Influence of parental nativity and perceived neighborhood environment on physical activity and screen time of United States youth.

OBJECTIVE: This study assessed how parental nativity and perceived environment are associated with physical activity and screen time of U.S. children and adolescents. METHODS: Data originated from the 2020-21 U.S. National Survey of Children's Health. We conducted multivariable Poisson regression to assess the cross-sectional association of parental nativity and perceived neighborhood environment variables on parental reports of youth meeting national physical activity and screen time guidelines. We tested interactions of parental nativity and neighborhood environment variables on both outcomes. Analyses were conducted using STATA v17 and p < 0.05 indicated statistical significance. RESULTS: The sample of 24,928 children and 30,951 adolescents was 11.6 years of age, on average, with approximately 39% under 200% of the federal poverty level. About one-third of the sample (27.5%) had foreign-born parents. In adjusted models, we found that compared to youth with U.S.-born parents, those with foreign-born parents had a lower prevalence of meeting physical activity guidelines. Youth whose parents reported living in safe neighborhoods had a higher prevalence of meeting guidelines for physical activity (children: PR = 1.20, 95%CI 1.14, 1.27; adolescents: PR = 1.23, 95%CI 1.14, 1.32) and screen time (children: PR = 1.19, 95%CI 1.13, 1.26; adolescents: PR = 1.16, 95%CI 1.06, 1.28) than youth whose parents reported unsafe neighborhoods. We found similar associations between neighborhoods considered supportive or with many amenities and meeting physical activity and screen time guidelines. CONCLUSIONS: Youth whose parents are foreign-born have a lower prevalence of sufficient activity, and perceived parental neighborhood safety and support may be significant influences on youth physical activity and screen time.

Using the Nutrition Facts Label to Make Food Choices Is Associated with Healthier Eating among 8th and 11th-Grade Students: An Analysis of Statewide Representative Data from the 2019-2020 Texas School Physical Activity and Nutrition Survey.

BACKGROUND: Nutrition labels are a tool to inform and encourage the public to make healthier food choices, but little information is available about use in multi-ethnic adolescent populations in the U.S. The purpose of this study was to examine associations between the level of nutrition label usage and healthy/unhealthy eating behaviors among a statewide representative sample of 8th and 11th-grade students in Texas. METHODS: We analyzed cross-sectional associations between the Nutrition Facts label use and eating behaviors from a statewide sample of 8th and 11th-grade students in Texas, (n = 4730, weighted n = 710,731, mean age = 14.7 ± 1.6 years; 49% female, 51% Hispanic), who completed the 2019-2020 Texas School Physical Activity and Nutrition (TX SPAN) survey. Students self-reported their level of nutrition label usage to make food choices (5-point Likert scale from "Never" to "Always") and previous day consumption of 26 food items (13 healthy, 13 unhealthy). The 26 food items were used to calculate a Healthy Eating Index (HEI) score (0-100), a Healthy Foods Index (HFI) score (0-100), and an Unhealthy Foods Index (UFI) score (0-100). Weighted linear regression models were employed to examine the associations between self-reported use of nutrition labels to make food choices and HEI, HFI, and UFI scores. Marginal predicted means of HEI, HFI, and UFI scores were calculated post hoc from linear regression models. The odds of consuming specific individual food items for nutrition label usage were also calculated from weighted logistic regression models. All linear and logistic regression models were adjusted for grade, sex, Body Mass Index (BMI), race/ethnicity, economic disadvantage, and percentage of English language learners by school. RESULTS: A total of 11.0% of students reported always/almost always using nutrition labels to make food choices, 27.9% reported sometimes using them, while 61.0% indicated they never/almost never used nutrition labels to make food choices. The average HEI score among students in the sample was 47.7 ± 5.9. Nutrition Facts label usage was significantly and positively associated with HEI (b = 5.79, 95%CI: 4.45, 7.12) and HFI (b = 7.28, 95%CI:4.48, 10.07), and significantly and negatively associated with UFI (b = -4.30, 95%CI: -6.25, -2.34). A dose-response relationship was observed between nutrition label usage and HEI, HFI, and UFI scores, such that the strength of these associations increased with each one-point increase in nutrition label usage. Students who reported using nutrition labels always/almost always to make food choices had significantly higher odds of consuming healthy foods including baked meat, nuts, brown bread, vegetables, whole fruit, and yogurt (ORrange = 1.31-3.07), and significantly lower odds of consuming unhealthy foods including chips, cake, candy, and soda (ORrange = 0.48-0.68) compared to students who reported never/almost never using the Nutrition Facts label. CONCLUSIONS: Using the Nutrition Facts labels to make food choices is beneficially associated with healthy and unhealthy eating among 8th and 11th-grade students, although the proportion of students using nutrition labels to make their food choices was low. Public health efforts should be made to improve nutrition literacy and encourage nutrition label use among secondary students in the United States.

Evaluation of a Clinic-Based, Electronic Social Determinants of Health Screening and Intervention in Primary Care Pediatrics.

OBJECTIVE: Social determinants of health (SDOH) significantly affect individuals' health outcomes, yet universal electronic SDOH screening is not standard in primary care. Our study explores the implementation of an electronic SDOH screening in the electronic health record (EHR) and follow-up intervention among primary care pediatric patients within an academic clinic. METHODS: Beginning in August of 2022, patients and their families determined to have at least one SDOH need qualified for an in-clinic referral to a coordinated care team member. We assessed the overall efficacy and feasibility of the implementation. RESULTS: Over the 4-month pilot, 1473 of 2064 (71.4%) eligible patients were screened, with 472 (32%) patients screening positive on at least one SDOH domain. Of the 472 screened positive, 48 (10.2%) declined a referral. Two hundred and forty-seven of the 424 (58.3%) received a referral to a care coordination team member. CONCLUSIONS: This study demonstrates the feasibility of a universal electronic SDOH screening tool within the EHR within an urban, academic-based clinic.

Interpersonal and Environmental Protective Factors and Their Associations With Children's Weight Status.

Both external structure (ie, participating in extracurricular activities) and family factors (ie, parental emotional support) have separately been linked with children's physical health and well-being, however, their combined effects are less well known. The current study examined the longitudinal associations between participating in structured out-of-school activities and parent reports of warmth/emotional support with children's weight status (ie, zBMI) over time. Utilizing longitudinal data from the United States-based Early Childhood Longitudinal Study, Kindergarten Class of 2010 to 2011 (ECLS-K:2011), we employed a confirmatory factor analysis (CFA) and a latent variable cross-lagged path analysis to examine if emotional supportiveness and participation in structured activities predicted lower zBMI over the course of 1 year. The final sample included 18 135 participants. Mean age of the participants was 8.12 years (±0.38 years), and 51% of children were male. Mean zBMI was 0.54 (±1.12). Structure at baseline predicted increased zBMI in year 2 (ß = .03, P = .02) but did not predict parent emotional supportiveness at year 2 (ß = -.05, P = .09). Parent emotional supportiveness at baseline predicted greater zBMI at year 2 (ß = .02, P = <.01) but did not predict structure at year 2 (ß = .02, P = .39). zBMI at baseline did not predict structure (ß = .02, P = .25) or parent emotional supportiveness at year 2 (ß = -.01, P = .55). Our findings were inconsistent with our hypothesis with regard to directionality. Continued refinement about the role of internal structure (ie, family, and parenting practices) may inform public health prevention strategies to support the well-being of children and families.

Volunteering, Health, and Well-being of Children and Adolescents in the United States.

This cross-sectional study examines whether there is an association between volunteering and well-being among children and adolescents across the United States.

Examining Bullying Victimization, Bullying Perpetration, and Positive Childhood Experiences.

BACKGROUND: The National Academy of Sciences has recognized bullying as a serious public health issue, with the outcomes of bullying immediate and long-term. The purpose of this study was to examine the relationship between 7 selected positive childhood experiences, such as having a mentor, and bullying victimization, and bullying perpetration. METHODS: We used cross sectional data from the 2019-2020 National Survey of Children's Health, children ages 6 to 17 (n = 43,999). Bivariate analyses were used, with p < .05 deemed significant. To examine the association between experiences and bullying victimization or perpetration, multivariable regression models were used. RESULTS: Children who experienced any of the following positive childhood experiences had a lower odds of perpetration of bullying victimization: resilient family; safe neighborhood; supportive neighborhood; or connected caregiver. The following covariates had a lower odds of perpetrating bullying, across all models: race other than white, female sex, age of 13 or older, primary language not English, and a guardian education of a high school diploma or less. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Schools can play a formative role in promoting positive childhood experiences identified herein. CONCLUSIONS: Findings from this study may be beneficial for educators, policy makers, and child advocacy stakeholders as they design and implement school or community-based youth development programs.

Free Summer Day Camp to Address Childhood Obesity: Is There Demand?

Children from low-income households, and who are overweight or obese (OWOB), are at risk of accelerated weight gain during summer. Summer day camps (SDCs) have the potential to mitigate accelerated weight gain during summer as these settings can positively influence children's obesogenic behaviors (i.e., increase physical activity); however, barriers exist to attending, most notably cost. Little is known on whether low-income caregivers of children with OWOB would be interested in having their child attend SDC for free. Caregivers (n = 109, 82% mother respondents, >75% Medicaid and Minority Household) with a child attending pediatric endocrinology clinics completed a one-page survey to explore demand. Approximately 66% of respondents expressed interest for their child to attend SDC for free. Providing free SDC for children with OWOB and from low-income households is a possible strategy to tackle childhood obesity during summer.

Accuracy and Precision of Opportunistic Measures of Body Composition from the Tanita DC-430U.

Background: It is essential to quantify the accuracy and precision of bioelectrical impedance (BIA)-estimated percent body fat (%BF) to better interpret community-based research findings that utilize opportunistic measures. Methods: Study 1 measured the accuracy of a new dual-frequency foot-to-foot BIA device (Tanita DC-430U) compared with dual-energy X-ray absorptiometry (DXA) among healthy elementary school-aged children (N = 50). Study 2 examined the precision of BIA %BF estimates within and between days among children and adults (N = 38). Results: Regarding accuracy, Tanita DC-430U underestimated %BF by 8.0 percentage points compared with DXA (20.6% vs. 28.5%), but correctly ranked children in terms of %BF. Differences in %BF between BIA and DXA were driven by lower BIA-estimated fat mass (7.8 kg vs. 9.9 kg, p < 0.05) and higher BIA-estimated fat-free mass (25.3 kg vs. 24.1 kg, p < 0.05). The absolute agreement between BIA and DXA for estimated %BF was moderate (concordance correlation coefficients = 0.53). Regarding precision, measures taken at the same time, but on different days (root mean square standard deviation [RMSD] = 0.42-0.74) were more precise than the measures taken at different times within a single day (RMSD = 1.04-1.10). Conclusion: The Tanita DC-430U substantially underestimated %BF compared with DXA, highlighting the need to assess accuracy of new BIA devices when they are introduced to the market. Opportunistic measures of %BF estimates were most precise when taken at consistent times and in the morning, but may be utilized throughout the day with an understanding of within- and between-day variability.

COVID-19 mitigation strategies: A natural experiment highlighting the importance of structure in the prevention and treatment of childhood obesity.

Societal restrictions due to the Coronavirus Disease 2019 (COVID-19), such as the closure of schools, childcare centers, and community programs, were implemented to prevent the spread of the disease and to protect the health and well-being of the population. These mitigation efforts drastically interrupted the day-to-day environments of children and adolescents and influenced how they spent the majority of their waking hours. Evidence shows on days when children and adolescents are in "structured" settings, such as school or other extracurricular programs or day camps, their obesogenic behaviors (i.e., sleep, physical activity, diet, and screen/media time) are more favorable than on days with less structure (e.g., summer days, weekends). Although obesity is driven by complex interactions between environmental, behavioral, biological, and genetic factors, COVID-19 pandemic closures emphasized the importance of daily structure. This short communication used a tertiary examination of the literature to show how societal restrictions and mitigation strategies resulted in significant increases in childhood and adolescent obesity on a global scale and highlights the importance of key underlying principles of the Structured Days Hypothesis (SDH). Closure of schools and other structured programs as a result of COVID-19 exposed youth to prolonged periods of less-structured environments as youth spent considerably more time at home than normal. Societal restrictions and mitigation strategies as a result of COVID-19 inadvertently demonstrated the importance of structure in shaping children's health behaviors and weight-related outcomes. Public health practitioners and researchers should consider this framework in the development of interventions to prevent and treat obesity in youth.

Classroom teachers' "off-the-shelf" use of movement integration products and its impact on children's sedentary behavior and physical activity.

Movement integration (MI) products are one of many MI strategies that aim to reduce students' sedentary behavior (SB) and increase physical activity (PA) during classroom time. This study examined elementary classroom teachers' off-the-shelf (i.e., no researcher support) use of MI products (GoNoodle Plus [GN], ABC for Fitness [ABC], Take10) and their impact on students' SB and PA. Teachers (N = 57) at five schools received one MI product and reported MI strategy uses/day while student (n = 1,098, 52% female, 66% Black) accelerometer-determined SB and PA was assessed. Mixed regression models estimated changes in MI uses/day and SB and PA during the school day prior to and after teachers received the MI product. GoNoodle was the only MI product where overall MI strategy uses/day increased (∆ = 0.8, 95% CI = 0.1, 1.4). Across products, students' SB increased (∆ = 2.2, 95% CI = 1.2, 3.1) while light (∆ = -1.7, 95% CI = 1.2, 3.1) and MVPA (∆ = -0.5, 95% CI = -0.8, -0.2) decreased. For GN SB (∆ = -3.3, 95% CI = -7.8, 1.3), light (∆ = 2.5, 95% CI = -0.7, 5.7), and MVPA (∆ = 0.8, 95% CI = -0.9, 2.5), did not show statistically significant change. For Take10 SB (∆ = 1.0, 95% CI = -0.2, 2.2) and MVPA (∆ = 0.1, 95% CI = -0.3, 0.6) did not change while light PA decreased (∆ = -1.1, 95% CI = -2.0, -0.3). For ABC SB increased (∆ = 11.1, 95% CI = 8.4, 13.9) while light (∆ = -7.0, 95% CI = -8.9, -5.0) and MVPA (∆ = -4.2, 95% CI = -5.2, -3.1) decreased. GN shows promise for classroom teacher use. However, given limited uptake of the other products and the lack of change in children's SB and PA, this study suggests that off-the-shelf MI products cannot be integrated into classroom routines without additional support.

Home Smoke-Free Policies as Children Age: Urban, Rural, and Suburban Differences.

INTRODUCTION: Parents/guardians can effectively reduce tobacco smoking and secondhand smoke exposure among youth by adopting and enforcing rules against indoor tobacco smoking (ie, home smoke-free policies). We investigate home smoke-free policies from childhood to adolescence in the United States and across rural, suburban, and urban households. AIMS AND METHODS: We analyzed 2019-2020 National Survey of Children's Health data from n = 5,955 parents of youth aged 0-17, living at home with a tobacco smoker in the United States (U.S). Geographical categories were: rural, suburban, and urban. Home smoke-free policy reflected prohibiting tobacco smoking inside the home. Weighted logistic regressions examined the (1) association between youth age and home smoke-free policies, (2) interaction between geographic category and youth age, and (3) differing associations between youth age and home smoke-free policies by geography. Models controlled for youth race, ethnicity, sex, parental education, household annual income, and home structure. RESULTS: Approximately 13.2% of U.S. households with a smoker did not have a home smoke-free policy. Stratified analyses found one-year increase in youth age was associated with lower odds of having a home smoke-free policy in rural (aOR:0.91; 95%CI: 0.87-0.95) and urban (aOR: 0.96; 95%CI: 0.92-1.00; p = .039), but not suburban (aOR:1.00; 95%CI: 0.95-1.05) households, controlling for covariates. CONCLUSION: Odds of having a smoke-free home in the U.S. declined significantly in rural (9% per year) and urban (4%) but not suburban (0%) households. We quantify declines in home smoke-free policies as children age and identify geographic disparities for this environmental determinant of health. IMPLICATIONS: Health promotion efforts targeting secondhand smoke prevention is needed, particularly for parents of older youth. Furthermore, there is a clear geographic bias in secondhand smoke exposure among all youth particularly older youth. Tailored interventions are needed to address geographic disparities in secondhand smoke exposure among rural and urban youth.

Preliminary Evidence of Children's Weight Gain From 5 Months of Home Quarantine During the COVID-19 Pandemic.

Purpose. Preventive measures to curtail the spread of the Coronavirus Disease 2019 (COVID-19)-such as home quarantine, closure of schools/programs-are necessary, yet the impact of these restrictions on children's weight status is unknown. The purpose of this case report was to investigate changes in children's body mass index (BMI) and zBMI during COVID-19 quarantine. Methods. Children had their heights and weights recorded early March 2020 (pre-COVID-19) and 5 months later (early August 2020). Paired sample t tests examined changes in BMI and zBMI from baseline to follow-up. Results. Twenty-nine children (62% female; mean age 9.3 years; 27.5% with overweight or obesity) provided height and weight data at both time points. There was a significant difference in pre-COVID-19 BMI (mean [M] = 20.1, standard deviation [SD] = 6.0) and follow-up BMI (M = 20.7, SD = 6.4); t (57) = -3.8, P < .001, and pre-COVID-19 zBMI (M = 0.8, SD = 0.9) and follow-up zBMI (M = 0.9, SD = 0.9); t (57) = -3.1, P = .003. Five of the 29 children moved from normal weight to overweight (n = 4) or obese (n = 1) during 5 months of quarantine. Conclusions. Preliminary evidence shows most children increased their BMI and zBMI values from pre-COVID-19 assessment to the follow-up assessment, 5 months later. These initial findings identify potential incidental negative health consequences of children as a result of COVID-19 preventative measures such as home quarantine.

Healthy Summer Learners: An explanatory mixed methods study and process evaluation.

Healthy Summer Learners (HSL), a novel, 6-week summer program for 2-4th grade children from low-income families in the Southeastern United States, aimed to prevent accelerated summer BMI gain and academic learning loss by providing healthy meals and snacks, 15 min of nutrition education, 3 h of physical activity opportunities and 3.5 h of reading instruction daily. This three-armed pilot quasi-experimental study used a repeated measure within- and between-participant design to compare HSL, to an active comparator-21st Century Summer Learning Program (21 C), and no-treatment control. A mixed-methods process evaluation was employed to evaluate program implementation and provide insight for future program development. Though the program was well received, student attendance was lower than anticipated and full program fidelity was not achieved. During interviews, both parents and teachers noted that the bussing schedule was inconsistent, making attendance difficult for some families. These process evaluation findings may help explain why no statistically significant group-by-time interactions at 3- or 12-month follow up were found for the primary outcomes of zBMI or MAP reading score. Future iterations of HSL should seek to extend program hours, lengthen program duration, and explore ways to lower projected cost of attendance.

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

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.

Differences by School Location in Summer and School Monthly Weight Change: Findings from a Nationally Representative Sample.

OBJECTIVES: To examine changes in accelerations of Body Mass Index (BMI), age-and-sex specific body mass index (zBMI), and 95th percentile of BMI (%BMIp95) during the summer months and school year by school location designation (i.e., urban, suburban, exurban). This study utilized the Early Childhood Longitudinal Study Kindergarten Class of 2010-2011. METHODS: Of the 18,174 children in the ECLS-K:2011 dataset, I restricted participants to those with at least two consecutive measures that occurred August/September or April/May. Mixed-effect regression analyses estimated differences in monthly change in BMI, zBMI, and %BMIp95 between the summer and school year while accounting for the ECLS-K complex sampling design. Models also examined differences in the magnitude of BMI, zBMI, and %BMIp95 change between the summer and school year by school location. Post-hoc Benjamini-Hochberg (BH) procedure set at 10% false discovery was incorporated to account for multiple comparisons. RESULTS: A total of 1549 children (48% female, 42% White) had at least two consecutive measures that occurred in August/September or April/May. Among all locale classifications (i.e., urban, suburban, and exurban), children from high-income households comprised the largest proportions for each group (31%, 39%, and 37%), respectively. Among urban and suburban locations, Hispanic children comprised the largest proportions for both groups (43% and 44%), respectively. Among exurban locale classifications, White children comprised the largest proportion of children (60%). Children from suburban and exurban schools experienced significantly less accelerations in monthly zBMI gain when compared to their urban counterparts -0.038 (95CI = -0.071, -0.004) and -0.045 (95CI = -0.083, -0.007), respectively. Children from exurban schools experienced significantly less acceleration in monthly %BMIp95 during the summer months when compared to the school year -0.004 (95CI = -0.007, 0.000). CONCLUSIONS: This is one of the first studies to examine summer weight gain by school location. Summer appears to impact children more negatively from urban schools when compared to their suburban and exurban counterparts.

COVID-19 Leads to Accelerated Increases in Children's BMI z-Score Gain: An Interrupted Time-Series Study.

INTRODUCTION: The COVID-19 pandemic may have negatively impacted children's weight status owing to the closure of schools, increased food insecurity and reliance on ultraprocessed foods, and reduced opportunities for outdoor activity. METHODS: In this interrupted time-series study, height and weight were collected from children (n=1,770 children, mean age=8.7 years, 55.3% male, 64.6% Black) and were transformed into BMI z-score in each August/September from 2017 to 2020. Mixed-effects linear regression estimated yearly BMI z-score change before the COVID-19 pandemic year (i.e., 2017-2019) and during the COVID-19 pandemic year (i.e., 2019-2020). Subgroup analyses by sex, race (i.e., Black, White, other race), weight status (overweight or obese and normal weight), and grade (i.e., lower=kindergarten-2nd grade and upper=3rd-6th grade) were conducted. RESULTS: Before the COVID-19 pandemic, children's yearly BMI z-score change was +0.03 (95% CI= -0.10, 0.15). Change during the COVID-19 pandemic was +0.34 (95% CI=0.21, 0.47), an acceleration in BMI z-score change of +0.31 (95% CI=0.19, 0.44). For girls and boys, BMI z-score change accelerated by +0.33 (95% CI=0.16, 0.50) and +0.29 (95% CI=0.12, 0.46), respectively, during the pandemic year. Acceleration in BMI z-score change during the pandemic year was observed for children who were Black (+0.41, 95% CI=0.21, 0.61) and White (+0.22, 95% CI=0.06, 0.39). For children classified as normal weight, BMI z-score change accelerated by +0.58 (95% CI=0.40, 0.76). Yearly BMI z-score change accelerated for lower elementary/primary (+0.23, 95% CI=0.08, 0.37) and upper elementary/primary (+0.42, 95% CI=0.42, 0.63) children. CONCLUSIONS: If similar BMI z-score accelerations occurred for children across the world, public health interventions to address this rapid unhealthy BMI gain will be urgently needed.

Children's moderate-to-vigorous physical activity on weekdays versus weekend days: a multi-country analysis.

PURPOSE: The Structured Days Hypothesis (SDH) posits that children's behaviors associated with obesity - such as physical activity - are more favorable on days that contain more 'structure' (i.e., a pre-planned, segmented, and adult-supervised environment) such as school weekdays, compared to days with less structure, such as weekend days. The purpose of this study was to compare children's moderate-to-vigorous physical activity (MVPA) levels on weekdays versus weekend days using a large, multi-country, accelerometer-measured physical activity dataset. METHODS: Data were received from the International Children's Accelerometer Database (ICAD) July 2019. The ICAD inclusion criteria for a valid day of wear, only non-intervention data (e.g., baseline intervention data), children with at least 1 weekday and 1 weekend day, and ICAD studies with data collected exclusively during school months, were included for analyses. Mixed effects models accounting for the nested nature of the data (i.e., days within children) assessed MVPA minutes per day (min/day MVPA) differences between weekdays and weekend days by region/country, adjusted for age, sex, and total wear time. Separate meta-analytical models explored differences by age and country/region for sex and child weight-status. RESULTS/FINDINGS: Valid data from 15 studies representing 5794 children (61% female, 10.7 ± 2.1 yrs., 24% with overweight/obesity) and 35,263 days of valid accelerometer data from 5 distinct countries/regions were used. Boys and girls accumulated 12.6 min/day (95% CI: 9.0, 16.2) and 9.4 min/day (95% CI: 7.2, 11.6) more MVPA on weekdays versus weekend days, respectively. Children from mainland Europe had the largest differences (17.1 min/day more MVPA on weekdays versus weekend days, 95% CI: 15.3, 19.0) compared to the other countries/regions. Children who were classified as overweight/obese or normal weight/underweight accumulated 9.5 min/day (95% CI: 6.9, 12.2) and 10.9 min/day (95% CI: 8.3, 13.5) of additional MVPA on weekdays versus weekend days, respectively. CONCLUSIONS: Children from multiple countries/regions accumulated significantly more MVPA on weekdays versus weekend days during school months. This finding aligns with the SDH and warrants future intervention studies to prioritize less-structured days, such as weekend days, and to consider providing opportunities for all children to access additional opportunities to be active.

Comparison of multichannel and single-channel wrist-based devices with polysomnography to measure sleep in children and adolescents.

STUDY OBJECTIVES: To compare sleep parameters produced by the Fitbit Charge 3 (Fitbit) and Actigraph GT9X accelerometer (Actigraph) to polysomnography in children and adolescents. METHODS: Participants (n = 56, ages 9.2 ± 3.3 years) wore a Fitbit and an Actigraph on their nondominant wrist concurrently with polysomnography during an overnight observation at a children's sleep laboratory. Total sleep time, sleep efficiency, wake after sleep onset, sleep onset, and sleep offset were extracted from the Fitabase and Actilife software packages, respectively, with the Sadeh algorithm. Bland-Altman plots were used to assess the agreement between wearable devices and polysomnography. RESULTS: Seventy-nine percent of participants were diagnosed with OSA. Compared with polysomnography, the Fitbit and the Actigraph underestimated total sleep time by 6.1 minutes (absolute mean bias [AMB] = 27.7 minutes) and 31.5 minutes (AMB = 38.2 minutes), respectively. The Fitbit overestimated sleep efficiency by 3.0% (AMB = 6.3%), and the Actigraph underestimated sleep efficiency by 12.9% (AMB = 13.2%). The Fitbit overestimated wake after sleep onset by 18.8 minutes (AMB = 23.9 minutes), and the Actigraph overestimated wake after sleep onset by 56.1 minutes (AMB = 54.7 minutes). In addition, the Fitbit and the Actigraph underestimated sleep onset by 1.2 minutes (AMB = 13.9 minutes) and 10.2 minutes (AMB = 18.1 minutes), respectively. Finally, the Fitbit and the Actigraph overestimated sleep offset by 6.0 minutes (AMB = 12.0 minutes) and 10.5 minutes (AMB = 12.6 minutes). Linear regression indicated significant trends, with the Fitbit underestimating wake after sleep onset and sleep efficiency at higher values. CONCLUSIONS: The Fitbit provided comparable and in some instances better sleep estimates with polysomnography compared to the Actigraph. Findings support the use of multichannel devices to measure sleep in children and adolescents. Additional studies are needed in healthy children over several nights and in free-living settings.

Comparing measures of free-living sleep in school-aged children.

OBJECTIVE/BACKGROUND: Recent technological advances and emerging commercially-available consumer-friendly sleep assessment products affords researchers with a host of tools to consider for capturing free-living sleep in children. The purpose of this study was to compare free-living sleep characteristics (duration and bed/wake times) across different measures in children. METHODS: Elementary school-aged children (N = 30, mean age 7.2 years, 63% boys, 87% non-Hispanic white) wore an ActiGraph GT9X Link© and Fitbit Charge HR© on the non-dominant wrist, with a Beddit 3 Sleep Monitor© affixed to their mattress for two consecutive weekend nights of free-living sleep. Parents completed a sleep log of bed and wake times. Absolute differences in bed and wake times were examined and Bland Altman plots assessed the level of agreement across sleep measures. RESULTS: Across the four sleep measures, total sleep time (TST) ranged from 458 min/night (ActiGraph GT9X Link©) to 613 min/night (Parent report). Mean bed and wake times ranged from 8:46PM to 9:03PM, and 6:52AM to 7:16AM, respectively. Pearson correlation coefficients were moderate between all four sleep measures (range r = 0.30-0.71). Bland-Altman plots indicated the highest level of agreement for TST was between Beddit 3 Sleep Monitor© and Fitbit Charge HR© (mean difference -11.7, limits of agreement: 119.0, -142.4 min). CONCLUSIONS: The findings from this study show a high level of agreement of when a child goes to sleep and wakes up across a variety of sleep measures; however, more work is needed to classify TST once the sleep period has commenced.