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Opioid use disorder (OUD) and overdose deaths are a public health crisis. One contributing factor is stigma towards people who use opioids. We developed and conducted a public-facing, half-day educational event designed to challenge misperceptions about OUD from a contemporary neuroscience perspective. Participants engaged with three different resources on the neurobiology of addiction, and, at the end of the event, they rated its effectiveness. We also collected and compared pre- and post-event composite OUD stigma scales. Participants rated our approach and the overall event as highly effective. Additionally, OUD stigma scores were lower immediately following the event, and this decrease was primarily driven by decreased internalized stigma. Here, we demonstrate an effective proof-of-concept that an accessible, public-facing, neuroscience education event may reduce OUD stigma in the community.

Abstract Post-pandemic health operations have become a near-term reality, discussions around wearables are on the rise. How do wearable health solutions effectively deploy and use this opportunity to fill the gap between wellness and healthcare? In this paper, we will talk about wearable healthcare diagnosis, with a particular focus on monitoring skin hydration using optical multi-wavelength sensor fusion. Continuous monitoring of human skin hydration is a task of paramount importance for maintaining water loss dynamics for fitness lovers as well as for skin beauty, integrity and the health of the entire body. Preserving the appropriate levels of hydration ensures consistency of weight, positively affects psychological state, and proven to result in a decrease in blood pressure as well as the levels of “bad” cholesterol while slowing down the aging processes. Traditional methods for determining the state of water content in the skin do not allow continuous and non-invasive monitoring, which is required for variety of consumer, clinical and cosmetic applications. We present novel sensing technology and a pipeline for capturing, modeling and analysis of the skin hydration phenomena and associated changes therein. By expanding sensing capabilities built into the SmartWatch sensor and combining them with advanced modeling and Machine Learning (ML) algorithms, we identified several important characteristics of photoplethysmography (PPG) signal and spectral sensitivity corresponding to dynamics of skin water content. In a hardware aspect, we newly propose the expansion of SmartWatch capabilities with InfraRed light sources equipped with wavelengths of 970 nm and 1450 nm. Evaluation of the accuracy and characteristics of PPG sensors has been performed with biomedical optics-based simulation framework using Monte Carlo simulations. We performed rigorous validation of the developed technology using experimental and clinical studies. The developed pipeline serves as a tool in the ongoing studies of the next generation of optical sensing technology.

This paper describes the application of multiwavelength photoplethysmography (MW-PPG) in reflectance mode for noninvasive measurements of total hemoglobin concentration. Consumer wrist-wearable devices (smartwatches, wristbands) typically contain a photoplethysmography sensor operating in reflectance mode, as opposed to clinical pulse-oximetry sensors operating in transmittance mode. We assume that the generally accepted approach to the analysis of the transmittance-mode MW-PPG signal, based on the direct implementation of the Beer-Lambert law, is not applicable to the analysis of reflectance-mode MW-PPG signals. We propose that the shape of the MW-PPG signal carries information regarding the distribution of optical paths at different wavelengths within the tissue, which is crucial for the correct estimation of the absorption of light in the probe volume. We have developed and tested several machine-learning algorithms to analyze the reflectance-mode MW-PPG signal and to estimate the total hemoglobin concentration based on this analysis. To train and validate the algorithms, we collected a dataset of 840 MW-PPG signals measured from 170 volunteers by using a wrist-wearable PPG sensor. Three reference devices were used to label the data and test the performance of the developed algorithms: an invasive laboratory blood test, minimally invasive HemoCue Hb 201+, and noninvasive Masimo Radical-7 transmittance mode pulse-oximeter. The best performance achieved with the developed algorithm, mean absolute error MAE $\approx 12.6\pm $ 1.7 g/L and correlation coefficient R $\approx 0.66\pm $ 0.09, is comparable with the performance of the clinical noninvasive device.

This study presents a newly released algorithm for smartwatches – Sweat loss estimation for running activities. A machine learning model (polynomial Kernel Ridge Regression) is used to estimate the sweat loss in milliliters. A clinical dataset of 748 running tests of 568 people was collected and used for training / validation. The data presents a diversity of factors playing an important role in sweat loss: anthropometric parameters of users, distance, ambient temperature and humidity. The data augmentation technique was implemented. One of the key points of the algorithm is an accelerometer-based model for running distance estimation. The model we developed has a mean absolute percentage error (MAPE) = 7.7% and a coefficient of determination (R2) = 0.95 (at distances in the range of 2–20 km). The performance of the fully automatic sweat loss estimation algorithm provides an average root mean square error (RMSE) = 236 ml; more fundamentally, health-related parameter body weight percentage RMSE (RMSEBWP) = 0.33% and R2 = 0.79. To the best of the authors’ knowledge, the algorithm provides the best performance of any existing solution or described in the literature.

Current bioelectric impedance analysis (BIA) systems are often large, cumbersome devices which require strict electrode placement on the user, thus inhibiting mobile capabilities. In this work, we developed a handheld BIA device that measures impedance from multiple frequencies (5 kHz~200 kHz) with four contact electrodes and evaluated the BIA device against standard body composition analysis systems: a dual-energy X-ray absorptiometry (DXA) system (GE Lunar Prodigy, GE Healthcare, Buckinghamshire, UK) and a whole-body BIA system (InBody S10, InBody, Co. Ltd, Seoul, Korea). In the study, 568 healthy participants, varying widely in body mass index, age, and gender, were recruited at two research centers: the Samsung Medical Center (SMC) in South Korea and the Pennington Biomedical Research Center (PBRC) in the United States. From the measured impedance data, we analyzed individual body fat and skeletal muscle mass by applying linear regression analysis against target reference data. Results indicated strong correlations of impedance measurements between the prototype pathways and corresponding InBody S10 electrical pathways (R = 0.93, p < 0.0001). Additionally, body fat estimates from DXA did not yield significant differences (p > 0.728 (paired t-test), DXA mean body fat 29.45 ± 10.77 kg, estimated body fat 29.52 ± 12.53 kg). Thus, this portable BIA system shows a promising ability to estimate an individual’s body composition that is comparable to large stationary BIA systems.

Introduction: Estimates suggest that hundreds of thousands of sex trafficking victims live in the United States. Several screening tools for healthcare professionals to identify sex trafficking victims have been proposed, but the effectiveness of these tools in the emergency department (ED) remains unclear. Our primary objective in this study was to evaluate the feasibility of a screening survey to identify adult victims of sex trafficking in the ED. We also compared the sensitivity of emergency physician concern and a screening survey for identifying sex trafficking victims in the ED and determined the most effective question(s) for identifying adult victims of sex trafficking. Methods: We enrolled a convenience sample of medically stable female ED patients, age 18–40 years. Patients completed a 14-question survey. Physician concern for sex trafficking was documented prior to informing the physician of the survey results. A “yes” answer to any question or physician concern was considered a positive screen, and the patient was offered social work consultation. We defined a “true positive” as a patient admission for or social work documentation of sex trafficking. Demographic and clinical information were collected from the electronic medical record. Results: We enrolled 143 patients, and of those 39 (27%, 95% confidence interval [CI] [20%–35%]) screened positive, including 10 (25%, 95% CI [13%–41%]) ultimately identified as victims of sex trafficking. Sensitivity of the screening survey (100%, 95% CI [74%–100%]) was better than physician concern (40%, 95% CI [12%–74%]) for identifying victims of sex trafficking, difference 60%, 95% CI [30%–90%]. Physician specificity (91%, 95% CI [85%–95%]), however, was slightly better than the screening survey (78%, 95% CI [70%–85%]), difference 13%, 95% CI [4%–21%]. All 10 (100%, 95%CI [74%–100%]) “true positive” cases answered “yes” to the screening question regarding abuse. Conclusion: Identifying adult victims of sex trafficking in the ED is feasible. A screening survey appears to have greater sensitivity than physician concern, and a single screening question may be sufficient to identify all adult victims of sex trafficking in the ED.