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JMIR Pediatrics and Parenting

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Published on 25.04.18 in Vol 1, No 1 (2018): Jan-Jun

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/10320, first published Mar 09, 2018.

This paper is in the following e-collection/theme issue:

    Original Paper

    Clustering of Obesity-Related Risk Behaviors Among Families With Preschool Children Using a Socioecological Approach: Cross-Sectional Study

    Department of Nutritional Sciences, Rutgers University, New Brunswick, NJ, United States

    Corresponding Author:

    Virginia Quick, RD, PhD

    Department of Nutritional Sciences

    Rutgers University

    26 Nichol Avenue

    New Brunswick, NJ, 08520

    United States

    Phone: 1 848 932 0950

    Fax:1 732 932 6522

    Email:


    ABSTRACT

    Background: Limited attention has been given to assessing home environments of parents with preschool-aged children using a socioecological approach to better understand potential influencers of obesity risk.

    Objective: The purpose of this cross-sectional study was to examine the clustering of obesity-related risk behaviors among mothers with preschool children.

    Methods: Mothers with preschool-aged children (ages 2 to 5 years) who participated in the online Home Obesogenic Measure of Environments (HOMES) survey were examined in clustering of four healthy recommended behaviors (ie, mother’s fruit and vegetable intake ≥5 per day, sedentary screen time <4 hours per day, sugar-sweetened beverage intake <1 time/day, and increased physical activity level). Frequencies and percents of the clustering variables were conducted along with Spearman rank order correlations to determine significant associations. Ward’s method with squared Euclidean distances were performed for the cluster analysis using the four standardized continuous variables. Identification of total cluster number was determined by visually inspecting the dendogram. Sociodemographic, intrapersonal, social environment, and home physical environment characteristic differences between cluster groups were further examined by independent t tests and chi-square analysis to validate findings.

    Results: Of the 496 participants (72.6%, 360/496 white; age mean 32.36, SD 5.68 years), only a third (37.1%, 184/496) consumed five or more servings of fruits/vegetables daily, had low sedentary screen time of <4 hours/day, and reported moderate to high levels of physical activity (34.1%, 169/496). More than half (57.7%, 286/496) consumed <1 sugar-sweetened beverage serving daily. A positive correlation (r=.34, P<.001) between physical activity level and fruit/vegetable intake (≥5 servings/day), and a positive correlation (r=.15, P=.001) between low sedentary screen time (<4 hours/day) and low sugar-sweetened beverage intake (<1 serving/day) were found. Ward’s hierarchical analysis revealed a two-cluster solution: less healthy/inactive moms (n=280) and health conscious/active moms (n=216). Health conscious/active moms were significantly (P<.010) likely to be more physically active, have lower sedentary screen time, lower daily intake of sugar-sweetened beverages, and greater daily intake of fruits and vegetables compared to less healthy/inactive moms. Less healthy/inactive moms were significantly more likely to have a higher body mass index and waist circumference compared to the other cluster; however, there were no significant sociodemographic differences. There were many intrapersonal (eg, importance of physical activity for child and self) and home physical environment (eg, home availability of fruits/vegetables and salty/fatty snacks) characteristic differences between clusters, but few significant differences emerged for social environment characteristics (eg, family meals, family cohesion).

    Conclusions: Findings may have implications in tailoring future obesity prevention interventions among families with young children.

    JMIR Pediatr Parent 2018;1(1):e10320

    doi:10.2196/10320

    KEYWORDS



    Introduction

    In the United States, more than one-third of children are either overweight or obese [1]. The high prevalence rate among all pediatric age groups, in both sexes, and in various ethnic and racial groups has been at a steady climb over the last three decades and this trend continues to this day [2]. Additionally, the overall estimated annual medical costs and physical and mental health consequences of obesity are very high [3].

    An energy imbalance in which too few calories are expended for the amount of calories consumed is often the primary focus of obesity research and interventions; however, the many intrapersonal and environmental (social and physical) factors facilitating this energy imbalance are critical to understand. The socioecological model posits that health and well-being of an individual is determined by multiple levels of influence [4]. At the macro level, factors such as economic policies and political and legal structures (eg, gross domestic product) have a more indirect effect in influencing behaviors. At the micro level, factors of the near physical environment (eg, home, neighborhood), family social environment, and intrapersonal characteristics more directly influence behaviors.

    Application of ecological theory in obesity research has indicated that intrapersonal characteristics, social environments, and physical environment factors all play a role in obesity [5]. Environments that lack support for weight-management behaviors make it difficult for individuals to engage in behaviors that prevent weight gain. Currently, obesity prevention interventions in children younger than 5 years of age have shown limited effectiveness in reducing or limiting weight gain [6], perhaps due to little attention being given to the social and physical environments within which diet and physical activity behaviors are endorsed [7].

    The research to date on the prevention and treatment of obesity among children and adults highlights the importance of increased consideration of the social and physical environment [8]. At the micro level, the home environment is shared among parents and their children. Parents are considered the “gate keepers” of the home and role models for their children. That is, parents can strongly influence food and physical activity behaviors and practices which, in turn, may influence their child’s obesity risk [9,10]. Prior research has reported a number of parent and social and physical environment factors in the home associated with children’s overweight status, such as limited physical activity supports [11], infrequent family meals [12], low household availability of fruits and vegetables [13], excessive sedentary screen time [12], and less parental modeling of healthy behaviors [14].

    Mothers, in particular, can have a strong influence on their child’s weight-related behaviors from a young age that develop during the preschool years and track later into childhood and adulthood [15-19]. Given the home environment may influence child obesity risk, it is important to better understand these factors. Limited attention has been given to comprehensively assess the home environment of parents with preschool-aged children using a socioecological approach with reliable and validated measures, which is necessary for better understanding the potential influencers of obesity risk among families with young children [20]. Thus, a secondary analysis from a rich dataset of socioecological factors related to the obesogenic home environments of mothers with preschool-aged children (2 to 5 years of age) was examined to assess the clustering of obesity-related behaviors.


    Methods

    The Institutional Review Board at Rutgers University approved this research study. All participants gave informed consent to participate.

    Recruitment

    A global research company (ie, Survey Sampling International) whose services include survey participant recruitment, sent invitations to panel members who were mothers in the United States, inviting them to complete the online Home Obesogenic Measure of Environments (HOMES) survey [4,21,22]. Recruitment notices asked mothers to participate in a survey to help researchers “learn more about families with young kids” and to help them develop “a program for parents to build healthier kids.” To be eligible, panel members had to be female, 18 to 45 years of age, English speaking, have at least one preschool child (aged 2 to 5 years), and be the main household food gatekeeper (ie, make most or all food purchasing and meal decisions). Participants and their spouse/partner could not be employed in a health-related profession. As an incentive to complete the survey, participating mothers accrued points from Survey Sampling International that were redeemable for gifts.

    Instruments

    Details on the development and content of the online HOMES survey and research protocol are described elsewhere [4,21,22]. In brief, the HOMES cross-sectional survey was developed by researchers at Rutgers University as part of a larger research study exploring obesity risk in mothers of young children and included an array of valid, reliable measures that focused on mother’s sociodemographic, health-related, intrapersonal, social, and home physical environment characteristics. Measures were selected to yield an understanding of socioecological factors pertaining to diet and physical activity. All measures were self-reported and underwent rigorous selection to ensure they were valid and reliable. The survey was posted online using Qualtrics platform and pilot-tested with 48 participants 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. Administration of an online format was chosen 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 (ie, distance from researchers or limited time to meet in-person). Multimedia Appendix 1 lists the variables used in this secondary analysis of the online survey including number of items, possible score range, scale type, and internal consistency (Cronbach alpha when applicable).

    Sociodemographics and Health-Related Characteristics

    Maternal sociodemographic data collected included race/ethnicity, highest education level achieved, number of children in the household, family affluence [23,24], and food insecurity risk [25]. Health-related characteristics assessed were general health status (Centers for Disease Control and Prevention [CDC] Health-Related Quality of Life) [26,27], depression severity (Patient Health Questionnaire-2) [28], body dissatisfaction (Eating Disorder Examination Questionnaire) [29], and primary relative with history of obesity.

    Weight Status and Waist Circumference

    Mothers reported their current height, weight, and waist circumference. Height and weight were used to calculate body mass index (BMI) as recommended by the CDC [30]. Mothers reported their child’s height and weight, which were used to calculate age- and sex-specific child BMI percentile.

    Intrapersonal Characteristics

    Mothers’ weight-related behaviors assessed were physical activity level (streamlined International Physical Activity Questionnaire) [31-33] and sleep quality and duration (Pittsburgh Sleep Quality Index) [34,35]. Maternal dietary intake was assessed using the following food frequency questionnaires: Block Fruit-Vegetable-Fiber Screener, Block Dietary Fat Screener [36-38], and a sugar-sweetened beverage screener [39]. Maternal eating styles measured from the Three-Factor Eating Questionnaire-18 [40,41] were disinhibited eating, emotional eating, and dietary restraint eating. Mothers’ self-perceptions assessed were personal organization [42], need for cognition [43,44], parenting self-efficacy [45,46], stress management [47], and stress management self-efficacy (created de novo). Value of engaging in healthy behaviors for self and child (eg, 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 healthy eating) [11,21,48-50] were also measured.

    Social Environment

    Family meal patterns and family meal environment (eg, frequency of meals, family meal atmosphere) [11,51-55] data were collected. Scales assessing family functioning and engagement included family conflict and lack of cohesion [56-58], and family support for healthy behaviors [59-61].

    Home Physical Environment

    Evaluation of the home environment’s accessibility and availability to physical activity and sedentary activity supports (eg, media devices in the home, TV accessibility for child) [11,48-50,60,62,63] were assessed along with measures of household food availability (eg, fruit/vegetables, sugar-sweetened beverages) [36,39,64-66].

    Obesity-Related Behaviors

    It was decided a priori that healthy recommended behaviors, such as mother’s fruit and vegetable intake of five or more per day, sedentary screen time (<4 hours per day), sugar-sweetened beverage intake (<1 time/day), and increased physical activity level, would be the variables used in clustering mother’s obesity-related behaviors. Currently, the Dietary Guidelines for Americans [67] recommend adults consume 5 to 9 servings of fruits and vegetables per day and limit the amount of daily sugar-sweetened beverage consumption; thus, a cut-off of five or more fruits and vegetables per day and less than one serving of sugar-sweetened beverage intake per day, respectively, were proxies for healthy behaviors. Although the American Academy of Pediatrics recommends parents monitor and limit their preschool child’s screen time to less than 1 hour per day, there are no set time limits for adults [68]. For this reason, a liberal approach was taken in giving a cut-off of less than 4 hours daily of sedentary screen time for mothers. Physical activity levels, using the streamlined International Physical Activity Questionnaire [31-33], were categorized into low, moderate, and high levels using cut-off scores previously set by other researchers [31,33]. That is, self-report physical activity level was calculated as: (number of days walking per week) + 2 * (number of days moderate-intensity activities per week) + 3 * (number of days vigorous-intensity activities per week), with a possible score range of 0 to 42 (categorized as low: 0 to <20; moderate: 20 to <30; high: ≥30 physical activity levels).

    Statistical Analysis

    Frequencies and percents of clustering variables were examined to describe the sample of mother’s meeting these defined fruit and vegetable intake, sugar-sweetened beverage intake, sedentary screen time, and physical activity level behaviors. Before clustering, correlations among the four cluster variables were examined to determine significant associations using Spearman rank order correlations. For cluster analysis, the clustering variables were standardized (z scores) to permit comparisons of means and variances [69]. The four clustering variables (ie, mother’s physical activity level, sedentary screen time, daily fruit and vegetable intake, and sugar-sweetened beverage intake) were considered as continuous variables in the model. Ward’s method with squared Euclidean distances was used for the cluster analysis using the standardized continuous variables mentioned previously. Ward’s method was used because it has yielded useful results in previously similar settings [70], and tends to result in clusters of more equal size, which increases the robustness of cross-cluster comparisons [71]. The number of clusters that emerged was identified by visually inspecting the dendogram and noting the point at which the scree graph angled most sharply upward.

    To further establish how the two clusters differed from one another, variables used in the cluster analysis as well as intrapersonal, social environment, and home physical environment variables not used in defining the clusters, were examined using independent t tests for continuous variables and chi-square analysis for categorical variables. Given the large number of tests, P values were reduced to P<.010 to be considered statistically significant. All analyses were performed using SPSS version 24.


    Results

    Participant Characteristics

    A total of 496 participants (72.6%, 360/496 white; age mean 32.36, 5.68 SD years) with complete and plausible data were included in the analyses (48 had implausible numbers for sedentary screen time [≥15 hours per day] and three each had missing data for daily fruit/vegetable servings and sugar-sweetened beverage intake). More than one-third of participants (37.1%, 184/496) met daily fruit and vegetable serving recommendations of five or more and had relatively low sedentary screen time of less than 4 hours per day (Table 1). More than half of participants (57.7%, 286/496) consumed less than one sugar-sweetened beverage serving daily. Sugar-sweetened beverages included soft drinks, fruit drinks, energy drinks, and sugar-sweetened specialty coffee drinks. Additionally, approximately one-third (34.1%, 169/496) of mothers reported moderate to high levels of physical activity in the last week.

    Spearman rank order correlations revealed a positive correlation (r=.34, P<.001) between physical activity level and fruit and vegetable intake (≥5 servings/day), and a positive correlation (r=.15, P=.001) between low sedentary screen time (<4 hours/day) and low sugar-sweetened beverage intake (<1 serving/day; Table 2).

    Cluster Group Characteristics

    Ward’s hierarchical analysis revealed a two-cluster solution for the participants using the four standardized measures (ie, mother’s physical activity level, sedentary screen time, daily fruit and vegetable intake, and sugar-sweetened beverage intake). The two clusters of mothers were broadly divided as less healthy/inactive moms (n=280) and health conscious/active moms (n=216).

    Table 1. Proportions of mother’s fruit and vegetable intake, sedentary screen time, sugar-sweetened beverage intake, and physical activity level (N=496).
    View this table
    Table 2. Spearman rank correlations among healthy behaviors of mothers with a preschool child (N=496).
    View this table
    Table 3. Independent t tests and chi-square tests of sociodemographic, intrapersonal, interpersonal, and home environment characteristics of participants by cluster (N=496).
    View this table

    Health conscious/active moms were significantly likely to be more physically active, have lower sedentary screen time, lower daily intake of sugar-sweetened beverages, and greater daily intake of fruits and vegetables compared to less healthy/inactive moms (Table 3). These two clusters were further validated when examining associations with maternal health status, weight, and intrapersonal, social environment, and the home physical environment characteristics.

    There were no significant sociodemographic characteristic differences between the two clusters, except health conscious/active moms had a greater tendency to be employed (part-time/full-time) and be white, non-Hispanic. Less healthy/inactive moms were significantly more likely to have a higher BMI and waist circumference, and have a primary relative with a history of obesity compared to the other cluster. However, there were no significant differences between cluster groups on their child’s BMI percentile. Additionally, less healthy/inactive moms reported greater body dissatisfaction and poorer general health status compared to health conscious/active moms.

    There were many intrapersonal characteristics that differed between the two clusters. For example, less healthy/inactive moms were significantly more likely to be have lower sleep quality and consume less daily servings of milk compared to health conscious/active moms. Additionally, health conscious/active moms were significantly more likely to have greater personal organization, need for cognition, parenting self-efficacy, and stress management self-efficacy compared to less healthy/inactive moms. Nearly, all health behavior value variables were significantly different between the two clusters. That is, health conscious/active moms were significantly more likely to place greater importance on physical activity for self and their child, and encourage and model physical activity for their child compared to less healthy/inactive moms.

    The only social environment characteristics that differed between the two clusters was family meal frequency and family cohesion. That is, health conscious/active moms had significantly more family meals per week and more family cohesion compared to less healthy/inactive moms.

    As anticipated, health conscious/active moms’ home physical activity environments had significantly greater availability of physical activity and stricter limits on children’s daily screen time compared to less healthy/inactive moms. Additionally, health conscious/active moms had greater availability in the home for fruits and vegetables, fatty/salty snacks, and sugar-sweetened beverages compared to less healthy/inactive moms.


    Discussion

    Principal Results

    Overall, cluster analysis findings demonstrate two distinct patterns of obesity-related behaviors among mothers of preschool-aged children as evidenced by the external validation of clusters when examining associations with maternal health status, weight, intrapersonal, social environment, and the home physical environment characteristics. Comparing behavioral profiles of mothers of young children assigned to groups via cluster analysis adds qualitative insights, which may improve tailoring of interventions intended to effect behavior change. Thus, the two-cluster profiles found in our study may have implications for future obesity prevention interventions among families with young children.

    Obesity-related behaviors were not strongly defined by sociodemographic characteristics, variables typically used to tailor nutrition interventions. Instead, a number of health-related variables and anthropometric markers (ie, weight status, waist circumference) were related to cluster membership. For instance, less healthy/inactive moms had a higher BMI, waist circumference, body dissatisfaction, and poorer general health status compared to health conscious/active moms. Given the negative health outcomes associated with membership in the less healthy/inactive moms cluster and mothers’ influence on their children living in the same home environments [70], interventions that focus on improving obesity-related risk behaviors of mothers with young children are warranted.

    The four obesity-related variables (ie, mother’s physical activity level, sedentary screen time, daily fruit and vegetable intake, and sugar-sweetened beverage intake) used to define clusters in this study may be useful markers for tailoring obesity interventions. However, regardless of cluster groupings, a large proportion of participants were still not meeting the recommended daily intakes of fruits and vegetables along with suggested limits of daily sedentary screen time and sugar-sweetened beverage intake [67]. Further examination of cluster groupings by intrapersonal, social environment, and home physical environment characteristics illustrates the complexity of obesity and potential target areas for influencing behavior change among mothers with preschool-aged children. This study found cluster grouping associations at all levels; however, more significant associations were found at the intrapersonal and home physical environment levels suggesting parents might be strong influencers of their child’s behaviors.

    Comparison With Prior Work

    Prior research has found concordance of clustering patterns between children and their mothers suggesting that modeling of obesity-related risk behaviors by parents may be particularly important influences on children’s behavior [70]. Although not formerly tested in this study, health conscious/active moms reported significantly greater health behavior values on the importance of modeling physical activity for their child and placing limits on child sedentary screen time compared to less healthy/inactive moms. Both physical activity levels and eating behaviors of parents are predictive of obesity in children [72]. Additionally, prior research has found a positive relationship between family support and increased physical activity along with family-based obesity treatment programs being the most effective at combating pediatric obesity [73]. Thus, obesity interventions that are family based may be more effective when encouraging parents to improve their own obesity-related risk behaviors along with developing the parenting skills needed to model these same healthy behaviors for their children.

    Over time, diets have changed dramatically during the preschool period with an increase intake of added sugars that persists into adolescence [74]. Interestingly, health conscious/active moms had greater home availability of fruits and vegetables per family member, but also greater home availability of low nutrient-dense foods, such as sugar-sweetened beverages and salty/fatty snacks. Having greater availability of these food items does not necessarily correlate with greater food consumption of these low nutrient-dense food items. Future research should further explore whether greater availability of high nutrient-dense versus low nutrient-dense foods in the home has any direct effect on food intake and, in turn, obesity risk.

    Limitations

    Taking a simplistic approach can obscure true interrelationships between health behaviors [75]. Considering the complexity of behaviors is important when developing obesity prevention interventions; however, it is also important to note that cluster analysis is an exploratory technique. The cluster profiles observed in our study may have differed with the inclusion of different variables, clustering algorithm, or sample. Other limitations are the cross-sectional design that limits the ability to make interferences of causality from the observed associations, and the potential for reporting error and bias given all information was self-reported by participants. Although the sample included mothers of preschool-aged children who had demographics similar to the overall US population, findings may not be generalizable to fathers or families with children of different ages and in other countries.

    Conclusions

    Despite these study limitations, this is one of only a few studies that have examined the clustering of obesity-related risk behaviors in mothers of preschool-aged children. Additionally, this study took a socioecological approach to comprehensively examine an array of obesity-related risk behavior factors among mothers with preschool-aged children using reliable and validated measures. Given the strong influence mothers have on their child’s weight-related behaviors during the preschool years and subsequent years following into adulthood [15-19], study findings further suggest targeting obesity prevention interventions using a socioecological approach that encourages mothers to model positive health behaviors for their children at the intrapersonal, social environment, and home physical environment levels. Future research assessing the value of this socioecological approach is warranted.

    Acknowledgments

    Supported by United States Department of Agriculture, National Institute of Food and Agriculture (Grant Number 2011-68001-30170), and Rutgers Research Council Grant. I would also like to acknowledge Dr Jennifer Martin-Biggers for her prior dissertation work on the HOMES survey.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Descriptive Statistics of Sociodemographic, Intrapersonal, Interpersonal and Home Environment Characteristics of Participants (N=496).

    PDF File (Adobe PDF File), 42KB

    References

    1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014 Feb 26;311(8):806-814. [CrossRef] [Medline]
    2. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, et al. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 Through 2013-2014. JAMA 2016 Jun 07;315(21):2292-2299. [CrossRef] [Medline]
    3. Finkelstein EA, Graham WC, Malhotra R. Lifetime direct medical costs of childhood obesity. Pediatrics 2014 May;133(5):854-862 [FREE Full text] [CrossRef] [Medline]
    4. Martin-Biggers J, Worobey J, Byrd-Bredbenner C. Recent Advances in Obesity in Children. Berlin: Avid Science Publications; 2016. Interpersonal characteristics in the home environment associated with childhood obesity   URL: http://www.avidscience.com/wp-content/uploads/2016/05/OIC-15-03_May-06-2016.pdf [accessed 2018-03-06] [WebCite Cache]
    5. Hawkins SS, Cole TJ, Law C, Millennium Cohort Study Child Health Group. An ecological systems approach to examining risk factors for early childhood overweight: findings from the UK Millennium Cohort Study. J Epidemiol Community Health 2009 Feb;63(2):147-155 [FREE Full text] [CrossRef] [Medline]
    6. Wang Y, Wu Y, Wilson R, Bleich S, Cheskin L, Weston C, et al. Childhood Obesity Prevention Programs: Comparative Effectiveness Review and Meta-Analysis. Rockville, MD: Agency for Healthcare Research and Quality; 2013 Jun.   URL: https://www.ncbi.nlm.nih.gov/books/NBK148737/pdf/Bookshelf_NBK148737.pdf [accessed 2018-04-18] [WebCite Cache]
    7. Monasta L, Batty GD, Macaluso A, Ronfani L, Lutje V, Bavcar A, et al. Interventions for the prevention of overweight and obesity in preschool children: a systematic review of randomized controlled trials. Obes Rev 2011 May;12(5):e107-e118. [CrossRef] [Medline]
    8. Monasta L, Batty GD, Cattaneo A, Lutje V, Ronfani L, Van Lenthe FJ, et al. Early-life determinants of overweight and obesity: a review of systematic reviews. Obes Rev 2010 Oct;11(10):695-708. [CrossRef] [Medline]
    9. Ogata BN, 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 Aug;114(8):1257-1276. [CrossRef] [Medline]
    10. Skouteris H, McCabe M, Ricciardelli L, Milgrom J, Baur L, Aksan N, et al. Parent–child interactions and obesity prevention: a systematic review of the literature. Early Child Development and Care 2012 Feb;182(2):153-174. [CrossRef]
    11. Spurrier NJ, Magarey AA, Golley R, Curnow F, Sawyer MG. 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 May 30;5:31 [FREE Full text] [CrossRef] [Medline]
    12. Anderson SE, Whitaker RC. Household routines and obesity in US preschool-aged children. Pediatrics 2010 Mar;125(3):420-428. [CrossRef] [Medline]
    13. Rolls BJ, Ello-Martin JA, Tohill BC. What can intervention studies tell us about the relationship between fruit and vegetable consumption and weight management? Nutr Rev 2004 Jan;62(1):1-17. [Medline]
    14. He M, Piché L, Beynon C, Harris S. Screen-related sedentary behaviors: children's and parents' attitudes, motivations, and practices. J Nutr Educ Behav 2010;42(1):17-25 [FREE Full text] [CrossRef] [Medline]
    15. Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study. Pediatrics 2001 Sep;108(3):712-718. [Medline]
    16. Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. The relation of childhood BMI to adult adiposity: the Bogalusa Heart Study. Pediatrics 2005 Jan;115(1):22-27. [CrossRef] [Medline]
    17. Wang Y, Beydoun MA. The obesity epidemic in the United States--gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev 2007;29:6-28. [CrossRef] [Medline]
    18. Guo SS, Wu W, Chumlea WC, Roche AF. Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr 2002 Sep;76(3):653-658 [FREE Full text] [Medline]
    19. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 1997 Sep 25;337(13):869-873. [CrossRef] [Medline]
    20. Pinard CA, Yaroch AL, Hart MH, Serrano EL, McFerren MM, Estabrooks PA. Measures of the home environment related to childhood obesity: a systematic review. Public Health Nutr 2012 Jan;15(1):97-109. [CrossRef] [Medline]
    21. Martin-Biggers J. Home Environment Characteristics Associated with Obesity Risk in Preschool-Aged Children and their Parents [PhD dissertation]. New Brunswick, NJ: Rutgers University; 2016.
    22. Martin-Biggers J, Cheng C, Spaccarotella K, Byrd-Bredbenner C. Recent Advances in Obesity in Children. Berlin: Avid Science Publications; 2016. The physical activity environment in homes and neighborhoods   URL: http://www.avidscience.com/wp-content/uploads/2016/05/OIC-15-04_May-06-2016.pdf [accessed 2018-03-06] [WebCite Cache]
    23. Hartley JEK, Levin K, Currie C. A new version of the HBSC Family Affluence Scale - FAS III: Scottish Qualitative Findings from the International FAS Development Study. Child Indic Res 2016;9:233-245 [FREE Full text] [CrossRef] [Medline]
    24. Currie C, Molcho M, Boyce W, Holstein B, Torsheim T, Richter M. Researching health inequalities in adolescents: the development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale. Soc Sci Med 2008 Mar;66(6):1429-1436. [CrossRef] [Medline]
    25. Hager ER, Quigg AM, Black MM, Coleman SM, Heeren T, Rose-Jacobs R, et al. Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics 2010 Jul;126(1):e26-e32. [CrossRef] [Medline]
    26. Centers for Disease Control and Prevention. HRQOL concepts: Why is quality of life important?   URL: http://www.cdc.gov/hrqol/concept.htm [accessed 2017-12-12] [WebCite Cache]
    27. Centers for Disease Control and Prevention. CDC HRQOL-14 Healthy Days Measure   URL: http://www.cdc.gov/hrqol/hrqol14_measure.htm [accessed 2017-12-12] [WebCite Cache]
    28. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 2003 Nov;41(11):1284-1292. [CrossRef] [Medline]
    29. Fairburn CG, Beglin SJ. Assessment of eating disorders: interview or self-report questionnaire? Int J Eat Disord 1994 Dec;16(4):363-370. [Medline]
    30. Centers for Disease Control and Prevention. About BMI for Adults   URL: http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html [accessed 2017-12-12] [WebCite Cache]
    31. Quick V, Byrd-Bredbenner C, Shoff S, White A, Lohse B, Horacek T, et al. A streamlined, enhanced self-report physical activity measure for young adults. Int J Health Pr Educ 2016 May 03;54(5):245-254 [FREE Full text] [CrossRef]
    32. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act 2011 Oct 21;8:115 [FREE Full text] [CrossRef] [Medline]
    33. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003 Aug;35(8):1381-1395. [CrossRef] [Medline]
    34. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989 May;28(2):193-213. [Medline]
    35. Carpenter JS, Andrykowski MA. Psychometric evaluation of the Pittsburgh Sleep Quality Index. J Psychosom Res 1998 Jul;45(1):5-13. [Medline]
    36. Block G, Gillespie C, Rosenbaum EH, Jenson C. A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med 2000 May;18(4):284-288. [Medline]
    37. Block G, Hartman AM, Naughton D. A reduced dietary questionnaire: development and validation. Epidemiology 1990 Jan;1(1):58-64. [Medline]
    38. Block G, Thompson FE, Hartman AM, Larkin FA, Guire KE. Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. J Am Diet Assoc 1992 Jun;92(6):686-693. [Medline]
    39. West DS, Bursac Z, Quimby D, Prewitt TE, Spatz T, Nash C, et al. Self-reported sugar-sweetened beverage intake among college students. Obesity (Silver Spring) 2006 Oct;14(10):1825-1831 [FREE Full text] [CrossRef] [Medline]
    40. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29(1):71-83. [Medline]
    41. Karlsson J, Persson LO, Sjöström L, Sullivan M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int J Obes Relat Metab Disord 2000 Dec;24(12):1715-1725. [Medline]
    42. 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 Jul;16(3):429-444 [FREE Full text] [CrossRef]
    43. Cacioppo J, Petty R. The need for cognition. J Pers Soc Psychol 1982;42(1):116-131 [FREE Full text]
    44. Cacioppo JT, Petty RE, Kao CF. The efficient assessment of need for cognition. J Pers Assess 1984 Jun;48(3):306-307. [CrossRef] [Medline]
    45. Johnston C, Mash E. A measure of parenting satisfaction and efficacy. J Clin Child Psychol 1989 Jun;18(2):167-175 [FREE Full text] [CrossRef]
    46. Gibaud-Wallston J, Wandersmann L. Development and Utility of the Parenting Sense of Competence Scale. Washington, DC: John F Kennedy Center for Research on Education and Human Development; 1978.
    47. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983 Dec;24(4):385-396. [Medline]
    48. Gattshall ML, Shoup JA, Marshall JA, Crane LA, Estabrooks PA. 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 Jan 11;5:3 [FREE Full text] [CrossRef] [Medline]
    49. Bryant MJ, Ward DS, Hales D, Vaughn A, Tabak RG, 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 Apr 28;5:23 [FREE Full text] [CrossRef] [Medline]
    50. Earls F, Brooks-Gunn J, Raudenbush S, Sampson R. Project on human development in Chicago neighborhoods (PHDCN): home and life interview, wave 2. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 1997.
    51. Neumark-Sztainer D, Story M, Hannan PJ, Perry CL, Irving LM. Weight-related concerns and behaviors among overweight and nonoverweight adolescents: implications for preventing weight-related disorders. Arch Pediatr Adolesc Med 2002 Feb;156(2):171-178. [Medline]
    52. Neumark-Sztainer D, Larson NI, Fulkerson JA, Eisenberg ME, Story M. Family meals and adolescents: what have we learned from Project EAT (Eating Among Teens)? Public Health Nutr 2010 Jul;13(7):1113-1121. [CrossRef] [Medline]
    53. 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. [CrossRef]
    54. Neumark-Sztainer D, Wall MM, Story M, Perry CL. Correlates of unhealthy weight-control behaviors among adolescents: implications for prevention programs. Health Psychol 2003 Jan;22(1):88-98. [Medline]
    55. Storfer-Isser. Association of Maternal Employment with Attitudes, Subjective Norms, and Perceived Behavioral Control Regarding Meal Preparation Among Mothers of 4-5 Year Old Children [PhD Dissertation]. Bowling Green, OH: Bowling Green State University; 2013.
    56. Moos RM, Moos B. Family Environment Scale Manual: Development, Applications, Research. 3rd edition. Palo Alto, CA: Center for Health Care Evaluation, Dept of Veterans Affairs, Stanford University Medical Centers; 2009.
    57. Saucier G, Wilson KR, Warka J. The structure of retrospective accounts of family environments: related to the structure of personality attributes. J Pers Assess 2007 Jun;88(3):295-308. [CrossRef] [Medline]
    58. Greene R, Plank R. The Short-Form Family Environment Scale: testing a different response format. Psychol Rep 2016 Dec 06;74(2):451-464 [FREE Full text] [CrossRef]
    59. Trost SG, Sallis JF, Pate RR, Freedson PS, Taylor WC, Dowda M. Evaluating a model of parental influence on youth physical activity. Am J Prev Med 2003 Nov;25(4):277-282. [Medline]
    60. Sallis JF, Prochaska JJ, Taylor WC, Hill JO, Geraci JC. Correlates of physical activity in a national sample of girls and boys in grades 4 through 12. Health Psychol 1999 Jul;18(4):410-415. [Medline]
    61. Ball K, Crawford D. An investigation of psychological, social and environmental correlates of obesity and weight gain in young women. Int J Obes (Lond) 2006 Aug;30(8):1240-1249. [CrossRef] [Medline]
    62. Boles RE, Halbower AC, Daniels S, Gunnarsdottir T, Whitesell N, Johnson SL. Family chaos and child functioning in relation to sleep problems among children at risk for obesity. Behav Sleep Med 2017;15(2):114-128 [FREE Full text] [CrossRef] [Medline]
    63. Timperio A, Crawford D, Telford A, Salmon J. Perceptions about the local neighborhood and walking and cycling among children. Prev Med 2004 Jan;38(1):39-47. [Medline]
    64. Byrd-Bredbenner C, Abbot JM, Cussler E. Nutrient profile of household food supplies of families with young children. J Am Diet Assoc 2009 Dec;109(12):2057-2062. [CrossRef] [Medline]
    65. Hunsberger M, O'Malley J, Block T, Norris JC. Relative validation of Block Kids Food Screener for dietary assessment in children and adolescents. Matern Child Nutr 2015 Apr;11(2):260-270. [CrossRef] [Medline]
    66. Nelson MC, Lytle LA. Development and evaluation of a brief screener to estimate fast-food and beverage consumption among adolescents. J Am Diet Assoc 2009 Apr;109(4):730-734 [FREE Full text] [CrossRef] [Medline]
    67. 2015 Dietary Guidelines Advisory Committee. Scientific Report of the 2015 Dietary Guidelines Advisory Committee. Washington, DC: United States Department of Agriculture, United States Department of Health and Human Services; 2015.
    68. AAP Council on Communications and Media. Media use in school-aged children and adolescents. Pediatrics 2016 Nov;138(5) [FREE Full text] [CrossRef] [Medline]
    69. Milligan G, Cooper M. Methodology review: clustering methods. Appl Psych Meas 2016 Jul 27;11(4):329-354 [FREE Full text] [CrossRef]
    70. Cameron AJ, Crawford DA, Salmon J, Campbell K, McNaughton SA, Mishra GD, et al. Clustering of obesity-related risk behaviors in children and their mothers. Ann Epidemiol 2011 Feb;21(2):95-102. [CrossRef] [Medline]
    71. Everitt B, Landau S, Morven L. Cluster Analysis. London: Oxford University Press; 2001.
    72. Krahnstoever DK, Francis LA, Birch LL. Reexamining obesigenic families: parents' obesity-related behaviors predict girls' change in BMI. Obes Res 2005 Nov;13(11):1980-1990 [FREE Full text] [CrossRef] [Medline]
    73. Pradinuk M, Chanoine J, Goldman RD. Obesity and physical activity in children. Can Fam Physician 2011 Jul;57(7):779-782 [FREE Full text] [Medline]
    74. Emmett PM, Jones LR. Diet, growth, and obesity development throughout childhood in the Avon Longitudinal Study of Parents and Children. Nutr Rev 2015 Oct;73 Suppl 3:175-206 [FREE Full text] [CrossRef] [Medline]
    75. Patterson RE, Haines PS, Popkin BM. Health lifestyle patterns of US adults. Prev Med 1994 Jul;23(4):453-460. [CrossRef] [Medline]


    Abbreviations

    BMI: body mass index
    HOMES: Home Obesogenic Measure of Environments


    Edited by G Eysenbach; submitted 09.03.18; peer-reviewed by K Tamura, S Barbieri; comments to author 05.04.18; revised version received 12.04.18; accepted 12.04.18; published 25.04.18

    ©Virginia Quick. Originally published in JMIR Pediatrics and Parenting (http://pediatrics.jmir.org), 25.04.2018.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information, a link to the original publication on http://pediatrics.jmir.org, as well as this copyright and license information must be included.