Mobile Crowd Sensing in Clinical and Psychological Trials – A Case Study

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 TrackYourTinnitus 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

Mobile Crowd Sensing Services for Tinnitus Assessment, Therapy and Research

Tinnitus, the phantom sensation of sound, is a highly prevalent disorder that is difficult to treat; i.e., available treatments are only effective for patient subgroups. Sufficiently large and qualitative longitudinal data sets, which aggregate the individuals’ demographic and clinical characteristics, together with their response to specific therapeutic interventions, would therefore facilitate evidence-based treatment suggestions for individual patients. Currently, clinical trials are the standard instrument for realizing evidence-based medicine. However, the related information gathering is limited. For example, clinical trials try to reduce the complexity of the individual case by generating homogeneous groups to obtain significant results. From the latter, individual treatment decisions are inferred. A complementary approach would be to assess the effect of specific interventions in large samples considering the individual peculiarity of each subject. This allows providing individualized treatment decisions. Recently, mobile crowd sensing emerged as an approach for collecting large and ecological valid datasets at rather low costs. By providing mobile crowd sensing services to large numbers of patients, large datasets can be gathered cheaply on a daily basis. In the TrackYourTinnitus project, we implemented a mobile crowd sensing platform to reveal new medical aspects on tinnitus and its treatment. Additionally, we work on mobile services exploring approaches for understanding tinnitus and for improving its diagnostic and therapeutic management. We present the TrackYourTinnitus platform as well as its goals, architecture and preliminary results. Overall, the platform and its mobile services offer promising perspectives for tinnitus research and treatment

Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms

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

Using Smart Mobile Devices for Collecting Structured Data in Clinical Trials: Results From a Large-Scale Case Study

In future, more and more clinical trials will rely on smart mobile devices for collecting structured data from subjects during trial execution. Although there have been many projects demonstrating the benefits of mobile digital questionnaires, the scenarios considered in literature have been rather limited so far. In particular, the number of subjects is rather low in respective studies and a well controllable infrastructure is usually presumed, which not always applies in practice. This paper gives insights into the lessons learned in a clinical psychology trial when using tablets for mobile data collection. In particular, more than 1.700 subjects have participated so far, providing us with valuable feedback on collecting trial data with smart mobile devices in the large scale. Furthermore, issues related to an insufficient infrastructure (e.g., unstable Internet connections) have been addressed as well. Overall, the paper provides valuable insights gained during trial execution. In future, electronic questionnaires executable on smart mobile devices will replace paper-based ones

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

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

Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback

Assessment of chronic disorders requires new ways of data collection compared to the traditional pen & paper based approaches. For example, tinnitus, the phantom sensation of sound, is a highly prevalent disorder that is difficult to treat; i.e., available treatments are only effective for patient subgroups. In most individuals with tinnitus, loudness and annoyance of tinnitus varies over time. Currently, established assessment methods of tinnitus neither systematically assess this moment-to-moment variability nor environmental factors having an effect on tinnitus loudness and distress. However, information of individual fluctuations and the effect of envi-ronmental factors on the tinnitus might represent important information for tinnitus subtyping and for individualized treat-ment. In this context, a promising approach for collecting ecological valid longitudinal datasets at rather low costs is mobile crowdsensing. In the TrackYourTinnitus project, we developed an advanced mobile crowdsensing platform to reveal more detailed information about the course of tinnitus over time. In this paper, the patient mobile feedback service as a particular component of the platform is presented. It was developed to provide patients with aggregated information about the variation of their tinnitus over time. This mobile feedback service shall help a patient to demystify the tinnitus and to get better control of it, which should facilitate coping with this chronic health condition. As the basic principles and design of this mobile services are also applicable to other chronic disorders, promising perspectives for disorder management and clinical research arise

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

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

Conception and realization of a mobile data acquisition and assistance application for intersession processes of patients in psychotherapeutic treatments at the example of the iOS platform

Conventional effectiveness and impact factor studies in psychotherapy research deal mainly with the therapy session per se. In contrast, a current trend is the increasing focus on patient advancement between therapy sessions, the so-called intersession processes. Traditionally, patient data is collected and evaluated in the form of paper questionnaires. In the context of intersession research, where this is done just prior to the therapy session, this means that their results often can not be properly included immediately afterwards. With the proliferation of mobile devices such as smartphones, tablet computers, and wearables, mobile crowd sensing is a promising approach for capturing and analyzing large amounts of distributed data. This is attributed to the fact that modern mobile devices are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform complex tasks and provide countless possibilities for user interactions. Contemporaneous, in the course of digitization, both the topic of electronic health and mobile health (mHealth) are gaining increasingly more importance in the healthcare industry. Furthermore, simple and efficient interaction with mobile applications, as well as the exchange of information between the health care provider, here the therapist, and the patients, are essential aspects in applications in the mHealth field. Properly implemented, this can both improve and simplify the patient's treatment process. Within the scope of this thesis, in cooperation with the Institute of Psychology of the University of Klagenfurt, a mHealth application is developed, which allows to scientifically record intersession processes of patients in psychotherapeutic treatments. The patient automatically receives questionnaires via the mobile application, depending on therapy session dates and the results of previous evaluations, as well as manual interventions by the therapist. Thus, it should be significantly easier and more efficient for the therapist to collect and evaluate data on the patient's intersession processes and to prepare in advance for the upcoming therapy session