Differences in the proportion of children meeting behavior guidelines between summer and school by socioeconomic status and race

Abstract Objective Children who fail to meet activity, sleep, and screen‐time guidelines are at increased risk for obesity. Further, children who are Black are more likely to have obesity when compared to children who are White, and children from low‐income households are at increased risk for obesity when compared to children from higher‐income households. The objective of this study was to evaluate the proportion of days meeting obesogenic behavior guidelines during the school year compared to summer vacation by race and free/reduced priced lunch (FRPL) eligibility. Methods Mixed‐effects linear and logistic regressions estimated the proportion of days participants met activity, sleep, and screen‐time guidelines during summer and school by race and FRPL eligibility within an observational cohort sample. Results Children (n = 268, grades = K − 4, 44.1%FRPL, 59.0% Black) attending three schools participated. Children's activity, sleep, and screen‐time were collected during an average of 23 school days and 16 days during summer vacation. During school, both children who were White and eligible for FRPL met activity, sleep, and screen‐time guidelines on a greater proportion of days when compared to their Black and non‐eligible counterparts. Significant differences in changes from school to summer in the proportion of days children met activity (−6.2%, 95CI = −10.1%, −2.3%; OR = 0.7, 95CI = 0.6, 0.9) and sleep (7.6%, 95CI = 2.9%, 12.4%; OR = 2.1, 95CI = 1.4, 3.0) guidelines between children who were Black and White were observed. Differences in changes in activity (−8.5%, 95CI = −4.9%, −12.1%; OR = 1.5, 95CI = 1.3, 1.8) were observed between children eligible versus uneligible for FRPL. Conclusions Summer vacation may be an important time for targeting activity and screen‐time of children who are Black and/or eligible for FRPL.


| INTRODUCTION
There is growing evidence that the socioeconomic status of a child's family is a key risk factor for becoming obese. 1,2 Compelling evidence exists that school-aged children [3][4][5][6][7] and adults 8,9 from low-income families are at elevated risk for obesity. Recent data from the National Health and Nutrition Examination Survey show that 20% of children from families with a household income at ≤130% of the federal poverty level have obesity, while only 10% of children from families with incomes ≥350% of the federal poverty level have obesity. 10 Importantly, this gap has increased over time. Independent of income, children who are Black are at an increased risk for obesity when compared to children who are White. 5,11 This is most likely due to the well-documented effects of structural racism on health behaviors which underlie health disparities. 12 Summer vacation from school is a critically important time for addressing obesity. A large body of evidence indicates that body mass index (BMI) gain accelerates during the summer. [13][14][15][16][17][18] Further, at least one study has shown that the prevalence of children with obesity increases during the months of summer. 15 This acceleration in BMI may be due to engagement in unhealthy behaviors during the summer. For instance, a growing number of studies demonstrate that children engage in less physical activity, spend more time sedentary, and spend more time on screens during the summer than during the school year. [19][20][21] Studies are also emerging that show children engage in healthier amounts of sleep and less variable sleep on nights prior to school days, compared to extended breaks from school, like summer. [20][21][22] The degradation of health behaviors during summer vacation likely leads to decreased rates of meeting activity, 23 sleep, 24 and screen-time guidelines. 25,26 Failing to meet these guidelines has been associated with increased risk for obesity, insulin resistance, cardiovascular and other diseases. 27 The structured days hypothesis, 28 which posits that structure, defined as a pre-planned, segmented, and adult-supervised compulsory environment, plays a protective role for children against unhealthy behaviors and, ultimately, prevents the occurrence of negative health-outcomes, such as excessive BMI gain. The structured days hypothesis draws upon concepts in the 'filled-time perspective' literature, which posits that time filled with favorable activities cannot be filled with unfavorable activities. 29 This perspective leads to the hypothesis that children engage in a greater number of unhealthy behaviors that lead to increased BMI gain during times that are less-structured (e.g., summer days) than during times that are more structured (e.g., school days). Correspondingly, the Health Gap Hypothesis posits that children from low-income households and children who are Black have relatively less access to structured summer programming (e.g., summer camps) than their middle-to-high income and White counterparts due to financial barriers and insufficient community resources. 30 Thus, summer may disproportionately impact the health behaviors of children from lowincome and Black households and ultimately lead to greater accelerated summer BMI gain in these children. Indeed preliminary evidence suggests that children who are Black and children from lowincome households experience greater increases in summer BMI gain compared to other children. 31 Ultimately, greater accelerated summer BMI gain may partially explain the disproportionate risk for obesity born by children from low-income and Black households.
The purpose of this study was to examine the proportion of days children met guidelines for moderate-to-vigorous physical activity (MVPA ≥ 60 min/day), 23 sleep (10-13 h/night for 5 year olds, 9-12 h/night for 6-12 year olds), 24 and screen-time (<2 h/day) 25,26 during the school year compared to the summer, and to examine if these rates differed by race and free/reduced priced lunch (FRPL) eligibility, a proxy of household income. It is hypothesized that (1) during the summer all children will meet physical activity, sleep, and screen-time guidelines on fewer days than during the school year, and (2) children who are eligible for FRPL and children who are Black will experience greater declines in the number of days that they meet physical activity, sleep, and screen-time guidelines than children who are not eligible for FRPL and children who are White.

| Study sample and design
This study utilized data from a larger natural experiment that examined changes in BMI and fitness during the summer vacation and school year for children attending a year-round school and two match paired traditional schools. 32,33 Physical activity, sleep, and screen-time behavioral data were collected on a subset of (n = 267) children participating in the larger study from Spring 2018-Fall 2019.
This study presents obesogenic behavior data from school years

| Race
Parents reported child's race on a single item screener once at enrollment into the study. Children whose parents reported a race other than White or Black were excluded from the current analysis (n = 26).

| Physical activity and sleep
Details fully describing the study can be found elsewhere. 33 Physical activity and sleep were measured using a Fitbit Charge 2 TM (Fitbit Inc.). Fitbits were chosen because they provide good agreement with polysomnography and electrocardiography, 34 they use multiple heartrate and actigraphy channels to classify sleep which is superior to a single-channel actigraphy, 35 can be charged at home, and data is stored in the cloud allowing for data collection over extended periods of time (e.g., 3-months summer vacation). Data processing for physical activity and sleep were informed by the International Study of Childhood Obesity, Lifestyle and the Environment data processing protocols. 36 For this analyses, only nocturnal sleep was considered. A valid night of sleep was considered sleep onset that occurred between 5 PM and 6 AM and lasted for greater than 240 min. 37 If sleep segments were separated by less than 20 min they were considered one continuous sleep segment. 36 Sleep duration was identified as the number of minutes that the Fitbit device classified a child as asleep during a sleep episode. To distill heartrate into activity intensity levels, each child's resting heartrate was calculated as the lowest mean beats-per-minute for 10 consecutive minutes each day. 38 Heartrates were distilled into activity intensity levels based on percent heart rate reserve (HRR). Intensity levels are classified as follows: 0.0-19.9% HRR equaled sedentary, 20.0-49.9% of HRR equaled light physical activity, and ≥50.0% equaled MVPA. 39 An individual day of at least 10 h of waking wear was considered a valid day. 36

| Screen-time
Screen-time was assessed via parent proxy-report. Parents completed a questionnaire with their child/children to report their children's screen-time twice per week during measurement periods.
Parents were asked to report on their child's daily screen-time on at least 4 days during each 30-days collection period. Parents/children estimated total amount of time (hours and minutes) children spent in front of a screen that day (e.g., TV, computer, video game, smartphone, and tablet).

| Household income
Poverty-to-income ratio (PIR) was used as a measure of household income. PIR is the ratio of household income to poverty and is calculated by dividing the total reported household income by the Department of Health and Human Services' poverty level. 40 Parents/ guardians were asked to select a household income as a single item in $10,000 increments. For this analysis, PIR was dichotomized by FRPL status according to the National School Lunch Program. 41 Children living in a household with a PIR < 1.85 were classified as eligible to receive FRPL and a PIR ≥ 1.85 was classified as not eligible to receive FRPL.

| Statistical analyses
First, means and standard deviations of school and child characteristics were examined. Subsequently, regression analysis was used to assess the difference between meeting guidelines (dependent variable) on a school or summer day (independent variable). For each behavior, the dependent variable was operationalized as a binary variable (meeting vs. not meeting the guidelines) or as the proportion of days a child met guidelines. The independent variable was also binary (i.e., school or summer day). Multi-level mixed effect logistic and linear regressions, respectively, were conducted to account for clustering (i.e., days nested within children and children nested within schools). One set of models included race and race-by-condition in-

| RESULTS
Characteristics of the participating children are presented in Table 1.     The findings of the current study align with past studies that have found children are less active and engage in more screen-time during periods of less structure (i.e., summer, weekends, or holidays). 20,28,33,[42][43][44][45] Given that children who are Black and children from low-income households experience more dramatic accelerations in BMI during the summer than their White and middle-to-highincome counterparts, 31

| CONCLUSIONS
During summer, children are less likely to meet guidelines for physical activity and screen-time, providing partial support for the Structured Days Hypothesis. 28 This is particularly true for children who are Black or eligible for FRPL, providing support forthe Health Gap Hypothesis. 30 Interventions that target MVPA and screen-time during times of less structure (i.e., summer), may be warranted.

ACKNOWLEDGEMENT
The first author acknowledges that the publication was supported by award/project number R21HD090647-01A1S1 of the Eunice Ken-