A growing number of observational studies are utilizing the advances of mobile technology. In addition to diverse strategies such as digital phenotyping, ecological momentary assessments (EMA) or mobile Crowdsensing, intervention studies are becoming increasingly relevant in this context. More specifically, Just-in-Time Adaptive Interventions (JITAIs) appear to be a key component in mobile health for behavioral support, aimed at providing the proper kind of aid at the right time by dynamically adapting to a person's changing condition. Fully or partially electronically supported treatments can relieve the health system and bridge or shorten the waiting time for treatments for patients. In this work we combine our technical expertise with the theoretical foundations of JITAIs in an interdisciplinary collaboration to facilitate the appropriate use of theory in building JITAIs in a dynamic system. As a result, we present a highly configurable, generic and modular EMA and JITAI mobile framework which enabled us to generate a cross-platform breast cancer aftercare mobile app in a large-scale German study. The aim is to learn more about the validity, usefulness and feasibility of such mobile-device-assisted studies, taking into account administrative burden as well as user acceptance. We discuss the background, implementation, and whether these features could leverage similar study types in the future to overcome the static nature of existing behavioral and interventional apps.

Mental disorders represent an ongoing challenge to global health and can affect anyone at any age from any region in the world. The response of healthcare providers to mental health disorders still lags behind that of other diseases and a significant number of people who are affected by mental health disorders do not receive adequate treatment. The widespread usage of Internet-connected devices provides new opportunities to deliver treatment to more people using innovative approaches. The groundwork is being laid for the adoption of Internet- and mobile-based interventions, providing mental and behavioral health support to more people and narrowing the treatment gap. This paper discusses the main technical details of the backend API of the eSano eHealth platform as an example for a complex and comprehensive IT-framework for large-scale and flexible Internet- and mobile-based interventions. An overview of eSano is provided and the platform is compared with other technical solutions in the field. In addition, the components of eSano are described and further technical insights are elaborated in more detail. To this end, the work at hand demonstrates the main requirements of the backend API powering eSano, its concepts and the overall developed solution. It will as such inform researchers and practitioners about state-of-the-art backend API development in the eHealth context.

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.

A stream of users' interactions with an mHealth app can be seen as the result of a stochastic process that can be captured by an algorithm that learns over the whole stream. But is it only one process? We investigate to what extend learning for each user separately delivers better predictions than learning one model over the whole stream. Our application scenario is the prediction of Ecological Momentary Assessments (EMA) for an mHealth app (TinnitusTipps) on tinnitus. The data were recorded as part of a pilot study, in which one group of users received non-personalized suggestions (tips) throughout the study, while the other group received tips only during the second half of the study. Our method encompasses user-centric and global stream learning for EMA prediction, combined under a Contextual Multi-Armed Bandit (CMAB) that captures the context of each user group and incorporates the prediction quality of each learner into the reward function. We show that user-centric learning is beneficial for users who contribute many EMA, while a learner over the whole stream is better for users with few EMA.

The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

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.

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.