Researchers have increasingly begun to use consumer wearables or wrist-worn smartwatches and fitness monitors for measurement of cardiovascular psychophysiological processes related to mental and physical health outcomes. These devices have strong appeal because they allow for continuous, scalable, unobtrusive, and ecologically valid data collection of cardiac activity in “big data” studies. However, replicability and reproducibility may be hampered moving forward due to the lack of standardization of data collection and processing procedures, and inconsistent reporting of technological factors (e.g., device type, firmware versions, and sampling rate), biobehavioral variables (e.g., body mass index, wrist dominance and circumference), and participant demographic characteristics, such as skin tone, that may influence heart rate measurement. These limitations introduce unnecessary noise into measurement, which can cloud interpretation and generalizability of findings. This paper provides a brief overview of research using commercial wearable devices to measure heart rate, reviews literature on device accuracy, and outlines the challenges that non-standardized reporting pose for the field. We also discuss study design, technological, biobehavioral, and demographic factors that can impact the accuracy of the passive sensing of heart rate measurements, and provide guidelines and corresponding checklist handouts for future study data collection and design, data cleaning and processing, analysis, and reporting that may help ameliorate some of these barriers and inconsistencies in the literature.
Nearly all applications of 3D printing for surgical planning have been limited to bony structures and simple morphological descriptions of complex organs due to the fundamental limitations in accuracy, quality, and efficiency of the current modeling paradigms and technologies. Current approaches have largely ignored the constitution of soft tissue critical to most surgical specialties where multiple high-resolution variations transition gradually across the interior of the volume. Differences in the scales of organization related to unique organs require special attention to capture fine features critical to surgical procedures. We present a six-material bitmap printing technique for creating 3D models directly from medical images, which are superior in spatial and contrast resolution to current 3D modeling methods, and contain previously unachievable spatial fidelity for soft tissue differentiation. A retrospective exempt IRB was obtained for all data through the Colorado Multiple Institution Review Board #21-3128.
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