@article{GuederReinFlohretal.2023,
  author    = {G{\"u}der, G{\"u}lmisal and Rein, Eva von and Flohr, Thomas and Weismann, Dirk and Schmitt, Dominik and St{\"o}rk, Stefan and Frantz, Stefan and Kratzer, Vincent and Kendi, Christian},
  title     = {Motion detectors as additional monitoring devices in the intensive care unit — a proof-of-concept study},
  series = {Applied Sciences},
  volume    = {13},
  journal   = {Applied Sciences},
  number    = {16},
  issn      = {2076-3417},
  doi       = {10.3390/app13169319},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-362404},
  year      = {2023},
  abstract  = {Background: Monitoring the vital signs of delirious patients in an intensive care unit (ICU) is challenging, as they might (un-)intentionally remove devices attached to their bodies. In mock-up scenarios, we systematically assessed whether a motion detector (MD) attached to the bed may help in identifying emergencies. Methods: We recruited 15 employees of the ICU and equipped an ICU bed with an MD (IRON Software GmbH, Gr{\"u}nwald, Germany). Participants were asked to replay 22 mock-up scenes of one-minute duration each: 12 scenes with movements and 10 without movements, of which 5 were emergency scenes ("lying dead-still, with no or very shallow breathing"). Blinded recordings were presented to an evaluation panel consisting of an experienced ICU nurse and a physician, who was asked to assess and rate the presence of motions. Results: Fifteen participants (nine women; 173 ± 7.0 cm; 78 ± 19 kg) joined the study. In total, 286 out of 330 scenes (86.7\%) were rated correctly. Ratings were false negative (FN: "no movements detected, but recorded") in 7 out of 180 motion scenes (3.9\%). Ratings were false positive (FP: "movements detected, but not recorded") in 37 out of 150 scenes (24.7\%), more often in men than women (26 out of 60 vs. 11 out of 90, respectively; p \< 0.001). Of note, in 16 of these 37 FP-rated scenes, a vibrating mobile phone was identified as a potential confounder. The emergency scenes were correctly rated in 64 of the 75 runs (85.3\%); 10 of the 11 FP-rated scenes occurred in male subjects. Conclusions: The MD allowed for identifying motions of test subjects with high sensitivity (96\%) and acceptable specificity (75\%). Accuracy might increase further if activities are recorded continuously under real-world conditions.},
  language  = {en}
}
@article{WeismannMoeckelPaethetal.2023,
  author    = {Weismann, Dirk and M{\"o}ckel, Martin and Paeth, Heiko and Slagman, Anna},
  title     = {Modelling variations of emergency attendances using data on community mobility, climate and air pollution},
  series = {Scientific Reports},
  volume    = {13},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-023-47857-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357578},
  year      = {2023},
  abstract  = {Air pollution is associated with morbidity and mortality worldwide. We investigated the impact of improved air quality during the economic lockdown during the SARS-Cov2 pandemic on emergency room (ER) admissions in Germany. Weekly aggregated clinical data from 33 hospitals were collected in 2019 and 2020. Hourly concentrations of nitrogen and sulfur dioxide (NO2, SO2), carbon and nitrogen monoxide (CO, NO), ozone (O3) and particulate matter (PM10, PM2.5) measured by ground stations and meteorological data (ERA5) were selected from a 30 km radius around the corresponding ED. Mobility was assessed using aggregated cell phone data. A linear stepwise multiple regression model was used to predict ER admissions. The average weekly emergency numbers vary from 200 to over 1600 cases (total n = 2,216,217). The mean maximum decrease in caseload was 5 standard deviations. With the enforcement of the shutdown in March, the mobility index dropped by almost 40\%. Of all air pollutants, NO2 has the strongest correlation with ER visits when averaged across all departments. Using a linear stepwise multiple regression model, 63\% of the variation in ER visits is explained by the mobility index, but still 6\% of the variation is explained by air quality and climate change.},
  language  = {en}
}