A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

Requirements for a Flexible and Generic API Enabling Mobile Crowdsensing mHealth Applications

Presently, mHealth becomes increasingly important in supporting patients in their everyday life. For example, diabetes patients can monitor themselves by the use of their smartphones. On the other, clinicians as well as medical researchers try to exploit the advantages of mobile technology. More specifically, mHealth applications can gather data in everyday life and are able to easily collect sensor or context data (e.g., the current temperature). Compared to clinical trials, these advantages enable mHealth applications to gather more data in a rather short time. Besides, humans often behave atypically in a clinical environment and, hence, mHealth applications collect data in a setting that reflects the daily behavior more naturally. Hitherto, many technical solutions emerged to deal with such data collection settings. Mobile crowdsensing is one prominent example in this context. We utilize the latter technology in a multitude of large-scale projects to gather data of several chronic disorders. In the TrackYourTinnitus project, for example, we pursue the goal to reveal new medical insights to the tinnitus disorder. We learned in the realized projects that a sophisticated API must be provided to cope with the requirements of researchers from the medical domain. Notably, the API must be able to flexibly deal with requirement changes. The work at hand presents the elicited requirements and illustrate the pillars on which our flexible and generic API is built on. Although we identified that the maintenance of such an API is a challenging endeavor, new data evaluation opportunities arise that are promising in the context of chronic disorder management

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

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

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

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

Concept learning challenged

In my thesis, I argue that the philosophical and psychological study of concept-learning mechanisms has failed to take the diversity of learning mechanisms into account, and that consequently researchers should embrace a new way of thinking about concept learning: `concept learning' as a class of psychological mechanisms is not a natural kind lending itself to unified study and should be eliminated. To arrive at this, I discuss several concept-learning models that attempt to overcome Jerry Fodor's challenge and base my judgement on the plurality of feasible concept-learning mechanisms and on criteria for theoretical notions from the philosophy of science. Chapter 1 serves as an introduction to the topic `concept learning' and highlights its importance as a research topic in the study of the mind. I argue that a mechanistic understanding of the shape of concept learning is best suited to explain the phenomena, in line with the recent resurgence of mechanism-based explanation in the philosophy of mind. As the main challenge to the idea that concepts can be learnt, I proceed to set up Fodor's challenge for concept learning in Chapter 2. This challenge is the idea that concepts cannot be learnt given the logically possible mechanisms of concept learning. I lay out the argumentative structure and background assumptions that support Fodor's argument, and propose to scrutinise his empirically based premise most closely in my thesis: this empirically based premise is that the only possible mechanism of concept learning is the process of forming and testing hypotheses. As replies to Fodor's challenge, I discuss Perceptual Learning (R. Goldstone), Perceptual Meaning Analysis (J. Mandler), Quinean Bootstrapping (S. Carey), pattern-governed learning (W. Sellars), joint-attentional learning (M. Tomasello), and the Syndrome-Based Sustaining Mechanism Model (E. Margolis and S. Laurence). I argue that almost every mechanism I discuss has some leverage against Fodors argument, suggesting that there may be a wide variety of non-hypothesis-based concept-learning mechanisms. The final chapter of my thesis, Chapter 7, takes a step back and reviews the fate of the notion of concept learning in light of the diverse set of learning mechanisms brought up in my thesis. My first and main worry is that it is questionable whether the previously discussed mechanisms of concept learning share many scientifically relevant properties that would justify seeing them as instances of the natural kind 'concept learning mechanism'. I argue that the substantiation of this worry would necessitate the elimination of 'concept learning' and 'concept-learning mechanism' as terms of the cognitive sciences. The chapter lays out the argumentative structure on which Concept Learning Eliminativism (CLE) rests, along with a discussion of questions about natural kinds and pragmatics in theory construction. This is inspired by Edouard Machery's argument for the elimination of 'concept', but independent of Machery's own project. With this in place, I go on to give a conclusive argument that supports CLE, based on the claims that 'concept learning' is not a natural kind and that there are pragmatic advantages to eliminating 'concept learning'. In this final chapter, I also raise pragmatic considerations that support the argument for CLE, and propose new research directions that could pro t from the eliminativist position

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