displays demographic characteristics of the youth and parent samples. The study population was almost exclusively White and well-educated.
Demographic characteristics of the Identifying Determinants of Eating and Activity (IDEA) Study sample*
Descriptive statistics for youth and parent EB knowledge scores have been presented elsewhere (34
). Means of the youth and parent scales were 7.5 (range, 1-14) and 10.7 (range, 3-15), respectively. Overall, parents scored significantly (p<0.0001) higher than youth, and knowledge scale scores were weakly correlated between youth and parents (r=0.17, p=0.001). The mean of the paired knowledge scores was 9.1 (range, 3-14).
presents results from eight separate linear regression models. Only paired knowledge was modeled after initial analyses revealed this score was generally more strongly correlated with the HFI and PAMI variables than either the youth or parent knowledge scores alone. After adjusting for education level, paired EB knowledge was negatively associated with the MAASS (p=0.005), while knowledge was positively associated with fruit (p=0.0004), vegetable (p=0.032), healthful snack (p=0.005), and healthful beverage availability (p=0.003), and the Activity:Media Ratio Score (p=0.003). Knowledge was not associated with media density (p=0.122) and the PAASS (p=0.114) in the adjusted models.
Table 2 Results from linear regression models of associations between paired energy balance (EB) knowledge scores and selected Home Food Inventory (HFI) and Physical Activity and Media Inventory (PAMI) variables from the Identifying Determinants of Eating and (more ...)
In support of the original hypothesis, paired EB knowledge was significantly associated with six of eight home environment variables. Specifically, greater EB knowledge was associated with greater availability of fruit, vegetables, healthful snacks, and healthful beverages. These correlations between EB knowledge and healthy home environments are supported by prior associations between nutrition knowledge and healthy food purchasing behaviors among adults (24
) and are consistent with relationships proposed by the TREC IDEA conceptual model (33
). Also according to this model, higher consumption of fruit, vegetables, healthful snacks and beverages, and reduced consumption of less healthful foods would be expected in more healthful home environments. Previous research among youth indicates that associations between home availability and consumption are particularly apparent for fruits and vegetables (2
). Calcium intake has also been linked to milk availability among adolescents (38
). However, no known studies have observed associations between availability of healthful foods and consumption of other categories of healthful or less healthful foods.
This study failed to find a statistically significant association between EB knowledge and availability or accessibility of PA equipment in the home. Investigating other potential correlates of home PA equipment may be worthwhile, since some previous studies have linked home PA equipment availability to benefits in the form of higher levels of PA (15
), lower levels of sedentary behavior (15
), and lower body mass index (girls only) (14
) among youth. Studies of accessibility are currently lacking.
Media accessibility (MAASS) appeared to have a stronger association with EB knowledge than did media density. This finding reinforces the need for further investigation into equipment accessibility in the home, particularly in examining the extent to which these factors influence behavior. Assessing whether televisions and other media equipment are located in bedrooms may be one of the best surrogates of accessibility currently available. There is evidence that the presence of media equipment in bedrooms of youth is associated with more sedentary behavior (15
), less PA (39
), poor eating habits (39
), and a higher body mass index (15
In general, youth or parent knowledge alone had weaker associations with the home environment than paired knowledge. This may be a consequence of the stronger buying power and overall synergistic effect of the family as a whole on the home environment. In light of these results, future behavioral intervention programs may wish to target the entire family rather than only one family member.
This study had a number of strengths. For example, the instruments used were previously pilot-tested and shown to be reliable and valid, and high response rates translated into a low amount of missing data. Most importantly, few studies have investigated home environmental factors related to nutrition and PA. This unique analysis can help us understand how EB knowledge and aspects of the home environment may impact each other before ultimately influencing behavior.
Despite these strengths, the ability to infer causality is limited due to the cross-sectional nature of the data. It is hypothesized that knowledge influences specific behaviors that influence home environments, and changes in knowledge may lead to changes in home environments. While these concepts are fairly intuitive, these conclusions cannot be made without longitudinal data. Because of the racially and socioeconomically homogeneous nature of the study population, it is difficult to generalize the findings of this study. However, if small differences in knowledge were associated with various outcomes in this group of families, one would expect that larger disparities in knowledge among more heterogeneous populations would generally lead to stronger associations.