Residual inhibition (RI) is a phenomenon observed in many tinnitus patients, where tinnitus remains temporarily suppressed for a short duration—typically less than a minute-after the cessation of an appropriate masking stimulus. Despite decades of clinical interest in RI, machine learning (ML)-based, feature-driven classification approaches remain scarce. In this study, we investigate the potential of ML models to classify RI by developing a dedicated data analysis pipeline. Given the heterogeneous nature of the features—including numerical, binary, and ordinal variables-we apply feature importance techniques tailored for mixed-type data to ensure a comprehensive evaluation and improve interpretability. Our results demonstrate a clear separation between RI classes, highlighting the relevance of specific clinical and audiological factors in distinguishing them. Building on this, we assess the predictive power of RI classifications with high confidence within a supervised learning framework to determine their relevance for treatment outcome prediction. While our findings confirm that RI can be effectively classified, they also suggest that RI alone is not sufficient to serve as a reliable predictor for treatment outcomes.

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 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.

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.

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.

Electronic health records (EHR) often include multiple perspectives on a patient's current state of well-being (e.g. vital signs and subjective indicators measured by questionnaires). In this study, we use these perspectives to build phenotypes of chronic tinnitus patients and investigate how these phenotypes are associated with response to treatment. Therefore, we model patients as nodes in a network, where those perspectives are interpreted as layers of a multi-layer network. To identify phenotypes of patients in the network, we implement a community detection algorithm. Some of these communities can be considered as phenotypes if they represent subgroups of patients that are similar according to the investigated perspectives. Furthermore, we analyze the influence of the layers on the final community structure of patients. We then propose a method to add layers given their community structure similarity. Finally, we fit a model, per community, to predict the treatment outcome. In some communities, this prediction outperformed the baseline scenario where the predictor was fitted to all patients.

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.

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.

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.

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.