Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Sep 1.
Published in final edited form as: Appetite. 2025 May 9;213:108038. doi: 10.1016/j.appet.2025.108038

Profiles of maternal feeding practices and their associations with parent feeding styles, child diet quality, and the home food environment

Amara Channell Doig 1, Leah M Lipsky 1, Grace Shearrer 2, Tonja R Nansel 1
PMCID: PMC12168140  NIHMSID: NIHMS2082909  PMID: 40349932

Abstract

Parent feeding practices influence child diet, yet studies have primarily examined these practices individually. This study examines latent profiles of parent feeding practices and tests their relations with feeding styles, child diet quality, and the home food environment. Comprehensive Feeding Practices Questionnaire (CFPQ, comprised of 12 subscales) assessed feeding practices in 118 parent-child dyads in North Carolina, U.S.A. Profiles of the 12 CFPQ subscales were identified using Latent Profile Analysis. Multinomial regression estimated associations of CFPQ latent profiles with the Caregiver Feeding Styles Questionnaire. ANOVA estimated associations of profile membership with the Home Food Inventory obesogenic (HFI-OB) and fruit/vegetable score (HFI-FV). Linear regression examined associations of profile membership with child Healthy Eating Index 2020 (HEI) overall and separately for adequacy (HEI-adq) and moderation components (HEI-mod). Fit indices supported a three-profile model: 1-Optimal (high supportive and low controlling), 2-Mixed (high supportive and high controlling), and 3-Laissez-Faire (low supportive and moderate controlling). Feeding styles at age 3.5 years did not predict CFPQ profile membership. Child diet quality was lower in the laissez-faire profile than the optimal profile (Total HEI β=−5.85, SE=2.98; HEI-adq β=−3.65, SE=1.96; HEI-mod β=−2.19, SE=1.34), but did not meet the threshold for statistical significance. HFI-OB was significantly lower in the optimal profile than the mixed profile (mean difference= −4.85, 95%CI= −9.00, 0.70) and laissez-faire profile (mean difference= −4.68, 95%CI= −8.40, −0.97), while HFI-FV was significantly higher for the optimal profile than laissez-faire profile (mean difference= 3.30, 95%CI= 0.10, 6.49). The optimal feeding practices profile was associated with better diet-related outcomes compared with the other profiles. Examining profiles of parent feeding practices may improve understanding of the complex relationships of inter-related feeding practices with child diet and health outcomes.

Keywords: Parental feeding practices, parental feeding styles, child diet quality, home food environment

1. Background

Early childhood nutrition is critical for development and lifelong health (Kerr et al., 2021; Schwarzenberg et al., 2018). However, diet quality in young children is low (Liu et al., 2020; Thomson et al., 2020), characterized by inadequate vegetable and fruit intake and excessive intake of sodium, refined grains, and sugar (Banfield et al., 2016; Hamner et al., 2023; Herrick et al., 2020; Powell, Smith-Taillie, & Popkin, 2016). Given that diet quality and the associated health risks of suboptimal diet quality track throughout development (Nicklaus & Remy, 2013), understanding modifiable determinants of early nutrition is critical for improving public health.

Parent feeding practices, which refer to strategies parents use to feed their children, may facilitate or inhibit children’s healthful dietary intake. Practices such as modeling, environment, and encouraging balance and variety are associated with increased intake of vegetables, fruit, and fish intake (Draxten et al., 2014; Haszard et al., 2015; Mazza, Morseth, & Torheim, 2022; Melbye, Øverby, & Øgaard, 2012), while pressure to eat, child control, and feeding for emotional regulation or as a reward are associated with lower overall diet quality and vegetable and fruit intake (Masento et al., 2022; Mou et al., 2021; Warkentin et al., 2020). The Comprehensive Feeding Practices Questionnaire (CFPQ) is the predominant measure of parent feeding practices that contains 12 subscales (Musher-Eizenman & Holub, 2007) assessing a broad range of positive and negative practices. Previous studies using this measure have primarily examined individual practices rather than patterns across practices or composite scores (Draxten et al., 2014; Haszard et al., 2015; Masento et al., 2022; Mazza, Morseth, & Torheim, 2022; Melbye, Øverby, & Øgaard, 2012; Mou et al., 2021; Warkentin et al., 2020). However, since the influence of any given practice may depend on the parent’s use of other practices, examining patterns simultaneously would provide a more comprehensive understanding of the parent-child feeding interactions and their association with child diet. For example, the impact of parents’ teaching about nutrition or encouraging balance and variety may be influenced by concurrent use of controlling feeding practices such as restriction.

In contrast to parent feeding practices, parent feeding styles describe the emotional environment parents create in feeding interactions with their children through patterns of behaviors in the dimensions of responsiveness (warmth and involvement) and demandingness (control and demands) (Hughes et al., 2005). Parent feeding styles, which parallel general parenting styles, are categorized into one of: authoritative (high responsiveness and demandingness), authoritative (low responsiveness and high demandingness), indulgent (low demandingness and high responsiveness), and uninvolved (low responsiveness and demandingness). Evidence suggests parent feeding styles influence specific feeding practices (Chen et al., 2021; Lopez et al., 2018). For example, authoritative feeding styles are associated with more parent meal-time structural practices, greater autonomy support, and greater child involvement (Chen et al., 2021; Lopez et al., 2018). However, no studies have examined how parent feeding styles relate to comprehensive measures of parent feeding practices. Elucidating this relationship will improve understanding of determinants of parent feeding practices and mechanisms for how parent feeding styles impact child eating behaviors.

Parent food acquisition and the subsequent healthfulness of foods in the home also shape the feeding environment and are positively associated with child diet quality (Couch et al., 2014; Rex et al., 2021). The home food environment may be associated with parent feeding practices via several mechanisms. For example, parent nutrition knowledge and motivations for health may drive both the home food environment and use of positive parent feeding practices. Conversely, greater presence of obesogenic foods in the house may increase the use of feeding practices such as restriction. To date, only one study has examined the association of the physical home food environment with a single construct of parent feeding practices, finding that the availability of obesogenic foods was not associated with parental modeling (Loth et al., 2016). Clarifying the relationship of the home food environment with parent feeding practices will provide a better understanding of the overall feeding environment and potentially inform effective intervention approaches.

To address these gaps, this study explores patterns of parent feeding practices and examines how these patterns are related to parent feeding styles, the home food environment, and child diet quality, in a pregnancy cohort followed through child age 5 years.

2. Methods

2.1. Study procedure and sample

Sprouts: Development of Eating Behaviors in Young Children (Sprouts) is a follow-up to the Pregnancy Eating Attributes Study (PEAS), which examined behavioral and environmental influences on diet and weight change in pregnancy and postpartum (Nansel et al., 2016). PEAS participants were enrolled from 2014–2016 at the University of North Carolina Women’s Hospital during the first trimester of pregnancy and followed through the first year postpartum. Exclusion criteria included a pre-existing diabetes diagnosis, a self-reported eating disorder, a medical condition that contraindicated participation, or any medications that could affect diet or weight. The only additional exclusion criteria for Sprouts was a child neurocognitive disability or diagnosis of attention deficit/hyperactivity disorder. Of 331 participants who were not withdrawn from PEAS, 162 maternal/child dyads were enrolled in Sprouts from 2019 to 2021. Of these, 113 completed the second visit at child age 5 years. Since initial enrollment was interrupted by the COVID-19 pandemic, participants who did not respond to enrollment requests at the first visit were invited to enroll at the second visit. An additional 19 dyads enrolled at age 5 years, for a total of 132 mother/child dyads at visit 2. Questionnaires measuring feeding practices and styles were completed either during in-person visits or virtually (during the COVID-19 pandemic) using an online data collection platform; the home food inventory was completed virtually for all participants to allow parents to complete while observing foods in the home. The first 24-hour recall was completed at the assessment (or virtually during the COVID-19 pandemic) and the other was completed virtually approximately 10 days later, ensuring that a weekday and weekend were captured. The Sprouts study protocol was approved by the University of North Carolina Institutional Review Board (study #18–2030) and conducted under a reliance agreement from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

2.2. Measures

Caregiver feeding practices questionnaire.

At child age 5 years, parents completed the Caregiver Feeding Practices Questionnaire (CFPQ). The CFPQ includes 49 questions about 12 parental feeding practice domains (3–5 items each) (Musher-Eizenman & Holub, 2007). Response options are on a five-point Likert scale for either frequency of use (1= “rarely,” 5= “always”) or agreement (1= “disagree,” 5= “agree”). Subscales are calculated as the mean of responses (with items reverse coded as appropriate) and include: child control (child control of eating behaviors and parent-child interactions), emotional regulation (using food), food as a reward, pressure (to increase intake), restriction for health, restriction for weight control, monitoring (keeping track of less healthy food), encourage balance and variety, environment (making healthy food available), involvement (involving the child in food preparation and planning), modeling (parental modeling of healthy eating), and teaching about nutrition.

Caregiver feeding styles questionnaire.

At child age 3.5 years, parents completed the Caregiver Feeding Styles Questionnaire (CFSQ), which queries 19 strategies for getting their child to eat (7 child-centered and 12 parent-centered) to measure demandingness and responsiveness. Response options are on a 5-point Likert scale (1= “never”, 5= “always”). The demandingness score is a mean of all 19 items and the responsiveness score is a ratio of the mean of the child-centered items to the mean of all items. Responsiveness and demandingness scores are used to categorize parents according to one of four feeding styles, including uninvolved (< 2.70 demandingness, < 1.09 responsiveness), authoritarian (≥ 2.70 demandingness, < 1.09 responsiveness), indulgent (< 2.70 demandingness, ≥ 1.09 responsiveness), or authoritative (≥ 2.70 demandingness, ≥ 1.09 responsiveness) (Hughes et al., 2012).

Home food Inventory.

At child age 5 years, participants completed the Home Food Inventory questionnaire (HFI), which assesses the presence of foods in the home based on a comprehensive list of items from the major food categories using a yes/no checklist (Fulkerson et al., 2008). Summative scores for obesogenic foods and for fruits and vegetables were calculated as the number of foods in each category present in the home (Fulkerson, 2013; Fulkerson et al., 2008). The obesogenic score includes foods with added fats, desserts and candy, sugar-sweetened beverages, savory snacks, high fat dairy, and processed meats, and ranges from 0–71. The fruit and vegetable score includes 26 fruits and 20 vegetables for a possible score of 0–46. Higher scores on the obesogenic and fruit and vegetables scores represent higher home food availability of these foods.

Diet Quality.

Child dietary intake was assessed at age 5 using two non-consecutive parent-proxy 24-hour recalls. Trained research staff queried all food consumption, including the source, timing, preparation, and portion size, from midnight to midnight for the previous day. The recalls were processed using the Nutrition Data System for Research (NDSR) version 2020 (Minneapolis, Minnesota). To assess diet quality, Healthy Eating Index (HEI)-2020 scores were calculated for each participant using the simple scoring algorithm (Kennedy et al., 1995; Shams-White et al., 2023). The HEI total score ranges from 0 – 100, with higher scores reflecting closer adherence to the Dietary Guidelines for Americans – 2020 – 2025 (U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2020). The HEI-total score) is the sum of 13 components, including 9 adequacy components (total fruit, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafoods and plant proteins, and fatty acids , for which higher scores reflect greater intake) and 4 moderation components (refined grains, sodium, added sugars, and saturated fats, for which higher scores reflect lower intake) Additionally, we created two subscores by summing the 9 adequacy components (HEI-adequacy, range = 0 – 60) and 4 moderation components (HEI-moderation, range = 0 – 40) to enable examining differential impacts of parent feeding practices on increasing foods to encourage versus decreasing foods to limit

Demographics.

Participants reported race, ethnicity, and education at PEAS baseline, and breastfeeding status at each postpartum visit through child age 12 months. At each Sprouts visit, participants reported family income, household size, and employment status. The income to poverty ratio was calculated using family income and household size (United States Census Bureau, 2023).

2.6. Data Analysis

This analysis included all participants who completed the CFPQ (n=118); 14 participants were excluded due to missing data. Latent Profile analysis was conducted using the 12 CFPQ subscales using the mclust package in R (Scrucca et al., 2016; Team, 2023). The program uses an integrated approach to finite mixture-models using model-based hierarchical clustering and assesses 14 possible models with varying geometric characteristics to find the best fit. Models were assessed using multiple fit statistics beginning with the Bayesian Information Criterion (BIC) and Integrated Completed Likelihood (ICL) and followed by a Bootstrap Likelihood Ratio test (BLRT) to confirm the model fit based on the number of profiles. The best model solutions were then line-plotted for interpretation and to determine whether the profiles were theoretically meaningful.

All other analyses were conducted using SAS 9.4 (SAS Institute Inc). Analysis of variance (ANOVA) and chi-square analysis were conducted to examine profile differences by sociodemographic characteristics. Since none significantly differed between profiles at a threshold of p<0.1, no covariates were included in subsequent statistical models. Multinomial regression was used to estimate associations between the CFSQ feeding styles and latent profiles. This analysis included participants who completed both the age 3.5 and 5 year assessments (n=105). Linear regression was used to examine the association of profile membership with HEI, HEI moderation, and HEI adequacy. ANOVA was used to examine the association of the latent profiles with the HFI obesogenic score and the HFI fruit and vegetable score. Finally, sensitivity analyses were conducted to control for potential confounding by SES. To address missing data on HEI and HFI scores, values were carried forward from the most recent time point. Statistical significance was set at p<0.05.

3. Results

3.1. Latent Profile Model Selection

MCLUST identified the three best models based on BIC and ICL as the VVI 3- profile model, and VII 4 and 3- profile models (Table 1). Of the two VII models, the BLRT indicated the four-profile model was better than the three-profile model (p<0.001). The VVI three-profile model and the VII three- profile model had similar profiles, but the fit statistics were superior for the VVI model.

Table 1.

Latent Profile Analysis Model Fit Statistics

Model Number of components log-likelihood df BIC ICL Cases per Profile
VVI 3 −1796.12 74 −3945.28 −3953.73 32, 34, 52
VII 4 −1861.04 55 −3984.47 −3997.76 20, 34, 7, 57
VII 3 −1895.44 41 −3986.47 −4010.23 34, 33, 51
Open in a new tab

Both the VVI three-profile and VII four-profile models were lined-plotted (Figures 1 and 2) and found to be theoretically meaningful, but the smallest profile (n=7) in the VII was determined to be too small to have utility in subsequent analyses of relationships with other variables in this sample. As a result, the three-profile VVI model was selected as the best solution.

Figure 1.

Figure 1.

Open in a new tab

Standardized means of the CFPQ subscales for VVI three-profile model.

Figure 2.

Figure 2.

Open in a new tab

Standardized means of the CFPQ Subscales for Four-Profile model

Figure 1 shows the standardized means of the CFPQ subscales for each of the three profiles. The first profile comprised high supportive practices (teaching about nutrition, encouraging balance and variety, monitoring, modeling, involvement, and environment), low use of controlling practices (pressure and restriction), and low use of child control or use of food as a reward or for emotional regulation, and so was named Optimal. Profile two comprised high use of food as a reward, emotional regulation, and use of both controlling and supportive practices, and so was named Mixed. The third profile comprised low supportive and controlling practices, high child control, and moderate emotional regulation or use of food as a reward and so was named Laissez-Faire. Table 2 shows the descriptive statistics for each profile. There were no demographic differences between the profiles, and all the CFPQ subscales differed across profiles except for pressure and child control.

Table 2.

CFPQ Subscale and Sample Characteristics for Each Latent Profile

Optimal
n=34		 Mixed
n=32		 Laissez-Faire
n=52		 p-value
n (%) or Mean ± SD
Race 0.33
 Non-Hispanic White 29 (31.52) 26 (28.26) 37 (40.22)
 Non-Hispanic Black 1 (9.09) 3 (27.27) 7 (63.64)
 Hispanic or Latino 2 (28.57) 2 (28.57) 3 (42.86)
 Asian 1 (25.00) 1 (25.00) 2 (50.00)
 Other or Multi-Racial 1 (25.00) - 3 (75.00)
Education 0.56
 <Bachelor's Degree 5 (20.83) 8 (33.33) 11 (45.83)
 Bachelor's Degree or More 28 (31.5) 23 (25.84) 38 (42.70)
Employment 0.94
 Full-Time 18 (26.09) 19 (27.54) 32 (46.38)
 Part-Time 8 (33.33) 6 (25.00) 10 (41.67)
 Student - 1 (50.00) 1 (50.00)
 Not Working 7 (33.33) 6 (28.57) 8 (38.10)
Income to Poverty Ratio 3.38 ± 1.22 3.31 ± 1.12 3.27 ± 1.25 0.92
Breastfeeding Duration (months) 10.06 ± 3.82 10.61 ±3.41 9.62 ± 4.33 0.55
CFPQ
 Teaching about Nutrition 4.94 ± 0.13 4.71 ± 0.38 4.14 ± 0.62 <.0001
 Encourage Balance and Variety 4.82 ± 0.21 4.81 ± 0.19 4.24 ± 0.47 <.0001
 Environment 4.30 ± 0.54 3.84 ± 0.70 3.72 ± 0.85 0.002
 Emotional Regulation 1.24 ± 0.28 1.85 ± 0.59 1.74 ± 0.72 <.0001
 Food as Reward 1.57 ± 0.64 3.65± 0.67 2.47 ± 1.08 <.0001
 Involvement 4.13 ± 0.77 3.80 ± 0.80 3.17 ± 0.84 <.0001
 Modeling 4.75 ± 0.27 4.55 ± 0.37 4.00 ± 0.75 <.0001
 Monitoring 4.43 ± 0.53 4.13 ± 0.55 3.74 ± 0.95 0.0002
 Pressure 2.42 ± 0.88 2.92 ± 0.85 2.56 ± 1.07 0.09
 Restriction for Health 2.99 ± 0.90 3.64 ± 0.82 3.24 ± 0.84 0.009
 Restriction for Weight Control 1.34 ± 0.32 1.78 ± 0.76 1.43 ± 0.40 0.001
 Child Control 2.58 ± 0.59 2.74 ± 0.33 2.74 ± 0.69 0.39
CFSQ 0.34
 Authoritative 3 (21.43) 6 (42.86) 5 (35.71)
 Authoritarian 7 (21.21) 12 (36.36) 14 (42.42)
 Indulgent 16 (38.10) 8 (19.05) 18 (42.86)
 Uninvolved 4 (25.00) 3 (18.75) 9 (56.25)
HEI Scores
 HEI 62.17 ± 14.52 57.38 ± 14.17 56.33 ± 12.28 0.14
 HEI Moderation 25.25 ± 6.47 23.00 ± 6.38 23.06 ± 5.59 0.20
 HEI Adequacy 36.92 ± 9.46 34.38 ± 8.78 33.26 ± 8.46 0.18
Home Food Inventory
 HFI Obesogenic 19.59 ± 6.45 24.44± 6.13 24.27 ± 7.98 0.006
 HFI Fruits and Vegetables 23.35 ± 6.21 22.84 ± 5.94 20.06 ± 6.11 0.03
Open in a new tab

Abbreviations: CFPQ, Comprehensive Feeding Practices Questionnaire; CFSQ, Caregiver Feeding Styles Questionnaire; HEI, Healthy Eating Index; HFI, Home Food Inventory; SD, Standard Deviation.

3.2. Associations of Caregiver Feeding Styles with Feeding Practices

Caregiver feeding styles at age 3.5 years did not predict feeding practice latent profile membership at age 5 years (Table 3).

Table 3.

Multinomial regression results for caregiver feeding style at child age 3.5 year predicting feeding practice latent profile membership at age 5 years (n=105)

Optimal Mixed Laissez-Faire
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value
Authoritarian Ref 0.857 (0.161– 4.554) 0.295 1.200 (0.220– 6.534) 0.709
Indulgent Ref 0.250 (0.049–2.884) 0.081 0.675 (0.139–3.283) 0.259
Uninvolved Ref 0.375 (0.049–2.884) 0.572 1.350 (0.211–8.617) 0.584
Authoritative Ref Ref Ref
Open in a new tab

Abbreviations: OR, Odds Ratio; CI, confidence interval.

3.3. Associations of Caregiver Feeding Practices with the Home Food Environment

Both HFI scores were associated with the latent profiles (Table 4). Pairwise comparison showed the optimal profile was associated with a lower HFI obesogenic score compared to both the mixed (mean difference= −4.85, 95%CI= −9.00 – 0.70) and laissez-fare profiles (mean difference= −4.68, 95%CI= −8.40 - −0.97). The optimal profile was associated with a higher HFI fruit and vegetable score compared to the laissez-faire profile (mean difference= 3.30, 95%CI= 0.10–6.49).

Table 4.

Home Food Inventory scores across the latent profiles

Dependent variable Comparison Across Groups Comparison between groups
F-statistic p-value Latent profiles compared mean difference 95% CI
HFI Obesogenic 5.42 0.006 Optimal vs Mixed −4.85* (−9.00– −0.70)
Optimal vs Laissez-Faire −4.68* (−8.40 – −0.97)
Mixed vs Laissez-Faire 0.17 (−3.62 – 3.95)
HFI Fruits and Vegetables 3.69 0.03 Optimal vs Mixed 0.51 (− 3.06 – 4.07)
Optimal vs Laissez-Faire 3.29* (0.10 – 6.49)
Mixed vs Laissez-Faire 2.79 (−0.47 – 6.04)
Open in a new tab
*

p<0.05

Abbreviations: HFI, Home Food Inventory; CI, confidence interval.

3.4. Associations of Caregiver Feeding Practices with Child Diet Quality

Mean HEI, HEI adequacy, and HEI moderation were highest in the optimal profile (Table 2). In the linear regression models, HEI, HEI adequacy, and HEI moderation were higher in the optimal profile than in the mixed and laissez-faire profiles, but the differences did not meet the threshold for statistical significance (Table 5).

Table 5.

Linear Regression for associations of feeding practice latent profile membership with child diet quality (n=117)

Dependent variable Latent profile membership Parameter Estimate (SE) 95% CI p-value
HEI
Optimal ref
Mixed −4.79 (3.32) −11.37 – 1.79 0.15
Laissez-Faire −5.85 (2.98) −11.76 – 0.07 0.05
HEI Adequacy
Optimal ref
Mixed −2.53 (2.18) −6.85 – 1.78 0.25
Laissez-Faire −3.65 (1.96) −7.53 – 0.23 0.06
HEI Moderation
Optimal ref
Mixed −2.26 (1.50) −5.22 – 0.71 0.13
Laissez-Faire −2.19 (1.34) −4.86 – 0.47 0.11
Open in a new tab

Abbreviations: HEI, Healthy Eating Index; SE, Standard Error; CI, confidence interval.

3.5. Sensitivity analyses by SES

No differences in findings were observed after controlling for SES.

4. Discussion

Latent profile analysis revealed three distinct profiles across the twelve parent feeding practices of the CFPQ: Optimal (29%), Mixed (27%), and Laissez-Faire (44%). There were no sociodemographic differences across the profiles, and profile membership was not associated with parent feeding styles. Parents in the optimal profile had higher mean child quality compared to the other profiles, but diet quality ranged widely within the profiles and the differences did not meet the threshold for statistical significance. Parents in the optimal profile reported significantly higher home food fruit and vegetable availability (HFI) compared to those in the Laissez-Faire profile and lower obesogenic food availability compared to the other two profiles.

The identification of three district profiles of parent feeding practices is comparable with a previous study which used a different measure and found a profile with similar distinctions by the use of structure and responsive feeding practices (Burnett et al., 2022). Although the latent profiles aligned conceptually with parent feeding styles (Hughes et al., 2005; Wood et al., 2020), they were not significantly associated. Specifically, an authoritative feeding style, which employs autonomy-supportive approaches to influence child eating, would be expected to align with the use of optimal feeding practices (Wood et al., 2020). Conversely, parents with an uninvolved feeding style would be expected to demonstrate Laissez-Faire feeding practices (Wood et al., 2020). The lack of association between parent feeding styles and the feeding practice profiles suggests that the measures are assessing different aspects of parent feeding; there may be a benefit to using both measures as has been done previously (Jennings et al., 2019; Marr, Breeze, & Caton, 2022; Power et al., 2015; Vollmer, 2019). Alternatively, the lack of association may be due to the different timing of the administration of each measure. However, research has found that the CFSQ and CFPQ are stable over time (Silva Garcia et al., 2018). Null findings herein contrast with previous research showing associations of general parenting style with parent feeding practices (Blissett & Haycraft, 2008; Collins, Duncanson, & Burrows, 2014; Hubbs-Tait et al., 2008; Kiefner-Burmeister et al., 2016; van der Horst & Sleddens, 2017; Vollmer & Mobley, 2013). Parental feeding practices may align better with general parenting styles rather than feeding styles, and future research could investigate this question.

The associations observed between the home food environment and parent feeding practices profiles may reflect differences in how parents value healthful eating. Specifically, healthful food purchasing behaviors and supportive feeding practices could both be related to prioritizing healthful eating, consistent with previous findings that stronger health motivation was associated with more vegetables and fruits in the home (Røed et al., 2020) and parents who valued healthful eating used more modeling and involvement (Russell, Worsley, & Campbell, 2015). Thus, the home food environment may mediate the relationship of parental feeding practices with child diet quality. Conversely, the home food environment may shape parental feeding practices; for example, availability of discretionary foods in the home may lead to greater use of controlling feeding practices, consistent with findings that higher accessibility of snack foods in the home was associated with more parental use of restriction (Corsini et al., 2018). Additional research is needed to elucidate the directionality of these associations.

Although associations of the profiles with child diet quality did not meet the threshold for statistical significance, there were meaningful mean differences between profiles, with the optimal profile having an approximately 5–6 point (8–10%) higher mean total HEI and 2–3 point (6–10%) higher adequacy and moderation component scores compared to the other two profiles. HEI differences of this magnitude are thought to be associated with differences in risk of diet related disease (Kirkpatrick et al., 2018). Additionally, parents in the optimal profile reported a significantly less obesogenic home food environment and greater availability of fruits and vegetables in the home. While these findings are consistent with previous research demonstrating associations of individual parent feeding practices with child eating behaviors (Draxten et al., 2014; Haszard et al., 2015; Masento et al., 2022; Mazza, Morseth, & Torheim, 2022; Mou et al., 2021; Vollmer, 2019) , they also suggest the utility of examining patterns across the CFPQ feeding practices. Both the optimal and mixed profiles reported high supportive practices, yet showed meaningful differences in the home food environment and child diet quality, suggesting the importance of considering both supportive and controlling practices simultaneously.

4.1. Strengths and Limitations

Strengths of this study include the use of 24-hour dietary recalls to assess dietary intake, and of validated measures of parental feeding practices and styles. While two 24-hour dietary recalls are regarded as one of the most valid dietary assessment methods, two days of intake may not be representative of an individual’s usual intake. Additional limitations include the modest sample size that although sufficient to assess latent profile membership, may have decreased power to detect differences in child diet quality across profiles. Although there were no sociodemographic differences in profile membership and SES did not confound the relationships observed, the sample for this study was from a single geographic region with higher average education and income than the U.S. general population.

5. Conclusions

Findings suggest that parent use of twelve feeding practices can be summarized by classification within a smaller number of distinct profiles, which differ meaningfully on diet-related outcomes. As such, these profiles offer a way to examine parent feeding behaviors in combination, providing a more comprehensive characterization of parent feeding practices than examining each subscale in isolation.

Funding:

This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development Intramural Research Program (contract #HHSN275201300012C, #HHSN275201300026I/HHSN27500002, #HHSN275201800002I, and #HHSN275201800013I). The funding body had no role in the study design; collection, analysis, and interpretation of data; or writing of the manuscript.

List of Abbreviations

CFPQ

Comprehensive Feeding Practices Questionnaire

PEAS

Pregnancy Eating Attributes Study

CFSQ

Caregiver Feeding Styles Questionnaire

HEI

Healthy Eating Index

HFI

Home Food Inventory questionnaire

BIC

Bayesian Information Criterion

ICL

Integrated Completed Likelihood

BLRT

Bootstrap Likelihood Ratio test

ANOVA

Analysis of variance

Footnotes

Competing interests: The authors have no competing interests to declare.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics approval and consent to participate: The study protocol was approved by the University of North Carolina Institutional Review Board, IRB #13–3848 and #18–2030 and conducted under a reliance agreement from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Written informed consent was obtained from all participants.

Consent for publication: Not applicable.

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.

Availability of data and materials:

Data described in the manuscript, code book, and analytic code will be made available upon request pending approval of a data use agreement. Following publication of study objectives, de-identified data will be shared in the NICHD Data and Specimen Hub.

References

  1. Banfield EC, Liu Y, Davis JS, Chang S, & Frazier-Wood AC (2016). Poor Adherence to US Dietary Guidelines for Children and Adolescents in the National Health and Nutrition Examination Survey Population. Journal of the Academy of Nutrition and Dietetics, 116(1), 21–27. 10.1016/j.jand.2015.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Blissett J, & Haycraft E (2008). Are parenting style and controlling feeding practices related? Appetite, 50(2), 477–485. 10.1016/j.appet.2007.10.003 [DOI] [PubMed] [Google Scholar]
  3. Burnett AJ, Lacy KE, Russell CG, Spence AC, Worsley A, & Lamb KE (2022). Groups of mothers based on feeding practices and their associations with dietary quality of pre-school children: A latent profile analysis. Appetite, 168, 105754. 10.1016/j.appet.2021.105754 [DOI] [PubMed] [Google Scholar]
  4. Chen B, Kattelmann K, Comstock C, McCormack L, Wey H, & Meendering J (2021). Parenting Styles, Food Parenting Practices and Dietary Intakes of Preschoolers. Nutrients, 13(10), 3630. https://www.mdpi.com/2072-6643/13/10/3630 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Collins C, Duncanson K, & Burrows T (2014). A systematic review investigating associations between parenting style and child feeding behaviours. Journal of Human Nutrition and Dietetics, 27(6), 557–568. 10.1111/jhn.12192 [DOI] [PubMed] [Google Scholar]
  6. Corsini N, Kettler L, Danthiir V, & Wilson C (2018). Parental feeding practices to manage snack food intake: Associations with energy intake regulation in young children. Appetite, 123, 233–240. 10.1016/j.appet.2017.12.024 [DOI] [PubMed] [Google Scholar]
  7. Couch SC, Glanz K, Zhou C, Sallis JF, & Saelens BE (2014). Home Food Environment in Relation to Children’s Diet Quality and Weight Status. Journal of the Academy of Nutrition and Dietetics, 114(10), 1569–1579.e1561. 10.1016/j.jand.2014.05.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Draxten M, Fulkerson JA, Friend S, Flattum CF, & Schow R (2014). Parental role modeling of fruits and vegetables at meals and snacks is associated with children’s adequate consumption. Appetite, 78, 1–7. 10.1016/j.appet.2014.02.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fulkerson JA (2013). Scoring Protocol of the Home Food Inventory (2013). University of Minnesota. [Google Scholar]
  10. Fulkerson JA, Nelson MC, Lytle L, Moe S, Heitzler C, & Pasch KE (2008). The validation of a home food inventory. International Journal of Behavioral Nutrition and Physical Activity, 5(1), 55. 10.1186/1479-5868-5-55 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hamner HC, Dooyema CA, Blanck HM, Flores-Ayala R, Jones JR, Ghandour RM, & Petersen R (2023). Fruit, Vegetable, and Sugar-Sweetened Beverage Intake Among Young Children, by State — United States, 2021 (MMWR Morb Mortal Wkly Rep, Issue. 10.15585/mmwr.mm7207a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Haszard JJ, Skidmore PML, Williams SM, & Taylor RW (2015). Associations between parental feeding practices, problem food behaviours and dietary intake in New Zealand overweight children aged 4–8 years. Public Health Nutrition, 18(6), 1036–1043. 10.1017/S1368980014001256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Herrick KA, Fryar CD, Hamner HC, Park S, & Ogden CL (2020). Added Sugars Intake among US Infants and Toddlers. Journal of the Academy of Nutrition and Dietetics, 120(1), 23–32. 10.1016/j.jand.2019.09.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hubbs-Tait L, Kennedy TS, Page MC, Topham GL, & Harrist AW (2008). Parental Feeding Practices Predict Authoritative, Authoritarian, and Permissive Parenting Styles. Journal of the American Dietetic Association, 108(7), 1154–1161. 10.1016/j.jada.2008.04.008 [DOI] [PubMed] [Google Scholar]
  15. Hughes SO, Cross MB, Hennessy E, Tovar A, Economos CD, & Power TG (2012). Caregiver’s Feeding Styles Questionnaire. Establishing cutoff points. Appetite, 58(1), 393–395. 10.1016/j.appet.2011.11.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hughes SO, Power TG, Orlet Fisher J, Mueller S, & Nicklas TA (2005). Revisiting a neglected construct: parenting styles in a child-feeding context. Appetite, 44(1), 83–92. 10.1016/j.appet.2004.08.007 [DOI] [PubMed] [Google Scholar]
  17. Jennings KM, Loth KA, Tate AD, Miner MH, & Berge JM (2019). Application of latent profile analysis to define subgroups of parenting styles and food parenting practices. Appetite, 139, 8–18. 10.1016/j.appet.2019.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kennedy ET, Ohls J, Carlson S, & Fleming K (1995). The Healthy Eating Index: Design and Applications. Journal of the American Dietetic Association, 95(10), 1103–1108. 10.1016/S0002-8223(95)00300-2 [DOI] [PubMed] [Google Scholar]
  19. Kerr JA, Liu RS, Gasser CE, Mensah FK, Burgner D, Lycett K, Gillespie AN, Juonala M, Clifford SA, Olds T, Saffery R, Gold L, Liu M, Azzopardi P, Edwards B, Dwyer T, & Wake M (2021). Diet quality trajectories and cardiovascular phenotypes/metabolic syndrome risk by 11–12 years. International Journal of Obesity, 45(7), 1392–1403. 10.1038/s41366-021-00800-x [DOI] [PubMed] [Google Scholar]
  20. Kiefner-Burmeister A, Hoffmann D, Zbur S, & Musher-Eizenman D (2016). Implementation of parental feeding practices: does parenting style matter? Public Health Nutrition, 19(13), 2410–2414. 10.1017/S1368980016000446 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kirkpatrick SI, Reedy J, Krebs-Smith SM, Pannucci TE, Subar AF, Wilson MM, Lerman JL, & Tooze JA (2018). Applications of the Healthy Eating Index for Surveillance, Epidemiology, and Intervention Research: Considerations and Caveats. Journal of the Academy of Nutrition and Dietetics, 118(9), 1603–1621. 10.1016/j.jand.2018.05.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Liu J, Rehm CD, Onopa J, & Mozaffarian D (2020). Trends in Diet Quality Among Youth in the United States, 1999–2016. JAMA, 323(12), 1161–1174. 10.1001/jama.2020.0878 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lopez NV, Schembre S, Belcher BR, O’Connor S, Maher JP, Arbel R, Margolin G, & Dunton GF (2018). Parenting styles, food-related parenting practices, and children’s healthy eating: A mediation analysis to examine relationships between parenting and child diet. Appetite, 128, 205–213. 10.1016/j.appet.2018.06.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Loth KA, Friend S, Horning ML, Neumark-Sztainer D, & Fulkerson JA (2016). Directive and non-directive food-related parenting practices: Associations between an expanded conceptualization of food-related parenting practices and child dietary intake and weight outcomes. Appetite, 107, 188–195. 10.1016/j.appet.2016.07.036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Marr C, Breeze P, & Caton SJ (2022). A comparison between parent and grandparent dietary provision, feeding styles and feeding practices when caring for preschool-aged children. Appetite, 168, 105777. 10.1016/j.appet.2021.105777 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Masento NA, Dulay KM, Harvey K, Bulgarelli D, Caputi M, Cerrato G, Molina P, Wojtkowska K, Pruszczak D, Barlińska J, Messer D, & Houston-Price C (2022). Parent, child, and environmental predictors of vegetable consumption in Italian, Polish, and British preschoolers [Original Research]. Frontiers in Nutrition, 9. 10.3389/fnut.2022.958245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mazza M, Morseth M, & Torheim LE (2022). Association between parental feeding practices and children’s dietary intake: a cross-sectional study in the Gardermoen Region, Norway. Food Nutr Res, 66. 10.29219/fnr.v66.8050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Melbye EL, Øverby NC, & Øgaard T (2012). Child consumption of fruit and vegetables: the roles of child cognitions and parental feeding practices. Public Health Nutrition, 15(6), 1047–1055. 10.1017/S1368980011002679 [DOI] [PubMed] [Google Scholar]
  29. Mou Y, Jansen PW, Raat H, Nguyen AN, & Voortman T (2021). Associations of family feeding and mealtime practices with children’s overall diet quality: Results from a prospective population-based cohort. Appetite, 160, 105083. 10.1016/j.appet.2020.105083 [DOI] [PubMed] [Google Scholar]
  30. Musher-Eizenman D, & Holub S (2007). Comprehensive Feeding Practices Questionnaire: Validation of a New Measure of Parental Feeding Practices. Journal of Pediatric Psychology, 32(8), 960–972. 10.1093/jpepsy/jsm037 [DOI] [PubMed] [Google Scholar]
  31. Nansel TR, Lipsky LM, Siega-Riz AM, Burger K, Faith M, & Liu A (2016). Pregnancy eating attributes study (PEAS): a cohort study examining behavioral and environmental influences on diet and weight change in pregnancy and postpartum. BMC Nutr, 2. 10.1186/s40795-016-0083-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nicklaus S, & Remy E (2013). Early Origins of Overeating: Tracking Between Early Food Habits and Later Eating Patterns. Current Obesity Reports, 2(2), 179–184. 10.1007/s13679-013-0055-x [DOI] [Google Scholar]
  33. Powell ES, Smith-Taillie LP, & Popkin BM (2016). Added Sugars Intake Across the Distribution of US Children and Adult Consumers: 1977–2012. Journal of the Academy of Nutrition and Dietetics, 116(10), 1543–1550.e1541. 10.1016/j.jand.2016.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Power TG, O’Connor TM, Orlet Fisher J, & Hughes SO (2015). Obesity Risk in Children: The Role of Acculturation in the Feeding Practices and Styles of Low-Income Hispanic Families. Childhood Obesity, 11(6), 715–721. 10.1089/chi.2015.0036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rex SM, Kopetsky A, Bodt B, & Robson SM (2021). Relationships Among the Physical and Social Home Food Environments, Dietary Intake, and Diet Quality in Mothers and Children. Journal of the Academy of Nutrition and Dietetics, 121(10), 2013–2020.e2011. 10.1016/j.jand.2021.03.008 [DOI] [PubMed] [Google Scholar]
  36. Røed M, Vik FN, Hillesund ER, Lippevelde WV, & Øverby NC (2020). Associations between parental food choice motives, health-promoting feeding practices, and infants’ fruit and vegetable intakes: the Food4toddlers study. Food Nutr Res, 64. 10.29219/fnr.v64.3730 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Russell CG, Worsley A, & Campbell KJ (2015). Strategies used by parents to influence their children’s food preferences. Appetite, 90, 123–130. 10.1016/j.appet.2015.02.038 [DOI] [PubMed] [Google Scholar]
  38. Schwarzenberg SJ, Georgieff MK, NUTRITION CO, Daniels S, Corkins M, Golden NH, Kim JH, Lindsey CW, & Magge SN (2018). Advocacy for Improving Nutrition in the First 1000 Days to Support Childhood Development and Adult Health. Pediatrics, 141(2). 10.1542/peds.2017-3716 [DOI] [PubMed] [Google Scholar]
  39. Scrucca L, Fop M, Murphy TB, & Raftery AE (2016). mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R j, 8(1), 289–317. [PMC free article] [PubMed] [Google Scholar]
  40. Shams-White MM, Pannucci TE, Lerman JL, Herrick KA, Zimmer M, Meyers Mathieu K, Stoody EE, & Reedy J (2023). Healthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans, 2020–2025. Journal of the Academy of Nutrition and Dietetics, 123(9), 1280–1288. 10.1016/j.jand.2023.05.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Silva Garcia K, Power TG, Beck AD, Fisher JO, Goodell LS, Johnson SL, O’Connor TM, & Hughes SO (2018). Stability in the feeding practices and styles of low-income mothers: questionnaire and observational analyses. International Journal of Behavioral Nutrition and Physical Activity, 15(1), 28. 10.1186/s12966-018-0656-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Team RC (2023). R: A Language and Environment for Statistical Computing. In R Foundation for Statistical Computing. https://www.R-project.org/ [Google Scholar]
  43. Thomson JL, Landry AS, Tussing-Humphreys LM, & Goodman MH (2020). Diet quality of children in the United States by body mass index and sociodemographic characteristics. Obesity Science & Practice, 6(1), 84–98. 10.1002/osp4.388 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. U.S. Department of Agriculture and U.S. Department of Health and Human Services, U. a. H. (2020). Dietary Guidelines for Americans, 2020–2025. . DietaryGuidelines.gov.
  45. United States Census Bureau. (2023). Poverty Thresholds. Retrieved 12/18/2023, from https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html
  46. van der Horst K, & Sleddens EFC (2017). Parenting styles, feeding styles and food-related parenting practices in relation to toddlers’ eating styles: A cluster-analytic approach. PLOS ONE, 12(5), e0178149. 10.1371/journal.pone.0178149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Vollmer RL (2019). Parental feeding style changes the relationships between children’s food preferences and food parenting practices: The case for comprehensive food parenting interventions by pediatric healthcare professionals. Journal for Specialists in Pediatric Nursing, 24(1), e12230. 10.1111/jspn.12230 [DOI] [PubMed] [Google Scholar]
  48. Vollmer RL, & Mobley AR (2013). Parenting styles, feeding styles, and their influence on child obesogenic behaviors and body weight. A review. Appetite, 71, 232–241. 10.1016/j.appet.2013.08.015 [DOI] [PubMed] [Google Scholar]
  49. Warkentin S, Mais LA, Ranganath K, Jansen E, & Carnell S (2020). Controlling and less controlling feeding practices are differentially associated with child food intake and appetitive behaviors assessed in a school environment. Pediatric Obesity, 15(10), e12714. 10.1111/ijpo.12714 [DOI] [PubMed] [Google Scholar]
  50. Wood AC, Blissett JM, Brunstrom JM, Carnell S, Faith MS, Fisher JO, Hayman LL, Khalsa AS, Hughes SO, Miller AL, Momin SR, Welsh JA, Woo JG, & Haycraft E (2020). Caregiver Influences on Eating Behaviors in Young Children. Journal of the American Heart Association, 9(10), e014520. doi: 10.1161/JAHA.119.014520 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data described in the manuscript, code book, and analytic code will be made available upon request pending approval of a data use agreement. Following publication of study objectives, de-identified data will be shared in the NICHD Data and Specimen Hub.

RESOURCES