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Mental health problems are becoming more common while access to treatment is often not available to everyone who needs help. Recent advances in information technology, the wide availability of the internet, the emergence of smartphones and their common usage worldwide raise hope for more treatment options for mental health disorders. Many mobile phone apps that claim to assist in treating a variety of mental health disorders are already available, and the number of such apps continues to increase. The availability of such apps raises many questions about their effectiveness, suitable treatment methods, possibilities for use alongside traditional treatment methods, possible risks and other uncertainties. Beside mobile apps, internet-based apps are also being introduced with similar sets of challenges and ambiguities. One area of research that is gaining a lot of attention recently is Persuasive System Design and Behavior Change. Persuasive System Design is considered one solution that has the potential to help solve the challenges of lack of user motivation and adherence when utilizing mental health applications. The goal of this paper is to perform a literature review, in order to determine the most essential requirements for a persuasively designed mental health application. As part of this process, the challenges and requests of the end-user will be taken into account in order to make recommendations for the future design of such applications.

Complex traits, such as growth and fitness, are typically controlled by a very large number of variants, which can interact in both additive and non-additive fashion. In an attempt to gauge the relative importance of both types of genetic interactions, we have turned to hybrids, which provide a facile means for creating many novel allele combinations. We focused on the interaction between alleles of the same locus and performed a transcriptomic study involving 141 random crosses between different accessions of the plant model speciesArabidopsis thaliana. Additivity is rare, consistently observed for only about 300 genes enriched for roles in stress response and cell death. Regulatory rare-allele burden affects the expression level of these genes but does not correlate with F1rosette size. Non-additive gene expression in F1hybrids is much more common, with the vast majority of genes (over 90%) being expressed below parental average. Unlike in the additive genes, regulatory rare-allele burden in the non-additive gene set is strongly correlated with F1rosette size, even though it only mildly covary with the expression level of these genes. Our study underscores under-dominance as the predominant gene action associated with emergence of rosette growth trajectories in theA. thalianahybrid model. Our work lays the foundation for understanding molecular mechanisms and evolutionary forces that lead to dominance complementation of rare regulatory alleles.

The ubiquity of smart mobile devices facilitates data collection in the healthcare domain. Two of the concepts, which can be applied in this context, are mobile crowdsensing (MCS) and ecological momentary assessment (EMA). TrackYourTinnitus (TYT) is an advanced mobile healthcare platform that combines both concepts enabling the monitoring and evaluation of the users’ individual variability of tinnitus symptoms. This paper describes the underlying data set and structure of the TYT mobile platform and highlights selected issues whose investigation provides advanced insights into the users of this mobile platform as well as their data.

Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients&rsquo, quality of life. Recent advances in mHealth have enabled patients to maintain a &lsquo, disease journal&rsquo, of ecologically-valid momentary assessments, improving patients&rsquo, own awareness of their disease while also providing clinicians valuable data for research. In this study, we investigate the effect of non-personalised tips on patients&rsquo, perception of tinnitus, and on their continued use of the application. The data collected from the study involved three groups of patients that used the app for 16 weeks. Groups A &amp, Y were exposed to feedback from the start of the study, while group B only received tips for the second half of the study. Groups A and Y were run by different supervisors and also differed in the number of hospital visits during the study. Users of Group A and B underwent assessment at baseline, mid-study, post-study and follow-up, while users of group Y were only assessed at baseline and post-study. It is seen that the users in group B use the app for longer, and also more often during the day. The answers of the users to the Ecological Momentary Assessments are seen to form clusters where the degree to which the tinnitus distress depends on tinnitus loudness varies. Additionally, cluster-level models were able to predict new unseen data with better accuracy than a single global model. This strengthens the argument that the discovered clusters really do reflect underlying patterns in disease expression.

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.

Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.

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.

BACKGROUND The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. OBJECTIVE In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS. METHODS Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. RESULTS Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). CONCLUSIONS TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.

Background The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. Objective In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS. Methods Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. Results Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). Conclusions TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.

Tinnitus is a condition associated with a continuous noise in the ears or head and can arise from many different medical disorders. The perception of tinnitus can vary within and between days. In the recent years, Ecological Momentary Assessments of tinnitus have been used to investigate these tinnitus variations during the daily life of the patients. In the last five years, several independent studies have used Ecological Momentary Assessment to assess tinnitus. With this chapter, we want to review the current state of this research. All the EMA studies revealed a considerable variability of tinnitus loudness and tinnitus distress. It has been found that emotional states and emotional dynamics, the subjectively perceived stress level and the time of the day exert influence on the tinnitus variability. In summary, the EMA method revealed a good potential to improve our scientific understanding of tinnitus. Furthermore, it also showed that it can be used to understand the individual differences of tinnitus—and may even be used as a tool for individualized diagnostic and treatment. We conclude, that the results of the EMA studies can lead to improvements of existing research methods in the field of tinnitus.

At a time, in which people are more and more suffering from lifestyle-related diseases such as cardiovascular diseases, diabetes, or obesity, changing health behavior and preserving a healthy lifestyle are salient factors of any public health effort. Hence, research on predictors and pathways of health behavior change is increasingly important. Following this, new ways of implementing behavior change interventions become possible based on internet technologies, allowing for technological approaches fostering behavior change. Such union of media informatics and psychology is denoted as persuasive design and refers to all technological intervention components, which help people to take, regularly use and re-take (after relapses into unwanted behavior) interventions. Along this trend, the present chapter introduces (1) theories of health behavior change and summarizes (2) present persuasive design approaches, thereby ending with (3) future directions in the field.

The advent of chatbots may influence many treatment procedures in the medical and psychological fields. In particular, chatbots may be useful in many situations before and after medical procedures when patients are back at home. For example, while being in the preparation phase of a colonoscopy, a chatbot might answer patient questions more quickly than a doctor. Moreover, it is more and more discussed whether chatbots may be the first entry point for (urgent) medical questions instead of the consultation of a medical expert, as there exist already well-established algorithms for some of these situations. For example, if a new medical symptom occurs, a chatbot might serve as the first “expert” to relieve a patient’s condition. Note that the latter situation to use chatbots is mainly driven by the trend that patients often have to wait too long for appointments with a proper medical expert due to capacity problems of many healthcare systems. While the usage of supporting “at home actions” of patients with chatbot technologies is typically welcomed by medical experts, the use of this technology to “replace” them in their core competence, namely diagnosis and therapy, is generally seen highly critical. Apart from the domain side, it must be carefully considered what currently available chatbot technologies can do or cannot do. Moreover, it has also to be considered, how existing technologies can be established in highly critical medical and interdisciplinary fields with possible emergency situations (e.g., if a chatbot gets the message of a patient that indicates to commit suicide), involving ethical questions as well as questions of responsibility and accountability. Therefore, this work raises aspects that might be the basis for medical as well as technical experts to better work together for proper chatbot solutions. Thereby, the work at hand proposes an architecture that should serve as a reference for various medical and psychological scenarios. When using suitable technical solutions, we argue that chances emerge, which mitigate upcoming challenges significantly.

The characteristics and optimal management of heart failure with a moderately reduced ejection fraction (HFmrEF, LV-EF 40&ndash, 50%) are still unclear. Advanced cardiac MRI offers information about function, fibrosis and inflammation of the myocardium, and might help to characterize HFmrEF in terms of adverse cardiac remodeling. We, therefore, examined 17 patients with HFpEF, 18 with HFmrEF, 17 with HFrEF and 17 healthy, age-matched controls with cardiac MRI (Phillips 1.5 T). T1 and T2 relaxation time mapping was performed and the extracellular volume (ECV) was calculated. Global circumferential (GCS) and longitudinal strain (GLS) were derived from cine images. GLS (&minus, 15.7 &plusmn, 2.1) and GCS (&minus, 19.9 &plusmn, 4.1) were moderately reduced in HFmrEF, resembling systolic dysfunction. Native T1 relaxation times were elevated in HFmrEF (1027 &plusmn, 40 ms) and HFrEF (1033 &plusmn, 54 ms) compared to healthy controls (972 &plusmn, 31 ms) and HFpEF (985 &plusmn, 32 ms). T2 relaxation times were elevated in HFmrEF (55.4 &plusmn, 3.4 ms) and HFrEF (56.0 &plusmn, 6.0 ms) compared to healthy controls (50.6 &plusmn, 2.1 ms). Differences in ECV did not reach statistical significance. HFmrEF differs from healthy controls and shares similarities with HFrEF in cardiac MRI parameters of fibrosis and inflammation.

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.

This study sought to examine whether early cardiac alterations could be detected by left atrial (LA) strain in patients with risk for cardiac abnormalities. In this cross-sectional and retrospective study, we included patients with (n = 234) and without (n = 48) risk for cardiac abnormalities (i.e. those with arterial hypertension, diabetes mellitus and/or a history of coronary artery disease) of similar age and with preserved left ventricular (LV) systolic and diastolic function according to standard criteria. LA strain was significantly altered in patients with risk for cardiac abnormalities in comparison to those without risk (29.2 ± 8.6 vs. 38.5 ± 12.6%; rate of impaired LA strain: 18.8% vs. 0%; all p

Aims The aim of the present study was to determine the lower limit of normality and the clinical relevance of left ventricular (LV) early diastolic strain rate (LVSRe) for the detection of LV diastolic dysfunction (LVDD). Methods and results Using 2D speckle-tracking echocardiography, we analysed 377 healthy subjects and 475 patients with risk for LVDD with preserved LV ejection fraction (LVEF). The normal range of LVSRe analysing the healthy subjects was 1.56 ± 0.28 s-1, with a lower limit of normality at 1.00 s-1. Using this cut-off, LVSRe was able to detect high rates of LV diastolic alterations (rate 71.1%), which was significantly better than using indirect diastolic parameters such as left atrial volume index (LAVI) and tricuspid regurgitation velocity (TR) (rates 22.9% and 9.1%) and similar to annular mitral parameters such as lateral and septal e' velocity (rates 70.9% and 72.4%). In line, adding LVSRe to the current evaluation of LVDD increased significantly the rate of detection of LVDD (absolute rate of increase 18.9%; rate of detection of LVDD: from 14.3% to 33.2%, P

As the wild tiger population in tropical Asia dropped from about 100,000 to 3,500 in the last century, the need to conserve tiger habitats poses a challenge for the Global Tiger Recovery Program. This paper develops and uses a high-resolution monthly forest clearing database for 74 tiger habitat areas in ten countries to investigate habitat threats for Bengal, Indochinese, Malayan and Sumatran tigers. The econometric model links forest habitat loss and forest clearing to profitability calculations that are affected by market expectations, environmental conditions and evolving patterns of settlement, among others. It uses new spatial panel estimation methods that allow for temporal and spatial autocorrelation. The econometric results emphasize the role of short-run market variables, including the exchange rate, real interest rate and prices of agricultural products in forest clearing, with considerable variation in the estimated timing for response and impact elasticities across countries. The results highlight a critical message for the conservation policy community: Changes in world agricultural-product markets and national financial policies have significant, measurable effects on tropical forest clearing, with variable time lags and degrees of responsiveness across countries. Measuring these effects and pinpointing areas at risk can provide valuable guidance for policymakers, conservation managers, and donor institutions.

This paper uses a large panel database to investigate the determinants of forest clearing in Indonesian kabupatens since 2005. Our study incorporates short-run changes in prices and demands for palm oil and wood products, as well as the exchange rate, the real interest rate, land-use zoning, forest protection, the estimated opportunity cost of forested land, the quality of local governance, the poverty rate, population density, the availability of communications infrastructure, transport cost, local rainfall and terrain slope. The econometric results highlight the role of dynamic economic factors in forest clearing: significant roles for lagged changes in all the short-run economic variables—product prices, demands, the exchange rate and the real interest rate—as well as communications infrastructure, some types of commercial zoning, rainfall and terrain slope. We find no significance for the other variables, although our measure of protected-area status is too general for strong conclusions to be drawn about the empirical relationship between forest clearing and protection. It is also possible that the insignificance of transport cost in our results is due to collinearity with our proxy for communications cost, and/or the offsetting effects of transport cost on the direct profitability of forest clearing (negative) and the effectiveness of local forest regulation (positive). Our results strongly support a model of forest clearing as an investment that is highly sensitive to expectations about future forest product prices and demands, as well as changes in the cost of capital (indexed by the real interest rate), the relative cost of local inputs (indexed by the exchange rate), and the cost of land clearing (indexed by local precipitation). By implication, the opportunity cost of forested land fluctuates widely with changes in international markets and decisions by Indonesia's financial authorities about the exchange and interest rates. Our results suggest that forest conservation programs are unlikely to succeed if they ignore such powerful forces.

A basic reason for backend systems in mobile application architectures is the centralized management of state. Mobile clients synchronize local states with the backend in order to maintain an up-to-date view of the application state. As not all mobile social applications require strong consistency guarantees, we survey an alternative approach using special data structures for mobile applications. These data structures only provide eventual consistency, but allow for conflict-free replication between peers. Our analysis collects the requirements of social mobile applications for being suitable for this approach. Based on exemplary mobile social applications, we also point out the benefits of serverless architecture or architectures with a thin backend layer.