Motion detectors as additional monitoring devices in the intensive care unit — a proof-of-concept study
Please always quote using this URN: urn:nbn:de:bvb:20-opus-362404
- 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, Grünwald, Germany). Participants were asked to replay 22 mock-up scenes of one-minute duration each: 12 scenes with movementsBackground: 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, Grünwald, 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.…
Author: | Gülmisal GüderORCiD, Eva von Rein, Thomas Flohr, Dirk Weismann, Dominik SchmittORCiD, Stefan StörkORCiD, Stefan Frantz, Vincent Kratzer, Christian Kendi |
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URN: | urn:nbn:de:bvb:20-opus-362404 |
Document Type: | Journal article |
Faculties: | Medizinische Fakultät / Medizinische Klinik und Poliklinik I |
Medizinische Fakultät / Deutsches Zentrum für Herzinsuffizienz (DZHI) | |
Language: | English |
Parent Title (English): | Applied Sciences |
ISSN: | 2076-3417 |
Year of Completion: | 2023 |
Volume: | 13 |
Issue: | 16 |
Article Number: | 9319 |
Source: | Applied Sciences (2023) 13:16, 9319. https://doi.org/10.3390/app13169319 |
DOI: | https://doi.org/10.3390/app13169319 |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Tag: | Internet of Things devices; motion detector; noncontact monitoring |
Release Date: | 2024/06/10 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |