The Day-Level Association Between Child Care Attendance and 24-Hour Movement Behaviors in Preschool-Aged Children

BACKGROUND

Twenty-four hour movement behaviors (ie, physical activity [PA], screen time [ST], and sleep) are associated with children's health outcomes. Identifying day-level contextual factors, such as child care, that positively influence children's movement behaviors may help identify potential intervention targets, like improving access to child care programs. This study aimed to examine the between- and within-person effects of child care on preschoolers' 24-hour movement behaviors.

METHODS

Children (N = 74, 4.7 [0.9] y, 48.9% girls, 63.3% White) wore an Axivity AX3 accelerometer on their nondominant wrist 24 hours per day for 14 days to measure PA and sleep. Parents completed surveys each night about their child's ST and child care attendance that day. Linear mixed effects models predicted day-level 24-hour movement behaviors from hours spent in child care.

RESULTS

Children spent an average of 5.0 (2.9) hours per day in child care. For every additional hour of child care above their average, children had 0.3 hours (95% CI, -0.3 to -0.2) less ST that day. Between-person effects showed that compared with children who attended fewer overall hours of child care, children who attended more hours had less overall ST (B = -0.2 h; 95% CI, -0.4 to 0.0). Child care was not significantly associated with PA or sleep.

CONCLUSIONS

Child care attendance was not associated with 24-hour PA or sleep; however, it was associated with less ST. More research utilizing objective measures of ST and more robust measures of daily schedules or structure is necessary to better understand how existing infrastructure may influence preschool-aged children's 24-hour movement behaviors. In addition, future research should consider how access to child care may influence child care attendance.

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.

Examination of co-parenting support and parenting stress as mediators of the food insecurity-maternal depression/anxiety relationship

BACKGROUND

Identifying family environment factors related to food insecurity and maternal mental health could inform additional support for mothers who experience food insecurity. This study seeks to examine the mechanistic roles of co-parenting support and parenting stress on the food insecurity-maternal mental health relationship.

METHODS

Data from the Future of Families and Child Well-being Study, which recruited mothers post-delivery from 75 urban hospitals, was utilized. Analysis includes 1808 mothers followed for 15 years. Food insecurity was assessed at year 5, co-parenting support and parenting stress at year 9, and maternal depression and anxiety at year 15. Structural equation models evaluated the role of food insecurity on maternal depression (model 1) and anxiety (model 2) through co-parenting support and parenting stress simultaneously, adjusting for socio-demographics.

RESULTS

Co-parenting support did not mediate the relationships of food insecurity and maternal depression and anxiety, controlling for parenting stress. Controlling for co-parenting support, parenting stress did not mediate the food insecurity-maternal depression relationship, but partially mediated the food insecurity-maternal anxiety relationship (specific indirect: B = 0.026, CI:0.01, 0.05; specific direct: B = 0.131, CI:-0.04, 0.32).

LIMITATIONS

There was a significant period of time (10 years) between assessment of food insecurity and assessment of maternal mental health. Self-reported data on sensitive topics may be susceptible to bias. With observational research, it is possible that unobserved confounding variables impact the findings.

CONCLUSIONS

Cumulative support in the form of - parenting, economic (e.g., utilities), and food - may help reduce parenting stress and anxiety among mothers who experience food insecurity.

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.

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

Measuring Micro Temporal Processes Underlying Preschoolers Screen Use and Behavioral Health: Protocol for the Tots & Tech Study (Preprint)

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

Later sleep timing predicts accelerated summer weight gain among elementary school children: a prospective observational study

OBJECTIVES AND BACKGROUND

Social demands of the school-year and summer environment may affect children's sleep patterns and circadian rhythms during these periods. The current study examined differences in children's sleep and circadian-related behaviors during the school-year and summer and explored the association between sleep and circadian parameters and change in body mass index (BMI) during these time periods.

METHODS

This was a prospective observational study with 119 children ages 5 to 8 years with three sequential BMI assessments: early school-year (fall), late school-year (spring), and beginning of the following school-year in Houston, Texas, USA. Sleep midpoint, sleep duration, variability of sleep midpoint, physical activity, and light exposure were estimated using wrist-worn accelerometry during the school-year (fall) and summer. To examine the effect of sleep parameters, physical activity level, and light exposure on change in BMI, growth curve modeling was conducted controlling for age, race, sex, and chronotype.

RESULTS

Children's sleep midpoint shifted later by an average of 1.5 h during summer compared to the school-year. After controlling for covariates, later sleep midpoints predicted larger increases in BMI during summer, (γ = .0004, p = .03), but not during the school-year. Sleep duration, sleep midpoint variability, physical activity levels, and sedentary behavior were not associated with change in BMI during the school-year or summer. Females tended to increase their BMI at a faster rate during summer compared to males, γ = .06, p = .049. Greater amounts of outdoor light exposure (γ = -.01, p = .02) predicted smaller increases in school-year BMI.

CONCLUSIONS

Obesity prevention interventions may need to target different behaviors depending on whether children are in or out of school. Promotion of outdoor time during the school-year and earlier sleep times during the summer may be effective obesity prevention strategies during these respective times.

239 Changes in Children’s Schoolyear and Summer Sleep during the COVID-19 Pandemic

Introduction

In spring 2020, elementary schools closed to minimize the spread of COVID-19. Questionnaire data suggest children’s sleep was impacted during the pandemic, yet device-based data (i.e. accelerometry) on this topic is lacking. The purpose of this study was to examine children’s sleep during the COVID-19 pandemic (i.e. spring and summer 2020) compared to previous data collected from the same children during each of the two previous years (spring and summer 2018 and 2019).

Methods

68 children (age = 9.9±1.2 years, 56% Black, 53% male) previously recruited for an observational cohort study wore a Fitbit Charge 2 on their wrist during the spring and summer from 2018-2020 (i.e. six 30-day measurement periods). We used multilevel mixed models to examine how children’s sleep patterns changed during the pandemic accounting for previous trajectory (i.e. 2018 to 2019). Models included age, sex, and race as covariates.

Results

Children had an average of 84 nights of sleep data across all six 30-day measurement periods. In the spring of the pandemic, children slept 24.6 minutes more (95%CI = 11.6, 37.5) compared to previous springs. During the pandemic summer, they slept 40.0 minutes more (95%CI = 24.6, 58.5) compared to previous summers. Sleep midpoint was 117.1 minutes later (95%CI = 103.6, 130.6) in the spring during the pandemic and 46.0 minutes later (95% CI = 26.9, 65.2) in the summer during the pandemic compared to previous years. Sleep efficiency improved slightly by 1.3% (95% CI = 0.7, 1.9) and 3.6% (95% CI = 2.7, 4.5) in spring and summer, respectively, during the pandemic compared to previous years.

Conclusion

During the COVID-19 pandemic, children slept longer after accounting for previous developmental trends. Notably, the shift in sleep timing during the pandemic was nearly two hours later in the spring compared to previous years, potentially due to the lack of structure usually provided by school. Later sleep timing is independently associated with poor health behaviors (e.g., nutrition, physical activity, screen time). Future studies should examine if these changes in sleep persist over time and have potential long-term effects on children’s health.

Support (if any)

R21HD095164 (PI Weaver) & UofSC COVID-19 Research Initiative Grant (PI Armstrong)

Food insecurity and housing instability during early childhood as predictors of adolescent mental health

This study examined the effects of food insecurity and housing instability experiences during early childhood on adolescent anxiety and depressive symptoms through maternal depression and parenting stress. This longitudinal study included 4 waves of data from the Fragile Families and Child Well-Being Study (n = 2,626). Food insecurity was measured when the child was 5 years of age using the U.S. Department of Agriculture's 18-item Food Security Scale. Housing instability was also measured when the child was 5 years of age based on an affirmative response to 6 housing adversity items. Maternal depression and parenting stress were measured when the child was 9 years of age. Anxiety and depressive symptoms were assessed when the child (now adolescent) was 15 years of age using 6 items of the Brief Symptom Inventory 18 anxiety subscale and 5 items of the Centers for Epidemiologic Studies Depression Scale, respectively. Two structural equation models assessed the associations between food insecurity and housing instability on adolescent anxiety (Model 1) and depressive symptoms (Model 2) through maternal depression and parenting stress simultaneously, controlling for sociodemographic characteristics. Results suggest that experiencing both food insecurity and housing instability during early childhood increases the risk of long-term adolescent depressive (indirect: B = 0.008, 95% CI [0.002, 0.016]) and anxiety (indirect: B = 0.012, 95% CI [0.002, 0.026]) symptoms through maternal depression to parenting stress. Screening for food insecurity and housing instability during early childhood could potentially identify both mothers who are at risk for depression and parenting stress and children who are at increased risk for anxiety or depressive symptoms during adolescence. (PsycInfo Database Record (c) 2020 APA, all rights reserved).