ABSTRACT
Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. Many COVID-19 technology solutions leverage positioning systems, generally using Bluetooth and GPS, and can theoretically be adapted to monitor safety compliance within dedicated environments. However, they may not be the ideal modalities for indoor positioning. This article conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, our analysis demonstrates how coarse-grained WiFi data can sufficiently reflect the indoor occupancy spectrum when different COVID-19 policies were enacted. Our work analyzes staff and students' mobility data from three university campuses. Two of these campuses are in Singapore, and the third is in the Northeastern United States. Our results show that online learning, split-team, and other space management policies effectively lower occupancy. However, they do not change the mobility for individuals transitioning between spaces. We demonstrate how this data source can be a practical application for institutional crowd control and discuss the implications of our findings for policymaking.
ABSTRACT
The COVID-19 pandemic has accelerated a long-term trend in libraries toward the generation of online teaming objects. These materials in the library space are unique compared to those generated in other fields, as libraries frequently make them available for access on their website, or through library-owned social media channels, in formats meant to be available to all. This article looks at major types of library online learning objects and surfaces the most impactful areas of work for librarians and other education professionals interested in improving accessibility and accessibility compliance. The article and discussion are meant to provide an overview of critical WCAG 2.1 standards in relation to learning object types to make the practical implementation of accessibility less overwhelming. To supplement these recommendations, the authors have provided exhaustive lists of WCAG 2.1 criteria applicable to each learning object in the Appendixes.
ABSTRACT
Introduction: Lockdowns imposed to stem the spread of COVID-19 have disrupted the lifestyles of many worldwide, but studies to date are mostly confined to observations within a limited number of countries, based on subjective reports and survey from a narrow time window. In the present study, we investigate associations between the severity of lockdown policies and objective sleep and resting-heart rate measures. Methods: Data from 113,000 users of a consumer sleep tracker across 20 countries were gathered between Jan-Jul 2020 and compared with an equivalent period in 2019 as a control for naturally occurring seasonal fluctuations. Lockdown stringency was derived using scores from the Oxford Government Response Tracker. Multilevel growth curve models were used to quantify the effect of lockdown stringency on changes to sleep patterns (midsleep time and midsleep variability) and resting heart rate changes, and to predict changes in resting heart rate from changes to sleep patterns. Results: Lockdown severity modulated the size of shifts in sleep midpoint and regularity during this period. Midsleep times were delayed in all countries during strict lockdowns, particularly on weekdays, while midsleep variability reduced. The largest shifts in midsleep time (+0.09 to +0.58 hours), midsleep variability (-0.12 to -0.26 hours) and resting heart rate (-0.35 to -2.08 bpm) occurred in April and May - when most countries imposed their strictest lockdown measures. In addition, multilevel modelling revealed that for each unit increase in stringency index, midsleep time was delayed by 0.96 min, midsleep variability decreased by 0.46 min and resting heart rate decreased by 0.06 bpm. Finally, in models predicting changes in resting heart rate from changes to sleep patterns, midsleep variability was shown to be the strongest predictor of resting heart rate, wherein an hour increase in the standard deviation of midsleep variability predicted a 5.12 increase in bpm, while an hour increase in midsleep time only predicted a 1.25 decrease in bpm. Conclusion: Our findings demonstrate the utility of large-scale data from consumer wearables in providing population-level insights into how lockdown severity directly impacts sleep health during this pandemic period.
ABSTRACT
As the neurobiology of COVID-19 advances, delirium looms ever larger as a predictor of poor outcome. That is why it is important to identify when delirium is already present and developing. That will require a change in standards of practice which only call presently for daily delirium assessments when patients are in an intensive care unit (ICU). We argue that quick delirium assessments should be added to routine nursing assessments from the time a patient suspected of COVID-19 arrives through the acute care hospital course of all COVID-19 positive patients.