Longitudinal assessment of sweat-based TNF-alpha in inflammatory bowel disease using a wearable device

Wearable devices can non-invasively monitor patients with chronic diseases. Sweat is an easily accessible biofluid for continuous sampling of analytes, including inflammatory markers and cytokines. We evaluated a sweat sensing wearable device in subjects with and without inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract. Participants with an IBD related hospital admission and a C-reactive protein level above 5 mg/L wore a sweat sensing wearable device for up to 5 days. Tumor necrosis factor-alpha (TNF-α) levels were continually assessed in the sweat via the sensor, and daily in the blood. A second cohort of healthy subjects without chronic diseases wore the device for up to 48 h. Twenty-eight subjects were enrolled. In the 16 subjects with IBD, a moderate linear relationship between serum and sweat TNF-α levels was observed (R2 = 0.72). Subjects with IBD were found to have a mean sweat TNF-α level of 2.11 pg/mL, compared to a mean value of 0.19 pg/mL in 12 healthy controls (p < 0.0001). Sweat TNF-α measurements differentiated subjects with active IBD from healthy subjects with an AUC of 0.962 (95% CI 0.894-1.000). A sweat sensing wearable device can longitudinally measure key sweat-based markers of IBD. TNF-α levels in the sweat of subjects with IBD correlate with serum values, suggesting feasibility in non-invasive disease monitoring.

Longitudinal monitoring of IL-6 and CRP in inflammatory bowel disease using IBD-AWARE

There are limitations to monitoring modalities for chronic inflammatory conditions, including inflammatory bowel disease (IBD). Wearable devices are scalable mobile health technology that present an opportunity to monitor markers that have been linked to worsening, chronic inflammatory conditions and enable remote monitoring. In this research article, we evaluate and demonstrate a proof-of-concept wearable device to longitudinally monitor inflammatory and immune markers linked to IBD disease activity in sweat compared to expression in serum. Sixteen participants with an IBD-related hospital admission and a C-reactive protein (CRP) > 5 μg/mL were followed for up to 5 days. The sweat sensing device also known as IBD AWARE was worn to continuously measure CRP and interleukin-6 (IL-6) in the sweat of participants via electrochemical impedance spectroscopy. Serum samples were collected daily. A linear relationship between serum and sweat readings for CRP and IL-6 was demonstrated based on individual linear correlation coefficients. Pooled CRP and IL-6 serum-to-sweat ratios demonstrated improving correlation coefficients as serum cutoffs decreased. Between the first and last day of observation, significant and non-significant trends in serum CRP and IL-6 were observed in the sweat. Comparison of sweat measurements between the subjects with active IBD and 10 healthy subjects distinguished an inflamed and uninflamed state with an AUC of 0.85 (95% CI: 0.68-1.00) and a sensitivity and specificity of 82% and 70% at a CRP cutoff of 938.9 pg/mL. IBD AWARE wearable device holds promise in longitudinally monitoring individuals with IBD and other inflammatory diseases.

Geography Influences Susceptibility to SARS-CoV-2 Serological Response in Patients With Inflammatory Bowel Disease: Multinational Analysis From the ICARUS-IBD Consortium

BACKGROUND

Beyond systematic reviews and meta-analyses, there have been no direct studies of serological response to COVID-19 in patients with inflammatory bowel disease (IBD) across continents. In particular, there has been limited data from Asia, with no data reported from India. The ICARUS-IBD (International study of COVID-19 Antibody Response Under Sustained immunosuppression in IBD) consortium assessed serological response to SARS-CoV-2 in patients with IBD in North America, Europe, and Asia.

METHODS

The ICARUS-IBD study is a multicenter observational cohort study spanning sites in 7 countries. We report seroprevalence data from 2303 patients with IBD before COVID-19 vaccination between May 2020 and November 2021. SARS-CoV-2 anti-spike and anti-nucleocapsid antibodies were analyzed.

RESULTS

The highest and lowest SARS-CoV-2 anti-spike seropositivity rates were found in Asia (81.2% in Chandigarh and 57.9% in Delhi, India; and 0% in Hong Kong). By multivariable analysis, country (India: odds ratio [OR], 18.01; 95% confidence interval [CI], 12.03-26.95; P < .0001; United Kingdom: OR, 2.43; 95% CI, 1.58-3.72; P < .0001; United States: OR, 2.21; 95% CI, 1.27-3.85; P = .005), male sex (OR, 1.46; 95% CI, 1.07-1.99; P = .016), and diabetes (OR, 2.37; 95% CI, 1.04-5.46; P = .039) conferred higher seropositivity rates. Biological therapies associated with lower seroprevalence (OR, 0.22; 95% CI, 0.15-0.33; P < .0001). Multiple linear regression showed associations between anti-spike and anti-nucleocapsid titers with medications (P < .0001) but not with country (P = .3841).

CONCLUSIONS

While the effects of medications on anti-SARS-CoV-2 antibody titers in patients with IBD were consistent across sites, geographical location conferred the highest risk of susceptibility to serologically detectable SARS-CoV-2 infection. Over half of IBD patients in India were seropositive prior to vaccination. These insights can help to inform shielding advice, therapeutic choices, and vaccine strategies in IBD patients for COVID-19 and future viral challenges.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort

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.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers

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.

Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

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.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

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.