Volume 9

Abstract

Objectives: Women play an important role in promoting healthy eating and physical activity within their households, influencing the current and life-long health behaviors of children. The purpose of this study is to describe changes in fruit/vegetable consumption and physical activity among children living with racially and ethnically diverse women participating in a lifestyle-change intervention.

Methods: The study involves secondary analysis of data from a randomized trial of a wellness-coaching intervention led by Community Health Workers, called Community Wellness Coaches in this study. Study participants came from five diverse racial/ethnic communities. Participants received monthly vs. quarterly wellness coaching. Data on changes in the health behaviors of children at four months after enrollment were collected through interviews. Children’s behavior changes were compared by study arm, demographics, and women’s health behaviors.

Results: Overall, 71.9% of women reported increases in the fruit/vegetable intake of children living in their household and 59.4% reported increases in children’s physical activity. There were no differences in children’s fruit/vegetable intake or physical activity by study arm (p=0.88).  Women who reported that their own fruit/vegetable intake increased were more likely to report an increase in children’s consumption (aOR=2.55, 95%CI 1.05 – 6.21).

Conclusion and Implications: Among women of color participating in a health-behavior change intervention, women’s behavior changes were associated with improvements in child health behaviors. Interventions focused on improving women’s health behaviors may also impact the behaviors of children and other household members. Emphasizing the role of women on the health of children in such interventions may magnify this impact.

Introduction

The U.S has experienced nearly a two-fold increase in obesity in less than 30 years.¹ Disparities in obesity rates exist among women from different racial/ethnic groups. For example,Utah data from 2021 reveal overweight/obesity rates of 39.2% (95% CI 32.5 – 46.3) among people of Asian descent, 62.8% (95% CI 54.5 – 70.5) among Black people, 64.2% (95% CI 63.3 – 65.1) among white people, 71% (95% CI 66.9 – 74.7) among people of Hispanic/Latinx descent, 74.9% (95% CI 68.8 – 80.2) among people of American Indian/Alaska Native descent, and 87.9% (95% CI 78.9 – 93.4) among people of Native Hawaiian/Pacific Islander descent.² These rates differ slightly when compared to national data from 2021, although data indicate national disparities by race and ethnicity, with overweight/obesity rates of 14.5% (95% CI 11.4 – 18.1) among women of Asian descent, 39.6% (95% CI 36.2 – 43.0) among white non-Hispanic/Latinx women, 45.7% (95% CI 42.4 – 49.1) among women of Hispanic/Latinx descent, and 57.9% (95% CI 54.0 – 61.7) among Black women.³ When a child has a parent who is obese, a child is three times more likely to be overweight or obese.⁴ Thus, identifying novel and effective ways to address and prevent obesity in both mothers and their children is critical. Educational interventions that are delivered to mothers and impact their children have the potential to address childhood obesity.

Given the racial/ethnic disparities in obesity rates among women and children, the Coalition for a Healthier Community for Utah Women and Girls (UWAG) conducted a randomized trial of a 12-month wellness-coaching intervention for women of color [Hispanic/Latinx, American Indian/Alaska Native, African American, African Refugees/Immigrants, and Native Hawaiian/Pacific Islander] residing in Utah. The aim of the present secondary analysis using baseline and four-month data was to better understand whether wellness coaching focused on fruit/vegetable intake (FVI) and physical activity among these women was associated with changes in the health behaviors of children living in their homes. This research question is important because obesity is on the rise for both adults and children. Communities of color are also disproportionately impacted by high overweight and obesity rates, due to social determinants of health, such as disparities in food access (e.g., readily available highly processed and energy-dense foods and fewer low-cost, fresh foods), neighborhood environment resources, and lack of health-insurance coverage.⁵

Methods

The UWAG study was developed through a partnership between the Utah Women’s Health Coalition (UWHC) and Community Faces of Utah (CFU). CFU is an established partnership that includes leaders from five different community organizations along with University of Utah and Utah Department of Health personnel. The communities include African immigrants and refugees from Burundi, Central African Republic, Democratic Republic of Congo, and Rwanda, African Americans, American Indian/Alaska Natives, Hispanic/Latinx, and Native Hawaiian/Pacific Islanders. Community-based participatory research (CBPR) best practices were employed to build trust between community leaders and academic researchers.^6, The process began with the leaders identifying the priority health issues for the intervention—diabetes and obesity—and the focus on increasing healthy diet and exercise behaviors.⁷ The UWAG partnership involved the community leaders and academic partners as research collaborators throughout all phases of the study, including planning, design, implementation, data analysis, and dissemination of study findings.⁷

Women from each of the CFU communities were trained to serve as Community Wellness Coaches (CWCs), helping to address issues related to obesity and diabetes by promoting healthy eating and exercise among women in their own communities. The CWCs understood the culture and context of their communities and thus were well suited to help women make healthy behavior changes.

The CWCs were also trained to collect height and  weight, and to use a set of computer-assisted interview and coaching tools developed in Research Electronic Data Capture (REDCap) software.^{8, 9} REDCap is an application for building, collecting, and managing online surveys and data hosted by the University of Utah through the Center for Clinical and Translational Science (recently renamed the Clinical and Translational Science Institute). Height was measured in inches using a measuring tape. While resting against the measurement surface, participants were instructed to look straight ahead, to relax their shoulders with arms resting at their sides and legs straight with knees close together and bare feet flat and almost together. Height was recorded to the quarter-inch at the beginning of the study. Weight was measured in pounds using an analog scale that was zeroed before each use. Participants were instructed to remove their shoes and to wear light clothing. Weight was recorded to the whole pound, and was taken at baseline, and once at each of the three follow-up time points. While published protocols were not used for these measurements, best practices were used based on the clinical expertise of our team, which included a nurse midwife and a physician. BMI was calculated by multiplying by 703 the result of weight in pounds divided by height squared {i.e., BMI = [weight in pounds / (height in inches x height in inches) * 703]}. For all racial/ethnic categories except Native Hawaiian/Pacific Islanders, BMI categories were: underweight <18.5, normal weight 18.5-24.9, overweight 25-29.9, obese 30 or greater.¹⁰ BMI categories used for Native Hawaiian/Pacific Islanders were: normal weight <26, overweight 26-31.9, obese 32 or greater.¹¹ The interview questions were developed and adapted with input from CWCs and in collaboration with community partners to ensure cultural appropriateness and clarity. In addition to demographic questions, women were asked about their health knowledge and health behaviors, as well as barriers and facilitators they perceived to their health behaviors. After all baseline information was gathered, participants were randomized to receive quarterly wellness coaching (low-intensity intervention arm) or monthly wellness coaching with monthly group activities (high-intensity intervention arm). The CWCs planned monthly activities based on the interests and needs of participants. Examples of these activities included grocery-store tours, healthy-cooking classes, group participation in 5K races, group hikes, bowling, and stress-management activities. Participants received tools related to these activities, such as measuring cups, exercise bands, recipes, and pedometers. Participants in both the low-intensity and high-intensity intervention arms were asked the same set of questions four months after randomization. At this follow-up interview, participants were asked to report changes in their diet and exercise behaviors as well as changes that they had observed in their children, spouse, and any other people living in the home during the prior four months. Data for analyses described in this paper came from baseline and four-month computer assisted participant interviews led by CWCs.

Questions on diet came from the Behavioral Risk Factor Surveillance System survey.¹² Participants were asked by a CWC about the number of times they drank pure fruit juice in the past month, ate fruit (including fresh, frozen, or canned fruit juice and fruit), and ate vegetables (including beans, dark green vegetables, orange vegetables, and “other” vegetables). To acquire accurate comparable answers on servings of fruits and vegetables, CWCs showed women images of serving sizes compared to familiar items such as dice and playing cards next to the actual item. CWCs also provided physical versions of these items to women to hold as they answered the questions. Physical activity was assessed using the following question: “In an average week, how much time do you spend being physically active or doing exercise?” The answer category options available for describing the weekly amount of physical activity time were: none, less than 20 minutes, 20-29 minutes, 30-59 minutes, 1 hour to less than 1.5 hours, 2 to less than 2.5 hours, 2.5 to less than 3 hours, and 3 or more hours. The level used in the data analysis for achieving recommended physical activity was 150 minutes per week, and the change in daily physical activity from baseline to follow-up was described as increasing more than one level or as not increasing.

Statistical and Data Analysis

The goal of these secondary analyses was to explore whether participants reported changes in the health behaviors of their children between the baseline and the four-month interview. We created Directed Acyclic Graphs (DAGs) of the hypothesized relationships between maternal and child changes in fruit and vegetable consumption and physical activity, in order to visually encode: 1) assumptions; 2) a priori knowledge; and 3) identification of the minimally sufficient set of covariates needed for confounding control. DAGs were created using the browser-based Daggity.net .¹³ The DAGs provide a visual representation of the relationships among a complex system of interacting components (i.e., variables) and our assumptions about those relationships, in a mathematically grounded framework of non-parametric structural equation models, enabling a fuller understanding by a broader audience.^14-16 See Figures 1 and 2.

Key variables were compared between women reporting an increase or no increase in the FVI or physical-activity behaviors of children in the home and were tested using Fisher’s exact 2-sided test. We explored whether demographics, health behaviors, or knowledge about diet/exercise were associated with children’s health-behavior changes. Paired T-tests were used to examine differences in the mother’s mean fruit and vegetable consumption and mean physical activity between baseline and follow-up. The association between women’s age and children’s increases in FVI and physical activity were assessed using pooled (equal variance) 2-sided t-tests as indicated by Folded F-Test (p=0.55, p=0.85, respectively).

Odds of reporting an increase in children’s FVI and physical activity at follow-up were compared to reporting no increase at follow-up and were calculated using Firth’s penalized-likelihood logistic regression in SAS software.^{17, 18} Sensitivity of the effect estimates were also examined between the non-pregnant study population and the total study population, and effect-measure modification (EMM) was assessed, using the full-regression model, for any covariate in either population with an interaction term chi-square p-value≤0.10. The data were analyzed using IBM SPSS Statistics for Windows, Version 21.0. and SAS 9.4 software; copyright © 2016 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.  Research ethics approval was obtained from the University of Utah Institutional Review Board and the Phoenix Area Indian Health Service Institutional Review Board prior to implementing the study (IRB 00055195).

Results

The study involved 485 Utah women from African American, African, American Indian/Alaska Native, Hispanic/Latinx, and Native Hawaiian/Pacific Islander communities living in households across the Wasatch front that included a total of 2,499 individuals. Participants were excluded from this analysis if they did not have four-month data or had no children living in their home at baseline. Of the 485 women in the study, 53.2% of them (n=258) had children under the age of 18 in the home at baseline. A total of 224 women had both children in the home and four-month follow-up data and thus were included in this analysis.  These women had a total of n=607 children living in their homes. Demographic data are shown in Table 1. 

At baseline, participants had a mean age of 40.0 years (SD=9.6) and nearly 60% were living below the federal poverty line. Overall, the women had an average of 2.7 (SD=1.4) children per household with variation across communities (data not shown). Approximately 90% of these women were either sedentary or not meeting guidelines for recommended weekly exercise at baseline and almost 70% were not meeting guidelines for fruits/vegetables per day at baseline. Notably, 81.7% of women were overweight/obese at baseline; four months after randomization, 16.1% of the participants experienced a 5% or greater weight loss from their baseline weight, and only 12.0% experienced weight gain of 5% or greater (data not shown). When stratified, 5.2% (n=3) of women reporting an increase in fruit and vegetable consumption for their children, also experienced weight gain of 5% or greater; 10.2% (n=12) of women reporting an increase in the physical activity of their child(ren), also experienced weight gain of 5% or greater, which did not differ significantly from women that did not experience weight gain during the study (p>.30) (data not shown).

As shown in Table 2 and Table 3, similar findings were observed among participants randomized to high-intensity and low-intensity study arms in the participant-reported changes in FVI among children at four months. There was a statistically significant difference in children’s behavior changes by women’s dietary behaviors, with 45.3% of women reporting that their own FVI increased, also reporting an increase in child consumption (aOR=2.55, 95%CI 1.05 – 6.21) in the minimally adjusted model. Fewer women who felt that preparation of fruits and vegetables was too time-consuming reported positive dietary changes in children. In fact, 94.4% of women who disagreed that “fruits and vegetables take too much time to prepare” reported increases in children’s consumption compared to only 5.6% of women who agreed/strongly agreed that preparation of fruits/vegetables was time-consuming (aOR=2.20, 95%CI 0.70 – 6.57). While we observed participant-reported increases in the FVI of children across all groups, Hispanic/Latinx women were the most likely group to report an increase in children’s consumption of fruit and vegetables, at 40.4%; and American Indian/Alaska Native women were the least likely to report an increase in their children’s fruit and vegetable intake, at 10.6% (cOR=0.40, 95%CI 0.16 – 0.97).

The proportion of participants reporting increases in physical activity among children was similar between those randomized to the high- and low-intensity arms of the study (p=0.88), as described in Table 3. We observed differences in the changes in children’s physical activity by a woman’s own physical-activity level. Although not statistically significant, if the woman’s physical activity had increased at four months by more than one category level, she had 1.60 times the odds of also reporting an increase in children’s physical activity (aOR, 95%CI 0.89 – 2.94). Women who had a baseline BMI considered normal or underweight had 2.48 times the adjusted odds (95%CI 1.08 – 6.09) of reporting an increase in physical activity for children at follow-up. While not statistically significant, differences were observed in the proportion of women reporting increases in physical activity in children based on their own self-reported physical activity and environmental barriers to physical activity. We observed lower numbers of women reporting increases in the physical activity of their children who also reported not having a safe place (93.2% vs 6.8%, p=.16), and/or too much pollution and/or noise to exercise (83.5% vs 16.5%, p=.44). Similarly, women who expressed a belief that exercise is important had 1.38 times the odds (95%CI 0.64 – 2.95) of also reporting an increase in physical activity at four months for their children. Among racial/ethnic groups, Hispanic/Latinx mothers reported increases in their children’s physical activity at a higher frequency (40.6%) compared to all other racial/ethnic groups (p=.27).

To examine the impact of pregnancy, we conducted a sensitivity analysis limiting the data analysis to women who were not pregnant at baseline and who were not pregnant at four-month follow-up (n=204, data not shown). We observed changes in excess of 10% of the reported effect estimates (aORs) from the minimally sufficient regression models in 64% of the estimates reported in Table 2 (fruit and vegetable consumption of children) and in 39% of the estimates reported in Table 3 (physical activity of children). The only estimate observed to cross the threshold for statistical significance (α=0.95) in the non-pregnant population was the odds of reporting an increase in fruit and vegetable consumption among women who reported being encouraged by their children to do healthy things a few times a week, compared to only being encouraged yearly or never. This estimate differed between the entire study population of women (aOR=2.57, 95%CI 0.99 – 6.62) and the non-pregnant women (aOR=3.51, 95%CI 1.28 – 9.76) in the study population.

We examined interaction in both the total and the non-pregnant study populations using the full-regression models and observed p-values ≥ 0.14 for all interaction terms in the fruit and vegetable consumption models. In the physical activity full models, we found evidence of interaction by BMI categories among the non-pregnant population (p≤.05), and weak evidence among the total study population (p≤.10). We also observed weak evidence of interaction by race and ethnicity in the non-pregnant study population (p≥.08), but not in the full study population (p≥.32). In further examining the interaction between BMI and women reporting increased physical activity at follow-up for children in their households, we observed BMI stratum-specific crude ORs that varied (39% change) between strata (with mostly overlapping 95% CIs), and that differed from the overall crude OR, suggesting the presence of confounding and effect measure modification (EMM). Similarly, we observed evidence of both confounding and EMM by race and ethnicity on the estimate of odds of mothers reporting increased physical activity for their children at follow-up, whereby a stratum-specific crude OR varied (59% change in estimate, with imprecise and overlapping 95% CIs), and differed from the overall crude OR. We observed greater percent-changes in estimate between the stratum-specific crude odds in the full study population, as compared to the non-pregnant population.  

     

Discussion and Health Implications

In this study, we found that four months after the wellness-coaching intervention, 59.4% of participants reported an increase in their children’s exercise behaviors and 71.9% reported an increase in their children’s FVI, with no notable difference between women randomized into the high-intensity versus the low-intensity study arms. These behavior changes reported in children indicate that wellness-coaching programs for women may impact their children’s diet and exercise behaviors in the short-term, though the long-term implication is not clear.

Research has explored parents as “agents of change” within families and has shown that women’s participation in lifestyle-change programs can have long-term positive impacts on their children’s health behaviors. Examples include an increase in children’s consumption of fruits and vegetables (longitudinal follow-up over two years)¹⁹ and the improvement of their children’s insulin resistance (longitudinal follow-up over three months).²⁰ These studies have focused on improving lifestyle behaviors (e.g., family nutrition/eating behaviors) and parenting skills related to these behaviors. These interventions prioritized the enhancement of parental self-efficacy through increasing nutritional knowledge and parenting skills related to eating and physical-activity practices in the home environment.^{21,22, 23} These parent-focused studies have demonstrated improvements to the health of offspring, including: 1) decreases in offspring BMI z scores;^24-28 2) parental healthy BMI change as a predictor of healthy BMI change in offspring;²⁹ 3) improvements in family weight-related parenting practices that are associated with improvements in offspring’s dietary intake;^{19, 25, 30} and 4) improvements in diet and activity behaviors in mothers, associated with improvements in diet and activity of offspring.³¹

The present data analysis found several factors that were associated with lower levels of behavior change in children. For example, we found that women who reported not having a safe place to exercise in their communities were less likely to report an increase in their children’s exercise behaviors. Some participants, such as those from the African Immigrant/Refugee community felt uncomfortable exercising in public spaces. Other studies have found that children who live in unsafe neighborhoods, as reported by their parents, engage in physical activity approximately one day less per week than children whose parents report living in a safe neighborhood.³² This finding emphasizes the importance of creating safe environments for physical activity as an important factor in increasing children’s physical activity. We also found that women who felt that fruits/vegetables took too much time to prepare were less likely to report increases in their children’s FVI. Higher rates of at-home food preparation are associated with a higher diet quality.³³ Educating women about ways to reduce preparation time, and providing meal-planning resources may lead to a higher likelihood of women cooking healthy foods like fruits and vegetables in the home.³⁴ We do not have data on women’s shopping practices or grocery budgets.  Future research should assess these factors in order to more fully understand barriers/facilitators to healthy household eating behaviors.

Key strengths of the UWAG study were the CBPR framework and the employment of women from each community as wellness coaches, which helped recruit and retain diverse study participants.³⁵ Involving community leaders as members of the research team may also have increased the number of participants who were recruited and retained through building community trust in the study. Another strength of this study was that it included participants from five different racial/ethnic groups residing in the same urban area, increasing the diversity and generalizability of the study. The two major ethnicities that were not included in this study were Caucasian and Asian individuals; these could be added in a future replication of this study.

A significant limitation of this study is the fact that assessment relied on self-report by the study’s participants. For example, the health-behavior changes in children were reported by women, as directly measuring changes in children’s behaviors was not a primary aim of the study. There were no objective data collected about physical activity in the women or children; all physical activity data were self-reported by participants. As a result of assessment based on self-report, the study’s findings may reflect some response bias, such as recall bias or social-desirability bias, if women desired to portray their family health behaviors as healthier than they were in reality. Further, assessing dietary intake at baseline and again at four months—just one time over the course of a month—is a study limitation. For example, seasonal changes in the availability of fruits and vegetables have been shown to impact servings consumed.^{36, 37} Another limitation of this study is the short duration of assessment; future research should assess sustainability of behavior change over longer time periods. Future research focused on children’s health behaviors should incorporate interview data with children, conducted by trained CWCs under the supervision of registered dietitian nutrionists, as well as objective measures of physical activity in both children and women. Additionally, future research should assess the role of other members of the household (e.g., significant other, other children) in the facilitation of household health-behavior changes.

This study provides some evidence that interventions targeting women may have an impact on children’s diet and exercise behaviors in the short term. Studies targeting overweight/obese parents who have overweight/obese children aged five and under are particularly needed. Hesketh and Campbell noted that interventions aimed toward children under the age of five are rare, and interventions with infants and their mothers even rarer.³⁸ This lack of interventions is alarming, as data show how important maternal influence is on children’s health, with an effect across the lifespan. 

Acknowledgements

The authors would like to acknowledge the contributions of the Coalition for a Healthier Community for Utah Women and Girls (which includes many of this paper’s authors) and the Community Wellness Coaches. This includes:

• Utah Women’s Health Coalition
• Community Faces of Utah
  • Best of Africa: Valentine Mukundente
  • Calvary Baptist Church: Pastor France A. Davis, Doriena Lee
  • Hispanic Health Care Task Force: Sylvia Rickard (deceased), Ana Sanchez-Birkhead, Jeannette Villalta
  • National Tongan American Society: O. Fahina Tavake-Pasi, Ivoni Nash
  • Urban Indian Center: Eruera “Ed” Napia
  • Utah Department of Health: Brenda Ralls
  • University of Utah and Utah Clinical & Translational Science Institute: Stephen C. Alder, B. Heather Brown, Louisa A. Stark
  • University of Utah: Grant Sunada
  • Community Wellness Coaches: Claudia Gonzalez, Natalie Gutierrez, Patricia Otiede, Penelope Pinnecoose, Olga Rubiano, Esperance Rugamwa, Se Toki, Jeanette Villalta, and Cathy Wolfsfeld
• University of Utah Center of Excellence in Women’s Health: Leanne Johnston, Sara E. Simonsen, Kathleen B. Digre
• University of Utah College of Health: Patricia Eisenman
• University of Utah Department of Biomedical Informatics: Bernie LaSalle

Funding: This study was supported by a grant from the Office on Women’s Health, Department of Health and Human Services Grant number: 1CCEWH111018-01-00 (KBD and SES). This study is registered under the Coalition for a Healthier Community–Utah Women and Girls–Phase II (UWAGII); University of Utah, Center of Excellence in Women’s Health and Center for Clinical and Translational Science; and National Institutes of Health, National Center for Advancing Translational Sciences [Grant (8UL1TR000105 (formerly UL1RR025764) NCATS/NIH)] (SCA, BHB, LAS). Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number F31NR020431 (JKM). The research and content reported in this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Office on Women’s Health. The study is registered at https://clinicaltrials.gov/ct2/show/ NCT02470156 (No. NCT02470156).

References

1. Ingram DD, Malec DJ, Makuc DM, Kruszon-Moran D, Gindi RM, Albert M, Beresovsky V, Hamilton BE, Holmes J, Schiller J, Sengupta M. National Center for Health Statistics Guidelines for Analysis of Trends. Vital Health Stat 2. 2018(179):1-71. Epub 2018/05/19. PubMed PMID: 29775435.

2. (IBIS) PHIBIS. Health Indicator Report of Overweight or Obese by Race, Utah, 2020-2021. Utah Department of Health and Human Services: Healthy Environments Active Living BoHP, Division of Disease Control and Prevention, Utah Department of Health and Human Services; 2022.

3. Stierman BA, Joseph; Carroll, Margaret D.; Chen, Te-Ching; Davy, Orlando; Fink, Steven; Fryar, Cheryl D.; Gu, Qiuping; Hales, Craig M.; Hughes, Jeffery P.; Ostchega, Yechiam; Storandt, Renee J.; Akinbami, Lara J. National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health Outcomes. http://dx.doi.org/10.15620/cdc:106273: Centers for Disease Control and Prevention, (U.S.) NCfHS; 2021 6/14/2021. Report No.

4. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869-73. Epub 1997/09/26. doi: 10.1056/nejm199709253371301. PubMed PMID: 9302300.

5. Mujuru P, Jean-Francois B, Pérez-Stable EJ. National Institute on Minority Health and Health Disparities Specialized Centers of Excellence on Minority Health and Health Disparities. Am J Prev Med. 2022;63(1 Suppl 1):S6-s7. Epub 2022/06/21. doi: 10.1016/j.amepre.2022.03.006. PubMed PMID: 35725142; PMCID: PMC9212883.

6. Blacksher E, Nelson C, Van Dyke E, Echo-Hawk A, Bassett D, Buchwald D. Conversations about Community-Based Participatory Research and Trust: “We Are Explorers Together”. Prog Community Health Partnersh. 2016;10(2):305-9. Epub 2016/06/28. doi: 10.1353/cpr.2016.0039. PubMed PMID: 27346777.

7. Simonsen SE, Digre KB, Ralls B, Mukundente V, Davis FA, Rickard S, Tavake-Pasi F, Napia EE, Aiono H, Chirpich M, Stark LA, Sunada G, Keen K, Johnston L, Frost CJ, Varner MW, Alder SC. A gender-based approach to developing a healthy lifestyle and healthy weight intervention for diverse Utah women. Eval Program Plann. 2015;51:8-16. Epub 2015/01/07. doi: 10.1016/j.evalprogplan.2014.12.003. PubMed PMID: 25559947.

8. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-81. Epub 2008/10/22. doi: 10.1016/j.jbi.2008.08.010. PubMed PMID: 18929686; PMCID: PMC2700030.

9. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019;95:103208. Epub 2019/05/13. doi: 10.1016/j.jbi.2019.103208. PubMed PMID: 31078660; PMCID: PMC7254481.

10. Centers for Disease Control and Prevention. About Adult BMI: Centers for Disease Control and Prevention; 2022 [cited 2023 July 31]. Available from: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html.

11. World Health Organization. The Asia-Pacific perspective: redefining obesity and its treatment. Sydney: Health Communications Australia: Regional Office for the Western Pacific, 2000.

12. National Center for Chronic Disease Prevention and Health Promotion DoPH. Behavioral Risk Factor Surveillance System: Centers for Disease Control and Prevention; 2022 [cited 2018 September 04]. Available from: https://www.cdc.gov/brfss/index.html.

13. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol. 2016;45(6):1887-94. Epub 2017/01/17. doi: 10.1093/ije/dyw341. PubMed PMID: 28089956.

14. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669-88. doi: 10.1093/biomet/82.4.669.

15. Pearl J. Causality : models, reasoning, and inference. 2nd edition. ed: Cambridge : Cambridge University Press; 2000.

16. Pearl J. Causal inference in statistics: An overview. Statistics surveys. 2009;3(none):96-146. doi: 10.1214/09-SS057.

17. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409-19. Epub 2002/09/05. doi: 10.1002/sim.1047. PubMed PMID: 12210625.

18. Karabon P, Beaumont W, editors. Rare Events or Non-Convergence with a Binary Outcome? The Power of Firth Regression in PROC LOGISTIC2020.

19. Flores-Barrantes P, Iglesia I, Cardon G, Willems R, Schwarz P, Timpel P, Kivelä J, Wikström K, Iotova V, Tankova T, Usheva N, Rurik I, Antal E, Liatis S, Makrilakis K, Karaglani E, Manios Y, Moreno LA, González-Gil EM, On Behalf Of The Feel Diabetes-Study G. Longitudinal Associations between Food Parenting Practices and Dietary Intake in Children: The Feel4Diabetes Study. Nutrients. 2021;13(4). Epub 2021/05/01. doi: 10.3390/nu13041298. PubMed PMID: 33920052; PMCID: PMC8071003.

20. López-Contreras IN, Vilchis-Gil J, Klünder-Klünder M, Villalpando-Carrión S, Flores-Huerta S. Dietary habits and metabolic response improve in obese children whose mothers received an intervention to promote healthy eating: randomized clinical trial. BMC Public Health. 2020;20(1):1240. Epub 2020/08/17. doi: 10.1186/s12889-020-09339-4. PubMed PMID: 32795294; PMCID: PMC7427732.

21. Loveman E, Al-Khudairy L, Johnson RE, Robertson W, Colquitt JL, Mead EL, Ells LJ, Metzendorf MI, Rees K. Parent-only interventions for childhood overweight or obesity in children aged 5 to 11 years. Cochrane Database Syst Rev. 2015;2015(12):Cd012008. Epub 2015/12/23. doi: 10.1002/14651858.Cd012008. PubMed PMID: 26690844; PMCID: PMC8761478 EM: none known. LE: none known. MIM: none known. KR: none known.

22. Golan M, Crow S. Parents are key players in the prevention and treatment of weight-related problems. Nutr Rev. 2004;62(1):39-50. Epub 2004/03/05. doi: 10.1111/j.1753-4887.2004.tb00005.x. PubMed PMID: 14995056.

23. Golan M. Parents as agents of change in childhood obesity–from research to practice. Int J Pediatr Obes. 2006;1(2):66-76. Epub 2007/10/02. doi: 10.1080/17477160600644272. PubMed PMID: 17907317.

24. Janicke DM, Sallinen BJ, Perri MG, Lutes LD, Huerta M, Silverstein JH, Brumback B. Comparison of parent-only vs family-based interventions for overweight children in underserved rural settings: outcomes from project STORY. Arch Pediatr Adolesc Med. 2008;162(12):1119-25. Epub 2008/12/03. doi: 10.1001/archpedi.162.12.1119. PubMed PMID: 19047538; PMCID: PMC3782102.

25.  West F, Sanders MR, Cleghorn GJ, Davies PS. Randomised clinical trial of a family-based lifestyle intervention for childhood obesity involving parents as the exclusive agents of change. Behav Res Ther. 2010;48(12):1170-9. Epub 2010/10/05. doi: 10.1016/j.brat.2010.08.008. PubMed PMID: 20883981.

26. Golley RK, Magarey AM, Baur LA, Steinbeck KS, Daniels LA. Twelve-month effectiveness of a parent-led, family-focused weight-management program for prepubertal children: a randomized, controlled trial. Pediatrics. 2007;119(3):517-25. Epub 2007/03/03. doi: 10.1542/peds.2006-1746. PubMed PMID: 17332205.

27.  Jansen E, Mulkens S, Jansen A. Tackling childhood overweight: treating parents exclusively is effective. Int J Obes (Lond). 2011;35(4):501-9. Epub 2011/03/03. doi: 10.1038/ijo.2011.16. PubMed PMID: 21364527.

28. Magarey AM, Perry RA, Baur LA, Steinbeck KS, Sawyer M, Hills AP, Wilson G, Lee A, Daniels LA. A parent-led family-focused treatment program for overweight children aged 5 to 9 years: the PEACH RCT. Pediatrics. 2011;127(2):214-22. Epub 2011/01/26. doi: 10.1542/peds.2009-1432. PubMed PMID: 21262890.

29. Wrotniak BH, Epstein LH, Paluch RA, Roemmich JN. Parent weight change as a predictor of child weight change in family-based behavioral obesity treatment. Arch Pediatr Adolesc Med. 2004;158(4):342-7. Epub 2004/04/07. doi: 10.1001/archpedi.158.4.342. PubMed PMID: 15066873.

30. Arredondo EM, Ayala GX, Soto S, Slymen DJ, Horton LA, Parada H, Campbell N, Ibarra L, Engelberg M, Elder JP. Latina mothers as agents of change in children’s eating habits: findings from the randomized controlled trial Entre Familia: Reflejos de Salud. Int J Behav Nutr Phys Act. 2018;15(1):95. Epub 2018/10/05. doi: 10.1186/s12966-018-0714-0. PubMed PMID: 30285755; PMCID: PMC6167856.

31. Klohe-Lehman DM, Freeland-Graves J, Clarke KK, Cai G, Voruganti VS, Milani TJ, Nuss HJ, Proffitt JM, Bohman TM. Low-income, overweight and obese mothers as agents of change to improve food choices, fat habits, and physical activity in their 1-to-3-year-old children. J Am Coll Nutr. 2007;26(3):196-208. Epub 2007/07/20. doi: 10.1080/07315724.2007.10719602. PubMed PMID: 17634164.

32. Galaviz KI, Zytnick D, Kegler MC, Cunningham SA. Parental Perception of Neighborhood Safety and Children’s Physical Activity. J Phys Act Health. 2016;13(10):1110-6. Epub 2016/11/03. doi: 10.1123/jpah.2015-0557. PubMed PMID: 27254849.

33. McLaughlin C, Tarasuk V, Kreiger N. An examination of at-home food preparation activity among low-income, food-insecure women. J Am Diet Assoc. 2003;103(11):1506-12. Epub 2003/10/25. doi: 10.1016/j.jada.2003.08.022. PubMed PMID: 14576717.

34. Mook K, Laraia BA, Oddo VM, Jones-Smith JC. Food Security Status and Barriers to Fruit and Vegetable Consumption in Two Economically Deprived Communities of Oakland, California, 2013-2014. Prev Chronic Dis. 2016;13:E21. Epub 2016/02/13. doi: 10.5888/pcd13.150402. PubMed PMID: 26866947; PMCID: PMC4752515.

35. Frerichs L, Lich KH, Dave G, Corbie-Smith G. Integrating Systems Science and Community-Based Participatory Research to Achieve Health Equity. Am J Public Health. 2016;106(2):215-22. Epub 2015/12/23. doi: 10.2105/ajph.2015.302944. PubMed PMID: 26691110; PMCID: PMC4815818.

36. Button BLG, McEachern LW, Martin G, Gilliland JA. Intake of Fruits, Vegetables, and Sugar-Sweetened Beverages among a Sample of Children in Rural Northern Ontario, Canada. Children (Basel). 2022;9(7). Epub 2022/07/28. doi: 10.3390/children9071028. PubMed PMID: 35884012; PMCID: PMC9320505.

37. Stelmach-Mardas M, Kleiser C, Uzhova I, Peñalvo JL, La Torre G, Palys W, Lojko D, Nimptsch K, Suwalska A, Linseisen J, Saulle R, Colamesta V, Boeing H. Seasonality of food groups and total energy intake: a systematic review and meta-analysis. Eur J Clin Nutr. 2016;70(6):700-8. Epub 2016/01/14. doi: 10.1038/ejcn.2015.224. PubMed PMID: 26757837.

38. Hesketh KD, Campbell KJ. Interventions to prevent obesity in 0-5 year olds: an updated systematic review of the literature. Obesity (Silver Spring). 2010;18 Suppl 1:S27-35. Epub 2010/01/29. doi: 10.1038/oby.2009.429. PubMed PMID: 20107458.

Citation

Nava M, Christini K, Kepka D, Kent-Marvick J, Digre KB, Stark LA, Davis FA, Lee D, Mukundente V, Napia E, Sanchez-Birkhead A, Tavake-Pasi OF, Villalta J, Brown H, & Simonsen S. (2023). Do Interventions Targeting Women Impact Children’s Health Behaviors?. Utah Women’s Health Review. doi: 10.26054/0d-jacn-53wd

PDF

View / download