Rüdiger Pryss’s research while affiliated with University Hospital Würzburg and other places

Despite major advances in recent years, the timely detection and prevention of incipient congestion in patients with chronic heart failure remains challenging. Most approaches are either invasive or require the acquisition of additional hardware. Leveraging voice analysis for the purposes of diagnosing, predicting risks, and telemonitoring clinical outcomes of patients with heart failure represents a promising, cost-effective, and convenient alternative compared with hitherto deployed methods. To expand this field, close collaboration of several disciplines of medicine and computer science is an obligatory requirement. The current review aims to lay out the state-of-the-art in this quickly advancing area of research. It elucidates the foundation for voice feature extraction, describes the prospective capabilities of this evolving technology, and outlines the challenges involved in identifying vocal biomarkers both in general and in the context of heart failure.

This study explored differences in COVID-19 infections and symptoms between Android and iOS users using data from the CoronaCheck app. This cross-sectional analysis included 23,063 global users (20,753 Android and 2310 iOS) from April 2020 to February 2023. Participants reported COVID-19 symptoms and contact risks, with data analyzed to adjust for age, sex, education, and country. Android users were generally younger, more often male, had a lower educational level, and reported more symptoms on average (2.1 vs. 1.6) than iOS users. Android users also had higher suspected COVID-19 infection rates (24% vs. 11%), with an adjusted odds ratio of 2.21 (95% CI: 1.93–2.54). These findings suggest platform-based differences in COVID-19 infection rates and symptom reporting, highlighting potential biases in mobile health research. Adjusting for device operating systems may be crucial in improving the reliability of population-based health data collected through mobile platforms.

The dataset published in this work is derived from the ‘Corona Health’ app, developed in collaboration with the German Robert Koch Institute (RKI) during the early stages of the COVID-19 pandemic. The smartphone application aimed to monitor the mental and physical health of the public through real-time data collection. The dataset incorporates Ecological Momentary Assessments (EMA), Patient-reported Outcome Measures (PROMs), GPS data, and digital phenotyping from app users who consented. The data includes responses from 1805 mostly German users who completed baseline and follow-up questionnaires, capturing their physical health status over time. These questionnaires cover health-related topics, including medical history, cardiovascular risk factors, lifestyle habits, and the impact of the pandemic on health behaviors. The resulting dataset offers insights into health trajectories and behaviors during the pandemic and can be utilized for further research on physical health, user engagement, and the efficacy of EMA and digital phenotyping in health monitoring. The data is publicly available under a Creative Commons license on zenodo.org/records/11093394.

Hintergrund Chronische Rückenschmerzen betreffen rund ein Drittel der deutschen Bevölkerung und stellen eine erhebliche Herausforderung im Gesundheitswesen dar. Die Förderung von Selbstwirksamkeit und Autonomie der Patient*innen spielt eine zentrale Rolle in der erfolgreichen Schmerztherapie. Dazu können digitale Interventionen eingesetzt werden. Bisherige digitale Interventionen zum Einsatz bei Rückenschmerzen sind jedoch häufig nicht an die aktuellen Beschwerden und die aktuelle Situation von Patient*innen angepasst. Ziele Ziel der Studie war die Entwicklung der BackUp-App, die durch individualisierte selbstmanagementbasierte Mikrointerventionen die Autonomie der Patient*innen stärkt und zur Reduktion von Schmerzintensität und Schmerzkatastrophisierung beitragen soll. Methoden Die BackUp-App folgt einem Just-in-Time Adaptive Intervention (JITAI)-Ansatz, um bedarfsorientierte Interventionen zu bieten. Mehrfach tägliche Ecological Momentary Assessments (EMA) erfassen Schmerzempfinden und Schmerzkatastrophisierung im Alltag der Patien*tinnen und ermöglichen so bedarfsgerechte Anpassungen. Die Inhalte der App umfassen Interventionen aus Physiotherapie, Entspannung und Achtsamkeit, entwickelt auf Basis einer 2023 durchgeführten Meta-Analyse zur Wirksamkeit digitaler Selbstmanagement-Interventionen. Strukturierte Interviews mit 15 Expert*innen und 15 Patient*innen lieferten Einblicke in bevorzugte Mikrointerventionen und Designpräferenzen. Schließlich wurde die BackUp-App auf Grundlage der eSano Plattform für digitale Interventionen programmiert. Ergebnisse Die Interviews ergaben wertvolle Hinweise zur Individualisierung der App und offenbarten Präferenzunterschiede zwischen Expert*innen und Patienti*nnen . Insgesamt konnten 14 Mikrointerventionen ausgewählt werden und Design-Kriterien wie die Schwelle der Schmerzkatastrophisierung, die zur Auslösung einer Mikrointerventionen notwendig ist, festgelegt werden. Zur individualisierten Umsetzung der JITAI-Prinzipien wurden die Funktionen der eSano-Plattform um zufällige Auswahlmöglichkeiten von Mikrointerventionen erweitert. Diskussion Die nächste Projektphase umfasst eine randomisierte kontrollierte Studie (RCT), die den Einfluss der BackUp-App auf Schmerzkatastrophisierung und Schmerzintensität evaluieren soll. Diese Ergebnisse sollen wertvolle Einblicke in den Nutzen adaptiver digitaler Interventionen in der Schmerztherapie liefern und zur Optimierung zukünftiger Therapieansätze beitragen.

In most tinnitus patients, tinnitus can be masked by external sounds. However, evidence for the efficacy of sound-based treatments is scarce. To elucidate the effect of sounds on tinnitus under real-world conditions, we collected data through the TrackYourTinnitus mobile platform over a ten-year period using Ecological Momentary Assessment and Mobile Crowdsensing. Using this dataset, we analyzed 67,442 samples from 572 users. Depending on the effect of environmental sounds on tinnitus, we identified three groups (T-, T+, T0) using Growth Mixture Modeling (GMM). Moreover, we compared these groups with respect to demographic, clinical, and user characteristics. We found that external sound reduces tinnitus (T-) in about 20% of users, increases tinnitus (T+) in about 5%, and leaves tinnitus unaffected (T0) in about 75%. The three groups differed significantly with respect to age and hearing problems, suggesting that the effect of sound on tinnitus is a relevant criterion for clinical subtyping.

Background Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches. Methods The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study. To ensure high data quality, measures for standardized training, monitoring as well as plausibility and data quality tests were implemented. Results The clinical core data set comprises information about medical history, clinical symptoms, information about deep brain stimulation surgery, complications and outcome for the main neurological movement disorders Parkinson’s disease, tremor, and dystonia. Its applicability as well as data exchange and quality control was tested within the first phase of the study in 51 patients from Würzburg. Conclusions Within the ReTune project, a standardised clinical core data set for Parkinson’s disease, dystonia and tremor was developed. The collection as well as concepts for the implementation of monitoring and data exchange were elaborated and successfully tested. Trial registration number ClinicalTrials.gov (DRKS-ID: DRKS00031878).

Background We recently showed that population data-based machine-learning can improve the automated echocardiographic quantification of cardiac structure and function. The respective gain in accuracy and precision strengthens the confidence into automated echocardiographic readings and carries potential for applications in various settings. Purpose We applied the automated detector to high-resolution standard echocardiograms and to echocardiograms acquired with a handheld device. Methods and Results PAVE (Pathology-oriented reading of echocardiography) is a cooperation project between University Hospital Wuerzburg (UKW) and Tomtec Imaging Systems using an established federated machine-learning environment. We recruited 2043 patients (mean age 64±16, 44% women) presenting at the UKW for transthoracic echocardiography (TTE). After high-resolution standard TTE (Vivid E95, GE) trained sonographers acquired images with a handheld ultrasound device (Lumify 2.0, Philips) according to a pre-specified protocol. Images of both modalities were loaded into the analysis platform of the Academic CoreLab Ultrasound-based Cardiovascular Imaging (TomtecArena®, Tomtec, Germany). For the current analysis, we selected n=51 random patients (mean age 65±16, 39% women) from the PAVE cohort. Reading of high-resolution and handheld TTE, respectively, was performed by a trained sonographer (>14 days apart and blinded to the reading results) as well as by the automated detector. We here present first results regarding 2D (parasternal long axis) measurements of interventricular septum (IVS), left ventricular diameter (LVD) and posterior wall thickness (PW) at end-diastole from manual CoreLab reading (CL) and automated detector reading (AD) of high-resolution TTE (11.3±4.1mm, 10.9±2.5mm; 48.3±8.1mm, 49.5±7.4mm; PW 9.3±3.2mm, 10.0±1.7mm) as well as of the respective handheld echocardiograms (IVS 10.7±3.4mm, 11.0±2.2mm; LVD 47.7±7.4mm, 48.3±6.6mm; PW 9.7±2.7mm, 10.2±1.6mm). The agreement between measurements performed in high-resolution and handheld TTE was assessed using Bland-Altman analysis (Table 1; Figure 1). Conclusions The application of an automated detector to handheld TTE was feasible and led to measurement values in the same range but with higher agreement of measurements when compared to human reading. Our results await extension to further echocardiographic parameters and confirmation in larger cohorts but suggest that automated echocardiographic reading might become a valuable tool in patient care. Accurate automated reading of handheld TTE might extend applicability of echocardiography to non-expert settings.