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Background: The Structured Days Hypothesis posits that structure protects children against obesogenic behaviors (e.g., physical inactivity, unhealthy dietary intake) and, ultimately, prevents the occurrence of excessive weight gain. The hours following school (i.e., 3-6 pm school days) and summer vacation are two "windows of vulnerability" when children may experience less structure. Programs that provide a healthy structured environment and may prevent BMI gain exist for both time periods (i.e., after-school programs and summer day camps). Unfortunately, these programs are cost prohibitive for children from low-income families to attend. This study will test the impact of providing vouchers to access existing, community-operated after-school and summer programs on BMI z-score, body composition, and obesogenic behaviors (i.e., physical activity, screen use, diet, and sleep) of children (5-12 years) from schools that primarily serve families with low income., Methods: The study will employ a 2x2 factorial design. Participants (N = 480) attending 4 elementary schools in one school district will be randomly assigned to a no treatment control, after-school program voucher only, summer day camp voucher only, or after-school and summer day camp vouchers. Vouchers will cover the full cost of attending a pre-existing community-based after-school or summer camp program. The primary outcome (BMI z-score) will be measured at baseline (before end of school year, ~ May), 3-month follow-up (after summer, ~ August), and 12-month follow-up (end the following school year, ~ May). Secondary outcomes include body composition (i.e., whole-body fat mass, fat free mass, and percent body fat) and obesogenic behaviors (i.e., physical activity, sedentary time, sleep, screen-time, and diet). The study will also employ a rigorous process evaluation which will consider after-school and summer camp program attendance and content. Analyses will examine differences between the four groups in BMI z-score, body composition, and obesogenic behaviors. Incremental cost effectiveness ratios will determine the cost effectiveness of the intervention., Discussion: The current study will provide critical information for researchers, practitioners, and policy makers seeking to combat the childhood obesity epidemic in children from families with low-income during the school year and summer., Trial Registration: NCT05880901 . Registered 27 May 2023., (© 2024. The Author(s).)

Background: Drivers of summer body mass index (BMI) gain in children remain unclear. The Circadian and Circannual Rhythm Model (CCRM) posits summer BMI gain is biologically driven, while the Structured Days Hypothesis (SDH) proposes it is driven by reduced structure. Objectives: Identify the mechanisms driving children's seasonal BMI gain through the CCRM and SDH. Methods: Children's ( N  = 147, mean age = 8.2 years) height and weight were measured monthly during the school year, and once in summer (July-August). BMI z-score (zBMI) was calculated using CDC growth charts. Behaviors were measured once per season. Mixed methods regression estimated monthly percent change in children's height (%HΔ), weight (%WΔ), and monthly zBMI for school year vs. summer vacation, seasonally, and during school months with no breaks vs. school months with a break ≥1 week. Results: School year vs. summer vacation analyses showed accelerations in children's %WΔ (Δ = 0.9, Standard Error (SE) = 0.1 vs. Δ = 1.4, SE = 0.1) and zBMI (Δ = -0.01, SE = 0.01 vs. Δ = 0.04, SE = 0.3) during summer vacation, but %HΔ remained relatively constant during summer vacation compared with school (Δ = 0.3, SE = 0.0 vs. Δ = 0.4, SE = 0.1). Seasonal analyses showed summer had the greatest %WΔ (Δ = 1.8, SE = 0.4) and zBMI change (Δ = 0.05, SE = 0.03) while %HΔ was relatively constant across seasons. Compared with school months without a break, months with a break showed higher %WΔ (Δ = 0.7, SE = 0.1 vs. Δ = 1.6, SE = 0.2) and zBMI change (Δ = -0.03, SE = 0.01 vs. Δ = 0.04, SE = 0.01), but %HΔ was constant (Δ = 0.4, SE = 0.0 vs. Δ = 0.3, SE = 0.1). Fluctuations in sleep timing and screen time may explain these changes. Conclusions: Evidence for both the CCRM and SDH was identified but the SDH may more fully explain BMI gain. Interventions targeting consistent sleep and reduced screen time during breaks from school may be warranted no matter the season.