Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

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.

Prioritize "Food is Medicine" initiatives in the 2024 Farm Bill for human and planetary health.

The Society of Behavioral Medicine (SBM) supports increased funding for policies in the 2024 Farm Bill that align with a Food is Medicine (FIM) framework and address multiple dimensions of human and planetary health.

The Society of Behavioral Medicine (SBM) supports funding for policies in 2024 Farm Bill that align with a Food is Medicine (FIM) framework and address multiple dimensions of human and planetary health. Recommendations include increasing funding for produce prescription programs, establishing systems to align federal- and state-funded initiatives, and the allocation of funding for financial incentives when sustainable agricultural practices are utilized in government-funded local and regional farm-to-institution programs.

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.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Are parent-reported sleep logs essential? A comparison of three approaches to guide open source accelerometry-based nocturnal sleep processing in children.

We examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5-12 years) wore a wrist-based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent-reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z-Angle (HDCZA) algorithm (no log), and an 8 p.m.-8 a.m. window (generic log) using the R-package 'GGIR' (version 2.6-4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland-Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4-10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.

Use of guidelines, checklists, frameworks, and recommendations in behavioral intervention preliminary studies and associations with reporting comprehensiveness: a scoping bibliometric review.

BACKGROUND: Guidelines, checklists, frameworks, and recommendations (GCFRs) related to preliminary studies serve as essential resources to assist behavioral intervention researchers in reporting findings from preliminary studies, but their impact on preliminary study reporting comprehensiveness is unknown. The purpose of this study was to conduct a scoping bibliometric review of recently published preliminary behavioral-focused intervention studies to (1) examine the prevalence of GCFR usage and (2) determine the associations between GCFR usage and reporting feasibility-related characteristics. METHODS: A systematic search was conducted for preliminary studies of behavioral-focused interventions published between 2018 and 2020. Studies were limited to the top 25 journals publishing behavioral-focused interventions, text mined to identify usage of GCFRs, and categorized as either not citing GCFRs or citing ≥ 2 GCFRs (Citers). A random sample of non-Citers was text mined to identify studies which cited other preliminary studies that cited GCFRs (Indirect Citers) and those that did not (Never Citers). The presence/absence of feasibility-related characteristics was compared between Citers, Indirect Citers, and Never Citers via univariate logistic regression. RESULTS: Studies (n = 4143) were identified, and 1316 were text mined to identify GCFR usage (n = 167 Citers). A random sample of 200 studies not citing a GCFR were selected and categorized into Indirect Citers (n = 71) and Never Citers (n = 129). Compared to Never Citers, Citers had higher odds of reporting retention, acceptability, adverse events, compliance, cost, data collection feasibility, and treatment fidelity (ORrange = 2.62-14.15, p < 0.005). Citers also had higher odds of mentioning feasibility in purpose statements, providing progression criteria, framing feasibility as the primary outcome, and mentioning feasibility in conclusions (ORrange = 6.31-17.04, p < 0.005) and lower odds of mentioning efficacy in purpose statements, testing for efficacy, mentioning efficacy in conclusions, and suggesting future testing (ORrange = 0.13-0.54, p < 0.05). Indirect Citers had higher odds of reporting acceptability and treatment fidelity (ORrange = 2.12-2.39, p < 0.05) but lower odds of testing for efficacy (OR = 0.36, p < 0.05) compared to Never Citers. CONCLUSION: The citation of GCFRs is associated with greater reporting of feasibility-related characteristics in preliminary studies of behavioral-focused interventions. Researchers are encouraged to use and cite literature that provides guidance on design, implementation, analysis, and reporting to improve the comprehensiveness of reporting for preliminary studies.

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.

Free Healthy School Meals for All as a Means to Advance Child Health Equity.

This Viewpoint discusses how providing free, healthy meals to all schoolchildren can advance health equity.

Barriers to Optimal Child Sleep among Families with Low Income: A Mixed-Methods Study to Inform Intervention Development.

This study gathered formative data on barriers to optimal child sleep to inform the development of a sleep intervention for parents of preschool-aged children in low-income households. Parents (n = 15, age: 34 ± 8 years, household income: $30,000 ± 17,845/year) reporting difficulties with their child's sleep participated in this study. Mixed methods included an online survey and semi-structured phone interview. Items assessed barriers/facilitators to optimal child sleep and intervention preferences. Interview transcripts were coded using inductive analyses and constant-comparison methods to generate themes. Derived themes were then mapped onto the Theoretical Domains Framework to contextualize barriers and inform future intervention strategies. Themes that emerged included: stimulating bedtime activities, child behavior challenges, variability in children's structure, parent work responsibilities, sleep-hindering environment, and parent's emotional capacity. Parent's intervention preferences included virtual delivery (preferred by 60% of parents) to reduce barriers and provide flexibility. Mixed preferences were observed for the group (47%) vs. individual (53%) intervention sessions. Parents felt motivated to try new intervention strategies given current frustrations, the potential for tangible results, and knowing others were in a similar situation. Future work will map perceived barriers to behavior change strategies using the Behavior Change Wheel framework to develop a parenting sleep intervention.

Food Insecurity, Federal Nutrition Support, and Parent Feeding Practices During COVID-19: A 1-Year Follow-up Study.

OBJECTIVES: COVID-19 caused stark increases in food insecurity. To maintain food provision, policy changes to the National School Lunch Program (NSLP) and Supplemental Nutrition Assistance Program (SNAP) were instated. This longitudinal study examined (1) food security patterns across the timeline of COVID-19; (2) the relationship among food security patterns, NSLP/SNAP use, and parent feeding practices; and (3) parent perceptions of NSLP/SNAP policy changes. METHODS: A total of 333 US parents completed online surveys during the COVID-19 pandemic: May 2020 (T1), September 2020 (T2), and May 2021 (T3). Food security and parent feeding practices were reported at each time point; pre-COVID-19 behaviors were retrospectively reported at T1. Use and perceptions of NSLP/SNAP policy changes were reported at T3. We examined associations between food security and parent feeding practices using repeated-measures mixed models. RESULTS: The percentage of parents with very low food security increased from pre-COVID-19 (9.6%) to T1 (29.1%) and remained elevated at T3 (16.8%). One-third (31.2%) of families fluctuated between food security and food insecurity, with 27.0% remaining food insecure at T3. Thirty percent of consistently food-insecure families reported not receiving school-provided meals, and 45% did not receive SNAP benefits. Most parents reported that pickup school meal sites (71.4%), Pandemic Electronic Benefit Transfer cards (51.4%), and increased SNAP benefits (79.6%) were beneficial. Initial changes in parent feeding practices reported at T1 returned to pre-COVID-19 levels by T3, yet concern for child overweight remained significantly elevated. CONCLUSION: Continued policy efforts to support food-insecure families via expanded food access in NSLP/SNAP are critical.

Estimating Physical Activity and Sleep using the Combination of Movement and Heart Rate: A Systematic Review and Meta-Analysis.

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.

Impact of Virtual vs. In-Person School on Children Meeting the 24-h Movement Guidelines during the COVID-19 Pandemic.

The pandemic mitigation strategy of closing schools, while necessary, may have unintentionally impacted children's moderate-to-vigorous physical activity (MVPA), sleep, and time spent watching screens. In some locations, schools used hybrid attendance models, with some days during the week requiring in-person and others virtual attendance. This scenario offers an opportunity to evaluate the impact of attending in-person school on meeting the 24-h movement guidelines. Children (N = 690, 50% girls, K-5th) wore wrist-placed accelerometers for 14 days during October/November 2020. Parents completed daily reports on child time spent on screens and time spent on screens for school. The schools' schedule was learning for 2 days/week in-person and 3 days/week virtually. Using only weekdays (M-F), the 24-h movement behaviors were classified, and the probability of meeting all three was compared between in-person vs. virtual learning and across grades. Data for 4956 weekdays (avg. 7 d/child) were collected. In-person school was associated with a greater proportion (OR = 1.70, 95% CI: 1.33-2.18) of days that children were meeting the 24-h movement guidelines compared to virtual school across all grades. Students were more likely to meet the screen time (OR = 9.14, 95% CI: 7.05-11.83) and MVPA (OR = 1.50, 95% CI: 1.25-1.80) guidelines and less likely to meet the sleep (OR = 0.73, 95% CI: 0.62-0.86) guidelines on the in-person compared to the virtual school days. Structured environments, such as school, have a protective effect on children's movement behaviors, especially physical activity and screen time.

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.

Seasonal Shifts in Children's Sedentary Behaviors, Physical Activity, and Sleep: A Systematic Review and meta-Analysis.

Children's movement behaviors (ie, sedentary behaviors, physical activity, and sleep) are related to obesity risk and may vary throughout the year. The purpose of this systematic review is to summarize existing literature on the seasonal variation in physical activity and sleep in children. This study found that children's behaviors fluctuate seasonally and thus, interventions must target behaviors during the times when children's behaviors are the least healthy, specifically during the summer (when children are not in school) and winter. Finally, the paucity of data on seasonal variation in sleep indicates a need for further research in this area.

Sleep onset, duration, or regularity: which matters most for child adiposity outcomes?

BACKGROUND/OBJECTIVES: Sleep measures, such as duration and onset timing, are associated with adiposity outcomes among children. Recent research among adults has considered variability in sleep and wake onset times, with the Sleep Regularity Index (SRI) as a comprehensive metric to measure shifts in sleep and wake onset times between days. However, little research has examined regularity and adiposity outcomes among children. This study examined the associations of three sleep measures (i.e., sleep duration, sleep onset time, and SRI) with three measures of adiposity (i.e., body mass index [BMI], waist circumference, and waist-to-height ratio [WHtR]) in a pediatric sample. SUBJECTS/METHODS: Children (ages 4-13 years) who were part of the U.S. Newborn Epigenetic STudy (NEST) participated. Children (N = 144) wore an ActiGraph for 1 week. Sleep measures were estimated from actigraphy data. Weight, height, and waist circumference were measured by trained researchers. BMI and WHtR was calculated with the objectively measured waist and height values. Multiple linear regression models examined associations between child sleep and adiposity outcomes, controlling for race/ethnicity, child sex, age, mothers' BMI and sleep duration. RESULTS: When considering sleep onset timing and duration, along with demographic covariates, sleep onset timing was not significantly associated with any of the three adiposity measures, but a longer duration was significantly associated with a lower BMI Z-score (ß = -0.29, p < 0.001), waist circumference (ß = -0.31, p < 0.001), and WHtR (ß = -0.38, p < 0.001). When considering SRI and duration, duration remained significantly associated with the adiposity measures. The SRI and adiposity associations were in the expected direction, but were non-significant, except the SRI and WHtR association (ß = -0.16, p = 0.077) was marginally non-significant. CONCLUSIONS: Sleep duration was consistently associated with adiposity measures in children 4-13 years of age. Pediatric sleep interventions should focus first on elongating nighttime sleep duration, and examine if this improves child adiposity outcomes.

Sleep parenting practices are associated with infant self-soothing behaviors when measured using actigraphy.

OBJECTIVE/BACKGROUND: Night wakings are common during infancy, with variability in infant self-soothing or requiring parent involvement to fall back asleep. Reasons for variable soothing behaviors are unclear and may be influenced by early-life sleep parenting practices. The study applied a novel method using sleep actigraphy in mother-father-infant triads to quantify infant-only wake bouts (i.e., "self-soothing") and examined associations between sleep parenting practices from 6 to 24 weeks of age. PATIENTS/METHODS: Mother-father-infant triads (N = 20) wore sleep monitors for 1 week at infant ages 6, 15, and 24 weeks. Data were time-matched within families to quantify infant-only wake bouts (when infants woke in the night and parents remained asleep) to be indicative of infant self-soothing. Mothers reported on sleep parenting practices (e.g., feeding to sleep, putting infants to bed while awake) using the Brief Infant Sleep Questionnaire at each age. Mixed models tested associations between parenting practices and infant-only wake bouts. RESULTS: Most mothers (69.5%) reported typically feeding their infants to sleep at bedtime; 27.1% reported typically putting infants to bed while still awake. Parenting practices associated with a greater proportion and frequency of infant-only wake bouts included not typically feeding infants to sleep during night wakings, putting infants to bed while still awake (at 24 weeks of age), and using a greater proportion of low-, rather than high-, stimulus soothing strategies during night wakings. CONCLUSION: These data suggest parenting practices, such as putting infants to bed when drowsy but still awake, are important strategies for promoting infant self-soothing during night wakings.

Home Food Environment Changes and Dietary Intake during an Adolescent Behavioral Weight Loss Intervention Differ by Food Security Status.

Behavioral weight loss (BWL) for pediatric obesity includes guidance on improving the home food environment and dietary quality; yet food insecurity presents barriers to making these changes. This study examined if home food environment, dietary quality, energy intake, and body weight changes during adolescent obesity treatment differed by food security status, and if changes in the home food environment were associated with changes in dietary quality and energy intake by food security status. Adolescents (n = 82; 13.7 ± 1.2 years) with obesity participated in a 4-month BWL treatment. Food insecurity, home food environment (Home Food Inventory [HFI]), dietary quality (Healthy Eating Index [HEI]), energy intake, and body mass index (BMI) were assessed at baseline and post-treatment. A reduced obesogenic home food environment and improved dietary quality were observed for food secure (ps < 0.01), but not insecure households (ps > 0.05) (mean difference, HFI: −6.6 ± 6.4 vs. −2.4 ± 7.4; HEI: 5.1 ± 14.4 vs. 2.7 ± 17.7). Energy intake and BMI decreased for adolescents in food secure and insecure households (ps < 0.03) (mean difference; energy intake: −287 ± 417 vs. −309 ± 434 kcal/day; BMI: −1.0 ± 1.4 vs. −0.7 ± 1.4). BWL yielded similar reductions in energy intake and body weight yet did not offer the same benefits for improved dietary quality and the home food environment for adolescents with food insecurity.

Nutrient Intake During School Lunch in Title I Elementary Schools With Universal Free Meals.

BACKGROUND: The National School Lunch Program (NSLP) provides >30 million meals to children daily; however, the specific nutrient composition of NSLP-selected and consumed meals for students from lower income and racial/ethnic minoritized backgrounds is unknown. AIMS: To quantify the nutrients in school lunch selection and consumption among students participating in the NSLP and compare these values to nutrient recommendations. METHOD: Students (1st-5th graders; 98.6% from racial/ethnic minoritized backgrounds; 92.5% NSLP participation) from six Title I elementary schools serving universal free meals participated. Digital images of students' lunch meal selection and consumption were obtained (n = 1,102 image pairs). Plate waste analyses quantified portions consumed. Nutrient composition of students' lunch selection and consumption were calculated and compared with the 2010 Healthy Hunger-Free Kids Act and 2009 Institute of Medicine recommendations. RESULTS: Most lunches selected (59%-97%) met recommendations for all nutrients except for total calories (23%), vitamin C (46%), and dietary fiber (48%). Based on lunch consumption, most students' lunches met recommendations for sodium (98%), protein (55%), calories from fat (82%), and saturated fat (89%); however, few met recommendations for total calories (5%), calcium (8%), iron (11%), vitamin A (18%), vitamin C (16%), and fiber (7%). DISCUSSION: Meals selected met most nutrient recommendations for the majority of children; yet overall consumption patterns reflect suboptimal nutrient intake. CONCLUSION: Meals served under the NSLP policy mandates align with recommended nutrient patterns, highlighting the importance of maintaining these standards. Strategies to optimize children's intake of nutrient-rich portions of these meals are needed to optimize policy impact.