Technical Challenges of a Mobile Application Supporting Intersession Processes in Psychotherapy

The usage of mobile applications in healthcare is gaining popularity in recent years. The ubiquity of a sophisticated mobile appliance that is applicable to sample ecological patient data in real life by acquiring both mental state and environmental data has enabled new possibilities for researchers and healthcare providers. Collecting data using the mentioned approach is often called Ecological Momentary Assessment (EMA) and is characterized by an unidirectional data flow towards the platform provider. A more challenging approach, in turn, is called Ecological Momentary Intervention (EMI). The latter requires a bidirectional data flow in order to enable the possibility of sending feedback to the patients and controlling their experiences through interventions. Although both approaches are established parts of IT-supported treatments in the field of psychology and psychotherapy until now, the so-called intersession process has not been technically supported appropriately yet. Therefore, the Intersession-Online platform was developed in order to (a) assess intersession processes systematically, (b) monitor a patient, and (c) intervene by suppressing negative thoughts concerning the therapy. In this paper, the technical requirements, architecture, and features of the mobile application of the Intersession-Online platform are presented. In this context, the development of a patient data sampling mechanism, which consists of a sophisticated, inter-questionnaire dependent sampling schedule and synchronization strategy is particularly illustrated and discussed. Altogether, the technical challenges will show that a mobile application supporting intersession processes in psychotherapy is an endeavor which requires many considerations. However, on the other, such a mobile application may be the basis for new technical as well as psychological insights

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

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

Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems

Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service providers often create lock-in effects making it inconvenient for the user to switch providers. In this paper, we argue that the user's smartphone already holds a lot of the data that feeds into typical recommender systems for movies, music, or POIs. With the ubiquity of the smartphone and other users in proximity in public places or public transportation, data can be exchanged directly between users in a device-to-device manner. This way, each smartphone can build its own database and calculate its own recommendations. One of the benefits of such a system is that it is not restricted to recommendations for just one user - ad-hoc group recommendations are also possible. While the infrastructure for such a platform already exists - the smartphones already in the palms of the users - there are challenges both with respect to the mobile recommender system platform as well as to its recommender algorithms. In this paper, we present a mobile architecture for the described system - consisting of data collection, data exchange, and recommender system - and highlight its challenges and opportunities.Comment: Accepted for publication at the 2019 IEEE 16th International Conference on Ubiquitous Intelligence and Computing (IEEE UIC 2019

Flexible development of location-based mobile augmented reality applications with AREA

Mobile applications have garnered a lot of attention in the last years. The computational capabilities of mobile devices are the mainstay to develop completely new application types. The provision of augmented reality experiences on mobile devices paves one alley in this feld. For example, in the automotive domain, augmented reality applications are used to experience, inter alia, the interior of a car by moving a mobile device around. The device’s camera then detects interior parts and shows additional information to the customer within the camera view. Another application type that is increasingly utilized is related to the combination of serious games with mobile augmented reality functions. Although the latter combination is promising for many scenarios, technically, it is a complex endeavor. In the AREA (Augmented Reality Engine Application) project, a kernel was implemented that enables location-based mobile augmented reality applications. Importantly, this kernel provides a fexible architecture that fosters the development of individual location-based mobile augmented reality applications. The work at hand shows the fexibility of AREA based on a developed serious game. Furthermore, the algorithm framework and major features of it are presented. As the conclusion of this paper, it is shown that mobile augmented reality applications require high development eforts. Therefore, fexible frameworks like AREA are crucial to develop respective applications in a reasonable time

“You Don’t Know Where It Will Stop” -- An Inquiry into Smartphone Users' Privacy Mental Models of Contextual Integrity

The Contextual Integrity (CI) theory provides a benchmark for privacy protection or violation according to the appropriateness of information collection and flows in a certain context. As privacy threats and protections develop and vie in various mobile contexts, how smartphone users represent the benchmark CI in their minds deserves exploration. In this study, we inquired into 18 smartphone users’ privacy mental models of CI. We found that they verbalized and visualized three patterns of information flow (i.e., unidirectional lines, branching tree, and complex network) and two categories of information collection (i.e., monetization-oriented and monitoring-based). With these mental models, our participants expressed numerous privacy concerns, such as unstoppable information sharing, data monetization, and surveillance. We discussed these findings and concluded that even though mobile operating systems and apps have claimed to be privacy-friendly and protective, some users remain dubious about such claims even though their privacy mental models may not accurately reflect reality

Extraversion moderates the relationship between social media use and depression

Background: There is evidence that extraversion and associated frequent personal and digital social contacts are associated with mental health, reflected in reduced risk for anxiety or depression. However, excessive social media use (SMU) has been related to a decrease of mental health. We test how extraversion moderates the effect of SMU on anxiety and depression in times of social distancing. Methods: Data were collected with an app-based survey combined with passive sensing of social media usage time. We analyzed SMU (objective average duration of communication app usage) and cross-sectional questionnaire data from 486 adults (mean age = 42.42). Using multiple regression models, we tested how SMU, extraversion and their interaction relate to individual depression and anxiety scores. Results: Depression scores were associated with a higher SMU and lower extraversion. There was a significant positive relationship between SMU and extraversion that predicted higher depression scores. Limitations: In the present sample, there is a recruitment bias since only data from smartphones running iOS were included. Future research should also take a closer look at the purpose behind SMU. Conclusions: We conclude that extraversion might be a protective factor for depression which can turn into a harmful one if it is related to higher SMU. Thus, the interplay between SMU and extraversion needs to be considered when predicting individual differences in mental health.Peer Reviewe

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

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