Corona Health -- A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures

Untersuchung der Geodaten von Benutzern der Moodpath mHealth App mithilfe von Methoden des Maschinellen Lernens

Depressive Störungen sind ein ernstzunehmendes weitverbreitetes Krankheitsbild. Sie bleiben häufig unentdeckt oder unbehandelt. Dies hat weitreichende Auswirkungen, nicht nur auf den Betroffenen und nahestehende Personen, sondern auch auf ökonomischer Ebene. Die indirekten Kosten, welche beispielsweise durch Produktionsausfälle aufgrund Krankschreibungen oder Frühpensionierungen entstehen, belaufen sich Schätzungen zufolge in der EU auf jährlich 260 Milliarden Euro. Um mögliche depressive Störungen zu erkennen bieten Smartphones und themenspezifische Applikationen eine große Möglichkeit. Man hat die Möglichkeit Verhaltensmuster der Nutzer aufzuzeichnen und so potenzielle depressive Episoden frühzeitig zu erkennen oder bei schon diagnostizierter Depression Faktoren für depressive Schübe zu identifizieren. Des Weiteren bieten Smartphones die Chance Bewegungsdaten aufzuzeichnen und legen somit die Grundlage um eventuelle Einflüsse der verschiedenen Aufenthaltsorte auf Depressionen zu untersuchen. In dieser Arbeit werden Geodaten verwendet, um den allgemeinen Gemütszustand vorherzusagen. Dafür werden Daten der Moodpath Applikation verwendet und es wird wie folgt vorgegangen. Mithilfe der Geodaten werden Cluster erstellt, die als exogene Variable dienen. Der sogenannte ‚Happiness Score‘, welchen die Nutzer mehrmals täglich in einem Fragebogen angegeben haben, stellt die endogene Variable dar. Nun soll festgestellt werden, ob ein Zusammenhang zwischen den Clustern und dem Happiness Score besteht. Dafür werden Methoden des maschinellen Lernens zur Klassifikation herangezogen: Support Vector Machine, Decision Trees und Random Forest. Die Performances der entwickelten Modelle werden anhand einer Kreuzvalidierung evaluiert. Das Ergebnis zeigt einen Zusammenhang zwischen den Geodaten und dem Gemütszustand. Die SVM erzielt eine minimal bessere geschätzte Klassifikationsgenauigkeit als der Decision Tree oder der Random Forest, allerdings mit einer höheren Rechenzeit. Auch andere Untersuchungen belegen eine Korrelation zwischen Bewegungsdaten und Depressionen. All diese Befunden sollten in größer angelegten Studien repliziert werden

Call to Action: Investigating Interaction Delay in Smartphone Notifications

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users’ interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users’ interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device’s charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user’s sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively

Medical device regulation efforts for mHealth apps during the COVID-19 pandemic — an experience report of Corona Check and Corona Health

Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a “medical app”. However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team — especially software engineers — are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted

Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI

Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%–20% among the adult population. The EU is funding a new collaborative project entitled “Unification of Treatments and Interventions for Tinnitus Patients” (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure.Fil: Winfried, Schlee. Universitat Regensburg; AlemaniaFil: Stefan, Schoisswohl. Universitat Regensburg; AlemaniaFil: Susanne, Staudinger. Universitat Regensburg; AlemaniaFil: Axel, Schiller. Universitat Regensburg; AlemaniaFil: Astrid, Lehner. Universitat Regensburg; AlemaniaFil: Berthold, Langguth. Universitat Regensburg; AlemaniaFil: Martin, Schecklmann. Universitat Regensburg; AlemaniaFil: Jorge, Simoes. Universitat Regensburg; AlemaniaFil: Patrick, Neff. Universitat Regensburg; AlemaniaFil: Steven, Marcrum. Universitat Regensburg; AlemaniaFil: Myra, Spiliopoulou. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Uli, Niemann. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Miro, Schleicher. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Vishnu, Unnikrishnan. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Clara, Puga. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Lena, Mulansky. University Hospital Wuerzburg; AlemaniaFil: Ruediger, Pryss. University Hospital Wuerzburg; AlemaniaFil: Carsten, Vogel. University Hospital Wuerzburg; AlemaniaFil: Johannes, Allgaier. University Hospital Wuerzburg; AlemaniaFil: Efi, Giannopoulou. Zeincro Egeszegugyi Szolgaltato Korlatolt Felelossegu Tarsasag; HungríaFil: Katalin, Birki. Zeincro Egeszegugyi Szolgaltato Korlatolt Felelossegu Tarsasag; HungríaFil: Klairi, Liakou. Zeincro Egeszegugyi Szolgaltato Korlatolt Felelossegu Tarsasag; HungríaFil: Rilana, Cima. Katholikie Universiteit Leuven; BélgicaFil: Johan, Vlaeyen. Katholikie Universiteit Leuven; BélgicaFil: Nicolas, Verhaert. Katholikie Universiteit Leuven; BélgicaFil: Saskia, Ranson. Adelante Tinnitus Expertise Centre; Países BajosFil: Birigt, Mazurek. Charite—Universit atsmedizin Berlin; AlemaniaFil: Petra, Brueggemann. Charite—Universit atsmedizin Berlin; AlemaniaFil: Benjamin, Boecking. Charite—Universit atsmedizin Berlin; AlemaniaFil: Nyamaa, Amarjargal. Charite—Universit atsmedizin Berlin; AlemaniaFil: Elgoyhen, Ana Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentin

Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures