Eye-tracking technology has been increasingly utilized in various medical domains, yet its adoption in pathology remains limited. The lack of standardized methodologies and the complexity of analyzing whole-slide images pose significant challenges for applying eye-tracking in this domain. Existing studies often rely on proprietary and heterogeneous software solutions, reducing reproducibility and comparability of results across researchers. To address these issues, we present a novel eye-tracking study platform specifically designed for digital pathology. The proposed platform enables standardized and reproducible eye-tracking studies by providing a modular architecture that supports common eye-tracking software and hardware. It features a web-based frontend for study execution and a backend deployed via Docker, ensuring platformindependent usability while maintaining local data storage for privacy compliance and GDPR adherence. An experimental study was conducted with pathologists, trainees, and medical students to validate the applicability of the platform, as well as to demonstrate its reliable performance and ease-of-use. While the platform proved technically robust, areas for improvement remain. Future enhancements will focus on refining the user experience by integrating an improved tutorial system and posttask feedback mechanisms, incorporating gamification elements to boost participant engagement and data quality. Additionally, the next major version will introduce full support for whole-slide images, including zooming and advanced navigation features, to provide more comprehensive insights into pathologists' visual attention patterns. By providing a structured and adaptable research framework, our work represents a significant step toward standardizing eye-tracking research in pathology. The developed platform provides the foundation for more consistent study designs and reproducible findings, ultimately contributing to the advancement of digital pathology and diagnostic training methodologies.

Health apps have the potential to collect data in real life, which makes them a useful tool. Users can monitor themselves, and researchers can gain comprehensive insights. However, user behavior is subject to fluctuations. On the one hand, this makes it complicated to use machine learning techniques, and on the other hand, there is a need to predict activity to stimulate it when necessary.This paper investigates user participation, focusing on their usage behavior, possible patterns of engagement, and resulting neighborhoods. The goal is to use these insights to predict a user’s engagement. Three ways of modeling patterns of engagement are presented. Neighborhood methods are used to infer conclusions about future behavior from the information of other users. Three methods are proposed to predict the length of a phase of inactivity, the length of the next phase of activity, and its values and structure.Applied to two real-world datasets, the results show that the approaches are suitable for making these predictions.These findings can be used to understand better and predict the behavior of mHealth users and plan possible interventions. This research expands the boundaries of research using self-monitoring mHealth apps, as user behavior can now be more easily formalized and applied. Furthermore, the approach can help to influence user engagement in a targeted way. Thus, this contribution exceeds our previous state-of-the-art work in its possibilities.

Glioblastoma is the most common malignant brain tumor with a poor survival rate due to its high intra- and intertumor heterogeneity. The current heterogeneity determination is based on a microscopic analysis of Hematoxilyn and Eosinstained tumor slides carried out by experienced neuropathologists. There is no standardized procedure yet, to quantify heterogeneity, though. With the hypothesis that the amount of heterogeneity impacts overall survival, we aim to develop an objective method to capture heterogeneity. We were able to successfully implement an initial Machine Learning classification model for determining heterogeneity. However, the available dataset was insufficient to train a resilient and stable system. Therefore, we propose an architecture for a semi-automatic data collection and preprocessing framework easing the collection of large quantities of required data. While there exists a multitude of frameworks tackling parts of the tumor research area, no simple ready-to-use solution is present for the easy collection of tumor data in daily clinical routine, especially for high-resolution pathological images. We plan to implement the proposed architecture at the University Hospital Augsburg, Germany in 2023. The dataset created in this process will then be used as a resilient basis for heterogeneity classification and for analyzing glioblastomas in general.

The last three years showed that a deep understanding of pandemics, how they spread, and how they can be managed is of utmost importance. However, teaching students such complex topics is hard and cumbersome. Studies from other domains have already found, that students highly benefit from game-based learning approaches. Therefore, we developed a concept for a serious game, teaching students pandemic management. In this paper, we introduce our core gameplay concept, the methods of how requirements for such a serious game were derived as well as the final system architecture we developed to meet the identified requirements. We believe that such a game-based learning approach may significantly improve the understanding of pandemics and how to properly manage decisions in such a complex scenario.

Orofacial myofunctional disorders in children are a common problem with most therapy consisting of interventions to be done at home. Parents often find it difficult to motivate their children to exercise, and it is impossible for speech therapists to effectively track how much the children have practiced at home. Gamified mHealth applications have already been successfully applied in other domains of speech therapy, such as speech apraxia and aphasia. For orofacial myofunctional therapy, a sophisticated and well-established solution is still lacking. We describe the requirements and development of LudusMyo, an mHealth application to support OMD therapy specifically. We expect that such a gamified mHealth application will significantly increase the children's motivation to continue their therapy at home while enabling therapists to reliably track their patients' progress and tailor the outpatient therapy accordingly.

At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems. Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

Applications in mobile health (mHealth) empower self-monitoring of chronic conditions of the user and also offer insights to medical experts. The data generated by these apps constitute one time series per user. These time series vary substantially in length and contain ‘gaps’, as users pause or stop interacting with the app. In order to design measures that promote patient engagement with the app, it is necessary to predict and understand decline in engagement. We measured the performance of the algorithms on two real-world datasets from an mHealth app. We show that all approaches outperform the baseline and that shapelet, dictionary and matrix distance approach perform similarly for long-term prediction. This is particularly important because it allows early intervention towards increase of engagement. In this paper, we present an approach that uses the missingness information to process time series with large gaps.

Adherence and phases of non-adherence in the usage of mHealth apps for self-monitoring generate time series characterized by gaps of varying duration. These reduce the knowledge that can be gained from the data because important observations are not captured. In this paper, an approach is presented that allows experts to estimate the possible duration of a user’s absence in order to build on it and take appropriate action.For this purpose, the users’ time series (with gaps and of different lengths) are decomposed into sequences that have no gaps anymore. Each sequence is associated with the duration of the subsequent gap. Using unsupervised binning, these gap values are sorted into a predefined number of categories. Sequences with similar gap durations are thus assigned the same category labels, grouped using unsupervised time series clustering and are assigned with cluster labels. Thus triplets are derived consisting of user ID, cluster label and gap category label. These triplets can then be used for collaborative filtering with matrix factorization. It is now possible to estimate the gap duration even if the same sequence has not yet been observed for the app user.The results show that the quality of binning depends on the appropriate choice of the number of categories, on the technique used, and on the maximum length of the gaps. In this example, a 5-star rating, the Fischer-Jenks algorithm and a maximum gap length of 30 days. We demonstrate how clustering can be used to gradually adapt the time series to the conditions of collaborative filtering and how complex matrix factorization models respond better to the complex structures of the data and lead to better results. In this work, K-Medoids and Agglomerative Clustering obtained applicable results and for matrix factorization SVD++.We believe that our approach provides a valuable back-end tool for experts to better assess the adherence of users to a selfmonitoring app.

Ecological momentary assessment (EMA) has been used in many mHealth apps. EMA captures valuable insights into many diseases. Identifying the Granger causal relationships across the EMA variables may contribute to the interpretation of a disease and improve treatment decisions. In our study, we perform a circadian conditional Granger causality analysis on multivariate time series. The analysis was done on EMA data of 270 users of an mHealth app on tinnitus and on their registration data, using the latter to explain the circadian conditional Granger causal relationships. We discovered that some EMA items Granger cause others for more than 8% of the mHealth app users and that these users have answered 8 out of 40 questions at registration differently.

A stream of users' interactions with an mHealth app can be seen as the result of a stochastic process that can be captured by an algorithm that learns over the whole stream. But is it only one process? We investigate to what extend learning for each user separately delivers better predictions than learning one model over the whole stream. Our application scenario is the prediction of Ecological Momentary Assessments (EMA) for an mHealth app (TinnitusTipps) on tinnitus. The data were recorded as part of a pilot study, in which one group of users received non-personalized suggestions (tips) throughout the study, while the other group received tips only during the second half of the study. Our method encompasses user-centric and global stream learning for EMA prediction, combined under a Contextual Multi-Armed Bandit (CMAB) that captures the context of each user group and incorporates the prediction quality of each learner into the reward function. We show that user-centric learning is beneficial for users who contribute many EMA, while a learner over the whole stream is better for users with few EMA.

Mobile Health (mhealth) applications are increasing in popularity, and the collection of disease-specific time series data using Ecological Momentary Assessment (EMA) questionnaires has been shown to help in the creation of personalised predictors for next-step forecasting, which can be crucial in giving preemptive interventions. In this work, we propose a framework that aims to mitigate a common issue in EMA data - that some users contribute a bulk of the data while most users contribute too little. Our proposed framework aims to discover a `useful' neighbourhood of `long' users for `short' ones, by optimising for the error of the user-level predictors for users with little data available for learning. For each user-level predictor, this is done by iteratively adding the next-most-similar long user from a similarity-ordered list as long as the error of the learned model does not increase. This method is compared against a baseline that exploits all available data for the long users, as well as an exhaustive search model that retains only only those users that yield the lowest error predictor. We also explore multiple ways to define similarity, and study the impact of each on the two search strategies on two EMA datasets from an mHealth app `TrackYourDiabetes' - with users from Bulgaria and Spain. Our experiments over the two datasets show a 2.5% and 36.6% improvement respectively for RMSE while using on average 42.8% and 69.9% less data than the baseline method.

Abstract Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings. Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.

Mobile applications can help patients with a chronical disease to record their Ecological Momentary Assessments (EMA) and to get a more precise impression of how their disease manifests itself during day and night and over longer time periods. Such crowdsensing applications contribute to patient empowerment, in which patients monitor their disease and, sometimes, learn to cope better with it. An open question is whether physicians can also be helped in assisting their patients, by understanding similarities and differences in the patients' evolution. We study the EMA of patients with the chronical disease tinnitus, as recorded with the mobile crowdsensing application Track Your Tinnitus. We propose a method that captures similarities in patient evolution, taking account of the differences in the frequency of each patient's EMA recordings. We incorporate this method into a complete workflow that encompasses following components: an algorithm that captures similarities among patients on the basis of their registration data, a method that juxtaposes static patient similarity to EMA-based patient similarity, and a method that identifies those subspaces of the static feature space and those of the EMA-based feature space, which are mainly contributing to patient similarity. We report on our results for the time period recordings from 2014 till 2017 of 450 tinnitus patients from TrackYourTinnitus mobile application.

Many studies have revealed that therapeutic homework is beneficial for the efficacy of therapies. Interestingly, the latter have been less supported by IT systems so far and, hence, therapeutic opportunities have been neglected. For example, mobile devices can be used to notify patients about assigned homework and help them to accomplish it in a timely manner. In general, the use of mobile devices as well as their sensors seem to be promising for the support of remote therapeutic interventions. In the Albatros project, we have been developing a framework that enables domain experts to flexibly define the homework required in the context of a remote therapeutic intervention. More precisely, the various tasks of a homework can be specified as a mobile process, which is then run on the mobile device of the respective patient. To realize this vision, a configurator component using a model-driven approach was developed. In particular, the Albatros configurator shall relieve domain experts from complex technical issues when defining a homework. The study presented in this paper investigates whether domain experts are actually able to use the configurator component. In particular, the study revealed three insights. First, basic interventions can be easily defined with an acceptable number of errors. Second, for defining complex interventions (e.g., using a sensor when performing an exercise) several issues could be identified that will contribute to improve the Albatros configurator. Third, additional studies are needed to evaluate the overall mental effort of domain experts when using the configurator. Altogether, the Albatros framework may be a reasonable alley to empower domain experts in creating homework in the context of remote therapeutic interventions.

Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and vary over time. Usually, in interviews or self-report questionnaires, patients are asked to report symptoms as well as their severity and duration retrospectively. However, only little is known to what degree such retrospective reports reflect the symptoms experienced in daily life some time ago. Mobile technologies can help to bridge this gap: mobile self-help services allow patients to record their symptoms prospectively when (or shortly after) they occur in daily life. In this study, we present results that we obtained with the mobile crowdsensing platform TrackYourTinnitus to show that there is a discrepancy between the prospective assessment of symptom variability and the retrospective report thereof. To be more precise, we evaluated the real-time entries provided to the platform by individuals experiencing tinnitus. The results indicate that mobile technologies like the TrackYourTinnitus crowdsensing platform may go beyond the role of an assistive service for patients by contributing to more accurate diagnosis and, hence, to a more elaborated treatment.

In the context of therapeutic interventions, smart mobile devices are becoming increasingly important. First, they can properly assist patients in performing their homework - a support required for more efficient therapeutic interventions. Second, mobile applications enable therapists to monitor homework outcomes. From a technical perspective, frequently required changes of the mobile applications supporting therapeutic interventions constitute a major challenge. To tackle the latter for a multitude of remote therapeutic interventions, e.g., in psychotherapy or physiotherapy, we deploy process management technology to smart mobile devices. This paper discusses flexibility issues addressed by the mobile processes. Particularly, the achieved flexibility, in turn, increases the practical benefits of smart mobile devices in the context of remote therapeutic interventions.

The development of information systems, which support homework in the context of therapeutic interventions, has not been sufficiently addressed so far. However, both therapists and patients crave for a mobile assistance managing complex homework procedures. For example, smart mobile devices can automatically inform therapists about corresbonding outcomes, giving them the opportunity to timely adjust homework if required. When realizing information systems that integrate smart mobile devices, the common procedure of therapeutic interventions in general and homework in particular must be carefully captured by the system. Therefore, relevant requirements were elicitated in real-world projects. Based on these requirements, we realized the Albatros platform enabling therapists to manage therapeutic interventions remotely. Using the platform, homework can be created with a web-based component and be performed by patients with the help of smart mobile devices. In this paper, elicitated requirements for realizing the platform as well as its features and architecture are presented. Altogether, the Albatros platform enables therapists as well as patients to manage therapeutic interventions and homework more efficiently.

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.

Smart mobile devices are variously used in the health sector. Some mobile applications empower patients to better understand their health problems, others guide them in health behavior. Moreover, smart mobile devices can be used in clinical research. Mobile crowd sensing has proven high usefulness for collecting health data with high ecological validity in this context. As the core idea, individually recorded health data are evaluated and fed back to individuals to better control their symptoms. For this purpose, the Track-YourTinnitus mobile crowd sensing platform was developed to empower patients to cope better with their tinnitus. So far, the platform has solely gathered patient data based on mobile questionnaires. When filling in a questionnaire, however, the analysis of the heartrate might provide novel information to medical experts. As monitoring the heartrate with smart mobile devices is costly, the trend towards wearables offers promising perspectives. Using smartwatches instead of smartphones in TrackYourTinnitus, however, requires questionnaire management on smartwatches. This work presents results of a prestudy related to the feasibility of sophisticated questionnaires on smartwatches. A prototype was developed and evaluated with 24 subjects. The obtained results are promising regarding the use of smartwatches for mobile crowd sensing in the context of chronic disorders.

Tinnitus ("ringing in the ear") is characterized by the perception of a sound in the absence of a corresponding acoustic stimulus. While many affected people habituate to the phantom sound, others are severely bothered and impaired in their quality of life. It is assumed that the latter group is characterized by a deficient noise cancelling mechanism in the brain. To train tinnitus patients to focus on target sounds and hence to suppress irrelevant background sounds, we developed a mobile serious game application, which is presented in this paper. The application runs on three mobile operating systems. We describe its goals and architecture as well as results from an evaluation study. Study results indicate that the gaming approach is feasible for training affected patients in focusing on directional hearing and, thereby, to suppress their tinnitus. Compared to traditional hearing training, advances of this approach are anytime availability, higher enjoyment, immediate feedback, and the option to stepwise increase game difficulty. From this, we expected an increased patient motivation and adherence as well as improved training and learning effects.

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.

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.

The technical capabilities of modern smart mobile devices more and more enable us to run desktop-like applications with demanding resource requirements in mobile environments. Along this trend, numerous concepts, techniques, and prototypes have been introduced, focusing on basic implementation issues of mobile applications. However, only little work exists that deals with the design and implementation (i.e., the engineering) of advanced smart mobile applications and reports on the lessons learned in this context. In this paper, we give profound insights into the design and implementation of such an advanced mobile application, which enables location-based mobile augmented reality on two different mobile operating systems (i.e., iOS and Android). In particular, this kind of mobile application is characterized by high resource demands since various sensors must be queried at run time and numerous virtual objects may have to be drawn in realtime on the screen of the smart mobile device (i.e., a high frame count per second be caused). We focus on the efficient implementation of a robust mobile augmented reality engine, which provides location-based functionality, as well as the implementation of mobile business applications based on this engine. In the latter context, we also discuss the lessons learned when implementing mobile business applications with our mobile augmented reality engine.

In application domains like healthcare, psychology and e-learning, data collection is based on specifically tailored paper & pencil questionnaires. Usually, such a paper-based data collection is accomplished by a massive workload regarding the processing, analysis, and evaluation of the data collected. To relieve domain experts from these manual tasks and to increase the efficiency of the data collection process, we developed a generic approach for realizing process-driven smart mobile device applications based on process management technology. According to this approach, the logic of a questionnaire is described in terms of an explicit process model whose enactment is driven by a generic process engine. Our goal is to demonstrate that such a process-aware design of mobile business applications is useful with respect to mobile data collection. Hence, we developed a generic architecture comprising the main components of mobile data collection applications. Furthermore, we used these components for developing mobile electronic questionnaires for psychological studies. The paper presents the challenges identified in this context and discusses the lessons learned. Overall, process management technology offers promising perspectives for developing mobile business applications at a high level of abstraction.