- Email: [email protected]
Maternal executive function and the family food environment
Accepted Manuscript Maternal executive function and the family food environment Katherine W. Bauer, Heidi M. Weeks, Julie C. Lumeng, Alison L. Miller, Ashley N. Gearhardt PII:
S0195-6663(18)31562-9
DOI:
https://doi.org/10.1016/j.appet.2019.02.004
Reference:
APPET 4185
To appear in:
Appetite
Received Date: 22 October 2018 Revised Date:
7 January 2019
Accepted Date: 10 February 2019
Please cite this article as: Bauer K.W., Weeks H.M., Lumeng J.C., Miller A.L. & Gearhardt A.N., Maternal executive function and the family food environment, Appetite (2019), doi: https:// doi.org/10.1016/j.appet.2019.02.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Maternal Executive Function and the Family Food Environment
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Katherine W. Bauer PhD1,2, Heidi M. Weeks PhD , Julie C. Lumeng MD1,2,3, Alison L. Miller
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PhD2,4, Ashley N. Gearhardt PhD2,5
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Affiliations:
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1. Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI.
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2. Center for Human Growth and Development, University of Michigan, Ann Arbor, MI
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3. Division of Pediatrics, School of Medicine, University of Michigan, Ann Arbor, MI
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4. Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI
5. Department of Psychology, University of Michigan, Ann Arbor, MI
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13 Corresponding Author:
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Katherine W. Bauer, PhD
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Assistant Professor
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Department of Nutritional Sciences
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University of Michigan School of Public Health
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3854 SPH I, 1415 Washington Heights
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Ann Arbor, MI 48109-2029
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Phone: (734)763-2546
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Email: [email protected]
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ABSTRACT
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The family food environment plays an important role in supporting children’s dietary quality,
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regulating eating behaviors, and promoting a healthy weight status. However, relatively little is
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known regarding parent-level factors that support or hinder parents’ ability to create health-
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promoting family food environments. The current study examines whether executive function
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among mothers, or mothers’ capacity to control their thoughts, emotions, and actions, is
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associated with qualities of the family food environment that support children’s healthy eating
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and weight. Cross-sectional data were collected from 492 US-based mothers of 2 to 9-year-old
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children in August 2017 (Mean maternal age = 34.2 years (SD=6.7), 76.5% White race).
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Mothers’ difficulties with executive function were measured using the Behavior Rating of
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Executive Function-Adult Version and family food environment characteristics were measured
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via psychometrically-sound, self-report surveys. Standardized, linear regression models were
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used to examine covariate-adjusted associations between mothers’ executive function difficulties
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and family food environment characteristics, as well as the potential for differences in these
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associations by family sociodemographic characteristics. Mothers with more executive function
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difficulties consistently reported less use of recommended food-related parenting practices and
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less healthful home food environment characteristics including providing frequent family meals,
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implementing consistent mealtime schedules and structure, and avoiding using food to regulate
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children’s emotions. No differences in these associations were observed by mothers’ educational
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attainment, household income-to-needs ratio, or child age. Results suggest that lower executive
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function may interfere with mothers’ ability to create family food environments that support
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children’s healthy eating and weight.
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Keywords: Executive Function; Parenting; Diet, food, and nutrition
Abbreviations:
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EF: Executive Function
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BMI: Body Mass Index
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MTurk: Amazon Mechanical Turk
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BRIEF-A: Behavior Rating of Executive Function-Adult Version
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GEC: Global Executive Composite
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SD: Standard Deviation
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SE: Standard Error
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57 Acknowledgements: N/A
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Funding: This work was supported by the University of Michigan Momentum Center
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(www.momentumcenter.org).
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BACKGROUND
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Executive functions (EF) refer to the higher order cognitive processes that allow individuals to
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control their thoughts, emotions, and actions (Miyake, et al., 2000). Individuals with lower EF
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are more likely to engage in dysregulated eating behavior (Allom & Mullan, 2014; Dohle, Diel,
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& Hofmann, 2017), have less healthy dietary intake (Wyckoff, Evans, Manasse, Butryn, &
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Forman, 2017), and experience obesity more often than individuals with stronger EF (Yang,
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Shields, Guo, & Liu, 2018). These associations are believed to exist because lower EF interferes
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with one’s ability to create routines and environments that promote healthy eating. For example,
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individuals with lower EF may have difficulty avoiding unhealthy food purchases, implementing
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recipes to prepare home-cooked food, and maintaining regular eating schedules (Dohle, et al.,
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2017; Hayes, Eichen, Barch, & Wilfley, 2018).
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While there is substantial evidence suggesting that an individual’s EF impacts their own eating
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and weight, it has rarely been considered that within families, parents’ EF may impact their
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children’s eating and weight. The family food environment plays an important role in children’s
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dietary intake and eating behaviors (Kral & Faith, 2009; Wardle & Carnell, 2009). For example,
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parents’ use of specific food-parenting practices, such as coercively controlling children’s eating
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and promoting eating to soothe emotions, may contribute to dysregulated eating behaviors
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among children (Savage, Fisher, & Birch, 2007; Vaughn, et al., 2016). Further, home food
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environment characteristics such as availability of healthy food, frequent family meals, and
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limited consumption of meals prepared outside the home have all been associated with healthier
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dietary quality among children (Bauer, Neumark-Sztainer, Fulkerson, Hannan, & Story, 2011;
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Hammons & Fiese, 2011). Deficits in parents’ EF may limit their ability to sustain such healthy
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home food environments and use positive food parenting practices.
94 A small body of research has examined the role of EF in parents’ general parenting approaches
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with findings suggesting that mothers with lower EF engage in harsher parenting (Deater-
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Deckard & Bell, 2017; Monn, Narayan, Kalstabakken, Schubert, & Masten, 2017). Meanwhile,
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mothers with stronger EF are more likely to demonstrate sensitive, involved parenting and
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provide developmentally-appropriate supports for their children (Mazursky-Horowitz, et al.,
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2017). This research lends support for a conceptual framework suggesting that parents’ EF
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impacts parenting and the home environment, ultimately affecting children’s outcomes
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(Crandall, Deater-Deckard, & Riley, 2015). However to date, only one study has examined the
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role of parent EF in the domain of child feeding. Among mothers of infants, Fuglestad, et al.
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(Fuglestad, et al., 2017) observed that stronger EF was associated with more appropriate
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responsiveness to babies’ hunger cues at 2 weeks of age, which in turn predicted healthier
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growth between 2 weeks and 3 months.
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The current study seeks to contribute to this emerging area of research by examining associations
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between mothers’ EF and characteristics of the family food environment relevant to children’s
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eating and weight beyond infancy. Further, to increase understanding of whether there are
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specific families for whom mothers’ EF may be more or less strongly associated with the family
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food environment, differences in the associations between mother’s EF and family food
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environment characteristics were examined by family sociodemographic characteristics
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(mothers’ educational attainment, household income-to-needs ratio, and child age). We
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hypothesized that mothers with lower EF will be less likely to engage in food parenting practices
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and report home food environment characteristics that are known to promote children’s healthy
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eating (e.g., having frequent family meals, limiting access to unhealthy foods). We further
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hypothesized that in families of lower income, families with mothers with lower educational
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attainment, and families with younger children, poor maternal EF will be even less likely to
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report healthy family food environment characteristics. This hypothesis is informed by previous
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research suggesting that having limited resources and more physically and emotionally
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dependent children is taxing on mothers’ EF, resulting in more negative parenting outcomes
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(Deater-Deckard, Wang, Chen, & Bell, 2012; Mokrova, O'Brien, Calkins, & Keane, 2010).
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Understanding the relations between mothers’ EF and the family food environment can provide
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insight into why some families have difficulty implementing parenting behaviors for child
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nutrition promotion and obesity prevention, and ultimately, may help guide the development of
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more effective family-based child health interventions.
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METHODS
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Study Sample and Data Collection Method
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Mothers of 2 to 9-year-old children living in the US were recruited to complete an online survey
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via Amazon Mechanical Turk (MTurk) in August 2017. This relatively wide age range of
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children was selected because it allowed for examination of potential differences in the relations
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between maternal EF and the family food environment among families with toddler and
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preschool-aged children, who often present physical, feeding demands on mothers, versus
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elementary school-aged children, who may be more independent in feeding but introduce other
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time demands on families that may affect the family food environment (e.g., extracurricular
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activities). MTurk is an increasingly common, as well as cost and time effective, approach to
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collecting data from parents in studies of children’s eating and weight status (Burrows, et al.,
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2017; Darling, Sato, van Dulmen, Flessner, & Putt, 2017; Emley, Taylor, & Musher-Eizenman,
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2017). Recent studies demonstrate that MTurk samples do not differ from study samples
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obtained by conventional recruitment methods (Mortensen & Hughes, 2018) and may be more
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socio-demographically diverse than participant samples recruited through other means, such as
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Facebook or email listservs (Dworkin, Hessel, Gliske, & Rudi, 2016). The study was advertised
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as a study to understand families’ eating habits. Individuals interested in participating completed
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a screening survey and were eligible if they reported they were female, 18 years of age or older,
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had a child between the ages of 2 and 9 years old, were their child’s legal guardian, and lived
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with their child at least 5 days per week. Of the 1013 individuals who completed the screener,
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557 were eligible based on the above criteria. Eligible participants were then invited to complete
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the study survey. If participants had multiple children within the target age range, they were
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asked to complete the survey with their youngest child in mind. Participants received a $0.05
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payment for completing the screening survey and a $2.00 payment for completing at least 90%
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of the study survey. These compensation rates were determined after reviewing the
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compensation rates of similar MTurk-based studies. Among the individuals who were eligible,
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95% completed at least 90% of the study survey (N=527). Among these 527 mothers, 6.6%
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(N=35) were excluded due to infrequent or inconsistent responses on the BRIEF-A measure of
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EF difficulties, resulting in a final sample of 492. The University of Michigan determined this
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study was exempt from Institutional Review Board review.
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Measures
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Executive function difficulties. Mothers’ EF difficulties were assessed using the Behavior
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Rating of Executive Function-Adult Version (BRIEF-A) (Roth, Isquith, & Gioia, 2005), a
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standardized rating scale used to assess adults’ EF difficulties as manifested in everyday life
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(Roth, Lance, Isquith, Fischer, & Giancola, 2013). The 75-item measure assesses 9 aspects of
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EF: 3 core EF processes (response inhibition, working memory, and cognitive flexibility) and 6
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EF-related skills (emotional control, initiation, planning and organizing, organization of
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materials, and self-monitoring). Participants rated statements (e.g., “I have trouble changing from
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one activity or task to another” and “I forget instructions easily”) from 1 (Never) to 3 (Often) and
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responses were summed to create each sub-scale. The Global Executive Composite (GEC)
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(Cronbach’s α = 0.97) is a summary measure of the 9 sub-scales, with higher scores indicating a
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greater degree of EF difficulties. GEC T-scores were calculated in comparison to a normative
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sample comprised of 1050 self- and 1200 informant reports. The BRIEF-A has been used to
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assess EF difficulties among healthy and clinical populations (Adler, et al., 2013; Løvstad, et al.,
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2016; Roth, et al., 2013).
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Family food environment. Selection of family food environment measures used in this study
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was based on evidence-driven hypotheses regarding characteristics known to promote positive
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nutrition among children that may present a particular challenge for parents with EF deficits. For
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example, as suggested by the parenting literature (Deater-Deckard, et al., 2012), inhibiting
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instincts to appease children exhibiting negative emotions with food may be difficult for mothers
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with lower EF and as suggested by studies demonstrating that cognitive flexibility supports
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consistent physical activity routines (Kelly & Updegraff, 2017), implementing regular family
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meals may be difficult for mothers with lower EF.
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184 Home food environment. Two scales were used to measure the availability of healthy food at
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home (4 items) and availability of unhealthy food at home (4 items) (Bauer, Hearst, Escoto,
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Berge, & Neumark-Sztainer, 2012). Mothers reported how often fruits, vegetables, and whole
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wheat bread were available and/or served with meals in their home (healthy foods) and how
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often ‘junk food’, salty snacks, candy, and soda pop were available in their home (unhealthy
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foods). Response options ranged from “Never” (1) to “Always” (4) on a 4-point scale.
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Cronbach’s α for availability of healthy food = 0.65 and unhealthy food = 0.81. Several qualities
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of family meals were also assessed including the frequency of family evening meals during the
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past 7 days (Lytle, et al., 2011), frequency of family evening meals prepared outside the home
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during the past 7 days (sum of 3 items assessing frequency of meals purchased from fast food
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restaurants (Lytle, et al., 2011), from another kind of restaurant, and purchased pre-prepared
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from a store), and the frequency of family evening meals cooked at home during the past 7 days.
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Response options were on a 5-point scale ranging from “Never” (0) to “7 times” (7). Mothers
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also reported on mealtime structure (8 items) and mealtime schedule (5 items) using sub-scales
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of The Feeding Strategies Questionnaire (Berlin, Davies, Silverman, & Rudolph, 2011).
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Response options for all of these sub-scales were on a 5-point scale that ranged from “Strongly
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Disagree” (1) to “Strongly Agree” (5). In the current study, Mealtime Structure Cronbach’s α =
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0.85, and Mealtime Schedule = 0.86.
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Food parenting practices. Two food parenting practices, regulating children’s emotions with
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food and monitoring children’s eating, were measured using the Emotion Regulation Scale (3
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items) and the Monitoring Scale (4 items) of The Comprehensive Feeding Practices
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Questionnaire (Musher-Eizenman & Holub, 2007). Response options for these scales were on a
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5-point scale ranging from “Never” (1) to “Always” (5). Cronbach’s α for the Emotion
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Regulation Scale = 0.82 and 0.87 for the Monitoring Scale in the current sample. The extent to
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which mothers allow between meal grazing was also measured using a 3-item sub-scale of The
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Feeding Strategies Questionnaire (Cronbach’s α= 0.88 in the current sample) (Berlin, et al.,
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2011).
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Sociodemographic characteristics and maternal body mass index (BMI). Mothers reported
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their race/ethnicity, relationship status, educational attainment, total income of their household in
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the past year before taxes (6 response options ranging from “<$20,000” to “$100,000 or more”),
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and how many people live in their household. Household income and number of people living in
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the household were used to calculate income-to-needs ratio as a proportion of the US federal
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poverty line. Mothers also reported their height in feet and inches and weight in pounds, from
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which BMI was calculated. Adult height and weight self-reported via online surveys has
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demonstrated high validity (Lassale, et al., 2013).
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Statistical Analysis
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Univariate statistics were used to examine the distributions of GEC T-scores, sociodemographic
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characteristics, and mothers’ BMI. Associations between these variables were examined using
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ANOVA and Pearson correlations. Measures of the family food environment were standardized
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to a mean of 0 and standard deviation of 1, and separate linear regression models were developed
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with GEC T-score as the independent variable and each family food environment measure as
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dependent variables, adjusted for maternal race/ethnicity as a categorical variable and maternal
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education, household income-to-needs ratio, and maternal BMI as continuous variables. To
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account for multiple comparisons, a Bonferroni adjustment was implemented and p values <.005
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used to indicate statistical significance. Effect modification of the relations between maternal EF
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difficulties and family food environment characteristics by mothers’ educational attainment,
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household income-to-needs ratio, and child age was examined through the addition of interaction
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terms including continuous variables to the linear regression models. Given the more limited
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statistical power of these models, interactions were indicated in cases where the interaction term
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was significant at p<.05.
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Three-fourths (76.5%) of mothers identified as White, 9.2% Black, 5.7% Hispanic/Latina, and
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5.5% Asian (Table 1). The majority (53.2%) completed college or had a graduate degree.
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Mothers’ mean age was 34.2 years (SD=6.7) and mean BMI was 27.3 (SD=7.0). The mean GEC
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T-score for the sample was 50.7 (SD=11.4) and 13.2% of mothers had a GEC-T score ≥ 65,
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indicating clinically-elevated EF difficulties (Roth, et al., 2005). GEC T-scores differed across
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levels of maternal education and were modestly correlated with maternal BMI (r=0.14, p=.002),
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indicating higher maternal BMI was associated with greater EF difficulties.
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Table 1. Maternal Characteristics: Descriptive Statistics and Bivariate Associations with Executive Function Difficulties (N=492)1
11.6 (57) 35.2 (173) 53.2 (261)
52.1 (11.5) 52.1 (9.5) 49.5 (11.7)
87.2 (428) 12.8 (63)
50.4 (11.4) 52.7 (11.7)
Test Statistic F 1.29
p-value
3.23
.04
2.30
.13
.27
50.8 (11.3) 48.8 (11.0) 49.9 (11.6) 49.7 (11.9) 56.2 (13.6)
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76.5 (375) 9.2 (45) 5.7 (28) 5.5 (27) 3.1 (15)
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Race/ethnicity White Black Hispanic/Latina Asian Mixed/other Highest educational attainment Less than high school degree or high school completed Some college Completed college or advanced degree Relationship status Married or in committed relationship Other
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Total % (n)
Global Executive Composite T-Score Mean (SD)
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Mean (SD) r 2.3 (1.2) --0.08 .08 Household income-to-needs ratio 34.2 (6.7) -0.03 .46 Maternal age (years) 4.8 (2.1) -0.06 .18 Child age (years) 27.3 (7.0) -0.14 .002 Maternal Body Mass Index 1 F statistic from ANOVA, r statistic = Pearson Correlation Coefficient; SD = Standard Deviation; higher GEC T-scores indicate greater executive function difficulties
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Greater maternal EF difficulties were associated with lower availability of healthy food at home and higher availability of unhealthy food (Table 2). Mothers with greater EF difficulties also reported less frequent family meals, with fewer family meals prepared at home and more
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frequent family meals prepared outside the home, such as meals purchased from fast food
restaurants, compared to mothers with fewer EF difficulties. Maternal EF difficulties were also negatively correlated with mealtime structure and scheduling; families of mothers with greater
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EF difficulties experience less structured meals and less consistent scheduling of meals. Mothers with greater EF difficulties were more likely to report regulating children’s emotions with food
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and were less likely to monitor children’s eating. The only family food characteristic not associated with mothers’ EF difficulties was allowing between meal grazing.
Table 2. Associations Between Maternal Executive Function Difficulties and Family Food Environment Characteristics.1
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Total Sample Unadjusted Mean (SD)
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β (SE) p-value Home Food Environment Healthy home food availability 3.3 (0.57) -0.25 (0.04) <.001 Unhealthy home food availability 2.3 (0.65) 0.13 (0.05) .004 Frequency of family evening meals 5.2 (1.79) -0.17 (0.05) <.001 Frequency of family evening meals 5.0 (1.57) -0.25 (0.05) <.001 prepared at home Frequency of family evening meals 2.9 (2.22) 0.23 (0.05) <.001 prepared outside home Mealtime structure 3.8 (0.75) -0.30 (0.04) <.001 Mealtime schedule 3.3 (0.88) -0.20 (0.05) <.001 Food Parenting Practices Regulates child emotions with food 1.9 (0.73) 0.23 (0.05) <.001 Monitors child eating 4.0 (0.90) -0.21 (0.05) <.001 Allows between meal grazing 3.3 (0.96) 0.04 (0.05) .38 SD = Standard Deviation, SE = Standard Error; higher GEC T-scores indicate greater executive function difficulties; linear regression adjusted for maternal race/ethnicity (categorical), maternal educational attainment (continuous), household income-to-needs ratio (continuous), and maternal body mass index (continuous)
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Models examining interactions between maternal EF difficulties and maternal educational attainment, household income-to-needs ratio, and child age indicated that there were no
by these sociodemographic characteristics (data not shown).
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DISCUSSION
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differences in the associations between maternal EF difficulties and family food characteristics
In the current study that sought to examine associations between maternal EF and family food
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characteristics via an MTurk-based survey of US mothers, we effectively and resourceefficiently recruited a study sample whose reported EF difficulties reflected other healthy populations based on GEC T-score mean and variability, but were less than populations with clinical conditions commonly associated with impaired EF including eating disorders and obesity (Ciszewski, Francis, Mendella, Bissada, & Tasca, 2014; Roth, et al., 2005; Rouel, Raman, Hay,
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& Smith, 2016). Consistent with our hypothesis, mothers with greater EF difficulties reported less engagement in several family food environment characteristics that promote children’s
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healthy eating. For example, families of mothers with greater EF difficulties reported less frequent family meals, were less likely to have healthy food available at home, and were more
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likely to have unhealthy food available at home. Mothers with greater EF difficulties also were more likely to regulate children’s emotions with food and were less likely to monitor their children’s intake. These family food environment characteristics have been associated with less healthy dietary intake, higher weight status, and/or a greater likelihood of developing maladaptive eating behaviors among children (Bauer, et al., 2011; Hammons & Fiese, 2011; Savage, et al., 2007; Vaughn, et al., 2016). These findings provide some of the first evidence suggesting maternal EF may play an important role in children’s nutrition and weight status.
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Counter to our hypotheses, the relations between maternal EF difficulties and family food environment characteristics did not differ by family sociodemographic characteristics. That is, lower maternal EF may present a similar barrier to healthy family food environments across
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diverse types of families.
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This study contributes to our growing understanding of how adults’ EF is not only associated with their own dietary intake and weight status (Allom & Mullan, 2014; Dohle, et al., 2017;
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Wyckoff, et al., 2017; Yang, et al., 2018), but may affect their children’s eating and weight. Building on evidence that as early as infancy, stronger EF among mothers is associated with more responsive feeding (Fuglestad, et al., 2017), the current study suggests that stronger EF may also support mothers’ engagement in parenting practices that impact older children’s eating and establish positive nutrition and eating behaviors throughout the lifespan. Recent evidence
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also demonstrates that children of parents with lower EF are less likely to have optimal bedtime routines (Kitsaras, Goodwin, Allan, Kelly, & Pretty, 2018), another avenue through which parent
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EF may impact children’s obesity risk (Anderson, Sacker, Whitaker, & Kelly, 2017). Further, one study has identified that children of parents with greater impulsivity, a component of EF, are
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more likely to experience obesity (Stoklosa, et al., 2018). Together, these findings suggest that families with parents with lower EF may be a population in particular need of nutrition promotion and obesity prevention programming.
While behavior change can be difficult for adults with lower EF (Gettens & Gorin, 2017), several novel treatment approaches are demonstrating promise in improving emotional and behavioral self-regulation among individuals with lower EF to address dysregulated eating and
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obesity (Dassen, Houben, Van Breukelen, & Jansen, 2018; Eichen, Matheson, Appleton-Knapp, & Boutelle, 2017; Raman, Hay, Tchanturia, & Smith, 2017). For example, Episodic Future Thinking, where individuals are guided through exercises to visualize positive future events in
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great detail, has been associated with improvements in impulse control and reductions in
obesogenic eating behaviors (Bromberg, Wiehler, & Peters, 2015; Daniel, Stanton, & Epstein, 2013; Dassen, Jansen, Nederkoorn, & Houben, 2016). Further, Cognitive Remediation Therapy,
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which seeks to improve thinking processes and strengthen metacognition, has been shown to improve cognitive flexibility and has led to meaningful improvements in weight status among
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individuals seeking care for obesity (Allom, Mullan, Smith, Hay, & Raman, 2018). If future studies provide consistent support for the hypothesis that lower EF among parents has a negative impact on children’s nutrition and weight outcomes, integrating intervention strategies such as these into family-based interventions may enhance behavior change among parents with lower
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EF, providing improved opportunities for children to engage in healthier behaviors.
Findings should be considered in light of study limitations. Although use of MTurk to conduct
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population-based survey research is growing, it is more difficult than in-person data collection methods to verify participant-provided information. However, electronic and mailed survey-
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based studies are prone to similar limitations and validity checks built into the BRIEF-A identified only a small number of incongruent response patterns. Second, while the BRIEF-A contains two theoretically-distinct indices assessing behavior regulation (the ability to regulate behavior and emotional responses) and metacognition (the ability to self-manage tasks and monitor performance) (Roth, et al., 2005), a high correlation (r=0.81) between the two indices in the current sample led us to rely on a single summary measure of EF difficulties, the GEC.
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Future studies using task-based measures of EF may provide greater insight into whether specific EF processes underlie parent engagement in family food practices. There was also a limited number of measures of child characteristics; for example, children’s heights and weights were
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not collected due to the relatively low validity of parent report of these measures among parents of children in this age range (Weden, et al., 2013), limiting the opportunity to examine
associations between mothers’ EF and child weight status. Children’s EF was also not measured.
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As lower EF among children is associated with obesogenic eating behaviors and obesity (Liang, Matheson, Kaye, & Boutelle, 2014), it would be beneficial to understand how mothers’ EF
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predicts family and child outcomes independent of child EF, and how mothers’ and children’s EF may interact to impact children’s nutrition and growth. Finally, this study only focused on mothers, despite fathers and other caregivers often having substantial roles in supporting the family food environment, and the study sample was predominantly of White race and had
CONCLUSIONS
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relatively high educational attainment, limiting generalizability to other population groups.
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This study provides some of the first evidence that mothers’ EF is associated with elements of the family food environment that support children’s healthy eating and weight status. These
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associations were consistent across families with varying maternal education, household income, and child age. Future research using diverse assessments of parent EF, assessing a wide range of family food environment characteristics, and measuring child-level characteristics including children’s own EF, diet, and BMI, will greatly enhance our understanding of the role of parents’ EF in children’s nutrition and growth. Additionally, as poor parent EF is hypothesized to interfere with even strong intentions to engage in nutrition-promoting parenting practices
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(Larsen, et al., 2018), it is important to consider the intersections of parent intention, motivation, and EF, and how these factors together support behavior change. This knowledge may guide the development of novel intervention strategies to better promote healthy family food environments
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S0195-6663(18)31562-9
DOI:
https://doi.org/10.1016/j.appet.2019.02.004
Reference:
APPET 4185
To appear in:
Appetite
Received Date: 22 October 2018 Revised Date:
7 January 2019
Accepted Date: 10 February 2019
Please cite this article as: Bauer K.W., Weeks H.M., Lumeng J.C., Miller A.L. & Gearhardt A.N., Maternal executive function and the family food environment, Appetite (2019), doi: https:// doi.org/10.1016/j.appet.2019.02.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Maternal Executive Function and the Family Food Environment
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Katherine W. Bauer PhD1,2, Heidi M. Weeks PhD , Julie C. Lumeng MD1,2,3, Alison L. Miller
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PhD2,4, Ashley N. Gearhardt PhD2,5
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Affiliations:
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1. Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI.
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2. Center for Human Growth and Development, University of Michigan, Ann Arbor, MI
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3. Division of Pediatrics, School of Medicine, University of Michigan, Ann Arbor, MI
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4. Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI
5. Department of Psychology, University of Michigan, Ann Arbor, MI
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13 Corresponding Author:
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Katherine W. Bauer, PhD
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Assistant Professor
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Department of Nutritional Sciences
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University of Michigan School of Public Health
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3854 SPH I, 1415 Washington Heights
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Ann Arbor, MI 48109-2029
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Phone: (734)763-2546
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Email: [email protected]
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ABSTRACT
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The family food environment plays an important role in supporting children’s dietary quality,
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regulating eating behaviors, and promoting a healthy weight status. However, relatively little is
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known regarding parent-level factors that support or hinder parents’ ability to create health-
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promoting family food environments. The current study examines whether executive function
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among mothers, or mothers’ capacity to control their thoughts, emotions, and actions, is
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associated with qualities of the family food environment that support children’s healthy eating
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and weight. Cross-sectional data were collected from 492 US-based mothers of 2 to 9-year-old
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children in August 2017 (Mean maternal age = 34.2 years (SD=6.7), 76.5% White race).
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Mothers’ difficulties with executive function were measured using the Behavior Rating of
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Executive Function-Adult Version and family food environment characteristics were measured
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via psychometrically-sound, self-report surveys. Standardized, linear regression models were
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used to examine covariate-adjusted associations between mothers’ executive function difficulties
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and family food environment characteristics, as well as the potential for differences in these
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associations by family sociodemographic characteristics. Mothers with more executive function
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difficulties consistently reported less use of recommended food-related parenting practices and
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less healthful home food environment characteristics including providing frequent family meals,
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implementing consistent mealtime schedules and structure, and avoiding using food to regulate
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children’s emotions. No differences in these associations were observed by mothers’ educational
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attainment, household income-to-needs ratio, or child age. Results suggest that lower executive
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function may interfere with mothers’ ability to create family food environments that support
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children’s healthy eating and weight.
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Keywords: Executive Function; Parenting; Diet, food, and nutrition
Abbreviations:
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EF: Executive Function
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BMI: Body Mass Index
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MTurk: Amazon Mechanical Turk
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BRIEF-A: Behavior Rating of Executive Function-Adult Version
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GEC: Global Executive Composite
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SD: Standard Deviation
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SE: Standard Error
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57 Acknowledgements: N/A
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Funding: This work was supported by the University of Michigan Momentum Center
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(www.momentumcenter.org).
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BACKGROUND
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Executive functions (EF) refer to the higher order cognitive processes that allow individuals to
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control their thoughts, emotions, and actions (Miyake, et al., 2000). Individuals with lower EF
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are more likely to engage in dysregulated eating behavior (Allom & Mullan, 2014; Dohle, Diel,
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& Hofmann, 2017), have less healthy dietary intake (Wyckoff, Evans, Manasse, Butryn, &
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Forman, 2017), and experience obesity more often than individuals with stronger EF (Yang,
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Shields, Guo, & Liu, 2018). These associations are believed to exist because lower EF interferes
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with one’s ability to create routines and environments that promote healthy eating. For example,
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individuals with lower EF may have difficulty avoiding unhealthy food purchases, implementing
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recipes to prepare home-cooked food, and maintaining regular eating schedules (Dohle, et al.,
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2017; Hayes, Eichen, Barch, & Wilfley, 2018).
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While there is substantial evidence suggesting that an individual’s EF impacts their own eating
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and weight, it has rarely been considered that within families, parents’ EF may impact their
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children’s eating and weight. The family food environment plays an important role in children’s
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dietary intake and eating behaviors (Kral & Faith, 2009; Wardle & Carnell, 2009). For example,
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parents’ use of specific food-parenting practices, such as coercively controlling children’s eating
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and promoting eating to soothe emotions, may contribute to dysregulated eating behaviors
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among children (Savage, Fisher, & Birch, 2007; Vaughn, et al., 2016). Further, home food
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environment characteristics such as availability of healthy food, frequent family meals, and
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limited consumption of meals prepared outside the home have all been associated with healthier
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dietary quality among children (Bauer, Neumark-Sztainer, Fulkerson, Hannan, & Story, 2011;
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Hammons & Fiese, 2011). Deficits in parents’ EF may limit their ability to sustain such healthy
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home food environments and use positive food parenting practices.
94 A small body of research has examined the role of EF in parents’ general parenting approaches
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with findings suggesting that mothers with lower EF engage in harsher parenting (Deater-
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Deckard & Bell, 2017; Monn, Narayan, Kalstabakken, Schubert, & Masten, 2017). Meanwhile,
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mothers with stronger EF are more likely to demonstrate sensitive, involved parenting and
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provide developmentally-appropriate supports for their children (Mazursky-Horowitz, et al.,
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2017). This research lends support for a conceptual framework suggesting that parents’ EF
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impacts parenting and the home environment, ultimately affecting children’s outcomes
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(Crandall, Deater-Deckard, & Riley, 2015). However to date, only one study has examined the
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role of parent EF in the domain of child feeding. Among mothers of infants, Fuglestad, et al.
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(Fuglestad, et al., 2017) observed that stronger EF was associated with more appropriate
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responsiveness to babies’ hunger cues at 2 weeks of age, which in turn predicted healthier
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growth between 2 weeks and 3 months.
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The current study seeks to contribute to this emerging area of research by examining associations
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between mothers’ EF and characteristics of the family food environment relevant to children’s
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eating and weight beyond infancy. Further, to increase understanding of whether there are
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specific families for whom mothers’ EF may be more or less strongly associated with the family
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food environment, differences in the associations between mother’s EF and family food
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environment characteristics were examined by family sociodemographic characteristics
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(mothers’ educational attainment, household income-to-needs ratio, and child age). We
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hypothesized that mothers with lower EF will be less likely to engage in food parenting practices
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and report home food environment characteristics that are known to promote children’s healthy
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eating (e.g., having frequent family meals, limiting access to unhealthy foods). We further
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hypothesized that in families of lower income, families with mothers with lower educational
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attainment, and families with younger children, poor maternal EF will be even less likely to
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report healthy family food environment characteristics. This hypothesis is informed by previous
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research suggesting that having limited resources and more physically and emotionally
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dependent children is taxing on mothers’ EF, resulting in more negative parenting outcomes
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(Deater-Deckard, Wang, Chen, & Bell, 2012; Mokrova, O'Brien, Calkins, & Keane, 2010).
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Understanding the relations between mothers’ EF and the family food environment can provide
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insight into why some families have difficulty implementing parenting behaviors for child
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nutrition promotion and obesity prevention, and ultimately, may help guide the development of
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more effective family-based child health interventions.
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METHODS
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Study Sample and Data Collection Method
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Mothers of 2 to 9-year-old children living in the US were recruited to complete an online survey
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via Amazon Mechanical Turk (MTurk) in August 2017. This relatively wide age range of
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children was selected because it allowed for examination of potential differences in the relations
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between maternal EF and the family food environment among families with toddler and
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preschool-aged children, who often present physical, feeding demands on mothers, versus
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elementary school-aged children, who may be more independent in feeding but introduce other
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time demands on families that may affect the family food environment (e.g., extracurricular
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activities). MTurk is an increasingly common, as well as cost and time effective, approach to
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collecting data from parents in studies of children’s eating and weight status (Burrows, et al.,
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2017; Darling, Sato, van Dulmen, Flessner, & Putt, 2017; Emley, Taylor, & Musher-Eizenman,
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2017). Recent studies demonstrate that MTurk samples do not differ from study samples
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obtained by conventional recruitment methods (Mortensen & Hughes, 2018) and may be more
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socio-demographically diverse than participant samples recruited through other means, such as
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Facebook or email listservs (Dworkin, Hessel, Gliske, & Rudi, 2016). The study was advertised
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as a study to understand families’ eating habits. Individuals interested in participating completed
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a screening survey and were eligible if they reported they were female, 18 years of age or older,
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had a child between the ages of 2 and 9 years old, were their child’s legal guardian, and lived
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with their child at least 5 days per week. Of the 1013 individuals who completed the screener,
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557 were eligible based on the above criteria. Eligible participants were then invited to complete
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the study survey. If participants had multiple children within the target age range, they were
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asked to complete the survey with their youngest child in mind. Participants received a $0.05
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payment for completing the screening survey and a $2.00 payment for completing at least 90%
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of the study survey. These compensation rates were determined after reviewing the
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compensation rates of similar MTurk-based studies. Among the individuals who were eligible,
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95% completed at least 90% of the study survey (N=527). Among these 527 mothers, 6.6%
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(N=35) were excluded due to infrequent or inconsistent responses on the BRIEF-A measure of
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EF difficulties, resulting in a final sample of 492. The University of Michigan determined this
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study was exempt from Institutional Review Board review.
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Measures
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Executive function difficulties. Mothers’ EF difficulties were assessed using the Behavior
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Rating of Executive Function-Adult Version (BRIEF-A) (Roth, Isquith, & Gioia, 2005), a
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standardized rating scale used to assess adults’ EF difficulties as manifested in everyday life
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(Roth, Lance, Isquith, Fischer, & Giancola, 2013). The 75-item measure assesses 9 aspects of
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EF: 3 core EF processes (response inhibition, working memory, and cognitive flexibility) and 6
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EF-related skills (emotional control, initiation, planning and organizing, organization of
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materials, and self-monitoring). Participants rated statements (e.g., “I have trouble changing from
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one activity or task to another” and “I forget instructions easily”) from 1 (Never) to 3 (Often) and
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responses were summed to create each sub-scale. The Global Executive Composite (GEC)
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(Cronbach’s α = 0.97) is a summary measure of the 9 sub-scales, with higher scores indicating a
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greater degree of EF difficulties. GEC T-scores were calculated in comparison to a normative
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sample comprised of 1050 self- and 1200 informant reports. The BRIEF-A has been used to
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assess EF difficulties among healthy and clinical populations (Adler, et al., 2013; Løvstad, et al.,
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2016; Roth, et al., 2013).
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Family food environment. Selection of family food environment measures used in this study
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was based on evidence-driven hypotheses regarding characteristics known to promote positive
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nutrition among children that may present a particular challenge for parents with EF deficits. For
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example, as suggested by the parenting literature (Deater-Deckard, et al., 2012), inhibiting
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instincts to appease children exhibiting negative emotions with food may be difficult for mothers
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with lower EF and as suggested by studies demonstrating that cognitive flexibility supports
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consistent physical activity routines (Kelly & Updegraff, 2017), implementing regular family
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meals may be difficult for mothers with lower EF.
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184 Home food environment. Two scales were used to measure the availability of healthy food at
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home (4 items) and availability of unhealthy food at home (4 items) (Bauer, Hearst, Escoto,
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Berge, & Neumark-Sztainer, 2012). Mothers reported how often fruits, vegetables, and whole
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wheat bread were available and/or served with meals in their home (healthy foods) and how
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often ‘junk food’, salty snacks, candy, and soda pop were available in their home (unhealthy
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foods). Response options ranged from “Never” (1) to “Always” (4) on a 4-point scale.
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Cronbach’s α for availability of healthy food = 0.65 and unhealthy food = 0.81. Several qualities
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of family meals were also assessed including the frequency of family evening meals during the
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past 7 days (Lytle, et al., 2011), frequency of family evening meals prepared outside the home
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during the past 7 days (sum of 3 items assessing frequency of meals purchased from fast food
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restaurants (Lytle, et al., 2011), from another kind of restaurant, and purchased pre-prepared
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from a store), and the frequency of family evening meals cooked at home during the past 7 days.
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Response options were on a 5-point scale ranging from “Never” (0) to “7 times” (7). Mothers
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also reported on mealtime structure (8 items) and mealtime schedule (5 items) using sub-scales
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of The Feeding Strategies Questionnaire (Berlin, Davies, Silverman, & Rudolph, 2011).
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Response options for all of these sub-scales were on a 5-point scale that ranged from “Strongly
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Disagree” (1) to “Strongly Agree” (5). In the current study, Mealtime Structure Cronbach’s α =
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0.85, and Mealtime Schedule = 0.86.
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203
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185
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Food parenting practices. Two food parenting practices, regulating children’s emotions with
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food and monitoring children’s eating, were measured using the Emotion Regulation Scale (3
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items) and the Monitoring Scale (4 items) of The Comprehensive Feeding Practices
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Questionnaire (Musher-Eizenman & Holub, 2007). Response options for these scales were on a
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5-point scale ranging from “Never” (1) to “Always” (5). Cronbach’s α for the Emotion
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Regulation Scale = 0.82 and 0.87 for the Monitoring Scale in the current sample. The extent to
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which mothers allow between meal grazing was also measured using a 3-item sub-scale of The
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Feeding Strategies Questionnaire (Cronbach’s α= 0.88 in the current sample) (Berlin, et al.,
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2011).
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Sociodemographic characteristics and maternal body mass index (BMI). Mothers reported
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their race/ethnicity, relationship status, educational attainment, total income of their household in
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the past year before taxes (6 response options ranging from “<$20,000” to “$100,000 or more”),
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and how many people live in their household. Household income and number of people living in
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the household were used to calculate income-to-needs ratio as a proportion of the US federal
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poverty line. Mothers also reported their height in feet and inches and weight in pounds, from
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which BMI was calculated. Adult height and weight self-reported via online surveys has
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demonstrated high validity (Lassale, et al., 2013).
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Statistical Analysis
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Univariate statistics were used to examine the distributions of GEC T-scores, sociodemographic
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characteristics, and mothers’ BMI. Associations between these variables were examined using
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ANOVA and Pearson correlations. Measures of the family food environment were standardized
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to a mean of 0 and standard deviation of 1, and separate linear regression models were developed
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with GEC T-score as the independent variable and each family food environment measure as
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dependent variables, adjusted for maternal race/ethnicity as a categorical variable and maternal
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education, household income-to-needs ratio, and maternal BMI as continuous variables. To
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account for multiple comparisons, a Bonferroni adjustment was implemented and p values <.005
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used to indicate statistical significance. Effect modification of the relations between maternal EF
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difficulties and family food environment characteristics by mothers’ educational attainment,
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household income-to-needs ratio, and child age was examined through the addition of interaction
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terms including continuous variables to the linear regression models. Given the more limited
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statistical power of these models, interactions were indicated in cases where the interaction term
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was significant at p<.05.
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238 RESULTS
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Three-fourths (76.5%) of mothers identified as White, 9.2% Black, 5.7% Hispanic/Latina, and
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5.5% Asian (Table 1). The majority (53.2%) completed college or had a graduate degree.
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Mothers’ mean age was 34.2 years (SD=6.7) and mean BMI was 27.3 (SD=7.0). The mean GEC
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T-score for the sample was 50.7 (SD=11.4) and 13.2% of mothers had a GEC-T score ≥ 65,
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indicating clinically-elevated EF difficulties (Roth, et al., 2005). GEC T-scores differed across
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levels of maternal education and were modestly correlated with maternal BMI (r=0.14, p=.002),
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indicating higher maternal BMI was associated with greater EF difficulties.
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Table 1. Maternal Characteristics: Descriptive Statistics and Bivariate Associations with Executive Function Difficulties (N=492)1
11.6 (57) 35.2 (173) 53.2 (261)
52.1 (11.5) 52.1 (9.5) 49.5 (11.7)
87.2 (428) 12.8 (63)
50.4 (11.4) 52.7 (11.7)
Test Statistic F 1.29
p-value
3.23
.04
2.30
.13
.27
50.8 (11.3) 48.8 (11.0) 49.9 (11.6) 49.7 (11.9) 56.2 (13.6)
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76.5 (375) 9.2 (45) 5.7 (28) 5.5 (27) 3.1 (15)
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Race/ethnicity White Black Hispanic/Latina Asian Mixed/other Highest educational attainment Less than high school degree or high school completed Some college Completed college or advanced degree Relationship status Married or in committed relationship Other
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Total % (n)
Global Executive Composite T-Score Mean (SD)
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248 249
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Mean (SD) r 2.3 (1.2) --0.08 .08 Household income-to-needs ratio 34.2 (6.7) -0.03 .46 Maternal age (years) 4.8 (2.1) -0.06 .18 Child age (years) 27.3 (7.0) -0.14 .002 Maternal Body Mass Index 1 F statistic from ANOVA, r statistic = Pearson Correlation Coefficient; SD = Standard Deviation; higher GEC T-scores indicate greater executive function difficulties
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Greater maternal EF difficulties were associated with lower availability of healthy food at home and higher availability of unhealthy food (Table 2). Mothers with greater EF difficulties also reported less frequent family meals, with fewer family meals prepared at home and more
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frequent family meals prepared outside the home, such as meals purchased from fast food
restaurants, compared to mothers with fewer EF difficulties. Maternal EF difficulties were also negatively correlated with mealtime structure and scheduling; families of mothers with greater
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EF difficulties experience less structured meals and less consistent scheduling of meals. Mothers with greater EF difficulties were more likely to report regulating children’s emotions with food
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and were less likely to monitor children’s eating. The only family food characteristic not associated with mothers’ EF difficulties was allowing between meal grazing.
Table 2. Associations Between Maternal Executive Function Difficulties and Family Food Environment Characteristics.1
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Total Sample Unadjusted Mean (SD)
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β (SE) p-value Home Food Environment Healthy home food availability 3.3 (0.57) -0.25 (0.04) <.001 Unhealthy home food availability 2.3 (0.65) 0.13 (0.05) .004 Frequency of family evening meals 5.2 (1.79) -0.17 (0.05) <.001 Frequency of family evening meals 5.0 (1.57) -0.25 (0.05) <.001 prepared at home Frequency of family evening meals 2.9 (2.22) 0.23 (0.05) <.001 prepared outside home Mealtime structure 3.8 (0.75) -0.30 (0.04) <.001 Mealtime schedule 3.3 (0.88) -0.20 (0.05) <.001 Food Parenting Practices Regulates child emotions with food 1.9 (0.73) 0.23 (0.05) <.001 Monitors child eating 4.0 (0.90) -0.21 (0.05) <.001 Allows between meal grazing 3.3 (0.96) 0.04 (0.05) .38 SD = Standard Deviation, SE = Standard Error; higher GEC T-scores indicate greater executive function difficulties; linear regression adjusted for maternal race/ethnicity (categorical), maternal educational attainment (continuous), household income-to-needs ratio (continuous), and maternal body mass index (continuous)
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Models examining interactions between maternal EF difficulties and maternal educational attainment, household income-to-needs ratio, and child age indicated that there were no
by these sociodemographic characteristics (data not shown).
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DISCUSSION
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differences in the associations between maternal EF difficulties and family food characteristics
In the current study that sought to examine associations between maternal EF and family food
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characteristics via an MTurk-based survey of US mothers, we effectively and resourceefficiently recruited a study sample whose reported EF difficulties reflected other healthy populations based on GEC T-score mean and variability, but were less than populations with clinical conditions commonly associated with impaired EF including eating disorders and obesity (Ciszewski, Francis, Mendella, Bissada, & Tasca, 2014; Roth, et al., 2005; Rouel, Raman, Hay,
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& Smith, 2016). Consistent with our hypothesis, mothers with greater EF difficulties reported less engagement in several family food environment characteristics that promote children’s
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healthy eating. For example, families of mothers with greater EF difficulties reported less frequent family meals, were less likely to have healthy food available at home, and were more
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likely to have unhealthy food available at home. Mothers with greater EF difficulties also were more likely to regulate children’s emotions with food and were less likely to monitor their children’s intake. These family food environment characteristics have been associated with less healthy dietary intake, higher weight status, and/or a greater likelihood of developing maladaptive eating behaviors among children (Bauer, et al., 2011; Hammons & Fiese, 2011; Savage, et al., 2007; Vaughn, et al., 2016). These findings provide some of the first evidence suggesting maternal EF may play an important role in children’s nutrition and weight status.
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Counter to our hypotheses, the relations between maternal EF difficulties and family food environment characteristics did not differ by family sociodemographic characteristics. That is, lower maternal EF may present a similar barrier to healthy family food environments across
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diverse types of families.
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This study contributes to our growing understanding of how adults’ EF is not only associated with their own dietary intake and weight status (Allom & Mullan, 2014; Dohle, et al., 2017;
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Wyckoff, et al., 2017; Yang, et al., 2018), but may affect their children’s eating and weight. Building on evidence that as early as infancy, stronger EF among mothers is associated with more responsive feeding (Fuglestad, et al., 2017), the current study suggests that stronger EF may also support mothers’ engagement in parenting practices that impact older children’s eating and establish positive nutrition and eating behaviors throughout the lifespan. Recent evidence
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also demonstrates that children of parents with lower EF are less likely to have optimal bedtime routines (Kitsaras, Goodwin, Allan, Kelly, & Pretty, 2018), another avenue through which parent
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EF may impact children’s obesity risk (Anderson, Sacker, Whitaker, & Kelly, 2017). Further, one study has identified that children of parents with greater impulsivity, a component of EF, are
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more likely to experience obesity (Stoklosa, et al., 2018). Together, these findings suggest that families with parents with lower EF may be a population in particular need of nutrition promotion and obesity prevention programming.
While behavior change can be difficult for adults with lower EF (Gettens & Gorin, 2017), several novel treatment approaches are demonstrating promise in improving emotional and behavioral self-regulation among individuals with lower EF to address dysregulated eating and
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obesity (Dassen, Houben, Van Breukelen, & Jansen, 2018; Eichen, Matheson, Appleton-Knapp, & Boutelle, 2017; Raman, Hay, Tchanturia, & Smith, 2017). For example, Episodic Future Thinking, where individuals are guided through exercises to visualize positive future events in
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great detail, has been associated with improvements in impulse control and reductions in
obesogenic eating behaviors (Bromberg, Wiehler, & Peters, 2015; Daniel, Stanton, & Epstein, 2013; Dassen, Jansen, Nederkoorn, & Houben, 2016). Further, Cognitive Remediation Therapy,
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which seeks to improve thinking processes and strengthen metacognition, has been shown to improve cognitive flexibility and has led to meaningful improvements in weight status among
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individuals seeking care for obesity (Allom, Mullan, Smith, Hay, & Raman, 2018). If future studies provide consistent support for the hypothesis that lower EF among parents has a negative impact on children’s nutrition and weight outcomes, integrating intervention strategies such as these into family-based interventions may enhance behavior change among parents with lower
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EF, providing improved opportunities for children to engage in healthier behaviors.
Findings should be considered in light of study limitations. Although use of MTurk to conduct
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population-based survey research is growing, it is more difficult than in-person data collection methods to verify participant-provided information. However, electronic and mailed survey-
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based studies are prone to similar limitations and validity checks built into the BRIEF-A identified only a small number of incongruent response patterns. Second, while the BRIEF-A contains two theoretically-distinct indices assessing behavior regulation (the ability to regulate behavior and emotional responses) and metacognition (the ability to self-manage tasks and monitor performance) (Roth, et al., 2005), a high correlation (r=0.81) between the two indices in the current sample led us to rely on a single summary measure of EF difficulties, the GEC.
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Future studies using task-based measures of EF may provide greater insight into whether specific EF processes underlie parent engagement in family food practices. There was also a limited number of measures of child characteristics; for example, children’s heights and weights were
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not collected due to the relatively low validity of parent report of these measures among parents of children in this age range (Weden, et al., 2013), limiting the opportunity to examine
associations between mothers’ EF and child weight status. Children’s EF was also not measured.
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As lower EF among children is associated with obesogenic eating behaviors and obesity (Liang, Matheson, Kaye, & Boutelle, 2014), it would be beneficial to understand how mothers’ EF
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predicts family and child outcomes independent of child EF, and how mothers’ and children’s EF may interact to impact children’s nutrition and growth. Finally, this study only focused on mothers, despite fathers and other caregivers often having substantial roles in supporting the family food environment, and the study sample was predominantly of White race and had
CONCLUSIONS
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relatively high educational attainment, limiting generalizability to other population groups.
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This study provides some of the first evidence that mothers’ EF is associated with elements of the family food environment that support children’s healthy eating and weight status. These
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associations were consistent across families with varying maternal education, household income, and child age. Future research using diverse assessments of parent EF, assessing a wide range of family food environment characteristics, and measuring child-level characteristics including children’s own EF, diet, and BMI, will greatly enhance our understanding of the role of parents’ EF in children’s nutrition and growth. Additionally, as poor parent EF is hypothesized to interfere with even strong intentions to engage in nutrition-promoting parenting practices
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(Larsen, et al., 2018), it is important to consider the intersections of parent intention, motivation, and EF, and how these factors together support behavior change. This knowledge may guide the development of novel intervention strategies to better promote healthy family food environments
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