Myalgic Encephalomyelitis/Chronic Fatigue Syndrome After SARS-CoV-2 Infection.

Importance: Chronic symptoms reported following an infection with SARS-CoV-2, such as cognitive problems, overlap with symptoms included in the definition of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Objective: To evaluate the prevalence of ME/CFS-like illness subsequent to acute SARS-CoV-2 infection, changes in ME/CFS symptoms through 12 months of follow-up, and the association of ME/CFS symptoms with SARS-CoV-2 test results at the acute infection-like index illness. Design, Setting, and Participants: This prospective, multisite, longitudinal cohort study (Innovative Support for Patients with SARS-CoV-2 Infections Registry [INSPIRE]) enrolled participants from December 11, 2020, to August 29, 2022. Participants were adults aged 18 to 64 years with acute symptoms suggestive of SARS-CoV-2 infection who received a US Food and Drug Administration-approved SARS-CoV-2 test at the time of illness and did not die or withdraw from the study by 3 months. Follow-up surveys were collected through February 28, 2023. Exposure: COVID-19 status (positive vs negative) at enrollment. Main Outcome and Measures: The main outcome was the weighted proportion of participants with ME/CFS-like illness based on the 2015 Institute of Medicine clinical case definition using self-reported symptoms. Results: A total of 4378 participants were included in the study. Most were female (3226 [68.1%]). Mean (SD) age was 37.8 (11.8) years. The survey completion rates ranged from 38.7% (3613 of 4738 participants) to 76.3% (1835 of 4738) and decreased over time. The weighted proportion of participants identified with ME/CFS-like illness did not change significantly at 3 through 12 months of follow-up and was similar in the COVID-19-positive (range, 2.8%-3.7%) and COVID-19-negative (range, 3.1%-4.5%) groups. Adjusted analyses revealed no significant difference in the odds of ME/CFS-like illness at any time point between COVID-19-positive and COVID-19-negative individuals (marginal odds ratio range, 0.84 [95% CI, 0.42-1.67] to 1.18 [95% CI, 0.55-2.51]). Conclusions and Relevance: In this prospective cohort study, there was no evidence that the proportion of participants with ME/CFS-like illness differed between those infected with SARS-CoV-2 vs those without SARS-CoV-2 infection up to 12 months after infection. A 3% to 4% prevalence of ME/CFS-like illness after an acute infection-like index illness would impose a high societal burden given the millions of persons infected with SARS-CoV-2.

Ethnic and racial differences in self-reported symptoms, health status, activity level, and missed work at 3 and 6 months following SARS-CoV-2 infection.

Introduction: Data on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection. Methods: Participants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys (N = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point. Results: Following SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36-2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06-2.02; much less, OR: 2.23; 95%CI: 1.38-3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25-2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21-2.46; much less, OR: 2.08; 95%CI: 1.18-3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13-3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60-5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10-3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02-2.51; much less, OR: 2.49; 95%CI: 1.40-4.44), and more missed work (OR: 2.25; 95%CI: 1.27-3.98). Discussion: Awareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes.

TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers.

We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.

Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization.

Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.