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Purpose: Studying physical activity in toddlers using accelerometers is challenging due to noncompliance with wear time (WT) and activity log (AL) instructions. The aims of this study are to examine relationships between WT and AL completion and (1) demographic and socioeconomic variables, (2) parenting style, and (3) whether sedentary time differs by AL completion. Methods: Secondary analysis was performed using baseline data from a community wellness program randomized controlled trial for parents with toddlers (12–35 mo). Parents had toddlers wear ActiGraph wGT3x accelerometers and completed ALs. Valid days included ≥600-minute WT. Analysis of variance and chi-square analyses were used. Results: The sample (n = 50) comprised racial and ethnically diverse toddlers (mean age = 27 mo, 58% male) and parents (mean age = 31.7 y, 84% female). Twenty-eight families (56%) returned valid accelerometer data with ALs. Participants in relationships were more likely to complete ALs (P <.05). Toddler sedentary time did not differ between those with ALs and those without. Conclusions: We found varied compliance with WT instructions and AL completion. Returned AL quality was poor, presenting challenges in correctly characterizing low-activity counts to improve internal validity of WT and physical activity measures. Support from marital partners may be important for adherence to study protocols. [ABSTRACT FROM AUTHOR]

The use of screen media has significantly increased and several lifestyle consequences have occurred following this change. Screen media exposure is more common among younger age children. Multiple factors may be the reason for this; however, their use is likely reinforced. Parents seek support from screen media devices, which can help calm and occupy their children in a matter of minutes. However, resorting to electronic devices for instant relief is likely to persist due to both negative and positive reinforcement. Reinforcing screen media in this way will make the use of screen devices inextricable for parents and their children. Explaining the issue clearly and coming up with feasible solutions will help minimize the negative health effects of prolonged screen use, the general desirability of screen media devices, and also improve (and increase) healthy screen time habits in children. [ABSTRACT FROM AUTHOR]

This systematic review with the Delphi study aimed to identify effective and resource-efficient (optimal) strategies for recruiting schools into health promotion interventions in the United States. A literature search was conducted in PubMed, Cochrane Library, and CINAHL (EBSCO). A total of 116 interventions reported in 160 articles were included. Most school-based interventions did not report data regarding school recruitment duration (81%), target school size (63%), and school recruitment strategies (78%). Further, no details were provided regarding the reasons for declining to participate despite being eligible. For the Delphi, responses from 23 researchers in school-based clinical trials were collected. A qualitative descriptive approach was used for coding responses and collapsed into higher-order categories based on school recruitment strategies. Delphi participants reported that (1) creating new or leveraging pre-existing partnerships, (2) intervention champion, (3) minimal school disruptions, (4) working with open mind/flexibility, and (5) transparent communication are the most optimal school recruitment strategies. Staff time and travel were the most frequently reported costs for implementing those strategies. The overall trend in school-based obesity prevention intervention studies illustrates the importance of a better understanding school recruitment. Improved reporting can allow researchers to budget their time and resources better and provide greater confidence in reaching their target school size., (© 2024 World Obesity Federation.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)