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.

Abstract Compositional data analysis (CoDA) is widely used to examine the associations between one’s balance of movement behaviors (sedentary behavior, physical activity, and sleep) and health outcomes. Existing reviews have primarily focused on reporting standards for physical activity and sedentary behavior, but have not considered the contribution of sleep reporting to our understanding of CoDA. Purpose To characterize device-based sleep data measurement, processing, and reporting in studies using CoDA to examine associations between movement behaviors and health indicators. Methods A systematic search was conducted in seven databases, along with supplemental strategies (forward and backward citation searches and expert review). Observational studies published since 2015 that employed CoDA approaches using isometric log-ratio transformations to examine the associations between movement behavior compositions utilizing device-based measures of sleep and health outcomes were included. Data extraction included items based on sleep actigraphy measurement, processing, and reporting practices recommended by the American Academy of Sleep Medicine. The National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess study quality. Results Among the 70 included studies (n = 60 cross-sectional, n = 10 longitudinal), few articles reported key sleep measurement and processing information. Most articles (n = 67) included only one sleep component in the time-use composition, and less than half of the articles (n = 29) acknowledged sleep-related limitations. Reports were classified as having good (n = 60) or poor (n = 10) study quality. Conclusions This review identified inconsistencies in the measurement, processing, and reporting of device-measured sleep in studies using CoDA. Varying protocols and reporting on sleep data processing highlight the need for adoption of current standardized approaches and reporting practices. Future research should prioritize transparency and consistency to improve the validity and comparability of findings on sleep’s role as a key component of the integrative 24-h approach to health.

Background:Children engage in less healthful behaviors during the summer compared to the school year, when they are exposed to a highly structured school environment. It is unclear whether changes in children's health behaviors may also be explained by parents relaxing rules/routines in summer (e.g., related to sleep, screens, diet). If so, this represents potentially modifiable drivers of children meeting health behavior guidelines. The purpose of this study was to determine if parents have different rules/routines in summer compared to the school year. Methods:This study used data from the What's UP with Summer three-year, longitudinal observational cohort study (2021-2023) that followed elementary-aged children from 17 elementary schools in the southeastern United States. Parents of 1,084 children (age range 5-14 years, 48% girls) completed surveys during school (April/May) and summer (July) each year, yielding six total timepoints. Survey items assessed parental rules related to dietary behaviors (6 questions), screen-use (weekdays and weekends, 3 questions each), and sleep (weekdays and weekends, 3 questions each). Lower scores reflected fewer rules/routines that supported these health behaviors. Mixed-effects models examined changes in parenting rules from school year to summer controlling for socioeconomic status (poverty-income ratio, parent education), child age and sex. Results:Compared to the school year, parent rules in summer decreased for weekday screen-use (b= -0.31, 95% CI -0.34, -0.28), weekend screen-use (b= -0.09, 95% CI -0.13, -0.06), weekday sleep (b= -0.81, 95% CI -0.84, -0.78), and weekend sleep (b = -0.21, 95%CI -0.24, -0.17). There was no significant change in diet-related rules (b= -0.01, 95% CI -0.02, 0.00). Age had a significant interaction effect for screen and sleep rules/routines, while povertyincome ratio and child sex did not. Conclusions:Parents reported fewer rules for screen use and sleep during summer compared to the school year, and overall rules/routines declined as children grew older. The gap between summer and school sleep rules also widened with age. These findings suggest that the structure of school year routines help support parents in maintaining rules and routines that shape children's health behaviors.

Screen time is frequently used as a proxy for sedentary behaviour in physical activity and public health research, an assumption largely derived from television viewing. However, because smartphones are portable, screentime can occur across diverse physical contexts. This raises uncertainty about whether it reliably reflects sedentary behaviour at the momentary level. This opportunistic analysis used data from an ongoing cohort study of adult caregivers of preschool-aged children. Smartphone screen time was objectively assessed using the Android-based passive sensing app Chronicle, and movement behaviour was assessed using wrist-worn accelerometers. Accelerometer data were processed using GGIR and the SedUp algorithm to classify epochs as sedentary or non-sedentary. Smartphone and movement data were aligned at 5-s epochs during waking wear time. Primary outcomes included the proportion of smartphone screen-use time occurring during sedentary versus non-sedentary behaviour. Secondary analyses examined inactive versus active behaviour during non-sedentary screen use, and app-level differences. The analytic sample included 63 participants contributing 785 person-days (3.3-million 5-s epochs). At the participant level, the median proportion of smartphone use occurring during non-sedentary behaviour was 25.9

Purpose: Children experience excessive weight gain during the summer months, and changes in sleep behaviors may contribute. Children in low-income families are at elevated risk of engaging in poorer health behaviors and health outcomes and may be exposed to greater changes in sleep during summer. The purpose of this study was to assess differences in school-aged children’s sleep health by household income separately while in school and summer. Methods: Children (n=1,007, age range: 5-14yrs, 49% female) wore an Actigraph GT9X for 24 hours per day over 14 days in spring and summer across a maximum of 3 years (6 timepoints). Sleep duration, bedtime, and waketime were calculated using the GGIR (v3.1.2) R package and HDCZA algorithm. Variability of these metrics was calculated as the individual standard deviation across days at each timepoint. Income groups were determined as parent-report of household income, categorized as low-income (<2.0 income-to-poverty ratio), middle-income (2.0-3.0 income-to-poverty ratio), or high-income (>3.0 income-to-poverty ratio). We utilized mixed effects models to examine mean levels and variability of sleep metrics to compare income groups in school and summer. Results: Analyses included 34,767 days of accelerometer data. Children had similar sleep duration and timing during school and summer across income groups. However, children in low-income families went to bed significantly later in summer than children in middle (+13.2mins, 95%CI: 5.3, 21.1) and high-income (+21.0mins, 95%CI: 10.1, 31.9) families. Children in low-income families also woke up significantly later in summer than children in high-income families (+12.1mins, 95%CI: 2.4, 21.9). Bedtime was more variable in summer (low vs. middle: +5.2mins; 95%CI: 2.5, 7.9; low vs. high: +8.5mins; 95%CI: 4.8, 12.3), while waketime was more variable in school (low vs. middle: +10.3mins; 95%CI: 7.8, 12.8; low vs. high-income: +13.1mins; 95%CI: 9.6, 16.6) and in summer (low vs. middle: +12.0mins; 95%CI: 9.4, 14.5; low vs. high: +18.7mins; 95%CI: 15.3, 22.2). Conclusions: Children from low-income households had later sleep timing and more sleep variability as compared to their middle and high-income counterparts. Future studies should examine the contextual and behavioral time-use factors associated with sleep health in children across income groups, especially in low-income families, to identify where or when to intervene for effective sleep intervention.

Physical performance impairments are common in cancer survivors and can limit daily activities, quality of life, and long-term health. Although structured exercise programs have proven beneficial for improving physical performance, maintenance of these benefits is unclear. This study aimed to systematically evaluate whether improvements in physical performance are maintained following structured exercise oncology interventions. A systematic search was conducted for randomized controlled trials (RCTs) published between January 1990 and March 2025. Eligible trials engaged adult cancer survivors in structured exercise interventions and reported objective measures of cardiorespiratory fitness, muscular strength, and/or walking capacity at the end of the intervention and ≥ 3 months after program completion. Data were pooled using random-effects meta-analyses with weighted mean differences (WMD) used to summarize effects. Twenty-four RCTs (2289 participants; mean follow-up post-intervention = 8 months) were included. Exercise significantly improved cardiorespiratory fitness at post-intervention (WMD = + 1.76 ml/kg/min; p = 0.008); however, improvements were attenuated at follow-up (WMD = + 1.24 ml/kg/min; p = 0.130). Similarly, upper and lower body strength improved post-intervention (WMD = + 3.35 kg; p = 0.001; WMD = + 12.7 kg; p = 0.045), but effects diminished at follow-up (WMD = + 1.80 kg; p = 0.081; WMD = + 10.0 kg; p = 0.093). In contrast, walking capacity increased post-intervention (WMD = + 40.3 m; p = 0.002) and remained elevated at follow-up (WMD = + 49.4 m; p = 0.006). Certainty of evidence ranged from very low to low across outcomes, primarily due to risk of bias, inconsistency, and imprecision in effect estimates. Structured exercise interventions were found to produce short-term improvements in physical performance among cancer survivors. Although gains in cardiorespiratory fitness and muscular strength appeared to persist at follow-up, they were attenuated compared with post-intervention and supported by very low certainty evidence. In contrast, walking capacity demonstrated sustained improvements at follow-up, though the certainty of evidence remained low. Future work is needed to identify longer-term effects (> 12 months) and develop strategies to better maintain improved physical performance. While exercise programs can improve physical performance, these benefits may not persist without ongoing support. Cancer survivors should be encouraged to continue self-directed exercise after program completion, and exercise programs should incorporate strategies to maintain longer-term improvements in physical performance.

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

Understanding how 24-h movement behaviors (i.e., physical activity, sedentary behavior, sleep) differ among children with developmental disabilities (e.g., delays, disorders, disabilities) is crucial, with evidence indicating that variations seen in older children may begin to emerge in early childhood. However, reviews have not acknowledged such developmental considerations in young participants. Therefore, a scoping review of the growing body of research focused on preschool-aged children is essential for gaining deeper insights into how these behaviors evolve and differ during this critical period. This scoping review aimed to (1) identify 24-h movement behavior levels in preschool-aged children with developmental disabilities, (2) determine whether 24-h movement behaviors differed between children with and without developmental disabilities, and (3) determine if 24-h movement behaviors varied by the domain of developmental difference. We conducted a systematic search in nine databases. Inclusion criteria were: (1) children between 33 and 72 months, (2) inclusion of children with developmental disabilities in the sample, (3) measures of at least two movement behaviors, (4) written in English, and (5) empirical, original research designs. The date of publication was unrestrained. Title/abstract and full text screening were completed by two independent reviewers. The date of publication was unrestrained. Twenty predominantly cross-sectional articles published between 2004 and 2024 were included. Studies examined movement behaviors across various developmental domains such as autism spectrum disorder, cerebral palsy, and developmental coordination disorder. Findings on sleep, sedentary behavior, and physical activity were mixed, with some studies reporting significant group differences while others found no or inconsistent group differences. This review highlights the variability and gaps in current research on movement behaviors among children with developmental disabilities, underscoring the need for more comprehensive and consistent measurements that consider all 24-h movement behaviors to better inform targeted interventions.

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.

BACKGROUND:Wearable devices that measure energy expenditure are not designed for children. Therefore, we developed the PATCH (Platform for Accurate Tracking of Children's Health), an open-source device to measure children's energy expenditure using heart rate (HR) and acceleration. This study examines three models to estimate children's oxygen consumption using HR and acceleration compared with a criterion of indirect calorimetry. METHODS:Fifty-two children aged 3-8 years (mean age, 6.4 ± 1.7 years; 42% female; 73% White) completed a semistructured protocol ranging in intensity from inactive (e.g., using iPad) to vigorous (e.g., running). The PATCH was attached to the chest and measured HR (photoplethysmography) and acceleration (three-axis accelerometer, ±16 g). The criterion (Cosmed K5) measured breath-by-breath oxygen uptake (V̇O 2 ; mL·kg -1 ·min -1 ). We used cross-sectional time series (CSTS) models, generalized additive mixed models (GAMM), and random forest (RF) to predict oxygen consumption from a combination of HR, acceleration, and participant characteristics (biological sex, age, weight, height). We used 10-fold cross-validation, testing each fold and training on the rest, repeated for robustness. Model fit was assessed using mean bias, mean absolute error, mean absolute percent error, and variance explained ( R2 ). We reported out-of-sample R2 values without subject-specific random effects to ensure broad applicability. RESULTS:Mean bias values for CSTS, GAMM, and RF were -0.01, 0.01, and 0.08 mL·kg -1 ·min -1 , respectively. Mean absolute error values were 1.54, 1.56, and 1.99 mL·kg -1 ·min -1 ; mean absolute percent error was 10% for CSTS and GAMM, and 13% for RF. The CSTS explained 86% (SD, 5%) of variance, GAMM explained 86% (SD, 5%), and RF explained 81% (SD, 6%) in oxygen consumption. CONCLUSIONS:CSTS, GAMM, and RF models provide similarly accurate estimates of children's oxygen consumption using the PATCH device compared with indirect calorimetry. Further validation in larger, free-living samples is needed.