Modern mobile devices are mainstream and ubiquitous devices. The widespread adoption of mobile devices has resulted in surge of mobile applications (apps) hosted on marketplaces (app stores) of several mobile platforms. Besides other benefits, these apps are also applied in healthcare-related and medical use, for instance, in case of tinnitus, where tinnitus disorder is associated with the perception of ringing sound without external sound source. In particular, for tinnitus, these apps allow provision of tinnitus-related relief, self-help, and general management. The collective aim of this chapter is to foster and report on Mobile Health (mHealth) solutions, in particular mobile apps within the tinnitus context. First, this chapter provides an up-to-date overview of existing mHealth apps available for major mobile platforms. Second, this chapter provides deep insights into quality and effectiveness of said mobile apps for tinnitus treatment and management. Finally, this chapter provides discussions in relation to the tinnitus-related mHealth apps.

To our knowledge, there is no published study investigating the characteristics of people experiencing tinnitus in Albania. Such a study would be important, providing the basis for further research in this region and contributing to a wider understanding of tinnitus heterogeneity across different geographic locations. The main objective of this study was to develop an Albanian translation of a standardised questionnaire for tinnitus research, namely the European School for Interdisciplinary Tinnitus Research-Screening Questionnaire (ESIT-SQ). A secondary objective was to assess its applicability and usefulness by conducting an exploratory survey on a small sample of the Albanian tinnitus population.Design and study sample Three translators were recruited to create the Albanian ESIT-SQ translation following good practice guidelines. Using this questionnaire, data from 107 patients attending otolaryngology clinics in Albania were collected.Results Participants reporting various degrees of tinnitus symptom severity had distinct phenotypic characteristics. Application of a random forest approach on this preliminary dataset showed that self-reported hearing difficulty, and tinnitus duration, pitch and temporal manifestation were important variables for predicting tinnitus symptom severity.Conclusions Our study provided an Albanian translation of the ESIT-SQ and demonstrated that it is a useful tool for tinnitus profiling and subgrouping.

ntroduction: Tinnitus, a perception of ringing and buzzing sound in the ear, has not been completely understood yet. It is well known that tinnitus-related distress and loudness can change over time. However, proper comparability for the data collection approaches requires further focused studies. In this context, technology such as the use of mobile devices may be a promising approach. Repeated assessments of tinnitus-related distress and loudness in Ecological Momentary Assessment (EMA) studies require a short assessment, and a Visual Analogic Scale (VAS) is often used in this context. Yet, their comparability with psychometric questionnaires remains unclear and thus was the focus of this study. Research goals: The evaluation of the appropriateness of VAS in measuring tinnitus-related distress and loudness is pursued in this paper. Methods: The Mini Tinnitus Questionnaire (Mini-TQ) measured tinnitus-related distress once. Tinnitus-related distress and tinnitus loudness were measured repeatedly using VAS on a daily basis during 7 days in the TrackYourTinnitus (TYT) smartphone app and were summarized per day using mean and median results. Then, correlations between summarized VAS tinnitus-related distress and summarized VAS tinnitus loudness, on the one side, and Mini-TQ, on the other side, were calculated. Results: Correlations between Mini-TQ and VAS tinnitus-related distress ranged between r = 0.36 and r = 0.52, while correlations between Mini-TQ and VAS tinnitus loudness ranged between r = 0.25 and r = 0.36. The more time difference between the Mini-TQ and the VAS assessments is, the lower the correlations between them. Mean and median VAS values per day resulted in similar correlations. Conclusions: Mobile-based VAS seems to be an appropriate approach to utilize daily measurements of tinnitus-related distress.

Background: Modern smartphones contain sophisticated high-end hardware features, offering high computational capabilities at extremely manageable costs and have undoubtedly become an integral part in users' daily life. Additionally, smartphones offer a well-established ecosystem that is easily discoverable and accessible via the marketplaces of differing mobile platforms, thus encouraging the development of many smartphone apps. Such apps are not exclusively used for entertainment purposes but are also commonplace in health care and medical use. A variety of those health and medical apps exist within the context of tinnitus, a phantom sound perception in the absence of any physical external source. Objective: In this paper, we shed light on existing smartphone apps addressing tinnitus by providing an up-to-date overview. Methods: Based on PRISMA guidelines, we systematically searched and identified existing smartphone apps on the most prominent app markets, namely Google Play Store and Apple App Store. In addition, we applied the Mobile App Rating Scale (MARS) to evaluate and assess the apps in terms of their general quality and in-depth user experience. Results: Our systematic search and screening of smartphone apps yielded a total of 34 apps (34 Android apps, 26 iOS apps). The mean MARS scores (out of 5) ranged between 2.65-4.60. The Tinnitus Peace smartphone app had the lowest score (mean 2.65, SD 0.20), and Sanvello—Stress and Anxiety Help had the highest MARS score (mean 4.60, SD 0.10). The interrater agreement was substantial (Fleiss κ=0.74), the internal consistency was excellent (Cronbach α=.95), and the interrater reliability was found to be both high and excellent—Guttman λ6=0.94 and intraclass correlation, ICC(2,k) 0.94 (95% CI 0.91-0.97), respectively. Conclusions: This work demonstrated that there exists a plethora of smartphone apps for tinnitus. All of the apps received MARS scores higher than 2, suggesting that they all have some technical functional value. However, nearly all identified apps were lacking in terms of scientific evidence, suggesting the need for stringent clinical validation of smartphone apps in future. To the best of our knowledge, this work is the first to systematically identify and evaluate smartphone apps within the context of tinnitus.

Recent developments in Brain-Computer Interfaces (BCI)—technologies to collect brain imaging data—allow recording of Electroencephalography (EEG) data outside of a laboratory setting by means of mobile EEG systems. Brain imaging has been pivotal in understanding the neurobiological correlates of human behavior in many complex disorders. This is also the case for tinnitus, a disorder that causes phantom noise sensations in the ears in absence of any sound source. As studies have shown that tinnitus is also influenced by complexities in non-auditory brain areas, mobile EEG can be a viable solution in better understanding the influencing factors causing tinnitus. Mobile EEG will become even more useful, if real-time EEG analysis in mobile experimental environments is enabled, e.g., as an immediate feedback to physicians and patients or in undeveloped areas where a laboratory setup is unfeasible. The volume and complexity of brain imaging data have made preprocessing a pertinent step in the process of analysis, e.g., for data cleaning and artifact removal. We introduce the first smartphone-based preprocessing pipeline for real-time EEG analysis. More specifically, we present a mobile app with a rudimentary EEG preprocessing pipeline and evaluate the app and its resource consumption underpinning the feasibility of smartphones for EEG preprocessing. Our proposed approach will allow researchers to collect brain imaging data of tinnitus and other patients in real-world environments and everyday situations, thereby collecting evidence for previously unknown facts about tinnitus and other conditions.

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.

martphones 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.

Tinnitus is a complex and heterogeneous psycho-physiological disorder responsible for causing a phantom ringing or buzzing sound albeit the absence of an external sound source. It has a direct influence on affecting the quality of life of its sufferers. Despite being around for a while, there has not been a cure for tinnitus, and the usual course of action for its treatment involves use of tinnitus retaining and sound therapy, or Cognitive Behavioral Therapy (CBT). One positive aspect about these therapies is that they can be administered face-to-face as well as delivered via internet or smartphone. Smartphones are especially helpful as they are highly personalized devices, and offer a well-established ecosystem of apps, accessible via respective marketplaces of differing mobile platforms. Note that current therapeutic treatments such as CBT have shown to be effective in suppressing the tinnitus symptoms when administered face-to-face, their effectiveness when being delivered using smartphones is not known so far. A quick search on the prominent market places of popular mobile platforms (Android and iOS) yielded roughly 250 smartphone apps offering tinnitus-related therapies and tinnitus management. As this number is expected to steadily increase due to high interest in smartphone app development, a contemporary review of such apps is crucial. In this paper, we aim to review scientific studies validating the smartphone apps, particularly to test their effectiveness in tinnitus management and treatment. We use the PRISMA guidelines for identification of studies on major scientific literature sources and delineate the outcomes of identified studies.

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.

Tinnitus is a common medical condition which interfaces many different disciplines, yet it is not a priority for any individual discipline. A change in its scientific understanding and clinical management requires a shift toward multidisciplinary cooperation, not only in research but also in training. The European School for Interdisciplinary Tinnitus research (ESIT) brings together a unique multidisciplinary consortium of clinical practitioners, academic researchers, commercial partners, patient organizations, and public health experts to conduct innovative research and train the next generation of tinnitus researchers. ESIT supports fundamental science and clinical research projects in order to: (1) advancing new treatment solutions for tinnitus, (2) improving existing treatment paradigms, (3) developing innovative research methods, (4) performing genetic studies on, (5) collecting epidemiological data to create new knowledge about prevalence and risk factors, (6) establishing a pan-European data resource. All research projects involve inter-sectoral partnerships through practical training, quite unlike anything that can be offered by any single university alone. Likewise, the postgraduate training curriculum fosters a deep knowledge about tinnitus whilst nurturing transferable competencies in personal qualities and approaches needed to be an effective researcher, knowledge of the standards, requirements and professionalism to do research, and skills to work with others and to ensure the wider impact of research. ESIT is the seed for future generations of creative, entrepreneurial, and innovative researchers, trained to master the upcoming challenges in the tinnitus field, to implement sustained changes in prevention and clinical management of tinnitus, and to shape doctoral education in tinnitus for the future.

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.

Smartphones have become an integral part in life of users, mainly because over the course of recent years, they have become extremely mainstream, cheap, flexible, and they pack high-end hardware that offers high computational capabilities. Many, if not all of today’s smartphones are equipped with sophisticated sensors which enable smart mobile sensing. The programmable nature of these sensors in the smartphones enable a wide array of possibilities to achieve user-centric or environmental sensing. Even though there have been different approaches proposed to develop a smartphone app, platform, design frameworks, APIs, and even application-specific architectures, there is a lack of generalised referenceable architecture in the literature. In this paper, we propose a generic reference architecture, which can be derived to create more concrete mobile sensing or mobile app architectures. Furthermore, we realise the proposed reference architecture in a healthcare use case, specifically in the context of applying smart mobile sensing to support tinnitus research.