Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration

INTRODUCTION

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr.

METHODS

Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC).

RESULTS

Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95.

CONCLUSIONS

The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

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.

Balancing best practice and reality in behavioral intervention development: A survey of principal investigators funded by the National Institutes of Health

Preliminary studies play a prominent role in the development of large-scale behavioral interventions. Though recommendations exist to guide the execution and interpretation of preliminary studies, these assume optimal scenarios which may clash with realities faced by researchers. The purpose of this study was to explore how principal investigators (PIs) balance expectations when conducting preliminary studies. We surveyed PIs funded by the National Institutes of Health to conduct preliminary behavioral interventions between 2000 and 2020. Four hundred thirty-one PIs (19% response rate) completed the survey (November 2021 to January 2022, 72% female, mean 21 years post-terminal degree). Most PIs were aware of translational models and believed preliminary studies should precede larger trials but also believed a single preliminary study provided sufficient evidence to scale. When asked about the relative importance of preliminary efficacy (i.e. changes in outcomes) and feasibility (i.e. recruitment, acceptance/adherence) responses varied. Preliminary studies were perceived as necessary to successfully compete for research funding, but among PIs who had peer-reviewed federal-level grants applications (n = 343 [80%]), responses varied about what should be presented to secure funding. Confusion surrounding the definition of a successful, informative preliminary study poses a significant challenge when developing behavior interventions. This may be due to a mismatch between expectations surrounding preliminary studies and the realities of the research enterprise in which they are conducted. To improve the quality of preliminary studies and advance the field of behavioral interventions, additional funding opportunities, more transparent criteria in grant reviews, and additional training for grant reviewers are suggested.

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 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.

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.

Are the Risk of Generalizability Biases Generalizable? A Meta-Epidemiological Study

Background Preliminary studies (e.g., pilot/feasibility studies) can result in misleading evidence that an intervention is ready to be evaluated in a large-scale trial when it is not. Risk of Generalizability Biases (RGBs, a set of external validity biases) represent study features that influence estimates of effectiveness, often inflating estimates in preliminary studies which are not replicated in larger-scale trials. While RGBs have been empirically established in interventions targeting obesity, the extent to which RGBs generalize to other health areas is unknown. Understanding the relevance of RGBs across health behavior intervention research can inform organized efforts to reduce their prevalence. Purpose The purpose of our study was to examine whether RGBs generalize outside of obesity-related interventions. Methods A systematic review identified health behavior interventions across four behaviors unrelated to obesity that follow a similar intervention development framework of preliminary studies informing larger-scale trials (i.e., tobacco use disorder, alcohol use disorder, interpersonal violence, and behaviors related to increased sexually transmitted infections). To be included, published interventions had to be tested in a preliminary study followed by testing in a larger trial (the two studies thus comprising a study pair). We extracted health-related outcomes and coded the presence/absence of RGBs. We used meta-regression models to estimate the impact of RGBs on the change in standardized mean difference (ΔSMD) between the preliminary study and larger trial. Results We identified sixty-nine study pairs, of which forty-seven were eligible for inclusion in the analysis (k = 156 effects), with RGBs identified for each behavior. For pairs where the RGB was present in the preliminary study but removed in the larger trial the treatment effect decreased by an average of ΔSMD=-0.38 (range - 0.69 to -0.21). This provides evidence of larger drop in effectiveness for studies containing RGBs relative to study pairs with no RGBs present (treatment effect decreased by an average of ΔSMD =-0.24, range - 0.19 to -0.27). Conclusion RGBs may be associated with higher effect estimates across diverse areas of health intervention research. These findings suggest commonalities shared across health behavior intervention fields may facilitate introduction of RGBs within preliminary studies, rather than RGBs being isolated to a single health behavior field.

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

BACKGROUND

Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children's screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation.

OBJECTIVE

The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement.

METHODS

We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach.

RESULTS

Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14.

CONCLUSION

The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first.

SYSTEMATIC REVIEW REGISTRATION

The protocol has been pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

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.

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.

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

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

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.

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.

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.

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.

The mysterious case of the disappearing pilot study: a review of publication bias in preliminary behavioral interventions presented at health behavior conferences

BACKGROUND

The number of preliminary studies conducted and published has increased in recent years. However, there are likely many preliminary studies that go unpublished because preliminary studies are typically small and may not be perceived as methodologically rigorous. The extent of publication bias within preliminary studies is unknown but can prove useful to determine whether preliminary studies appearing in peer-reviewed journals are fundamentally different than those that are unpublished. The purpose of this study was to identify characteristics associated with publication in a sample of abstracts of preliminary studies of behavioral interventions presented at conferences.

METHODS

Abstract supplements from two primary outlets for behavioral intervention research (Society of Behavioral Medicine and International Society of Behavioral Nutrition and Physical Activity) were searched to identify all abstracts reporting findings of behavioral interventions from preliminary studies. Study characteristics were extracted from the abstracts including year presented, sample size, design, and statistical significance. To determine if abstracts had a matching peer-reviewed publication, a search of authors' curriculum vitae and research databases was conducted. Iterative logistic regression models were used to estimate odds of abstract publication. Authors with unpublished preliminary studies were surveyed to identify reasons for nonpublication.

RESULTS

Across conferences, a total of 18,961 abstracts were presented. Of these, 791 were preliminary behavioral interventions, of which 49% (388) were published in a peer-reviewed journal. For models with main effects only, preliminary studies with sample sizes greater than n = 24 were more likely to be published (range of odds ratios, 1.82 to 2.01). For models including interactions among study characteristics, no significant associations were found. Authors of unpublished preliminary studies indicated small sample sizes and being underpowered to detect effects as barriers to attempting publication.

CONCLUSIONS

Half of preliminary studies presented at conferences go unpublished, but published preliminary studies appearing in peer-reviewed literature are not systematically different from those that remain unpublished. Without publication, it is difficult to assess the quality of information regarding the early-stage development of interventions. This inaccessibility inhibits our ability to learn from the progression of preliminary studies.

Scientists' perception of pilot study quality was influenced by statistical significance and study design

OBJECTIVES

Preliminary studies play a key role in developing large-scale interventions but may be held to higher or lower scientific standards during the peer review process because of their preliminary study status.

STUDY DESIGN AND SETTING

Abstracts from 5 published obesity prevention preliminary studies were systematically modified to generate 16 variations of each abstract. Variations differed by 4 factors: sample size (n = 20 vs. n = 150), statistical significance (P < 0.05 vs. P > 0.05), study design (single group vs. randomized 2 groups), and preliminary study status (presence/absence of pilot language). Using an online survey, behavioral scientists were provided with a randomly selected variation of each of the 5 abstracts and blinded to the existence of other variations. Respondents rated each abstract on aspects of study quality.

RESULTS

Behavioral scientists (n = 271, 79.7% female, median age 34 years) completed 1,355 abstract ratings. Preliminary study status was not associated with perceived study quality. Statistically significant effects were rated as more scientifically significant, rigorous, innovative, clearly written, warranted further testing, and had more meaningful results. Randomized designs were rated as more rigorous, innovative, and meaningful.

CONCLUSION

Findings suggest reviewers place a greater value on statistically significant findings and randomized control design and may overlook other important study characteristics.

Feasibility and preliminary efficacy of structured programming and a parent intervention to mitigate accelerated summer BMI gain: a pilot study

BACKGROUND

This study assessed the initial feasibility and preliminary efficacy of providing children a free summer day camp and a parent intervention to improve self-regulation and mitigate accelerated summer BMI gain.

METHODS

This pilot 2x2 factorial randomized control trial used a mixed-methods design to evaluate providing children a free summer day camp (SCV), a parent intervention (PI), and the combination of these two strategies (SCV+PI) to mitigate accelerated summer body mass index (BMI) gain. Progression criteria for feasibility and efficacy were assessed to determine if a full-scale trial was warranted. Feasibility criteria included recruitment capability (≥80 participants recruited) retention (≥70% participants retained), compliance (≥80% of participants attending the summer program with children attending ≥60% of program days, and ≥80% of participants completing goal setting calls with ≥60% of weeks syncing their child's Fitbit), and treatment fidelity (≥80% of summer program days delivered for ≥9 h/day, and ≥80% of participant texts delivered). Efficacy criteria were assessed via achieving a clinically meaningful impact on zBMI (i.e., ≥0.15). Changes in BMI were estimated using intent-to-treat and post hoc dose-response analyses via multilevel mixed-effects regressions.

RESULTS

For recruitment, capability and retention progression criteria were met with a total of 89 families participating and 24 participants randomized to the PI group, 21 randomized to the SCV group, 23 randomized to the SCV+PI group, and 21 randomized to the control. However, fidelity and compliance progression criteria were not achieved due to COVID-19 and lack of transportation. Progression criteria for efficacy was also not achieved as intent-to-treat analyses did not show changes in BMI gain that were clinically meaningful. Post hoc dose-response analyses showed that for each day (0 to 29) of summer programming children attended they gained -0.009 (95CI= -0.018, -0.001) less in BMI z score.

CONCLUSIONS

Engagement in both the SCV and PI was not ideal due to COVID-19 and lack of transportation. Providing children with structured summer programming to mitigate accelerated summer BMI gain may be an effective strategy. However, because feasibility and efficacy progression criteria were not met, a larger trial is not warranted until further pilot work is completed to ensure children attend the programming.

TRIAL REGISTRATION

The trial reported herein was prospectively registered at ClinicalTrials.gov. Trial #: NCT04608188.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement.

METHODS

We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE).

RESULTS

ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG.

CONCLUSION

This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

Influence of pilot and small trials in meta-analyses of behavioral interventions: a meta-epidemiological study

BACKGROUND

Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status.

METHODS

Searches were to identify systematic reviews that conducted meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from January 2016 to October 2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size but not a pilot/feasibility study (N ≤ 100, N > 100, and N > 370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance of summary ES between the four categories of studies was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N ≤ 100 studies on the estimated summary ES.

RESULTS

A total of 1602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 studies per meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N ≤ 100 studies comprised 22% (0-58%) and 21% (0-83%) of studies included in the meta-analyses. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES ranged from 0.20 to 0.46 depending on the proportion of studies comprising the original ES were either mostly small (e.g., N ≤ 100) or mostly large (N > 370). Concordance was low when removing both pilot/feasibility and N ≤ 100 studies (kappa = 0.53) and restricting analyses only to the largest studies (N > 370, kappa = 0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES.

CONCLUSIONS

When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N ≤ 100 studies, summary ES can be affected markedly and should be interpreted with caution.

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.

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.

Measurement of Physical Activity with Wrist-Worn ActiGraph GT3X+ in Older Women

Higher wear compliance has been seen with wrist placed accelerometers versus hip placed. Performance of wrist placed ActiGraph GT3X+ accelerometer (GT3X+, ActiGraph LLC, Pensacola, FL) in assessing physical activity (PA) remains unclear.

PURPOSE

This study examined GT3X+'s performance in measuring PA energy expenditure (PAEE) and classifying PA intensity in older women.

METHODS

Women [n = 89, age = 65.6 (4.3)] wore GT3X+ and SenseWear Armband Mini (SWAM, BodyMedia Inc. Pittsburgh, PA) for 2 weeks. Concurrently, doubly labeled water (DLW) determined total daily energy expenditure (TDEE). Resting energy expenditure (REE) was determined by Indirect Calorimetry. Data was processed using manufacturer-provided software. Bivariate correlations, Intra Class Correlations, and Bland-Altman plots were performed to evaluate agreement between GT3X+ and criterion measures for sedentary time, light and moderate-to-vigorous PA (determined by SWAM) and PAEE (determined by SWAM and by DLW and REE). Epoch-by-epoch analysis evaluated discrepancy and agreement of PA intensity classification between GT3X+ and SWAM.

RESULTS

For PAEE, GT3X+ showed moderate correlations with criterion measures (r = 0.413, 0.400 with SWAM; r = 0.564, 0.501 with DLW and REE), but Bland-Altman plots showed large variability. When estimating time spent in PA intensity, GT3X+ underestimated sedentary time and overestimated PA intensity compared to SWAM. During epoch-by-epoch analysis, GT3X+ misclassified light intensity PA as moderate-to-vigorous PA 72% of the time. Counts per minute showed strong correlations with criterion measures (r = 0.68, 0.625 for SWAM and DLW and REE respectively).

CONCLUSION

Current equations and cut points do not provide accurate measures of PA with wrist-worn GT3X+ in older women.

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

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

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

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.

Use of Child Care Attenuates the Link Between Decreased Maternal Sleep and Increased Depressive Symptoms

OBJECTIVE

Sleep disruption is common among postpartum women and is linked with depression. Child care may alleviate parenting stress and protect new mothers against symptoms of depression.

METHODS

Mothers from the NURTURE study, a birth cohort of 666 women of full-term infants, completed questionnaires during home visits when their infants were 3, 6, 9, and 12 months old. The Edinburgh Postnatal Depression Scale and the Perceived Stress Scale were used to measure depressive symptoms and stress, respectively. Mothers reported total nightly sleep duration for themselves and their infants, child care arrangements, marital status, and income. We used mixed-effects models adjusting for income, marital status, and child age to examine the indirect effect of infants' sleep on maternal mental health through mothers' sleep and assessed whether patterns differed depending on child care.

RESULTS

Decreased maternal sleep mediated the association between infant sleep and maternal mental health. When infants slept 1 hour less than usual, mothers slept 7 fewer minutes (B = 0.19, 95% confidence interval [CI], 0.01 to 0.27) and reported more depressive symptoms (B = -0.27, 95% CI, -0.43 to -0.11) and stress (B = -0.33, 95% CI -0.58 to -0.09). Child care moderated the indirect effect; decreased maternal sleep was not associated with increased depressive symptoms (and was not a mediator) when mothers had child care (indirect effect = -0.001, 95% CI, -0.03 to 0.03).

CONCLUSION

Use of infant child care reduced the link between maternal sleep and depressive symptoms. Regular access to child care may be a method to mitigate feelings of depression for new mothers.

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.

Feasibility &amp; Preliminary Efficacy of Structured Programming and a Parent Intervention to Mitigate Accelerated Summer BMI Gain: A pilot study

BackgroundThis study assessed initial feasibility and preliminary efficacy of providing children a free summer day camp and a parent intervention to improve self-regulation and mitigate accelerated summer BMI gain.MethodsThis pilot 2x2 factorial randomized control trial used a mixed methods design to evaluate providing children a free summer day camp (SCV), a parent intervention (PI), and the combination of these two strategies (SCV + PI) to mitigate accelerated summer body mass index (BMI) gain. Feasibility (i.e., recruitment capability, retention, compliance, treatment fidelity, acceptability) was examined using means, standard deviations, and percentages for relevant variables. Changes in BMI were estimated using intent-to-treat and post-hoc dose response analyses via multilevel mixed effects regressions.ResultsA total of 89 families participated, with 24 participants randomized to the PI group, 21 randomized to the SCV group, 23 randomized to the SCV + PI group, and 21 randomized to the control. Parents and children found the summer program acceptable but attendance at the summer program and engagement in the PI were low due to COVID-19 and lack of transportation. Intent-to-treat analyses showed no statistically significant difference between groups in summer BMI gain. Post-hoc dose response analyses showed that for each day (0 to 29) of summer programming children attended they gained - 0.009 (95CI= -0.018, -0.001) less in BMI z-score.ConclusionsEngagement in both the SCV and PI was not ideal and was likely due to COVID-19 and lack of transportation. Providing children with structured summer programming to mitigate accelerated summer BMI gain may be an effective strategy. Thus, a larger trial may be warranted, but more work is needed to ensure children attend the programming.Trial registration: The trial reported herein was prospectively registered at clinicaltrials.gov. Trial #:NCT04608188.

Foods and beverages provided in out of school hours care services: an observational study

Introduction

Out of school hours care (OSHC) is a fast-growing childcare setting in Australia, however the types of foods and beverages offered are relatively unknown. This study describes the food and beverages offered and investigates sector-level and setting-level factors which may impact OSHC in meeting the Australian Dietary Guidelines (ADG).

Methods

This cross-sectional, observational study was conducted in 89 OSHC services (between 2018 and 2019). Food and beverages offered, kitchen facilities and menus were captured via direct observation. Foods were categorised into five food groups or discretionary foods, based on the ADG, and frequencies determined. Short interviews with OSHC directors ascertained healthy eating policies, staff training, food quality assessment methods and food budgets. Fisher’s exact test explored the influence of sector-level and setting-level factors on food provision behaviours.

Results

Discretionary foods (1.5 ± 0.68) were offered more frequently than vegetables (0.82 ± 0.80) (p < .001), dairy (0.97 ± 0.81) (p = .013) and lean meats (0.22 ± 0.54) (p < .001). OSHC associated with long day care and reported using valid food quality assessment methods offered more lean meats (p= .002, and p= .004). Larger organisations offered more vegetables (p = .015) and discretionary foods (p= .007). Menus with clearly worded instructions to provide fruits and vegetables daily offered more fruit (p= .009), vegetables (p < .001) and whole grains (p= .003). No other sector or setting-level factors were associated with services aligning with the ADG.

Conclusion

Future interventions could benefit from trialling menu planning training and tools to assist OSHC services in NSW meet the ADG requirements.

Impact of risk of generalizability biases in adult obesity interventions: A meta-epidemiological review and meta-analysis

Biases introduced in early-stage studies can lead to inflated early discoveries. The risk of generalizability biases (RGBs) identifies key features of feasibility studies that, when present, lead to reduced impact in a larger trial. This meta-study examined the influence of RGBs in adult obesity interventions. Behavioral interventions with a published feasibility study and a larger scale trial of the same intervention (e.g., pairs) were identified. Each pair was coded for the presence of RGBs. Quantitative outcomes were extracted. Multilevel meta-regression models were used to examine the impact of RGBs on the difference in the effect size (ES, standardized mean difference) from pilot to larger scale trial. A total of 114 pairs, representing 230 studies, were identified. Overall, 75% of the pairs had at least one RGB present. The four most prevalent RGBs were duration (33%), delivery agent (30%), implementation support (23%), and target audience (22%) bias. The largest reductions in the ES were observed in pairs where an RGB was present in the pilot and removed in the larger scale trial (average reduction ES -0.41, range -1.06 to 0.01), compared with pairs without an RGB (average reduction ES -0.15, range -0.18 to -0.14). Eliminating RGBs during early-stage testing may result in improved evidence.

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

Impact of the COVID-19 pandemic on elementary schoolers' physical activity, sleep, screen time and diet: A quasi-experimental interrupted time series study

BACKGROUND

COVID-19 school closures pose a threat to children's wellbeing, but no COVID-19-related studies have assessed children's behaviours over multiple years .

OBJECTIVE

To examine children's obesogenic behaviours during spring and summer of the COVID-19 pandemic compared to previous data collected from the same children during the same calendar period in the 2 years prior.

METHODS

Physical activity and sleep data were collected via Fitbit Charge-2 in 231 children (7-12 years) over 6 weeks during spring and summer over 3 years. Parents reported their child's screen time and dietary intake via a survey on 2-3 random days/week.

RESULTS

Children's behaviours worsened at a greater rate following the pandemic onset compared to pre-pandemic trends. During pandemic spring, sedentary behaviour increased (+79 min; 95% CI = 60.6, 97.1) and MVPA decreased (-10 min, 95% CI = -18.2, -1.1) compared to change in previous springs (2018-2019). Sleep timing shifted later (+124 min; 95% CI = 112.9, 135.5). Screen time (+97 min, 95% CI = 79.0, 115.4) and dietary intake increased (healthy: +0.3 foods, 95% CI = 0.2, 0.5; unhealthy: +1.2 foods, 95% CI = 1.0, 1.5). Similar patterns were observed during summer.

CONCLUSIONS

Compared to pre-pandemic measures, children's PA, sedentary behaviour, sleep, screen time, and diet were adversely altered during the COVID-19 pandemic. This may ultimately exacerbate childhood obesity.

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

OBJECTIVE

Children who fail to meet activity, sleep, and screen-time guidelines are at increased risk for obesity. Further, children who are Black are more likely to have obesity when compared to children who are White, and children from low-income households are at increased risk for obesity when compared to children from higher-income households. The objective of this study was to evaluate the proportion of days meeting obesogenic behavior guidelines during the school year compared to summer vacation by race and free/reduced priced lunch (FRPL) eligibility.

METHODS

Mixed-effects linear and logistic regressions estimated the proportion of days participants met activity, sleep, and screen-time guidelines during summer and school by race and FRPL eligibility within an observational cohort sample.

RESULTS

Children (n = 268, grades = K - 4, 44.1%FRPL, 59.0% Black) attending three schools participated. Children's activity, sleep, and screen-time were collected during an average of 23 school days and 16 days during summer vacation. During school, both children who were White and eligible for FRPL met activity, sleep, and screen-time guidelines on a greater proportion of days when compared to their Black and non-eligible counterparts. Significant differences in changes from school to summer in the proportion of days children met activity (-6.2%, 95CI = -10.1%, -2.3%; OR = 0.7, 95CI = 0.6, 0.9) and sleep (7.6%, 95CI = 2.9%, 12.4%; OR = 2.1, 95CI = 1.4, 3.0) guidelines between children who were Black and White were observed. Differences in changes in activity (-8.5%, 95CI = -4.9%, -12.1%; OR = 1.5, 95CI = 1.3, 1.8) were observed between children eligible versus uneligible for FRPL.

CONCLUSIONS

Summer vacation may be an important time for targeting activity and screen-time of children who are Black and/or eligible for FRPL.

Supporting Physical Activity in Patients and Populations During Life Events and Transitions: A Scientific Statement From the American Heart Association

Achieving recommended levels of physical activity is important for optimal cardiovascular health and can help reduce cardiovascular disease risk. Emerging evidence suggests that physical activity fluctuates throughout the life course. Some life events and transitions are associated with reductions in physical activity and, potentially, increases in sedentary behavior. The aim of this scientific statement is to first provide an overview of the evidence suggesting changes in physical activity and sedentary behavior across life events and transitions. A second aim is to provide guidance for health care professionals or public health workers to identify changes and promote physical activity during life events and transitions. We offer a novel synthesis of existing data, including evidence suggesting that some subgroups are more likely to change physical activity behaviors in response to life events and transitions. We also review the evidence that sedentary behavior changes across life events and transitions. Tools for health care professionals to assess physical activity using simple questions or wearable devices are described. We provide strategies for health care professionals to express compassion as they ask about life transitions and initiate conversations about physical activity. Last, resources for life phase-specific, tailored physical activity support are included. Future research needs include a better characterization of physical activity and sedentary behavior across life events and transitions in higher-risk subgroups. Development and testing of interventions designed specifically to combat declines in physical activity or increases in sedentary behavior during life events and transitions is needed to establish or maintain healthy levels of these cardiovascular health-promoting behaviors.

Disparities by household income and race/ethnicity: the utility of BMI for surveilling excess adiposity in children

Objectives: Low-income children (6-19 years) are at higher risk for BMI-determined overweight and obesity, but this relationship varies by children's race/ethnicity. BMI, however, is a poor marker of excess adiposity in minority children. The objective of this study was to determine if the relationships of income and/or race/ethnicity with weight status was consistent between BMI-determined overweight or obesity and adiposity measured via dual energy X-ray absorptiometry (DXA).Design: This study included a nationally representative sample of U.S. children (N = 9857, 14.0 years, 52.8% male, 31.8% low-income, 52.1% middle-income). Disparities in household income-to-poverty ratio (low-income = 0.00-1.00, middle-income = 1.01-4.00, high-income > 4.00) was the exposure with prevalence of BMI-determined overweight or obesity (i.e. age/sex specific CDC cutoffs) and DXA-determined excess adiposity (i.e. body fat%≥75th percentile) as the outcome.Results: For DXA, children from high-income households were 0.47 (95CI = 0.35, 0.65) and 0.55 (95CI = 0.44, 0.70) times as likely to have excess adiposity compared to children in middle and low-income households, respectively. Similar findings were observed with BMI-determined overweight and obesity. Stratified analyses by individual racial/ethnic groups showed children from high-income households were less likely to have excess adiposity compared to their low-income peers for White, Black, and Hispanic children. However, these relationships did not hold for BMI-determined overweight and obesity in Black and Hispanic children.Conclusions: This study revealed that the relationships between income and DXA-determined adiposity differed from the relationships between income and BMI-determined overweight and obesity for children who are Black and Hispanic. This suggests that BMI may be an inappropriate surveillance tool when exploring relationships between race/ethnicity, income, and adiposity.

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.

Physical activity in out of school hours care: an observational study

Background

Opportunities for physical activity within out of school hours care (OSHC) are not well documented in Australia. This study explored factors associated with children (5–12 years) meeting 30 min of moderate to vigorous physical activity (MVPA) while attending OSHC in the afternoon period.

Methods

A cross-sectional study, conducted in 89 OSHC services in New South Wales, Australia, serving 4,408 children. Each service was visited twice between 2018–2019. Physical activity promotion practices were captured via short interviews and System for Observing Staff Promotion of Physical Activity and Nutrition (SOSPAN). Physical activity spaces was measured (m²) and physical activity of 3,614 child days (42% girls), were collected using Acti-Graph accelerometers. Association between program practices and children accumulation of MVPA was tested using mixed effects logistic regression, adjusted by OSHC service and child.

Results

Twenty-six percent of children (n = 925) accumulated 30 min or more of MVPA. Factors associated with children reaching MVPA recommendations included: services scheduling greater amounts of child-led free play, both 30–59 min (OR 2.6, 95%CI 1.70, 3.98) and ≥ 60 min (OR 6.4, 95%CI 3.90, 10.49); opportunities for staff-led organised play of ≥ 30 min (OR 2.3, 95%CI 1.47, 3.83); and active games that engaged the majority of children (OR 1.7, 95%CI 1.11, 2.61). Children were less likely to meet MVPA recommendations if services played games with elimination components (OR 0.56, 95%CI 0.37, 0.86).

Conclusion

Improvements to service-level physical activity promotion practices, specifically the type of physical activity scheduled and the structure of games, may be an effective strategy to increase MVPA of children attending OSHC afterschool in NSW, Australia.

Identifying effective intervention strategies to reduce children's screen time: a systematic review and meta-analysis

BACKGROUND

Excessive screen time ([Formula: see text] 2 h per day) is associated with childhood overweight and obesity, physical inactivity, increased sedentary time, unfavorable dietary behaviors, and disrupted sleep. Previous reviews suggest intervening on screen time is associated with reductions in screen time and improvements in other obesogenic behaviors. However, it is unclear what study characteristics and behavior change techniques are potential mechanisms underlying the effectiveness of behavioral interventions. The purpose of this meta-analysis was to identify the behavior change techniques and study characteristics associated with effectiveness in behavioral interventions to reduce children's (0-18 years) screen time.

METHODS

A literature search of four databases (Ebscohost, Web of Science, EMBASE, and PubMed) was executed between January and February 2020 and updated during July 2021. Behavioral interventions targeting reductions in children's (0-18 years) screen time were included. Information on study characteristics (e.g., sample size, duration) and behavior change techniques (e.g., information, goal-setting) were extracted. Data on randomization, allocation concealment, and blinding was extracted and used to assess risk of bias. Meta-regressions were used to explore whether intervention effectiveness was associated with the presence of behavior change techniques and study characteristics.

RESULTS

The search identified 15,529 articles, of which 10,714 were screened for relevancy and 680 were retained for full-text screening. Of these, 204 studies provided quantitative data in the meta-analysis. The overall summary of random effects showed a small, beneficial impact of screen time interventions compared to controls (SDM = 0.116, 95CI 0.08 to 0.15). Inclusion of the Goals, Feedback, and Planning behavioral techniques were associated with a positive impact on intervention effectiveness (SDM = 0.145, 95CI 0.11 to 0.18). Interventions with smaller sample sizes (n < 95) delivered over short durations (< 52 weeks) were associated with larger effects compared to studies with larger sample sizes delivered over longer durations. In the presence of the Goals, Feedback, and Planning behavioral techniques, intervention effectiveness diminished as sample size increased.

CONCLUSIONS

Both intervention content and context are important to consider when designing interventions to reduce children's screen time. As interventions are scaled, determining the active ingredients to optimize interventions along the translational continuum will be crucial to maximize reductions in children's screen time.

Small studies, big decisions: the role of pilot/feasibility studies in incremental science and premature scale-up of behavioral interventions

Background

Careful consideration and planning are required to establish “sufficient” evidence to ensure an investment in a larger, more well-powered behavioral intervention trial is worthwhile. In the behavioral sciences, this process typically occurs where smaller-scale studies inform larger-scale trials. Believing that one can do the same things and expect the same outcomes in a larger-scale trial that were done in a smaller-scale preliminary study (i.e., pilot/feasibility) is wishful thinking, yet common practice. Starting small makes sense, but small studies come with big decisions that can influence the usefulness of the evidence designed to inform decisions about moving forward with a larger-scale trial. The purpose of this commentary is to discuss what may constitute sufficient evidence for moving forward to a definitive trial. The discussion focuses on challenges often encountered when conducting pilot/feasibility studies, referred to as common (mis)steps, that can lead to inflated estimates of both feasibility and efficacy, and how the intentional design and execution of one or more, often small, pilot/feasibility studies can play a central role in developing an intervention that scales beyond a highly localized context.

Main body

Establishing sufficient evidence to support larger-scale, definitive trials, from smaller studies, is complicated. For any given behavioral intervention, the type and amount of evidence necessary to be deemed sufficient is inherently variable and can range anywhere from qualitative interviews of individuals representative of the target population to a small-scale randomized trial that mimics the anticipated larger-scale trial. Major challenges and common (mis)steps in the execution of pilot/feasibility studies discussed are those focused on selecting the right sample size, issues with scaling, adaptations and their influence on the preliminary feasibility and efficacy estimates observed, as well as the growing pains of progressing from small to large samples. Finally, funding and resource constraints for conducting informative pilot/feasibility study(ies) are discussed.

Conclusion

Sufficient evidence to scale will always remain in the eye of the beholder. An understanding of how to design informative small pilot/feasibility studies can assist in speeding up incremental science (where everything needs to be piloted) while slowing down premature scale-up (where any evidence is sufficient for scaling).

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)

Dynamics of sleep, sedentary behavior, and moderate-to-vigorous physical activity on school versus nonschool days

STUDY OBJECTIVES

Studies examining time-use activity behaviors (sleep, sedentary behavior, and physical activity) on school days compared with nonschool days have examined these behaviors independently, ignoring their interrelated nature, limiting our ability to optimize the health benefits of these behaviors. This study examines the associations of school-day (vs. nonschool day) with time-use activity behaviors.

METHODS

Time series data (6,642 days) from Fitbits (Charge-2) were collected (n = 196, 53% female, 5-10 years). We used a variable-centered dynamic structural equation modeling approach to estimate day-to-day associations of time-use activity behaviors on school days for each child. We then used person-centered cluster analyses to group individuals based on these estimates.

RESULTS

Within-participant analysis showed that on school days (vs. nonschool days), children (1) slept less (β = -0.17, 95% CI = -0.21, -0.13), (2) were less sedentary (β = -0.05, 95% CI = -0.09, -0.02), and (3) had comparable moderate-to-vigorous physical activity (MVPA; β = -0.05, 95% CI = -0.11, 0.00). Between-participant analysis showed that, on school days, children with higher sleep carryover experienced greater decreases in sleep (β = 0.44, 95% CI = 0.08, 0.71), children with higher body mass index z-score decreased sedentary behavior more (β = -0.41, 95% CI = -0.64, -0.13), and children with lower MVPA increased MVPA more (β = -0.41, 95% CI -0.64, -0.13). Cluster analysis demonstrated four distinct patterns of connections between time-use activity behaviors and school (High Activity, Sleep Resilient, High Sedentary, and Dysregulated Sleep).

CONCLUSIONS

Using a combination of person-centered and more traditional variable-centered approaches, we identified patterns of interrelated behaviors that differed on school, and nonschool days. Findings can inform targeted intervention strategies tailored to children's specific behavior patterns.

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.

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

Purpose

The Structured Days Hypothesis (SDH) posits that children’s behaviors associated with obesity – such as physical activity – are more favorable on days that contain more ‘structure’ (i.e., a pre-planned, segmented, and adult-supervised environment) such as school weekdays, compared to days with less structure, such as weekend days. The purpose of this study was to compare children’s moderate-to-vigorous physical activity (MVPA) levels on weekdays versus weekend days using a large, multi-country, accelerometer-measured physical activity dataset.

Methods

Data were received from the International Children’s Accelerometer Database (ICAD) July 2019. The ICAD inclusion criteria for a valid day of wear, only non-intervention data (e.g., baseline intervention data), children with at least 1 weekday and 1 weekend day, and ICAD studies with data collected exclusively during school months, were included for analyses. Mixed effects models accounting for the nested nature of the data (i.e., days within children) assessed MVPA minutes per day (min/day MVPA) differences between weekdays and weekend days by region/country, adjusted for age, sex, and total wear time. Separate meta-analytical models explored differences by age and country/region for sex and child weight-status.

Results/findings

Valid data from 15 studies representing 5794 children (61% female, 10.7 ± 2.1 yrs., 24% with overweight/obesity) and 35,263 days of valid accelerometer data from 5 distinct countries/regions were used. Boys and girls accumulated 12.6 min/day (95% CI: 9.0, 16.2) and 9.4 min/day (95% CI: 7.2, 11.6) more MVPA on weekdays versus weekend days, respectively. Children from mainland Europe had the largest differences (17.1 min/day more MVPA on weekdays versus weekend days, 95% CI: 15.3, 19.0) compared to the other countries/regions. Children who were classified as overweight/obese or normal weight/underweight accumulated 9.5 min/day (95% CI: 6.9, 12.2) and 10.9 min/day (95% CI: 8.3, 13.5) of additional MVPA on weekdays versus weekend days, respectively.

Conclusions

Children from multiple countries/regions accumulated significantly more MVPA on weekdays versus weekend days during school months. This finding aligns with the SDH and warrants future intervention studies to prioritize less-structured days, such as weekend days, and to consider providing opportunities for all children to access additional opportunities to be active.

Physical activity and sedentary time of youth in structured settings: a systematic review and meta-analysis

BACKGROUND

Structured settings, such as school, childcare, afterschool programs, summer camps, and physical activity/sport programs are crucial to promoting physical activity (PA) opportunities and reducing sedentary (ST) for children and adolescents. However, little is known about how much PA and ST children and adolescents accumulate in structured settings. The aim of this study is to conduct a systematic review and meta-analysis of the absolute amount of time youth spend physically active and sedentary in different structured settings (Prospero number: 42018111804).

METHODS

Observational and experimental/quasi-experimental studies (baseline data only) with full-text available, written in English and published in a peer-reviewed journal, reporting the total amount of objectively measured PA (light, moderate, vigorous, and/or total physical activity) and/or time spent ST during structured settings among youth (3 to 18 years) were eligible. Adjusted meta-analysis was conducted to estimate the pooled mean of time spent in PA and ST, by settings and sex.

RESULTS

A total of 187 studies (childcare n=60; school n=91; afterschool programs n=14; summer camp n=4; and Physical activity/ sport programs n=18) from 30 countries (47.9% United States), representing 74,870 youth (mean age 8.6 years old) were included. Overall, there was a high variation between studies in outcomes and settings. The meta-analyses revealed, on average, youth spend 221.8 minutes (36.7 min/hour) in ST and 32.1 minutes (5.1 min/hour) in MVPA during childcare hours, and 223.9 minutes (36.7min/hour) in ST and 27.8 min (4.4 min/hour) in MVPA at school. Relatively, youth are engaged in more MVPA in afterschool programs (11.7 min/hour), PA/ sport programs (20.9 min/hour), and summer camps (6.4 min/hour), when compared to childcare and school.

CONCLUSION

Total PA accumulated during childcare and MVPA accumulated during schools hours were close to recommendations, despite high proportion of ST. Afterschool programs, summer camp and PA/ sport programs are important settings that can contribute to daily PA and reduced ST. Ensuring all youth have access to these structured settings may be an important step forward for public health.

The impact of summer vacation on children's obesogenic behaviors and body mass index: a natural experiment

BACKGROUND

Children's BMI gain accelerates during summer. The Structured Days Hypothesis posits that the lack of the school day during summer vacation negatively impacts children's obesogenic behaviors (i.e., physical activity, screen time, diet, sleep). This natural experiment examined the impact of summer vacation on children's obesogenic behaviors and body mass index (BMI).

METHODS

Elementary-aged children (n = 285, 5-12 years, 48.7% male, 57.4% African American) attending a year-round (n = 97) and two match-paired traditional schools (n = 188) in the United States participated in this study. Rather than taking a long break from school during the summer like traditional schools, year-round schools take shorter and more frequent breaks from school. This difference in school calendars allowed for obesogenic behaviors to be collected during three conditions: Condition 1) all children attend school, Condition 2) year-round children attend school while traditional children were on summer vacation, and Condition 3) summer vacation for all children. Changes in BMI z-score were collected for the corresponding school years and summers. Multi-level mixed effects regressions estimated obesogenic behaviors and monthly zBMI changes. It was hypothesized that children would experience unhealthy changes in obesogenic behaviors when entering summer vacation because the absence of the school day (i.e., Condition 1 vs. 2 for traditional school children and 2 vs. 3 for year-round school children).

RESULTS

From Condition 1 to 2 traditional school children experienced greater unhealthy changes in daily minutes sedentary (∆ = 24.2, 95CI = 10.2, 38.2), screen time minutes (∆ = 33.7, 95CI = 17.2, 50.3), sleep midpoint time (∆ = 73:43, 95CI = 65:33, 81:53), and sleep efficiency percentage (-∆ = 0.7, 95CI = -1.1, - 0.3) when compared to year-round school children. Alternatively, from Condition 2 to 3 year-round school children experienced greater unhealthy changes in daily minutes sedentary (∆ = 54.5, 95CI = 38.0, 70.9), light physical activity minutes (∆ = - 42.2, 95CI = -56.2, - 28.3) MVPA minutes (∆ = - 11.4, 95CI = -3.7, - 19.1), screen time minutes (∆ = 46.5, 95CI = 30.0, 63.0), and sleep midpoint time (∆ = 95:54, 95CI = 85:26, 106:22) when compared to traditional school children. Monthly zBMI gain accelerated during summer for traditional (∆ = 0.033 95CI = 0.019, 0.047) but not year-round school children (∆ = 0.004, 95CI = -0.014, 0.023).

CONCLUSIONS

This study suggests that the lack of the school day during summer vacation negatively impacts sedentary behaviors, sleep timing, and screen time. Changes in sedentary behaviors, screen time, and sleep midpoint may contribute to accelerated summer BMI gain. Providing structured programming during summer vacation may positively impact these behaviors, and in turn, mitigate accelerated summer BMI gain.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03397940 . Registered January 12th 2018.

Physical Activity Opportunities of Low-Income Elementary School-Aged Children During the Segmented School Day

BACKGROUND

In this study, we examined moderate-to-vigorous physical activity (MVPA) of children in a school district serving children from low-income and minority households.

METHODS

This observational study was conducted in 8 rural elementary schools in South Carolina. Children (N = 719, age = 7.7 years, 48.0% girls, 88.0% African American) wore accelerometers during school hours. Physical activity was distilled into time engaged in MVPA during 4 distinct opportunities. These 4 opportunities were non-activity time (eg, class-time), extended lunch (lunch recess after eating), physical education (PE), and recess. Mixed effects linear regressions estimated MVPA on days that had no activity opportunities, extended lunch, recess, PE, and multiple activity opportunities (eg, PE and recess).

RESULTS

On days with multiple activity opportunities, girls and boys accumulated 8.0 (95% CI = 4.9, 11.1) and 7.1 (95% CI = 3.6, 10.7) additional minutes of MVPA compared to a no activity day. On PE days boys accumulated 5.2 (95% CI = 0.3, 10.2) additional minutes of MVPA, whereas recess days provided girls with 3.0 (95% CI = 0.1, 6.0) additional MVPA minutes. No other activity opportunities provided statistically significant increases in school-day MVPA.

CONCLUSIONS

In low-income schools it may be necessary to provide multiple physical activity opportunities during the school day to increase boys' and girls' MVPA.