Danube University Krems

About: Danube University Krems is a education organization based out in Krems, Niederösterreich, Austria. It is known for research contribution in the topics: Stroke & Population. The organization has 498 authors who have published 1572 publications receiving 68797 citations.

Current methods for development of rapid reviews about diagnostic tests: an international survey.

Abstract: Rapid reviews (RRs) have emerged as an efficient alternative to time-consuming systematic reviews—they can help meet the demand for accelerated evidence synthesis to inform decision-making in healthcare. The synthesis of diagnostic evidence has important methodological challenges. Here, we performed an international survey to identify the current practice of producing RRs for diagnostic tests. We developed and administered an online survey inviting institutions that perform RRs of diagnostic tests from all over the world. All participants (N = 25) reported the implementation of one or more methods to define the scope of the RR; however, only one strategy (defining a structured question) was used by ≥90% of participants. All participants used at least one methodological shortcut including the use of a previous review as a starting point (92%) and the use of limits on the search (96%). Parallelization and automation of review tasks were not extensively used (48 and 20%, respectively). Our survey indicates a greater use of shortcuts and limits for conducting diagnostic test RRs versus the results of a recent scoping review analyzing published RRs. Several shortcuts are used without knowing how their implementation affects the results of the evidence synthesis in the setting of diagnostic test reviews. Thus, a structured evaluation of the challenges and implications of the adoption of these RR methods is warranted.

ToA ranging using subsample peak estimation and equalizer-based multipath reduction

Abstract: In the presence of multipath propagation received signals are composed of multiple echoes which are correlated to the direct signal. When using bandlimited signals for time-of-arrival (ToA) locating in indoor environments, the radio bandwidth is often not sufficient to decompose the received signal and to identify the first arriving path. This paper presents a ToA estimation approach based on a high-resolution correlation receiver with subsample interpolation. It is combined with a multipath error estimator based on the inverse channel impulse response determined by an equalizer. Simulation results confirm that the proposed approach can reduce the multipath error by a factor of 3.42 compared to matched filtering. Finally, measurements using an FPGA-based prototype implementation for wireless LAN show the applicability of the proposed approach for real-time locating systems.

Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform

Abstract: Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.

Just the other side of the coin? From error to insight analysis:

Abstract: To shed more light on data explorers dealing with complex information visualizations in real-world scenarios, new methodologies and models are needed which overcome existing explanatory gaps. There...

Provision of Psychotherapy One Year after the Beginning of the COVID-19 Pandemic in Austria.

Abstract: A previous study revealed that the majority of Austrian psychotherapists switched to remote settings during the first months of the COVID-19 pandemic. The current study investigated whether this change in treatment format was maintained after one year of the COVID-19 pandemic. From 16 February until 2 April 2021, a total of 238 Austrian psychotherapists completed an online survey. They were asked about the number of patients currently treated in-person, via telephone and via the internet. Psychotherapists rated three different aspects of psychotherapy (ability to actively listen to patients, ability to understand what is going on in the patients and ability to support patients emotionally) for three different formats (in-person with facemasks, telephone and internet) separately. The results show that, after one year of the pandemic, the majority (78.4%) of patients were treated in-person (compared to 21.7% during the first months of the COVID-19 pandemic; p < 0.001). This change in the treatment format was accompanied by a strong increase in the total number of patients treated by 77.2% on average (p < 0.001). Psychotherapists reported no differences between in-person psychotherapy with facemasks and psychotherapy via the internet with regard to the three investigated aspects of psychotherapy, while the surveyed aspects were rated less favorably for psychotherapy conducted via telephonic communication (p < 0.05). Further studies are needed to investigate the reasons why most psychotherapists switched back to the in-person format with the continuation of the COVID-19 pandemic.