Ein Rahmenwerk zur mobilen Unterstützung therapeutischer Interventionen

Immer mehr Menschen leiden in der heutigen Zeit unter psychischen Erkrankungen, wie Depressionen oder Posttraumatischen Belastungsstörungen, die mithilfe therapeutischer Interventionen im Rahmen einer Psychotherapie behandelt werden können. Die hierbei zur Anwendung kommenden Interventionen hängen jeweils grundsätzlich von den zu Beginn der Therapie definierten Therapiezielen ab, und erstrecken sich teilweise über mehrere Sitzungen hinweg. Viele Interventionen nutzen therapeutische Hausaufgaben, um die Zeit zwischen den Therapiesitzungen effizient zu gestalten bzw. eine bestmögliche Wirksamkeit der Intervention zu erzielen. Hierbei spielt die korrekte Durchführung der Hausaufgabe eine große Rolle, d.h. diese sollte einerseits im definierten Kontext (z.B. Zeit, Ort oder maximale Herzfrequenz) erfolgen und andererseits entsprechend den Vorgaben des Therapeuten ausgeführt werden. Darüber hinaus ist eine wahrheitsgetreue und lückenlose Rückmeldung (sog. Feedback) von Seiten des Patienten über den Verlauf der Hausaufgabe essentiell, damit der Therapeut wichtige Erkenntnisse hinsichtlich der Wirksamkeit der Hausaufgabe bzw. therapeutischen Intervention erhält. Aufgrund fehlender technischer Lösungen ist es Therapeuten heute weder möglich, die Korrektheit der durchgeführten Hausaufgabe zu überprüfen noch das direkte Feedback während oder im Anschluss an die Hausaufgabe zu erfahren. Aber auch auf Seiten des Patienten fehlt eine maßgeschneiderte technische Unterstützung, um eine kontinuierliche und angemessene Hausaufgabendurchführung gewinnbringend zu gewährleisten. Die vorliegende Arbeit adressiert die erwähnten Aspekte und Anforderungen seitens der Therapeuten und Patienten durch Einführung eines umfassenden Rahmenwerks zur mobilen Unterstützung therapeutischer Interventionen. Die hierbei erarbeiteten Konzepte erlauben einerseits eine robuste und flexible Ausführung therapeutischer Interventionen auf einem mobilen Endgerät des Patienten, andererseits ermöglichen sie deren flexible Modellierung und Konfiguration durch den Therapeuten. Als weiteren Beitrag dieser Arbeit wurden Konzepte entwickelt, die durch den Einsatz von End-User Development Techniken den Therapeuten in die Lage versetzen, das technische Management therapeutischer Interventionen ohne Einbeziehung eines IT-Experten durchzuführen. Mithilfe eines umfangreichen Prototyps wurde das Rahmenwerk schließlich validiert und in mehreren praktischen Projekten getestet. Letztere haben gezeigt, dass das vorgestellte Rahmenwerk einen erheblichen Beitrag in der aktuellen Gesundheitsforschung leisten kann

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

Supporting Remote Therapeutic Interventions with Mobile Processes

Many studies have revealed that homework (e.g., relaxation exercises) are crucial for remote therapeutic inter-ventions. In this context, to monitor whether patients actually perform their homework and to check whether they perform it in the right way constitute complex tasks. So far, therapeutic interventions have not been properly supported by IT systems and, hence, the opportunities provided by mobile assistance have been neglected. For example, a smart mobile device may notify a patient about an assigned homework or motivate him to accomplish it in time. Moreover, the patient might be further assisted through a video providing detailed instructions. In turn, the smart mobile device could inform the therapist of the homework outcome. In practice, a proper support of the various types of homework is challenging, even when using modern IT systems. To remedy this drawback, we propose an approach integrating mobile services with process management technology in order to enable the complex coordination tasks that become necessary in connection with homework. For example, a process might enable remote monitoring of home-work, giving therapists the opportunity of timely adjustments. In addition, the approach allows involving researchers by providing them with valuable data (e.g., heart rate) gathered during and after homework. This paper presents an approach for creating processes that run on smart mobile devices and enable flexible remote therapeutic intervention support. Such mobile approach significantly enhances therapy assistance on one hand and mobile homework-related scenarios on the other

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

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

Engineering an Advanced Location-Based Augmented Reality Engine for Smart Mobile Devices

Daily business routines more and more require to access information systems in a mobile manner, while preserving a desktop-like feeling at the same time. The goal of this work is to outline the engineering process of a sophisticated mobile service running on a smartphone. More precisely, we show how to develop the core of a location-based augmented reality engine for the iPhone 4S based on the operating system iOS 5.1 (or higher). We denote this engine as AREA. In particular, we develop concepts for coping with limited resources on a mobile device, while providing a smooth user augmented reality experience at the same time. We further present and develop a suitable application architecture in this context, which easily allows integrating augmented reality with a wide range of applications

Context-Based Assignment and Execution of Human-Centric Mobile Services

Performing tasks with the help of smart mobile devices is demanded for various areas in everyday life. In business environments, for example, tasks requiring complex paper work (e.g., paper-based documentation in the context of machine maintenance) shall be digitally transformed with the use of smart mobile devices. However, the realization of respective mobile applications is challenging as coordination issues have to be addressed in this context. For example, mobile application A performing task A may have to be finished before mobile application B performing task B may be started, i.e., human-centric mobile services need to be coordinated. To accomplish the latter, a formal context capturing service dependencies is required, while at the same time considering the mobile context of each involved human-centric mobile service needs to be considered. The presented approach extends existing process management technology with mobile activities to enable this. More precisely, we developed a mobile context framework that allows for a robust and flexible execution of mobile activities. The feasibility of the approach is demonstrated through a prototypical implementation as well as case studies. Altogether, the support of human-centric mobile services is promising regarding work efficiency in numerous scenarios and application domains in everyday life

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)

Towards Patterns for Defining and Changing Data Collection Instruments in Mobile Healthcare Scenarios

Especially in healthcare scenarios and clinical trials, a large amount of data needs to be collected in a rather short time. In this context, smart mobile devices can be a feasible instrument to foster data collection scenarios. To enable domain experts to create and maintain mobile data collection applications themselves, the QuestionSys framework relies on a model-driven approach to digitize paper-based questionnaires. This digital transformation is based on manual as well as automated tasks. The manual tasks applied by the domain experts can be eased by the use of change patterns. They describe features to easily add or delete the elements of a questionnaire. This work summarizes crucial change patterns and shows how they can be applied in practice. We believe that the patterns constitute an important means to implement sophisticated mobile data collection applications by domain experts themselves

Process-Driven Mobile Data Collection (Extended Abstract)

Structured instruments are commonly used to collect data in various application domains (e.g., psychology). Still, the former are handled in a traditional paper-based fashion. In this context, the widespread use of smart mobile devices offers promising perspectives with respect to the con-trolled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects, turning both the program-ming and maintenance of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. In order to empower domain experts to create mobile data collection applica-tions themselves, a powerful framework, applying process management concepts in a broader scope, was developed. The framework enables the development of sophisticated mobile data collection ap-plications by orders of magnitude faster compared to current practices on one hand. On the other, domain experts are relieved from manual tasks, like digitizing the data collected