Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Highlights d Bacteroides ovatus strain variation drives either high or low fecal IgA d CD4 + T cells are critical in B. ovatus-mediated gut IgA production d Cocktails of IgA high B. ovatus strains convert mice from lowto high-IgA producers

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Formation of mutagenic heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs) is one pathway believed to drive the association of colon cancer with meat consumption. Limited data exist on the associations of individual HCAs and PAHs in red or white meat with colon cancer. Analyzing data from a validated meat preparation questionnaire completed by 1,062 incident colon cancer cases and 1,645 population controls from an ongoing case-control study, risks of colon cancer were estimated using unconditional logistic regression models, comparing the fourth to the first quartile of mutagen estimates derived from a CHARRED based food frequency questionnaire. Total dietary intake of 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) (adjusted odds ratio (aOR) = 1.88, 95% CI = 1.45–2.43, Ptrend < 0.0001), 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline (DiMeIQx) (aOR = 1.73, 95% CI = 1.34–2.23, Ptrend < 0.0001) and meat-derived mutagenic activity (aOR = 1.84, 95% CI = 1.42–2.39, Ptrend < 0.0001) were statistically significantly associated with colon cancer risk. Meat type specific analyses revealed statistically significant associations for red meat-derived MeIQx, DiMeIQx and mutagenic activity, but not for the same mutagens derived from white meat. Our study adds evidence supporting red meat-derived, but not white-meat derived HCAs and PAHs, as an important pathway for environmental colon cancer carcinogenesis.

Objective To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods Health care workers from seven hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of COVID-19 were answered in the app. Results We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined five machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (Confidence Interval 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking. Lay Summary The goal of the study is to determine if SARS-CoV-2 infections, which cause Coronavirus Disease 2019 (COVID-19), can be detected using machine learning algorithms applied to the information collected by wearable devices. Four hundred and nine health care workers were enrolled from 7 hospitals in New York City. Participants downloaded a custom smart phone application and were provided with an Apple Watch, if they did not have one of their own. Daily questions collected information from participants about how they feel and whether they were diagnosed with COVID-19. We found that a type of machine learning algorithm, called gradient boosting machines was able to reliably predict SARS-CoV-2 infections by combining various metrics collected from the Apple Watch. We found markers of heart rate variability, or the calculation of the small-time differences between each heartbeat, to be important in identifying infections. These findings demonstrate that wearable devices may improve screening for SARS-CoV-2 infections and the overall tracking of infections.