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

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

Konzeption und Realisierung einer mobilen Android Anwendung zur Depressionserkennung

Depressionen werden heute immer häufiger und sollen 2020 die zweithäufigste Volkskrankheit sein. Sie zeichnen sich z. B. durch Interessenlosigkeit und Traurigkeit aus, können das Leben der Betroffenen stark beeinträchtigen und im schlimmsten Fall sogar zum Suizid führen. Um Depressionen zu erkennen, können Smartphones genutzt werden. Diese werden immer leistungsfähiger und somit wird auch das mobile Datensammeln immer häufiger eingesetzt. Das Datensammeln kann mit Hilfe von Fragebögen erfolgen. Gerade durch Fortschritte im Bereich von mobilem Internet wird dies ebenfalls unterstützt, denn so können Fragebögen jederzeit online von zu Hause oder unterwegs ausgefüllt werden. Ebenfalls ist eine direkte Auswertung und das Anzeigen von Feedback für den Nutzer leichter. Daraus ergibt sich die Problemstellung Depressionen effizient zu erkennen. Das Personal in Arztpraxen erkennt Depressionen oftmals nicht oder Menschen mit Depressionen bekommen keine Behandlung, da sie sich durch Vermeidungsverhalten nicht die Möglichkeiten eröffnen, die bereitstehen. Doch durch frühzeitige Erkennung können Depressionen oftmals gut behandelt werden. Im Rahmen dieser Arbeit wird eine mobile Android Anwendung für Smartphones und Tablets konzipiert und implementiert. Es soll ein Modell aus Rollen, Studien und Fragebögen umgesetzt werden. Als Rollen gibt es Patienten und Ärzte, die jeweils in ihrem Bereich der App verschiedene Funktionalitäten bereitgestellt bekommen. Ein Patient kann an Studien teilnehmen und die Fragebögen dieser ausfüllen und abgeben. Ein Arzt ist als Verantwortlicher einer Studie dazu in der Lage alle abgegebenen Fragebögen dieser Studie auswerten zu lassen und für den entsprechenden Patienten freizuschalten. Die Anwendung soll durch das Ausfüllen und direkte Auswerten von Fragebögen dabei helfen Depressionen frühzeitig zu erkennen, zur Behandlung wurde sie aber nicht konzipiert

Optimizing Expectations via Mobile Apps: A New Approach for Examining and Enhancing Placebo Effects

There is growing interest in interventions that enhance placebo responses in clinical practice, given the possibility that this would lead to better patient health and more effective therapy outcomes. Previous studies suggest that placebo effects can be maximized by optimizing patients’ outcome expectations. However, expectancy interventions are difficult to validate because of methodological challenges, such as reliable blinding of the clinician providing the intervention. Here we propose a novel approach using mobile apps that can provide highly standardized expectancy interventions in a blinded manner, while at the same time assessing data in everyday life using experience sampling methodology (e.g., symptom severity, expectations) and data from smartphone sensors. Methodological advantages include: 1) full standardization; 2) reliable blinding and randomization; 3) disentangling expectation effects from other factors associated with face-to-face interventions; 4) assessing short-term (days), long-term (months), and cumulative effects of expectancy interventions; and 5) investigating possible mechanisms of change. Randomization and expectancy interventions can be realized by the app (e.g., after the clinic/lab visit). As a result, studies can be blinded without the possibility for the clinician to influence study outcomes. Possible app-based expectancy interventions include, for example, verbal suggestions and imagery exercises, although a large number of possible interventions (e.g., hypnosis) could be evaluated using this innovative approach

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

Access and Engagement of MoodTools, an mHealth Application for Depression

mHealth (mobile health) serves as a potential solution for circumventing barriers to traditional psychotherapy, but few studies evaluate mHealth technologies available in real-world settings with real-world users. This study evaluated the extent to which MoodTools, a self-help app for depression, circumvents barriers to traditional psychotherapy and engages users. App behavior from 159,00 Android users were assessed. Results showed that MoodTools could circumvent barriers to traditional psychotherapy, as it was downloaded in 198 countries, and the number of users was positively correlated with rates of unmet mental health need in the US. App use during and outside of traditional business hours were not significantly different. Regarding engagement, app sessions averaged 4 minutes and half of users returned to the app after their first session. There was no correlation between users’ initial depressive symptom severity score and total amount of time spent in MoodTools. Implications and future directions are discussed

Ein Rahmenwerk zur mobilen Unterstützung therapeutischer Interventionen

Immer mehr Menschen leiden in der heutigen Zeit unter psychischen Erkrankungen, wie Depressionen oder Posttraumatischen Belastungsstörungen, die mithilfe therapeutischer Interventionen im Rahmen einer Psychotherapie behandelt werden können. Die hierbei zur Anwendung kommenden Interventionen hängen jeweils grundsätzlich von den zu Beginn der Therapie definierten Therapiezielen ab, und erstrecken sich teilweise über mehrere Sitzungen hinweg. Viele Interventionen nutzen therapeutische Hausaufgaben, um die Zeit zwischen den Therapiesitzungen effizient zu gestalten bzw. eine bestmögliche Wirksamkeit der Intervention zu erzielen. Hierbei spielt die korrekte Durchführung der Hausaufgabe eine große Rolle, d.h. diese sollte einerseits im definierten Kontext (z.B. Zeit, Ort oder maximale Herzfrequenz) erfolgen und andererseits entsprechend den Vorgaben des Therapeuten ausgeführt werden. Darüber hinaus ist eine wahrheitsgetreue und lückenlose Rückmeldung (sog. Feedback) von Seiten des Patienten über den Verlauf der Hausaufgabe essentiell, damit der Therapeut wichtige Erkenntnisse hinsichtlich der Wirksamkeit der Hausaufgabe bzw. therapeutischen Intervention erhält. Aufgrund fehlender technischer Lösungen ist es Therapeuten heute weder möglich, die Korrektheit der durchgeführten Hausaufgabe zu überprüfen noch das direkte Feedback während oder im Anschluss an die Hausaufgabe zu erfahren. Aber auch auf Seiten des Patienten fehlt eine maßgeschneiderte technische Unterstützung, um eine kontinuierliche und angemessene Hausaufgabendurchführung gewinnbringend zu gewährleisten. Die vorliegende Arbeit adressiert die erwähnten Aspekte und Anforderungen seitens der Therapeuten und Patienten durch Einführung eines umfassenden Rahmenwerks zur mobilen Unterstützung therapeutischer Interventionen. Die hierbei erarbeiteten Konzepte erlauben einerseits eine robuste und flexible Ausführung therapeutischer Interventionen auf einem mobilen Endgerät des Patienten, andererseits ermöglichen sie deren flexible Modellierung und Konfiguration durch den Therapeuten. Als weiteren Beitrag dieser Arbeit wurden Konzepte entwickelt, die durch den Einsatz von End-User Development Techniken den Therapeuten in die Lage versetzen, das technische Management therapeutischer Interventionen ohne Einbeziehung eines IT-Experten durchzuführen. Mithilfe eines umfangreichen Prototyps wurde das Rahmenwerk schließlich validiert und in mehreren praktischen Projekten getestet. Letztere haben gezeigt, dass das vorgestellte Rahmenwerk einen erheblichen Beitrag in der aktuellen Gesundheitsforschung leisten kann