Inadequate fruit and vegetable consumption is associated with increased risk of chronic disease. Yet, many individuals consume below the recommended intake according to the Dietary Guidelines for Americans (DGA). This study aimed to examine the association of adherence to the DGA (2020–2025) recommendations for fruit and vegetable (FV) intake of 1.5–2 cups of fruits and 2–3 cups of vegetables daily for adults among caretakers with a child(ren) living in households eligible for a Supplemental Nutrition Assistance Program (SNAP). We conducted a cross-sectional analysis of 85 caretakers with children in an urban neighborhood of low-income in the Bronx, New York (NY). Log-binomial regressions demonstrated that having more children (RR 1.36; 95% CI 1.15–1.59), younger children (RR 1.22; 95% CI 1.07–1.39), or children participating in a school lunch program (RR 1.47; 95% CI 1.16–1.85) was positively associated with caretakers’ probability of adhering to the DGA recommendations for FV intake. Our study highlights the eating behaviors of families living with children ≤ 10 years of age, many of whom were participating in a school lunch program, and underscores the dietary benefits associated with these characteristics. 1. Introduction Previous studies examining household composition and diet have focused on household food purchases [ 12, 13, 14, 15, 16], intake [ 17], or the children’s diet [ 17, 18, 19, 20]. These studies have yielded mixed results. Some reported that having more children in the household predicted higher fruit intake in children [ 20], whereas others showed a negative association between the number of children and their FV intake [ 17, 18] as well as vegetable variety [ 18]. Nonetheless, few studies consider the role of household composition in adult caretakers’ FV intake [ 21, 22, 23, 24, 25]. It is important to understand how indicators of household composition may be associated with the caretakers’ diet, as they are important role models and may shape children’s food choices over time [ 26]. Most of the existing literature compared FV intake between parents and non-parents, with results showing that caretakers consume more sugar-sweetened beverages, total calories, and percent saturated fat as well as fewer FVs [ 21, 22, 23, 24, 25] due to having less time to eat healthy and consuming more snacks with their children [ 21]. However, there may be important heterogeneity aside from the children’s presence within households that warrants investigation regarding caretakers’ FV intake. Numerous studies have examined children’s characteristics, such as age and participation in a school lunch program, as indicators of household composition [ 27, 28, 29, 30, 31, 32]. Two cross-sectional studies [ 27, 28] found that children who are older tend to consume fewer fruits [ 31] and adopt unhealthier dietary patterns compared to their younger counterparts [ 32] due to different levels of support from caretakers on FV intake and their emerging independence in food choices [ 27]. Thus, caretakers with children who are older may purchase and consume fewer FVs. Other studies found that having more children in the household participating in a school lunch program decreased food spending, providing the opportunity to reallocate the food budget to higher-quality food items among other household members [ 29, 30]. Such studies have urged the current body of literature to move beyond the measure of children’s presence in the household to include the number of children and children’s characteristics within the household composition. Therefore, our study contributes to the current body of literature by considering an array of indicators of household composition, including household size, number of children, children’s age, their participation in a school lunch program, and number of adult caretakers, to examine the association with adherence to DGA recommendations for FV intake among caretakers with a child(ren) living in households eligible for a Supplemental Nutrition Assistance Program (SNAP). 2. Materials and Methods 2.1. Participants We conducted a cross-sectional analysis of 85 caretakers eligible for the SNAP with a child(ren) in the Bronx, New York (NY), USA to examine the association between indicators of household composition and adherence to the DGA 2020–2025 recommendations for FV intake among caretakers, defined as biological parents, non-biological parents, or blood relatives, such as grandparents. Our analysis was part of a parent study that examined the feasibility of an online grocery intervention to support healthy food selection among households of low income with children. The New York University Institutional Review Board (IRB-FY2022-6623) approved our study protocol, and all individual participants provided written informed consent virtually. 2.2. Data Collection We collected data between July and September 2022 in five Head Start centers located in two neighborhoods in the Bronx, NYC, with the highest poverty rates compared to the city estimates [ 33]. We approached caretakers who, if interested, scanned a barcode or texted our study’s texting line to receive a link to an online survey available in English and Spanish via Qualtrics. Caretakers answered screening questions to assess eligibility for the parent study. They were included if they (1) are the primary food shopper of a household with a child(ren) ≤ age 10, (2) have been a SNAP participant in the past year or have an annual household income ≤ 130% of the federal poverty level, (3) have a smartphone to facilitate participation in the parent intervention, and (4) speak English or Spanish. Those who met the eligibility requirements continued to answer other survey questions regarding demographic characteristics (i.e., age, sex, race/ethnicity, and preferred language), household food security via the United States Department of Agriculture (USDA) 6-item Food Security Survey [ 34], SNAP participation in the past year, indicators of household composition (i.e., household size, number of children ≤ age 10, children ≤ age 5, and children participating in a school lunch program), and FV intake via the National Care Institute (NCI) FV Screener [ 35]. We used reCAPTCHA verification and cookie technology to confirm human answers and ensure only a single submission was received per participant, respectively. Caretakers took approximately 15 min to complete the online survey and received $20 after completing it. 2.3. Measures 2.3.1. Outcome Variable 2.3.2. Exposure Variable Caretakers self-reported their household size via multiple-choice questions (with options ranging between 2 and 10+), number of children ≤ age 10 (between 1 and 6+), number of children ≤ age 5 (between 1 and 5+), and number of children participating in a school lunch program (between 1 and 6+). We calculated the ratio of adults to children by dividing the number of adults by the number of children ≤ age 10 (adult-children). We obtained the number of adults by subtracting the number of children ≤ age 10 from the household size. Similarly, we calculated the ratio of younger children (≤age 5) to older children (ages 6–10) by dividing the number of younger children by older children (younger–older children). We obtained the number of older children by subtracting the number of younger children from the number of children ≤ age 10. Given that participation in our study required having at least one adult caretaker and one child ≤ 10 years of age, denominator variables were always ≥1. 2.3.3. Covariates The online survey included multiple-choice questions regarding age (with options ranging between 18–24 and 65+), sex (female, male, non-binary, prefer not to say, or prefer to self-describe), race/ethnicity (African American/Black, American Indian/Alaska Native, Asian American, Asian Indian/Asian Chinese/Asian Filipino/Asian Korean/Asian Vietnamese, other Asian, Hispanic/Latin American, Middle Eastern/North African, Native Hawaiian/other Pacific Islander, White/Caucasian, another race/ethnicity not listed [as an open-ended question], or do not know), preferred language (English or Spanish), and SNAP participation in the past year (yes or no). 2.4. Analysis We conducted descriptive analyses to include frequencies and percentages for categorical variables as well as the median and interquartile range (IQR) for continuous variables of the caretakers’ demographic characteristics (i.e., age, sex, race/ethnicity, and preferred language), household food security, SNAP participation in the past year, indicators of household composition (i.e., household size, number of children ≤ age 10, children ≤ age 5, children ages 6–10, children participating in a school lunch program, and adults), and caretakers’ FV intake. We analyzed the normality of residuals using the Shapiro–Wilk test which demonstrated violations of the assumption when regressing indicators of household composition on FV daily servings. We categorized FV intake as meeting or not meeting the DGA recommendations as follows: ≥2 servings/day as meeting the fruit intake recommendation, ≥2.5 servings/day as meeting the vegetable intake recommendation, and ≥3.5 servings/day as meeting the FV intake recommendation [ 8]. Since the NCI FV Screener collects data on serving sizes in addition to cup equivalents, we chose to report serving sizes rather than cup equivalents to reflect the varied definitions of cup equivalents across different types of fruits and vegetables. Reporting serving sizes accounts for varied intake of raw and cooked vegetables, whole fruits, and juice consumption among our population. We conducted log-binomial regression models [ 40, 41] to assess the association between indicators of household composition and adherence to FV recommendations adjusted for the caretakers’ age, sex, race/ethnicity, preferred language, household food security, and SNAP participation in the past year. Despite the relatively moderate sample size, log binomial models were appropriate to analyze the common binary outcome of FV intake [ 42]. We treated each indicator of household composition as an independent variable and analyzed it in separate multivariate models. To ensure strict model stability, prevent overfitting, and guarantee robust parameter estimation, we employed the logbin package in R, utilizing the combinatorial EM (expectation-maximization) algorithm. The convergence tolerance was set to an absolute deviance/parameter change of 1 × 10 −6 with a maximum iteration threshold of 100 to guarantee stable optimization. Type III effects were evaluated using Wald chi-square tests via the car package to assess the independent contribution of each predictor. All models converged successfully and Type III effects are reported in Table A1. We used R to perform all analyses and considered 5% as the significance level. 4. Discussion The number of children, children’s age, their participation in a school lunch program, and the number of adults were associated with caretakers’ adherence to the DGA FV intake recommendations among those eligible for the SNAP with a child(ren) in the Bronx, NYC. Our results highlight the importance of further examining the role of indicators of household composition in FV intake and elucidate their possible contribution to disparities of FV intake among urban populations of low income. When specifying the number of children as an indicator of household composition, we found a positive association with the caretakers’ adherence to the DGA recommendations for vegetable and FV intake. Our results challenged previous studies that found unhealthier dietary patterns among caretakers with children compared to those without [ 21, 22, 23, 24, 25]. Possible explanations for the discordance are that previous studies compared parents versus non-parents, finding that households with biological parent caretakers, especially mothers, reported higher consumption of sugar-sweetened beverages, higher energy, and higher percent total saturated fat than non-parent caretakers (step parents or adoptive, non-biological parents) [ 21, 22, 23, 24, 25]. Many of these studies were also conducted outside of the U.S., providing a different socioeconomic and cultural context, thus not accounting for these influences on food availability and choice in the U.S. While our study participants were homogenous in the sense of having low income and food insecurity, they represent a heterogeneous sample of adults with children, including non-parent caretakers, such as grandparents, relatives, and adoptive parents, thus showing the eating behaviors of all types of caretakers as a whole. Furthermore, the scope of one study limited participants’ eligibility to parents of children ≤ age 5 [ 21], while our study included a wider age range of ≤age 10, thus broadening the findings of FV consumption. We found a positive association between the number of children participating in a school lunch program and the caretakers’ adherence to the DGA recommendations for FV intake. Our result is consistent with the implications of previous studies in the literature reporting that participation in a school lunch program increased food security among families of low income with children by providing nutritional resources for children and consequently creating opportunities to reallocate food to other household members [ 29, 30]. It is important to note that previous studies in the literature investigating enrollment in the National School Lunch Program were large prospective observational studies across the U.S. Given our small sample size and cross-sectional study design, several differences among population, including location, ethnicity, and income level, cannot be accounted for. Lastly, we found that having a greater adult–children ratio reduced adherence to the DGA FV intake recommendation. Our results contradict the implications of two studies that found healthier dietary habits among adults living with other adults than adults living with children [ 22, 23]. A possible explanation for the discrepancy is that the composition of adults in the household (i.e., spouse/partner or other adults who are not spouse/partner) may play a role in the FV intake of other members in the household [ 24]. For instance, a study found that men have healthier dietary patterns when living with a spouse compared to another adult who was not a spouse [ 46]. Our study did not explore the roles of adult caretakers in the household or their employment status. It could be that there were adults living in the household who were employed or receiving food assistance among our population sample, which could increase the ability to purchase and consume FV. In addition, our study did not inquire about the number of adults in the household. We estimated the total number of adults as household members > age 10 by subtracting the number of children ≤ age 10 from the household size. Considering adults as household members > age 10 may include older children and adolescents who may share more similar dietary habits to children than adults. For instance, according to a report about the Healthy Eating Index (HEI) score of children and adolescents, individuals ages 5–10 and ages 11–18 have a similar HEI score ranging from 51 to 55 [ 32]. Our study has several limitations, one of which is its cross-sectional design that does not allow causal inference and the use of a small sample size of caretakers. Moreover, the use of online surveys may have introduced selection bias towards participants who have smartphones, access to the Internet, and higher possible engagement with institutional programs. To address this bias, we had tablets available during recruitment to allow interested participants to answer the online survey. Our sample size was small and homogenous (i.e., low-income families with young children) and not representative of the heterogenous population in the Bronx in terms of household composition, food security status, and cultural dietary patterns, thereby limiting the generalizability and external validity of the findings. Additionally, an a priori statistical power analysis was not conducted, and the study may be underpowered to detect smaller effects. Given our small sample size relative to the number of covariates adjusted for in the regression models, there is an inherent risk of model overfitting. Consequently, these findings should be interpreted with caution as preliminary and hypothesis-generating rather than definitive. Nevertheless, we were able to successfully recruit and assess a community-based sample of a hard-to-reach population. As secondary analysis of a parent pilot study [ 47] that utilized a small sample by design due to limited funding, these exploratory findings provide valuable preliminary insights that can inform larger, fully powered future investigations. Lastly, residual confounding may still be possible regardless of statistical adjustment on several covariates. We did not ask about the number of adults in the household, which does not account for children ≥ 10 or adolescents living in the household. Participation in other food assistance programs besides the SNAP or whether the caretakers resided in a household with a single parent were not measured. These variables may contribute valuable information pertaining to household composition. 5. Conclusions Our results challenge the understanding that having fewer children or more adults living in a household increases the likelihood of caretakers’ healthier dietary habits. Conversely, our results align with the findings of the existing literature that a household with more children who are younger or more children participating in a school lunch program is a protective factor to FV intake among caretakers, especially those of Hispanic families eligible for the SNAP. Our results have implications that may benefit not only caretakers with children but also families living in these households, as the diets of caretakers will influence a child’s food choices throughout life. Lastly, our results urge future studies to move beyond children’s presence in the household as the only indicator of household composition when considering caretakers’ FV intake and to support the investigation of social, cultural, and gender dynamics that may influence intrahousehold food distribution and intake. Author Contributions S.W., A.C.B.T., K.M. and P.E.R. were involved in the study’s conception and design. S.W., A.C.B.T. and Z.N.R. conducted data collection and management. S.W. and A.C.B.T. performed the data analysis. S.W. wrote the first draft of the manuscript and all authors commented on previous versions. All authors have read and agreed to the published version of the manuscript. Funding Our work was supported by the New York University Institute of Human Development and Social Change’s Seed Award Program under Grant No. RB281. Institutional Review Board Statement Our study was conducted in accordance with the Declaration of Helsinki and approved by the New York University Institutional Review Board (IRB-FY2022-6623, approved on 6 September 2022). All individual participants provided written informed consent virtually. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data that support the findings of our study are available from the corresponding author, K.M., upon reasonable request. Acknowledgments We would like to thank all the families who participated in our study and the staff at Head Start centers who facilitated our data collection process. We would also like to acknowledge the contributions of Madeline Lowney, Yu-Ting Hung, Yiyang Zhong, and Mary Kathryn Edwards for their assistance with data collection. Conflicts of Interest The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: FV Fruit and Vegetable DGA Dietary Guidelines for Americans SNAP Supplemental Nutrition Assistance Program Appendix A Table A1. Type III effects of binomial logistic regression models of study variables (n = 85). Table A1. Type III effects of binomial logistic regression models of study variables (n = 85). Model Wald Chi-Square adf bp-Value Fruit Intake Household size 0.398 1 0.528 Children ≤ age 10 receiving school lunch 0.395 1 0.530 Ratio of adults to children 0.111 1 0.739 Ratio of younger to older children 0.295 1 0.587 Children ≤ age 10 living in the household 0.009 1 0.924 Vegetable Intake Household size 0.029 1 0.865 Children ≤ age 10 receiving school lunch 2.50 1 0.114 Ratio of adults to children 2.169 1 0.141 Ratio of younger to older children 0.088 1 0.767 Children ≤ age 10 living in the household 5.17 1 0.023 Fruit and Vegetable Intake Household size 0.541 1 0.462 Children ≤ age 10 receiving school lunch 4.566 1 0.033 Ratio of adults to children 3.110 1 0.078 Ratio of younger to older children 0.000 1 0.996 Children ≤ age 10 living in the household 4.515 1 0.034 a Chi-square; b degrees of freedom. References Nour, M.; Lutze, S.A.; Grech, A.; Allman-Farinelli, M. The Relationship between Vegetable Intake and Weight Outcomes: A Systematic Review of Cohort Studies. 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Feasibility of an Online Grocery Intervention Pilot to Improve Fruit and Vegetable Purchase and Food Security Among Adults With Children Eligible for SNAP. J. Nutr. Educ. Behav. 2025, 57, 989–998. [] [ CrossRef] Table 1. Descriptive information of participants in terms of demographic characteristics, household food security, SNAP participation, and indicators of household composition (n = 85). Table 1. Descriptive information of participants in terms of demographic characteristics, household food security, SNAP participation, and indicators of household composition (n = 85). Demographics Sample Size (%) Median (IQR) Age (years) 18–29 27 (31.8) 30–39 40 (47.1) 40 or older 18 (21.2) Gender, Female 80 (94.1) Race/Ethnicity 84 (100) African American 14 (16.7) Hispanic 65 (77.4) Multiracial 3 (3.6) Other 2 (2.4) Preferred Language English 37 (43.5) Spanish 48 (56.5) Food Security 4 (2–6) High or marginal food security (0–1) 8 (9.5) Low food security (2–4) 38 (44.7) Very low food security (5–6) 39 (45.9) SNAP Participation Received SNAP benefits in the past year 68 (80) Indicators of Household Composition Household Size 5 (3–5) Two 9 (10.6) Three 13 (15.3) Four 20 (23.5) Five 28 (32.9) Six or more 15 (17.7) Number of Children ≤ Age 10 2 (1–3) One 31 (36.9) Two 28 (33.3) Three or more 25 (29.8) Number of Children ≤ Age 5 1 (1–2) None 7 (8.4) One 54 (65.1) Two or more 22 (26.5) Number of Older Children (Age 6–10) 1 (0–1) None 38 (45.8) One 32 (38.6) Two or more 13 (15.6) Number of Children Participating in a School Lunch Program 1 (1–2) One 42 (56) Two 26 (34.7) Three or more 7 (9.3) Number of Adults 2 (2–3) None 4 (4.8) One 14 (16.7) Two 33 (39.3) Three 15 (17.9) Four or more 18 (21.5) Abbreviations: SNAP = Supplemental Nutrition Assistance Program; IQR = interquartile range. Other ethnicities included American Indian/Alaska Native, Asian descent, White, another ethnicity not listed, or unknown. Table 2. Descriptive characteristics of participants adhering to the DGA 2020–2025 recommendations and median intake among participants. Table 2. Descriptive characteristics of participants adhering to the DGA 2020–2025 recommendations and median intake among participants. Fruit and Vegetable Intake (Servings/Day) n (%) Median (IQR) Fruits only 31 (36.5) 1.13 (0.5–2.21) Vegetables only 31 (36.5) 1.96 (1.18–2.98) Fruits and vegetables 42 (49.4) 3.46 (2.36–5.27) Abbreviations: DGA, Dietary Guidelines for Americans; IQR, interquartile range. Table 3. Log-binomial regression model of the association between indicators of household composition and caretakers’ adherence to the DGA b 2020–2025 recommendations for FV a intake. Table 3. Log-binomial regression model of the association between indicators of household composition and caretakers’ adherence to the DGA b 2020–2025 recommendations for FV a intake. Indicators of Household Fruit Intake Vegetable Intake FV Intake RR c95% CI dp-Value RR 95% CI p-Value RR 95% CI p-Value Household size 1 0.86–1.17 0.997 0.97 0.79–1.18 0.759 0.98 0.85–1.14 0.806 Number of children 1.19 0.95–1.50 0.13 1.44 1.11–1.86 0.005 ** 1.36 1.15–1.59 <0.001 *** Ratio of younger children (<age 5) to older children (age 6–10) 0.98 0.60–1.59 0.925 0.93 0.59–1.46 0.746 1.22 1.07–1.39 0.003 ** Number of children participating in a school lunch program 1.25 0.94–1.66 0.133 1.52 1.03–2.24 0.035 * 1.47 1.16–1.85 0.001 ** Ratio of adults to children 0.84 0.65–1.09 0.191 0.79 0.60–1.05 0.101 0.82 0.68–0.99 0.049 * a FV = fruit and vegetable. b DGA = Dietary Guidelines for Americans. c RR = relative risk. d CI = confidence interval model adjusted on the caregiver’s sex, race/ethnicity, preferred language, household food security, and SNAP participation in the past year. We treated each indicator of household composition as an independent variable and analyzed it in separate models. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. MDPI and ACS Style McLean, K.; Wiloejo, S.; Rehman, Z.N.; Rummo, P.E.; Trude, A.C.B. Indicators of Household Composition Are Associated with Adherence to Fruit and Vegetable Intake Recommendations Among Caretakers Eligible for SNAP with Children. Int. J. Environ. Res. Public Health 2026, 23, 765. https://doi.org/10.3390/ijerph23060765 AMA Style McLean K, Wiloejo S, Rehman ZN, Rummo PE, Trude ACB. Indicators of Household Composition Are Associated with Adherence to Fruit and Vegetable Intake Recommendations Among Caretakers Eligible for SNAP with Children. International Journal of Environmental Research and Public Health. 2026; 23(6):765. https://doi.org/10.3390/ijerph23060765 Chicago/Turabian Style McLean, Kellie, Stefani Wiloejo, Zoya N. Rehman, Pasquale E. Rummo, and Angela C. B. Trude. 2026. "Indicators of Household Composition Are Associated with Adherence to Fruit and Vegetable Intake Recommendations Among Caretakers Eligible for SNAP with Children" International Journal of Environmental Research and Public Health 23, no. 6: 765. https://doi.org/10.3390/ijerph23060765 APA Style McLean, K., Wiloejo, S., Rehman, Z. N., Rummo, P. E., & Trude, A. C. B. (2026). Indicators of Household Composition Are Associated with Adherence to Fruit and Vegetable Intake Recommendations Among Caretakers Eligible for SNAP with Children. International Journal of Environmental Research and Public Health, 23(6), 765. https://doi.org/10.3390/ijerph23060765
