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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.)

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.

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Unrelated to this work Dr. Weaver and Dr. Armstrong report board membership and ownership shares in Trackster LLC. Unrelated to this work Dr. de Zambotti reports grants from Noctrix Health and Verily Life Science LLC (Alphabet Inc.), and is a co-founder and Chief Scientific Officer at Lisa Health Inc. and has ownership of shares in Lisa Health., (Copyright: © 2024 White et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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 )., (© 2024. The Author(s).)

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.

Predators can reduce herbivory by consuming herbivores (a consumptive effect) and by altering herbivore behavior, life history, physiology or distribution (non-consumptive effects). The non-consumptive, or trait-mediated, effects of predators on prey may have important functions in the dynamics of communities. In a facultative ant-plant mutualism, we investigated whether these non-consumptive effects influenced the host plants of prey. Here, predaceous ants (Forelius pruinosus) consume and disturb a dominant lepidopteran folivore (Bucculatrix thurberiella) of wild cotton plants (Gossypium thurberi). Season-long ant exclusion experiments revealed that ants had a larger proportional effect on damage by B. thurberiella than on caterpillar abundance, a result that suggests ants have a strong non-consumptive effect. Behavioral experiments conducted in two populations over 2 years demonstrated that B. thurberiella caterpillars were substantially less likely to damage wild cotton leaves in the presence of ants due to ant-induced changes in caterpillar behavior. In the absence of ants caterpillars spent more time stationary (potential feeding time) and less time dropping from leaves by a thread of silk than when ants were present. Furthermore, ants altered the spatial distribution of both caterpillars and damage; caterpillars spent relatively more time on the upper surfaces of leaves and caused damage further from the leaf margin in ant exclusion treatments. Both direct encounters with ants and information conveyed when ants walked onto leaves were key events leading to the anti-predator behaviors of caterpillars. This study contributes to a small body of evidence from terrestrial systems demonstrating that the trait-mediated effects of predators can cascade to the host plants of prey.