Johannes Schobel

Measuring the Moment-to-Moment Variability of Tinnitus: The TrackYourTinnitus Smart Phone App

Tinnitus, the phantom perception of sound without a corresponding external sound, is a frequent disorder which causes significant morbidity. So far there is no treatment available that reliably reduces the tinnitus perception. The research is hampered by the large heterogeneity of tinnitus and the fact that the tinnitus perception fluctuates over time. It is therefore necessary to develop tools for measuring fluctuations of tinnitus perception over time and for analyzing data on single subject basis. However, this type of longitudinal measurement is difficult to perform using the traditional research methods such as paper-and-pencil questionnaires or clinical interviews. Ecological momentary assessment (EMA) represents a research concept that allows the assessment of subjective measurements under real-life conditions using portable electronic devices and thereby enables the researcher to collect longitudinal data under real-life conditions and high cost efficiency. Here we present a new method for recording the longitudinal development of tinnitus perception using a modern smartphone application available for iOS and Android devices with no costs for the users. The TrackYourTinnitus (TYT) app is available and maintained since April 2014. A number of 857 volunteers with an average age of 44.1 years participated in the data collection between April 2014 and February 2016. The mean tinnitus distress at the initial measurement was rated on average 13.9 points on the Mini-Tinnitus Questionnaire (Mini-TQ; max. 24 points). Importantly, we could demonstrate that the regular use of the TYT app has no significant negative influence on the perception of the tinnitus loudness nor on the tinnitus distress. The TYT app can therefore be proposed as a safe instrument for the longitudinal assessment of tinnitus perception in the everyday life of the patient.

End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains.

An Engine Enabling Location-Based Mobile Augmented Reality Applications

Contemporary smart mobile devices are already capable of running advanced mobile applications with demanding resource requirements. However, utilizing the technical capabilities of such devices constitutes a challenging task (e.g., when querying their sensors at run time). This paper deals with the design and implementation of an advanced mobile application, which enables location-based mobile augmented reality on different mobile operating systems (i.e., iOS and Android). In particular, this kind of application is characterized by high resource demands. For example, at run time various calculations become neccessary in order to correctly position and draw virtual objects on the screen of the smart mobile device. Hence, we focus on the lessons learned when implementing a robust and efficient, location-based mobile augmented reality engine as well as efficient mobile business applications based on it.

Using Smart Mobile Devices for Collecting Structured Data in Clinical Trials: Results from a Large-Scale Case Study

In future, more and more clinical trials will rely on smart mobile devices for collecting structured data from subjects during trial execution. Although there have been many projects demonstrating the benefits of mobile digital questionnaires, the scenarios considered in literature have been rather limited so far. In particular, the number of subjects is rather low in respective studies and a well controllable infrastructure is usually presumed, which not always applies in practice. This paper gives insights into the lessons learned in a clinical psychology trial when using tablets for mobile data collection. In particular, more than 1.700 subjects have participated so far, providing us with valuable feedback on collecting trial data with smart mobile devices in the large scale. Furthermore, issues related to an insufficient infrastructure (e.g., unstable Internet connections) have been addressed as well. Overall, the paper provides valuable insights gained during trial execution. In future, electronic questionnaires executable on smart mobile devices will replace paper-based ones.

Process-Driven Data Collection with Smart Mobile Devices

Paper-based questionnaires are often used for collecting data in application domains like healthcare, psychology or education. Such paper-based approach, however, results in a massive workload for processing and analyzing the collected data. In order to relieve domain experts from these manual tasks, we propose a process-driven approach for implementing as well as running respective mobile business applications. In particular, the logic of a questionnaire is described in terms of an explicit process model. Based on this process model, in turn, multiple questionnaire instances may be created and enacted by a process engine. For this purpose, we present a generic architecture and demonstrate the development of electronic questionnaires in the context of scientific studies. Further, we discuss the major challenges and lessons learned. In this context the presented process-driven approach offers promising perspectives in respect to the development of mobile data collection applications.

Outpatient Tinnitus Clinic, Self-Help Web Platform, or Mobile Application to Recruit Tinnitus Study Samples?

For understanding the heterogeneity of tinnitus, large samples are required. However, investigations on how samples recruited by different methods differ from each other are lacking. In the present study, three large samples each recruited by different means were compared: N = 5017 individuals registered at a self-help web platform for tinnitus (crowdsourcing platform Tinnitus Talk), N = 867 users of a smart mobile application for tinnitus (crowdsensing platform TrackYourTinnitus), and N = 3786 patients contacting an outpatient tinnitus clinic (Tinnitus Center of the University Hospital Regensburg). The three samples were compared regarding age, gender, and duration of tinnitus (month or years perceiving tinnitus; subjective report) using chi-squared tests. The three samples significantly differed from each other in age, gender and tinnitus duration (p < 0.05). Users of the TrackYourTinnitus crowdsensing platform were younger, users of the Tinnitus Talk crowdsourcing platform had more often female gender, and users of both newer technologies (crowdsourcing and crowdsensing) had more frequently acute/subacute tinnitus (<3 months and 4–6 months) as well as a very long tinnitus duration (>20 years). The implications of these findings for clinical research are that newer technologies such as crowdsourcing and crowdsensing platforms offer the possibility to reach individuals hard to get in contact with at an outpatient tinnitus clinic. Depending on the aims and the inclusion/exclusion criteria of a given study, different recruiting strategies (clinic and/or newer technologies) offer different advantages and disadvantages. In general, the representativeness of study results might be increased when tinnitus study samples are recruited in the clinic as well as via crowdsourcing and crowdsensing.

A Mobile Service Engine Enabling Complex Data Collection Applications

The widespread distribution of smart mobile devices offers promising perspectives for the timely collection of huge amounts of data. When realizing sophisticated mobile data collection applications, numerous technical issues arise. For example, as many real-world projects require the support of different mobile operating systems, platform-specific peculiarities must be properly handled. Existing approaches often rely on specifically tailored mobile applications. As a drawback, changes to the data collection procedure result in costly code adaptations. To remedy this drawback, a model-driven approach is proposed, enabling end-users (i.e., domain experts) to create mobile data collection applications themselves. This model relies on complex questionnaires called instruments. An instrument not only contains all information about the data to be collected, but additionally comprises information on how it shall be processed on different mobile operating systems. For this purpose, we developed an advanced mobile (kernel) service being capable of processing sophisticated instruments on various platforms. This paper discusses fundamental kernel requirements and introduces the developed architecture. Altogether, the mobile service allows for the effective use of smart mobile devices in data collection application scenarios (e.g., clinical trials).

A Lightweight Process Engine for Enabling Advanced Mobile Applications

The widespread dissemination of smart mobile devices offers new perspectives for timely data collection in large-scale scenarios. However, realizing sophisticated mobile data collection applications raises various technical issues like the support of different mobile operating systems and their platform-specific features. Often, specifically tailored mobile applications are implemented in order to meet particular requirements. In this context, changes of the data collection procedure become costly and profound programming skills are needed to adapt the respective mobile application accordingly. To remedy this drawback, we developed a model-driven approach, enabling end-users to create mobile data collection applications themselves. Basis to this approach are elements for flexibly defining sophisticated questionnaires, called instruments, which not only contain information about the data to be collected, but also on how the instrument shall be processed on different mobile operating systems. For the latter purpose, we provide an advanced mobile (kernel) service that is capable of processing the logic of sophisticated instruments on various platforms. The paper discusses fundamental requirements for such a kernel and introduces a generic architecture. The feasibility of this architecture is demonstrated through a prototypical implementation. Altogether, the mobile service allows for the effective use of smart mobile devices in a multitude of different data collection application scenarios (e.g., clinical and psychological trials).

A Configurator Component for End-User Defined Mobile Data Collection Processes

The widespread dissemination of smart mobile devices offers promising perspectives for collecting huge amounts of data. When realizing mobile data collection applications (e.g., to support clinical trials), challenging issues arise. For example, many real-world projects require support for heterogeneous mobile operating systems. Usually, existing data collection approaches are based on specifically tailored mobile applications. As a drawback, changes of a data collection procedure require costly code adaptations. To remedy this drawback, we implemented a model-driven approach that enables end-users to realize mobile data collection applications themselves. This paper demonstrates the developed configurator component, which enables domain experts to implement digital questionnaires. Altogether, the configurator component allows for the fast development of questionnaires and hence for collecting data in large-scale scenarios using smart mobile devices.

Towards Flexible Mobile Data Collection in Healthcare

The widespread dissemination of smart mobile devices offers promising perspectives for a variety of healthcare data collection scenarios. Usually, the implementation of mobile healthcare applications for collecting patient data is cumbersome and time-consuming due to scenario-specific requirements as well as continuous adaptations to already existing mobile applications. Emerging approaches, therefore, aim to empower domain experts to create mobile data collection applications themselves. This paper discusses flexibility issues considered by a generic and sophisticated framework for realizing mobile data collection applications. Thereby, flexibility is discussed along different phases of data collection scenarios. Altogether, the realized flexibility significantly increases the practical benefit of smart mobile devices in healthcare data collection scenarios.