A comparison of perceived barriers to optimal child sleep among families with low and high income.

OBJECTIVE: Families with low-income experience suboptimal sleep compared to families with higher-income. Unique drivers likely contribute to these disparities, along with factors that universally impede sleep patterns, despite income level. To inform intervention tailoring, this mixed-methods study gathered parent's perceptions about child sleep challenges to identify similarities/differences in families with lower-income and higher-income. METHODS: Parents who experienced difficulties with their child (ages 2-4years) sleep were categorized as lower income (n = 15; $30,000 ± 17,845/year) or higher income (n = 15; $142,400 ± 61,373/year). Parents completed a survey and semistructured interview to explore barriers and facilitators for child sleep. Two coders independently evaluated transcripts for lower-income and higher-income groups using inductive analyses. Constant-comparison methods generated themes and characterized similarities/differences by income group. RESULTS: Groups were similar in themes related to diverse bedtime routines, nighttime struggles with child sleep, parent strategies to reduce night wakings, parent effort to provide a sleep-promoting environment, and presence of electronic rules. Groups differed in themes related to factors influencing routine setting (eg, lower income: external factors influencing routines; higher income: personal attributes for structure), parent appraisal of child sleep (eg, higher income: ambivalence; lower income: mostly negative appraisal), nap timing and duration (eg, lower income: longer naps), and strategy utilization and pursuit of resources (eg, higher income: more parents tried various strategies and accessed online/print resources). CONCLUSIONS: Parents experienced many similar barriers to child sleep, with a few distinct differences by income group. These findings can inform future intervention components for all families, as well as customized components to address the unique needs of families across income levels.

Understanding Accelerated Summer Body Mass Index Gain by Tracking Changes in Children's Height, Weight, and Body Mass Index Throughout the Year.

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

What does it mean to use the mean? The impact of different data handling strategies on the proportion of children classified as meeting 24-hr movement guidelines and associations with overweight and obesity.

Background: Despite the widespread endorsement of 24-hour movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-hour movement guidelines (24hrG) and associations with overweight and obesity. Methods: A subset of 524 children (ages 5-12yrs) with complete 24-hour behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using 1) average of behaviors across all days (AVG-24hr), 2) classifying each day and evaluating the percentage meeting 24hrG from 10-100% of their measured days (DAYS-24hr), and 3) the average of a random sample of 4 days across 10 iterations (RAND-24hr). A second subset of children (N=475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression. Results: Classification for AVG-24hr resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24hr resulted in 63.5% meeting 24hrG on 10% of measured days with <1% meeting 24hrG on 100% of days. Classification for RAND-24hr resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24hr as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR=0.38, p<0.05). The RAND-24hr strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR=0.04, p<0.05). Conclusions: Varying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.

Integrating Equity Into Bicycle Infrastructure, Planning, and Programming: A Mixed Methods Exploration of Implementation Among Participants in the Bicycle Friendly Community Program.

INTRODUCTION: Integrating equity considerations into bicycle infrastructure, planning, and programming is essential to increase bicycling and reduce physical inactivity-related health disparities. However, little is known about communities' experiences with activities that promote equity considerations in bicycle infrastructure, planning, and programming or about barriers and facilitators to such considerations. The objective of this project was to gain in-depth understanding of the experiences, barriers, and facilitators that communities encounter with integrating equity considerations into bicycle infrastructure, planning, and programming. METHODS: We administered a web-based survey in 2022 to assess communities' experiences with 31 equity-focused activities in 3 areas: 1) community engagement, education, events, and programming (community engagement); 2) data collection, evaluation, and goal setting (data); and 3) infrastructure, facilities, and physical amenities (infrastructure). Respondents were people who represented communities in the US that participated in the League of American Bicyclists' Bicycle Friendly Community (BFC) Program. We then conducted 6 focus groups with a subset of survey respondents to explore barriers and facilitators to implementing equity-focused activities. RESULTS: Survey respondents (N = 194) had experience with a mean (SD) of 5.9 (5.7) equity-focused activities. Focus group participants (N = 30) identified themes related to community engagement (outreach to and engagement of underrepresented communities, cultural perceptions of bicycling, and funding and support for community rides and programs); data (locally relevant data); and infrastructure (political will, community design, and infrastructure). They described barriers and facilitators for each. CONCLUSION: Communities are challenged with integrating equity into bicycle infrastructure, planning, and programming. Multicomponent strategies with support from entities such as the BFC program will be required to make progress.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions.

GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Parental decision-making on summer program enrollment: A mixed methods Covid-19 impact study.

BACKGROUND: The closure of childcare organizations (e.g. schools, childcare centers, afterschool programs, summer camps) during the Covid-19 pandemic impacted the health and wellbeing of families. Despite their reopening, parents may be reluctant to enroll their children in summer programming. Knowledge of the beliefs that underlie parental concerns will inform best practices for organizations that serve children. METHODS: Parents (n = 17) participated in qualitative interviews (October 2020) to discuss Covid-19 risk perceptions and summer program enrollment intentions. Based on interview responses to perceived Covid-19 risk, two groups emerged for analysis- "Elevated Risk (ER)" and "Conditional Risk (CR)". Themes were identified utilizing independent coding and constant-comparison analysis. Follow-up interviews (n = 12) in the Spring of 2021 evaluated the impact of vaccine availability on parent risk perceptions. Additionally, parents (n = 17) completed the Covid-19 Impact survey to assess perceived exposure (Range: 0-25) and household impact (Range: 2-60) of the pandemic. Scores were summed and averaged for the sample and by risk classification group. RESULTS: Parents overwhelmingly supported the operation of summer programming during the pandemic due to perceived child benefits. Parent willingness to enroll their children in summer programming evolved with time and was contingent upon the successful implementation of safety precautions (e.g. outdoor activities, increased handwashing/sanitizing of surfaces). Interestingly, parents indicated low exposure (ER: Avg. 6.3 ± 3.1 Range [2-12], CR: Avg. 7.5 ± 3.6 Range [1-14]) and moderate family impact (ER: Avg. 27.1 ± 6.9 Range [20-36], CR: Avg. 33.7 ± 11.4 Range [9-48]) on the impact survey. CONCLUSION: Childcare organizations should mandate and evaluate the implementation of desired Covid-19 safety precautions for their patrons.

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.

Measuring Microtemporal Processes Underlying Preschoolers' Screen Use and Behavioral Health: Protocol for the Tots and Tech Study.

BACKGROUND: Excessive screen time is associated with poor health and behavioral outcomes in children. However, research on screen time use has been hindered by methodological limitations, including retrospective reports of usual screen time and lack of momentary etiologic processes occurring within each day. OBJECTIVE: This study is designed to assess the feasibility and utility of a comprehensive multibehavior protocol to measure the digital media use and screen time context among a racially and economically diverse sample of preschoolers and their families. This paper describes the recruitment, data collection, and analytical protocols for the Tots and Tech study. METHODS: The Tots and Tech study is a longitudinal, observational study of 100 dyads: caregivers and their preschool-age children (aged 3-5 years). Both caregivers and children will wear an Axivity AX3 accelerometer (Axivity Ltd) for 30 days to assess their physical activity, sedentary behavior, and sleep. Caregivers will complete ecological momentary assessments (EMAs) for 1 week to measure child behavioral problems, caregiver stress, and child screen time. RESULTS: The Tots and Tech study was funded in March 2020. This study maintains rolling recruitment, with each dyad on their own assessment schedule, depending on the time of enrollment. Enrollment was scheduled to take place between September 2020 and May 2022. We aim to enroll 100 caregiver-child dyads. The Tots and Tech outcome paper is expected to be published in 2022. CONCLUSIONS: The Tots and Tech study attempts to overcome previous methodological limitations by using objective measures of screen time, physical activity, sedentary behavior, and sleep behaviors with contextual factors measured by EMA. The results will be used to evaluate the feasibility and utility of a comprehensive multibehavior protocol using objective measures of mobile screen time and accelerometry in conjunction with EMA among caregiver-child dyads. Future observational and intervention studies will be able to use this study protocol to better measure screen time and its context. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36240.

Feasibility of Measuring Screen Time, Activity, and Context Among Families With Preschoolers: Intensive Longitudinal Pilot Study.

BACKGROUND: Digital media has made screen time more available across multiple contexts, but our understanding of the ways children and families use digital media has lagged behind the rapid adoption of this technology. OBJECTIVE: This study evaluated the feasibility of an intensive longitudinal data collection protocol to objectively measure digital media use, physical activity, sleep, sedentary behavior, and socioemotional context among caregiver-child dyads. This paper also describes preliminary convergent validity of ecological momentary assessment (EMA) measures and preliminary agreement between caregiver self-reported phone use and phone use collected from passive mobile sensing. METHODS: Caregivers and their preschool-aged child (3-5 years) were recruited to complete a 30-day assessment protocol. Within 30-days, caregivers completed 7 days of EMA to measure child behavior problems and caregiver stress. Caregivers and children wore an Axivity AX3 (Newcastle Upon Tyne) accelerometer to assess physical activity, sedentary behavior, and sleep. Phone use was assessed via passive mobile sensing; we used Chronicle for Android users and screenshots of iOS screen time metrics for iOS users. Participants were invited to complete a second 14-day protocol approximately 3-12 months after their first assessment. We used Pearson correlations to examine preliminary convergent validity between validated questionnaire measures of caregiver psychological functioning, child behavior, and EMA items. Root mean square errors were computed to examine the preliminary agreement between caregiver self-reported phone use and objective phone use. RESULTS: Of 110 consenting participants, 105 completed all protocols (105/110, 95.5% retention rate). Compliance was defined a priori as completing ≥70%-75% of each protocol task. There were high compliance rates for passive mobile sensing for both Android (38/40, 95%) and iOS (64/65, 98%). EMA compliance was high (105/105, 100%), but fewer caregivers and children were compliant with accelerometry (62/99, 63% and 40/100, 40%, respectively). Average daily phone use was 383.4 (SD 157.0) minutes for Android users and 354.7 (SD 137.6) minutes for iOS users. There was poor agreement between objective and caregiver self-reported phone use; root mean square errors were 157.1 and 81.4 for Android and iOS users, respectively. Among families who completed the first assessment, 91 re-enrolled to complete the protocol a second time, approximately 7 months later (91/105, 86.7% retention rate). CONCLUSIONS: It is feasible to collect intensive longitudinal data on objective digital media use simultaneously with accelerometry and EMA from an economically and racially diverse sample of families with preschool-aged children. The high compliance and retention of the study sample are encouraging signs that these methods of intensive longitudinal data collection can be completed in a longitudinal cohort study. The lack of agreement between self-reported and objectively measured mobile phone use highlights the need for additional research using objective methods to measure digital media use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-36240.

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.

Patterns Of Food Security And Dietary Intake During The First Half Of The Child Tax Credit Expansion.

Temporary expansion of the Child Tax Credit (CTC) during the COVID-19 pandemic provided additional monthly income for US families, with no restrictions on use, from July through December 2021. This study examined food security and children's dietary intake after three months of expanded CTC payments. Parents completed online surveys before and after three months of CTC payments. Among parents participating in the expansion, food and beverage purchases were the most common use of expanded CTC funds (45.9 percent), particularly in households with very low food security (63.0 percent). From before to midway through the CTC expansion, very low food security decreased from 12.7 percent to 5.6 percent, and simultaneously, food security increased from 57.4 percent to 66.4 percent. The CTC expansion was also associated with decreases in children's consumption of added sugar, sugar-sweetened beverages, and sweetened fruit beverages. No changes were observed in children's intake of other dietary components. Our findings suggest that the expanded CTC payments may have helped lessen food insecurity and supported reductions in children's intake of added sugar in participating households.

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.

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.

Impact of a year-round school calendar on children's BMI and fitness: Final outcomes from a natural experiment.

BACKGROUND: Structure may mitigate children's accelerated summer BMI gain and cardiorespiratory-fitness (CRF) loss. OBJECTIVES: Examine BMI and CRF change during school and summer for year-round and traditional calendar school children. METHODS: Three schools (N = 2279, 1 year-round) participated in this natural experiment. Children's BMI z-score (zBMI) and CRF (PACER laps) were measured from 2017 to 2019 each May/August. Mixed effects regression estimated monthly zBMI and CRF change during school/summer. Secondary analyses examined differences by weight status and race. Spline regression models estimated zBMI and CRF growth from kindergarten-sixth grade. RESULTS: Compared to traditional school, children attending a year-round school gained more zBMI (difference = 0.015; 95CI = 0.002, 0.028) during school, and less zBMI (difference = -0.029; 95CI = -0.041, -0.018), and more CRF (difference = 0.834; 95CI = 0.575, 1.093) monthly during summer. Differences by weight status and race were observed during summer and school. Growth models demonstrated that the magnitude of overall zBMI and CRF change from kindergarten-sixth grade was similar for year-round or traditional school children. CONCLUSIONS: Contrary to traditional school children zBMI increased during the traditional 9-month school calendar and zBMI decreased during the traditional summer vacation for year-round school children. Structured summer programming may mitigate accelerated summer BMI gain and CRF loss especially for overweight or obese, and/or Black children.

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.

Elementary Classroom Teachers' Self-Reported Use of Movement Integration Products and Perceived Facilitators and Barriers Related to Product Use.

Movement integration (MI) products are designed to provide children with physical activity during general education classroom time. The purpose of this study was to examine elementary classroom teachers' self-reported use of MI products and subsequent perceptions of the facilitators of and barriers to MI product use. This study utilized a mixed-methods design. Elementary classroom teachers (n = 40) at four schools each tested four of six common MI products in their classroom for one week. Teachers completed a daily diary, documenting duration and frequency of product use. Following each product test, focus groups were conducted with teachers to assess facilitators and barriers. MI product use lasted for 11.2 (Standard Deviation (SD) = 7.5) min/occasion and MI products were used 4.1 (SD = 3.5) times/week on average. Activity Bursts in the Classroom for Fitness, GoNoodle, and Physical Activity Across the Curriculum were most frequently used. Facilitators of and barriers to MI product use were identified within three central areas-logistics, alignment with teaching goals, and student needs and interests. Teachers were receptive to MI products and used them frequently throughout the week. When considering the adoption of MI products, teachers, administrators, and policy makers should consider products that are readily usable, align with teaching goals, and are consistent with student needs and interests.