Purpose Youth physical activity (PA) levels differ by race/ethnicity and socioeconomic status (SES). It is well established that various multi-level factors may influence changes in PA. The current study examined whether the association between the change in individual, interpersonal, and environmental factors and the change in PA is modified by race/ethnicity or SES. Methods This study followed 643 youth and their parents from suburban and rural South Carolina participating in the Transitions and Activity Changes in Kids (TRACK) Study in 2008–09 and 2010–11. We assessed total PA in youth using accelerometry and categorized youth and parent survey data into blocks based on the socioecological model. Multivariate regression growth curve models evaluated whether the association between change in independent variables and change in PA was modified by race/ethnicity or SES. Results PA declined from 5th to 7th grade among all racial/ethnic and SES groups. Associations between the range of variables and change in PA were modified by race/ethnicity, but not SES. Blacks did not share any common predictors of change in PA with Whites or Hispanics. However, child-reported number of active friends was associated with total PA, and enjoyment of PA was associated with change in PA among both Whites and Hispanics. Significant interactions by time varied by racial/ethnic group. Conclusions Factors that influence changes in youth PA vary by race/ethnicity but not SES. These findings reinforce the complex nature of addressing PA behavior in diverse samples and further support the need for culturally-appropriate interventions to promote PA in youth.

This study examined the effects of the ActiGraph’s (AG) low-frequency extension (LFE) filter on steps and physical activity classification in the free-living environment. Thirty-four African-American women (age, 24.5±5.2 years; BMI, 24.9±4.5 kg/m2) had daily activity measured simultaneously with an AG-GT3X+ accelerometer and a New Lifestyles NL-800 pedometer for seven days. Steps per day (steps/day) and time (minutes/day) spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) were examined with and without the LFE filter (AG-LFE and AG-N, respectively). The AG-LFE recorded more total steps (13,723±4,983 steps/day) compared to AG-N and NL-800 (6,172±2,838 and 5,817±3,037 steps/day, respectively; p<0.001). Compared to the AG-N, the AG-LFE estimated less time in sedentary behaviors (518.7±92.1 vs. 504.2±105.4 min/day, respectively; p<0.001), and more time in light (247.7±70.4 vs. 279.1±74.7 min/day, respectively; p<0.001) and MVPA (18.9±16.9 vs. 21.5±18.2 min/day, respectively; p<0.001), respectively. These data suggest that steps and physical activity classifications will be affected when using the ActiGraph with and without the LFE filter. Future research should investigate the accuracy of these measures using the LFE filter.

Background: Youth with type 1 diabetes (T1D) are encouraged to participate in physical activity (PA). Studies have identified fear of hypoglycemia (FOH) as a barrier to participating in PA. Objectives: To examine (a) PA patterns in youth with T1D by age group and (b) the relationship between both parental and youth FOH and youth PA. Methods: A cross-sectional analysis from the SEARCH cohort study visit of youth ages 10 to 17 years with T1D (n = 1129) was conducted. Linear regression models estimated the association between self-reported number of days of vigorous PA (VPA) and moderate PA (MPA) and both youth-and parent-reported FOH. Multivariable models were adjusted for age, sex, race, duration of T1D, HbA1c, use of continuous glucose monitoring (CGM), recent severe hypoglycemia, primary insulin regimen, and BMI. Results: Participants were 52% female, had mean (sd) age 14.4 (4.2) years, diabetes duration 7.5 years (1.8), HbA1c 9.2% (1.7). Older youth were less likely to engage in VPA (P < .01), or sports teams (P < .01), but more likely to engage in MPA (P < .01). Higher youth FOH (behavior subscale) was associated with increased levels of VPA (β (se) 0.30 (0.11), P = .01) but not significantly associated with MPA (P = .06). There was no statistically significant association between parental FOH and youth PA. Conclusions: In SEARCH participants with T1D, VPA, and team sports participation declined with age, while MPA increased. We observed that higher scores on the youth FOH behavioral subscale were associated with increased VPA levels, suggesting that FOH may be less of a barrier to PA than previously thought.

A better understanding of these dynamics may inform the development of interventions.

Affordability and geographic accessibility are key health care access characteristics. We used data from 481 youth and young adults (YYA) with diabetes (389 type 1, 92 type 2) to understand the association between health care access and glycemic control as measured by HbA1c values. In multivariate models, YYA with state or federal health insurance had HbA1c percentage values 0.68 higher (P = 0.0025) than the privately insured, and those without insurance 1.34 higher (P < 0.0001). Not having a routine diabetes care provider was associated with a 0.51 higher HbA1c (P = 0.048) compared to having specialist care, but HbA1c did not differ significantly (P = 0.069) between primary vs specialty care. Distance to utilized provider was not associated with HbA1c among YYA with a provider (P = 0.11). These findings underscore the central role of health insurance and indicate a need to better understand the root causes of poorer glycemic control in YYA with state/federal insurance.