We Run This City: Impact of a Community-School Fitness Program on Obesity, Health, and Fitness

Physical fitness disparities among New York City public school youth using standardized methods, 2006-2017

Standardized physical fitness monitoring provides a more accurate proxy for youth health when compared with physical activity. Little is known about the utilization of broad-scale individual-level youth physical fitness testing to explore health disparities. We examined longitudinal trends in population-level fitness for 4^th-12^th grade New York City youth during 2006/7-2016/17 (average n = 510,293 per year). Analyses were performed in 2019. The primary outcome was whether or not youth achieved sex-/age-specific performance levels (called the Healthy Fitness Zone) on the aerobic capacity, muscular strength and muscular endurance tests using the NYC FITNESSGRAM. The Cooper Institute’s most recent Healthy Fitness Zone criteria were applied to all tests and years. Prevalence estimates were weighted, accounted for school clustering, adjusted for student-level sociodemographics, and run by sociodemographic subgroups and year. The overall prevalence for meeting 3 Healthy Fitness Zones increased from 15.5% (95%CI: 13.9%-17.0%) in 2006/7 to 23.3% (95%CI: 22.2%-24.4%) in 2016/17 for students in grades 4–12. Fitness for all student groups increased over time, although Hispanic and non-Hispanic black girls consistently had the lowest prevalence of meeting 3 Healthy Fitness Zones as compared to all other race/sex subgroups. Also, 9^th-12^th graders had a lower prevalence of meeting 3 Healthy Fitness Zones as compared to 4^th-8^th graders. Given forecasted sharp increases in cardiovascular disease prevalence, routine youth fitness surveillance using standardized, criterion referenced methods can identify important fitness disparities and inform interventions.

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research

BACKGROUND

Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution).

OBJECTIVES

To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research.

METHODS

We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM.

RESULTS

Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect.

CONCLUSIONS

The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

Two Family Interventions to Reduce BMI in Low-Income Urban Youth: A Randomized Trial

BACKGROUND

Our primary aim was to evaluate the effects of 2 family-based obesity management interventions compared with a control group on BMI in low-income adolescents with overweight or obesity.

METHODS

In this randomized clinical trial, 360 urban-residing youth and a parent were randomly assigned to 1 of 2 behaviorally distinct family interventions or an education-only control group. Eligible children were entering the sixth grade with a BMI ≥85th percentile. Interventions were 3 years in length; data were collected annually for 3 years. Effects of the interventions on BMI slope (primary outcome) over 3 years and a set of secondary outcomes were assessed.

RESULTS

Participants were primarily African American (77%), had a family income of <25 000 per year, and obese at enrollment (68%). BMI increased over time in all study groups, with group increases ranging from 0.95 to 1.08. In an intent-to-treat analysis, no significant differences were found in adjusted BMI slopes between either of the family-based interventions and the control group (P = .35). No differences were found between the experimental and control groups on secondary outcomes of diet, physical activity, sleep, perceived stress, or cardiometabolic factors. No evidence of effect modification of the study arms by sex, race and/or ethnicity, household income, baseline levels of child and parent obesity, or exposure to a school fitness program were found.

CONCLUSIONS

In this low-income, adolescent population, neither of the family-based interventions improved BMI or health-related secondary outcomes. Future interventions should more fully address poverty and other social issues contributing to childhood obesity.

CDC childhood physical activity strategies fail to show sustained fitness impact in middle school children

An increasing number of children are now obese and fail to meet minimum recommendations for physical activity (PA). Schools play a critical role in impacting children's activity behaviors, including PA. Our objective was to assess whether CDC-based school-centered strategies to promote PA increase long-term cardiovascular fitness (CVF) levels in students in schools. A prospective observational trial was conducted in 26 middle schools to implement CDC school-based strategies to increase PA for 3 years. Students had CVF assessed by Fitnessgram (PACER), a 20-meter shuttle run, at the start and end of each school year. A post-study questionnaire was administered to assess each school's strategy adherence. At baseline, 2402 students with a mean age 12.2 ± 1.1 years showed a mean CVF measured by PACER of 33.2 ± 19.0 laps (estimated VO2max 44.3 ± 5.3 ml/kg/min). During the first year, there was a significant increase in the mean PACER score (Δ = 3, 95% CI: 2-4.1 laps, p < 0.001) and PACER z-score (Δ = 0.09, 95% CI: 0.04-0.14, p = 0.001). Subsequently, however, a significant negative trend in PACER z-scores occurred (β = -0.02, p < 0.0001) so that over the 3-year study period, the intervention did not increase overall CVF. This effort to implement CDC school-based PA strategies in middle schools did not result in sustained increase in CVF over 3 years. It remains to be clarified whether this limited efficacy indicates that CDC physical activity strategies are not sufficiently robust to meaningfully impact health outcomes and/or additional support is needed in schools to improve fidelity of implementation.