ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
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the US comparing parental perception of their child’s diet with quantitatively assessed diet quality. We examined the association between parent perception of their child’s overall diet and the child’s diet quality, as measured by frequency of consumption of key food categories.
Methods: Secondary analysis was conducted using data from two independent cross- sectional panels of surveys with parents of a 3-18 year old child. Data collection took place in 2009-2010 and 2014, the random sample was drawn from low-income cities. Well-established survey questions assessed parental perception of their child’s diet and frequency of consumption of fruits, vegetables, sugar-sweetened beverages (SSB), fast food and unhealthy snacks. Diet quality scores were calculated for each child, with higher scores reflective of healthier diets (max score= 40). Ordered logistic regressions examined associations between parental perception and consumption of food categories. Multinomial logistic regressions examined associations between levels of concordance in parent perception and diet scores by demographic sub-groups.
Results: Almost half of children were non-Hispanic black (46%) and 40% were Hispanic. Overall 52% of parents strongly agreed, 33% somewhat agreed, 10% somewhat disagreed, and 4% strongly disagreed that their child eats a healthy diet. The mean diet quality score for the sample was 20.58 ± 6.7. Children from our sample with the unhealthiest diet had a mean frequency of fruit intake = 0.8 times/day and SSBs = 2.2 times/day. Children with the healthiest diet had a mean consumption of fruit=1.7/day and
SSBs= 0.4/day. Parental perception of their child’s diet was significantly higher when their child consumed more fruit (p<0.001) and vegetables (p<0.001) and lower when their child consumed more fast food (p<0.001), SSBs (p=0.01) and unhealthy snacks (p=0.02). Over half of parents overestimated the healthfulness of their child’s diet (61%). Parent, child and household demographics did not moderate this association.
Conclusions: Although parental perceptions that their child eats healthy are associated when their child eats more healthy foods and less unhealthy foods, parents’ perceptions still do not align with their child’s diet.
Investigation one revealed a moderate-to-large effect size for school-based interventions (n=10) increasing CVF (g=0.75; 95%CI [0.40-1.11]). Multi-level interventions (g=.79 [0.34-1.25]) were more effective than interventions focused on the individual (g=0.67 [0.12-1.22]). In investigations two and three children (78.3% Hispanic; mean ± SD age 53.2±4.5 months) completed a mean ± SD 3.7±2.3 PACER laps and 19.0±5.5 CSMP criteria. Individual and family factors associated with PACER laps included child sex (B=-0.96, p=0.03) and age (B=0.17, p<0.01), parents’ promotion of inactivity (B=0.66, p=0.08) and screen time (B=0.65, p=0.05), and parents’ concern for child’s safety during physical activity (B=-0.36, p=0.09). Child age (B=0.47, p<0.01) and parent employment (B=2.29, p=0.07) were associated with CMSP criteria. At the ECEC level, policy environment quality (B=-0.17; p=0.01) was significantly associated with number of PACER laps completed. Outdoor play environment quality (B=0.18; p=0.03), outdoor play equipment total (B=0.32; p<0.01) and screen time environment quality (B=0.60; p=0.02) were significantly associated with CMSP criteria. Researchers, ECEC teachers and policy makers should promote positive environmental changes to preschool-aged children’s family and ECEC environments, as these environments have the potential to improve CVF and GLS more than programs focused on the child alone.
Results: The accelerometer data demonstrated no significant difference in light physical activity or MVPA mean minutes per day between the groups. Few children reported engaging in activities sufficient for meeting the physical activity guidelines outside the AFL program. Of the 119 total distributed child physical activity tracker sheets (7 per family), 55 were returned. Of the 55 returned physical activity tracker sheets, parents reported engaging in physical activity with their children only 7 times outside of the program over seven weeks.
Conclusion: The combined intervention strategies implemented throughout the 12-week study did not appear to be effective at increasing habitual mean minutes per day spent engaging in light and MVPA among children beyond the directed program. Methodological limitations and low adherence to intervention strategies may partially explain these findings. Further research is needed to test successful strategies within community programs to increase habitual light physical activity and MVPA among 6-11 year old children.