Abstract 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.

BACKGROUND:In patients with inflammatory bowel disease (IBD), failure to adhere to treatment regimens due to insurance issues can lead to disease complications. Our aim was to examine patients' perceptions of the impact of insurance issues on their health.METHODS:Twenty-nine patients with IBD at a large US academic center and an insurance issue participated in a mixed-methods study. Retrospective chart review and an online questionnaire were completed to collect demographic information, IBD characteristics, and validated resilience scores. Semistructured interviews were completed for insurance experiences, which were coded independently by 2 coders for themes.RESULTS:Twenty-nine patients completed the interview, and 24 completed the online survey. Sixteen had Crohn's disease, 13 had ulcerative colitis, and 66% were female. The most common insurance issue was lapsed insurance. Many experienced physical consequences, with 58% having flares, 14% undergoing surgery, and 14% developing antibodies. All emotional responses were negative, with the majority feeling stressed (38%). Providers were uninformed of insurance issues in 28% of cases. When asked about perceived resilience, 41% felt incapable of managing the situation, and 45% gave up trying to solve the problem. When asked how to improve going forward, 38% requested an easily accessible advocate to guide them.CONCLUSIONS:A large proportion of our cohort chose not to inform their provider, felt incapable of managing on their own, and gave up on resolving their insurance issue. This highlights the need to consider restructuring the insurance system, to identify those at risk for insurance issues, and to make advocates available to avoid devastating consequences.

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 +/-similar to 4%) and specificity of 77% (CI +/-similar to 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 severe acute respiratory syndrome coronavirus-2 (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.

BACKGROUND & AIMS: Integrated inflammatory bowel disease (IBD) care is effective but not routinely implemented. Validated methods that simultaneously address mind and body targets such as resilience may improve access and outcomes. We describe the development and implementation of the GRITT method and its impact on resilience, health care utilization (HCU), and opioid use in IBD. METHODS: Consecutive patients from an academic IBD center were evaluated for low resilience on the basis of provider referral. Low resilience patients were invited to participate in the GRITT program. Primary outcome was % reduction in HCU. Secondary outcomes were change in resilience and corticosteroid and opioid use. Patients were allocated into 2 groups for analysis: GRITT participants (GP) and non-participants (NP). Clinical data and HCU in the year before enrollment were collected at baseline and 12 months. One-way repeated measures multivariate analysis of covariance evaluated group 3 time interactions for the primary outcome. Effect size was calculated for changes in resilience over time. RESULTS: Of 456 screened IBD patients 394 were eligible, 184 GP and 210 NP. GP had greater reduction in HCU than NP: 71% reduction in emergency department visits, 94% reduction in unplanned hospitalizations. There was 49% reduction in opioid use and 73% reduction in corticosteroid use in GP. Resilience increased by 27.3 points (59%), yielding a large effect size (d = 2.4). CONCLUSIONS: Mind-body care that focuses on building resilience in the context of IBD care may be a novel approach to reduce unplanned HCU and opioid use, but large, multicenter, randomized controlled trials are needed.

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.

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.

AbstractImportancePassive 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.