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. Author manuscript; available in PMC: 2014 Mar 1.
Published in final edited form as: J Adolesc Health. 2012 Aug 29;52(3):351–357. doi: 10.1016/j.jadohealth.2012.07.006

Family Functioning: Associations with Weight Status, Eating Behaviors, and Physical Activity in Adolescents

Jerica M Berge 1, Melanie Wall 2, Nicole Larson 3, Katie A Loth 3, Dianne Neumark-Sztainer 3
PMCID: PMC3580029  NIHMSID: NIHMS404341  PMID: 23299010

Abstract

Purpose

This paper examines the relationship between family functioning (e.g. communication, closeness, problem solving, behavioral control) and adolescent weight status and relevant eating and physical activity behaviors.

Methods

Data are from EAT 2010 (Eating and Activity in Teens), a population-based study that assessed eating and activity among socioeconomically and racially/ethnically diverse youth (n = 2,793). Adolescents (46.8% boys, 53.2% girls) completed anthropometric assessments and surveys at school in 2009–2010. Multiple linear regression was used to test the relationship between family functioning and adolescent weight, dietary intake, family meal patterns, and physical activity. Additional regression models were fit to test for interactions by race/ethnicity.

Results

For adolescent girls, higher family functioning was associated with lower body mass index z-score and percent overweight, less sedentary behavior, higher intake of fruits and vegetables, and more frequent family meals and breakfast consumption. For adolescent boys, higher family functioning was associated with more physical activity, less sedentary behavior, less fast food consumption, and more frequent family meals and breakfast consumption. There was one significant interaction by race/ethnicity for family meals; the association between higher family functioning and more frequent family meals was stronger for non-white boys compared to white boys. Overall, strengths of associations tended to be small with effect sizes ranging from - 0.07 to 0.31 for statistically significant associations.

Conclusions

Findings suggest that family functioning may be protective for adolescent weight and weight-related health behaviors across all race/ethnicities, although assumptions regarding family functioning in the homes of overweight children should be avoided given small effect sizes.

Keywords: Family Functioning, Adolescent Obesity, Dietary Intake, Family Meals, Physical Activity

BACKGROUND

Family functioning refers to the structural/organizational properties and the interpersonal interactions of the family group such as problem solving, communication, roles, adaptability, warmth/closeness, and behavior control.[1, 2] According to family systems theory, the interactions that occur within the family are reciprocal.[3] That is, each family member is shaping and being shaped by other family members’ actions. These mutual influencing patterns may give particular insight into the behaviors that ultimately determine dietary intake and physical activity in youth. Although family functioning has not been well-researched in relation to youth weight and health behaviors, its influence on the physical, social and emotional wellbeing of youth has been studied in social science and medical research. Specifically, studies have shown associations between poor family functioning and higher depressive symptoms,[4] less academic success,[5] more high risk behaviors,[6] and higher disordered eating behaviors,[7] and worse metabolic control in youth with diabetes.[8] It is of interest to explore if and how family functioning is associated with weight status, eating patterns, and physical activity behaviors, given the youth obesity public health problem.[9]

Numerous expert panels and researchers have pointed to the influence of the family and home environment as an important and neglected area of research related to adolescent obesity.[10, 11] A recent review found fewer than ten studies in the last decade that investigated overall family functioning (e.g. communication, closeness, problem solving, interpersonal relationships) in relation to childhood and adolescent obesity,[12] whereas several studies have looked at the association between parenting style (i.e. authoritative, authoritarian, permissive, neglectful) and parenting practices (e.g. modeling health behaviors) and childhood weight and weight-related behaviors.[13–15] Although parenting behaviors (e.g. parenting style, modeling behaviors, encouraging healthful behaviors) have been found to be related to child weight and health behaviors, they may not account for the overall impact of interpersonal behaviors that occur at the family level that might have an influence on child health behaviors and weight status.

The few studies that have looked at the association between family functioning and youth weight and behaviors of potential relevance for weight status have mainly shown that poor family communication[16] and lower family functioning[17, 18] were associated with higher body mass index (BMI) in youth, although two studies did not find a significant association.[19, 20] Thus, research looking at the influence of family functioning on childhood obesity has shown mixed results and study designs have mainly utilized small samples,[18] white participants,[19] higher income populations,[16] and younger children,[17] making it difficult to extrapolate findings to diverse adolescent populations. Understanding whether the results found in these previous studies hold across racial/ethnic and socio-economically diverse groups of youth and with adolescents, is an important next step needed to better inform the development of public health interventions targeting youth at greatest risk for obesity. In addition, it is important to identify whether, and how family functioning is associated with other behavioral outcomes beyond BMI or weight status, such as eating and physical activity behaviors. Understanding the potential influence of family functioning on variables that are upstream from adolescent weight status might allow for targeting modifiable family behaviors (e.g. communication, behavioral control, problem solving) in order to improve youth health behaviors.

This paper investigates the association between overall family functioning and adolescent BMI and multiple weight-related health behaviors that occur on a daily basis. The main research questions include: (a) What are the associations between family functioning and BMI, meal patterns (i.e., family meals, breakfast consumption) dietary intake (i.e., fruit and vegetable intake, fast food intake), and physical and sedentary activity? and (b) Do these associations differ by race/ethnicity?

METHODS

Study Design and Population

EAT 2010 (Eating and Activity in Teens) is a population-based study designed to assess dietary intake, physical activity, weight control behaviors, and weight status in adolescents. Surveys and anthropometric measures were completed by 2,793 adolescents from 20 public middle schools and high schools in the Minneapolis/St. Paul metropolitan area of Minnesota during the 2009–2010 academic year. The mean age of the study population was 14.4 years (SD=2.0); 46.1% were in middle school (6th–8th grades) and 53.9% were in high school (9th–12th grades). Participants were equally divided by gender (46.8% boys, 53.2% girls) and were racial/ethnically and socio-economically diverse (Table 1).

Table 1.

Socio-demographic characteristics, BMI, and health behaviors of EAT 2010 adolescent study participants

Girls Boys p-value
n = 1486 n = 1307
Ethnicity/race, %
    White 16.7 21.2 0.004a
    Black 28.9 29.0
    Hispanic 17.2 16.5
    Asian 19.9 19.9
    Native American 3.6 3.7
    Mixed/Other 13.7 9.7
Socio-economic status, %
    Low 42.8 33.4 <.001a
    Low-middle 20.9 21.8
    Middle 16.2 17.7
    High-middle 11.2 13.8
    High 5.9 8.8
    Missing 3.0 4.6
Age in years, mean(SD) 14.4(1.9) 14.5(2.1) 0.037
Body Mass Index, mean(SD) 23.8(5.8) 23.7(5.7) 0.481
Overweight (>85 Percentile) %(n) 38.6 (565) 41.7 (535) 0.099
Family meals, mean meals per week(SD) 3.8(2.6) 4.0(2.6) 0.008
Fast food intake, mean times per week(SD) 3.7 (4.4) 3.6 (4.2) 0.512
Fruits & vegetables, mean servings per day(SD) 2.7 (2.1) 2.7 (2.2) 0.789
Breakfast intake, mean times per week(SD) 4.0(2.6) 4.4(2.6) <0.001
Moderate to vigorous physical activity, mean hours per week(SD) 5.0(4.4) 6.7(4.9) <0.001
Sedentary activity, mean hours per week(SD) 36.3(24.1) 44.5(28.8) <0.001
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a

p-value for chi-square test of any differences across categories by gender

Trained research staff administered surveys and measured adolescents’ height and weight during selected, required health, physical education, and science classes. Measurements were completed in a private area and surveys were administered during two class periods that were typically 45–50 minutes. Following survey completion, participants were given a $10 gift card. Study procedures were approved by the University of Minnesota’s Institutional Review Board Human Subjects Committee and by the research boards of the participating school districts. All adolescents within selected classes were given the opportunity to assent if their parent/guardian did not return a signed consent form indicating their refusal to have their child participate. Among adolescents who were at school on the days of survey administration, 96.3% had parental consent and chose to participate.

Adolescent Survey Development

The EAT 2010 survey is a 235-item self-report instrument assessing a range of factors of potential relevance to weight status and weight-related behaviors among adolescents. Survey development was guided by a review of previous Project EAT surveys[21] to identify the most salient items; a theoretical framework, which integrates an ecological perspective with social cognitive theory;[22] expert review by professionals from different disciplines; and extensive pilot testing with adolescents. The test-retest reliability of measures over a one-week period was examined in a diverse sample of 129 middle school and high school students.

Measures

Family functioning

Six items were drawn from the general functioning scale of the Family Assessment Device (FAD)[1, 2] to measure overall family functioning. Previous research has shown high validity (r = 0.92) and test-retest reliability (r = 0.71) for the general functioning scale with racially/ethnically and socio-economically diverse populations.[23] Additionally, the general functioning scale has been found to correlate highly with other longer measures of family functioning (r > 0.50) and to have a positive linear relationship with two other reliable and valid measures of family functioning; the Family Environment Scale (FES) and the Family Adaptability and Cohesion Evaluation Scale (FACES IV).[24]

The general functioning scale on the FAD measures structural, organizational, and interaction patterns of the family including: problem solving, communication, roles, affective responsiveness, affective involvement, and behavior control among family members. Adolescents were asked, “How strongly do you agree with the following statements? For these questions, think about your family in general (including your parents and your brothers and sisters)… [Strongly disagree, Somewhat disagree, Somewhat agree, Strongly agree] (a) Family members are accepted for who they are; (b) Making decisions is a problem for the family; (c) We don’t get along well together; (d) We can express feelings to each other; (e) Planning family activities is difficult because we misunderstand each other; (f) We confide in each other (By ‘confide’ we mean to trust your family members enough to tell them something that is important to you).” Responses were assigned values 1–4 and all statements were converted to the positive form before the values were summed. The range of responses for this scale was 6–24, with higher scores representing higher family functioning (Scale alpha = .70).

Body Mass Index (BMI) z-score

All measurements were completed following standardized procedures [25]. Students were first asked to remove shoes, outerwear (e.g., heavy sweaters), and items of considerable weight (e.g., wallets) from their pockets. Height was assessed to the nearest 0.1 cm using a Shorr Board and weight to the nearest 0.1 kg using a calibrated scale. BMI values were calculated according to the following formula: weight (kg)/height (meters)2 and converted to z-scores, standardized for gender and age.[26]

Family meals

Family meal frequency was assessed by asking adolescents: “During the past seven days, how many times did all, or most, of your family living in your house eat a meal together?” Response options included: never, one to two times, three to four times, five to six times, seven times, and more than seven times (Test-retest r = .63). The highest two categories were collapsed.

Fast food intake

Fast food intake was assessed with the question, “In the past month, how often did you eat something from the following types of restaurants (include take-out and delivery)?” Types of restaurants included: traditional burger-and-fries, Mexican fast food, fried chicken, sandwich or sub shop, pizza place. Response options ranged from never/rarely to one or more times a day (Test-retest r = .49). To prevent outlying values from influencing results, responses were trimmed at 90 times per month (i.e. 3 fast food meals per day).

Fruit/vegetable intake

Dietary intake was assessed with the 149-item Youth and Adolescent Food Frequency Questionnaire (YAQ).[27] For fruit and vegetable intake, a daily serving was defined as the equivalent of one-half cup. Validity and reliability of the YAQ have been previously tested with youth.[27] Test-retest correlations between two YAQs over a one-year period were .49 for fruit, and .48 for vegetables. Responses to questions on the frequency of intake of fruits and vegetables (excluding potatoes), were summed to assess average total daily intake.

Breakfast consumption

Adolescents were asked: “During the past week, how many days did you eat breakfast?” Five response options ranged from never to every day (Test-retest r = .76).

Physical activity

Physical activity questions were adapted from the Godin Leisure-Time Exercise Questionnaire.[28] Adolescents were asked: “In a usual week, how many hours do you spend doing the following activities: (1) strenuous exercise (e.g. biking fast, aerobics, jogging, swimming laps, soccer, rollerblading) (2) moderate exercise (e.g. walking quickly, easy bicycling, skiing, dancing, skateboarding, snowboarding)”. Response options ranged from “none” to “6+ hours a week”. (Test-retest r = .73).

Sedentary behavior

Adolescents were asked, “In your free time on an average weekday (Monday-Friday), how many hours do you spend doing the following activities?…[0 hour, ½ hour, 1 hour, 2 hours, 3 hours, 4 hours, 5+ hours].” The activities assessed included: Watching TV/DVDs/videos, Using a computer (not for homework), and Xbox/Play-Station/other electronic games that you play when sitting. This same question was asked for an average weekend day. For each sedentary behavior an “hours per week” variable was created by calculating a weighted sum of weekday and weekend use.(Test-retest r = .86).

Covariates

Race/ethnicity, socio-economic status (SES), and age were assessed by self-report. Race/ethnicity was assessed with the question: “Do you think of yourself as…? (1) White, (2) Black or African American, (3) Hispanic or Latino, (4) Asian American, (5) Native Hawaiian or other Pacific Islander, (6) American Indian or Native American, or (7) Other”. The responses “Native Hawaiian or other Pacific Islander” and “Other” were coded as “mixed/other” due to small numbers. Classification tree methodology[29] was used to generate five categories of SES (Low SES, Low-Middle SES, Middle SES, Upper-Middle SES, High SES)[30]. The prime determinant of SES was the higher education level of either parent. Subsidiary variables were family eligibility for free/reduced-price school meals, family receipt of public assistance, and parent employment status. Age was calculated using self-reported birth date and survey completion date.

Statistical Analysis

Tests of mean differences across gender for all outcome variables and family functioning were conducted using two-sample t-tests. One-way ANOVA was used to test for differences in family functioning by race/ethnicity, SES and age categories. Hierarchical linear regression including a random effect to account for clustering within schools was used to estimate and test the relationship between family functioning and each of the outcomes controlling for age, SES, and race/ethnicity. All regressions were stratified by sex a priori due to previous research findings showing sex differences in BMI, fruit and vegetable intake, and physical activity in boys and girls.[31, 32] Regression results describing the association of each outcome with family functioning are presented in two ways. First, outcomes and family functioning were standardized within gender and standardized betas represent the expected standard deviation difference in the outcome variable associated with a one standard deviation increase in family functioning controlling for covariates. The standardized beta represents an effect size with benchmarks for describing its magnitude as: small = .10–.29, moderate = .30–.49, or large > .50 [33]. Second, regression adjusted mean outcome values on their original scales (unstandardized) are presented at fixed values corresponding to an adolescents reporting a family functioning value at the 5th and 95th percentile. Additional regression models were fit to test for interactions by race/ethnicity in six categories: White, Black or African American, Hispanic or Latino, Asian, Native American, Mixed/Other (combining Hawaiian Pacific Islander, Other, and Mixed race/ethnicity). In cases where the F-test for the five degree of freedom interaction of family functioning and race/ethnicity had a p-value <0.05, additional multiple regressions were fit stratified by race/ethnicity. Analyses were performed in SAS (V9.2, Cary NC, 2011).

RESULTS

Descriptive Analysis

Adolescent boys reported significantly higher frequencies of family meals and breakfast consumption, and more hours of both physical and sedentary activity than girls (Table 1). In addition, boys reported higher family functioning scores compared to girls; African American adolescents reported higher family functioning scores compared to adolescents from other racial/ethnic backgrounds; and adolescents from higher SES reported higher family functioning compared to adolescents from lower SES (Table 2).

Table 2.

Family functioning score* by adolescent demographics

Family Functioning
Score
Mean (SD)** 		p-value
Gender
    Female 17.7 (3.75)a 0.005
    Male 18.1 (3.33)b
Ethnicity/race, %
    White 17.9 (3.70)ab <0.001
    Black 18.5 (3.68)a
    Hispanic 18.1 (3.37)ab
    Asian 17.2 (3.26)c
    Native American 17.7 (3.47)bc
    Mixed/Other 17.8 (3.63)b
Socio-economic status, %
    Low 17.6 (3.54)a <.001
    Low-middle 17.7 (3.39)a
    Middle 18.3 (3.58)b
    High-middle 18.3 (3.79)b
    High 19.0 (3.53)c
Age
    Less than 14 years 18.0 (3.56) 0.244
    14 years or older 17.9 (3.57)
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*

Scores are mean family functioning scores. The range is 6–24; higher scores indicate higher family functioning.

**

For categories within each demographic variable, means with differing letter superscriptsa,b,c are statistically different from one another (p-value <0.05).

Family Functioning: Associations with Adolescent BMI z-scores and Health Behaviors

Adolescent Girls

Higher family functioning was significantly associated with more frequent family meals (p<0.001) and greater breakfast consumption (p<0.001) in girls after adjusting for age, SES, and race/ethnicity (Table 3). Specifically, the 0.31 standardized beta estimate for family meals represents the standard deviation increase in number of family meals, given one standard deviation increase in family functioning, after controlling for demographic variables. On the original scale, this effect corresponds to a mean number of family meals of 2.61 for girls with family functioning at the 5th percentile and a mean number of family meals of 5.12 for those at the 95th percentile.

Table 3.

Relationship between family functioning and adolescent girls’ BMI and health behavior outcomes

Outcome: Family Functioning
Score at 5th
percentilea 		Family Functioning
Score at 95th
percentilea 		Standardized Beta** SE t-value p-value
Meal Patterns
    Family Meals (meals per week) 2.61 5.12 0.31 0.02 12.38 <0.001
    Breakfast Consumption (daily) 3.31 4.75 0.18 0.03 6.78 <0.001
Dietary Intake
    Fruits & Vegetables (servings/per day) 2.52 2.91 0.06 0.03 2.31 0.037
    Fast Food Intake (times per week) 3.86 3.49 −0.03 0.03 −0.86 0.285
Physical Activity
    Moderate-to-vigorous Activity hrs./week) 4.65 5.12 0.03 0.03 1.17 0.207
    Sedentary Behavior (hrs./week) 39.0 33.2 −0.08 0.03 −2.74 0.004
Weight
    Body Mass Index z-score 0.79 0.62 −0.06 0.03 −2.33 0.020
    Percent Overweight (>85th percentile) 41.4 33.2 −0.05 0.01 −2.01 0.044
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*

All results adjusted for age, race, and socio-economic status

**

Standardized Beta estimates represent the standard deviation increase (or decrease) in the outcome, given one standard deviation increase in family functioning. Small = 0.10–0.29; medium = 0.30–0.49; large = > 0.50.

a

Adjusted mean outcomes at 5th percentile and 95th percentile score for family functioning (i.e. 12 vs. 24)

Higher family functioning was also modestly associated with greater daily intake of fruits and vegetables (p=0.037), more frequent breakfast consumption (p<0.001), fewer hours of sedentary behavior (p=0.004), lower BMI z-scores (p=0.020) and lower percent overweight (p=.044) in adolescent girls after adjusting for age, SES, and race/ethnicity. There were no significant associations between family functioning and fast food intake or hours of physical activity per week for adolescent girls. The standardized betas in all results for adolescent girls ranged from −0.07 to 0.31 for all statistically significant results, which represent small to moderate effect sizes.

Adolescent Boys

Higher family functioning was associated with more frequent family meals (p<0.001) in boys after adjusting for age, SES, and race/ethnicity (Table 3). Higher family functioning was also modestly associated with more frequent breakfast consumption (p=0.002), less frequent fast food intake (p<0.001), more hours of physical activity (p<0.001), and fewer hours of sedentary behaviors (p=0.001) after adjusting for age, SES, and race/ethnicity (Table 3). There were no significant associations found between family functioning and BMI z-score or servings of fruits and vegetables. The standardized betas in all results for adolescent boys ranged from −0.11 to 0.25 for all statistically significant results, which represent small effect sizes.

Interactions by Race/Ethnicity

There was only one significant interaction by race/ethnicity for adolescent boys related to family meal frequency (F5=2.47; p =0.031). Post-hoc analyses stratified by race/ethnicity indicated that the effect of family functioning was significantly stronger in non-white compared to white adolescent boys (p=0.012) and especially for Hispanic (p= 0.002) and Native American (p=0.057) boys. This means that for adolescent non-white boys, higher family functioning was associated with more frequent family meals than white boys. There were no other interactions between family functioning and race/ethnicity that were statistically significant.

DISCUSSION

Findings from the current study indicate that family functioning may be a small, but relevant correlate of adolescent weight and weight-related health behaviors. Specifically, positive family functioning, including healthy communication, having rules and structure, and using problem solving skills may be protective for adolescent girls in relation to greater family meal participation, breakfast consumption, fruit and vegetable intake, less sedentary activity, and lower BMI z-scores. For boys, positive family functioning may be protective for greater family meal participation, breakfast consumption, physical activity and less sedentary activity and fast-food intake. These findings extend the results of a limited number of previous studies on family functioning and adolescent health,[16–18] by showing that there are associations between higher family functioning and positive health behaviors (e.g. fruit and vegetable intake, family meals, breakfast consumption, physical activity) and fewer unhealthful behaviors (e.g. sedentary behaviors) in adolescents, in addition to lower BMI found in previous studies.[16–18] Furthermore, the comparisons between the 5th and 95th percentiles of family functioning scores suggest that at the more extreme ends, family functioning is strongly associated with a number of weight and weight-related health behaviors in adolescents, versus the middle range of scores, suggesting that the majority of adolescents family functioning was adequate.

Current findings can be explained using our guiding theoretical model. According to family systems theory, children live within a family context that shapes their health behaviors, and their understanding of health and well-being. Under conditions of poor family functioning (e.g. less structure/rules, warmth/communication, problem-solving skills) people become vulnerable to developing risk behaviors (e.g. less family structure leads to fewer family meals).[7, 17, 34] However, it may also be the case that families who have regular family meals are also more likely to have higher family functioning.

The interaction analyses showed that for non-white adolescent boys, and for Hispanic and Native American boys especially, higher family functioning scores were associated with more frequent family meals. Thus, higher family functioning may be highly protective for racially/ethnically diverse adolescent boys compared to white boys in regards to regular family meals occurring. The lack of overall significant interactions by race/ethnicity for other variables, suggests the importance of family functioning for adolescents across all race/ethnicities for outcomes that were found to be significant in the main analyses.

It is important to note that all findings in the current study had small to moderate effect sizes. This suggests that although family functioning is contributing to adolescent health behavior outcomes, it likely does not represent the entire story. For example, individual and environmental factors such as adolescent taste preferences and home availability of fruits and vegetables may play a greater role in shaping health behavior outcomes than family functioning. Furthermore, the small correlations suggest that for many families of overweight adolescents, the level of family functioning may be just as high as in families where all adolescent members are not overweight. Therefore assumptions about levels of family functioning within families in which there are overweight children should be avoided. However, findings in the current study are supported by previous research on family meals. For example, the finding that adolescent girls with low family functioning had an average of approximately three family meals per week while girls with high family functioning (at the 95th percentile) had an average of five family meals per week is significant because having five or more family meals per week has been linked to better dietary intake, and lower risk for substance use and disordered eating among diverse adolescents in previous research.[31, 35, 36]

Study strengths and limitations should be taken into account when interpreting the study findings. Study strengths included the use of a large and diverse population-based sample, the ability to examine many health behaviors among the same sample, and to test for interactions by race/ethnicity. One limitation of this study is the cross-sectional design. Because we were unable to examine longitudinal associations, we cannot determine causality or temporality of associations between family functioning and adolescent BMI and health behavior outcomes. However, theoretically it is more likely that family functioning would be influencing these outcomes rather than the adolescent’s behaviors (e.g. adolescent fruit and vegetable intake, sedentary behaviors) influencing family functioning. In addition, the family functioning measure used in the survey was not the full measure and may not have been inclusive of all family behaviors that contribute to measuring family functioning. Six of the 12 items on the scale were used in this study, thus we may have underestimated the association between family functioning and adolescent weight and health behaviors.

CONCLUSIONS

Study findings suggest that higher family functioning may be protective for adolescent weight and weight-related health behaviors that occur on a daily basis. These findings are consistent with other findings showing the value of healthy family functioning for other health-related domains and overall psychosocial development.[4–8, 37–39] Obesity prevention efforts may want to consider targeting family functioning as a way of improving adolescent health behaviors that precede, or moderate, weight outcomes. For example, public health interventions may want to include education for families about the importance of family communication, structure/roles, problem-solving, and closeness/warmth. Pointing out that higher family functioning may be protective for youth related to weight and weight-related health behaviors, in addition to emotional well-being, academic success and reduced high-risk behaviors may give families an increased incentive to work on improving their family functioning.[4–8, 37–40] Future research should look longitudinally at the relationship found between family functioning and adolescent weight and health behaviors in order to confirm temporality of associations.

Table 4.

Relationship between family functioning and adolescent boys’ BMI and health behavior outcomes

Outcome: Family Functioning
Score at 5th
percentilea 		Family Functioning
Score at 95th
percentilea 		Standardized Beta** SE t-value p-value
Meal Patterns
  Family Meals (meals per week) 2.87 5.13 0.25 0.03 8.98 <0.001
  Breakfast Consumption (daily) 3.95 4.94 0.10 0.03 3.70 0.002
Dietary Intake
  Fruits & Vegetables (servings/per day) 2.54 2.85 0.04 0.03 1.38 0.178
    Fast Food Intake (times per week) 4.37 3.00 −0.09 0.03 −3.27 <0.001
Physical Activity
  Moderate-to-vigorous Activity hrs./week) 5.81 7.60 0.10 0.03 3.71 <.0.001
  Sedentary Behavior (hrs./week) 50.0 40.3 −0.10 0.03 −3.38 0.001
Weight
  Body Mass Index (BMI) z-score 0.76 0.65 −0.03 0.03 −0.91 0.357
  Percent Overweight (>85th percentile) 46.0 38.0 −0.09 0.05 −1.75 0.080
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*

All results adjusted for age, race, and socio-economic status

**

Standardized Beta estimates represent the standard deviation increase (or decrease) in the outcome, given one standard deviation increase in family functioning. Small = 0.10–0.29; medium = 0.30–0.49; large = > 0.50.

a

Adjusted mean outcomes at 5th percentile and 95th percentile score for family functioning (i.e. 12 vs. 24)

Implications and Contributions.

Family functioning (e.g. communication, closeness, problem solving, behavioral control) has not been well-researched in relation to adolescent weight and weight-related behaviors that occur on a daily basis. The current study showed significant associations between higher family functioning and higher fruit and vegetable intake, more hours of physical activity and family meals and lower BMI in adolescents.

Acknowledgements

Research is supported by grant number R01 HL093247 from the National Heart, Lung, and Blood Institute (PI: Dianne Neumark-Sztainer). Dr. Berge’s time is supported by a grant from Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) grant Number K12HD055887 from the National Institutes of Child Health and Human Development, administered by the Deborah E. Powell Center for Women’s Health at the University of Minnesota. Dr. Wall is supported by a grant from the National Institutes of Health (U01-HD061940). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute, the National Institute of Child Health and Human Development, the National Cancer Institute or the National Institutes of Health.

Footnotes

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Financial disclosure and conflict of interest: Authors have no conflicts of interest, nor financial disclosures to report.

CONTRIBUTORS STATEMENT

All co-authors made a substantial contribution to the paper. Dr. Berge conceptualized the paper, interpreted the data and wrote all drafts of the paper. Dr. Wall conducted the data analysis and assisted with the interpretation of the data and critical review of the paper. She also critically revised it and gave final approval of the version to be published. Dr. Larson assisted with the acquisition of data and critical review of the paper. She also critically revised the paper and gave final approval of the version to be published. Dr. Loth assisted with the interpretation of the data. She also critically revised the paper and gave final approval of the version to be published. Dr. Neumark-Sztainer assisted in conceptualizing the paper and contributed to the design of the study. She also critically revised it and gave final approval of the version to be published.

References

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