Study Objective:Shiftwork increases risk for numerous chronic diseases, which is hypothesized to be linked to disruption of circadian timing of lifestyle behaviors. However, empirical data on timing of lifestyle behaviors in real-world shift workers are lacking. To address this, we characterized the regularity of timing of lifestyle behaviors in shift-working police trainees.Methods:Using a two-group observational study design (N=18), we compared lifestyle behavior timing during 6 weeks of in-class training during dayshift, followed by 6 weeks of field-based training during either dayshift or nightshift. Lifestyle behavior timing, including sleep/wake patterns, physical activity, and meals, was captured using wearable activity trackers and mobile devices. The regularity of lifestyle behavior timing was quantified as an index score, which reflects day-to-day stability on a 24h time scale: Sleep Regularity Index (SRI), Physical Activity Regularity Index (PARI) and Mealtime Regularity Index (MRI). Logistic regression was applied to these indices to develop a composite score, termed the Behavior Regularity Index (BRI).Results:Transitioning from dayshift to nightshift significantly worsened the BRI, relative to maintaining a dayshift schedule. Specifically, nightshift led to more irregular sleep/wake timing and meal timing; physical activity timing was not impacted. In contrast, maintaining a dayshift schedule did not impact regularity indices.Conclusion:Nightshift imposed irregular timing of lifestyle behaviors, which is consistent with the hypothesis that circadian disruption contributes to chronic disease risk in shift workers. How to mitigate the negative impact of shiftwork on human health as mediated by irregular timing of sleep/wake patterns and meals deserves exploration.

Abstract Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model’s precision-recall curve (AUC-PR) by 38–50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.

Meditation, a mental and physical exercise which helps to focus attention and reduce stress has gained more popularity in recent years. However, meditation requires a concerted effort and regular practice. To explore the feasibility of using Augmented Reality(AR) Devices to assist in meditating, we recruited ten subjects to perform a five-minute meditation task integrated into AR devices. Heart Rate, Heart Rate Variability, and skin conductance response(SCR) are analyzed based on an Electrocardiogram(ECG), Electrodermal activity to monitor the physiological changes during and after a meditation session. Additionally, participants filled out surveys containing the Perceived Stress Questionnaire (PSQ), a clinically validated survey designed to evaluate stress levels before and after meditation to analyze the change in stress levels. Finally, we found significant differences in Heart Rate and Mean SCR Recovery Time for participants between the three study procedure periods (before, during, and after guided meditation).

ABSTRACTNight shift work, characterized by behavioral circadian disruption, increases cardiometabolic disease risk. Our long-term goal is to develop a novel methodology to quantify behavioral circadian disruption in field-based settings and to explore relations to four metabolic salivary biomarkers of circadian rhythm. This pilot study enrolled 36 police academy trainees to test the feasibility of using wearable activity trackers to assess changes in behavioral patterns. Using a two-group observational study design, participants completed in-class training during dayshift for six weeks followed by either dayshift or nightshift field-training for six weeks. We developed a novel data-post processing step that improves sleep detection accuracy of sleep episodes that occur during daytime. We next assessed changes to resting heart rate (RHR) and sleep regularity index (SRI) during dayshift versus nightshift field training. Secondarily, we examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alternations to sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid, testosterone, and melatonin did not change. These findings show that nightshift work—a form of behavioral circadian rhythm disruption—was detectable in police trainees using activity trackers alone and in combination with a specialized data analysis methodology.KEY POINTSNight shift work increases cardiometabolic disease risk and this may be a consequence of behavioral circadian misalignment.To advance this hypothesis, methodologies to quantify behavioral irregularities during nightshift in field-based settings are needed.In this pilot study, commercially available activity trackers combined with a novel data processing step were used to assess alterations in sleep/wake patterns in police trainees during dayshift versus nightshift.We also explored relations with four metabolic salivary biomarkers of circadian rhythm during dayshift versus nightshift.Compared to dayshift, nightshift resulted in larger perturbations of resting heart rate, sleep regularity index (indicating reduced regularity), and alterations in sleep and activity patterns; this was accompanied by blunted cortisol.This novel data processing step extends commercially available technology for successful application in real-world shift work settings.