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Objective To assess whether an individual’s degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5–7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

Despite the fundamental role of strain variation in gut microbiota function, the number of unique strains of a species that can stably colonize the human gut is still unknown. In this work, we determine the strain richness of common gut species using thousands of sequenced bacterial isolates and metagenomes. We find that strain richness varies across species, is transferable by fecal microbiota transplantation, and is low in the gut compared to other environments. Therapeutic administration of supraphysiologic numbers of strains per species only temporarily increases recipient strain richness, which subsequently converges back to the population average. These results suggest that properties of the gut ecosystem govern the number of strains of each species colonizing the gut and provide a theoretical framework for strain engraftment and replacement in fecal microbiota transplantation and defined live biotherapeutic products.

BACKGROUND Clinical guidelines recommend the incorporation of risk stratification assessments when making decisions about Crohn’s disease (CD) treatment. We aimed to evaluate if patient and gastroenterologist (GI) decision making and confidence about starting or changing therapy were impacted by reviewing a CD risk stratification assessment. METHODS We conducted a multicenter, prospective study evaluating the impact of a risk stratification tool on decision making by GIs and biologic naïve adult CD patients considering starting or changing therapy. Medical records were reviewed by a GI at the organizing site to determine risk according to the American Gastroenterological Association (AGA) Crohn’s Disease Care Pathway risk assessment tool. Patients with any risk factors for rapid disease progression (age RESULTS 47 patient-GI pairs were enrolled across 14 sites. Among CD patients, mean age was 40.2 years, 59% were men, 70% were white, 80% had inflammatory (B1) disease behavior, 74% had no prior CD-related hospitalizations, and 82% no prior CD-related surgeries. 64% of patients were deemed moderate-high risk and 36% low risk by AGA criteria. In comparison, 40% of patients self-assessed their risk as moderate-high at baseline. The most common moderate-high risk factors were age CONCLUSION Incorporation of a simple risk stratification assessment when considering treatment change in CD can assist in patient and GI decision making. Low risk assessments are associated with lower confidence in decision making.

BACKGROUND Inflammatory bowel disease (IBD) flares are common and unpredictable. Disease monitoring relies on symptom reporting or single timepoint assessments of stool, blood, imaging, or endoscopy—these are inconvenient and invasive and do not always reflect the patient perspective. Advances in wearable technology allow for passive, continuous and non-invasive assessment of physiological metrics including heart rate variability (HRV), the measure of small time differences between each heartbeat, a marker of autonomic nervous system function. Our group has previously demonstrated that changes in autonomic function precedes an IBD flare, can predict psychological state transitions and even identify inflammatory events including SARS-CoV-2 infection. To develop algorithms that can predict IBD flares using wearable device signatures, we launched a national wearable device study called The IBD Forecast study. To assess data quality and feasibility, the first 125 Apple Watch users to enroll were evaluated. METHODS The IBD Forecast study is a prospective cohort study enrolling anyone ≥18 years of age in the United States (US) with IBD who is willing to (1) use a commercially available wearable device, (2) download our custom eHive app and (3) answer daily survey questions. HRV metrics (mean of the standard deviations of all the NN intervals [SDNN]) were analyzed using a mixed-effect cosigner model that incorporated body mass index, age, and sex. SDNN is a time domain HRV index that reflects both sympathetic and parasympathetic nervous system activity and is calculated from the variance of intervals between adjacent QRS complexes (the normal-to-normal [NN] intervals). Clinical flare was assessed with daily Patient Reported Outcome (PRO)-2 surveys (flare; PRO-2 Crohn’s disease >7, PRO-2 ulcerative colitis >2). Inflammatory flare was assessed via patient reported C-reactive protein (CRP), with inflammatory flare defined as >5 mg/L. RESULTS The first 125 study participants were enrolled across 29 states in the US (Table 1). Circadian features of changes of HRV were modelled (Figure 1). The mesor, or midline of the circadian pattern of the SDNN was higher in those with clinical flare (mean 44.43; 95% CI 41.25-47.75) compared to those in clinical remission (mean 43.03; 95% CI 39.94-46.22) (p CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device can identify symptomatic and inflammatory flares. This preliminary analysis of a small proportion of the IBD Forecast Study cohort demonstrates the feasibility of using wearable devices to identify, and may potentially predict, IBD flares.

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 7 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 Coronavirus Disease 2019 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 5 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 [CI] 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.

BACKGROUND Current methods to monitor inflammatory bowel disease (IBD) activity, such as colonoscopy, blood and stool markers, or imaging, are suboptimal and rely on single timepoint assessments that are inconvenient, expensive, or invasive. Wearable devices are a highly scalable mobile health technology that can non-invasively, continuously, and accurately monitor several physiological parameters that have been linked to flare in IBD. We performed a pilot feasibility study in subjects with IBD of a wearable device to monitor sweat-based inflammatory markers. METHODS Eligible participants were aged ≥ 18 years, diagnosed with IBD, admitted to the Mount Sinai Hospital for an IBD-related admission and had an elevated CRP (>5 mg/L). The sweat sensing device was worn by subjects to continuously measure the inflammatory markers C-reactive protein (CRP), tumor necrosis factor (TNF)α, and interleukin-6 (IL-6). The immunoassay on the sweat sensor was developed by immobilizing a specific capture probe antibody to capture CRP, TNFα and IL-6 in sweat. Data were transmitted from the device via Bluetooth to the cloud server. Sensor response was measured using impedance spectroscopy. Serum samples were collected daily. To evaluate the relationship between inflammatory markers in the sweat and serum, the Pearson correlation coefficient was calculated between longitudinal sweat measurements and serum CRP, TNFα and IL-6 levels. This was calculated for each subject with 2 or more consecutive serum values. The coefficient of variation was then calculated for each inflammatory marker. RESULTS A total of 16 subjects were enrolled. The sweat sensor device demonstrated the ability to measure in sweat CRP in the range of 525 to 1175.38 pg/ml, IL-6 in the range of 0.45 to 3.14 pg/ml, and TNFα in the range of 10.22 to 11.32 pg/ml. These measurements were correlated to their serum levels which were in the range of 2,200,000 to 12,7800,000 ug/ml for CRP, 0.68 to 31.66 pg/ml for IL-6, and 0.15 to 15.01 pg/ml for TNFα. The Pearson correlation coefficients between sweat and serum readings for CRP, IL-6 and TNFα are presented in Table 1. The coefficient of variation for CRP is 16.2% (mean 33,6863.14; standard deviation [std] 54,654.71), IL-6 is 19.5% (mean 33.23; std 7.06), and TNFα is 16.3% (mean 2.37; std 0.39). Figure 1 demonstrates temporal serum and sweat trends in a participant. CONCLUSION Longitudinally collected sweat CRP, IL-6 and TNFα, measured using a wearable device, correlate with serum measurements. This work is the first demonstration of continuous, non-invasive methods to longitudinally monitor inflammatory markers in sweat, and suggests the feasibility of using a non-invasive sweat-sensing wearable device to monitor IBD disease activity.

ImportancePassive and non-invasive identification of SARS-CoV-2 infection remains a challenge. Widespread use of wearable devices represents an opportunity to leverage physiological metrics and fill this knowledge gap.ObjectiveTo determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.DesignA multicenter observational study enrolling health care workers with remote follow-up.SettingSeven hospitals from the Mount Sinai Health System in New York CityParticipantsEligibility criteria included health care workers who were ≥18 years, employees of one of the participating hospitals, with at least an iPhone series 6, and willing to wear an Apple Watch Series 4 or higher. We excluded participants with underlying autoimmune/inflammatory diseases, and medications known to interfere with autonomic function. We enrolled participants between April 29th, 2020, and March 2nd, 2021, and followed them for a median of 73 days (range, 3-253 days). Participants provided patient-reported outcome measures through a custom smartphone application and wore an Apple Watch, collecting heart rate variability and heart rate data, throughout the follow-up period.ExposureParticipants were exposed to SARS-CoV-2 infection over time due to ongoing community spread.Main Outcome and MeasureThe primary outcome was SARS-CoV-2 infection, defined as ±7 days from a self-reported positive SARS-CoV-2 nasal PCR test.ResultsWe enrolled 407 participants with 49 (12%) having a positive SARS-CoV-2 test during follow-up. We examined five machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable 10-CV performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC)=85% (Confidence Interval 83-88%). The model was calibrated to improve sensitivity over specificity, achieving an average sensitivity of 76% (CI ±∼4%) and specificity of 84% (CI ±∼0.4%). The most important predictors included parameters describing the circadian HRV mean (MESOR) and peak-timing (acrophase), and age.Conclusions and RelevanceWe show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV2 infection. Utilizing physiological metrics from wearable devices may improve screening methods and infection tracking.

BACKGROUND The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. OBJECTIVE This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. METHODS HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. RESULTS A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial. CONCLUSIONS High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

Patients with immune-mediated inflammatory diseases (IMIDs) and acquired and genetic immunodeficiencies receiving therapeutic infusions are considered high risk for SARS-CoV-2 infection. However, the seroprevalance in this group and the safety of routine administrations at outpatient infusion centers are unknown. To determine the infection rate and clinical-social factors related to SARS-CoV-2 in asymptomatic patients with IMIDs and immunodeficiencies receiving routine non-cancer therapeutic infusions, we conducted a seroprevalence study at our outpatient infusion center. We report the first prospective SARS-CoV-2 sero-surveillance of 444 IBD/IMID, immunodeficiency, and immune competent patients at an outpatient infusion center in the U.S. showing lower seroprevalence in patients compared with the general population and provide clinical and social characteristics associated with seroprevalence in this group. These data suggest that patients can safely continue infusions at outpatient centers.

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.

IntroductionThe Coronavirus Disease 2019 (COVID-19) pandemic has resulted in psychological distress in health care workers (HCWs). There is a need to characterize which HCWs are at increased risk of psychological sequela from the pandemic.MethodsHCWs across seven hospitals in New York City were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study App. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Surveys were obtained daily.ResultsThree hundred and sixty-one HCWs were enrolled. Multivariable analysis found New York City COVID-19 case count to be significantly associated with increased longitudinal stress (p=0.008). A non-significant decrease in stress (p=0.23) was observed following COVID-19 diagnosis, though there was a borderline significant increase following the 4-week period after a COVID-19 diagnosis via nasal PCR (p=0.05). Baseline emotional support, baseline quality of life and baseline resilience were associated with decreased longitudinal stress (pConclusionOur findings demonstrate that low resilience, emotional support, and quality of life identify HCWs at risk of high perceived longitudinal stress secondary to the COVID-19 pandemic and have a distinct physiological stress profile. Assessment of HCWs for these features can identify and permit allocation of psychological support to these at-risk individuals as the COVID-19 pandemic and its psychological effects continue in this vulnerable population.

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.

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms.MethodsHealth 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. Survey’s assessing infection and symptom related questions were obtained daily.FindingsUsing 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), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). 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=0.01). Significant changes in the mean MESOR 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=0.01).InterpretationLongitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.FundingSupport was provided by the Ehrenkranz Lab For Human Resilience, the BioMedical Engineering and Imaging Institute, The Hasso Plattner Institute for Digital Health at Mount Sinai, The Mount Sinai Clinical Intelligence Center and The Dr. Henry D. Janowitz Division of Gastroenterology.

Background The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. Objective This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. Methods HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. Results A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P Conclusions High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

Fecal IgA production depends on colonization by a gut microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. By accessing the IgA-inducing capacity of a diverse set of human gut microbial strains, we identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied biomodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through both T-cell-dependent and T-cell-independent B cell-activation pathways. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Thus, microbial strain specificity is essential for the optimal induction of high-IgA responses in the gut. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a gut immune phenotype, including IgA production.

Summary Fecal IgA production depends on colonization by a gut microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. Here, we assessed the IgA-inducing capacity of a diverse set of human gut microbial strains by monocolonizing mice with each strain. We identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied bimodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through the T cell-dependent B cell-activation pathway. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a gut immune phenotype, including IgA production.