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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., (Copyright © 2024 by the American College of Sports Medicine.)

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.)

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.)

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.

Metal node engineering in combination with modularity, topological diversity, and porosity of metal-organic frameworks (MOFs) could advance energy and optoelectronic sectors. In this study, we focus on MOFs with multinuclear heterometallic nodes for establishing metal-property trends, i.e. , connecting atomic scale changes with macroscopic material properties by utilization of inductively coupled plasma mass spectrometry, conductivity measurements, X-ray photoelectron and diffuse reflectance spectroscopies, and density functional theory calculations. The results of Bader charge analysis and studies employing the Voronoi-Dirichlet partition of crystal structures are also presented. As an example of frameworks with different nodal arrangements, we have chosen MOFs with mononuclear, binuclear, and pentanuclear nodes, primarily consisting of first-row transition metals, that are incorporated in HHTP-, BTC-, and NIP-systems, respectively (HHTP 3- = triphenylene-2,3,6,7,10,11-hexaone; BTC 3- = 1,3,5-benzenetricarboxylate; and NIP 2- = 5-nitroisophthalate). Through probing framework electronic profiles, we demonstrate structure-property relationships, and also highlight the necessity for both comprehensive analysis of trends in metal properties, and novel avenues for preparation of heterometallic multinuclear isoreticular structures, which are critical components for on-demand tailoring of properties in heterometallic systems., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The development of porous well-defined hybrid materials (e.g., metal-organic frameworks or MOFs) will add a new dimension to a wide number of applications ranging from supercapacitors and electrodes to "smart" membranes and thermoelectrics. From this perspective, the understanding and tailoring of the electronic properties of MOFs are key fundamental challenges that could unlock the full potential of these materials. In this work, we focused on the fundamental insights responsible for the electronic properties of three distinct classes of bimetallic systems, M x-y M' y -MOFs, M x M' y -MOFs, and M x (ligand-M' y )-MOFs, in which the second metal (M') incorporation occurs through (i) metal (M) replacement in the framework nodes (type I), (ii) metal node extension (type II), and (iii) metal coordination to the organic ligand (type III), respectively. We employed microwave conductivity, X-ray photoelectron spectroscopy, diffuse reflectance spectroscopy, powder X-ray diffraction, inductively coupled plasma atomic emission spectroscopy, pressed-pellet conductivity, and theoretical modeling to shed light on the key factors responsible for the tunability of MOF electronic structures. Experimental prescreening of MOFs was performed based on changes in the density of electronic states near the Fermi edge, which was used as a starting point for further selection of suitable MOFs. As a result, we demonstrated that the tailoring of MOF electronic properties could be performed as a function of metal node engineering, framework topology, and/or the presence of unsaturated metal sites while preserving framework porosity and structural integrity. These studies unveil the possible pathways for transforming the electronic properties of MOFs from insulating to semiconducting, as well as provide a blueprint for the development of hybrid porous materials with desirable electronic structures.

We report on a method to produce free-standing graphene sheets from epitaxial graphene on silicon carbide (SiC) substrate. Doubly clamped nanomechanical resonators with lengths up to 20 microm were patterned using this technique and their resonant motion was actuated and detected optically. Resonance frequencies of the order of tens of megahertz were measured for most devices, indicating that the resonators are much stiffer than expected for beams under no tension. Raman spectroscopy suggests that the graphene is not chemically modified during the release of the devices, demonstrating that the technique is a robust means of fabricating large-area suspended graphene structures.

The ultrafast relaxation and recombination dynamics of photogenerated electrons and holes in epitaxial graphene are studied using optical-pump terahertz-probe spectroscopy. The conductivity in graphene at terahertz frequencies depends on the carrier concentration as well as the carrier distribution in energy. Time-resolved studies of the conductivity can therefore be used to probe the dynamics associated with carrier intraband relaxation and interband recombination. We report the electron-hole recombination times in epitaxial graphene for the first time. Our results show that carrier cooling occurs on subpicosecond time scales and that interband recombination times are carrier density dependent.