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Background: Acute respiratory failure is the most important organ dysfunction of COVID-19 patients. While non-invasive ventilation (NIV) and high-flow nasal cannula (HFNC) oxygen are frequently used, efficacy and safety remain uncertain. Benefits and harms of awake prone positioning (APP) in COVID-19 patients are unknown. Methods: We searched for randomized controlled trials (RCTs) comparing HFNC vs. NIV and APP vs. standard care. We meta-analyzed data for mortality, intubation rate, and safety. Results: Five RCTs (2182 patients) were identified. While it remains uncertain whether HFNC compared to NIV alters mortality (RR: 0.92, 95% CI 0.65–1.33), HFNC may increase rate of intubation or death (composite endpoint; RR 1.22, 1.03–1.45). We do not know if HFNC alters risk for harm. APP compared to standard care probably decreases intubation rate (RR 0.83, 0.71–0.96) but may have little or no effect on mortality (RR: 1.08, 0.51–2.31). Conclusions: Certainty of evidence is moderate to very low. There is no compelling evidence for either HFNC or NIV, but both carry substantial risk for harm. The use of APP probably has benefits although mortality appears unaffected.
Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.