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.

BACKGROUND: Current clinical decision support systems (DSS) are trained and validated on observational data from the clinic in which the DSS is going to be applied. This is problematic for treatments that have already been validated in a randomized clinical trial (RCT), but have not yet been introduced in any clinic. In this work, we report on a method for training and validating the DSS core before introduction to a clinic, using the RCT data themselves. The key challenges we address are of missingness, foremost: missing rationale when assigning a treatment to a patient (the assignment is at random), and missing verification evidence, since the effectiveness of a treatment for a patient can only be verified (ground truth) if the treatment was indeed assigned to the patient - but then the assignment was at random. MATERIALS: We use the data of a multi-armed clinical trial that investigated the effectiveness of single treatments and combination treatments for 240+ tinnitus patients recruited and treated in 5 clinical centres. METHODS: To deal with the 'missing rationale for treatment assignment' challenge, we re-model the target variable that measures the outcome of interest, in order to suppress the effect of the individual treatment, which was at random, and control on the effect of treatment in general. To deal with missing features for many patients, we use a learning core that is robust to missing features. Further, we build ensembles that parsimoniously exploit the small patient numbers we have for learning. To deal with the 'missing verification evidence' challenge, we introduce counterfactual treatment verification, a verification scheme that juxtaposes the effectiveness of the recommendations of our approach to the effectiveness of the RCT assignments in the cases of agreement/disagreement between the two. RESULTS AND LIMITATIONS: We demonstrate that our approach leverages the RCT data for learning and verification, by showing that the DSS suggests treatments that improve the outcome. The results are limited through the small number of patients per treatment; while our ensemble is designed to mitigate this effect, the predictive performance of the methods is affected by the smallness of the data. OUTLOOK: We provide a basis for the establishment of decision supporting routines on treatments that have been tested in RCTs but have not yet been deployed clinically. Practitioners can use our approach to train and validate a DSS on new treatments by simply using the RCT data available to them. More work is needed to strengthen the robustness of the predictors. Since there are no further data available to this purpose, but those already used, the potential of synthetic data generation seems an appropriate alternative.

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.

Smartphones allow for prompting users with a short questionnaire about their current subjective experience, a technique often called Ecological Momentary Assessments. One of the biggest challenges for such studies is a lack of adherence, diminishing the benefits for both user and researcher. Being able to predict if a user is going to stop answering the questionnaire prompts would be beneficial for researchers and developers. This would allow for, for example, specifically addressing those users, or for over-sampling populations at higher risk of dropping out of a study. In this work, based on an observational study of the general population , we analyzed data from almost 1,000 users. The data include a large variety of sensor data from the users’ smartphones. We utilized machine learning to predict adherence on a day-to-day level, as well as predict adherence based on participant data after on-boarding. For day-to-day prediction, the best performing model was a model based on metadata features (days since first questionnaire was filled out, days since the last questionnaire was filled out, number of filled-out questionnaires, days since app installation), yielding an area under the precision–recall curve of 0.89. The inclusion of sensor data did not improve the model’s performance, indicating that the high cost of collecting and processing sensor data is not worth the benefits for predicting fill-out behavior . Predicting at sign-up if a user will adhere to a questionnaire prompt at least once was better than chance, but further studies are needed.

Advanced organizational processes today require many process-relevant artifacts to be formally represented in process models. As these processes become more complex, process designers face the challenge of maintaining high comprehensibility while adequately visualizing process-relevant information. Modularization has been introduced to address the increasing complexity and information overload of process models, tackling this issue. While research acknowledges the importance of process model comprehensibility in order to benefit from the merits of such models, there still needs to be more clarity about the benefits of process model modularization. Recent research has focused on the human visual system and investigated visual behavior in the context of process model literacy. Continuing this branch of research, this paper analyzes gaze behavior when comprehending modularized process models. A follow-up eye-tracking study was conducted to comprehend differently modularized BPMN 2.0 process models (i.e., vertical, horizontal, orthogonal). The results indicated that process model readers comprehend modularization approaches differently. Vertical modularization was perceived as the best comprehensible approach, whereas horizontal received the least acceptance. Considered eye movement performance measures confirm prior observation that attention is deployed more effectively in vertical modularization. Based upon the findings, the paper proposes a revised theoretical model from prior work that relates visual tasks to the execution of visual routines, explaining comprehension and information processing in modularized process models. The theoretical model elaborates how the visual system process and comprehends information obtained from modularized process models. Furthermore, the paper derived implications for modeling and practical applications, pointing out solid representational points and shortcomings of each modularization approach regarding how modularized process models are read and comprehended.

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.

Smartphones and other mobile devices offer a valu-able opportunity to gather patient-specific health data during everyday life. However, the increasing popularity of mobile health apps demands specialized data analysis methods that can handle the unique, patient-based, time-dependent, and often multivariate data collected by these apps. This work explores the analysis of patient-based mHealth data to develop personalized prediction models. The models can incor-porate data not only from the individual patient, but also from other similar patients using patient-specific neighborhoods. Our approach entails selecting the appropriate data for a particular prediction task and dataset. We also discuss when to utilize data from other patients and offer guidance on selecting similarity functions, models, and model combinations. The approach is illustrated on the case study of tinnitus, a perception of sound without an external source, which can be highly distressing. Its presentation and treatment success are patient-specific. As part of the UNITI project, daily diary data of the patients is collected. Evaluation favored the use of personalized models using patient-specific neighborhoods over a global model using all data, or only using a patient's own data for tinnitus distress prediction.

A growing number of observational studies are utilizing the advances of mobile technology. In addition to diverse strategies such as digital phenotyping, ecological momentary assessments (EMA) or mobile Crowdsensing, intervention studies are becoming increasingly relevant in this context. More specifically, Just-in-Time Adaptive Interventions (JITAIs) appear to be a key component in mobile health for behavioral support, aimed at providing the proper kind of aid at the right time by dynamically adapting to a person's changing condition. Fully or partially electronically supported treatments can relieve the health system and bridge or shorten the waiting time for treatments for patients. In this work we combine our technical expertise with the theoretical foundations of JITAIs in an interdisciplinary collaboration to facilitate the appropriate use of theory in building JITAIs in a dynamic system. As a result, we present a highly configurable, generic and modular EMA and JITAI mobile framework which enabled us to generate a cross-platform breast cancer aftercare mobile app in a large-scale German study. The aim is to learn more about the validity, usefulness and feasibility of such mobile-device-assisted studies, taking into account administrative burden as well as user acceptance. We discuss the background, implementation, and whether these features could leverage similar study types in the future to overcome the static nature of existing behavioral and interventional apps.

Moving toward a digital environment poses com-plex challenges for organizations. Digital blueprints, or pro-cess models, are crucial for facilitating digital transformation. However, it is vital to ensure that all stakeholders correctly understand these models to reap their benefits. Despite extensive research on process model comprehension, there is still a lack of in-depth knowledge about the role of cognitive aspects. To address this gap, this paper presents the findings of an empirical study that evaluated the predictive value of cognitive skills, namely working memory, selective attention, and reasoning, on process model comprehension. The study involved 50 partici-pants, and the analysis revealed that higher reasoning abilities were associated with better comprehension, emphasizing the critical role of this cognitive skill in understanding process models.

BACKGROUND: Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not clear how these systems arrive at their predictions. METHODS: In this paper, we provide a brief overview of the taxonomy of explainability methods and review popular methods. In addition, we conduct a systematic literature search on PubMed to investigate which explainable artificial intelligence (XAI) methods are used in 450 specific medical supervised ML use cases, how the use of XAI methods has emerged recently, and how the precision of describing ML pipelines has evolved over the past 20 years. RESULTS: A large fraction of publications with ML use cases do not use XAI methods at all to explain ML predictions. However, when XAI methods are used, open-source and model-agnostic explanation methods are more commonly used, with SHapley Additive exPlanations (SHAP) and Gradient Class Activation Mapping (Grad-CAM) for tabular and image data leading the way. ML pipelines have been described in increasing detail and uniformity in recent years. However, the willingness to share data and code has stagnated at about one-quarter. CONCLUSIONS: XAI methods are mainly used when their application requires little effort. The homogenization of reports in ML use cases facilitates the comparability of work and should be advanced in the coming years. Experts who can mediate between the worlds of informatics and medicine will become more and more in demand when using ML systems due to the high complexity of the domain.

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.

Therapeutic Interventions (TIs) play an important role in modern medical and psychological treatments, but their integration into the digital world still shows deficits, e.g., in the integration of the auditory interface. Initiatives to integrate this interface into existing Internet- and Mobile-Based Interventions (IMIs) are largely focused on a small group of Voice Assistants (VAs) and their specific capabilities. To mitigate these drawbacks, the presented concept seamlessly integrates arbitrary VAs into the treatment process of TIs. To this end, an architecture - including a discussion of relevant requirements - is presented that, on the one hand, uses VAs as the only point of contact with patients and, on the other hand, provides a comprehensive web-based backend for Healthcare Providers (HCPs). Based on the architecture, a proof-of-concept implementation using Amazon Alexa is presented. Finally, it is discussed that the scenario addressed and the solution presented have great potential, but still need a lot of work and technical considerations.

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.

Mental disorders represent an ongoing challenge to global health and can affect anyone at any age from any region in the world. The response of healthcare providers to mental health disorders still lags behind that of other diseases and a significant number of people who are affected by mental health disorders do not receive adequate treatment. The widespread usage of Internet-connected devices provides new opportunities to deliver treatment to more people using innovative approaches. The groundwork is being laid for the adoption of Internet- and mobile-based interventions, providing mental and behavioral health support to more people and narrowing the treatment gap. This paper discusses the main technical details of the backend API of the eSano eHealth platform as an example for a complex and comprehensive IT-framework for large-scale and flexible Internet- and mobile-based interventions. An overview of eSano is provided and the platform is compared with other technical solutions in the field. In addition, the components of eSano are described and further technical insights are elaborated in more detail. To this end, the work at hand demonstrates the main requirements of the backend API powering eSano, its concepts and the overall developed solution. It will as such inform researchers and practitioners about state-of-the-art backend API development in the eHealth context.

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.

Digitization is advancing rapidly in many prevalently analogue domains such as healthcare. For the latter domain, the synergies with modern information technologies (IT) have become an integral part regarding communication and collaboration. For this reason, a comprehensible language is of importance in order to allow a frictionless exchange of information between domain experts. The Business Process Model and Notation (BPMN) 2.0 represents a promising notation that may be applied as lingua franca. Although the BPMN 2.0 is widespread applied by experts in business and industry, little experience exists how BPMN 2.0 is adopted in healthcare. In order to assess how BPMN 2.0 is deployed in healthcare, we conducted a preliminary eye tracking study, in which n=16 professionals from healthcare comprehended a particular BPMN 2.0 process model. The results indicate that BPMN 2.0 might be a candidate for a lingua franca to foster the comprehensible exchange of information as well as collaboration between healthcare and IT.

Several governments introduced or promoted the use of contact-tracing apps during the ongoing COVID-19 pandemic. In Germany, the related app is called Corona—Warn-App, and by end of 2020, it had 22.8 million downloads. Contact tracing is a promising approach for containing the spread of the novel coronavirus. It is only effective if there is a large user base, which brings new challenges like app users unfamiliar with using smartphones or apps. As Corona-Warn-App is voluntary to use, reaching many users and gaining a positive public perception is crucial for its effectiveness. Based on app reviews and tweets, we are analyzing the public perception of Corona-Warn-App. We collected and analyzed all 78,963 app reviews for the Android and iOS versions from release (June 2020) to beginning of February 2021, as well as all original tweets until February 2021 containing #CoronaWarnApp (43,082). For the reviews, the most common words and n-grams point towards technical issues, but it remains unclear, to what extent this is due to the app itself, the used Exposure Notification Framework, system settings on the user's phone, or the user's misinterpretations of app content. For Twitter data, overall, based on tweet content, frequent hashtags, and interactions with tweets, we conclude that the German Twitter-sphere widely reports adopting the app and promotes its use.

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.

The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

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.

Cyber-physical systems (CPS) are often customized to meet customer needs and, hence, exhibit a large number of hard-/software configuration variants. Consequently, the processes deployed on a CPS need to be configured to the respective CPS variant. This includes both configuration at design time (i.e., before deploying the implemented processes on the CPS) and runtime configuration taking the current context of the CPS into account. Such runtime process configuration is by far not trivial, e.g., alternative process fragments may have to be selected at certain points during process execution of which one fragment is then dynamically applied to the process at hand. Contemporary approaches focus on the design time configuration of processes, while neglecting runtime configuration to cope with process variability. In this paper, a generic approach enabling context-aware process configuration at runtime is presented. With the Process Query Language process fragments can be flexibly selected from a process repository, and then be dynamically injected into running process instances depending on the respective contextual situations. The latter can be automatically derived from context factors, e.g., sensor data or configuration parameters of the given CPS. Altogether, the presented approach allows for a flexible configuration and late composition of process instances at runtime, as required in many application domains and scenarios.

Tinnitus is a disorder or symptom that causes phantom noise sensation in the ears without presence of any external sound source. Tinnitus is understood as a problem caused by underlying damage in the inner-ear. However, recent studies have shown that tinnitus is also influenced by complexities in non-auditory brain areas. Among different brain-imaging techniques, mobile Electroencephalography (EEG) can be a viable solution in better understanding the influencing factors in the brain causing tinnitus, but real-time analysis of EEG in real-world environments is faced by unique challenges and limitations. We present the first pure smartphone-based solution to acquire and analyze EEG data in real time and in everyday settings, as well as in any other scenario which does not allow large setups. More specifically, we propose TinnituSense a smartphone app for EEG recordings and visualization, and evaluate this app to claim the feasibility of our approach. On one hand, the proposed approach will open the opportunities to perform brain-imaging in real-world environment. On the other hand, the developed app will allow tinnitus researchers to collect evidence for new facts regarding tinnitus with the help of ambulatory brain-imaging data.

Smart devices and low-powered sensors are becoming increasingly ubiquitous and nowadays almost all of these devices are connected, which is a promising foundation for crowdsensing of data related to various environmental phenomena. Resulting data is especially meaningful when it is related to time and location. Interestingly, many existing approaches built their solution on monolithic backends that process data on a per-request basis. However, for many scenarios, such technical setting is not suitable for managing data requests of a large crowd. For example, when dealing with millions of data points, still many challenges arise for modern smartphones if calculations or advanced visualization features must be accomplished directly on the smartphone. Therefore, the work at hand proposes an architectural design for managing geospatial data of tinnitus patients, which combines a cloudnative approach with Big Data concepts used in the Internet of Things. The presented architectural design shall serve as a generic foundation to implement (1) a scalable backend for a platform that covers the aforementioned crowdsensing requirements as well as to provide (2) a sophisticated stream processing concept to calculate and pre-aggregate incoming measurement data of tinnitus patients. Following this, this paper presents a visualization feature to provide users with a comprehensive overview of noise levels in their environment based on noise measurements. This shall help tinnitus or hearing-impaired patients to avoid locations with a burdensome sound level.

Tinnitus is a disorder that is not entirely understood, and many of its correlations are still unknown. On the other hand, smartphones became ubiquitous. Their modern versions provide high computational capabilities, reasonable battery size, and a bunch of embedded high-quality sensors, combined with an accepted user interface and an application ecosystem. For tinnitus, as for many other health problems, there are a number of apps trying to help patients, therapists, and researchers to get insights into personal characteristics but also into scientific correlations as such. In this paper, we present the first approach to an app in this context, called TinnituSense that does automatic sensing of related characteristics and enables correlations to the current condition of the patient by a combined participatory sensing, e.g., a questionnaire. For tinnitus, there is a strong hypothesis that weather conditions have some influence. Our proof-of-concept implementation records weather-related sensor data and correlates them to the standard Tinnitus Handicap Inventory (THI) questionnaire. Thus, TinnituSense enables therapists and researchers to collect evidence for unknown facts, as this is the first opportunity to correlate weather to patient conditions on a larger scale. Our concept as such is limited neither to tinnitus nor to built-in sensors, e.g., in the tinnitus domain, we are experimenting with mobile EEG sensors. TinnituSense is faced with several challenges of which we already solved principle architecture, sensor management, and energy consumption.

The exploitation of sensor features offered by present smart mobile devices is a trend that becomes increasingly important in various domains. In healthcare, for example, these sensors are used to cheaply gather valuable data for chronic disease management or health care. Regarding the latter, health insurers crave for effective methods that can be offered to their customers. Moreover, smart mobile devices provide many advantages compared to approaches hitherto applied in the aforementioned contexts as they can be easily used in everyday life. Thereby, when taking these advantages properly into account, new mobile application types become possible. Body sensor networks are such an application type that aim at monitoring users in vivo. Furthermore, data gathered with body sensor networks may be a valuable basis to provide user interventions. This paper presents an application that shall support users to walk mindfully. The motivation was to create a mobile tool that can make mindful walking more effective to reduce stress and to target noncommunicable diseases such as diabetes or depression. It is a mobile personalized tool that senses the walking speed and provides haptic feedback thereof. The mindful walking procedure, the technical prototype as well as preliminary study results are presented and discussed in this work. The reported user feedback and the study results indicate promising perspectives for a tool that supports a mindful walking behavior. Altogether, the use of smart mobile device sensors constitutes a promising instrument for realizing mobile applications in the context of health care and disease management.

Treatment of patients with tinnitus is mainly pursued on the basis of screening data, encompassing answers to questionnaires or audiological examinations. Since tinnitus affects the quality of life of patients and is associated with comorbidities like depression, the screening step involves the acquisition of extensive amounts of information, which may contribute to the design of a personalized treatment. Often times, it can be observed that patients give up their treatment before completion (i.e., they constitute “dropout” cases). In this study, we investigate how multi-target classification on the screening data can contribute to characterize patients that will drop out of the study. For our analysis, we base or considerations on the target variable “tinnitus loudness”, i.e. the subjectively perceived loudness of the phantom signal. Following this, we attempt to identify variables that explain the tinnitus loudness together with the likelihood of interrupting the treatment. To be more precise, we report on results from gathered data of 1419 tinnitus patients from the University Hospital of Regensburg.

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.

Presently, mHealth technology is often applied in the context of chronic diseases to gather data that may lead to new and valuable medical insights. As many aspects of chronic diseases are not completely understood, new data sources might be promising. mHealth technology may help in this context as it can be easily used in everyday life. Moreover, the bring your own device principle encourages many patients to use their smartphone to learn more about their disease. The less is known about a disorder (e.g., tinnitus), the more patients crave for new insights and opportunities. Despite the fact that existing mHealth technology like mobile crowdsensing has already gathered data that may help patients, in general, less is known whether and how data gathered with different mobile technologies may differ. In this context, one relevant aspect is the contribution of the mobile operating system itself. For example, are there differences between Android and iOS users that utilize the same mHealth technology for a disease. In the TrackYourTinnitus project, a mobile crowdsensing mHealth platform was developed to gather data for tinnitus patients in order to reveal new insights on this disorder with high economic and patient-related burdens. As many data sets were gathered during the last years that enable us to compare Android and iOS users, the work at hand compares characteristics of these users. Interesting insights like the one that Android users with tinnitus are significantly older than iOS users could be revealed by our study. However, more evaluations are necessary for TrackYourTinnitus in particular and mHealth technology in general to understand how smartphones affect the gathering of data on chronic diseases when using them in the large.

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.

Stream classification algorithms traditionally treat arriving observations as independent. However, in many applications the arriving examples may depend on the "entity" that generated them, e.g. in product reviewing or in the interactions of users with an application server. In this study, we investigate the potential of this dependency by partitioning the original stream of observations into entity-centric substreams and by incorporating entity-specific information into the learning model. We propose a k Nearest Neighbour inspired stream classification approach (kNN), in which the label of an arriving observation is predicted by exploiting knowledge on the observations belonging to this entity and to entities similar to it. For the computation of entity similarity, we consider knowledge about the observations and knowledge about the entity, potentially transferred from another domain. To distinguish between cases where this kind of knowledge transfer is beneficial for stream classification and cases where the knowledge on the entities does not contribute to classifying the observations, we also propose a heuristic approach based on random sampling of substreams using k Random Entities (kRE). Our learning scenario is not fully supervised: after acquiring labels for the initial few observations of each entity, we assume that no additional labels arrive, and attempt to predict the labels of near-future and far-future observations from that initial seed. We report on our findings from three datasets.

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.

The identification of subpopulations with particular characteristics with respect to a disease is important for personalized diagnostics and therapy design. For some diseases, the outcome is described by more than one target variable. An example is tinnitus: the perceived loudness of the phantom signal and the level of distress caused by it are both relevant targets for diagnosis and therapy. In this work, we study the potential of multi-target classification for the identification of those screening variables, which separate best among the different subpopulations of patients, paying particular attention to subpopulations with discordant value combinations of loudness and distress. We analyse the screening data of 1344 tinnitus patients from the University Hospital Regensburg, including questions from 7 questionnaires, and report on the performance of our workflow in target separation and in ranking the questionnaires variables on their discriminative power.

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.

Despite the widespread use of process models in healthcare organizations, there are many unresolved issues regarding the reading and comprehension of these models by domain experts. This is aggravated by the fact that there exists a plethora of process modeling languages for the graphical documentation of processes, which are often not used consistently for various reasons. Hence, the identification of those factors fostering the comprehension of process models becomes crucial. We have developed a conceptual framework incorporating measurements and theories from cognitive neuroscience and psychology to unravel factors fostering the comprehension of process models within organizations. We believe that a better comprehension of process models will enhance the support of healthcare processes significantly.

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.

Many highly prevalent diseases (e.g., tinnitus, migraine, chronic pain) are difficult to treat and universally effective treatments are missing. Available treatments are only effective in patient subgroups, i.e., medical doctors and patients have to figure out which therapy might be helpful in the patient's situation. Sufficiently large and qualitative longitudinal data sets, however, would be desirable to facilitate evidence-based treatment decisions for individual patients. On one hand, traditional sensing techniques (i.e., clinical trials) have many merits enabling evidence-based medicine. On the other, they have inherent limitations. First, clinical trials are very cost- and labour-intensive. Second, the traditional approach aims at reducing ecological heterogeneity to enable the investigation of homogeneous subsamples. Recently, a new paradigm emerged that offers promising perspectives for collecting large amounts of longitudinal patient data -- Mobile Crowd Sensing. By utilizing smart mobile devices of a large number of patients, health information can be gathered from large patient collections as well as at many different time points and in various real life environmental situations. In the Track Your Tinnitus project, we implemented such a mobile crowd sensing platform to reveal new medical aspects about tinnitus with a particular focus on the variability of tinnitus over time depending on the environmental situation. In this paper, the current project status as well as first lessons learned from running the mobile application for twelve months are presented. In turn, the lessons learned are discussed in the context of the new perspectives offered by mobile crowd sensing in the medical field.

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.

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