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Association of parental body mass index (BMI) with child’s health behaviors and child’s BMI depend on child’s age

Association of parental body mass index (BMI) with child’s health behaviors and child’s BMI... Background: Parent’s and child’s body mass index (BMI) are strongly associated, but their relationship varies by child’s sex and age. Parental BMI reflects, among other factors, parents’ behaviors and home environment, which influence their child’s behaviors and weight. This study examined the indirect effect of parent’s BMI on child’s BMI via child health behaviors, conditional on child’s sex and age. Methods: Data from 2039 children and 1737 parents from eight cities of the U.S. involved in the Childhood Obesity Research Demonstration project tested the association between parental BMI and child’s percentage of 95th BMI percentile (%BMIp95). A generalized structural equation modeling approach to path analysis was used to estimate and test simultaneously the associations among parental BMI and child’s health behaviors and BMI across three age groups (preschool 2-4 yr., elementary 5-10 yr., and middle school 11-12 yr). Child’s health behaviors were examined as mediators. Results: Parental BMI was related to %BMIp95 across all age groups, and was strongest in 11-12 yr. children. Parental BMI was positively associated with boys’ fruit and vegetable (FV) intake and girls’ sugar-sweetened beverage (SSB) intake. Compared to 2-4 yr., older children had less FVs and physical activity, more screen time and SSB, and higher %BMIp95. Mediation effects were not significant. Conclusions: Parental BMI was associated with child’s %BMIp95 and some child behaviors, and this association was stronger in older children; older children also exhibited less healthy behaviors. Age- and sex-specific interventions that focus on age-related decreases in healthy behaviors and parental strategies for promoting healthy behaviors among at-risk children are needed to address this epidemic of childhood obesity. Keywords: Childhood obesity, Body mass index, Physical activity, Nutrition, Path analysis Background and children whose parents had a healthy BMI exhibited About one third of children aged 2 to 19 years are classi- healthier behaviors such as regular physical activity (PA) fied as having overweight or obesity in the United States, and improved dietary patterns [6], compared with children and this high prevalence of childhood obesity has been whose parents had higher BMI. Higher maternal BMI is present for decades [1]. Prevention of childhood obesity related to higher child’s BMI and sedentary behavior [7], is a public health priority, because obesity in childhood less fruit consumption [8], and more TV viewing [9, 10]. increases risk of obesity in adulthood [2] and is associ- These results are consistent with the notion that parental ated with long-term adverse health consequences [3]. BMI reflects parents’ health behaviors that influence Numerous studies have reported a strong association their child’s health behaviors and ultimately weight between parent’s and child’s body mass index (BMI) [4, 5], status [11, 12]. Thus, the development of obesity in childhood and persistence into adulthood is not entirely explained by inheritable factors [13], but also * Correspondence: dpoconno@central.uh.edu by the health and parenting behaviors of parents/care- Department of Health and Human Performance, University of Houston, 3875 givers [4]. Holman Street Garrison Gym 104, Houston, TX 77204-6015, USA Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lee et al. BMC Obesity (2019) 6:11 Page 2 of 10 Although the sharing of genetic and behavioral factors included only children with BMI ≥85th age and sex between parents and children results in a similar specific percentile, whereas the CA and MA projects propensity for obesity status [10, 14], the association of also included healthy weight children. Demographic in- parent and child BMI has been shown to vary by child’s formation for parents (sex, age, education, employment, sex and age. Both son’s and daughter’s BMI has been re- and family income) and children (sex, age, and ethnicity) ported to be significantly related to father’s BMI, while were collected from parents. Children were categorized daughter’s BMI was significantly related to mother’s BMI into three age groups based on school status: preschool only [15]. Two separate studies demonstrated that chil- (2-4 yr), elementary (5-10 yr), and middle school (11-12 dren’s PA was affected by shared environmental factors yr). The study was approved by the institutional review for parents and young children [16], but not for parents boards of the participating CORD organizations and in- and adolescents [17]. This can be explained by a stitutions, and written informed consent of parents and decreasing influence of parents on children’s behaviors assent of children were obtained prior to data collection. as children mature and become more independent from their parents [14]. Moreover, older children’s behaviors Anthropometric measures and obesity status may be affected by school programs Anthropometric measures were collected using the [18] and peer behaviors [19]. Given the influences of methods described in the National Health and Nutrition schools and peers on children’s health behaviors and Examination Survey Anthropometry Procedures Manual consequently their BMI, we assume that the association [21]. Parent and child’s height and weight were recorded of parental BMI on their child’s behaviors and BMI in centimeters (cm) to the nearest 0.1 cm and kilogram would be expected to vary as a function of child’s age. (kg) to the nearest 0.1 kg, respectively. Parental BMI was Thus, child age may moderate the association between calculated using the Quetelet index equation (kg/m ). parental BMI and child’s health behaviors and BMI. Each child’s relative BMI, computed as the percentage of This study investigated 1) the extent to which parental the respective age and sex specific BMI 95th percentile BMI was associated with the child’s health behaviors and value %BMIp95), was used as the child BMI variable, BMI, 2) the role of child’s health behaviors as mediators calculated using the CDC reference data and software al- between parental BMI and child’s BMI, and 3) whether gorithm [22]. This measure has been shown to be more these relationships are conditional on child’s sex and appropriate for the heaviest children (i.e., those >97th age. We hypothesized: 1) healthy parental BMI would be percentile), a characteristic of this sample given the associated with healthier child behaviors and BMI, 2) inclusion criteria in one site (TX) and the demographics healthy child behaviors would be related to a healthier of the other two sites (i.e., rural, racially/ethnically di- child BMI, 3) the relation between parental BMI and verse, low-income [23]). In addition, it has better statis- child BMI would be partly mediated by the child’s health tical properties for comparisons than other BMI-derived behaviors, and 4) these associations would vary by child’s measures for children [24]. sex and age. Child health behavior variables Methods Child health behaviors were measured by surveying par- Participants ents using a standard set of items selected from previ- This study was a secondary analysis of baseline data ously validated instruments [20, 25]. Child’s times per collected on the Childhood Obesity Research Demon- day eating fruits and vegetables (FVs) (sum of 2 items) stration project (CORD) [20]. CORD implemented inte- and sugar-sweetened beverages (SSBs) (sum of 2 items) grated primary care and public health interventions were assessed using items from the School Physical across eight communities in three states in the U.S. to Activity and Nutrition project survey [26] and the Child improve child and family health behaviors and to and Adolescent Trial for Cardiovascular Health prevent and reduce childhood obesity among families (CATCH) Kids Club After-school questionnaire [27]. eligible for benefits under Titles XIX (Medicaid) and Number of days per week engaged in 60 min or more of XXI (Children’s Health Insurance Plan (CHIP)) of the PA was assessed using one item from the Youth Risk Be- Social Security Act, which are programs intended to havior Survey [28]. The parent selected from 0 to 7 days serve families with low household income. for their child, which resulted in a highly negatively In 2012–2014, 2039 children aged 2–12 years and one skewed distribution, so the observed responses were di- of their parents or caregivers (N = 1737) were enrolled chotomized into 7 days/week (every day) versus less than from eight communities in three states (Brawley, 7 days/week (not every day) for analyses. Total hours Calexico, and El Centro of California (CA); Fitchburg, and minutes per week of screen time (TV/DVD, com- Lowell, and New Bedford of Massachusetts (MA); and puter/video game, etc.) were computed from hours and Austin and Houston of Texas (TX)). The TX project minutes per weekday and weekend day for screen time Lee et al. BMC Obesity (2019) 6:11 Page 3 of 10 collected using four items from the CATCH Kids Club (Hypothesis 2) and whether these effects varied by age After-school questionnaire [27]. group (Hypothesis 4, Fig. 1b), and 3) indirect (medi- ation) effects (Hypothesis 3), the path from parental Analyses BMI through the child’s health behavior to the child’s Separate analyses were conducted for boys and girls. %BMIp95 (Fig. 1b), and whether the mediation effect Descriptive statistics were reported as means and stand- varied by age. All analyses were adjusted for site and for ard deviations (mean ± SD) or percentages (%). A gener- different sample sizes among the age groups and across alized structural equation modeling approach to path cities. All statistical analyses were performed using Stata analysis was used to estimate and test simultaneously 14.2 (Stata Corp, Texas, USA), and significance was the associations between parental BMI and child’s BMI, defined as p < .05. parental BMI and child’s health behaviors, and child’s health behaviors and BMI among the three child age Results groups (Fig. 1). Associations were modeled using linear Only 4% of adults reported being non-parent guardians, links and normal distributions, except for children’sPA, and 91.5% reported being the mother of the enrolled which was a dichotomous variable and was modeled child (Table 1). About 64% of parents had graduated using a log link and binomial distribution. Parental BMI high school, less than 50% were employed, and about was mean-centered so that interaction effects could be 70% were living in households below the federal poverty interpreted at the average BMI of the parents. State (CA, level (FPL). Overall parental BMI was 31.6 ± 7.4. MA, and TX) and city within state (Brawley, Calexico, El The total number of boys and girls were 1014 and Centro, Fitchburg, Lowell, New Bedford, Austin, and 1025, respectively (Table 2). More than 80% of children Houston) were included as covariates in all statistical were reported to be Hispanic. Due to the inclusion cri- models to adjust for mean differences among study sites. terion in TX for children to be ≥85th BMI percentile, Four separate analyses were conducted to test the TX children had a higher mean %BMIp95. The TX sequentially: 1) direct effects of parental BMI on the children also had less frequent FV intake, more frequent child’s %BMIp95 (Hypothesis 1) and whether the direct SSB intake, more hours of screen time, and a lower pro- effects varied by age group (Hypothesis 4, Fig. 1a); 2) portion participating every day in ≥60 min/day of PA, associations in the full path model (Fig. 1b) between par- compared to the MA and CA children. The percentage ental BMI and the child’s health behaviors (Hypothesis of children with normal weight, overweight, and obesity 1), and between child’s health behaviors and %BMIp95 were 22.3, 21.8, and 55.9%, respectively. Fig. 1 Analytical models tested in the study. (a) Total effect of parent BMI on child BMI. (b) Direct effect of parent and child BMI and indirect effect through child health behaviors. FV: fruit and vegetable; SSB: sugar-sweetened beverage; PA: physical activity. Site and city within site were included in the models as covariates Lee et al. BMC Obesity (2019) 6:11 Page 4 of 10 Table 1 Characteristics of parents across the three sites Parent MA CA TX Total N 421 804 512 1737 Gender Female (%) 90.7% 98.4% 95.9% 95.0% Age (yr) Mean (SD) 33.4 ± 8.2 36.2 ± 8.4 34.4 ± 7.0 34.6 ± 7.9 Relation with child Mother (%) 86.7% 93.7% 94.1% 91.5% Education High school graduate (%) 68.8% 68.7% 55.6% 64.4% Employment Work for pay (%) 52.0% 39.7% 47.5% 46.4% Income Less than $15,000 (%) 53.2% 43.5% 60.0% 51.9% Poverty Index % below FPL 73.8% 60.6% 72.4% 68.9% BMI 29.8 ± 7.3 31.8 ± 7.2 33.2 ± 7.8 31.6 ± 7.4 Mean ± SD MA: Massachusetts; CA: California; TX: Texas FPL: federal poverty level BMI: body mass index, kg/m Age: years Table 2 Characteristics of children across the three sites Child MA CA TX Total/Average Boys N 218 552 244 1014 Age (yr) 6.6 ± 3.3 6.9 ± 2.7 7.7 ± 2.8 7.1 ± 2.9 Race (% of Hispanic) 57.8% 98.0% 88.9% 81.6% % of 95th BMI percentile 95.1 ± 18.9 98.8 ± 19.7 117.6 ± 19.2 103.9 ± 19.3 FV Intake (times/day) 2.3 ± 1.7 2.3 ± 1.5 1.9 ± 1.3 2.2 ± 1.5 SSB Intake (times/day) 0.7 ± 0.9 1.1 ± 1.2 1.3 ± 1.2 1.0 ± 1.1 Screen Time (hrs/wk) 23.2 ± 15.8 27.2 ± 16.1 28.5 ± 16.6 26.3 ± 16.2 7 days/wk. of PA 60 min/day 76.2% 43.5% 19.7% 46.4% Girls N 203 567 255 1025 Age (yr) 6.5 ± 3.1 6.9 ± 2.6 7.7 ± 2.7 7.0 ± 2.8 Race (% of Hispanic) 69.0% 98.9% 85.9% 84.6% % of 95th BMI percentile 92.1 ± 19.8 96.2 ± 20.3 115.99 ± 16.8 101.4 ± 18.6 FV Intake (times/day) 2.3 ± 1.4 2.4 ± 1.5 2.3 ± 1.4 2.3 ± 1.5 SSB Intake (times/day) 0.8 ± 1.1 1.0 ± 1.0 1.2 ± 1.3 1.0 ± 1.1 Screen Time (hrs/wk) 21.7 ± 13.0 22.3 ± 12.8 26.5 ± 15.5 24.2 ± 13.8 7 days/wk. of PA 60 min/day 69.5% 42.3% 14.9% 42.2% Overall N 421 1119 499 2039 Age (yr) 6.5 ± 3.2 6.9 ± 2.7 7.7 ± 2.8 7.1 ± 2.9 Race (% of Hispanic) 63.4% 98.5% 87.9% 83.1% % of 95th BMI percentile 93.6 ± 19.4 97.5 ± 20.0 116.80 ± 18.0 102.7 ± 19.0 FV Intake (times/day) 2.3 ± 1.6 2.4 ± 1.5 2.1 ± 1.4 2.3 ± 1.5 SSB Intake (times/day) 0.8 ± 1.0 1.1 ± 1.1 1.3 ± 1.3 1.0 ± 1.1 Screen Time (hrs/wk) 22.5 ± 14.4 24.8 ± 14.5 27.5 ± 16.1 25.3 ± 15.0 7 days/wk. of PA 60 min/day 72.9% 42.9% 17.3% 44.3% Mean ± SD MA: Massachusetts, CA: California, TX: Texas FV intake: time(s) per day fruit and vegetable intake; SSB intake: time(s) per day sugar-sweetened beverage (i.e., soda, punch) intake; screen time: total hour(s) of screen time (i.e., computer, TV, video game) during 5 weekdays and 2 weekend days; 7 days/wk. of PA 60 min/day: percentage of children who every day do physical activity that make the child breath hard for 60 min or more Lee et al. BMC Obesity (2019) 6:11 Page 5 of 10 Age group differences for child’s BMI and health be- more times per day per 10-unit difference in parent haviors after adjusting for parental BMI and city were BMI, p = .029) in boys and SSB intake (0.2 more times per reported in Table 3. Compared to the preschool boys, day per 10-unit difference in parent BMI, p = .007) in girls elementary school boys had significantly higher (Hypothesis 1). However, parental BMI was not associated %BMIp95. Elementary and middle school boys showed with SSB consumption (p =.931), screen time (p =.833), significantly higher screen time (p < .001) and lower FV or PA (p =.515) in boys and FV intake (p =.815), screen intake (p < .001) and PA (elementary: p = .001; middle time (p =.379), or PA (p = .794) in girls (Hypothesis 1, data school: p < .001) than preschool boys. Elementary and not shown). middle school girls showed higher screen time (p < .001) In comparing the age groups, higher parental BMI was and lower FV intake and PA (elementary: p < .001; mid- associated with more screen time (p = .045) and dle school: p = .001) than preschool girls. SSB intake in engaging in PA seven days/week (p = .031) in elementary middle school girls were significantly higher (p = .023) school boys and more FV intake (p = .013) in middle than preschool girls. school boys. Age did not moderate the association between parental BMI and girls’ health behaviors Direct effect of parental BMI on child’s BMI among age (Hypothesis 4, Tables 4 and 5). groups Parental BMI was significantly positively associated Association between child’s health behaviors and with child’s %BMIp95 in both boys and girls (Hypoth- child’sBMI esis 1, p < .001). The association of parental BMI and Across all age groups, FV intake (p = .262, p = .278), child’s %BMIp95 increased significantly with child’s SSB intake (p =.227, p = .372), and screen time (p =.480, age (i.e., Hypothesis 4, parent BMI*child age inter- p = .258) were not significantly related to boys’ and girls’ action) among boys (p = .016) and girls (p = .019). A %BMIp95, but engaging in PA seven days/week (p =.002) positive one-unit difference in parental BMI was was significantly associated with healthier %BMIp95 associated with a 0.3, 0.8, and 0.7% higher %BMIp95 among boys and girls. Middle school girls who did not en- in preschool, elementary, and middle school boys, re- gage in PA seven days/week (p = .020) and elementary spectively. A positive one-unit difference in parental school boys who consumed SSB more times/day (p =.046) BMI was associated with a 0.5, 0.9, and 1.1% higher had significantly higher %BMIp95. %BMIp95 in preschool, elementary, and middle school Preschool boys ate FVs 0.7 and 0.8 more times/day girls, respectively. than elementary and middle school boys, respectively (p < .001). SSB intake did not differ significantly across Association between parental BMI and child’s health boys’ age groups. Compared to preschool boys, behaviors elementary and middle school boys had significantly Averaged across all age groups, parental BMI was longer screen time (5 h and 8 h, respectively, p < .001), significantly positively associated with FV intake (0.2 and were less likely to engage in PA seven days/week Table 3 Age differences of child’s BMI and health behaviors in both boys and girls Child Preschool Elementary school Middle School Total/Average Boys N 299 617 98 1014 % of 95th BMI percentile 99.9 ± 1.1 103.3 ± 0.8* 100.1 ± 1.9 101.1 ± 1.3 FV Intake (times/day) 2.6 ± 0.1 1.9 ± 0.1*** 1.9 ± 0.2*** 2.1 ± 0.1 SSB Intake (times/day) 0.9 ± 0.1 1.1 ± 0.1 1.1 ± 1.1 1.0 ± 0.4 Screen Time (hrs/wk) 22.6 ± 0.1 27.9 ± 0.7*** 30.5 ± 1.6*** 27.0 ± 1.1 7 days/wk. of PA 60 min/day (OR) 0.6 ± 0.0 0.5 ± 0.0** 0.4 ± 0.1*** 0.5 ± 0.0 Girls N 282 666 77 1025 % of 95th BMI percentile 99.1 ± 1.2 100.8 ± 0.9 98.2 ± 2.1 99.3 ± 1.4 FV Intake (times/day) 2.6 ± 0.1 2.2 ± 0.1*** 2.0 ± 0.2** 2.3 ± 0.1 SSB Intake (times/day) 0.9 ± 0.1 1.0 ± 0.1 1.2 ± 0.1* 1.1 ± 0.1 Screen Time (hrs/wk) 20.6 ± 0.8 24.5 ± 0.6*** 29.2 ± 1.5*** 24.8 ± 1.0 7 days/wk. of PA 60 min/day (OR) 0.6 ± 0.0 0.4 ± 0.0*** 0.3 ± 0.1** 0.4 ± 0.0 Values are model-predicted means±SE, adjusted for mean of parental BMI and different numbers of children across the sites and cities *p < .05, **p = .001, ***p < .001 Preschool is the reference group Lee et al. BMC Obesity (2019) 6:11 Page 6 of 10 Table 4 The associations among parental BMI, child’s health behaviors, and child’s BMI in boys Boys P-BMI ➔ FV P-BMI ➔ SSB P-BMI ➔ Screen Time P-BMI ➔ PA Constant 2.51 ± 0.15*** 1.05 ± 0.11*** 22.99 ± 1.62*** 0.53 ± 0.12* Age2 −0.68 ± 0.10*** 0.11 ± 0.08 5.00 ± 1.13*** 0.57 ± 0.09*** Age3 −0.84 ± 0.17*** 0.14 ± 0.13 7.95 ± 1.90*** 0.34 ± 0.10*** P-BMI 0.001 ± 0.01 0.01 ± 0.008 −0.06 ± 0.12 0.97 ± 0.02* P-BMI*Age2 −0.003 ± 0.01 −0.001 ± 0.01 0.25 ± 0.15* 1.04 ± 0.02* P-BMI*Age3 0.06 ± 0.03* −0.03 ± 0.02 −0.12 ± 0.28 1.03 ± 0.04 FV ➔ C-BMI SSB ➔ C-BMI Screen Time ➔ C-BMI PA ➔ C-BMI Constant 112.54 ± 2.68*** 114.49 ± 2.15*** 113.63 ± 2.46*** 114.17 ± 2.20*** Age2 4.17 ± 2.59 0.80 ± 1.78 − 0.69 ± 2.42 3.92 ± 1.90* Age3 1.11 ± 3.92 1.60 ± 3.05 −3.59 ± 4.20 1.39 ± 2.86 P-BMI 0.25 ± 0.73* 0.26 ± 0.14* 0.26 ± 0.14* 0.24 ± 0.14* P-BMI*Age2 0.55 ± 0.17** 0.53 ± 0.17** 0.52 ± 0.17** 0.57 ± 0.17** P-BMI*Age3 0.46 ± 0.34 0.41 ± 0.33 0.47 ± 0.33 0.45 ± 0.33 Behavior 0.46 ± 0.73 −0.75 ± 0.96 0.004 ± 0.068 −14.05 ± 4.80* Behavior*Age2 −0.48 ± 0.90 1.97 ± 1.16* 0.13 ± 0.08 3.33 ± 2.66 Behavior*Age3 −0.53 ± 1.65 −1.54 ± 1.93 0.11 ± 0.13 6.96 ± 4.50 All values are fixed regression coefficient ± SE, except values of P-BMI ➔ PA are odd ratios (OR) ± SE *p < .05, **p = .001, ***p < .001 Constant: preschool (reference groups); Age2: elementary; Age3: middle school P-BMI: mean-centered parental BMI; C-BMI: child percent of 95th BMI percentile (%BMIp95) Behaviors: FV (fruit and vegetable intake [times per day]), SSB (sugar-sweetened beverage intake [times per day]), screen time (TV, DVD, computer, video game [hours per week]), and PA (physical activity [7 days/week vs. < 7 days/week]) of child Table 5 The associations among parental BMI, child’s health behaviors, and child’s BMI in girls Girls P-BMI ➔ FV P-BMI ➔ SSB P-BMI ➔ Screen Time P-BMI ➔ PA Constant 2.52 ± 0.15*** 0.92 ± 0.12*** 19.68 ± 1.40*** 0.37 ± 0.10*** Age2 −0.38 ± 0.10*** 0.12 ± 0.08 3.78 ± 0.94*** 0.52 ± 0.08*** Age3 −0.67 ± 0.19*** 0.30 ± 0.14* 8.36 ± 1.73*** 0.43 ± 0.13* P-BMI −0.008 ± 0.01 0.005 ± 0.009 −0.067 ± 0.105 1.02 ± 0.02 P-BMI*Age2 0.005 ± 0.04 0.015 ± 0.01 0.15 ± 0.13 0.98 ± 0.02 P-BMI*Age3 0.026 ± 0.02 0.021 ± 0.02 0.26 ± 0.22 0.95 ± 0.04 FV ➔ C-BMI SSB ➔ C-BMI Screen Time ➔ C-BMI PA ➔ C-BMI Constant 113.86 ± 2.76*** 112.78 ± 2.17*** 114.17 ± 2.60*** 112.22 ± 2.22*** Age2 2.12 ± 2.61 1.53 ± 1.76 −0.70 ± 2.54 2.54 ± 1.85 Age3 1.96 ± 4.40 −1.71 ± 3.35 −8.12 ± 4.78* 1.89 ± 3.12 PBMI 0.48 ± 0.15** 0.49 ± 0.15** 0.48 ± 0.15** 0.51 ± 0.15** P-BMI*Age2 0.37 ± 0.18* 0.37 ± 0.18* 0.37 ± 0.18* 0.35 ± 0.18* P-BMI*Age3 0.65 ± 0.31* 0.60 ± 0.31* 0.55 ± 0.31* 0.46 ± 0.31 Behavior −0.56 ± 0.75 −0.33 ± 1.03 −0.09 ± 0.09 −15.83 ± 5.30* Behavior*Age2 −0.45 ± 0.89 −0.16 ± 1.22 0.10 ± 0.10 3.67 ± 2.65 Behavior*Age3 −1.98 ± 1.76 0.23 ± 2.02 0.26 ± 0.16 10.39 ± 5.07* All values are fixed regression coefficient ± SE, except values of P BMI ➔ PA are odd ratios (OR), ±SE *p < .05, **p = .001, ***p < .001 Constant: preschool (reference groups); Age2: elementary; Age3: middle school PBMI: mean-centered parental BMI; CBMI: child percent of 95th BMI percentile (%BMIp95) Behaviors: FV (fruit and vegetable intake [times per day]), SSB (sugar-sweetened beverage intake [times per day]), screen time (TV, DVD, computer, video game [hours per week]), and PA (physical activity [7 days/week vs. < 7 days/week]) of child Lee et al. BMC Obesity (2019) 6:11 Page 7 of 10 (p < .001, elementary: OR = 0.57; middle school: OR = Consistent with energy balance theory, we hypothe- 0.34) (Table 4). sized that unhealthy child behaviors related to energy Preschool girls ate FVs 0.4 and 0.7 more times/day intake (FV and SSB intake) and to energy expenditure than elementary and middle school girls, respectively (PA and screen time) would be related to their (p < .001). Middle school girls consumed SSB 0.3 %BMIp95 (Hypothesis 2). In the present study, a large more times/day than preschool girls (p = .018). Com- difference in parental BMI was positively associated pared to preschool girls, elementary and middle with FV intake in boys and SSB intake in girls (al- school girls had significantly longer screen time (3.8 h though the effect size is very small, it was statistically and 8.4 h, respectively, p < .001), and were less likely significant due to the large sample size), but not with to engage in PA seven days/week (elementary: OR = child’s screen time and PA. Previous studies found an 0.52, p < .001; middle school: OR = 0.43, p = .003) association of higher parental BMI with their children (Table 5). viewing more TV [9, 10] and engaging in less PA [6]. Additionally, only children’s PA was associated with Indirect (mediation) effects their %BMIp95 in this study, whereas other studies In both boys and girls, across and within all age groups, found relationships between children’sBMI andtheir none of the indirect effects of parental BMI on child’s dietary and sedentary behaviors [6, 30, 31]. %BMIp95 via child’s health behaviors were statistically One explanation for inconsistencies between our significant (Hypothesis 3). Thus, there are no mediatory results and previous studies may be due to different effects of child’s health behaviors on the relationship measures of child’s health behaviors. In our study, observed between parental BMI and child’s %BMIp95. children’s FV and SSB intake were measured as times/ day of the prior day, which do not provide a complete Discussion quantification of a child’s dietary intake, whereas previ- In this study, parental BMI was positively related to ous studies measured both frequency and portion sizes child’s %BMIp95 in both boys and girls, as previous [32]. Nevertheless, our data indicate that parental BMI is studies have reported [4, 5]. The 90% of enrolled adults a correlate of some child’s health behaviors and %BMIp95, being mothers may have resulted in the stronger and the survey questions that we used in this study have observed association of mothers’ BMI on %BMIp95 in been validated and used in previous studies [33]. daughters since the association of BMI in The current study observed age differences in the as- mother-daughter dyads is higher than in mother-son, sociations among parental BMI, child’s health behaviors, father-daughter, or father-son dyads [5]. In our data, and %BMIp95 (Hypothesis 4). Overall, preschool-aged older boys (elementary) and girls (elementary and mid- children showed healthier behaviors such as more fre- dle school) showed a stronger positive association be- quent FV intake, less frequent SSB intake, less weekly tween %BMIp95 and parental BMI, compared to the screen time, and higher proportion engaging in daily PA preschool-aged children. Previous studies found that compared to elementary and middle school children in obesity status in older children was affected by both in- both boys and girls. In particular, engaging in PA seven heritable traits from parents and shared environment days/week was lower, while screen time was higher, over time and emphasized that environmental effects among the older children than the youngest children. were important determinants to develop behavior These results are consistent with reports that suggest a patterns and obesity among adolescents [13, 29]. One decrease of PA is significantly associated with an in- potential explanation for our results is that factors com- crease of screen time among children and adolescents mon to both parents and children, who live in the same [10, 34, 35]. The reasons why child’s PA declines with household, including genetic, environmental, and socio- age are unclear, but it is possible that social support fac- cultural influences, may result in higher %BMIp95 in tors (parental influence, schools’ academic programming older children and increase the association with parental and facilities, peers’ activity, etc.) may be associated with BMI. decreases in opportunities for moderate to vigorous An assumption in interpreting our results is that par- physical activity (MVPA) among adolescents [36, 37]. In ental BMI is an indicator of genetic, environmental, and this study, there was significant association of higher sociocultural factors common to both parents and chil- parental BMI and engagement in PA every day in dren, and potentially long-term parental dietary, PA, and elementary school boys. One potential explanation of sedentary behaviors, and that those health behaviors this result could be that boys’ PA are less dependent on would influence their child’s health behaviors and BMI their mother’s PA and BMI, since boys’ PA were more [11, 12]. Thus, we expected that unhealthy parental BMI associated with their fathers [38]. Higher SSB intake and would be associated with child’s unhealthy behaviors screen time in the oldest girls were observed in this such as less FV and more SSB intake (Hypothesis 1). study, consistent with research that reported an Lee et al. BMC Obesity (2019) 6:11 Page 8 of 10 association between higher soda intake and longer TV objective measures. Fourth, parental behavior data were viewing time in older children [39]. Higher FV intake not consistently collected across the sites and were in middle school boys with higher parental BMI could therefore not available for our analyses; parent behaviors be explained by greater overall frequency of food may be more directly and strongly related to child health consumption, including more fruit and vegetables, behaviors than is parent BMI. Fifth, the oldest age group with larger parent body size [40]. Another potential sample sizes were smaller than the other age groups, explanation is that the parents with higher BMI may thus limiting the precision of the estimates for that be more concerned with obesity in their sons and group. provide a better diet as reflected by their higher FV Despite these limitations, this study included consumption. low-income families from different states and cities Finally, we expected child’s health behaviors to be me- across the USA, which allows broader generalization diators partly explaining the association between parent’s of the results compared to single-site studies. Investi- and child’s BMI (Hypothesis 3). However, none of the gation of age differences of the relationships among mediation effects were statistically significant. This was parental BMI, child’s health behaviors, and child’s due to either non-significant associations between BMI is a novel aspect. Age-specific associations may parental BMI and child’s health behaviors or between be informative for considering different intervention child’s health behaviors and %BMIp95, or both. Our strategies, such as providing interventions for the model showed that some of the child’s health behaviors family and home environment for preschool children, (e.g., FV and SSB intake, and PA) were associated with but including additional interventions for older chil- parental and child’s BMI, but the direct relationship be- dren, since older children spend much time at school tween parental BMI and child’s BMI remained relatively as well as home, make decisions more independently, unchanged. One previous study found a stronger rela- and are influenced by peer groups in addition to tionship between parent’s and child’s health behaviors parents [14, 19]. compared to the relationship between parental and child’sBMI [39]. Although parental health behaviors Conclusion were not assessed across all sites, parents’ behavioral in- This study demonstrated a large association between fluence on modifiable child’s health behaviors may affect parent BMI and child’s %BMIp95 but failed to detect child’s BMI and thus explain part of the association any mediation through child health behaviors. The asso- between parental and child’s BMI. Such behaviors may ciation between parental BMI and older children’s be opportunities to consider when designing interven- %BMIp95 was stronger compared to younger children. tions with a goal of changing behaviors in both parent Older children also had unhealthier behaviors such as and child to affect BMI. Caution is warranted in inter- less daily FV intake and PA engagement and more preting that a direct or indirect association indicates that weekly screen time and SSB intake; these unhealthy “blame” should be placed on individuals, such as viewing behaviors were associated with their higher %BMIp95. parents as “the” causal agent of obesity in childhood. Parental BMI would impact unhealthy behaviors and Our data do not suggest this. Instead, these associations obesity in their children, but our results are consistent should be viewed as opportunities to determine factors with the notion that childhood obesity may be affected that may impact obesity in children. Because obesity is by multi-factors such as environmental factors, inherit- an intractable disease with multiple etiologies, “blaming” able factors, parental behaviors, and a child’s own un- individuals (either parents or children) is counterpro- healthy behaviors. Thus, interventions for the prevention ductive and fails to consider the environmental, genetic, and control of childhood obesity may consider focusing epigenetic, and biological aspects of obesity. on simultaneously changing the health behaviors of both This study has a number of limitations. First, the parents and children. Our findings are also consistent sample was primarily Hispanic families who were eligible with the notion that early life (before age 5) may be the for Medicaid and CHIP benefits, so the results may not best opportunity for interventions to prevent childhood generalize to populations with a different ethnicity or obesity, before children develop their own unhealthy higher household income. Second, the cross-sectional behaviors and weight status. data allows for only evaluating associations among par- ental BMI and child’s health behaviors and %BMIp95; there may be unmeasured causal variables and paths that Abbreviations were not included in the analyses. Third, the survey %BMIp95: the percentage of 95th BMI percentile; BMI: body mass index; CA: California; CATCH: Child and Adolescent Trial for Cardiovascular Health; items did not reflect long-term child health behaviors, CORD: Childhood Obesity Research Demonstration; FV: fruit and vegetables; asking only about behaviors on a single day or week, and MA: Massachusetts; PA: physical activity; SSB: sugar-sweetened beverage; self-reported behaviors may not be as accurate as more TX: Texas Lee et al. BMC Obesity (2019) 6:11 Page 9 of 10 Acknowledgements in Greece; dietary and lifestyle habits in the context of the family The authors thank the MA-CORD, CA-CORD, TX-CORD, and EC-CORD teams environment: the Vyronas study. Appetite. 2008;51:218–22. who participated and collected the data for this study, especially thanks to 7. Sijtsma A, Sauer PJ, Corpeleijn E. Parental correlations of physical activity Carrie A. Dooyema (CDC/NCCDPHP/DNPAO) and Dr. Thomas Land of and body mass index in young children--he GECKO Drenthe cohort. MA-CORD for critical comments on the manuscript. Int J Behav Nutr Phys Act. 2015;12:132. 8. Morello MI, Madanat H, Crespo NC, Lemus H, Elder J. Associations among Funding parent acculturation, child BMI, and child fruit and vegetable consumption in a Hispanic sample. J Immigr Minor Health. 2012;14:1023–9. This research was supported in part by cooperative agreement RFA-DP-11- 007 (grant U18DP003350 and U18DP003377–01) from the Centers for Dis- 9. Maffeis C, Talamini G, Tato L. Influence of diet, physical activity and parents' ease Control and Prevention (CDC). The content is solely the responsibility of obesity on children's adiposity: a four-year longitudinal study. Int J Obes the authors and does not necessarily represent the official views of the CDC. Relat Metab Disord. 1998;22:758–64. 10. Steffen LM, Dai S, Fulton JE, Labarthe DR. Overweight in children and adolescents associated with TV viewing and parental weight: project Availability of data and materials HeartBeat. Am J Prev Med. 2009;37:S50–5. The datasets used and analyzed during this study are available from the 11. Ventura AK, Birch LL. Does parenting affect children's eating and weight corresponding author upon reasonable request. status? Int J Behav Nutr Phys Act. 2008;5:15. 12. Carriere G. Parent and child factors associated with youth obesity. Health Author’s contributions Rep. 2003;14(Suppl):29–39. CYL conceived the study design, implemented the literature search, 13. Nelson MC, Gordon-Larsen P, North KE, Adair LS. Body mass index gain, fast conducted the data analyses and interpretation, and wrote the first draft of food, and physical activity: effects of shared environments over time. the manuscript. DPO participated in the study design, contributed to the Obesity (Silver Spring). 2006;14:701–9. design of the CORD project, and assisted with data collection, analysis, and 14. Silventoinen K, Rokholm B, Kaprio J, Sorensen TI. The genetic and interpretation. TAL and GXA contributed to the design of the CORD project environmental influences on childhood obesity: a systematic review of twin and data collection, and CAJ and reviewed the study design and data and adoption studies. Int J Obes. 2010;34:29–40. analysis and interpretation. All authors were involved in writing and revising 15. Shafaghi K, Shariff ZM, Taib MN, Rahman HA, Mobarhan MG, Jabbari H. the manuscript and approved the submitted and published versions. Parental body mass index is associated with adolescent overweight and obesity in Mashhad. Iran Asia Pac J Clin Nutr. 2014;23:225–31. Ethics approval and consent to participate 16. Franks PW, Ravussin E, Hanson RL, Harper IT, Allison DB, Knowler WC, All studies of this project were approved by the institutional review boards Tataranni PA, Salbe AD. Habitual physical activity in children: the role of of the participating research institutes and universities in CA, MA, and TX. genes and the environment. Am J Clin Nutr. 2005;82:901–8. Written informed consent of parents and assent of children were obtained 17. Stubbe JH, Boomsma DI, Vink JM, Cornes BK, Martin NG, Skytthe A, Kyvik KO, prior to data collection. Rose RJ, Kujala UM, Kaprio J, et al. Genetic influences on exercise participation in 37,051 twin pairs from seven countries. PLoS One. Consent for publication 2006;1:e22. Not applicable. 18. Fernandes MM, Sturm R. The role of school physical activity programs in child body mass trajectory. J Phys Act Health. 2011;8:174–81. Competing interests 19. Halliday TJ, Kwak S. Weight gain in adolescents and their peers. Econ Hum DPO and GXA report grants from Centers for Disease Control and Biol. 2009;7:181–90. Prevention (CDC), during the conduct of the study. Other authors 20. O'Connor DP, Lee RE, Mehta P, Thompson D, Bhargava A, Carlson C, Kao D, have nothing to disclose. Layne CS, Ledoux T, O'Connor T, et al. Childhood obesity research demonstration project: cross-site evaluation methods. Child Obes. 2015;11:92–103. Publisher’sNote 21. Centers for Disease Control and Prevention: National Health and Nutrition Springer Nature remains neutral with regard to jurisdictional claims in Examination Survey: Anthropometry Procedures Manual [https://www.cdc. published maps and institutional affiliations. gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf] Accessed 12 Dec Author details 22. Centers for Disease Control and Prevention: A SAS Program for the CDC Department of Health and Human Performance, University of Houston, 3875 Growth Charts [https://www.cdc.gov/nccdphp/dnpao/growthcharts/ Holman Street Garrison Gym 104, Houston, TX 77204-6015, USA. Division of resources/sas.htm] Accessed 12 Dec 2017. Health Promotion and Behavioral Science, Graduate School of Public Health, San Diego State University, 5500 Campanile Drive, San Diego 92182-4162, 23. Foltz JL, Belay B, Dooyema CA, Williams N, Blanck HM. Childhood obesity CA, USA. research demonstration (CORD): the cross-site overview and opportunities for interventions addressing obesity community-wide. Child Obes. Received: 21 December 2017 Accepted: 18 February 2019 2015;11:4–10. 24. Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin References Nutr. 2009;90:1314–20. 1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult 25. Davison KK, Falbe J, Taveras EM, Gortmaker S, Kulldorff M, Perkins M, Blaine obesity in the United States, 2011-2012. Jama. 2014;311:806–14. RE, Franckle RL, Ganter C, Baidal JW, et al. Evaluation overview for the 2. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of Massachusetts childhood obesity research demonstration (MA-CORD) childhood overweight into adulthood: a systematic review of the literature. project. Child Obes. 2015;11:23–36. Obes Rev. 2008;9:474–88. 26. University of Texas School of Public Health: School Physical Activity and 3. Biro FM, Wien M. Childhood obesity and adult morbidities. Am J Clin Nutr. Nutrition (SPAN) project - Student Survey [https://sph.uth.edu/dotAsset/ 2010;91:1499s–505s. b94d672a-ca59-4bcb-b2e9-1d893c503764.pdf, https://sph.uth.edu/dotAsset/ 4. Farajian P, Panagiotakos DB, Risvas G, Malisova O, Zampelas A. Hierarchical 4d09697e-e692-4e66-816e-d9d6ccb35499.pdf] Accessed 12 Dec 2017. analysis of dietary, lifestyle and family environment risk factors for childhood obesity: the GRECO study. Eur J Clin Nutr. 2014;68:1107–12. 27. National Institute of Health: CATCH Kids Club After-school Student 5. Liu Y, Chen HJ, Liang L, Wang Y. Parent-child resemblance in weight status Questionnaire [https://www.nhlbi.nih.gov/health/educational/wecan/ and its correlates in the United States. PLoS One. 2013;8:e65361. downloads/CKC-questionnaire.pdf] Accessed 12 Dec 2017. 6. Kosti RI, Panagiotakos DB, Tountas Y, Mihas CC, Alevizos A, Mariolis T, 28. Centers for Disease Control and Prevention: Youth Risk Behavior Survey Papathanassiou M, Zampelas A, Mariolis A. Parental body mass index in (YRBS) [http://www.cdc.gov/healthyyouth/yrbs/pdf/questionnaire/ association with the prevalence of overweight/obesity among adolescents 2009MiddleSchool.txt] Accessed 12 Dec 2017. Lee et al. BMC Obesity (2019) 6:11 Page 10 of 10 29. Ihmels MA, Welk GJ, Eisenmann JC, Nusser SM, Myers EF. Prediction of BMI change in young children with the family nutrition and physical activity (FNPA) screening tool. Ann Behav Med. 2009;38:60–8. 30. Fogelholm M, Nuutinen O, Pasanen M, Myohanen E, Saatela T. Parent-child relationship of physical activity patterns and obesity. Int J Obes Relat Metab Disord. 1999;23:1262–8. 31. Williams SL, Mummery WK. Links between adolescent physical activity, body mass index, and adolescent and parent characteristics. Health Educ Behav. 2011;38:510–20. 32. Hoelscher DM, Day RS, Kelder SH, Ward JL. Reproducibility and validity of the secondary level school-based nutrition monitoring student questionnaire. J Am Diet Assoc. 2003;103:186–94. 33. Pei Z, Flexeder C, Fuertes E, Standl M, Berdel D, von Berg A, Koletzko S, Schaaf B, Heinrich J. Mother's body mass index and food intake in school-aged children: results of the GINIplus and the LISAplus studies. Eur J Clin Nutr. 2014;68:898–906. 34. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc. 2002;34:350–5. 35. Fakhouri TH, Hughes JP, Brody DJ, Kit BK, Ogden CL. Physical activity and screen-time viewing among elementary school-aged children in the United States from 2009 to 2010. JAMA Pediatr. 2013;167:223–9. 36. Dumith SC, Gigante DP, Domingues MR, Kohl HW 3rd. Physical activity change during adolescence: a systematic review and a pooled analysis. Int J Epidemiol. 2011;40:685–98. 37. Lau EY, Faulkner G, Qian W, Leatherdale ST. Longitudinal associations of parental and peer influences with physical activity during adolescence: findings from the COMPASS study. Health Promot Chronic Dis Prev Can. 2016;36:235–42. 38. Johansson E, Mei H, Xiu L, Svensson V, Xiong Y, Marcus C, Zhang J, Hagstromer M. Physical activity in young children and their parents-an early STOPP Sweden-China comparison study. Sci Rep. 2016;6:29595. 39. Drenowatz C, Erkelenz N, Wartha O, Brandstetter S, Steinacker JM. Parental characteristics have a larger effect on children's health behaviour than their body weight. Obes Facts. 2014;7:388–98. 40. Field AE, Gillman MW, Rosner B, Rockett HR, Colditz GA. Association between fruit and vegetable intake and change in body mass index among a large sample of children and adolescents in the United States. Int J Obes Relat Metab Disord. 2003;27:821–6. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Obesity Springer Journals

Association of parental body mass index (BMI) with child’s health behaviors and child’s BMI depend on child’s age

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Springer Journals
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Copyright © 2019 by The Author(s).
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Medicine & Public Health; Endocrinology; Public Health
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Abstract

Background: Parent’s and child’s body mass index (BMI) are strongly associated, but their relationship varies by child’s sex and age. Parental BMI reflects, among other factors, parents’ behaviors and home environment, which influence their child’s behaviors and weight. This study examined the indirect effect of parent’s BMI on child’s BMI via child health behaviors, conditional on child’s sex and age. Methods: Data from 2039 children and 1737 parents from eight cities of the U.S. involved in the Childhood Obesity Research Demonstration project tested the association between parental BMI and child’s percentage of 95th BMI percentile (%BMIp95). A generalized structural equation modeling approach to path analysis was used to estimate and test simultaneously the associations among parental BMI and child’s health behaviors and BMI across three age groups (preschool 2-4 yr., elementary 5-10 yr., and middle school 11-12 yr). Child’s health behaviors were examined as mediators. Results: Parental BMI was related to %BMIp95 across all age groups, and was strongest in 11-12 yr. children. Parental BMI was positively associated with boys’ fruit and vegetable (FV) intake and girls’ sugar-sweetened beverage (SSB) intake. Compared to 2-4 yr., older children had less FVs and physical activity, more screen time and SSB, and higher %BMIp95. Mediation effects were not significant. Conclusions: Parental BMI was associated with child’s %BMIp95 and some child behaviors, and this association was stronger in older children; older children also exhibited less healthy behaviors. Age- and sex-specific interventions that focus on age-related decreases in healthy behaviors and parental strategies for promoting healthy behaviors among at-risk children are needed to address this epidemic of childhood obesity. Keywords: Childhood obesity, Body mass index, Physical activity, Nutrition, Path analysis Background and children whose parents had a healthy BMI exhibited About one third of children aged 2 to 19 years are classi- healthier behaviors such as regular physical activity (PA) fied as having overweight or obesity in the United States, and improved dietary patterns [6], compared with children and this high prevalence of childhood obesity has been whose parents had higher BMI. Higher maternal BMI is present for decades [1]. Prevention of childhood obesity related to higher child’s BMI and sedentary behavior [7], is a public health priority, because obesity in childhood less fruit consumption [8], and more TV viewing [9, 10]. increases risk of obesity in adulthood [2] and is associ- These results are consistent with the notion that parental ated with long-term adverse health consequences [3]. BMI reflects parents’ health behaviors that influence Numerous studies have reported a strong association their child’s health behaviors and ultimately weight between parent’s and child’s body mass index (BMI) [4, 5], status [11, 12]. Thus, the development of obesity in childhood and persistence into adulthood is not entirely explained by inheritable factors [13], but also * Correspondence: dpoconno@central.uh.edu by the health and parenting behaviors of parents/care- Department of Health and Human Performance, University of Houston, 3875 givers [4]. Holman Street Garrison Gym 104, Houston, TX 77204-6015, USA Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lee et al. BMC Obesity (2019) 6:11 Page 2 of 10 Although the sharing of genetic and behavioral factors included only children with BMI ≥85th age and sex between parents and children results in a similar specific percentile, whereas the CA and MA projects propensity for obesity status [10, 14], the association of also included healthy weight children. Demographic in- parent and child BMI has been shown to vary by child’s formation for parents (sex, age, education, employment, sex and age. Both son’s and daughter’s BMI has been re- and family income) and children (sex, age, and ethnicity) ported to be significantly related to father’s BMI, while were collected from parents. Children were categorized daughter’s BMI was significantly related to mother’s BMI into three age groups based on school status: preschool only [15]. Two separate studies demonstrated that chil- (2-4 yr), elementary (5-10 yr), and middle school (11-12 dren’s PA was affected by shared environmental factors yr). The study was approved by the institutional review for parents and young children [16], but not for parents boards of the participating CORD organizations and in- and adolescents [17]. This can be explained by a stitutions, and written informed consent of parents and decreasing influence of parents on children’s behaviors assent of children were obtained prior to data collection. as children mature and become more independent from their parents [14]. Moreover, older children’s behaviors Anthropometric measures and obesity status may be affected by school programs Anthropometric measures were collected using the [18] and peer behaviors [19]. Given the influences of methods described in the National Health and Nutrition schools and peers on children’s health behaviors and Examination Survey Anthropometry Procedures Manual consequently their BMI, we assume that the association [21]. Parent and child’s height and weight were recorded of parental BMI on their child’s behaviors and BMI in centimeters (cm) to the nearest 0.1 cm and kilogram would be expected to vary as a function of child’s age. (kg) to the nearest 0.1 kg, respectively. Parental BMI was Thus, child age may moderate the association between calculated using the Quetelet index equation (kg/m ). parental BMI and child’s health behaviors and BMI. Each child’s relative BMI, computed as the percentage of This study investigated 1) the extent to which parental the respective age and sex specific BMI 95th percentile BMI was associated with the child’s health behaviors and value %BMIp95), was used as the child BMI variable, BMI, 2) the role of child’s health behaviors as mediators calculated using the CDC reference data and software al- between parental BMI and child’s BMI, and 3) whether gorithm [22]. This measure has been shown to be more these relationships are conditional on child’s sex and appropriate for the heaviest children (i.e., those >97th age. We hypothesized: 1) healthy parental BMI would be percentile), a characteristic of this sample given the associated with healthier child behaviors and BMI, 2) inclusion criteria in one site (TX) and the demographics healthy child behaviors would be related to a healthier of the other two sites (i.e., rural, racially/ethnically di- child BMI, 3) the relation between parental BMI and verse, low-income [23]). In addition, it has better statis- child BMI would be partly mediated by the child’s health tical properties for comparisons than other BMI-derived behaviors, and 4) these associations would vary by child’s measures for children [24]. sex and age. Child health behavior variables Methods Child health behaviors were measured by surveying par- Participants ents using a standard set of items selected from previ- This study was a secondary analysis of baseline data ously validated instruments [20, 25]. Child’s times per collected on the Childhood Obesity Research Demon- day eating fruits and vegetables (FVs) (sum of 2 items) stration project (CORD) [20]. CORD implemented inte- and sugar-sweetened beverages (SSBs) (sum of 2 items) grated primary care and public health interventions were assessed using items from the School Physical across eight communities in three states in the U.S. to Activity and Nutrition project survey [26] and the Child improve child and family health behaviors and to and Adolescent Trial for Cardiovascular Health prevent and reduce childhood obesity among families (CATCH) Kids Club After-school questionnaire [27]. eligible for benefits under Titles XIX (Medicaid) and Number of days per week engaged in 60 min or more of XXI (Children’s Health Insurance Plan (CHIP)) of the PA was assessed using one item from the Youth Risk Be- Social Security Act, which are programs intended to havior Survey [28]. The parent selected from 0 to 7 days serve families with low household income. for their child, which resulted in a highly negatively In 2012–2014, 2039 children aged 2–12 years and one skewed distribution, so the observed responses were di- of their parents or caregivers (N = 1737) were enrolled chotomized into 7 days/week (every day) versus less than from eight communities in three states (Brawley, 7 days/week (not every day) for analyses. Total hours Calexico, and El Centro of California (CA); Fitchburg, and minutes per week of screen time (TV/DVD, com- Lowell, and New Bedford of Massachusetts (MA); and puter/video game, etc.) were computed from hours and Austin and Houston of Texas (TX)). The TX project minutes per weekday and weekend day for screen time Lee et al. BMC Obesity (2019) 6:11 Page 3 of 10 collected using four items from the CATCH Kids Club (Hypothesis 2) and whether these effects varied by age After-school questionnaire [27]. group (Hypothesis 4, Fig. 1b), and 3) indirect (medi- ation) effects (Hypothesis 3), the path from parental Analyses BMI through the child’s health behavior to the child’s Separate analyses were conducted for boys and girls. %BMIp95 (Fig. 1b), and whether the mediation effect Descriptive statistics were reported as means and stand- varied by age. All analyses were adjusted for site and for ard deviations (mean ± SD) or percentages (%). A gener- different sample sizes among the age groups and across alized structural equation modeling approach to path cities. All statistical analyses were performed using Stata analysis was used to estimate and test simultaneously 14.2 (Stata Corp, Texas, USA), and significance was the associations between parental BMI and child’s BMI, defined as p < .05. parental BMI and child’s health behaviors, and child’s health behaviors and BMI among the three child age Results groups (Fig. 1). Associations were modeled using linear Only 4% of adults reported being non-parent guardians, links and normal distributions, except for children’sPA, and 91.5% reported being the mother of the enrolled which was a dichotomous variable and was modeled child (Table 1). About 64% of parents had graduated using a log link and binomial distribution. Parental BMI high school, less than 50% were employed, and about was mean-centered so that interaction effects could be 70% were living in households below the federal poverty interpreted at the average BMI of the parents. State (CA, level (FPL). Overall parental BMI was 31.6 ± 7.4. MA, and TX) and city within state (Brawley, Calexico, El The total number of boys and girls were 1014 and Centro, Fitchburg, Lowell, New Bedford, Austin, and 1025, respectively (Table 2). More than 80% of children Houston) were included as covariates in all statistical were reported to be Hispanic. Due to the inclusion cri- models to adjust for mean differences among study sites. terion in TX for children to be ≥85th BMI percentile, Four separate analyses were conducted to test the TX children had a higher mean %BMIp95. The TX sequentially: 1) direct effects of parental BMI on the children also had less frequent FV intake, more frequent child’s %BMIp95 (Hypothesis 1) and whether the direct SSB intake, more hours of screen time, and a lower pro- effects varied by age group (Hypothesis 4, Fig. 1a); 2) portion participating every day in ≥60 min/day of PA, associations in the full path model (Fig. 1b) between par- compared to the MA and CA children. The percentage ental BMI and the child’s health behaviors (Hypothesis of children with normal weight, overweight, and obesity 1), and between child’s health behaviors and %BMIp95 were 22.3, 21.8, and 55.9%, respectively. Fig. 1 Analytical models tested in the study. (a) Total effect of parent BMI on child BMI. (b) Direct effect of parent and child BMI and indirect effect through child health behaviors. FV: fruit and vegetable; SSB: sugar-sweetened beverage; PA: physical activity. Site and city within site were included in the models as covariates Lee et al. BMC Obesity (2019) 6:11 Page 4 of 10 Table 1 Characteristics of parents across the three sites Parent MA CA TX Total N 421 804 512 1737 Gender Female (%) 90.7% 98.4% 95.9% 95.0% Age (yr) Mean (SD) 33.4 ± 8.2 36.2 ± 8.4 34.4 ± 7.0 34.6 ± 7.9 Relation with child Mother (%) 86.7% 93.7% 94.1% 91.5% Education High school graduate (%) 68.8% 68.7% 55.6% 64.4% Employment Work for pay (%) 52.0% 39.7% 47.5% 46.4% Income Less than $15,000 (%) 53.2% 43.5% 60.0% 51.9% Poverty Index % below FPL 73.8% 60.6% 72.4% 68.9% BMI 29.8 ± 7.3 31.8 ± 7.2 33.2 ± 7.8 31.6 ± 7.4 Mean ± SD MA: Massachusetts; CA: California; TX: Texas FPL: federal poverty level BMI: body mass index, kg/m Age: years Table 2 Characteristics of children across the three sites Child MA CA TX Total/Average Boys N 218 552 244 1014 Age (yr) 6.6 ± 3.3 6.9 ± 2.7 7.7 ± 2.8 7.1 ± 2.9 Race (% of Hispanic) 57.8% 98.0% 88.9% 81.6% % of 95th BMI percentile 95.1 ± 18.9 98.8 ± 19.7 117.6 ± 19.2 103.9 ± 19.3 FV Intake (times/day) 2.3 ± 1.7 2.3 ± 1.5 1.9 ± 1.3 2.2 ± 1.5 SSB Intake (times/day) 0.7 ± 0.9 1.1 ± 1.2 1.3 ± 1.2 1.0 ± 1.1 Screen Time (hrs/wk) 23.2 ± 15.8 27.2 ± 16.1 28.5 ± 16.6 26.3 ± 16.2 7 days/wk. of PA 60 min/day 76.2% 43.5% 19.7% 46.4% Girls N 203 567 255 1025 Age (yr) 6.5 ± 3.1 6.9 ± 2.6 7.7 ± 2.7 7.0 ± 2.8 Race (% of Hispanic) 69.0% 98.9% 85.9% 84.6% % of 95th BMI percentile 92.1 ± 19.8 96.2 ± 20.3 115.99 ± 16.8 101.4 ± 18.6 FV Intake (times/day) 2.3 ± 1.4 2.4 ± 1.5 2.3 ± 1.4 2.3 ± 1.5 SSB Intake (times/day) 0.8 ± 1.1 1.0 ± 1.0 1.2 ± 1.3 1.0 ± 1.1 Screen Time (hrs/wk) 21.7 ± 13.0 22.3 ± 12.8 26.5 ± 15.5 24.2 ± 13.8 7 days/wk. of PA 60 min/day 69.5% 42.3% 14.9% 42.2% Overall N 421 1119 499 2039 Age (yr) 6.5 ± 3.2 6.9 ± 2.7 7.7 ± 2.8 7.1 ± 2.9 Race (% of Hispanic) 63.4% 98.5% 87.9% 83.1% % of 95th BMI percentile 93.6 ± 19.4 97.5 ± 20.0 116.80 ± 18.0 102.7 ± 19.0 FV Intake (times/day) 2.3 ± 1.6 2.4 ± 1.5 2.1 ± 1.4 2.3 ± 1.5 SSB Intake (times/day) 0.8 ± 1.0 1.1 ± 1.1 1.3 ± 1.3 1.0 ± 1.1 Screen Time (hrs/wk) 22.5 ± 14.4 24.8 ± 14.5 27.5 ± 16.1 25.3 ± 15.0 7 days/wk. of PA 60 min/day 72.9% 42.9% 17.3% 44.3% Mean ± SD MA: Massachusetts, CA: California, TX: Texas FV intake: time(s) per day fruit and vegetable intake; SSB intake: time(s) per day sugar-sweetened beverage (i.e., soda, punch) intake; screen time: total hour(s) of screen time (i.e., computer, TV, video game) during 5 weekdays and 2 weekend days; 7 days/wk. of PA 60 min/day: percentage of children who every day do physical activity that make the child breath hard for 60 min or more Lee et al. BMC Obesity (2019) 6:11 Page 5 of 10 Age group differences for child’s BMI and health be- more times per day per 10-unit difference in parent haviors after adjusting for parental BMI and city were BMI, p = .029) in boys and SSB intake (0.2 more times per reported in Table 3. Compared to the preschool boys, day per 10-unit difference in parent BMI, p = .007) in girls elementary school boys had significantly higher (Hypothesis 1). However, parental BMI was not associated %BMIp95. Elementary and middle school boys showed with SSB consumption (p =.931), screen time (p =.833), significantly higher screen time (p < .001) and lower FV or PA (p =.515) in boys and FV intake (p =.815), screen intake (p < .001) and PA (elementary: p = .001; middle time (p =.379), or PA (p = .794) in girls (Hypothesis 1, data school: p < .001) than preschool boys. Elementary and not shown). middle school girls showed higher screen time (p < .001) In comparing the age groups, higher parental BMI was and lower FV intake and PA (elementary: p < .001; mid- associated with more screen time (p = .045) and dle school: p = .001) than preschool girls. SSB intake in engaging in PA seven days/week (p = .031) in elementary middle school girls were significantly higher (p = .023) school boys and more FV intake (p = .013) in middle than preschool girls. school boys. Age did not moderate the association between parental BMI and girls’ health behaviors Direct effect of parental BMI on child’s BMI among age (Hypothesis 4, Tables 4 and 5). groups Parental BMI was significantly positively associated Association between child’s health behaviors and with child’s %BMIp95 in both boys and girls (Hypoth- child’sBMI esis 1, p < .001). The association of parental BMI and Across all age groups, FV intake (p = .262, p = .278), child’s %BMIp95 increased significantly with child’s SSB intake (p =.227, p = .372), and screen time (p =.480, age (i.e., Hypothesis 4, parent BMI*child age inter- p = .258) were not significantly related to boys’ and girls’ action) among boys (p = .016) and girls (p = .019). A %BMIp95, but engaging in PA seven days/week (p =.002) positive one-unit difference in parental BMI was was significantly associated with healthier %BMIp95 associated with a 0.3, 0.8, and 0.7% higher %BMIp95 among boys and girls. Middle school girls who did not en- in preschool, elementary, and middle school boys, re- gage in PA seven days/week (p = .020) and elementary spectively. A positive one-unit difference in parental school boys who consumed SSB more times/day (p =.046) BMI was associated with a 0.5, 0.9, and 1.1% higher had significantly higher %BMIp95. %BMIp95 in preschool, elementary, and middle school Preschool boys ate FVs 0.7 and 0.8 more times/day girls, respectively. than elementary and middle school boys, respectively (p < .001). SSB intake did not differ significantly across Association between parental BMI and child’s health boys’ age groups. Compared to preschool boys, behaviors elementary and middle school boys had significantly Averaged across all age groups, parental BMI was longer screen time (5 h and 8 h, respectively, p < .001), significantly positively associated with FV intake (0.2 and were less likely to engage in PA seven days/week Table 3 Age differences of child’s BMI and health behaviors in both boys and girls Child Preschool Elementary school Middle School Total/Average Boys N 299 617 98 1014 % of 95th BMI percentile 99.9 ± 1.1 103.3 ± 0.8* 100.1 ± 1.9 101.1 ± 1.3 FV Intake (times/day) 2.6 ± 0.1 1.9 ± 0.1*** 1.9 ± 0.2*** 2.1 ± 0.1 SSB Intake (times/day) 0.9 ± 0.1 1.1 ± 0.1 1.1 ± 1.1 1.0 ± 0.4 Screen Time (hrs/wk) 22.6 ± 0.1 27.9 ± 0.7*** 30.5 ± 1.6*** 27.0 ± 1.1 7 days/wk. of PA 60 min/day (OR) 0.6 ± 0.0 0.5 ± 0.0** 0.4 ± 0.1*** 0.5 ± 0.0 Girls N 282 666 77 1025 % of 95th BMI percentile 99.1 ± 1.2 100.8 ± 0.9 98.2 ± 2.1 99.3 ± 1.4 FV Intake (times/day) 2.6 ± 0.1 2.2 ± 0.1*** 2.0 ± 0.2** 2.3 ± 0.1 SSB Intake (times/day) 0.9 ± 0.1 1.0 ± 0.1 1.2 ± 0.1* 1.1 ± 0.1 Screen Time (hrs/wk) 20.6 ± 0.8 24.5 ± 0.6*** 29.2 ± 1.5*** 24.8 ± 1.0 7 days/wk. of PA 60 min/day (OR) 0.6 ± 0.0 0.4 ± 0.0*** 0.3 ± 0.1** 0.4 ± 0.0 Values are model-predicted means±SE, adjusted for mean of parental BMI and different numbers of children across the sites and cities *p < .05, **p = .001, ***p < .001 Preschool is the reference group Lee et al. BMC Obesity (2019) 6:11 Page 6 of 10 Table 4 The associations among parental BMI, child’s health behaviors, and child’s BMI in boys Boys P-BMI ➔ FV P-BMI ➔ SSB P-BMI ➔ Screen Time P-BMI ➔ PA Constant 2.51 ± 0.15*** 1.05 ± 0.11*** 22.99 ± 1.62*** 0.53 ± 0.12* Age2 −0.68 ± 0.10*** 0.11 ± 0.08 5.00 ± 1.13*** 0.57 ± 0.09*** Age3 −0.84 ± 0.17*** 0.14 ± 0.13 7.95 ± 1.90*** 0.34 ± 0.10*** P-BMI 0.001 ± 0.01 0.01 ± 0.008 −0.06 ± 0.12 0.97 ± 0.02* P-BMI*Age2 −0.003 ± 0.01 −0.001 ± 0.01 0.25 ± 0.15* 1.04 ± 0.02* P-BMI*Age3 0.06 ± 0.03* −0.03 ± 0.02 −0.12 ± 0.28 1.03 ± 0.04 FV ➔ C-BMI SSB ➔ C-BMI Screen Time ➔ C-BMI PA ➔ C-BMI Constant 112.54 ± 2.68*** 114.49 ± 2.15*** 113.63 ± 2.46*** 114.17 ± 2.20*** Age2 4.17 ± 2.59 0.80 ± 1.78 − 0.69 ± 2.42 3.92 ± 1.90* Age3 1.11 ± 3.92 1.60 ± 3.05 −3.59 ± 4.20 1.39 ± 2.86 P-BMI 0.25 ± 0.73* 0.26 ± 0.14* 0.26 ± 0.14* 0.24 ± 0.14* P-BMI*Age2 0.55 ± 0.17** 0.53 ± 0.17** 0.52 ± 0.17** 0.57 ± 0.17** P-BMI*Age3 0.46 ± 0.34 0.41 ± 0.33 0.47 ± 0.33 0.45 ± 0.33 Behavior 0.46 ± 0.73 −0.75 ± 0.96 0.004 ± 0.068 −14.05 ± 4.80* Behavior*Age2 −0.48 ± 0.90 1.97 ± 1.16* 0.13 ± 0.08 3.33 ± 2.66 Behavior*Age3 −0.53 ± 1.65 −1.54 ± 1.93 0.11 ± 0.13 6.96 ± 4.50 All values are fixed regression coefficient ± SE, except values of P-BMI ➔ PA are odd ratios (OR) ± SE *p < .05, **p = .001, ***p < .001 Constant: preschool (reference groups); Age2: elementary; Age3: middle school P-BMI: mean-centered parental BMI; C-BMI: child percent of 95th BMI percentile (%BMIp95) Behaviors: FV (fruit and vegetable intake [times per day]), SSB (sugar-sweetened beverage intake [times per day]), screen time (TV, DVD, computer, video game [hours per week]), and PA (physical activity [7 days/week vs. < 7 days/week]) of child Table 5 The associations among parental BMI, child’s health behaviors, and child’s BMI in girls Girls P-BMI ➔ FV P-BMI ➔ SSB P-BMI ➔ Screen Time P-BMI ➔ PA Constant 2.52 ± 0.15*** 0.92 ± 0.12*** 19.68 ± 1.40*** 0.37 ± 0.10*** Age2 −0.38 ± 0.10*** 0.12 ± 0.08 3.78 ± 0.94*** 0.52 ± 0.08*** Age3 −0.67 ± 0.19*** 0.30 ± 0.14* 8.36 ± 1.73*** 0.43 ± 0.13* P-BMI −0.008 ± 0.01 0.005 ± 0.009 −0.067 ± 0.105 1.02 ± 0.02 P-BMI*Age2 0.005 ± 0.04 0.015 ± 0.01 0.15 ± 0.13 0.98 ± 0.02 P-BMI*Age3 0.026 ± 0.02 0.021 ± 0.02 0.26 ± 0.22 0.95 ± 0.04 FV ➔ C-BMI SSB ➔ C-BMI Screen Time ➔ C-BMI PA ➔ C-BMI Constant 113.86 ± 2.76*** 112.78 ± 2.17*** 114.17 ± 2.60*** 112.22 ± 2.22*** Age2 2.12 ± 2.61 1.53 ± 1.76 −0.70 ± 2.54 2.54 ± 1.85 Age3 1.96 ± 4.40 −1.71 ± 3.35 −8.12 ± 4.78* 1.89 ± 3.12 PBMI 0.48 ± 0.15** 0.49 ± 0.15** 0.48 ± 0.15** 0.51 ± 0.15** P-BMI*Age2 0.37 ± 0.18* 0.37 ± 0.18* 0.37 ± 0.18* 0.35 ± 0.18* P-BMI*Age3 0.65 ± 0.31* 0.60 ± 0.31* 0.55 ± 0.31* 0.46 ± 0.31 Behavior −0.56 ± 0.75 −0.33 ± 1.03 −0.09 ± 0.09 −15.83 ± 5.30* Behavior*Age2 −0.45 ± 0.89 −0.16 ± 1.22 0.10 ± 0.10 3.67 ± 2.65 Behavior*Age3 −1.98 ± 1.76 0.23 ± 2.02 0.26 ± 0.16 10.39 ± 5.07* All values are fixed regression coefficient ± SE, except values of P BMI ➔ PA are odd ratios (OR), ±SE *p < .05, **p = .001, ***p < .001 Constant: preschool (reference groups); Age2: elementary; Age3: middle school PBMI: mean-centered parental BMI; CBMI: child percent of 95th BMI percentile (%BMIp95) Behaviors: FV (fruit and vegetable intake [times per day]), SSB (sugar-sweetened beverage intake [times per day]), screen time (TV, DVD, computer, video game [hours per week]), and PA (physical activity [7 days/week vs. < 7 days/week]) of child Lee et al. BMC Obesity (2019) 6:11 Page 7 of 10 (p < .001, elementary: OR = 0.57; middle school: OR = Consistent with energy balance theory, we hypothe- 0.34) (Table 4). sized that unhealthy child behaviors related to energy Preschool girls ate FVs 0.4 and 0.7 more times/day intake (FV and SSB intake) and to energy expenditure than elementary and middle school girls, respectively (PA and screen time) would be related to their (p < .001). Middle school girls consumed SSB 0.3 %BMIp95 (Hypothesis 2). In the present study, a large more times/day than preschool girls (p = .018). Com- difference in parental BMI was positively associated pared to preschool girls, elementary and middle with FV intake in boys and SSB intake in girls (al- school girls had significantly longer screen time (3.8 h though the effect size is very small, it was statistically and 8.4 h, respectively, p < .001), and were less likely significant due to the large sample size), but not with to engage in PA seven days/week (elementary: OR = child’s screen time and PA. Previous studies found an 0.52, p < .001; middle school: OR = 0.43, p = .003) association of higher parental BMI with their children (Table 5). viewing more TV [9, 10] and engaging in less PA [6]. Additionally, only children’s PA was associated with Indirect (mediation) effects their %BMIp95 in this study, whereas other studies In both boys and girls, across and within all age groups, found relationships between children’sBMI andtheir none of the indirect effects of parental BMI on child’s dietary and sedentary behaviors [6, 30, 31]. %BMIp95 via child’s health behaviors were statistically One explanation for inconsistencies between our significant (Hypothesis 3). Thus, there are no mediatory results and previous studies may be due to different effects of child’s health behaviors on the relationship measures of child’s health behaviors. In our study, observed between parental BMI and child’s %BMIp95. children’s FV and SSB intake were measured as times/ day of the prior day, which do not provide a complete Discussion quantification of a child’s dietary intake, whereas previ- In this study, parental BMI was positively related to ous studies measured both frequency and portion sizes child’s %BMIp95 in both boys and girls, as previous [32]. Nevertheless, our data indicate that parental BMI is studies have reported [4, 5]. The 90% of enrolled adults a correlate of some child’s health behaviors and %BMIp95, being mothers may have resulted in the stronger and the survey questions that we used in this study have observed association of mothers’ BMI on %BMIp95 in been validated and used in previous studies [33]. daughters since the association of BMI in The current study observed age differences in the as- mother-daughter dyads is higher than in mother-son, sociations among parental BMI, child’s health behaviors, father-daughter, or father-son dyads [5]. In our data, and %BMIp95 (Hypothesis 4). Overall, preschool-aged older boys (elementary) and girls (elementary and mid- children showed healthier behaviors such as more fre- dle school) showed a stronger positive association be- quent FV intake, less frequent SSB intake, less weekly tween %BMIp95 and parental BMI, compared to the screen time, and higher proportion engaging in daily PA preschool-aged children. Previous studies found that compared to elementary and middle school children in obesity status in older children was affected by both in- both boys and girls. In particular, engaging in PA seven heritable traits from parents and shared environment days/week was lower, while screen time was higher, over time and emphasized that environmental effects among the older children than the youngest children. were important determinants to develop behavior These results are consistent with reports that suggest a patterns and obesity among adolescents [13, 29]. One decrease of PA is significantly associated with an in- potential explanation for our results is that factors com- crease of screen time among children and adolescents mon to both parents and children, who live in the same [10, 34, 35]. The reasons why child’s PA declines with household, including genetic, environmental, and socio- age are unclear, but it is possible that social support fac- cultural influences, may result in higher %BMIp95 in tors (parental influence, schools’ academic programming older children and increase the association with parental and facilities, peers’ activity, etc.) may be associated with BMI. decreases in opportunities for moderate to vigorous An assumption in interpreting our results is that par- physical activity (MVPA) among adolescents [36, 37]. In ental BMI is an indicator of genetic, environmental, and this study, there was significant association of higher sociocultural factors common to both parents and chil- parental BMI and engagement in PA every day in dren, and potentially long-term parental dietary, PA, and elementary school boys. One potential explanation of sedentary behaviors, and that those health behaviors this result could be that boys’ PA are less dependent on would influence their child’s health behaviors and BMI their mother’s PA and BMI, since boys’ PA were more [11, 12]. Thus, we expected that unhealthy parental BMI associated with their fathers [38]. Higher SSB intake and would be associated with child’s unhealthy behaviors screen time in the oldest girls were observed in this such as less FV and more SSB intake (Hypothesis 1). study, consistent with research that reported an Lee et al. BMC Obesity (2019) 6:11 Page 8 of 10 association between higher soda intake and longer TV objective measures. Fourth, parental behavior data were viewing time in older children [39]. Higher FV intake not consistently collected across the sites and were in middle school boys with higher parental BMI could therefore not available for our analyses; parent behaviors be explained by greater overall frequency of food may be more directly and strongly related to child health consumption, including more fruit and vegetables, behaviors than is parent BMI. Fifth, the oldest age group with larger parent body size [40]. Another potential sample sizes were smaller than the other age groups, explanation is that the parents with higher BMI may thus limiting the precision of the estimates for that be more concerned with obesity in their sons and group. provide a better diet as reflected by their higher FV Despite these limitations, this study included consumption. low-income families from different states and cities Finally, we expected child’s health behaviors to be me- across the USA, which allows broader generalization diators partly explaining the association between parent’s of the results compared to single-site studies. Investi- and child’s BMI (Hypothesis 3). However, none of the gation of age differences of the relationships among mediation effects were statistically significant. This was parental BMI, child’s health behaviors, and child’s due to either non-significant associations between BMI is a novel aspect. Age-specific associations may parental BMI and child’s health behaviors or between be informative for considering different intervention child’s health behaviors and %BMIp95, or both. Our strategies, such as providing interventions for the model showed that some of the child’s health behaviors family and home environment for preschool children, (e.g., FV and SSB intake, and PA) were associated with but including additional interventions for older chil- parental and child’s BMI, but the direct relationship be- dren, since older children spend much time at school tween parental BMI and child’s BMI remained relatively as well as home, make decisions more independently, unchanged. One previous study found a stronger rela- and are influenced by peer groups in addition to tionship between parent’s and child’s health behaviors parents [14, 19]. compared to the relationship between parental and child’sBMI [39]. Although parental health behaviors Conclusion were not assessed across all sites, parents’ behavioral in- This study demonstrated a large association between fluence on modifiable child’s health behaviors may affect parent BMI and child’s %BMIp95 but failed to detect child’s BMI and thus explain part of the association any mediation through child health behaviors. The asso- between parental and child’s BMI. Such behaviors may ciation between parental BMI and older children’s be opportunities to consider when designing interven- %BMIp95 was stronger compared to younger children. tions with a goal of changing behaviors in both parent Older children also had unhealthier behaviors such as and child to affect BMI. Caution is warranted in inter- less daily FV intake and PA engagement and more preting that a direct or indirect association indicates that weekly screen time and SSB intake; these unhealthy “blame” should be placed on individuals, such as viewing behaviors were associated with their higher %BMIp95. parents as “the” causal agent of obesity in childhood. Parental BMI would impact unhealthy behaviors and Our data do not suggest this. Instead, these associations obesity in their children, but our results are consistent should be viewed as opportunities to determine factors with the notion that childhood obesity may be affected that may impact obesity in children. Because obesity is by multi-factors such as environmental factors, inherit- an intractable disease with multiple etiologies, “blaming” able factors, parental behaviors, and a child’s own un- individuals (either parents or children) is counterpro- healthy behaviors. Thus, interventions for the prevention ductive and fails to consider the environmental, genetic, and control of childhood obesity may consider focusing epigenetic, and biological aspects of obesity. on simultaneously changing the health behaviors of both This study has a number of limitations. First, the parents and children. Our findings are also consistent sample was primarily Hispanic families who were eligible with the notion that early life (before age 5) may be the for Medicaid and CHIP benefits, so the results may not best opportunity for interventions to prevent childhood generalize to populations with a different ethnicity or obesity, before children develop their own unhealthy higher household income. Second, the cross-sectional behaviors and weight status. data allows for only evaluating associations among par- ental BMI and child’s health behaviors and %BMIp95; there may be unmeasured causal variables and paths that Abbreviations were not included in the analyses. Third, the survey %BMIp95: the percentage of 95th BMI percentile; BMI: body mass index; CA: California; CATCH: Child and Adolescent Trial for Cardiovascular Health; items did not reflect long-term child health behaviors, CORD: Childhood Obesity Research Demonstration; FV: fruit and vegetables; asking only about behaviors on a single day or week, and MA: Massachusetts; PA: physical activity; SSB: sugar-sweetened beverage; self-reported behaviors may not be as accurate as more TX: Texas Lee et al. BMC Obesity (2019) 6:11 Page 9 of 10 Acknowledgements in Greece; dietary and lifestyle habits in the context of the family The authors thank the MA-CORD, CA-CORD, TX-CORD, and EC-CORD teams environment: the Vyronas study. Appetite. 2008;51:218–22. who participated and collected the data for this study, especially thanks to 7. Sijtsma A, Sauer PJ, Corpeleijn E. Parental correlations of physical activity Carrie A. Dooyema (CDC/NCCDPHP/DNPAO) and Dr. Thomas Land of and body mass index in young children--he GECKO Drenthe cohort. MA-CORD for critical comments on the manuscript. Int J Behav Nutr Phys Act. 2015;12:132. 8. Morello MI, Madanat H, Crespo NC, Lemus H, Elder J. Associations among Funding parent acculturation, child BMI, and child fruit and vegetable consumption in a Hispanic sample. J Immigr Minor Health. 2012;14:1023–9. This research was supported in part by cooperative agreement RFA-DP-11- 007 (grant U18DP003350 and U18DP003377–01) from the Centers for Dis- 9. Maffeis C, Talamini G, Tato L. Influence of diet, physical activity and parents' ease Control and Prevention (CDC). The content is solely the responsibility of obesity on children's adiposity: a four-year longitudinal study. Int J Obes the authors and does not necessarily represent the official views of the CDC. Relat Metab Disord. 1998;22:758–64. 10. Steffen LM, Dai S, Fulton JE, Labarthe DR. Overweight in children and adolescents associated with TV viewing and parental weight: project Availability of data and materials HeartBeat. Am J Prev Med. 2009;37:S50–5. The datasets used and analyzed during this study are available from the 11. Ventura AK, Birch LL. Does parenting affect children's eating and weight corresponding author upon reasonable request. status? Int J Behav Nutr Phys Act. 2008;5:15. 12. Carriere G. Parent and child factors associated with youth obesity. Health Author’s contributions Rep. 2003;14(Suppl):29–39. CYL conceived the study design, implemented the literature search, 13. Nelson MC, Gordon-Larsen P, North KE, Adair LS. Body mass index gain, fast conducted the data analyses and interpretation, and wrote the first draft of food, and physical activity: effects of shared environments over time. the manuscript. DPO participated in the study design, contributed to the Obesity (Silver Spring). 2006;14:701–9. design of the CORD project, and assisted with data collection, analysis, and 14. Silventoinen K, Rokholm B, Kaprio J, Sorensen TI. The genetic and interpretation. TAL and GXA contributed to the design of the CORD project environmental influences on childhood obesity: a systematic review of twin and data collection, and CAJ and reviewed the study design and data and adoption studies. Int J Obes. 2010;34:29–40. analysis and interpretation. All authors were involved in writing and revising 15. Shafaghi K, Shariff ZM, Taib MN, Rahman HA, Mobarhan MG, Jabbari H. the manuscript and approved the submitted and published versions. Parental body mass index is associated with adolescent overweight and obesity in Mashhad. Iran Asia Pac J Clin Nutr. 2014;23:225–31. Ethics approval and consent to participate 16. Franks PW, Ravussin E, Hanson RL, Harper IT, Allison DB, Knowler WC, All studies of this project were approved by the institutional review boards Tataranni PA, Salbe AD. Habitual physical activity in children: the role of of the participating research institutes and universities in CA, MA, and TX. genes and the environment. Am J Clin Nutr. 2005;82:901–8. Written informed consent of parents and assent of children were obtained 17. Stubbe JH, Boomsma DI, Vink JM, Cornes BK, Martin NG, Skytthe A, Kyvik KO, prior to data collection. Rose RJ, Kujala UM, Kaprio J, et al. Genetic influences on exercise participation in 37,051 twin pairs from seven countries. PLoS One. Consent for publication 2006;1:e22. Not applicable. 18. Fernandes MM, Sturm R. The role of school physical activity programs in child body mass trajectory. J Phys Act Health. 2011;8:174–81. Competing interests 19. Halliday TJ, Kwak S. Weight gain in adolescents and their peers. Econ Hum DPO and GXA report grants from Centers for Disease Control and Biol. 2009;7:181–90. Prevention (CDC), during the conduct of the study. Other authors 20. O'Connor DP, Lee RE, Mehta P, Thompson D, Bhargava A, Carlson C, Kao D, have nothing to disclose. Layne CS, Ledoux T, O'Connor T, et al. Childhood obesity research demonstration project: cross-site evaluation methods. Child Obes. 2015;11:92–103. Publisher’sNote 21. Centers for Disease Control and Prevention: National Health and Nutrition Springer Nature remains neutral with regard to jurisdictional claims in Examination Survey: Anthropometry Procedures Manual [https://www.cdc. published maps and institutional affiliations. gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf] Accessed 12 Dec Author details 22. Centers for Disease Control and Prevention: A SAS Program for the CDC Department of Health and Human Performance, University of Houston, 3875 Growth Charts [https://www.cdc.gov/nccdphp/dnpao/growthcharts/ Holman Street Garrison Gym 104, Houston, TX 77204-6015, USA. Division of resources/sas.htm] Accessed 12 Dec 2017. Health Promotion and Behavioral Science, Graduate School of Public Health, San Diego State University, 5500 Campanile Drive, San Diego 92182-4162, 23. Foltz JL, Belay B, Dooyema CA, Williams N, Blanck HM. Childhood obesity CA, USA. research demonstration (CORD): the cross-site overview and opportunities for interventions addressing obesity community-wide. Child Obes. Received: 21 December 2017 Accepted: 18 February 2019 2015;11:4–10. 24. Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin References Nutr. 2009;90:1314–20. 1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult 25. Davison KK, Falbe J, Taveras EM, Gortmaker S, Kulldorff M, Perkins M, Blaine obesity in the United States, 2011-2012. Jama. 2014;311:806–14. RE, Franckle RL, Ganter C, Baidal JW, et al. Evaluation overview for the 2. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of Massachusetts childhood obesity research demonstration (MA-CORD) childhood overweight into adulthood: a systematic review of the literature. project. Child Obes. 2015;11:23–36. Obes Rev. 2008;9:474–88. 26. University of Texas School of Public Health: School Physical Activity and 3. Biro FM, Wien M. Childhood obesity and adult morbidities. Am J Clin Nutr. Nutrition (SPAN) project - Student Survey [https://sph.uth.edu/dotAsset/ 2010;91:1499s–505s. b94d672a-ca59-4bcb-b2e9-1d893c503764.pdf, https://sph.uth.edu/dotAsset/ 4. Farajian P, Panagiotakos DB, Risvas G, Malisova O, Zampelas A. Hierarchical 4d09697e-e692-4e66-816e-d9d6ccb35499.pdf] Accessed 12 Dec 2017. analysis of dietary, lifestyle and family environment risk factors for childhood obesity: the GRECO study. Eur J Clin Nutr. 2014;68:1107–12. 27. National Institute of Health: CATCH Kids Club After-school Student 5. Liu Y, Chen HJ, Liang L, Wang Y. Parent-child resemblance in weight status Questionnaire [https://www.nhlbi.nih.gov/health/educational/wecan/ and its correlates in the United States. PLoS One. 2013;8:e65361. downloads/CKC-questionnaire.pdf] Accessed 12 Dec 2017. 6. Kosti RI, Panagiotakos DB, Tountas Y, Mihas CC, Alevizos A, Mariolis T, 28. Centers for Disease Control and Prevention: Youth Risk Behavior Survey Papathanassiou M, Zampelas A, Mariolis A. Parental body mass index in (YRBS) [http://www.cdc.gov/healthyyouth/yrbs/pdf/questionnaire/ association with the prevalence of overweight/obesity among adolescents 2009MiddleSchool.txt] Accessed 12 Dec 2017. Lee et al. BMC Obesity (2019) 6:11 Page 10 of 10 29. Ihmels MA, Welk GJ, Eisenmann JC, Nusser SM, Myers EF. Prediction of BMI change in young children with the family nutrition and physical activity (FNPA) screening tool. Ann Behav Med. 2009;38:60–8. 30. Fogelholm M, Nuutinen O, Pasanen M, Myohanen E, Saatela T. Parent-child relationship of physical activity patterns and obesity. Int J Obes Relat Metab Disord. 1999;23:1262–8. 31. Williams SL, Mummery WK. Links between adolescent physical activity, body mass index, and adolescent and parent characteristics. Health Educ Behav. 2011;38:510–20. 32. Hoelscher DM, Day RS, Kelder SH, Ward JL. Reproducibility and validity of the secondary level school-based nutrition monitoring student questionnaire. J Am Diet Assoc. 2003;103:186–94. 33. Pei Z, Flexeder C, Fuertes E, Standl M, Berdel D, von Berg A, Koletzko S, Schaaf B, Heinrich J. Mother's body mass index and food intake in school-aged children: results of the GINIplus and the LISAplus studies. Eur J Clin Nutr. 2014;68:898–906. 34. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc. 2002;34:350–5. 35. Fakhouri TH, Hughes JP, Brody DJ, Kit BK, Ogden CL. Physical activity and screen-time viewing among elementary school-aged children in the United States from 2009 to 2010. JAMA Pediatr. 2013;167:223–9. 36. Dumith SC, Gigante DP, Domingues MR, Kohl HW 3rd. Physical activity change during adolescence: a systematic review and a pooled analysis. Int J Epidemiol. 2011;40:685–98. 37. Lau EY, Faulkner G, Qian W, Leatherdale ST. Longitudinal associations of parental and peer influences with physical activity during adolescence: findings from the COMPASS study. Health Promot Chronic Dis Prev Can. 2016;36:235–42. 38. Johansson E, Mei H, Xiu L, Svensson V, Xiong Y, Marcus C, Zhang J, Hagstromer M. Physical activity in young children and their parents-an early STOPP Sweden-China comparison study. Sci Rep. 2016;6:29595. 39. Drenowatz C, Erkelenz N, Wartha O, Brandstetter S, Steinacker JM. Parental characteristics have a larger effect on children's health behaviour than their body weight. Obes Facts. 2014;7:388–98. 40. Field AE, Gillman MW, Rosner B, Rockett HR, Colditz GA. Association between fruit and vegetable intake and change in body mass index among a large sample of children and adolescents in the United States. Int J Obes Relat Metab Disord. 2003;27:821–6.

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BMC Obesity – Springer Journals

Published: Apr 1, 2019

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