Accelerated summer body mass index (BMI) gain in children is well documented, but no studies have examined if these increases are accompanied by increases in percent body fat (%BF). Data from the Healthy Seasons (N = 418; mean age = 7.1 years) observational cohort was examined. Children's height, weight, and %BF were measured during January, May, August, and December from 2023 to 2025. Monthly BMI gain was 0.067 during the school year and 0.119 during the summer (difference in change = 0.052, 95% confidence interval [CI] = 0.025, 0.079), indicating that the 3 months of summer represent 36% of annual BMI gain. Monthly %BF gain was 0.009 during the school year and 0.456 during the summer (difference in change = 0.446, 95% CI = 0.355, 0.538), indicating that the 3 months of summer represent 93% of annual %BF gain. This study suggests that summer BMI gain is accompanied by increases in %BF, suggesting summer is a critical period for obesity prevention efforts.

Background: Summer holiday programs offer a promising solution to prevent unhealthy changes in health behaviors often experienced by children during the extended break from school. However, not all families are able to access summer programs. This qualitative study explored parents’ experiences of the summer holiday period and their perceptions of receiving free summer programs to identify potential benefits, facilitators, and challenges to access. Methods: Parents (N=24: 100% female, 63% Black) of families randomized to receive free summer programming or experience “summer as usual” (control) were interviewed at the conclusion of summer 2024. Parents of “high-attenders” (N=8), “low-/non-attenders” (N=8) and controls (N=8) were recruited to participate in a semi-structured interview. Participants were asked about general summer experiences and benefits/challenges of the free summer program. Interviews were audio recorded, transcribed and coded. Thematic analysis was conducted with themes compared across attendance groups. Results: Parents described variability in their summer holiday experiences compared with the school year. Themes included family functioning, daily routines, and children’s health behaviors. Families with greater work flexibility and financial resources more often reported psychosocial benefits, including reduced stress and increased family time. Families with fewer financial or social support reported greater stress managing childcare demands and activity costs. Control-group parents particularly described stress juggling family roles while trying to provide healthy and enjoyable summer experiences. Summer programs were perceived as providing structured, active, and socially engaging environments that supported children’s physical, social, and emotional wellbeing while meeting parents’ childcare needs. Cost remained a significant barrier. Families’ values, needs, and practical constraints shaped engagement decisions. Free programs improved access and reduced financial distress. Facilitators to engagement included program design, content, and delivery features. Conclusion: Families with stronger financial and social support were better able to offset the increased demands of the summer holidays. Summer programs functioned as a social safety net and provided opportunities for physical activity, social connection, and cognitive engagement while supporting family wellbeing. Ensuring equitable access to summer programs represents a practical and scalable opportunity to support children and families. Future research should explore sustainable funding models and fee structures that promote participation while remaining equitable. Trial registration: NCT05880901

Children and adolescents with intellectual and developmental disabilities (IDD) are at greater risk for obesity and poor obesogenic behaviors (e.g., physical activity, screen time, diet, sleep) than their typically developing counterparts. The Structured Days Hypothesis (SDH) suggests that in typically developing children and adolescents, obesogenic behaviors worsen during periods of reduced structure (e.g., weekend or summer vacation). However, children and adolescents with IDD have unique factors that may alter how structure (i.e., pre-planned, segmented, adult supervised, out-of-home programs) influences obesogenic behaviors. Therefore, the objective of this systematic review and meta-analysis is to examine obesogenic behaviors during periods of more and less structure among children and adolescents with IDD. A comprehensive search of PubMed, PsycINFO, Embase, and Web of Science was performed through the end of 2024 based on the PICO framework. Studies were eligible if they included youth with IDD and measured obesogenic behaviors across contexts with differing degrees of structure. Two reviewers independently completed the screening process, extracted all relevant information, and evaluated methodological quality using the NHLBI tool. Results were synthesized using fixed- and random-effects meta-analyses and visually represented with forest plots. A total of 4,236 papers were screened with 323 full-text articles retrieved. After screening, 33 total studies were identified (physical activity = 23, sedentary behaviors = 12, sleep = 11, diet = 1). Meta-analyses indicated that the standardized mean difference of physical activity (Random = 0.27, [95

Later meal timing, increased variability in timing, and meal skipping are associated with negative health outcomes among children. Few studies examine whether these behaviors are different between the school year and summer. This study analyzed changes in meal timing (i.e., absolute timing, variability in timing, and meal skipping) from the school year to summer in elementary-aged children, and whether changes differed by child age, child sex, and/or household income. Parents (n = 1,004) of 1,004 children (ages 5–14 years, 50

Scale-up penalty, a common phenomenon in which the promising effects found in early preliminary studies are substantially reduced when evaluated in a subsequent larger trial, can stall the advancement of health behavior interventions. In obesity-related behavioral interventions, changes to key features between a preliminary study and subsequent larger trials inflate scale-up penalty. The purpose of this study is to examine whether changes in intervention features occur in other behavioral disciplines that utilize a similar developmental continuum wherein smaller-scale preliminary studies inform larger-scale trials, and whether changes in key features inflate scale-up penalty. We conducted a systematic review identifying preliminary studies followed by a larger trial conducted by the same author(s) (i.e., a study pair) in four areas—tobacco/smoking cessation, alcohol use, interpersonal violence, and sexually transmitted diseases. We coded intervention features in the preliminary study and larger trial to capture changes in key study features (e.g., who delivered the intervention). Multi-level meta-regressions estimated the association between the changes to key study features and change in standardized mean difference for health outcomes and calculated scale-up penalty. We identified 222 effects across 69 study pairs of preliminary studies with subsequent larger trials. Fifty-eight study pairs (84

PURPOSE:Healthy sleep habits are important when children transition from variable preschool environments to a more structured elementary school setting. However, few studies have harnessed the unique partnership between teachers and parents in preventing suboptimal child sleep. This study aimed to engage key community partners (i.e., 4K & 5K teachers and parents) in developing a combined school- and home-based sleep promotion program for young children (4-6 years old). METHODS:Teachers (n=34, 100% female, 12.4±9.0 years' experience) from 2 school districts participated in semistructured focus groups or phone interviews (n=26 teachers in 3 focus groups; n=8 interviews), while parents (n=61, 97% female, age=33.1 ± 5.1 years) completed an online semistructured survey to inform the development of a sleep promotion program. Transcripts were independently coded using an inductive approach and consensus coding. Themes were generated using constant-comparison methods. Survey results were summarized using descriptive statistics. RESULTS:Four key themes emerged from teachers' perspectives: 1) suboptimal sleep impacts children during the school day across multiple domains, (2) several barriers to adequate sleep exist, 3) parent-teacher interactions about sleep are complex and require unique approaches, and 4) a sleep program must fit within parent and teacher needs. Teachers noted leveraging the parent-teacher relationship may increase parent buy-in. Parents reported challenges with their child's sleep and conveyed interest in a tailored sleep promotion program during 4K. CONCLUSIONS:Teachers are concerned about suboptimal child sleep and are invested in working with parents to support healthy sleep habits. A sleep promotion program that encompasses collaboration between the school and home environment would be well-received by families of young children. Findings will inform future content, engagement strategies, and delivery mode.

The purpose of this study was to (1) examine differences in child screen time from school to summer, and (2) assess how attending structured programming relates to child screen time during the summer in a diverse cohort of children. Parents completed daily time use diaries for their children (n = 1,032 children; 9.7 ± 1.8 years old; 33

BACKGROUND:Summer vacation is a time when youth gain excessive weight. A key driver of unhealthy weight gain is poor dietary quality. The absence of consistent structure (e.g., school), is hypothesized to be one of the reasons for lower diet quality during summer. This study examined differences in school and summer dietary quality among a diverse cohort of children across three years. We also examined the impact of attending structured programs on children's diets. METHODS:Parents of 1,032 children (age 5-14 years, 48% girls) completed a time use diary each day for 14-days during school (April/May) and again in summer (July) from 2021 to 2023, for a total of 6 timepoints. The daily diary collected information on the child's location and dietary intake for that day. Mixed-effects models examined the odds (OR) of consuming a food/beverage (e.g., fruit, vegetable, soda, salty snacks) on a given day during school vs. summer, overall and by income. Models also examined the impact of attending structured programming during summer (e.g., summer day camp) on the likelihood of consumption. RESULTS:A total of 39,983 time use diaries were completed. Overall, children were less likely to consume fruit, vegetables, milk, 100% juice, and salty snacks (OR range 0.63 to 0.87), and they were more likely to consume non-carbonated sweetened beverages, soda, frozen desserts, and fast food (OR range 1.17 to 1.63) during the summer compared to school. On summer days with structured programming, children were more likely to consume fruits, vegetables, milk, salty snacks, sweetened beverages (OR range 1.13 to 1.45), and they were less likely to consume frozen desserts, fast food, and soda (OR range 0.63 to 0.90). Few differences were observed between income groups. CONCLUSIONS:Children were less likely to report consumption of healthier foods/beverages and more likely to report consumption of unhealthier foods/beverages during summer compared to school. Attending structured programming during summer is associated with improved diet- suggesting such settings have potential to modify dietary intake.

Passive sensing applications are limited by their inability to determine who is using a device, a critical concern in child mobile device use research, where devices are often shared between siblings or between a child and their parent. Our previous work leveraged behavioral biometrics to identify a target child user; however, it is unknown what type of training data is necessary for optimal model performance. This study evaluated model performance across different characteristics of training data. Thirty-six children (11.3 ± 0.9 years, 56

IntroductionThis study examined the potential of a device agnostic approach for predicting physical activity energy expenditure (PAEE) from research-grade and consumer wearable accelerometry and heart rate (HR) raw data compared with indirect calorimetry in children.MethodsTwo hundred thirty-one 5- to 12-yr-olds (52.4% male) of diverse skin tone and body weights participated in a 60-min protocol with multiple activities at varying intensities. Children wore two of three consumer wearables (Apple Watch Series 7, Garmin Vivoactive 4S, Fitbit Sense) and a research-grade accelerometer (ActiGraph GT9X) on their nondominant wrist, and a chest-placed, research-grade HR monitor (Actiheart 5, ECG), concurrently. Children also wore a K5 criterion measure of PAEE (i.e., COSMED K5). Cross-sectional time series (CSTS), generalized additive mixed effects model (GAMM), and random forest (RF) were used to estimate minute-by-minute PAEE from features extracted from raw accelerometry and HR data. Variance explained (R2), in addition to other metrics, evaluated agreement between estimated and criterion measurements.ResultsFor the research-grade devices (i.e., ActiGraph accelerometry and Actiheart HR), R2 values were 0.74, 0.74, and 0.76 for CSTS, GAMM, and RF, respectively. For Apple, R2 values were 0.77, 0.76, and 0.78; Garmin's values were 0.73, 0.73, and 0.75; and Fitbit's values were 0.63, 0.65, and 0.67 for CSTS, GAMM, and RF, respectively. Across all other evaluation metrics, a similar pattern was observed with Fitbit performing the worst but with little variability between the modeling approaches or the other devices.ConclusionsExcept for Fitbit, accelerometry and HR data from consumer wearables predicted PAEE comparably to research-grade devices, and there was little variability across modeling approach. These outcomes support deploying a consumer wearable device-agnostic approach for PAEE estimation in children.

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5-12 years, 52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred because of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables (i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1 to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Background:Epoch-level accelerometry summary metrics have the potential to be device-agnostic, meaning that similar estimates should be obtained regardless of the device used. The objective of this study was to identify which metric (ENMO, MAD, MIMS) best harmonizes data across devices in an applied setting measuring children's activity. Methods:Children (n=239; 9.3 ± 2.1 years, 47% female, 30% Black) wore ActiGraph GT9X (+/-8g, 50Hz) accelerometers and were randomized to wear two of three consumer wearables including Apple Watch Series 7 (+/-16g, 50Hz), Garmin Vivoactive 4S (+/-8g, 25Hz), and Fitbit Sense (+/-4g, 50Hz) on their non-dominant wrist, while participating in 60 minutes of simulated free-living activities (i.e., walking, running, soccer). The standard deviation (SD) across z-scores for ENMO, MAD and MIMS of each device was calculated at the second-level to quantify variability across devices. Lin's Concordance Correlation Coefficient (LCCC) was calculated for each combination of devices by metric to determine agreement. Multi-level intra-class correlation coefficients (ICC) were additionally used to quantify harmonization. Results:The SD of z-score across devices was the lowest, indicating better harmonization, for MAD (0.13±0.23), followed by ENMO (0.24±0.50), and then MIMS (0.26±0.39). LCCC was strongest for MAD, with coefficients ranging from 0.89 to 0.96. LCCC for MIMS ranged from 0.70 to 0.83, and was lowest for ENMO, ranging from 0.62 to 0.76. Overall ICC was highest for MAD (0.88), followed by MIMS (0.73), and ENMO (0.62). Conclusions:MAD appears to perform best at harmonizing across all the sampled research-grade and consumer devices in children.

STUDY OBJECTIVES:Evaluate the performance of actigraphy-based open-source and proprietary sleep algorithms compared to polysomnography in children with suspected sleep disorders. METHODS:In a sleep clinic, 110 children (5-12 years, 54% female, 50% black, 82% with sleep disorders) wore wrist-placed ActiGraph GT9X during overnight polysomnography. Actigraphy data were scored as sleep or wake using open-source GGIR and proprietary ActiLife software. Discrepancy and epoch-by-epoch analyses were conducted to assess agreement between algorithms and polysomnography, along with equivalence testing. RESULTS:The open-source vanHees2015 algorithm showed good accuracy (79.5% ± 12.0%), sensitivity (81.1% ± 13.5%), and specificity (66.0% ± 23.8%) for sleep detection but was outperformed by the proprietary ActiLife algorithms. The magnitude and trend of bias for total sleep time (TST), sleep efficiency (SE), sleep onset latency, and wake after sleep onset were similar between algorithms. TST and SE were statistically equivalent for the Cole-Kripke (Actilife) and vanHees2015 algorithms compared to the Sadeh (Actilife) algorithm. The Cole-Kripke (ActiLife) demonstrated higher sensitivity (90.5%) to detect sleep but lower specificity (61.2%) than Cole-Kripke (GGIR) (sensitivity: 62.7%, specificity: 79.9%). Sadeh and Cole-Kripke estimated sleep outcomes were not statistically equivalent between implementations in ActiLife and GGIR. CONCLUSIONS:The open-source vanHees2015 algorithm performed well but slightly worse than the proprietary ActiLife algorithms in children. The open-source nature vanHees2015 makes it ideal for clinical pediatric use. Implementation of the Sadeh and Cole-Kripke algorithms in the proprietary ActiLife and open-source GGIR software yield different sleep estimates, so comparisons between studies using these different implementations should be avoided.

BACKGROUND:The time spent physically active outside of school (e.g., extracurricular physical activity) is an important contributor to children's total daily physical activity for health and well-being. Little is known about the opportunities available to children to engage in extracurricular physical activity from low- to middle-income countries. This study aims to answer the question: What are the main perceived barriers and facilitators of extracurricular physical activity among school-age children in Mexico? METHODS:A multi-method cross-sectional study was performed. Six focus groups with children (aged 9-12 years), six focus groups with parents, 10 one-on-one interviews with parents, 12 interviews with teachers, and six interviews with head teachers were conducted across Campeche, Morelos, and Mexico State, Mexico. A questionnaire was applied to explore children's physical activity frequency and preferences for time inside and outside of school. Qualitative data analyses were performed with inductive thematic analysis supported with NVivo software. Quantitative data were analysed with descriptive statistics using IBM SPSS 26. RESULTS:Three main themes summarise the study's findings: (1) how children spend their time outside of school, (2) the places that children access, and (3) the social environment for physical activity outside of the school. The data suggest that children in Mexico dedicate their spare time to screen, work, do housework, or perform unstructured physical activity mostly at home instead of playing sports or actively outdoors. Family support, enjoyment of physical activity, access to programs and facilities, time, living in a housing complex with open common areas, and mild weather were important facilitators identified. 69.4% of children engage in extracurricular physical activity, none of which was provided by schools. More children commute by walking than riding a bike to and from school. Children living inland spent three times more time at home compared to those in seafront areas. CONCLUSIONS:Children rely on their families to partake in extracurricular structured physical activity. Policies targeting children's health and well-being should include school-based extracurricular physical activity programs.

BACKGROUND:Summer is a period of accelerated body mass index (BMI) gain for elementary school-aged children. Summer day camps may provide a structured environment, which has been shown to mitigate accelerated summer BMI gain. Many of these programs have a fee-for-service structure, creating a financial barrier for families with low-income. Providing vouchers to pay for these programs may be an effective strategy for addressing this barrier and mitigating accelerated summer BMI gain but requires further investigation on the optimal dose - the minimum exposure needed to see meaningful results while not overextending resources. METHODS:This study will use a multi-arm randomized controlled trial with three treatment levels. Children (n = 360) ages 5-12 years from participating schools (n = 4) will be randomly assigned to either summer as usual (comparison group) or to receive a voucher to attend an existing summer day camp for 4-, 6-, or 8-, weeks. BMI will be objectively measured at baseline (i.e., ∼May), 3-months (i.e., ∼August), and 12-months (i.e., ∼May of following school year). Obesogenic behaviors (e.g., physical activity, diet, screen time, and sleep) will be assessed in spring (i.e., late May) and summer (i.e., late June and July). Implementation factors, such as content, attendance frequency, duration, and coverage, relationship with children's summer BMI gain and obesogenic behaviors will also be evaluated. The study will also evaluate the cost-effectiveness of each duration. DISCUSSION:The study's findings will identify the optimal dose of summer programming to mitigate excess summer BMI gain, informing effective public health initiatives to combat childhood obesity. TRIAL REGISTRATION:NCT06158594 https://clinicaltrials.gov/study/NCT06158594?titles=determining%20optimal%20amount%20of%20structured%20environments&rank=1.

INTRODUCTION:Summer day camps can mitigate summer weight gain by providing a structured daily environment that promotes healthy behaviors, but summer day camps are often cost prohibitive to families with low income. This study evaluated the cost effectiveness of providing free summer day camps to disadvantaged children to prevent summer weight gain. METHODS:A total of 422 children from a low-income school district in South Carolina were recruited and randomly assigned to receive 8-10 weeks of free summer day camps or to experience summer as usual in 2021-2023. The incremental cost-effectiveness ratio was calculated by dividing summer day camp cost by the difference between the intervention and control groups in changes in BMI z-scores from the start to end of summer. Incremental cost-effectiveness ratios at varied doses of summer day camp participation were also calculated. Sensitivity analyses were conducted using nonparametric bootstrapping of trial-based BMI z-score outcomes, matched with summer day camp costs from across the country. The probability of cost effectiveness was assessed over a range of potential costs at which policymakers may be willing to support. RESULTS:The summer day camp voucher program averted 0.0917 BMI z-score gain relative to the controls at a cost of $1,307, yielding an incremental cost-effectiveness ratio of $1,463 per 0.1 BMI z-score averted per child. Attending summer day camp 5 days per week, representing the highest dose, yielded the highest cost effectiveness. Sensitivity analyses showed that the bootstrapped incremental cost-effectiveness ratios averaged $2,187 per 0.1 BMI z-score averted, with 80% being <$3,500 per 0.1 BMI z-score averted. CONCLUSIONS:The voucher program is likely cost effective, with 80% probability of cost effectiveness if policymakers are willing to pay $3,500 per 0.1 BMI z-score averted.

Abstract Background In a stepped wedge design, schools are randomised to a sequence of measurements, with each sequence transitioning to intervention status at a different time. There are several advantages to such designs, including increased statistical power, logistical benefits and the ability to explore change over time. However, stepped wedge designs have not previously been used to evaluate school-based physical activity interventions in children. This paper aimed to explore the feasibility of this design, by identifying school constraints, balancing these with statistical considerations and exploring the power of this chosen design under different scenarios. Methods We conducted three interlinked studies, with the results from one informing the next. Study 1 was a qualitative study to identify school constraints that inform the choice of stepped wedge configuration. Study 2 used simulation to choose a configuration that balanced these school constraints and statistical properties. Study 3 explored the statistical power for the chosen design for different school and pupil sample sizes, using an open cohort design (a mixture of new and repeated pupils). Results School staff considered the proposed data collection feasible, and supported a maximum of 3–4 measurements per year and an implementation period of one school term. Study 2 therefore considered incomplete stepped wedge designs with five steps. Statistically, the best designs had a mix of control and intervention measurements in terms 2–4 and a spread of measurements across the whole study duration. Power depended on a combination of the overall recruitment rate and the retention rate. For 20 schools with an eligible class size of 30 pupils, we would be able to detect a 6 min difference in average weekday moderate-to-vigorous physical activity with 80% power, provided there were > 50% of pupils measured per school at each time. A similarly powered cluster randomised controlled trial would require 42 schools. Conclusion Stepped wedge trials are a viable design for evaluating school-based physical activity interventions. Incomplete designs, where not all schools are measured at each point, offer the flexibility to work around practical constraints.

Background:To examine the efficacy of providing free summer day camp (SDC) to children from low-income families on changes in physical activity, time spent sedentary, and screentime. Methods:Across three summers (2021-2023), we randomized 422 children (8.2±1.5yrs, 48% female, 51% Black, 69% at or below 200% Federal Poverty Level, 30% food insecure) from seven elementary schools to one of two conditions: summer as usual (control, n=199) or free SDC for 8-10wks (intervention, n=223). Accelerometry measured activity (moderate-to-vigorous PA [MVPA] and time spent sedentary) and parent daily report of screentime were measured using a 14-day in April/May (school) and July (summer). Intent-to-treat analysis examined changes in behaviors between school and summer. Exposure models examined differences in behaviors during summer on days when children attended vs. did not attend a SDC in both intervention and control children. Results:Intent-to-treat models indicated in the summer children in the intervention group accumulated +15.0mins/day (95CI 12.0 to 18.0) more MVPA and spent -29.7mins/day (-37.7 to -21.8) less time sedentary and -14.1 mins/day (-23.9 to -4.3) on screens, compared to children in the control group. Exposure models indicated, on days children attended SDCs, they accumulated more MVPA (+26.1mins/day, 22.5 to 29.7), and spent less time sedentary (-63.5mins/day, -72.9 to -54.1) and on screens (-9.5mins/day, -20.1 to 1.2), compared to days when children did not attend SDC. Conclusions:Policies targeting upstream structural factors, such as universal access to existing community SDCs during summer, could lead to improvements in health behaviors among children from low-income households. Clinical Trialsgov:NCT04072549.