@article{WestphalKalinowskiHoferichteretal.2022, author = {Westphal, Andrea and Kalinowski, Eva and Hoferichter, Clara Josepha and Vock, Miriam}, title = {K-12 teachers' stress and burnout during the COVID-19 pandemic}, series = {Frontiers in psychology}, volume = {13}, journal = {Frontiers in psychology}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2022.920326}, pages = {29}, year = {2022}, abstract = {We present the first systematic literature review on stress and burnout in K-12 teachers during the COVID-19 pandemic. Based on a systematic literature search, we identified 17 studies that included 9,874 K-12 teachers from around the world. These studies showed some indication that burnout did increase during the COVID-19 pandemic. There were, however, almost no differences in the levels of stress and burnout experienced by K-12 teachers compared to individuals employed in other occupational fields. School principals' leadership styles emerged as an organizational characteristic that is highly relevant for K-12 teachers' levels of stress and burnout. Individual teacher characteristics associated with burnout were K-12 teachers' personality, self-efficacy in online teaching, and perceived vulnerability to COVID-19. In order to reduce stress, there was an indication that stress-management training in combination with training in technology use for teaching may be superior to stress-management training alone. Future research needs to adopt more longitudinal designs and examine the interplay between individual and organizational characteristics in the development of teacher stress and burnout during the COVID-19 pandemic and beyond.}, language = {en} } @article{SchneidemesserSibiyaCaseiroetal.2021, author = {Schneidemesser, Erika von and Sibiya, Bheki and Caseiro, Alexandre and Butler, Tim and Lawrence, Mark and Leitao, Joana and Lupa{\c{s}}cu, Aura and Salvador, Pedro}, title = {Learning from the COVID-19 lockdown in Berlin}, series = {Atmospheric environment: X}, volume = {12}, journal = {Atmospheric environment: X}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2590-1621}, doi = {10.1016/j.aeaoa.2021.100122}, pages = {13}, year = {2021}, abstract = {Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30-50 \% in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 \% (traffic and urban background locations) and a ca. 22 \% increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.}, language = {en} } @article{grosseDetersMeierMileketal.2021, author = {große Deters, Fenne and Meier, Tabea and Milek, Anne and Horn, Andrea B.}, title = {Self-focused and other-focused health concerns as predictors of the uptake of corona contact tracing apps}, series = {Journal of medical internet research}, volume = {23}, journal = {Journal of medical internet research}, number = {8}, publisher = {Centre of Global eHealth Innovation}, address = {Toronto}, issn = {1438-8871}, doi = {10.2196/29268}, pages = {15}, year = {2021}, abstract = {Background: Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial. Objective: This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one's own health and concern to unknowingly infect others. Methods: A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables. Results: Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one's own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that "too much" concern leads to defensive reactions and reduces app uptake. Conclusions: As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed.}, language = {en} } @article{LiBuenningKaiseretal.2022, author = {Li, Jianghong and B{\"u}nning, Mareike and Kaiser, Till and Hipp, Lena}, title = {Who suffered most?}, series = {Journal of family research}, volume = {34}, journal = {Journal of family research}, number = {1}, publisher = {University of Bamberg Press}, address = {Bamberg}, issn = {2699-2337}, doi = {10.20377/jfr-704}, pages = {281 -- 309}, year = {2022}, abstract = {Objective: This study examines gender and socioeconomic inequalities in parental psychological wellbeing (parenting stress and psychological distress) during the COVID-19 pandemic in Germany. Background: The dramatic shift of childcare and schooling responsibility from formal institutions to private households during the pandemic has put families under enormous stress and raised concerns about caregivers' health and wellbeing. Despite the overwhelming media attention to families' wellbeing, to date limited research has examined parenting stress and parental psychological distress during the COVID-19 pandemic, particularly in Germany. Method: We analyzed four waves of panel data (N= 1,771) from an opt-in online survey, which was conducted between March 2020 and April 2021. Multivariable OLS regressions were used to estimate variations in the pandemic's effects on parenting stress and psychological distress by various demographic and socioeconomic characteristics. Results: Overall, levels of parenting stress and psychological distress increased during the pandemic. During the first and third wave of the COVID-19 pandemic, mothers, parents with children younger than 11 years, parents with two or more children, parents working from home as well as parents with financial insecurity experienced higher parenting stress than other sociodemographic groups. Moreover, women, respondents with lower incomes, single parents, and parents with younger children experienced higher levels of psychological distress than other groups. Conclusion: Gender and socioeconomic inequalities in parents' psychological wellbeing increased among the study participants during the pandemic.}, language = {en} } @article{JafarnezhadgeroNorooziFakhrietal.2022, author = {Jafarnezhadgero, Amir Ali and Noroozi, Raha and Fakhri, Ehsan and Granacher, Urs and Oliveira, Anderson Souza}, title = {The Impact of COVID-19 and muscle fatigue on cardiorespiratory fitness and running kinetics in female recreational runners}, series = {Frontiers in physiology}, volume = {13}, journal = {Frontiers in physiology}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2022.942589}, pages = {10}, year = {2022}, abstract = {Background: There is evidence that fully recovered COVID-19 patients usually resume physical exercise, but do not perform at the same intensity level performed prior to infection. The aim of this study was to evaluate the impact of COVID-19 infection and recovery as well as muscle fatigue on cardiorespiratory fitness and running biomechanics in female recreational runners. Methods: Twenty-eight females were divided into a group of hospitalized and recovered COVID-19 patients (COV, n = 14, at least 14 days following recovery) and a group of healthy age-matched controls (CTR, n = 14). Ground reaction forces from stepping on a force plate while barefoot overground running at 3.3 m/s was measured before and after a fatiguing protocol. The fatigue protocol consisted of incrementally increasing running speed until reaching a score of 13 on the 6-20 Borg scale, followed by steady-state running until exhaustion. The effects of group and fatigue were assessed for steady-state running duration, steady-state running speed, ground contact time, vertical instantaneous loading rate and peak propulsion force. Results: COV runners completed only 56\% of the running time achieved by the CTR (p < 0.0001), and at a 26\% slower steady-state running speed (p < 0.0001). There were fatigue-related reductions in loading rate (p = 0.004) without group differences. Increased ground contact time (p = 0.002) and reduced peak propulsion force (p = 0.005) were found for COV when compared to CTR. Conclusion: Our results suggest that female runners who recovered from COVID-19 showed compromised running endurance and altered running kinetics in the form of longer stance periods and weaker propulsion forces. More research is needed in this area using larger sample sizes to confirm our study findings.}, language = {en} } @article{WarschburgerKamrathLanzingeretal.2023, author = {Warschburger, Petra and Kamrath, Clemens and Lanzinger, Stefanie and Sengler, Claudia and Wiegand, Susanna and G{\"o}ldel, Julia Marlen and Weihrauch-Bl{\"u}her, Susann and Holl, Reinhard and Minden, Kirsten}, title = {A prospective analysis of the long-term impact of the COVID-19 pandemic on well-being and health care among children with a chronic condition and their families}, series = {BMC pediatrics}, volume = {23}, journal = {BMC pediatrics}, number = {1}, publisher = {BioMed Central}, address = {London}, issn = {1471-2431}, doi = {10.1186/s12887-023-03912-7}, pages = {15}, year = {2023}, abstract = {Background There is consistent evidence that the COVID-19 pandemic is associated with an increased psychosocial burden on children and adolescents and their parents. Relatively little is known about its particular impact on high-risk groups with chronic physical health conditions (CCs). Therefore, the primary aim of the study is to analyze the multiple impacts on health care and psychosocial well-being on these children and adolescents and their parents. Methods We will implement a two-stage approach. In the first step, parents and their underage children from three German patient registries for diabetes, obesity, and rheumatic diseases, are invited to fill out short questionnaires including questions about corona-specific stressors, the health care situation, and psychosocial well-being. In the next step, a more comprehensive, in-depth online survey is carried out in a smaller subsample. Discussion The study will provide insights into the multiple longer-term stressors during the COVID-19 pandemic in families with a child with a CC. The simultaneous consideration of medical and psycho-social endpoints will help to gain a deeper understanding of the complex interactions affecting family functioning, psychological well-being, and health care delivery.}, language = {en} } @article{VaidSomaniRussaketal.2020, author = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.}, title = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {22}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {11}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/24018}, pages = {19}, year = {2020}, abstract = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.}, language = {en} }