Longitudinal weight status and academic achievement in elementary schoolchildren in the United States

Study design

Data used to answer the research questions are from a cluster-randomized controlled trial conducted in 40 elementary schools (20 intervention schools; 20 control schools) in a large suburban school district in Georgia, US. Students were prospectively followed from Grade 4 to Grade 5 including Grade 4 Fall (Fall 2018), Grade 4 Spring (Spring 2019), Grade 5 Fall (Fall 2019), and Grade 5 Spring (Spring 2020), though study activities ended midway through Grade 5 Spring in March 2020 because of the COVID-19 pandemic. School selection and randomization are described in a previous manuscript [20]. The school district administration, district IRB, and Emory University IRB (CR001-IRB00095600) approved this study. This study was registered with the National Institutes of Health ClinicalTrials.gov system, with ID NCT03765047.

The intervention employed components from the evidence-based Health Empowers You! program, which was designed using the Comprehensive School PA Program approach promoted by the Centers for Disease Control and Prevention (CDC) [21]. The multilevel intervention aims to shift school PA practices and culture and help students reach at least 45 min of PA during the school day. Prior evaluations of Health Empowers You! document improvements in average daily steps, moderate-to-vigorous PA (MVPA) levels in physical education (PE) classes, and student fitness and BMI [22, 23]. The intervention was implemented with the goal of sustainably elevating student school-day MVPA, which was measured with ActiGraph wGT3X-BT accelerometers (ActiGraph LLC, Pensacola, FL). Intervention status was ultimately not included in this analysis because differences in MVPA between intervention and control students were small; intervention students had approximately 3 more daily minutes of MVPA in Grade 4 Fall, 4.5 min more in Grade 4 Spring, and 5 min more in Grade 5 Fall. Details about the intervention are provided in a previous manuscript [20].

Before study implementation, consent/assent forms were distributed through district and school protocol with a brief informational video to obtain guardian consent and student assent to measure PA via accelerometry, and authorization for the school district to share de-identified demographic, standardized test score, course grade, FitnessGram, and attendance data with the research team.

Study population

Participating elementary schools included diverse student race/ethnicity and a mix of higher and lower SES. The school selection procedure ensured the schools were representative of the school district [20]. Of 6525 fourth graders in the 40 study schools, 4966 (76%) returned consents. Special education teachers participated in training and received resources for the implementation of the intervention at their discretion in the intervention schools, but students in special education classrooms were not included in data collection because these classes include multiple grade levels, and students in special education classes received teacher-assigned grades based on unique grading criteria. After removing students in special education classrooms from the analytic sample, 4936 students were eligible for analysis.

Data sources

The study used routinely collected school district data to obtain information about demographics, attendance, FitnessGram, course grades, and standardized test scores.

Demographic data included parent/guardian-reported student sex and race/ethnicity and school-reported students with disabilities (SWD), English language learners (ELL), and participation in FRL during the Grade 4 school year.

Attendance data included the number of days students were absent, tardy, and enrolled during the Grade 4 school year.

FitnessGram data documented students’ performance on the FitnessGram, an assessment developed by The Cooper Institute [24]. The district’s PE instructors are routinely trained in FitnessGram data collection, and the intervention’s Physical Activity Specialists (PASs) delivered a refresher training on FitnessGram to PE instructors in both years of the study. Students complete the FitnessGram in September/October and May/June each year. PE instructors measured student height and weight to calculate student BMI. Results from the FitnessGram PACER, a 20-m shuttle run, were used to estimate CRF. Full FitnessGram data were collected in Grade 4 Fall and Spring and Grade 5 Fall. FitnessGram data were not collected in Grade 5 Spring due to COVID-19. The PACER test was also not completed in Grade 3 because it has not been validated among third-grade students, but BMI data were collected in the Grade 3 Fall FitnessGram.

Semesterly course grades data included mathematics, reading, spelling, and writing grades from Grade 3 Fall to Grade 5 Fall.

Georgia Milestones Test data included student scores for Grade 3 Spring and Grade 4 Spring for English language arts (ELA), mathematics, and Lexile reading level [25]. The Milestone test is designed to assess whether students’ knowledge and skills meet state-adopted content standards for each academic subject [26]. Standardized tests were not administered in Grade 5 due to COVID-19.

Study measuresExposure

The exposure for this analysis is longitudinal weight status based on BMI. CDC age and sex-specific growth charts [27] were used to categorize participants as obese, overweight, healthy weight, and underweight. Children with a BMI at or above the 95th percentile for their age and sex had obesity, those from the 85th–95th percentile had overweight, those from the 5th–85th percentile had a healthy weight, and those below the 5th percentile had underweight [28].

Longitudinal weight status was based on obesity status at two time points and had four categories. Students who were obese at baseline and at follow-up were assigned “persistent obesity,” those who were not obese at baseline but were at follow-up were assigned “developed obesity,” those who were obese at baseline but not at follow-up were assigned “former obesity,” and those who were not obese at both time points were assigned “persistent non-obesity.” For analyses examining Grade 4 standardized test scores as outcomes, baseline BMI was Grade 3 Fall and follow-up was Grade 4 Spring. For analyses examining Grade 5 fall course grades as outcomes, baseline BMI was Grade 3 Fall and follow-up was Grade 5 Fall.

Supplemental analyses also considered the association between longitudinal overweight/obesity status and academic achievement. For these analyses, students with overweight or obesity at baseline and at follow-up were assigned “persistent overweight/obesity,” those who were not overweight or obese at baseline but were at follow-up were assigned “developed overweight/obesity,” those who were overweight/obese at baseline but not at follow-up were assigned “former overweight/obesity,” and those who were not overweight/obese at both time points were assigned “persistent non-overweight/obesity.”

Outcomes

Two different types of academic achievement measures were assessed. The first was Grade 4 Spring ELA, math, and Lexile Georgia Milestones standardized test results. Participant math scale scores ranged from 394 to 715, ELA scale scores ranged from 357 to 775, and Lexile scores ranged from 190 to 1300. Analyses were conducted with Milestones scores as continuous variables.

The second type of academic achievement measure was teacher-assigned course grades for reading, writing, spelling, and math. Course grades for Grade 3 Fall to Grade 5 Fall were collected and ranged from 0 to 100, with 100 indicating the highest achievement.

Covariates

Variables examined as confounders included sex (male or female), race/ethnicity (Asian, Black, Latino, White, or Other), FRL, SWD, ELL, prior achievement, and CRF. FRL status was dichotomized as “receiving” or “not receiving” and was used as a proxy for poverty status since only students whose families earn less than 185% of the federal poverty level are eligible. SWD included those with physical or learning disabilities and was dichotomized as “yes” or “no.” Current ELL was also dichotomized as “yes” or “no”. Student prior achievement was defined as the previous year’s course grade or standardized test score, in accordance with the outcome assessed in analyses. For example, the analysis using Grade 4 Georgia Milestones math standardized test scores controlled for each student’s Grade 3 Georgia Milestones math standardized test score. PACER laps were converted to an estimated CRF using the Cooper Institute’s standard formula [29]. The median CRF across Grade 4 Fall, Grade 4 Spring, and Grade 5 Fall was assigned to each student. The “healthy fitness zone” cutoff for CRF in this age group is 40.2 [30]. A dichotomous CRF variable using this cutoff categorized students’ median CRF as “fit” or “unfit.”

Analysis

Variables were missing data either because students were not enrolled in the participating schools for the entirety of the study or because their observation did not meet inclusion criteria. Multiple imputation addressed missing data. Twenty imputed datasets were created using the multilevel multiple imputation program Blimp [31]. Implausible imputed values were set to variables’ upper or lower bounds, depending on the nature of the recorded implausible value.

Descriptive statistics were computed on the non-imputed data. Variance in academic outcomes was similar across longitudinal overweight/obesity and longitudinal obesity subgroups. Two-level multilevel models were then fit with students nested within schools and synthesizing data across the 20 imputed sets. The teacher level was not included in multilevel analyses since students with departmentalized teachers rotated across teachers for core subjects. All models were first run with longitudinal obesity status as exposure. First, models assessed crude associations between longitudinal weight status and academic outcomes (Model A). Then the same associations were assessed but adjusted for prior achievement, FRL, sex, race/ethnicity, SWD, and ELL (Model B). For analyses with Grade 4 standardized test outcomes, Grade 3 standardized test scores were used for prior achievement. For analyses with Grade 5 Fall course grade outcomes, average Grade 3 course grade was used for prior achievement. Model C was further adjusted for dichotomized CRF. Fixed and random effects were aggregated across imputations using Rubin’s rules [32]. The same analyses were then run using longitudinal overweight/obesity as the exposure. On the basis of Model C, moderation analyses were also conducted for sex, race/ethnicity, and dichotomized CRF.

It was critical to adequately control for SES since it is a key confounder of the association between weight status and academic achievement. FRL participation is an imperfect proxy for SES—there are instances where a student in FRL’s family is not impoverished and instances where a student not in FRL’s family is actually impoverished. We therefore conducted a bias analysis using values of sensitivity and specificity of poverty classification that were derived from the CDC’s National Health and Nutrition Examination Survey conducted from 2011 to 2018. On the survey, parents indicated their race, their household income, and their children’s FRL status, which allowed for the estimation of sensitivity and specificity of poverty classification by FRL. We used these sensitivity and specificity values to calculate positive and negative predictive values of poverty status based on FRL participation. We in turn used those values to run a jackknife-weighted multilevel regression across the 20 imputed sets to see whether uncertainty about FRL as an SES proxy could substantially change findings.

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