adaptive in an unpredictable and ever-changing environment; however, these immediately gratifying behaviors are often not as conducive to long-term goals, such as health goals (Ellis et al., 2009). These two dimensions of early adversity are particularly important to examine in the context of food as they have been associated with worse regulatory abilities (McLaughlin & Sheridan, 2016; Doom et al., 2024b), which can influence dietary quality and eating behaviors.

1.1. Conceptual Foundations for the Impact of Deprivation and Unpredictability on Eating

Deprivation has been associated with decreased regulatory skills in response to rewarding or emotional stimuli such as worse executive control (EC) (Doom, et al., 2024a; Doom et al., 2023; Lambert et al., 2017). Additionally, unpredictability has been associated with decreased effortful control, and greater impulsivity (Sosu & Scmidt et al., 2022; Fowler et al., 2015; Davis & Glynn, 2024). Evidence suggests that experience of deprivation and unpredictability may be key factors that drive ineffective regulatory response to rewards (Doom et al., 2023; Doom et al., 2024b; Lambert et al., 2017; McLaughlin et al., 2014; Sheridan et al., 2022). Challenges with these cognitive abilities that support reward may undermine the ability to self-regulate in the context of a highly rewarding stimuli (e.g., less healthful food) or emotionally charged situations (e.g., eating to cope with emotions) (Davis & Glynn, 2024; Doom, et al., 2024a; Fowler et al., 2015; Pechtel & Pizzagalli, 2011; Sosu & Schmidt, 2022; Tomaso et al., 2020; Tottenham et al., 2010).

The balance between reward and regulation is supported by cognitive abilities described prior (including EC and impulsivity), which are necessary for adherence to both short and long-term health goals and regulatory abilities (Doom, et al., 2024a; Dumas et al., 2005; Nelson & Stice, 2023). More impulsivity and greater EC deficits have been associated with poorer diet quality and eating behaviors (e.g., less mindful eating) (Bénard et al., 2019; Hendrickson & Rasmussen, 2017; Nelson et al., 2020). Additionally, poor ability to self-regulate emotions has been associated with increased impulsivity and subsequent health risk behaviors (Espeleta et al., 2018), such as binge eating and emotional eating (Howells et al., 2024; Pieper & Laugero, 2013; Reichenberger et al., 2020; Shriver et al., 2019). Binge eating and emotional eating, in turn, have been associated with concurrent less healthful food choices, such as consuming energy dense and nutrient poor foods and drinks that are high in sugar and fat, leading to excessive energy intake, consumption patterns that are commonly associated with unhealthful weight gain (Doom, et al., 2024a; Doom et al., 2023; Rzeszutek et al., 2025; Singh & Singh, 2023). As such, by way of decreased regulatory capacity, the experience of deprivation and/or unpredictability may contribute to poor dietary quality and eating behaviors.

In addition to poor emotion regulatory skills linking early environmental adversity (i.e., deprivation and unpredictability) to poorer eating behaviors, such as emotional eating (Howells et al., 2024), development of increased psychopathology symptoms following the experience of deprivation and/or unpredictability may also direct these eating behaviors. Experience of ACEs in general have been associated with poorer diet and eating behaviors, and this pathway between ACEs and later diet has been hypothesized to be at least partially explained by psychopathology symptoms (Suglia et al., 2018). Relevant to this study, deprivation and unpredictability have been well-documented within prior research to be associated with increased psychopathology (McLaughlin et al., 2014; Phillips et al., 2023; Usacheva et al., 2022). In turn, increased psychological distress and low self-esteem has been associated with greater risk of emotional eating (Dakanalis et al., 2023; Koçak & Cagatay, 2024). Eating in response to emotions may be an attempt (albeit ineffective) to cope with negative emotions; rather than engaging in an effective emotion regulatory strategy (Dakanalis et al., 2023; Hong et al., 2025; Koçak & Cagatay, 2024). Engaging in emotional eating may lead to greater psychological distress, emergence of poorer dietary patterns (e.g., binge eating), and development of an eating disorder and/or obesity (Dakanalis et al., 2023; Hong et al., 2025; Koçak & Cagatay, 2024).

1.2. Current Evidence Linking Early-Life Deprivation and Unpredictability to Eating

Only recently have studies examined associations between deprivation, unpredictability, and later dietary quality and eating behaviors, despite strong conceptual rationale for expected associations. Emerging evidence suggests that greater experience of early unpredictability is associated with poorer eating behaviors (i.e., overeating, restrained, and emotional eating) (Doom, et al., 2024a; Doom et al., 2023). Broadly, the perception of childhood unpredictability reported in adulthood is associated with worse health related quality of life in general among adults (Maner et al., 2023). Additionally, experiencing deprivation in early childhood is associated with increased engagement in worse eating behaviors (Doom et al., 2023).

1.3. Gaps and Limitations in the Existing Literature

Despite strong conceptual rationale and emerging evidence for links between unpredictability, deprivation, dietary quality, and eating behaviors, the extant literature has important limitations that preclude our understanding of the ways in which different dimensions of early adversity impact diet and eating during adolescence, within a developmental framework. Deprivation and unpredictability have been predominately studied in isolation, which largely precludes ability to determine the most salient dimension of early adversity in predicting later dietary quality and eating behaviors (apart from Doom and colleagues, 2023). Although the study by Doom and colleagues (2023) examined both deprivation and unpredictability and their later associations with weight status and diet, diet was measured only by a single question via parent report of their child’s amount of food intake at age 9. Additionally, previous studies focusing on deprivation or unpredictability in isolation have several methodological and measurement issues, including a lack of longitudinal design, a reliance on retrospective report of early adversity, little consideration for developmental periods in which key study variables are measured, and sub-optimal measurement of dietary quality and eating behaviors. The current study addresses each of these issues to advance our understanding of the role of early adversity in developing adolescent dietary quality and eating behaviors.

To address gaps within the literature, this study will leverage a longitudinal study spanning from preschool through late adolescence, that oversampled for low socioeconomic status (SES), with rigorous measurement of key study variables and multiple measurements of dietary quality and eating behaviors across adolescence. Using structural equation modeling (SEM), repeated measures of dietary quality and eating behaviors during adolescence will be modeled as latent variables, pulling out the shared variance for each eating variable across time points, capturing the consistency of each eating variable. This approach will allow for the stability and consistency of eating habits to be measured throughout adolescence, which is a particularly relevant way to measure eating given that consistent poor eating patterns can lead to physical health concerns such as obesity or an eating disorder (Ruiz et al., 2019). As such, this modeling approach reveals greater implications for the impact that experience of early adversity can have on later diet and eating patterns, beyond just at one time point during adolescence. It is hypothesized that early experience of deprivation and unpredictability in preschool will be associated with worse dietary quality (higher caloric intake and higher added sugar intake) and worse eating behaviors (increased emotional eating and unpredictable eating, and poorer cognitive restraint associated with eating) across adolescence. We predict that these associations will be significant even while controlling for maternal education, income-to-needs ratio, and sex.

2. Methods

2.1. Participants

In this longitudinal sample, 337 children (51% female) and their primary caregivers were recruited during preschool in a small Midwestern city. Eligibility requirements included no diagnosed behavioral, developmental, or language disorders at the time of the initial recruitment. Participants and their primary caregivers were invited to attend laboratory sessions beginning in preschool that continued through adolescence. During preschool, participants entered this longitudinal study in a lagged cohort sequential design, between ages 3-6. In total, the sample included four cohorts with varied ages of enrollment, age 3 (N=118), age 3.75 (N=34), age 4.5 (N=90), age 5.25 (N=84), age 6 (N=11). For this specific study, data are drawn from two phases: preschool, between ages 3 to 6 years (at study entry), and across adolescence, from three time points between ages 14-18. During adolescence, repeated measures of study outcome variables occurred across three different time points: time one: 15.44 years of age, time two: 16.34 years of age, time three: 16.95 years of age, on average.

Upon study entry during preschool, caregivers were asked to complete a series of questionnaires in the laboratory focused on demographic information, life stressors, and early health. In addition, trained research assistants visited the children’s homes at the time of enrollment and conducted validated observational assessments of their home environment. During the adolescence waves, adolescents completed several measures of diet and other relevant health behaviors in the laboratory and at home following in-laboratory participation.

During preschool, the child’s primary caregiver reported basic demographic information. Report of child ethnicity was 14.2% Hispanic and child race was 70.0% White, 3.9% Black, 0.3% Asian American, and 25.8% multiracial. Additionally, 57% of families were below the national poverty line at the time of enrollment. During the preschool phase, informed consent was obtained from participating caregivers; during the adolescent phase, assent was obtained from adolescents, in addition to caregiver consent. During preschool, caregivers were compensated for participation. During the adolescence phase, both the adolescent and caregiver were paid for participation at each time point. All procedures were approved by the institution’s human subjects review board.

2.2. Measures

2.2.1. Deprivation (Preschool)

The Early Childhood Home Observation for the Measurement of the Environment (EC-Home; (Bradley et al., 2001)) was used to measure deprivation during preschool. The EC-HOME is an observational measure; ratings were completed by trained research staff during the home visit upon study entry. Research staff rated either the presence of an item in the home (1) or the absence (0) across six subscales: language stimulation, learning materials, parental responsivity, physical environment, academic stimulation, and variety. 46 items used from the EC-Home were totaled and reversed scored; such that a higher score indicated more exposure to deprivation (α =0.80). This study calculated inter-rater reliability of scoring, which was high (Cohen’s κ = 0.85–1.00). The EC-Home observational measure has been validated in past literature and has consistently shown to reveal associations between the early home environment and later developmental outcomes (Totsika & Sylva, 2004).

2.2.2. Unpredictability (Preschool)

Questions for the unpredictability construct, consistent with the fast life theories hypothesis, (Frankenhuis et al., 2021; Doom et al., 2024b), were drawn from our standard caregiver report background survey in preschool. The preschooler’s primary caregiver reported on four questions that measured family life events over the past five years: employment loss (reporter or reporter’s spouse), partner separations, and residential moves upon study entry at the preschool time point. Scores were summed, with higher scores indicating greater unpredictability. On average, parents reported a score of 2.83, suggesting that, on average, preschoolers experienced 2-3 unpredictable events. Given unpredictability is a formative construct (aggregate score), internal consistency calculations are unnecessary. This approach has been successfully used in past studies to represent early environmental unpredictability (e.g., Phillips et al., 2023).

2.2.3. Dietary Quality (Adolescence)

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a validated measure (Kirkpatrick et al., 2014; Subar et al., 2012) of assessing total dietary intake and dietary quality and has been commonly used among children and adolescents (e.g., Duraccio et al., 2024; Gu & Tucker, 2017). The ASA24 is a web-based dietary recall measure that was developed by the National Cancer Institute to measure dietary intake over the course of the past 24 hours (Subar et al., 2012). In this study, the ASA24 was used to measure the specific aspects of dietary quality (i.e., caloric intake and added sugar) expected to be impacted by early adversity. For each year of data collection, prompts were sent to collect reports for three non-consecutive days, including two weekdays and one weekend day. For an adolescent’s data to be included in this analysis at a given time point, at least 2 days of ASA24 data were required at each time point, a validated number of inclusion days that balances sample size and valid representation of true intake (Foster et al., 2019). Daily amount, from 0-10, for added sugar was calculated based upon adolescent’s responses at each time point, such that higher scores represent less added sugar in daily diet. Consistent with the Dietary Guidelines for Americans, a score of 10 indicated that an adolescent consumed less than 10% of their daily energy from added sugars. For example, a score of 5 out of 10 would suggest an intake amount that is two times higher than the Dietary Guidelines for Americans. (Krebs-Smith et al., 2018). Caloric (energy) intake was also calculated, with higher scores indicating greater caloric intake (Krebs-Smith et al., 2018).

2.2.4. Eating Behaviors (Adolescence)

The Three-Factor Eating Questionnaire R-18 (TFEQ-R18) (De Lauzon et al., 2004), which is a revised version of the original TFEQ (Stunkard & Messick, 1985), was used to assess eating behaviors among adolescents. This 18-item scale measures three different types of eating constructs: emotional eating (reacting to emotions by eating), uncontrolled eating (unable to control consumption amount), and cognitive restraint (attending to food choices and dietary intake to prevent weight gain or aid weight loss). Participants responded to each question on a Likert scale from 1 - “definitely false” through 4 - “definitely true,” with higher scores on each scale represented greater engagement in each dietary behavior (i.e., emotional eating, uncontrolled eating, and cognitive restraint). The TFEQ-R18 has been validated among a community sample of adolescents and demonstrated good internal consistency (αs = 0.80, 0.78, and 0.80 for uncontrolled eating, emotional eating, and cognitive restraint scales, respectively), with evidence supporting the three-factor structure (De Lauzon et al., 2004).

2.2.5. Control Variables

Basic demographic variables were reported on by the child’s primary caregiver during preschool, including sex, race, ethnicity, age, maternal education achievement, and family SES, measured by the income-to-needs ratio based upon the federal poverty line.

2.3. Statistical Analysis

Models were tested in Mplus software, version 8.5 (Muthén & Muthén, 2017). Covariance coverage, reflecting the proportion of cases with non-missing values on a given pair of variables, ranged between 0.22-1.00. Missing data status at each follow-up time point across adolescence was not significantly associated with any predictors or control variables, suggesting no systematic pattern of missingness on measured exogenous variables. Missing data were addressed with full information maximum likelihood estimation (FIML), which retains all participants and minimizes bias (Enders, 2010). We used the robust maximum-likelihood estimator (MLR) to address non-normality and obtain robust standard errors. The CFI, RMSEA, and SRMR values were computed to assess global model fit. CFI above 0.95 (Bentler, 1990; Hu & Bentler, 1999), RMSEA under 0.06 (Steiger & Lind, 1980), and SRMR under 0.08 were interpreted as indicating acceptable model fit (Hu & Bentler, 1999).

Two separate models were proposed to separately analyze associations between deprivation, unpredictability, and dietary quality (i.e., higher caloric intake and added sugar) and between deprivation, unpredictability, and eating behaviors (i.e., emotional eating, uncontrolled eating, and cognitive restraint). Repeated scores of dietary qualities and eating behaviors spanning across adolescence (i.e., 1st time point, 2nd time point, and 3rd time point) were modeled as indicators of latent variables for caloric intake, added sugar, emotional eating, uncontrolled eating, and cognitive restraint. This modeling approach captures the stability and consistency of each diet variable across adolescence which was of theoretical significance in the present study, rather than modeling change trajectories. First, measurement models were evaluated to ensure salience of factor loadings (> 0.30), acceptable model fit statistics, and variable distinctiveness (i.e., factor correlations less than 0.80; (Brown, 2015)). In the dietary quality model, two latent variables were created for both caloric intake and added sugar. Given the large range of possible scores for caloric intake, scores were divided by 100 in the model. In the eating behaviors model, three latent variables were created for emotional eating, uncontrolled eating, and cognitive restraint. Across latent variables in each model, factor loadings from the same time point (e.g., caloric intake at time 1 and added sugar at time 1) were allowed to covary (Anderson & Gerbing, 1988; Cheung et al., 2024).

In both final path models, deprivation and unpredictability were included as predictor variables, allowing for assessment of the most salient indicator of early adversity for each later eating variable (i.e., the unique effect of one dimension while controlling for the other). In addition, sex assigned at birth (male=1 or female=0), SES, and maternal education were controlled for in both models.

3. Results

Descriptive statistics of demographic variables and key study variables are reported in Table 1. In addition, bivariate correlation tables for both models (dietary quality and eating behavior models) are included in Table 2 and 3, respectively.

Table 1.

Demographic Characteristics and Key Study Variables

Mean SD Minimum Maximum
Age
 Adolescence Time 1 15.44 1.21 14 18
 Adolescence Time 2 16.34 1.01 15 18
 Adolescence Time 3 16.95 .78 16 18
Income to Needs Ratio (preschool) 2.22 1.67 0.00 10.77
N (%)
Families At or Below Poverty Line 192 57.00%
Sex
Female 172 51.00%
Male 165 49.00%
Race
White 236 70.00%
Asian 1 0.30%
Black 13 3.90%
Multiracial 87 25.80%
Ethnicity
Not Hispanic or Latino 289 85.80%
Hispanic or Latino 48 14.20%
Maternal Education
Without HS Diploma (<12 years) 17 5.00%
High School Graduate without College Education (12 years) 51 15.10%
Some College Education (13-15 years) 140 41.50%
Degree from 4-year college or more (>19 years) 129 38.30%
Key Study Variables
Mean SD Minimum Maximum
Deprivation Sum 6.69 4.39 0 26
Unpredictability Sum 2.81 3.28 0 32
Kcal 1 (N=213) 1830.37 721.76 571.53 6482.81
Kcal 2 (N=122) 1696.45 704.50 333.91 4057.60
Kcal 3 (N=103) 1777.40 796.58 134.96 4168.37
Added Sugar 1 (N=213) 6.58 2.76 0 10
Added Sugar 2 (N=122) 7.11 2.70 0 10
Added Sugar 3 (N=103) 7.04 3.04 0 10
Uncontrolled Eating Sum 1 (N=260) 17.78 4.94 9 35
Uncontrolled Eating Sum 2 (N=137) 18.20 5.49 9 35
Uncontrolled Eating Sum 3 (N=106) 18.66 5.21 9 34
Emotional Eating Sum 1 (N=260) 5.19 2.13 3 12
Emotional Eating Sum 2 (N=137) 5.66 2.44 3 12
Emotional Eating Sum 3 (N=106) 5.90 2.30 3 12
Cognitive Restraint Sum 1 (N=260) 12.34 3.42 6 23
Cognitive Restraint Sum 2 (N=137) 12.71 3.73 5 23
Cognitive Restraint Sum 3 (N=106) 12.54 3.55 6 22
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Table 2.

Bivariate Correlation Table for Eating Behavior Model

Cal 1 Cal 2 Cal 3 Add S 1 Add S 2 Add S 3 Dep Unpre
Cal 1 1
Cal 2 .57** 1
Cal 3 .43** .52** 1
Add S 1 −.04 .02 .02 1
Add S 2 −.15 −.12 −.08 .24** 1
Add S 3 −.14 −.24* −.19 .26** .28* 1
Dep .10 .02 −.06 −.07 .05 .00 1
Unpre −.09 −.18* −.15 −.11 .06 −.00 .35** 1
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Note:

**

Correlation is significant at the 0.01 level (2-tailed).

*

Correlation is significant at the 0.05 level (2-tailed).

Cal 1: caloric intake at time 1, Cal 2: caloric intake at time 2, Cal 3: caloric intake at time 3, Add S 1: added sugar at time 1, Add S 2: added sugar at time 2, Add S 3: added sugar at time 3, Dep: deprivation, Unpre: unpredictability.

Table 3.

Bivariate Correlation Table for Eating Behavior Model

EE 1 EE 2 EE 3 UE 1 UE 2 UE 3 CR 1 CR 2 CR 3 Dep Unpre
EE 1 1
EE 2 .64** 1
EE 3 .56** .71** 1
UE 1 .60** .51** .42** 1
UE 2 .40** .62** .39** .62** 1
UE 3 .41** .56** .63** .58** .61** 1
CR 1 .19** .14 .01 .07 −.03 −.20* 1
CR 2 .24** .07 .12 .06 −.04 −.09 .59** 1
CR 3 .00 −.01 .02 −.13 −.17 −.07 .50** .57** 1
Dep .02 .01 .03 .05 .09 .07 .09 .09 .07 1
Unpre .01 −.04 .05 −.07 .03 .03 .20** .17* .09 .35** 1
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Note:

**

Correlation is significant at the 0.01 level (2-tailed).

*

Correlation is significant at the 0.05 level (2-tailed).

EE 1: emotional eating at time 1, EE 2: emotional eating at time 2, EE 3: emotional eating at time 3, UE 1: uncontrolled eating at time 1, UE 2: uncontrolled eating at time 2, UE 3: uncontrolled eating at time 3, CR 1: cognitive restraint at time 1, CR 2: cognitive restraint at time 2, CR 3: cognitive restraint at time 3, Dep: deprivation, Unpre: unpredictability.

3.1. Dietary Quality Measurement Model

The first measurement model estimated the correlations among the predictors (unpredictability and deprivation) and latent outcome variables (caloric intake and added sugar) and demonstrated adequate model fit, CFI = 1.00 RMSEA= 0.00 SRMR=0.05. All key study variables were determined as unique constructs; deprivation and unpredictability were positively and moderately associated (β=0.35, p<.001) and unpredictability was negatively associated with caloric intake (β=−.22, p=.03) All other key study variables were not significantly correlated. All factor loadings contributing to the added sugar and caloric intake factors were significant with standardized values ranging from 0.42-0.80.

3.2. Eating Behavior Measurement Model

The second measurement model estimated the correlations among the predictors (unpredictability and deprivation) and latent outcome variables (emotional eating, uncontrolled eating, and cognitive restraint) and demonstrated adequate model fit, CFI = 1.00 RMSEA= 0.00 SRMR=0.05. All key study variables were determined to be distinct constructs, latent emotional eating was positively associated with latent uncontrolled eating (β=0.70 p<.001) and with latent cognitive restraint (β=0.21, p=.03), In addition, deprivation and unpredictability were significantly associated (β=0.35 p<.001). Finally, unpredictability was associated with cognitive restraint (β=0.24, p=.01) All other key study variables were not associated. All factor loadings contributing to the emotional eating, uncontrolled eating, and cognitive restraint factors were significant with standardized values ranging from 0.70-0.93 (Cheung et al., 2024).

3.3. Dietary Quality Path Model (Figure 1)

Figure 1. Dietary Quality Model.

Figure 1

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Note: *p < .05; **p < .01; ***p < .001. Only significant effects (standardized are reported). Bold lines represent significant effects. Sex, maternal education and income-to-needs ratio were controlled for in this model.

Model fit for the dietary quality model was determined to be adequate, CFI =1.00, RMSEA=0.00, SRMR=0.05. Model results indicated that greater experience of deprivation during preschool was uniquely associated with greater caloric intake across adolescence, b=0.06, p=.013, when controlling for unpredictability, sex, mother’s highest education, and SES. Additionally, model results indicate that greater experience of unpredictability during preschool was uniquely associated with lower caloric intake across adolescence, b= −0.07, p=.013, when controlling for deprivation, sex, maternal education, and SES. Sex was associated with caloric intake, suggesting that boys tended to consume more calories than girls, b= .91, p<.001. Intake of added sugar during adolescence was not associated with deprivation or unpredictability during preschool. Unstandardized parameter estimates for this model are reported in Table 4.

Table 4.

Unstandardized parameter estimates for TFEQ and ASA Model

Unstandardized Estimate SE p-value
Outcomes with Predictors
Uncontrolled Eating
 Deprivation 0.13 0.07 .08
 Unpredictability −0.14 0.08 .10
 Sex 1.07 0.55 .05
 Maternal Education 0.06 0.15 .70
 SES −0.03 0.20 .88
Emotional Eating
 Deprivation 0.06 0.03 .02
 Unpredictability −0.01 0.03 .83
 Sex −0.71 0.21 .00
 Maternal Education 0.05 0.06 .46
 SES 0.18 0.10 .09
Cognitive Restraint
 Deprivation 0.08 0.05 .15
 Unpredictability 0.18 0.09 .06
 Sex −1.36 0.37 <.001
 Maternal Education 0.01 0.10 .90
 SES 0.17 0.14 .22
Caloric Intake
 Deprivation 0.06 0.02 .01
 Unpredictability −0.07 0.03 .01
 Sex 0.91 0.18 .00
 Maternal Education 0.05 0.05 .36
 SES 0.10 0.08 .20
Added Sugar
 Deprivation −0.00 0.03 .90
 Unpredictability −0.03 0.04 .47
 Sex 0.43 0.27 .11
 Maternal Education 0.02 0.07 .81
 SES −0.00 0.09 .98
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3.4. Eating Behavior Path Model (Figure 2)

Figure 2. Eating Behavior Model.

Figure 2

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Note: *p < .05; **p < .01; ***p < .001. Only significant effects (standardized are reported). Bold lines represent significant effects. Sex, maternal education and income-to-needs ratio were controlled for in this model.

Model fit for the eating behavior model was determined to be adequate, CFI= 1.00 RMSEA= 0.00 SRMR= .04. Model results indicate that greater experience of deprivation during preschool was significantly associated with increased engagement in emotional eating across adolescence, b=0.06, p=.02, when controlling for unpredictability, sex, maternal education, and SES. Additionally, sex was associated with emotional eating, such that girls tended to engage in more emotional eating than boys across adolescence, b= −0.71, p=.00. Deprivation and unpredictability did not predict uncontrolled eating or cognitive restraint during adolescence. Sex was associated with increased cognitive restraint; girls tended to use greater cognitive restraint to make food choices, compared to boys, b= −1.36, p<.001. Unstandardized parameter estimates for this model are included in Table 4.

4. Discussion

This longitudinal study, informed by dimensional theories of early adversity (McLaughlin & Sheridan, 2016; Usacheva et al., 2023), utilized rigorous measurement of adolescent eating to examine the effect of greater early adversity on eating behaviors and dietary quality approximately a decade later. Deprivation (i.e., lack of cognitive, social, and emotional inputs such as lack of parental responsiveness, learning materials, and language stimulation) and unpredictability (i.e., stressful variations or changes in the environment such as residential changes and parental job changes) have been suggested in past literature to be associated with poor diet (Doom et al., 2024b; Maner, 2023). However, it is unclear if deprivation and unpredictability are uniquely associated with later eating outcomes, while controlling for the other, and if their effects are significant over long periods across development.

This study addresses gaps in previous literature in several key ways. To our knowledge, this study is the first to simultaneously examine associations between deprivation, unpredictability, dietary quality, and eating behaviors during key developmental periods. Additionally, the use of repeated measurements of diet variables across adolescence allows us to capture the stability and consistency of these behaviors which utilizes a theoretically driven modeling approach to examine these associations. Relatedly, key diet variables were rigorously measured via multiple 24-hour dietary recalls, the gold standard in dietary assessment (Kirkpatrick et al., 2014) and a well validated measure of eating behaviors. Further, this study controlled for potential confounding variables including sex, SES, and maternal education.

Partially consistent with a priori hypotheses, results from this study suggest that experience of greater deprivation during early childhood was uniquely associated with greater emotional eating and caloric intake across adolescence, when controlling for unpredictability, SES, maternal education, and sex. Notably, associations between deprivation and eating variables only emerged when controlling for unpredictability and covariates, which is suggestive of a suppression effect. By suppressing the shared variance with the other predictors, a unique association between greater experience of deprivation during preschool and altered eating during adolescence emerged (e.g., it is deprivation that is not related to unpredictability that potentially drives eating). These findings emphasize the importance of distinguishing between distinct dimensions of early adversity and examining their incremental effects. Additionally, although emotional eating and uncontrolled eating were highly correlated (r = .70), deprivation significantly predicted emotional eating but not uncontrolled eating. Thus, despite emotional and uncontrolled eating being strongly correlated, they appear to have distinct associations with deprivation, highlighting the importance of distinguishing between these dimensions of eating behavior in research and intervention efforts.

While the pattern of findings for deprivation was partially consistent with theory and expectations, the lack of significant findings for added sugar consumption, uncontrolled eating, and cognitive restraint was surprising. This may suggest that early deprivation does not meaningfully contribute to later added sugar intake, uncontrolled eating, and cognitive control, and that associations are more specific to emotional eating and caloric intake. Future studies may consider examining other factors, such as threat, psychopathology, peer influence, and household food availability that may contribute to these other types of dietary quality and eating behaviors.

Results suggest that the degree to which one experienced unpredictability during preschool and caloric intake during adolescence was negatively associated, when controlling for deprivation and key demographic variables. Specifically, greater unpredictability during preschool was associated with lower caloric intake across adolescence, inconsistent with study hypotheses. Unpredictability and lower caloric intake were associated even while controlling for deprivation, SES, maternal education, and sex. Consistent with high caloric intake, extremely low caloric intake is also not a healthy pattern, as sufficient calories are needed for activity, emotional regulation, and effective cognitive functioning (Kaptan et al., 2015; Lassi et al., 2017), so it is possible that early unpredictability is contributing to a less healthful adolescent eating pattern, albeit not in the way that we expected. In this model, SES was controlled for and was not associated with lower caloric intake, suggesting that unpredictability and lower caloric intake cannot be explained by lower income and thus a reduced budget for food. Future studies are needed to further examine associations between these two constructs, including exploration of the possible role of other contextual factors, such as food and nutrition insecurity or availability.

While contrary with our initial rationale, experience of unpredictability, consistent with the fast-life-theory hypothesis, (Frankenhuis et al., 2021; Doom et al., 2024b), may operate in the opposite direction than initially hypothesized, such that stress or related psychopathology, caused by early unpredictability, results in lower caloric intake due to lack or loss of appetite. Lack or loss of appetite may be a result of focus on more immediately pressing (stressful) environmental stimuli that prevent one from attending to bodily needs. This approach may be adaptive in the context of an unpredictable environment, but not for long term health. One study has suggested that stress, due to lack of social support, is associated with reduced hunger and less desire to eat among adults (Swaffield & Guo, 2020). In addition, low caloric intake may be a result of intentional food restriction for healthful or unhealthful weight loss. In this sense, lower caloric intake is not a positive behavior, but rather a coping mechanism in response to or a byproduct of stress or significant psychopathology and could, in the long term, be associated with the emergence of an eating disorder (e.g., anorexia). This finding and hypothesis is interesting and underexplored, and it would be warranted to examine in future longitudinal investigations.

In addition, inconsistent with hypotheses, unpredictability was not associated with added sugar intake, emotional eating, unpredictable eating, and cognitive restraint. While past literature has not examined these constructs explicitly, one study suggests that unpredictability is associated with higher BMI during adolescence (Doom et al., 2023). As such, unpredictability may be associated with other dimensions of poor diet outside the scope of this study (e.g., low fruit and vegetable intake, high saturated fat intake, and consumption of fast food). In addition, in this study unpredictability was conceptualized consistent with the life theories model. However, unpredictability has been conceptualized in different ways within past literature (e.g., day-to-day unpredictability). Future studies may consider exploring different conceptualizations of unpredictability that may be potentially more relevant to dietary quality and eating behaviors. Finally, other factors that are associated with weight gain, such as low physical activity and poor sleep may be other health behaviors to examine.

4.1. Limitations

Despite the significant novelty and methodological strengths of this study, notable limitations exist. First, our study did not include a valid measure of threat, an additional dimension of early adversity, which precludes our ability to determine if deprivation or unpredictability have unique associations with eating when controlling for threat. Future studies that examine associations between dimensions of early adversity and eating could expand on our work by including measures of deprivation, unpredictability, and threat. In addition, examining factors that may give rise to both deprivation and unpredictability during preschool, such as parent dysfunction or psychopathology may serve as a confound variable and should be examined in future studies.

While a significant strength of this study was that it examined the stability and consistency of dietary quality and eating behaviors across adolescence, this approach limited the ability to determine if these different aspects of eating were associated with each other across adolescence. For example, there is evidence to suggest that emotional eating predicts poor dietary intake (Rzeszutek et al., 2025; Singh & Singh, 2023), so considering emotional eating as a factor that links early adversity with quality of dietary intake could be considered in future studies.

Finally, adolescent diet can be influence by a wide range of other factors, including environmental factors, such as food environment and peer influence. As such, future models should include these more contextualized factors to further examine how early adversity impacts later dietary and eating outcomes. In addition, other mechanistic factors that may link early adversity with later dietary and eating outcomes, such as psychopathology, executive functioning, and coping mechanisms should be examined.

4.2. Implications for prevention and intervention

The current findings could inform specific strategies for promoting healthy eating across key points in development. First, results suggest that it may be important to implement screening and subsequent interventions at a systemic level (e.g., through doctor’s offices, community centers, and preschools) to increase access to and knowledge about developmentally appropriate materials to support cognitive development. In addition, family-level interventions that increase parental engagement and support a range of novel experiences for the child may be beneficial. These intervention efforts at an early age, when the brain is highly plastic, could yield long-term health benefits (e.g., decreased likelihood of developing obesity or an eating disorder) via healthier eating and diet. This effort may be beneficial to ensure that adolescents are consuming sufficient energy to support health and cognition. Moreover, screening adolescents to determine experience of these specific dimensions of early adversity could be useful to identify if one is at-risk for poor eating behaviors and poorer dietary quality and may benefit from compensatory interventions (e.g., environmental modifications to reduce access to less healthful foods and drinks, etc.) that seek to support healthy eating and diet despite risk from earlier experiences.

In addition to implementing early screening, prevention, and subsequent intervention efforts to mitigate or address experience of early adversity to decrease risk for less healthy caloric intake and greater emotional eating, examining mediating variables of these associations is critical to support interventions following the experience of early adversity. Identification of factors, that are grounded in theory, such as psychopathology and regulatory skills, may help to explain how experience of early adversity is associated with later dietary quality and eating behaviors. Targeting potential mediating factors in interventions, such as psychopathology, is critical after early adversity has been experienced to mitigate negative outcomes. For example, experience of psychopathology after the experience of deprivation and/or unpredictability has been well documented (McLaughlin et al., 2014; Phillips et al., 2023; Usacheva et al., 2022); in turn, experience of psychopathology has been associated with greater emotional eating, as an (ineffective) coping mechanism to address psychological symptoms (Dakanalis et al., 2023; Koçak & Cagatay, 2024). As previously mentioned in the limitations, this study did not examine mediating pathways; a future study should examine these pathways to yield important intervention targets.

5. Conclusions

This study reveals associations between specific dimensions of early adversity and eating patterns across adolescence, even after controlling for potential confounding variables. Higher levels of deprivation, when controlling for unpredictability, appears to be a salient predictor of later emotional eating and greater caloric intake. In addition, and inconsistent with study hypotheses, unpredictability was associated with lower caloric intake. This study emphasizes the importance of examining these associations longitudinally, and across critical periods of development. Future studies are needed to examine other aspects of dietary quality, utilize other conceptualizations of unpredictability, and could examine other mechanisms that may link early adversity with eating outcomes. In addition, future studies should examine the potential moderating role that unpredictability may have in explaining the degree to which deprivation and eating variables are associated. Findings suggest that future interventions that target modifiable antecedents, focused on reducing deprivation, might be particularly effective in improving dietary quality and eating behaviors across adolescence.

Acknowledgments:

We are grateful to the participating families, as well as the research technicians, undergraduate and graduate students, and lab coordinators who made this research possible. This work was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116693, R01DK125651), the National Institute on Drug Abuse/National Institutes of Health (Award Number R01DA041738), and the National Institute of Mental Health (R01MH065668) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Funding:

This research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health (Award Number R01DK116693, R01DK125651), the National Institute on Drug Abuse (Award Number R01DA041738), and the National Institute of Mental Health (R01MH065668). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

Declaration of competing interest: The author(s) declare no conflict of interest.

CRediT authorship contribution statement:

Emily L. Goldberg: conceptualization, data curation, methodology, formal analysis, writing – original draft, writing – review and editing

Rebecca L. Brock: methodology, formal analysis, supervision, writing - review and editing

Amy Lazarus Yaroch: writing - review and editing, methodology

Jennie L. Hill: writing - review and editing, methodology

W. Alex Mason: funding acquisition, methodology, writing - review and editing

Jennifer Mize Nelson: funding acquisition, methodology

Kimberly Andrews Espy: funding acquisition, methodology

Timothy D. Nelson: conceptualization, methodology, writing - review and editing, supervision, funding acquisition

Data statement:

Individual-level data are not publicly available due to privacy or ethical restrictions, as authorization was not included in the original consent.

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Data Availability Statement

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