Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform

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 retrospectively report symptoms as well as their severity, duration and influencing factors. However, only little is known to what degree such retrospective reports reflect the actual experiences made in daily life. Mobile technologies can remedy this deficiency. In particular, mobile self-help services allow patients to prospectively record symptoms and their severity at the time (or shortly after) they occur in daily life. In this study, we present results we obtained with the mobile crowdsensing platform TrackYourTinnitus. In particular, we show that there is a discrepancy between prospective and retrospective assessments. To be more precise, we show that the prospective variation of tinnitus loudness does not differ between the users who retrospectively rate tinnitus loudness as “varying” and the ones who retrospectively rate it as “non-varying.” As another result, the subjectively reported stress-level was positively correlated with tinnitus (loudness and distress) in the prospective assessments, even for users who retrospectively rated that stress reduces their tinnitus or has no effect on it. The results indicate that mobile technologies, like the TrackYourTinnitus crowdsensing platform, go beyond the role of an assistive service for patients by contributing to more detailed information about symptom variability over time and, hence, to more elaborated diagnostics and treatments

Differences between Android and iOS Users of the TrackYourTinnitus Mobile Crowdsensing mHealth Platform

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

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

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

Towards Incentive Management Mechanisms in the Context of Crowdsensing Technologies based on TrackYourTinnitus Insights

The increased use of mobile devices has led to an improvement in the public health care through participatory interventions. For example, patients were empowered to contribute in treatment processes with the help of mobile crowdsourcing and crowdsensing technologies. However, when using the latter technologies, one prominent challenge constitutes a continuous user engagement. Incentive management techniques can help to tackle this challenge by motivating users through rewards and recognition in exchange of task completion. For this purpose, we aim at developing a conceptual framework that can be integrated with existing mHealth mobile crowdsourcing and crowdsensing platforms. The development of this framework is based on insights we obtained from the TrackYourTinnitus (TYT) mobile crowdsensing platform. TYT, in turn, pursues the goal to reveal insights to the moment-to-moment variability of patients suffering from tinnitus. The work at hands presents evaluated data of TYT and illustrates how the results drive the idea of a conceptual framework for an incentive management in this context. Our results indicate that a proper incentive management should play an important role in the context of any mHealth platform that incorporates the idea of the crowd

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more indepth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. TheTrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder

Smartphone Apps in the Context of Tinnitus: Systematic Review

Smartphones containing sophisticated high-end hardware and offering high computational capabilities at extremely manageable costs have become mainstream and an integral part of users' lives. Widespread adoption of smartphone devices has encouraged the development of many smartphone applications, resulting in a well-established ecosystem, which is easily discoverable and accessible via respective marketplaces of differing mobile platforms. These smartphone applications are no longer exclusively limited to entertainment purposes but are increasingly established in the scientific and medical field. In the context of tinnitus, the ringing in the ear, these smartphone apps range from relief, management, self-help, all the way to interfacing external sensors to better understand the phenomenon. In this paper, we aim to bring forth the smartphone applications in and around tinnitus. Based on the PRISMA guidelines, we systematically analyze and investigate the current state of smartphone apps, that are directly applied in the context of tinnitus. In particular, we explore Google Scholar, CiteSeerX, Microsoft Academics, Semantic Scholar for the identification of scientific contributions. Additionally, we search and explore Google’s Play and Apple's App Stores to identify relevant smartphone apps and their respective properties. This review work gives (1) an up-to-date overview of existing apps, and (2) lists and discusses scientific literature pertaining to the smartphone apps used within the context of tinnitus

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain

Conceptualization and Realization of a Database Migration Path for an International and mHealth Tinnitus Database

The mobile crowdsensing platform TrackYourTinnitus (TYT) was created to monitor and visualize fluctuations of tinnitus perception by affected individuals using smart devices. The platform aims to gather data of tinnitus patients for research purposes and to help those affected to better understand the fluctuations of tinnitus perception. Users have to answer specific questionnaires to assess tinnitus perception and tinnitus-related parameters during their daily routine. The gathered data from the questionnaires are stored in the MariaDB database running on the back-end of the application. In the future, the data of TrackYourTinnitus will be merged with clinical databases to broaden the researches related to the tinnitus symptom. Consequently, the amount of data stored in the relational database will notably increase. Additionally, MRI scans will be joined to patients? data to allow a better overview of the tinnitus development for individuals. For this purpose, it is considered to look for an alternative system for hosting the TYT database, since the current database running on MariaDB does not deliver the performance required. This work attempts to transfer the TrackYourTinnitus database from MariaDB to SQL Server. The system migration aims to ensure smooth database operations when dealing with data on a large scale as well as to benefit from the advanced features of T-SQL, the query language used by SQL Server