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Article

An Exploratory Study Examining Obesity Risk in Non-Obese Mothers of Young Children Using a Socioecological Approach

by
Jennifer Martin-Biggers
1,
Virginia Quick
1,*,
Kim Spaccarotella
2 and
Carol Byrd-Bredbenner
1
1
Department of Nutritional Sciences, Rutgers University, 26 Nichol Avenue, New Brunswick, NJ 08901, USA
2
Department of Biological Sciences, Kean University, 1000 Morris Avenue, Union, NJ 07082, USA
*
Author to whom correspondence should be addressed.
Nutrients 2018, 10(6), 781; https://doi.org/10.3390/nu10060781
Submission received: 26 March 2018 / Revised: 11 June 2018 / Accepted: 15 June 2018 / Published: 17 June 2018
(This article belongs to the Special Issue Eating Disorders and Obesity: The Challenge for Our Times)
Versions Notes

Abstract

:
This cross-sectional, exploratory study aimed to (1) develop an obesity risk score using a comprehensive set of variables assessing mothers’ intrapersonal weight-related characteristics and those of their homes’ interpersonal and physical environments, and (2) determine how weight-related characteristics differ by obesity risk level. U.S. mothers (N = 550) of preschool-aged children completed an online survey that assessed maternal self-report weight status, sociodemographics, health-related characteristics, and maternal intrapersonal and their homes’ interpersonal and physical environment weight-related characteristics. Binomial logistic regression analysis identified variables significantly associated with obesity. Scores for all obesity risk variables were summed to create a weighted obesity risk score for non-obese participants (n = 386). Analysis of variance and Tukey post-hoc tests determined how non-obese mothers’ sociodemographic, health-related, and intrapersonal and their homes’ interpersonal and physical environment characteristics differed among obesity risk score tertiles. Results revealed that eight variables explained 53 percent of maternal obesity risk, including African American race, lower education level, more children in household, poorer maternal health, higher weight teasing history, higher body dissatisfaction, primary relative with obesity, and greater concern about children’s overweight risk. Non-obese mothers in the highest obesity risk tertile had greater food insecurity risk, lower family affluence, worse sleep quality, less fruit/vegetable availability, and reported less frequent modeling of healthy behaviors and more family conflict. In conclusion, eight characteristics that explained more than half of the risk for obesity in non-obese mothers of young children, may help healthcare professionals identify mothers at increased risk of obesity and offer preventive care early.

1. Introduction

Recent data from the National Health and Nutrition Examination Survey (NHANES) indicate that nearly 34.9 percent of U.S. adults are obese [1]. It is no longer debated that obesity and its comorbidities are significantly impacting Americans both in financial and quality of life costs. A systematic review of 33 studies found that the overall estimated medical costs of obesity accounted for approximately 10 percent of all medical spending in the United States [2]. The physical health consequences of obesity are numerous and include effects on the pulmonary, orthopedic, neurological, gastroenterological, endocrine, and cardiovascular systems, as well as causing systemic inflammation, thereby greatly impacting quality of life [3,4,5,6].
The increase in obesity rates in the U.S. likely reflects changes in environmental factors and lifestyle choices related to increased energy intake and inadequate energy expenditure, rather than genetic changes because of the slow rate at which population-wide genetic changes occur [7,8,9]. Changes in lifestyles and the environment that have occurred in tandem with the increase in obesity include ready availability of food and shifting dietary patterns, which have led to an increase in calorie intake [10,11], combined with a decline in energy expenditure associated with a sedentary lifestyle [12]. Macro-level factors have a more indirect (yet important) role in influencing behaviors and include social norms, agriculture policies, economic policies, advertising, and more. Factors that are more directly influenced by an individual include his or her physical and social environments and personal factors (e.g., home environment, skills, behaviors).
In recent years, health behavior change experts have recognized the influence of factors in the physical environment, as well as in the social environment, on obesity and health outcomes [13]. This ecological approach to public health issues posits that an individual’s motivation and skills alone are not adequate to facilitate behavior change; environments also need to support and facilitate the practice of healthful behaviors [14,15,16]. Reciprocal determinism, a construct of the Social Cognitive Theory, describes how a person’s characteristics and behaviors, as well as the physical and social environment where behaviors occur, mutually affect each other [17]. Environments not supportive of weight-management behaviors make it difficult to engage in practices that prevent unhealthy weight gain. Research has increasingly provided evidence that environmental factors significantly influence diet, physical activity, and obesity in adults [15,18,19] and children [16,20,21,22], yet their relative contribution to obesity risk remains unknown.
The home environment deserves in-depth study given its potential influence on health behaviors [23]. An understanding of factors in the home environment associated with obesity could assist healthcare providers, researchers, parents, and caregivers in creating home environments that support healthy weights for the whole family. Mothers tend to be food gatekeepers in the home and, thus, are well suited to providing an appraisal of their homes’ social (interpersonal) and physical environment characteristics [23,24]. Mothers also are children’s role models and often the household food decision-maker, thus mothers’ own intrapersonal cognitions, behaviors, and weight status play a role in weight-related decisions affecting the entire family [23,24]. Moreover, behaviors directly affecting weight are developed during childhood and track into adulthood [25,26,27,28], thereby making it vital to identify factors affecting maternal obesity risk and address them in obesity prevention interventions. A deeper understanding of the full interplay of intrapersonal and interpersonal characteristics and behaviors along with environments could provide a more complete picture of the aspects of the home environment that may place mothers of young children at risk for weight gain. A method for assessing risk for weight gain could enable health care providers and researchers to tailor and design more effective nutrition education.
Health experts acknowledge that lifestyles and the environment play a role in obesity risk [29]; however, few studies have considered these factors when examining obesity risk and none could be located that examined a comprehensive array of factors. Thus, the aims of this exploratory study were to (1) develop an obesity risk score using a comprehensive set of variables assessing mothers’ own intrapersonal weight-related characteristics as well as the weight-related characteristics of their homes’ interpersonal and physical environments, and (2) determine how these weight-related characteristics differ among levels of obesity risk.

2. Materials and Methods

This research was approved by the Institutional Review Board at Rutgers University. All participants gave informed consent.

2.1. Sample

Survey Sampling International (SSI), a global research company whose services include survey sample participant recruitment (www.surveysampling.com), was utilized. SSI panel members received invitations to participate in an online survey that would help researchers create “a program to help parents to build healthier kids”. The goal was to recruit mothers of children in the target age range who had most of the food decision-making authority in their households to adequately capture the most representative responses. To meet eligibility requirements, panel members had to be female, between 18- and 45-years-old, have one or more children 2- to 5-years-old, and be the household’s primary food gatekeeper (i.e., made most or all food purchasing and preparation decisions). As an incentive to complete the cross-sectional online survey, participating mothers accrued points from SSI that were redeemable for gifts. A total sample size of 384 was estimated based on a 95% confidence interval, and total population of women in the U.S. that are 20–45 years old using 2010 U.S. Census data [30].

2.2. Instrument

Development and content of the online Home Obesogenicity Measure of EnvironmentS (HOMES) survey is described in detail elsewhere [31,32,33]. In summary, development began with a comprehensive literature review to identify salient weight-related demographic, environmental, behavioral, and psychographic characteristics. Self-report scales assessing these characteristics, preferably those previously used and validated with a diverse sample of U.S. adults, also were identified. When multiple scales for assessing a characteristic were found, each was reviewed to determine which was most relevant to the study sample, easy to administer and score, and had good reliability and validity. If an instrument assessing a characteristic of interest or fitting the needs of the study could not be located in the literature, items were developed de novo. For scales with items substantially modified from their original form or developed de novo, standard processes for developing and refining scales were applied [34]. That is, experts (n = 5) in subject matter areas appropriate to the scale content (e.g., nutrition, physical activity, psychology, child development, obesogenic environment), psychometrics, and survey design iteratively reviewed and refined them to ensure scale clarity and content validity [34,35]. The items then underwent cognitive testing with participants who were similar to the study population to assess whether the items were interpreted as intended [35] and to determine ways to reduce participant burden and increase understandability and acceptability [36].
After refinement, the scales were consolidated into a single survey that was posted online (using Qualtrics®). The survey was pilot-tested with 48 participants whose characteristics were the same as those of the target audience for the final study (but were not participants in the final sample) to gauge completion time, identify further refinements needed to improve clarity and ease of completion, ensure protocols for scoring of scales were accurate, and conduct preliminary psychometric analyses. The survey was administered online for ease of data collection and convenience to participants, to help reduce the potential for social desirability bias that can occur during in-person administration, and to increase researcher ability to reach individuals who would be otherwise difficult to access (i.e., distance from researchers or limited time to meet in-person) [37,38].
The HOMES survey included an array of measures that focused on mother’s intrapersonal weight-related characteristics and their homes’ interpersonal and physical environment characteristics and yielded 79 variables. Table 1 lists the variables in the HOMES survey considered in the creation of the obesity risk score, including number of items, possible score range, and Cronbach’s alpha (when applicable).

2.3. Sociodemographics and Health-Related Characteristics

Variables addressing maternal sociodemographic data included race/ethnicity, education level, number of children in the household, family affluence [39,40], maternal employment, and food insecurity risk [41]. There were eight variables examining health-related characteristics (e.g., general health status [42,43], health-related quality of life [42,43], depression severity [44], age at birth of first child, perception of weight teasing history [45], body dissatisfaction [46], primary relative with obesity and/or obesity.

2.3.1. Weight Status

Self-reported heights and weights of participants were used to calculate body mass index (BMI) (weight (kg)/(height (m2)). Participants also reported their children’s height, weight, sex, and age which were used to calculated their children’s BMI percentile [91].

2.3.2. Intrapersonal Characteristics

Mothers’ personal weight-related behaviors (e.g., physical activity level and screentime [47,48,49], sleep quality and duration [50,51], fruit/vegetable intake [52,53,54], percent calories from fat [52,53,54], milk intake [55,56], sugar-sweetened beverage intake [55,56]) accounted for eight variables. Scales evaluating maternal eating styles (e.g., disinhibited eating [57,58], emotional eating [57,58], dietary restraint eating [57,58], adventurous eating [59,60,61]) generated four variables. Five variables were produced from measures of mothers’ self-perceptions (i.e., personal organization [62], need for cognition [63,64], parenting self-efficacy [65,66], stress management [67], stress management self-efficacy). Cognitions related to children’s weight (i.e., belief that chubby kids are healthy, concern about own children’s overweight risk [68]) were assessed with two scales. The value of engaging in healthy behaviors for self and child (i.e., importance placed on physical activity, encouragement and facilitation of children’s physical activity, importance of modeling physical activity to children, frequency of engaging in active play with children, parent modeling of healthy eating, parenting cognitions and behaviors associated with children’s television viewing) [31,69,70,71,72] was examined with 12 measures, each generating a variable.

2.3.3. Home Interpersonal Characteristics

Assessments of mothers’ child feeding behaviors (e.g., food restriction, pressuring, rewarding) [68,69,70,71,73,74,75] yielded 10 variables. Family meal patterns (e.g., frequency and location of meals) [70,76,77,78,79,80] resulted in nine variables. Scales assessing family functioning and engagement (e.g., family support for healthy behaviors [81,82,83], family conflict and lack of cohesion [84,85,86], household organization [87,88]) included five variables.

2.3.4. Home Physical Environment

Appraisal of the home physical environment’s accessibility to and availability of physical activity and sedentary activity opportunities (e.g., physical activity supports, media devices in the home, children’s TV accessibility) [69,70,71,72,82,89] contributed five variables. Measures of household food availability (e.g., fruit/vegetables, sugar-sweetened beverages) [52,55,56,90] generated 4 variables.

2.4. Data Analysis

Internal consistencies of all measures, when applicable, were calculated using Cronbach’s alpha. Descriptive statistics of all variables in the total sample were evaluated in four steps before further development of the obesity risk score. First, Spearman rank order correlations of all variables, except mothers’ and children’s weight status, were examined for multicollinearity. In the correlation matrix, race/ethnicity was categorized into two dichotomous variables (i.e., white or non-white, black or non-black) and education level was dichotomized into low (some college or less) or high (baccalaureate degree or higher). Variables that were intercorrelated (i.e., r > 0.50) were reviewed to select a single variable from among them to use in further analyses. The criterion for selecting a single variable from among those that were intercorrelated was that the variable under consideration had to be significantly correlated (p < 0.05) with maternal BMI. If none of the intercorrelated variables met this criterion, none were considered in further analyses. If more than one variable met this criterion, the variable with the highest correlation (absolute value) with maternal BMI was selected. In the second step of data analysis, binomial logistic regression was conducted to identify variables significantly associated with obesity. Variables remaining after the first step of the data analysis served as independent variables. The dependent variable was maternal weight status of obese (i.e., BMI ≥ 30) vs. non-obese (i.e., BMI ≤ 30). In step three of data analysis, the significant obesity risk variables identified in step two were again entered into the binomial logistic regression analysis to determine the best model fit and confirm results. In step four, data for non-obese mothers’ were extracted from the data set and median scores were calculated for all variables found to be significantly associated with obesity.
To create the weighted obesity risk score, each non-obese mother was assigned a score for each obesity risk variable found in the regression model, as stated above, to be significantly associated with obesity. If a participant’s score for an obesity risk variable was above the median for continuous variables or mothers had the characteristic for dichotomous variables, the score assigned for the variable was the beta coefficient value generated by the binomial logistic regression (reflecting an increased risk for obesity); if a particiapant’s score was below the median for continuous variables or mothers did not have the characteristic for dichotomous variables, thereby indicating reduced obesity risk, a score of 0 was awarded for that variable. Scores for all obesity risk variables were then summed to yield a participant’s total weighted obesity risk score.
To determine how non-obese mothers differed by obesity risk level, they were assigned to groups based on their obesity risk score tertile (i.e., low, moderate, and high risk). ANOVA and Tukey post-hoc tests were conducted to determine how maternal sociodemographic, health-related, and intrapersonal and their homes’ interpersonal and physical environment characteristics differed among and between obesity risk score tertiles. For variables that were statically significant (p < 0.05), effect sizes were estimated by examining partial Eta squared, Analyses were performed with SPSS software version 24.0 (IBM corporation, Chicago, IL, USA). Given the number of variables investigated, significance level for main effects was set at 0.01 to reduce the risk of type 1 errors while maintaining sufficient power. Significance level for post-hoc procedures was set at p < 0.05.

3. Results

Out of 910 participants who responded to the online survey, only 550 participants were eligible and completed the survey (i.e., 188 did not complete the survey, 96 did not meet inclusion criteria, and 76 had implausible responses (e.g., multiple items had the same answers)) with a survey response rate of 60%. Most participants (n = 550; mean age 32.25 ± 5.81 SD years) were white (72%) with some post-secondary education (82%). Nearly all measures had good to excellent internal consistency as determined by Cronbach’s alpha (see Table 1). Spearman rank order correlation coefficients of the study variables revealed that several variables were multicollinear. Table 2 lists each group of multicollinear variables and the variable selected from each group based on the criteria previously described in the Data Analysis section.
Of the original 79 variables examined, scores for the 62 variables that were not highly intercorrelated were retained for further analysis. Binomial logistic regression on the dichotomous dependent variable of non-obese (n = 386) vs obese (n = 164) weight status revealed that all of the variables combined explained 64 percent of maternal obesity risk, with 12 of the variables being significantly associated with obesity risk. The 12 significant obesity risk variables were retained and again subjected to binomial logistic regression resulting in a final model with 8 variables explaining 53 percent of maternal risk for obesity (Table 3). Three of the obesity risk variables were dichotomous variables (i.e., being black or African American, having lower education level, having a primary relative with a history of obesity) and five were continuous variables (i.e., larger number of children in household, poorer general health rating, higher perceived weight teasing history, greater body dissatisfaction, more concern about their children’s overweight risk). Using the variable scoring procedure described in the Data Analysis section, the weighted obesity risk score averaged 1.66 ± 0.98 SD and ranged from 0 to 5.49.
ANOVA and Tukey post-hoc tests comparing obesity risk scores by tertiles, as shown in Table 4, revealed significant differences (p < 0.001) among low-, moderate-, and high risk groups with a large effect size (i.e., η2 = 0.833) for the obesity risk score, as estimated by partial Eta squared. A review of the characteristics related to sociodemographic variables, weight status, and health showed that non-obese mothers in the low obesity risk tertile had significantly higher family affluence (p < 0.001), lower food security risk (p = 0.006), and higher BMIs (p < 0.001) than those in the high obesity risk tertile. Additionally, the high obesity risk tertile reported significantly more days of “not good” health in the past month (p < 0.001), younger age at birth of first child (p = 0.007), and higher depression severity (p < 0.001) compared with those in lower obesity risk tertiles. There were no significant child BMI percentile differences among obesity risk tertiles although there was an increasing trend.
An examination of maternal intrapersonal characteristics showed that those in the high obesity risk tertile were significantly more likely to report worse sleep quality (p < 0.001), greater emotional eating (p < 0.001), perceive themselves as having less personal organization (p < 0.001), lower confidence in parenting (p = 0.004), and poorer stress management skills (p < 0.001) than those in the lower obesity risk tertiles. Compared to the high obesity risk tertile, mothers in the low obesity risk tertile were significantly more likely to value the importance of physical activity for self (p < 0.001) and child (p = 0.006), value the importance of modeling physical activity (p = 0.01), frequently model physical activity (p < 0.001) and less frequently model sedentary activity (p = 0.007), model healthy eating (p = 0.003), and place more child limits on children’s TV program choices (p = 0.01). None of the other intrapersonal characteristics differed significantly among obesity risk tertile groups.
The only interpersonal characteristics that differed significantly among obesity risk tertile groups were the frequency of family meals eaten at the kitchen or dining table and family conflict and lack of cohesion. That is, mothers in the high obesity risk tertile reported eating significantly (p = 0.006) fewer family meals at a kitchen or dining table and had more family conflict and less cohesion (p = 0.005) compared with those in the low obesity risk tertile.
Household fruit and vegetable availability was the only home physical environment characteristic that differed significantly among obesity risk tertiles. That is, mothers in the low obesity risk tertile had greater household availability of fruits and vegetables (p = 0.013) than those in the high obesity risk tertile. Estimated effect sizes, as determined by partial Eta squared, for the significantly different intrapersonal, interpersonal, and home environment characteristics were low.

4. Discussion

This study developed an obesity risk score for non-obese mothers of young children using a comprehensive array of sociodemographic and weight-related intrapersonal, interpersonal, and home environmental characteristics. The eight characteristics comprising the obesity risk score may help health professionals identify non-obese mothers with young children at increased risk for obesity and provide early obesity prevention intervention.
The eight independent variables identified in this study explained over half of maternal risk for obesity have been shown in other studies to be strongly associated with obesity. That is, women of African American race are more likely to be overweight or obese than other racial and ethnic groups [1]; lower education attainment is associated with overweight and obesity [1,83]; obese adults have more chronic disease [92,93] and report poorer health [94]; obese women are more likely to report being teased growing up [95,96]; and body shape dissatisfaction is associated with overweight and obesity [97,98]. The study reported here also provides insight into variables that are associated with obesity which are not changeable through nutrition and health promotion programs (e.g., race, family history, affluence), and those which are, yet are rarely targeted in nutrition education interventions (e.g., family conflict, body dissatisfaction).
Examining non-obese mothers by high, moderate, and low obesity risk tertiles provided an efficient means for exploring how risk level was associated with interpersonal, intrapersonal, and environmental characteristics. Several sociodemographic variables, health-related assessments, and intrapersonal, and interpersonal characteristics were associated with high obesity risk score among non-obese mothers whereas home physical environment factors tended to not be associated with obesity risk. Trends in sociodemographic and health-related characteristics noted among participants at high risk for obesity (e.g., food insecurity, lower family affluence, more unhealthy days in past month, and greater depression severity) mirror national data [1,83]. For instance, mothers in the high obesity risk group reported less family affluence and greater depression severity, which is consistent with literature indicating obesity rates are higher among adult women with low socio-economic status [99] and obese women are more likely to suffer from depression (although this association may be bi-directional) [100,101].
High obesity risk mothers did not significantly differ from lower obesity risk moms in their physical activity and screentime behaviors possibly because at risk, but not obese, women are not yet hindered by their weight interfering with their activity level. These findings suggest that activity level may not be a significant risk factor for maternal obesity but is associated with obesity weight status, raising the question of reverse causality. That is, are obese mothers inactive and more sedentary [102] because their weight inhibits physical activity instead of inactivity causing weight gain; or, are there other factors contributing to these relationships, such as the environmental supports for physical activity? Environments that promote access to and availability of physical activity are associated with more physical activity behaviors [103,104,105,106,107]. However, a recent systematic review has found inconsistent results in the associations between the built physical environment and obesity in adults [108], perhaps due to the great variation in metrics used and differing contexts of prior studies [108].
There is growing interest in the associations of sleep duration and quality with weight [109]. In the present study, poor sleep quality was significantly more prevalent in high obesity risk mothers. Although non-significant, the number of total hours slept each night linearly decreased as obesity risk increased, with those in the high obesity risk group receiving less than the recommended total sleep hours per night for adults of 7 to 9 h [110]. These findings suggest the need for continued investigation of the mechanisms of how sleep may be related to weight gain, and indicate the importance of including sleep management in weight-management interventions.
A linear trend showed that high obesity risk mothers were less active with their children and allowed children to have more minutes of screentime daily than lower obesity risk mothers. This finding is similar to reports of associations between maternal characteristics, child activity, and sedentary behaviors [111,112], and further highlights the importance of maternal modeling, encouraging, and facilitating physical activity for their children [69]. Not surprisingly, low obesity risk mothers engaged in significantly more modeling of both physical activity and less sedentary behaviors. Additionally, mothers at low obesity risk were less likely to model unhealthy emotional eating behaviors for their children. Parents and caregivers act as powerful socialization agents and serve as models of eating that children learn to emulate [113]. Future work should explore how low obesity risk mothers’ modeling of healthy behaviors affects the health and weight status of their children.
Home food availability, parental diet, and family eating habits are associated with diet quality of children [114,115]. In this study, high obesity risk moms had less household availability of fruits and vegetables and ate fewer family meals at a kitchen or dining room table which may have influenced their child’s diet quality and weight status. Although non-significant, there was a positive linear trend in child BMI percentile and maternal obesity risk score. Studies on weight-resilience (i.e., maintaining a healthy weight despite living in an obesogenic environment) suggest that homes with healthy weight children and teens are more likely to have healthier food options available and limit access to unhealthy foods [116,117]. In the study reported here, only a non-significant linear trend occurred, with mothers at low obesity risk limiting children’s access to low nutrient dense foods and storing nutrient dense foods in a manner that was clearly visible to children. Thus, future interventions should consider targeting family nutrition education that encourages positive changes in the home food supply and healthful dietary practices in the home.
This study found a non-significant decreasing trend in the relationships among obesity risk tertiles and family meal frequency. Other studies have found cross-sectional associations of family meal frequency were inversely associated with obesity in adolescents, but longitudinal analyses have not corroborated those results [118,119,120]. The results of this study contribute to the mixed associations among family meals and weight status [121]. It may be that characteristics of the family meal environment are confounding potential associations. For example, non-obese mothers at high risk for obesity reported significantly more family conflict and lack of cohesion. Having poor family functioning (i.e., more conflict and less cohesion) during mealtimes may lead to less frequent family meals, especially when there is a negative meal atmosphere [122,123,124]. Further research examining the influences of family dynamics with family meal frequency and weight-related behaviors are warranted [122,125].
The relationships between home food availability and maternal dietary intake are not clear. For instance, sugar-sweetened beverage intake was relatively lower among low obesity risk mothers yet household availability of sugar-sweetened beverages was similar in households across maternal obesity risk tertiles. Mothers at low obesity risk ate relatively more fruits and vegetables and had significantly greater household availability of fruits and vegetables than those of higher obesity risk. Whether foods were eaten from household food stores or outside the home was not investigated; the incongruence of beverage intake and household availability may indicate these beverages were consumed at home as well as outside the home, whereas the relative consistency between fruit and vegetable intake and household food supplies may indicate these foods are typically eaten at home. Further research is needed to understand where food and beverages are typically consumed and the impact of consumption location on household food supplies, overall family dietary intakes, and obesity risk.
A major strength of this study was the use of a socioecological framework to guide the comprehensive selection of constructs obesity risk in mothers of young children. A systematic method [34] was used to examine the potential scales for application to the study population (i.e., mothers and young children of varying races/ethnicities and educational attainment). Use of reliable and valid scales is vital to ensure the most accurate responses. In this survey, previously validated and refined tools were used when possible, and nearly all had good to high internal consistency with the study sample. Despite the strengths of this study, it is important to note its limitations. Inference of causality of observed associations cannot be made due to the cross-sectional study design. In addition, the study sample only included mothers of preschool-aged children in the U.S. with greater educational attainment than the national average, so findings may not be generalizable to mothers with lower education levels or mothers with children of different ages or those residing in other countries. This study also did not evaluate characteristics and behaviors of other family or friends or environments outside of the home environment. Although the estimated effect size for the obesity risk score was high, the significantly different intrapersonal, interpersonal, and home environment characteristics that occurred across obesity risk tertiles had low effect sizes. Lastly, data collected from participants were self-reported and may be subject to both reporting error and bias. However, heights and weights self-reported by adults are highly correlated with measured heights and weights [126] and online data collection offers privacy and relative anonymity that may improve veracity when answering questions [127,128,129].

5. Conclusions

In conclusion, this exploratory study identified eight characteristics that, together, explain more than half of the risk of obesity in non-obese mothers of young children. These eight characteristics may help healthcare professionals identify mothers at increased risk of obesity and offer preventive care early and more specifically tailor care (e.g., psychological assistance for those with body dissatisfaction). Many of the eight characteristics are not usually assessed in clinical practice, but are simple to assess and may yield valuable obesity risk information to healthcare providers. In addition, nutrition communication and health promotion professionals can apply the findings by targeting intervention efforts to those at increased risk and expanding intervention content to address topics not typically addressed in obesity prevention programs, such as strategies for managing family conflicts and changing the home food environment. Further research with larger, more diverse samples who are longitudinally assessed is needed to confirm the results of this study. Additional research to clarify the contribution of early identification of those at high risk for obesity and inclusion of new topics in obesity prevention programs also is warranted.

Author Contributions

C.B.-B. and J.M.-B. conceived and designed the study. J.M.-B. and C.B.-B. collected data. J.M.-B., V.Q., and C.B.-B. analyzed the data. J.M.-B., V.Q., K.S., and C.B.-B. were involved in manuscript preparation and revision and approved the final manuscript.

Funding

This research was funded by United States Department of Agriculture, National Institute of Food and Agriculture, Grant Number 2011-68001-30170.

Acknowledgments

This study was funded by USDA NIFA #2011-68001-30170. An earlier draft of parts of this manuscript were from J. Martin-Biggers (2016) Home Environment Characteristics Associated with Obesity Risk In Preschool-Aged Children and Their Mothers, doctoral dissertation, Rutgers, The State University of New Jersey, New Brunswick, NJ.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ogden, C.; Carroll, M.; Kit, B.; Flegal, K. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 2014, 311, 806–814. [Google Scholar] [CrossRef] [PubMed]
  2. Tsai, A.; Williamson, D.; Glick, H. Direct medical cost of overweight and obesity in the United States: A qunatitative systematic review. Obes. Rev. 2012, 12, 50–61. [Google Scholar] [CrossRef] [PubMed]
  3. Pi-Sunyer, F.X. The Obesity Epidemic: Pathophysiology and Consequences of Obesity. Obesity 2002, 10 (Suppl. 12), 97S–104S. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Organization, W.H. Obesity: Preventing and Managing the Global Epidemic; World Health Organization: Geneva, Switzerland, 2000. [Google Scholar]
  5. Lobstein, T.; Baur, L.; Uauy, R. Obesity in children and young people: A crisis in public health. Obes. Rev. 2004, 5, 4–85. [Google Scholar] [CrossRef] [PubMed]
  6. Hassan, M.K.; Joshi, A.V.; Madhavan, S.S.; Amonkar, M.M. Obesity and health-related quality of life: A cross-sectional analysis of the US population. Int. J. Obes. Relat. Metab. Disord. 2003, 27, 1227–1232. [Google Scholar] [CrossRef] [PubMed]
  7. Brinkley, J.; Eales, J.; Jekanowski, M. The relation between dietary change and rising US obesity. Int. J. Obes. Relat. Metab. Disord. 2000, 24, 1032–1039. [Google Scholar] [CrossRef] [Green Version]
  8. Golan, M.; Crow, S. Parents are key players in the prevention and treatment of weight-related problems. Nutr. Rev. 2004, 62, 39–50. [Google Scholar] [CrossRef] [PubMed]
  9. Hill, J.; Wyatt, H.; Reed, G.; Peters, J. Obesity and the environment: Where do we go from here? Science 2003, 203, 853–856. [Google Scholar]
  10. Wang, Y.C.; Bleich, S.N.; Gortmaker, S.L. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among US children and adolescents, 1988–2004. Pediatrics 2008, 121, e1604–e1614. [Google Scholar] [PubMed]
  11. Wang, Y.C.; Orleans, C.T.; Gortmaker, S.L. Reaching the healthy people goals for reducing childhood obesity: Closing the energy gap. Am. J. Prev. Med. 2012, 42, 437–444. [Google Scholar] [PubMed]
  12. Diet, Nutrition and the Prevention of Chronic Diseases; World Health Organization Technical Report Series 916, backcover; World Health Organization: Geneva, Switzerland, 2003; Volume i–viii, pp. 1–149.
  13. Richard, L.; Gauvin, L.; Raine, K. Ecological Models Revisited: Their Uses and Evolution in Health Promotion Over Two Decades. Annu. Rev. Public Health 2011, 32, 307–326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Sallis, J.; Owen, N. Ecological Models of Health Behavior, 5th ed.; Jossey-Bass: San Francisco, CA, USA, 2015. [Google Scholar]
  15. Giskes, K.; van Lenthe, F.; Avendano-Pabon, M.; Brug, J. A systematic review of environmental factors and obesogenic dietary intakes among adults: Are we getting closer to understanding obesogenic environments? Obes. Rev. 2011, 12, e95–e106. [Google Scholar] [CrossRef] [PubMed]
  16. Van der Horst, K.; Oenema, A.; Ferreira, I.; Wendel-Vos, W.; Giskes, K.; van Lenthe, F.; Brug, J. A systematic review of environmental correlates of obesity-related dietary behaviors in youth. Health Educ. Res. 2007, 22, 203–226. [Google Scholar] [CrossRef] [PubMed]
  17. Kelder, S.; Hoelscher, D.; Perry, C. How individuals, environments, and health behavior interact; Social Cognitive Theory. In Health Behavior: Theory, Research, and Practice, 5th ed.; Glanz, K., Rimer, B., Viswanath, K., Eds.; Jossey-Bass: San Francisco, CA, USA, 2015; pp. 159–182. [Google Scholar]
  18. Saelens, B.; Sallis, J.; Frank, L. Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Ann. Behav. Med. 2003, 25, 80–91. [Google Scholar] [CrossRef] [PubMed]
  19. Humpel, N.; Owen, N.; Leslie, E. Environmental factors associated with adults’ participation in physical activity: A review. Am. J. Prev. Med. 2002, 22, 188–199. [Google Scholar] [CrossRef]
  20. Berge, J.M.; Larson, N.; Bauer, K.W.; Neumark-Sztainer, D. Are parents of young children practicing healthy nutrition and physical activity behaviors? Pediatrics 2011, 127, 881–887. [Google Scholar] [CrossRef] [PubMed]
  21. Evans, A.E.; Dave, J.; Tanner, A.; Duhe, S.; Condrasky, M.; Wilson, D.; Griffin, S.; Palmer, M.; Evans, M. Changing the home nutrition environment: Effects of a nutrition and media literacy pilot intervention. Fam. Community Health 2006, 29, 43–54. [Google Scholar] [CrossRef] [PubMed]
  22. Children’s Food Environment State Indicator Report; Centers for Disease Control and Prevention: Atlanta, GA, USA, 2011.
  23. Vaughn, A.; Ward, D.; Fisher, J.; Faith, M.; Hughes, S.; Kremers, S.; Musher-Eizenman, D.; O’Connor, T.; Patrick, H.; Power, T. Fundamental constructs in food parenting practices: A content map to guide future research. Nutr. Rev. 2016, 74, 98–117. [Google Scholar] [CrossRef] [PubMed]
  24. Reid, M.; Worsley, A.; Mavondo, F. The obesogenic household: Factors influencing dietary gatekeeper satisfaction with family diet. Psychol. Mark. 2015, 32, 544–557. [Google Scholar] [CrossRef]
  25. Mamun, A.; O’Callaghan, M.; Callaway, L.; Williams, G.; Najman, J.; Lawlor, D. Associations of Gestational Weight Gain with Offspring Body Mass Index and Blood Pressure at 21 Years of Age Evidence from a Birth Cohort Study. Circulation 2009, 119, 1720–1727. [Google Scholar] [CrossRef] [PubMed]
  26. Lagström, H.; Hakanen, M.; Niinikoski, H.; Viikari, J.; Rönnemaa, T.; Saarinen, M.; Pahkala, K.; Simell, O. Growth Patterns and Obesity Development in Overweight or Normal-Weight 13-Year-Old Adolescents: The STRIP Study. Pediatrics 2008, 122, e876–e883. [Google Scholar] [CrossRef] [PubMed]
  27. Shibli, R.; Rubin, L.; Akons, H.; Shaoul, R. Morbidity of overweight (85th percentile) in the first 2 years of life. Pediatrics 2008, 122, 267–272. [Google Scholar] [CrossRef] [PubMed]
  28. Ogata, B.; Hayes, D. Position of the Academy of Nutrition and Dietetics: Nutrition guidance for healthy children ages 2 to 11 years. J. Acad. Nutr. Diet. 2014, 114, 1257–1276. [Google Scholar] [CrossRef] [PubMed]
  29. Centers for Disease Control and Prevention Adult Obesity Causes & Consequences. Available online: https://www.cdc.gov/obesity/adult/causes.html (accessed on 1 November 2017).
  30. U.S. Census Bureau Age and Sex Composition: 2010. Available online: https://www.census.gov/prod/cen2010/briefs/c2010br-03.pdf (accessed on 1 May 2017).
  31. Martin-Biggers, J. Home Environment Characteristics Associated with Obesity Risk in Preschool-Aged Children and THEIR mothers. Ph.D. Dissertation, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA, 2016. [Google Scholar]
  32. Martin-Biggers, J.; Cheng, C.; Spaccarotella, K.; Byrd-Bredbenner, C. The Physical Activity Environment in Homes and Neighborhoods. In Recent Advances in Obesity in Children; Avid Science Publications: Berlin, Germany, 2016; Available online: www.avidscience.com/wp-content/uploads/2016/05/OIC-15-04_May-06-2016.pdf (accessed on 1 November 2017).
  33. Martin-Biggers, J.M.; Worobey, J.; Byrd-Bredbenner, C. Interpersonal Characteristics in the Home Environment Associated with Childhood Obesity. In Recent Advances in Obesity in Children; Avid Science Publications: Berlin, Germany, 2016; Chapter 3. [Google Scholar]
  34. Redding, C.A.; Maddock, J.E.; Rossi, J.S. The sequential approach to measurement of health behavior constructs: Issues in selecting and developing measures. Calif. J. Health Promot. 2006, 4, 83–101. [Google Scholar]
  35. Linn, R.; Gronlund, N. Measuring and Assessment in Teaching, 8th ed.; Prentice-Hall: Englewood Cliffs, NJ, USA, 2000. [Google Scholar]
  36. Carbone, E.T.; Campbell, M.K.; Honess-Morreale, L. Use of cognitive interview techniques in the development of nutrition surveys and interactive nutrition messages for low-income populations. J. Am. Diet. Assoc. 2002, 102, 690–696. [Google Scholar] [CrossRef]
  37. Wright, K.B. Researching Internet-Based Populations: Advantages and Disadvantages of Online Survey Research, Online Questionnaire Authoring Software Packages, and Web Survey Services. J. Comput.-Med. Commun. 2005, 10. [Google Scholar] [CrossRef]
  38. Masse, L. Innovations in Measurement of Parenting Constructs, Parenting Meausurement: Current Status and Consensus Reports. In Proceedings of the International Society for Behavioral Nutrition and Physical Activity, Houston, TX, USA, 20 May 2012. [Google Scholar]
  39. Hartley, J.; Levin, K.; Currie, C. A new version of the HBSC Family Affluence Scale—FAS III: Scottish qualitative findings from the international FAS developments study. Child Indic. Res. 2016, 9, 233–245. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Currie, C.; Mollcho, M.; Boyce, W.; Holstein, B.; Torsheim, T.; Richter, M. Researching health inequalities in adolescents: The development of the health behavior in school-aged children (HBSC) family affluence scale. Soc. Sci. Med. 2008, 66, 1429–1436. [Google Scholar] [CrossRef] [PubMed]
  41. Hager, E.; Quigg, A.; Black, M.; Coleman, S.; Heeren, T.; Rose-Jacobs, R.; Cook, J.; Ettinger de Cuba, S.; Casey, P.; Chilton, M.; et al. Development and validity of a 2-Item screen to identify families at risk for food insecurity. Pediatrics 2010, 126, e26–e32. [Google Scholar] [CrossRef] [PubMed]
  42. Centers for Disease Control and Prevention HRQOL Concepts. Why Is Quality of Life Important? Available online: www.cdc.gov/hrqol/concept.htm (accessed on 9 May 2016).
  43. Centers for Disease Control and Prevention CDC HRQOL-14 Healthy Days Measure. Available online: www.cdc.gov/hrqol/hrqol14_measure.htm (accessed on 9 May 2016).
  44. Kroenke, K.; Spitzer, R.L.; Williams, J.B.W. The Patient Health Questionnaire-2: Validity of a two-item depression screener. Med. Care 2003, 41, 1284–1292. [Google Scholar] [CrossRef] [PubMed]
  45. Thompson, J.; Cattarin, J.; Fowler, B.; Fisher, E. The Perception of Teasing Scale (POTS): A revision and extension of the Physical Appearance Related Teasing Scale (PARTS). J. Personal. Assess. 1995, 65, 146–157. [Google Scholar] [CrossRef] [PubMed]
  46. Fairburn, C.; Beglin, S. Assessment of eating disorders: Interview or self-report questionnaire? Int. J. Eat. Disord. 1994, 16, 363–370. [Google Scholar] [PubMed]
  47. Quick, V.; Byrd-Bredbenner, C.; Shoff, S.; White, A.; Lohse, B.; Horacek, T.; Kattlemann, K.; Phillips, B.; Hoerr, S.; Greene, G. A streamlined, enhanced self-report physical activity measure for young adults. Int. J. Health Promot. Educ. 2016. [Google Scholar] [CrossRef]
  48. Lee, P.; Macfarlane, D.; Lam, T.; Stewart, S. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int. J. Behav. Nutr. Phys. Act. 2011, 8, 115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Craig, C.L.; Marshall, A.L.; Sjorstrom, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.L.; Yngve, A.; Sallis, J.F.; et al. International Physical Activity Questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef] [PubMed]
  50. Buysse, D.; Reynolds, C.; Monk, T.; Berman, S.; Kupfer, D. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatr. Res. 1989, 28, 193–213. [Google Scholar] [CrossRef]
  51. Carpenter, J.; Andrykowski, M. Psychometric evaluation of the Pittsburgh Sleep Quality Index. J. Psychosom. Res. 1998, 45, 5–13. [Google Scholar] [CrossRef]
  52. Block, G.; Gillespie, C.; Rosenbaum, E.H.; Jenson, C. A rapid food screener to assess fat and fruit and vegetable intake. Am. J. Prev. Med. 2000, 18, 284–288. [Google Scholar] [CrossRef] [Green Version]
  53. Block, G.; Hartman, A.; Naughton, D. A reduced dietary questionnaire: Development and validation. Epidemiology 1990, 1, 58–64. [Google Scholar] [CrossRef] [PubMed]
  54. Block, G.; Thompson, F.; Hartman, A.; Larkin, F.; Guire, K. Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. J. Am. Diet. Assoc. 1992, 92, 686–693. [Google Scholar] [PubMed]
  55. Nelson, M.; Lytle, L. Development and evaluation of a brief screener to estimate fast-food and beverage consumption among adolescents. J. Am. Diet. Assoc. 2009, 109, 730–734. [Google Scholar] [CrossRef] [PubMed]
  56. West, D.; Bursac, Z.; Quimby, D.; Prewit, T.; Spatz, T.; Nash, C.; Mays, G.; Eddings, K. Self-reported sugar-sweetened beverage intake among college students. Obesity 2006, 14, 1825–1831. [Google Scholar] [CrossRef] [PubMed]
  57. Stunkard, A.; Messick, S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J. Psychosom. Res. 1985, 29, 71–83. [Google Scholar] [CrossRef]
  58. Karlsson, J.; Persson, L.-O.; Sjostrom, L.; Sullivan, M. Psychometric properties and factor structure of the Three-Factor Eating (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int. J. Obes. 2000, 24, 1715–1725. [Google Scholar] [CrossRef]
  59. Pliner, P.; Hobden, K. Development of a scale to measure the trait of food neophobia in humans. Appetite 1992, 19, 105–120. [Google Scholar] [CrossRef]
  60. Ullrich, N.V.; Touger-Decker, R.; O’Sullivan-Maillet, J.; Tepper, B.J. PROP taster status and self-perceived food adventurousness influence food preferences. J. Am. Diet. Assoc. 2004, 104, 543–550. [Google Scholar] [CrossRef] [PubMed]
  61. Pliner, P.; Loewen, E. Temperament and food neophobia in children and their mothers. Appetite 1997, 28, 239–254. [Google Scholar] [CrossRef] [PubMed]
  62. Matheny, A.; Wachs, T.; Ludwig, J.; Phillips, K. Bringing order out of chaos: Psychometric characteristics of the confusion, hubbub, and order scale. J. Appl. Dev. Psychol. 1995, 16, 429–444. [Google Scholar] [CrossRef]
  63. Cacioppo, J.; Petty, R. The need for cognition. J. Personal. Soc. Psychol. 1982, 42, 116–131. [Google Scholar] [CrossRef]
  64. Cacioppo, J.; Petty, R.; Kao, C.F. The efficient assessment of need for cognition. J. Personal. Assess. 1984, 48, 306–307. [Google Scholar] [CrossRef] [PubMed]
  65. Johnston, C.; Mash, E. A measure of parenting satisfaction and efficacy. J. Clin. Child Psychol. 1989, 18, 167–175. [Google Scholar] [CrossRef]
  66. Gibaud-Wallston, J.; Wandersmann, L. Development and Utility of the Parenting Sense of Competence Scale; John F. Kennedy Center for Research on Education and Human Development: Washington, DC, USA, 1978. [Google Scholar]
  67. Cohen, S.; Kamarck, T.; Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 1983, 24, 385–396. [Google Scholar] [CrossRef] [PubMed]
  68. Birch, L.; Fisher, J.; Grimm-Thomas, K.; Markey, C.; Sawyer, R.; Johnson, S. Confirmatory factor analysis of the Child Feeding Questionnaire: A measure of parental attitudes, belief and practices about child feeding and obesity proneness. Appetite 2001, 36, 202–210. [Google Scholar] [CrossRef] [PubMed]
  69. Gattshall, M.; Shoup, J.; Marshall, J.; Crane, L.; Estabrooks, P. Validation of a survey instrument to assess home environments for physical activity and healthy eating in overweight children. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 3. [Google Scholar] [CrossRef] [PubMed]
  70. Spurrier, N.; Magarey, A.; Golley, R.; Curnow, F.; Sawyer, M. Relationships between the home environment and physical activity and dietary patterns of preschool children: A cross-sectional study. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Bryant, M.; Ward, D.; Hales, D.; Vaughn, A.; Tabak, R.; Stevens, J. Reliability and validity of the Healthy Home Survey: A tool to measure factors within homes hypothesized to relate to overweight in children. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Earls, F.; Brooks-Gunn, J.; Raudenbush, S.; Sampson, R. Project on human development in Chicago neighborhoods (PHDCN): Home and life interview, wave 2, 1997–2000. Instruments for ICPSR 13630; Inter-University Consortium for Political and Social Research: Ann Arbor, MI, USA, undated.
  73. Hughes, S.; Cross, M.; Hennessy, E.; Tovar, A.; Economos, C.; Power, T. Caregiver’s Feeding Styles Questionnaire. Establishing cutoff points. Appetite 2012, 58, 393–395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Wardle, J.; Sanderson, S.; Guthrie, C.A.; Rapoport, L.; Plomin, R. Parental feeding style and the inter-generational transmission of obesity risk. Obes. Res. 2002, 10, 453–462. [Google Scholar] [CrossRef] [PubMed]
  75. Ogden, J.; Reynolds, R.; Smith, A. Expanding the concept of parental control: A role for overt and covert control in children’s snacking behaviour? Appetite 2006, 47, 100–106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Neumark-Sztainer, D.; Story, M.; Hannan, P.; Perry, C.; Irving, L. Weight-related concerns and behaviors among overweight and nonoverweight adolescents implications for preventing weight-related disorders. Arch. Pediatr. Adol. Med. 2002, 156, 171–178. [Google Scholar] [CrossRef]
  77. Neumark-Sztainer, D.; Larson, N.; Fulkerson, J.; Eisenberg, M.; Story, M. Family meals and adolescents: What have we learned from Project EAT (Eating Among Teens)? Public Health Nutr. 2010, 13, 1113–1121. [Google Scholar] [CrossRef] [PubMed]
  78. Koszewski, W.; Behrends, D.; Nichols, M.; Sehi, N.; Jones, G. Patterns of family meals and food and nutrition intake in limited resource families. Fam. Consum. Sci. Res. J. 2011, 39, 431–441. [Google Scholar] [CrossRef]
  79. Neumark-Sztainer, D.; Wall, M.; Story, M.; Perry, C. Correlates of unhealthy weight-control behaviors among adolescents: Implications for prevention programs. Health Psychol. 2003, 22, 88–98. [Google Scholar] [CrossRef] [PubMed]
  80. Storfer-Isser, A. Association of Maternal Employment with Attitudes, Subjective Norms, and Perceived Behavioral Control Regarding Meal Preparation Among Mothers of 4–5 Years Old Children; Bowling Green State University: Bowling Green, OH, USA, 2013. [Google Scholar]
  81. Trost, S.G.; Sallis, J.F.; Pate, R.R.; Freedson, P.S.; Taylor, W.C.; Dowda, M. Evaluating a model of parental influence on youth physical activity. Am. J. Prev. Med. 2003, 25, 277–282. [Google Scholar] [CrossRef]
  82. Sallis, J.F.; Prochaska, J.J.; Taylor, W.C.; Hill, J.O.; Geraci, J.C. Correlates of physical activity in a national sample of girls and boys in Grades 4 through 12. Health Psychol. 1999, 18, 410–415. [Google Scholar] [CrossRef] [PubMed]
  83. Ball, K.; Crawford, D. An investigation of psychological, social and environmental correlates of obesity and weight gain in young women. Int. J. Obes. 2006, 30, 1240–1249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Moos RH, M.B. Family Environment Scale Manual: Development, Applications, Research, 3rd ed.; Consulting Psychologists Press: Palo Alto, CA, USA, 1994. [Google Scholar]
  85. Saucier, G.; Wilson, K.; Warka, J. The structure of retrospective accounts of family environments: Related to the structure of personality attributes. J. Personal. Assess. 2007, 88, 295–308. [Google Scholar] [CrossRef] [PubMed]
  86. Greene, R.; Plank, R. The short-form family environment scale: Testing a different response format. Psychol. Rep. 1994, 74, 451–464. [Google Scholar] [CrossRef]
  87. Coldwell, J.; Pike, A.; Dunn, J. Household chaos—Links with parenting and child behaviour. J. Child. Psychol. Psychiatry 2006, 47, 1116–1122. [Google Scholar] [CrossRef] [PubMed]
  88. Matheny, A.W.T., Jr.; Ludwig, J.L.; Phillips, K. Bringing order out of chaos: Psychometric characteristics of the confusion, hubbub, and order scale. J. Appl. Dev. Psychol. 1995, 16, 319–429. [Google Scholar] [CrossRef]
  89. Crawford, D.; Telford, A.; Salmon, J. Perceptions about the local neighborhood and walking and cycling among children. Prev. Med. 2004, 38, 39–47. [Google Scholar]
  90. Byrd-Bredbenner, C.; Maurer Abbot, J.; Cussler, E. Nutrient Profile of Household Food Supplies of Families with Young Children. J. Am. Diet. Assoc. 2009, 109, 2057–2062. [Google Scholar] [CrossRef] [PubMed]
  91. Centers for Disease Control and Prevention CDC Growth Charts: United States. Available online: http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/charts.htm (accessed on 2 June 2004).
  92. Chu, S.Y.; Callaghan, W.M.; Kim, S.Y.; Schmid, C.H.; Lau, J.; England, L.J.; Dietz, P.M. Maternal obesity and risk of gestational diabetes mellitus. Diabetes Care 2007, 30, 2070–2076. [Google Scholar] [CrossRef] [PubMed]
  93. Metzger, B.E.; Coustan, D.R.; Committee, O. Summary and recommendations of the fourth international workshop-conference on gestational diabetes mellitus. Diabetes Care 1998, 21, B161–B167. [Google Scholar] [PubMed]
  94. Propser, M.-H.; Moczulski, V.; Qureshi, A. Obesity as a predictor of self-rated health. Am. J. Health Behav. 2009, 33, 319–329. [Google Scholar]
  95. Jackson, T.D.; Grilo, C.M.; Masheb, R.M. Teasing history, onset of obesity, current eating disorder psychopathology, body dissatisfaction, and psychological functioning in binge eating disorder. Obes. Res. 2000, 8, 451–458. [Google Scholar] [CrossRef] [PubMed]
  96. Quick, V.; McWilliams, R.; Byrd-Bredbenner, C. Fatty, Fatty, Two-By-Four: Weight teasing history and disturbed eating in young adult women. Am. J. Public Health 2013, 103, 508–515. [Google Scholar] [CrossRef] [PubMed]
  97. Schwartz, M.B.; Brownell, K.D. Obesity and body image. Body Image 2004, 1, 43–56. [Google Scholar] [CrossRef]
  98. Quick, V.; Eisenberg, M.; Bucchianeri, M.; Neumark-Sztainer, D. Prospective predictors of body dissatifaction in young adults: 10-year longitudinal findings. Emerg. Adulthood 2013, 1, 271–282. [Google Scholar] [CrossRef] [PubMed]
  99. Newton, S.; Braithwaite, D.; Akinyemiju, T. Socio-economic status over the life course and obesity: Systematic review and meta-analysis. PLoS ONE 2017, 12, e0177151. [Google Scholar] [CrossRef] [PubMed]
  100. Stunkard, A.J.; Faith, M.S.; Allison, K.C. Depression and obesity. Biol. Psychiatry 2003, 54, 330–337. [Google Scholar] [CrossRef]
  101. Carey, M.; Small, H.; Yoong, S.; Boyes, A.; Bisquera, A.; Sanson-Fisher, R. Prevalence of comorbid depression and obesity in general practice: A cross-sectional survey. Br. J. Gen. Pract. 2014, 64, e122–e127. [Google Scholar] [CrossRef] [PubMed]
  102. Thorp, A.; Owen, N.; Neuhas, M.; Dunstan, D. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am. J. Prev. Med. 2011, 41, 207–215. [Google Scholar] [CrossRef] [PubMed]
  103. Stronegger, W.; Titze, S.; Oja, P. Perceived characteristics of the neighborhood and its association with physical activiy behavior and self-rated health. Health Place 2010, 16, 736–743. [Google Scholar] [CrossRef] [PubMed]
  104. Miller, W.D.; Pollack, C.E.; Williams, D.R. Healthy homes and communities: Putting the pieces together. Am. J. Prev. Med. 2011, 40 (Suppl. 1), S48–S57. [Google Scholar] [CrossRef] [PubMed]
  105. Davison, K.; Lawson, C. Do attributes in the physical environment influence children’s physical activity? A review of the literature. Int. J. Behav. Nutr. Phys. Act. 2006, 3, 19. [Google Scholar] [CrossRef] [PubMed]
  106. Kumanyika, S.K.; Parker, L.; Sim, L.J. Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making; Institute of Medicine of the National Academies: Washington, DC, USA, 2010. [Google Scholar]
  107. Trost, S.G.; Owen, N.; Bauman, A.E.; Sallis, J.F.; Brown, W. Correlates of adults’ participation in physical activity: Review and update. Med. Sci. Sports Exerc. 2002, 34, 1996–2001. [Google Scholar] [CrossRef] [PubMed]
  108. Mackenbach, J.; Rutter, H.; Compernolle, S.; Glonti, K.; Oppert, J.; Charreire, H.; Bourdeaudhuij, I.; Brug, J.; Nijpels, G.; Lakerveld, J. Obesogenic environments: A systematic review of the association between the physical environment and adult weight status, the SPOTLIGHT project. BMC Public Health 2014, 14, 233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Golem, D.L.; Martin-Biggers, J.T.; Koenings, M.M.; Davis, K.F.; Byrd-Bredbenner, C. An integrative review of sleep for nutrition professionals. Adv. Nut. Int. Rev. J. 2014, 5, 742–759. [Google Scholar] [CrossRef] [PubMed]
  110. Hirshkowitz, M.; Whiton, K.; Albert, S.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Katz, E.; Kheirandish-Gozal, L.; et al. National Sleep Foundation’s sleep time duration recommedations: Methodology and results summary. Sleep Health 2015, 1, 40–43. [Google Scholar] [CrossRef] [PubMed]
  111. Sallis, J.; Prochaska, J.; Taylor, W. A review of correlates of physical activity of children and adolescents. Med. Sci. Sports Exerc. 2000, 32, 963–975. [Google Scholar] [CrossRef] [PubMed]
  112. Sonneville, K.; Rifas-Shiman, S.; Kleinman, K.; Gortmaker, S.; Gillman, M.; Taveras, E. Associations of obesogenic behaviors in mothers and obese children participating in a randomized trial. Obesity 2012, 20, 1449–1454. [Google Scholar] [CrossRef] [PubMed]
  113. Savage, J.; Fisher, J.; Birch, L. Parental influence on eating behavior: Conception to adolescence. J. Law Med. Ethics 2007, 35, 22–34. [Google Scholar] [CrossRef] [PubMed]
  114. Santiago-Torres, M.; Adams, A.; Carrel, A.; LaRowe, T. Home food availability, parental dietary intake, and familial eating habits influence diet quality of urban Hispanic children. Child. Obes. 2014, 10, 408–415. [Google Scholar] [CrossRef] [PubMed]
  115. Couch, S.; Glanz, K.; Zhou, C.; Sallis, J.; Saelens, B. Home food environment in relation to children’s diet quality and weight status. J. Acad. Nutr. Diet. 2014, 114, 1569–1579. [Google Scholar] [CrossRef] [PubMed]
  116. Brogan, K.; Idalski Carone, A.; Jen, K.; Ellis, D.; Marshall, S.; Naar-King, S. Factors associated with weight resilience in obesogenic environments in female African-American adolescents. J. Acad. Nutr. Diet. 2012, 112, 718–724. [Google Scholar] [CrossRef] [PubMed]
  117. Williams, L.; Veitch, J.; Ball, K. What helps children eat well? A qualitative exploration of resilience among disadvantages families. Health Educ. Res. 2011, 26, 296–307. [Google Scholar] [CrossRef] [PubMed]
  118. Fulkerson, J.A.; Neumark-Sztainer, D.; Hannan, P.J.; Story, M. Family meal frequency and weight status among adolescents: Cross-sectional and 5-year longitudinal associations. Obesity 2008, 16, 2529–2534. [Google Scholar] [CrossRef] [PubMed]
  119. Sen, B. Frequency of family dinner and adolescent body weight status: Evidence from the national longitudinal survey of youth, 1997. Obesity 2006, 14, 2266–2276. [Google Scholar] [CrossRef] [PubMed]
  120. Chan, J.C.; Sobal, J. Family meals and body weight. Analysis of multiple family members in family units. Appetite 2011, 57, 517–524. [Google Scholar] [CrossRef] [PubMed]
  121. Martin-Biggers, J.; Spaccarotella, K.; Berhaupt-Glickstein, A.; Hongu, N.; Worobey, J.; Byrd-Bredbenner, C. Come and get it! A discussion of family mealtime literature and factors affecting obesity risk. Adv. Nutr. 2014, 5, 235–247. [Google Scholar] [CrossRef] [PubMed]
  122. Halliday, J.; Palma, C.; Mellor, D.; Green, J.; Renzaho, A. The relationship between family functioning and child and adolescent overweight and obesity: A systematic review. Int. J. Obes. 2014, 38, 480–493. [Google Scholar] [CrossRef] [PubMed]
  123. Cyril, S.; Halliday, J.; Green, J.; Renzaho, A.M. Relationship between body mass index and family functioning, family communication, family type and parenting style among African migrant parents and children in Victoria, Australia: A parent-child dyad study. BMC Public Health 2015, 15, 707. [Google Scholar] [CrossRef] [PubMed]
  124. Zeller, M.; Reiter-Purtill, J.; Modi, A.; Gutzwiller, J.; Vannatta, K.; Davies, W. Controlled study of critical parent and family factors in the obesigenic environment. Obesity 2007, 15, 126–136. [Google Scholar] [CrossRef] [PubMed]
  125. Sigman-Grant, M.; Hayes, J.; VanBrackle, A.; Fiese, B. Childhood obesity: Family Resiliency: A Neglected Perspective in Addressing Obesity in Young Children. Child. Obes. 2015, 11, 664–673. [Google Scholar] [CrossRef] [PubMed]
  126. Pursey, K.; Burrows, T.; Stanwell, P.; Collins, C. How accurate is web-based self-reported height, weight and body mass index in young adults? J. Med. Internet Res. 2014, 16, e4. [Google Scholar] [CrossRef] [PubMed]
  127. Chang, L.; Krosnick, J. National surveys via RDD elephone interviewing versus the internet comparing sample representativeness and response quality. Public Opin. Q. 2009, 73, 641–678. [Google Scholar] [CrossRef]
  128. Sheehan, K.B. Crowdsourcing research: Data collection with Amazon’s Mechanical Turk. Commun. Monogr. 2018, 85, 140–156. [Google Scholar] [CrossRef]
  129. Lefever, S.; Dal, M. Online data collection in academic research: Advantages and limitations. Br. J. Educ. Technol. 2007, 38, 574–582. [Google Scholar] [CrossRef]
Table
Table 1. Description of Sociodemographic, Intrapersonal, Interpersonal and Home Environment Characteristics of Participants (N = 550).
Table 1. Description of Sociodemographic, Intrapersonal, Interpersonal and Home Environment Characteristics of Participants (N = 550).
Measure# ItemsPossible Score RangeScale TypeCronbach’s αMean ± SD or N (%)
Sociodemographic Characteristics
 Race/Ethnicity1n/acategorical response*
 White 396 (72.0)
 Black or African American 52 (9.5)
 Hispanic 25 (4.5)
 Multi-racial 43 (7.8)
 Asian 31 (5.6)
 Other 3 (0.6)
Education Level1n/acategorical response*
 High School or less 99 (18.0)
 Some college or technical/Associate’s degree 245 (44.5)
 Bachelor’s degree or higher 206 (37.5)
Maternal Employment1n/acategorical response*
 Do not work 303 (55.1)
 Part-time (less than 30 h/week) 103 (18.7)
 Full-time (30 or more h/week) 143 (26.0)
Number of Children in Household10 to more than 6Total #*2.20 ± 1.01
Family Affluence Score [39,40]30 to 7varies per item A*5.61 ± 1.56
Food Insecurity Risk [41]21 to 44-point agreement rating B0.842.04 ± 1.91
Weight Status
 Mother’s BMI1n/aSelf-reported height/weight*27.69 ± 7.90
 Child’s BMI percentile (n = 339)1n/aSelf-reported height/weight/sex/age*63.93 ± 34.93
Health-Related Assessments
 General Health Rating [42,43]11 to 55-point excellence rating C*3.52 ± 0.87
 Health-related Quality of Life (# of unhealthy days) [42,43]10 to 30days/month*2.89 ± 4.56
 Depression Severity [44]21 to 44-point occurrence rating D0.811.05 ± 1.44
 Age at Birth of First Child (years)1n/ayears*24.46 ± 5.39
 Perception of Weight Teasing History [45]31 to 55-point frequency rating E0.951.84 ± 1.15
 Body Dissatisfaction [46]11 to 44-point frequency rating F*2.58 ± 1.10
 Primary Relative with History of Obesity (% yes)10 or 1yes/no*207 (37.6)
 Primary Relative with History of Diabetes (% yes)10 or 1yes/no*140 (25.5)
Intrapersonal Characteristics
Maternal Weight-Related Behaviors
 Physical Activity Level [47,48,49]30 to 428-point exercise scale G*15.44 ± 9.98
 Screentime10 to 1440minutes/day*273.52 ± 253.99
 Sleep Duration10 to 24hours/day*7.11 ± 1.84
 Sleep Quality [50,51]11 to 55-point rating H*3.24 ± 0.89
 Fruit and Vegetable Intake [52,53,54]100 to 12.176-point servings I eaten per day scale*4.56 ± 2.22
 % Calories from Total Fat [52,53,54]170 to 1005-point servings eaten scale J*37.4 ± 5.91
 Milk [55,56]10 to 89-point servings drank per day scale K*3.95 ± 3.08
 Sugar-Sweetened Beverage [55,56]40 to 4.69-point servings drank per day scale K*0.89 ± 0.88
Maternal Eating Styles
 Disinhibited Eating [57,58]31 to 44-point agreement rating B0.811.96 ± 0.76
 Emotional Eating [57,58]31 to 44-point agreement rating B0.752.07 ± 0.88
 Dietary Restraint Eating [57,58]41 to 44-point agreement rating B0.742.42 ± 0.74
 Adventurous Eating [59,60,61]21 to 44-point agreement rating B0.723.16 ± 0.68
Maternal Self-Perceptions
 Personal Organization [62]41 to 55-point agreement rating L0.693.68 ± 0.82
 Need for Cognition [63,64]11 to 55-point agreement rating L*3.49 ± 0.98
 Parenting Self-Efficacy [65,66]11 to 55-point agreement rating L*4.1 ± 0.81
 Stress Management [67]21 to 44-point agreement rating D0.843.94 ± 0.76
 Stress Management Self-Efficacy21 to 44-point agreement rating D0.792.63 ± 1.01
Child Weight Cognitions 1 to 5
 Belief that Chubby Kids are Healthy21 to 55-point agreement rating L0.652.70 ± 0.74
 Concern for Child’s Overweight Risk [68]31 to 55-point agreement rating L0.911.91 ± 1.03
Health Behavior Values [31,69,70,71,72]
 Importance of Physical Activity for Self31 to 55-point agreement rating L0.823.49 ± 0.97
 Importance of Physical Activity for Child31 to 55-point agreement rating L0.683.83 ± 0.87
 Encourages/Facilitates Child Physical Activity51 to 55-point agreement rating L0.884.23 ± 0.66
 Importance of Modeling Physical Activity to Child21 to 55-point agreement rating L0.794.13 ± 0.82
 Engages in Physical Activity with Child Frequently20 to 78-point frequency scale M*3.67 ± 1.85
 Models Physical Activity to Child Frequently20 to 78-point frequency scale M*3.08 ± 1.22
 Models Sedentary Behaviors Infrequently20 to 78-point frequency scale M*2.79 ± 2.18
 Models Healthy Eating to Child41 to 55-point agreement rating L0.563.51 ± 0.73
 Belief that TV Positively Affects Child Learning21 to 55-point agreement rating L0.853.89 ± 0.76
 Talks Often with Child about TV21 to 55-point agreement rating L0.503.24 ± 0.97
 Limits Child Exposure to TV Commercials and Inappropriate Programs Shows21 to 55-point agreement rating L0.503.67 ± 0.93
 Limits Child to Educational TV11 to 55-point agreement rating L*3.52 ± 1.09
Home Interpersonal Characteristics
Child Feeding Practices [68,69,70,71,73,74,75]
 Restricts Child Food Intake21 to 55-point agreement rating L0.633.84 ± 0.86
 Pressures Child to Eat31 to 55-point agreement rating L0.692.17 ± 0.96
 Maternal Control Over Child Food Access and Choices71 to 55-point agreement rating L0.653.33 ± 0.52
 Non-Acceptance of Food Waste21 to 55-point agreement rating L0.613.05 ± 0.97
 Instrumental Feeding Practices (uses food to reward children for eating a healthy food)31 to 55-point agreement rating L0.732.63 ± 0.91
 Non-Food Rewards (uses non-food (e.g., extra playtime) to reward children for eating a healthy food)21 to 55-point agreement rating L0.652.90 ± 0.95
 Allows Child to Independently Access Nutrient Dense Foods50 to 5yes/no*1.82 ± 1.74
 Allows Child to Independently Access Low Nutrient Density Foods60 to 6yes/no*0.68 ± 1.32
 Nutrient Dense Foods Stored in Locations Visible to Child50 to 5yes/no*2.44 ± 1.71
 Low Nutrient Dense Foods Stored in Locations Visible to Child60 to 6yes/no*0.82 ± 1.35
Family Meal Patterns [70,76,77,78,79,80]
 Family Meal Frequency30 to 210–7 days for breakfast, lunch, dinner; score is sum of 3 meals*13.64 ± 5.05
 Importance of Family Meals31 to 55-point agreement rating L0.704.52 ± 0.64
 Positive Family Meal Atmosphere31 to 55-point agreement rating L0.704.12 ± 0.85
 Fast Food Eaten at Family Meals10 to 7days/week*0.93 ± 1.18
 TV on During Family Meals10 to 7days/week*2.24 ± 2.48
 Family Meals Eaten at Kitchen or Dining Table10 to 7days/week*4.69 ± 2.51
 Family Meals Eaten in the Car10 to 7days/week*0.43 ± 1.16
 Family Meal Planning21 to 55-point agreement rating L0.703.40 ± 0.88
 Time and Energy for Family Meals21 to 55-point agreement rating L0.784.34 ± 0.85
Family Functioning and Maternal Engagement
 Family Support for Healthy Behaviors [81,82,83]41 to 55-point agreement rating L0.814.40 ± 0.73
 Family Conflict and Lack of Cohesion [84,85,86]51 to 55-point agreement rating L0.841.83 ± 0.70
 Household Disorganization [87,88]31 to 55-point agreement rating L0.762.47 ± 0.92
 Verbal Engagement with Children11 to 55-point agreement rating L*4.17 ± 0.93
 Physical Engagement with Children11 to 55-point agreement rating L*4.74 ± 0.51
Home Physical Environment Characteristics
Physical Activity [69,70,71,72,82,89]
 Physical Activity Availability121 to 55-point agreement rating L0.723.78 ± 0.67
 Physical Activity Accessibility †21 to 55-point agreement rating L0.904.21 ± 1.14
 Media Devices in the Home60 to more than 10Total devices N*11.57 ± 4.21
 Media Devices in Child’s Bedroom70 to 7Total # of media device types O*1.39 ± 1.62
 Daily Screentime Child Allowed10 to 1440minutes/day*495.14 ± 714.22
Food Availability
 Household Fruit and Vegetable Availability (serving/person/day) [52,90]100 to 19.949-point servings scale P*6.41 ± 2.53
 Household Fatty/Salty Snack Availability (serving/person/day) [52,90]40 to 329-point servings scale P*8.37 ± 7.22
 Household Sugar-Sweetened Beverage Availability (serving/person/day) [55,56]40 to 4.579-point servings scale P*1.87 ± 1.79
 Household Breakfast Cereal Availability (serving/person/day) [52,90]10 to 89-point servings scale P*5.35 ± 2.72
Note: n/a = not applicable. * Cronbach’s alpha is not appropriate for the scale type or because the scale has <2 items. † n = 524. A Family affluence assessed by three questions asking the total number of cars, vans, or trucks the family owns (0 = none, 1 = 1 vehicle, 2 = 2 or more vehicles), # of times family traveled on vacation in past year (0 = never, 1 = 1 time, 2 = 2 times, 3 = 3 or more times), and whether participant had their own bedroom (1 = yes, 0 = no); items are summed. B 4-point Agreement Rating: definitely false, mostly false, mostly true, definitely true; scored 1 to 4, respectively. Items averaged with higher scores indicating greater expression of the trait. C 5-point Excellence Rating: poor, fair, good, very good, excellent; scored 1 to 5 respectively; higher score indicates better health. D 4-point Occurrence Rating: not at all, several days, more than half the days, nearly every day; scored 1 to 4, respectively; scale scores equals average of items with higher scores indicating greater expression of the behavior. E 5-point Frequency Rating: never, rarely, sometimes, often, very often; scored 1 to 5 respectively; scale score equals average of item scores with higher scores indicating greater weight teasing history. F 4-point Frequency Rating: not at all, slightly, moderately, a lot; scored 1 to 4 respectively; higher scores indicate greater body dissatisfaction. G 8-point Exercise Days/week: 0, 1, 2, 3, 4, 5, 6, and 7; days/week weighted by exercise intensity (weights of 1, 2, 3 for walking, moderate, and vigorous activity, respectively) and summed to create scale score; higher scale score indicates greater activity level. H 5-point rating scale: very good, good, okay, bad, and very bad; scored 1 to 5 respectively with higher scores indicate poorer sleep quality. I 6-point Fruit/Vegetable Servings Rating: <1 serving/week, 1 serving /week, 2 to 3 servings/week, 4 to 6 servings/week, 1 serving/day, 2 or more servings/day; scored 0 to 5 respectively; scale scoring algorithm is protected by copyright and described in detail elsewhere. Possible score range = 0 to 12.17. J 5-point Fatty Food Servings Rating: 1 time/month or less, 2 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 or more times/week; scored 0 to 4 respectively; scale scoring algorithm is protected by copyright and described in detail elsewhere. K 9-point Beverage Servings Rating: <1 time/week, 1 day/week, 2 days/week, 3 days/week, 4 days/week, 5 days/week, 6 days/week, 7 days/week, >1 time/day; scored 0 to 8 respectively. Possible score ranges for Sugar-Sweetened Beverages = 0 to 4.6; Milk = 0 to 8. L 5-point Agreement Rating: strongly disagree, disagree, neither agree nor disagree, agree, strongly agree; scored 1 to 5 respectively; scale score equals average of item scores with higher scale score indicating greater expression of the trait. M 8-point Modeling Days/week: 0 (almost never), 1, 2, 3, 4, 5, 6, and 7; days averaged to create scale score with higher score indicating more frequent modeling. N 11-point Media Device Count: 1 = 1 to 10 = 10, 11 = more than 10; scale score equals sum of items; higher score indicates greater number of media devices. Possible score range = 0 to 66. O Sum of 7 media devices found in child’s bedroom (i.e., TV, DVD player, Computer/Laptop, Smarphone/Tablet/LeapPad, video game devices (Nintendo DS, XBoxKinect), and Internet access). P 9-point Household Servings Rating: <1 time/week, 1 day/week, 2 days/week, 3 days/week, 4 days/week, 5 days/week, 6 days/week, 7 days/week, >1 time/day; scored 0 to 8 respectively. Possible score ranges for fruits/vegetables (0 to 19.94), salty/fatty snacks (0 to 32), sugar-sweetened beverages (0 to 4.57), and breakfast cereal (0 to 8) servings/household/member/day.
Table
Table 2. Selection Rationale for Single Variable from Multicollinear Variable Groups.
Table 2. Selection Rationale for Single Variable from Multicollinear Variable Groups.
Multicollinear Variable GroupVariable Most Highly and Significantly Correlated with BMI Was Retained in Further Analysis
Depression Severity, Health-related Quality of Life, and Stress ManagementHealth-Related Quality of Life
Disinhibited Eating and Emotional EatingEmotional Eating
Importance of Physical Activity for Self with (1) Maternal Physical Activity Level and (2) Importance of Modeling Physical Activity to ChildImportance of Physical Activity for Self
Encourages/Facilitates Child Physical Activity with (1) Importance of Physical Activity for Child and (2) Importance of Modeling Physical Activity to ChildEncourages/Facilitate Child Physical Activity (Note: Importance of Modeling Physical Activity to Child had a higher correlation with BMI; however, it could not be selected because it is intercorrelated with Importance of Physical Activity for Self; see above)
Models Physical Activity to Child Frequently with (1) Maternal Physical Activity Level and (2) Mother: Child Co-Physical ActivityModels Physical Activity to Child Frequently
Instrumental Feeding Practices and Non-Food RewardsInstrumental Feeding Practices
Allows Child to Independently Access Nutrient Dense Foods and Nutrient Dense Foods Stored in Locations Visible to ChildNeither variable was significantly correlated with BMI (none included)
Allows Child to Independently Access Low Nutrient Density Foods and Low Nutrient Dense Foods Stored in Locations Visible to ChildLow Nutrient Dense Foods Stored in Locations Visible to Child
Family Meals Eaten at Kitchen or Dining Table and TV on During Family MealsFamily Meals Eaten at Kitchen or Dining Table
Positive Family Meal Atmosphere and Household DisorganizationNeither variable was significantly correlated with BMI (none included)
Importance of Family Meals and Time and Energy for Family MealsImportance of Family Meals
Household Fruit and Vegetable Availability with (1) Fruit and Vegetable Intake and (2) Household Sugar-sweetened Beverage AvailabilityHousehold Fruit and Vegetable Availability
Table
Table 3. Binomial Logistic Regression of Variables Associated with Maternal Obesity (n = 550).
Table 3. Binomial Logistic Regression of Variables Associated with Maternal Obesity (n = 550).
Dependent Variable: Maternal Obesity †
Independent VariablesB ‡SE #Odds Ratio95% CIp-value
Race (black or African American)1.250.413.48(1.56, 7.70)0.002
Education Level (some college or less)0.610.261.83(1.09, 3.07)0.021
Number of Children in Household0.320.121.38(1.08, 1.75)0.010
General Health Rating a0.890.172.43(1.73, 3.41)<0.001
Perception of Weight Teasing History0.520.111.69(1.35, 2.11)<0.001
Body Dissatisfaction0.910.142.29(1.91, 3.25)<0.001
Primary Relative with History of Obesity0.710.252.04(1.25, 3.23)0.004
Concern for Child’s Overweight Risk0.280.131.32(1.03, 1.69)0.026
R
Cox and Snell R Square0.374
Nagelkerke R Square0.531
Tests of Model CoefficientsDF *χ2p-value
8257.92<0.001
‡ Beta coefficient. * DF = Degrees of Freedom. # Standard Error. † Maternal obesity defined at body mass index ≥ 30. a Higher scores indicate poorer perceptions of health status.
Table
Table 4. ANOVA of Sociodemographic, Intrapersonal, Interpersonal and Home Environment Characteristics among Obesity Risk Score Tertiles of Non-Obese Participants.
Table 4. ANOVA of Sociodemographic, Intrapersonal, Interpersonal and Home Environment Characteristics among Obesity Risk Score Tertiles of Non-Obese Participants.
Obesity Risk Score Tertiles of Non-Obese Participants (N = 386)
Low Risk (n = 120)Moderate Risk (n = 135)High Risk (n = 131)
Weighted Score Cut-offs0 to 1.121.13 to 2.07≥2.08ANOVA *
Mean ± SD or N (%)Mean ± SD or N (%)Mean ± SD or N (%)F or χ2p-value
Obesity risk score (possible score range 0–5.49)0.58 ± 0.36 †,a1.52 ± 0.27 b2.78 ± 0.53 c953.53<0.001
Sociodemographic Characteristics
 Maternal Employment 5.030.284
 Do not work56 (46.7)73 (54.1)78 (59.5)
 Part-time (less than 30 h/week)24 (20.0)28 (20.7)21 (16.0)
 Full-time (30 or more h/week)40 (33.3)34 (25.2)32 (24.4)
 Number of Children in Household2.07 ± 0.972.21 ± 1.022.22 ± 0.960.940.392
 Family Affluence Score6.09 ± 1.49 a5.63 ± 1.61 b5.49 ± 1.53 b5.160.006
 Food Insecurity Risk1.38 ± 1.76 a1.78 ± 1.802.27 ± 1.82 b7.81<0.001
Weight Status
 Mother’s BMI22.15 ± 2.64 a22.93 ± 3.03 a25.19 ± 3.05 b37.36<0.001
 Child’s BMI percentile (n = 339)59.85 ± 34.5862.07 ± 34.7665.45 ± 35.450.890.413
Health-Related Assessments
 Health-Related Quality of Life (# of unhealthy days)0.94 ± 1.57 a1.61 ± 2.51 a4.16 ± 5.49 b27.97<0.001
 Depression Severity0.57 ± 1.02 a0.85 ± 1.41 a1.33 ± 1.48 b10.68<0.001
 Age at Birth of First Child (years)25.92 ± 4.72 a24.30 ± 5.12 a23.92 ± 5.59 b5.080.007
 Primary Relative with History of Diabetes (% yes)23 (19.2)21 (15.6)38 (29.0)7.640.022
Intrapersonal Characteristics
Maternal Weight-Related Behaviors
 Physical Activity Level17.43 ± 9.7815.93 ± 9.4914.65 ± 10.502.460.087
 Screentime (minutes/day)216.25 ± 214.87270.78 ± 249.99277.67 ± 251.362.430.089
 Sleep Duration (hours/day)7.49 ± 1.977.22 ± 1.856.87 ± 1.893.310.038
 Sleep Quality3.58 ± 0.87 a3.46 ± 0.82 a3.03 ± 0.83 b15.00<0.001
 Fruit and Vegetable (servings/day)4.98 ± 2.374.84 ± 2.304.24 ± 2.293.650.027
 % Calories from Total Fat36.83 ± 5.3637.61 ± 6.5536.98 ± 5.870.640.530
 Milk (servings/day)4.18 ± 3.194.31 ± 2.953.33 ± 3.113.790.023
 Sugar-Sweetened Beverage (servings/day)0.70 ± 0.780.95 ± 1.060.93 ± 0.862.830.060
Maternal Eating Styles
 Disinhibited Eating1.77 ± 0.771.95 ± 0.762.00 ± 0.732.250.040
 Emotional Eating1.73 ± 0.74 a1.87 ± 0.80 a2.19 ± 0.88 b10.95<0.001
 Dietary Restraint Eating2.31 ± 0.792.47 ± 0.792.49 ± 0.692.270.105
Adventurous Eating3.21 ± 0.693.16 ± 0.663.12 ± 0.670.520.597
Maternal Self-Perceptions
 Personal Organization (self-effectiveness)3.91 ± 0.75 a3.79 ± 0.79 a3.49 ± 0.83 b9.31<0.001
 Need for Cognition3.66 ± 1.013.47 ± 0.943.37 ± 0.982.760.065
 Parenting Self-Efficacy4.23 ± 0.71 a4.23 ± 0.75 a3.95 ± 0.82 b5.710.004
 Stress Management4.19 ± 0.54 a4.02 ± 0.71 a3.79 ± 0.83 b10.41<0.001
 Stress Management Self-Efficacy2.88 ± 1.012.51 ± 1.052.64 ± 0.991.430.242
Child Weight Cognitions
 Belief that Chubby Kids are Healthy2.72 ± 0.752.79 ± 0.682.61 ± 0.721.950.143
Health Behavior Values
 Importance of Physical Activity for Self3.90 ± 0.92 a3.75 ± 0.81 a3.34 ± 0.92 b13.94<0.001
 Importance of Physical Activity for Child4.03 ± 0.78 a3.85 ± 0.853.69 ± 0.87 b5.120.006
 Encourages/Facilitates Child Physical Activity4.37 ± 0.674.29 ± 0.614.16 ± 0.643.440.033
 Importance of Modeling Physical Activity to Child4.35 ± 0.82 a4.26 ± 0.714.07 ± 0.76 b4.610.010
 Engages in Physical Activity with Child Frequently3.92 ± 1.903.61 ± 1.913.44 ± 1.802.110.123
 Models Physical Activity to Child Frequently3.55 ± 1.10 a3.13 ± 1.21 b2.93 ± 1.18 b9.14<0.001
 Models Sedentary Behaviors Infrequently3.54 ± 2.11 a2.84 ± 2.08 b2.75 ± 2.27 b5.000.007
 Models Healthy Eating to Child3.72 ± 0.75 a3.62 ± 0.633.43 ± 0.69 b5.740.003
 Belief that TV Positively Affects Child Learning3.91 ± 0.683.91 ± 0.83.78 ± 0.781.300.274
 Talks Often with Child about TV3.38 ± 1.013.27 ± 0.943.26 ± 0.940.600.548
 Limits Child Exposure to TV Commercials and Inappropriate Programs Shows3.77 ± 0.903.71 ± 0.863.66 ± 0.930.480.621
 Limits Child to Educational TV3.68 ± 1.03 a3.64 ± 1.02 a3.31 ± 1.09 b4.660.010
Home Interpersonal Characteristics
Child Feeding Practices
 Restricts Child Food Intake3.85 ± 0.883.91 ± 0.843.83 ± 0.830.310.737
 Pressures Child to Eat2.22 ± 1.042.18 ± 0.952.15 ± 0.890.180.837
 Maternal Control Over Child Food Access and Choices3.44 ± 0.493.38 ± 0.553.30 ± 0.482.400.092
 Non-Acceptance of Food Waste3.12 ± 0.963.26 ± 0.943.05 ± 0.891.750.175
 Instrumental Feeding Practices (uses food to reward children for eating a healthy food)2.74 ± 0.972.77 ± 0.932.55 ± 0.872.010.136
 Non-Food Rewards (uses non-food [e.g., extra playtime] to reward children for eating a healthy food)2.93 ± 1.002.95 ± 0.982.89 ± 0.860.160.854
 Allows Child to Independently Access Nutrient Dense Foods1.74 ± 1.741.96 ± 1.681.73 ± 1.780.750.475
 Allows Child to Independently Access Low Nutrient Density Foods0.48 ± 1.040.78 ± 1.430.70 ± 1.401.810.166
 Nutrient Dense Foods Stored in Locations Visible to Child 2.30 ± 1.722.42 ± 1.712.61 ± 1.781.030.360
 Low Nutrient Dense Foods Stored in Locations Visible to Child0.58 ± 1.210.79 ± 1.390.94 ± 1.362.260.106
Family Meal Patterns
 Family Meal Frequency (per week)14.25 ± 5.0013.92 ± 4.4912.97 ± 5.092.390.093
 Importance of Family Meals4.64 ± 0.574.53 ± 0.644.42 ± 0.653.690.026
 Positive Family Meal Atmosphere4.18 ± 0.864.09 ± 0.884.08 ± 0.790.570.569
 Fast Food Eaten at Family Meals
 TV on During Family Meals (days/week)1.53 ± 2.172.22 ± 2.532.24 ± 2.433.550.030
 Family Meals Eaten at Kitchen or Dining Table (days/week)5.38 ± 2.23 a4.82 ± 2.484.42 ± 2.42 b5.130.006
 Family Meals Eaten in the Car0.51 ± 1.290.54 ± 1.390.35 ± 0.900.920.401
 Family Meal Planning3.53 ± 0.933.46 ± 0.93.4 ± 0.800.700.496
 Time and Energy for Family Meals4.46 ± 0.784.33 ± 0.854.22 ± 0.922.570.078
Family Functioning and Maternal Engagement
 Family Support for Healthy Behaviors4.54 ± 0.744.34 ± 0.864.37 ± 0.652.560.079
 Family Conflict and Lack of Cohesion1.65 ± 0.62 a1.76 ± 0.641.91 ± 0.66 b5.290.005
 Household Disorganization2.32 ± 0.962.48 ± 0.922.55 ± 0.872.030.133
 Verbal Engagement with Children4.33 ± 0.844.15 ± 0.914.05 ± 1.012.910.056
 Physical Engagement with Children4.81 ± 0.444.67 ± 0.64.73 ± 0.512.110.122
Home Physical Environment Characteristics
Physical Activity
 Physical Activity Availability3.90 ± 0.683.82 ± 0.623.78 ± 0.641.120.327
 Physical Activity Accessibility4.42 ± 0.984.39 ± 1.164.12 ± 1.152.840.060
 Media Devices in the Home (total number)11.53 ± 4.2911.09 ± 4.1911.5 ± 3.710.490.613
 Media Devices in Child’s Bedroom (total number)1.05 ± 1.551.37 ± 1.701.63 ± 1.753.710.025
 Daily Screentime Child Allowed (minutes/day)342.00 ± 413.14433.78 ± 642.76509.54 ± 710.462.390.093
Food Availability
 Household Fruit and Vegetable Availability (serving/person/day)6.96 ± 2.41 a6.80 ± 2.626.09 ± 2.44 b4.400.013
 Household Fatty/Salty Snack Availability (serving/person/day)9.21 ± 7.818.67 ± 7.738.07 ± 6.810.730.481
 Household Sugar-Sweetened Beverage Availability (serving/person/day)1.70 ± 2.022.21 ± 2.021.72 ± 1.513.150.044
 Household Breakfast Cereal Availability (servings/person/day)5.67 ± 2.785.39 ± 2.705.49 ± 2.520.360.698
* Analysis of Variance (ANOVA) for continuous variables and Chi-square analysis for categorical variables examined characteristic differences among the Obesity Risk Score Tertile groups (low risk, moderate risk, and high risk). Tukey post-hoc tests were conducted for characteristics that were statistically significant (p < 0.01) in the ANOVA to determine significant between group differences. † Differing superscript lowercase letters in the same row indicate significant (p < 0.05) between group differences as determined by Tukey post-hoc tests.

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MDPI and ACS Style

Martin-Biggers, J.; Quick, V.; Spaccarotella, K.; Byrd-Bredbenner, C. An Exploratory Study Examining Obesity Risk in Non-Obese Mothers of Young Children Using a Socioecological Approach. Nutrients 2018, 10, 781. https://doi.org/10.3390/nu10060781

AMA Style

Martin-Biggers J, Quick V, Spaccarotella K, Byrd-Bredbenner C. An Exploratory Study Examining Obesity Risk in Non-Obese Mothers of Young Children Using a Socioecological Approach. Nutrients. 2018; 10(6):781. https://doi.org/10.3390/nu10060781

Chicago/Turabian Style

Martin-Biggers, Jennifer, Virginia Quick, Kim Spaccarotella, and Carol Byrd-Bredbenner. 2018. "An Exploratory Study Examining Obesity Risk in Non-Obese Mothers of Young Children Using a Socioecological Approach" Nutrients 10, no. 6: 781. https://doi.org/10.3390/nu10060781

APA Style

Martin-Biggers, J., Quick, V., Spaccarotella, K., & Byrd-Bredbenner, C. (2018). An Exploratory Study Examining Obesity Risk in Non-Obese Mothers of Young Children Using a Socioecological Approach. Nutrients, 10(6), 781. https://doi.org/10.3390/nu10060781

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