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The proliferation of online eHealth has made it much easier for users to access healthcare services and interventions from the comfort of their own homes. This study looks at how well one such platform—eSano—performs in terms of user experience when delivering mindfulness interventions. In order to assess usability and user experience, several tools such as eye-tracking technology, think-aloud sessions, a system usability scale questionnaire, an application questionnaire, and post-experiment interviews were employed. Participants were evaluated while they accessed the first module of the mindfulness intervention provided by eSano to measure their interaction with the app, and their level of engagement, and to obtain feedback on both the intervention and its overall usability. The results revealed that although users generally rated their experience with the app positively in terms of overall satisfaction, according to data collected through the system usability scale questionnaire, participants rated the first module of the mindfulness intervention as below average. Additionally, eye-tracking data showed that some users skipped long text blocks in favor of answering questions quickly while others spent more than half their time reading them. Henceforth, recommendations were put forward to improve both the usability and persuasiveness of the app—such as incorporating shorter text blocks and more engaging interactive elements—in order to raise adherence rates. Overall findings from this study provide valuable insights into how users interact with the eSano's participant app which can be used as guidelines for the future development of more effective and user-friendly platforms. Moreover, considering these potential improvements will help foster more positive experiences that promote regular engagement with these types of apps; taking into account emotional states and needs that vary across different age groups and abilities.

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%.

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.

Ecological Momentary Assessments (EMA) deliver insights on how patients perceive tinnitus at different times and how they are affected by it. Moving to the next level, an mHealth app can support users more directly by predicting a user's next EMA and recommending personalized services based on these predictions. In this study, we analyzed the data of 21 users who were exposed to an mHealth app with non-personalized recommendations, and we investigate ways of predicting the next vector of EMA answers. We studied the potential of entity-centric predictors that learn for each user separately and neighborhood-based predictors that learn for each user separately but take also similar users into account, and we compared them to a predictor that learns from all past EMA indiscriminately, without considering which user delivered which data, i.e., to a “global model.” Since users were exposed to two versions of the non-personalized recommendations app, we employed a Contextual Multi-Armed Bandit (CMAB), which chooses the best predictor for each user at each time point, taking each user's group into account. Our analysis showed that the combination of predictors into a CMAB achieves good performance throughout, since the global model was chosen at early time points and for users with few data, while the entity-centric, i.e., user-specific, predictors were used whenever the user had delivered enough data—the CMAB chose itself when the data were “enough.” This flexible setting delivered insights on how user behavior can be predicted for personalization, as well as insights on the specific mHealth data. Our main findings are that for EMA prediction the entity-centric predictors should be preferred over a user-insensitive global model and that the choice of EMA items should be further investigated because some items are answered more rarely than others. Albeit our CMAB-based prediction workflow is robust to differences in exposition and interaction intensity, experimentators that design studies with mHealth apps should be prepared to quantify and closely monitor differences in the intensity of user-app interaction, since users with many interactions may have a disproportionate influence on global models.

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.

Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.

Introduction: Internet- and mobile-based interventions (IMIs) and their integration into routine psychotherapy (i.e., blended therapy) can offer a means of complementing psychotherapy in a flexible and resource optimized way.Objective: The present study will evaluate the non-inferiority, cost-effectiveness, and safety of two versions of integrated blended psychotherapy for depression and anxiety compared to standard cognitive behavioral therapy (CBT).Methods: A three-armed multicenter cluster-randomized controlled non-inferiority trial will be conducted comparing two implementations of blended psychotherapy (PSYCHOnlineTHERAPYfix/flex) compared to CBT. Seventy-five outpatient psychotherapists with a CBT-license will be randomized in a 1:1:1 ratio. Each of them is asked to include 12 patients on average with depressive or anxiety disorders resulting in a total sample size of N = 900. All patients receive up to a maximum of 16 psychotherapy sessions, either as routine CBT or alternating with Online self-help sessions (fix: 8/8; flex: 0–16). Assessments will be conducted at patient study inclusion (pre-treatment) and 6, 12, 18, and 24 weeks and 12 months post-inclusion. The primary outcome is depression and anxiety severity at 18 weeks post-inclusion (post-treatment) using the Patient Health Questionnaire Anxiety and Depression Scale. Secondary outcomes are depression and anxiety remission, treatment response, health-related quality of life, patient satisfaction, working alliance, psychotherapy adherence, and patient safety. Additionally, several potential moderators and mediators including patient characteristics and attitudes toward the interventions will be examined, complemented by ecological day-to-day digital behavior variables via passive smartphone sensing as part of an integrated smart-sensing sub-study. Data-analysis will be performed on an intention-to-treat basis with additional per-protocol analyses. In addition, cost-effectiveness and cost-utility analyses will be conducted from a societal and a public health care perspective. Additionally, qualitative interviews on acceptance, feasibility, and optimization potential will be conducted and analyzed.Discussion: PSYCHOnlineTHERAPY will provide evidence on blended psychotherapy in one of the largest ever conducted psychotherapy trials. If shown to be non-inferior and cost-effective, PSYCHOnlineTHERAPY has the potential to innovate psychotherapy in the near future by extending the ways of conducting psychotherapy. The rigorous health care services approach will facilitate a timely implementation of blended psychotherapy into standard care.Trial Registration: The trial is registered in the German Clinical Trials Register (DRKS00023973; date of registration: December 28th 2020).

Introduction: Internet- and mobile-based interventions (IMIs) and their integration into routine psychotherapy (i.e., blended therapy) can offer a means of complementing psychotherapy in a flexible and resource optimized way. Objective: The present study will evaluate the non-inferiority, cost-effectiveness, and safety of two versions of integrated blended psychotherapy for depression and anxiety compared to standard cognitive behavioral therapy (CBT). Methods: A three-armed multicenter cluster-randomized controlled non-inferiority trial will be conducted comparing two implementations of blended psychotherapy (PSYCHOnlineTHERAPYfix/flex) compared to CBT. Seventy-five outpatient psychotherapists with a CBT-license will be randomized in a 1:1:1 ratio. Each of them is asked to include 12 patients on average with depressive or anxiety disorders resulting in a total sample size of N = 900. All patients receive up to a maximum of 16 psychotherapy sessions, either as routine CBT or alternating with Online self-help sessions (fix: 8/8; flex: 0–16). Assessments will be conducted at patient study inclusion (pre-treatment) and 6, 12, 18, and 24 weeks and 12 months post-inclusion. The primary outcome is depression and anxiety severity at 18 weeks post-inclusion (post-treatment) using the Patient Health Questionnaire Anxiety and Depression Scale. Secondary outcomes are depression and anxiety remission, treatment response, health-related quality of life, patient satisfaction, working alliance, psychotherapy adherence, and patient safety. Additionally, several potential moderators and mediators including patient characteristics and attitudes toward the interventions will be examined, complemented by ecological day-to-day digital behavior variables via passive smartphone sensing as part of an integrated smart-sensing sub-study. Data-analysis will be performed on an intention-to-treat basis with additional per-protocol analyses. In addition, cost-effectiveness and cost-utility analyses will be conducted from a societal and a public health care perspective. Additionally, qualitative interviews on acceptance, feasibility, and optimization potential will be conducted and analyzed. Discussion: PSYCHOnlineTHERAPY will provide evidence on blended psychotherapy in one of the largest ever conducted psychotherapy trials. If shown to be non-inferior and cost-effective, PSYCHOnlineTHERAPY has the potential to innovate psychotherapy in the near future by extending the ways of conducting psychotherapy. The rigorous health care services approach will facilitate a timely implementation of blended psychotherapy into standard care. Trial Registration: The trial is registered in the German Clinical Trials Register (DRKS00023973; date of registration: December 28th 2020).

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.

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.

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

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 paper reports the methodology and computational strategy for a forest cover disturbance alerting system. Analytical techniques from time series econometrics are applied to imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to detect temporal instability in vegetation indices. The characteristics from each MODIS pixel's spectral history are extracted and compared against historical data on forest cover loss to develop a geographically localized classification rule that can be applied across the humid tropical biome. The final output is a probability of forest disturbance for each 500 m pixel that is updated every 16 days. The primary objective is to provide high-confidence alerts of forest disturbance, while minimizing false positives. We find that the alerts serve this purpose exceedingly well in Para, Brazil, with high probability alerts garnering a user accuracy of 98 percent over the training period and 93 percent after the training period (2000–2005) when compared against the PRODES deforestation data set, which is used to assess spatial accuracy. Implemented in Clojure and Java on the Hadoop distributed data processing platform, the algorithm is a fast, automated, and open source system for detecting forest disturbance. It is intended to be used in conjunction with higher-resolution imagery and data products that cannot be updated as quickly as MODIS-based data products. By highlighting hotspots of change, the algorithm and associated output can focus high-resolution data acquisition and aid in efforts to enforce local forest conservation efforts.

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.

Background The purpose of this meta-analysis was to confirm if the global longitudinal systolic function of the left ventricle (LV) is altered in patients with heart failure with preserved ejection fraction (HFpEF). Methods We searched in different databases (Medline, Embase and Cochrane) studies that analysed LV global longitudinal systolic strain (GLS) in patients with HFpEF and in controls (such as healthy subjects or asymptomatic patients with arterial hypertension, diabetes mellitus or coronary artery disease). Results Twenty-two studies (2284 patients with HFpEF and 2302 controls) were included in the final analysis. Patients with HFpEF had significantly lower GLS than healthy subjects (mean −15.7% (range −12% to −18.9%) vs mean −19.9% (range −17.1% to −21.5%), weighted mean difference −4.2% (95% CI −3.3% to −5.0%), p < 0.001, respectively). In addition, patients with HFpEF had also significantly lower GLS than asymptomatic patients (mean −15.5% (range −13.4% to −18.4%) vs mean −18.3% (range −15.1% to −20.4%), weighted mean difference −2.8%(95% CI −1.9% to −3.6%), p < 0.001, respectively). In line, 10 studies showed that the rate of abnormal GLS was significantly higher in patients with HFpEF (mean 65.4% (range 37%–95%)) than in asymptomatic subjects (mean 13% (range 0%–29.6%)). Regarding the prognostic relevance of abnormal GLS in HFpEF, two multicentre studies with large sample size (447 and 348) and high number of events (115 and 177) showed that patients with abnormal GLS had worse cardiovascular (CV) outcomes than those with normal GLS (HR for CV mortality and HF hospitalisation 2.14 (95% CI 1.26 to 3.66) and 1.94 (95% CI 1.22 to 3.07)), even adjusting these analyses for multiples clinical and echocardiographic variables. Conclusion The present meta-analysis analysing 2284 patients with HFpEF and 2302 controls confirms that the longitudinal systolic function of the LV is significantly altered in high proportion of patients with HFpEF. Further large multicentre studies with the aim to confirm the prognostic role of abnormal GLS in HFpEF are warranted.

Conservation of habitats is critical for survival of endangered tigers. This paper develops a resource allocation model for tiger habitat protection incorporating information about threats to particular tiger subspecies, the quality of remaining habitat areas, the observed effectiveness of habitat protection by country, and the potential costs of protection projects for 74 habitats in Asia. Implementation of the model moves through two stages. The first stage employs user-specified weights to combine numerous subindices into composite indices of species threat, habitat quality, potential project costs and protection effectiveness. The second stage employs additional user-specified weights to combine the composite indices into priority scores and potential project budget shares for all 74 habitat areas. Exploration of model results suggests that changes in user-specified weights can have very significant consequences for habitat priority scores. Illustrative scenarios indicate that no single priority ordering can be prescribed in such a diverse setting, and actual priorities will depend on the preferences of decision-makers, as revealed in the weights assigned to species threats, habitat quality, cost elements, and effective protection. At the same time, the model can make a useful contribution by identifying priority orderings that are consistent with different sets of preferences. And it can inform policy discussions by allowing for extended exploration of alternative strategies, along with providing feedback to decision makers about the implicit preferences associated with their resource allocation decisions.

Habitat conservation is critical to the survival of endangered tigers. This paper develops a resource-allocation model for the protection of tiger habitats, using information on threats to particular tiger subspecies, the quality of remaining habitat areas, the observed effectiveness of habitat protection by country, and the potential costs of protection projects in74 habitats in Asia. This model will be implemented in two stages. The first stage involves using user-specified weights to combine numerous sub-indices into composite indices, covering threats to species, habitat quality, potential project costs and the effectiveness of protections. At the second stage, additional user-specified weights are used to combine the composite indices into priority scores and potential project budget shares for all 74 habitat areas. Results suggest that changes in user-specified weights can have very a significant impact on habitat priority scores. Illustrative scenarios indicate that the model can make a useful contribution by identifying priority orderings that are consistent with different sets of preferences. It will also provide feedback to decision makers regarding the implicit preferences associated with their resource allocation decisions.

OBJECTIVE: To investigate the effects of linagliptin alone and in combination with the angiotensin II receptor blocker (ARB), telmisartan on blood pressure (BP), kidney function, heart morphology and oxidative stress in rats with renovascular hypertension. METHODS: Fifty-seven male Wistar rats underwent unilateral surgical stenosis of the renal artery [2-kidney-1-clip (2k1c) method]. Animals were randomly divided into four treatment groups (n = 14-18 per group) receiving: telmisartan (10 mg/kg per day in drinking water), linagliptin (89 ppm in chow), combination (linagliptin 89 ppm + telmisartan 10 mg/kg per day) or placebo. An additional group of 12 rats underwent sham surgery. BP was measured one week after surgery. Hypertensive animals entered a 16-week dosing period. BP was measured 2, 4, 8, 12 and 16 weeks after the initiation of treatment. Blood and urine were tested for assessment of kidney function and oxidative stress 6, 10, 14 and 18 weeks after surgery. Blood and urine sampling and organ harvesting were finally performed. RESULTS: Renal stenosis caused an increase in mean ± SD systolic BP as compared with the sham group (157.7 ± 29.3 vs. 106.2 ± 20.5 mmHg, respectively; P

This paper updates Working Paper 294, “FCPR — Forest Conservation Performance Rating for the Pan-Tropics.Forest Conservation Performance Rating (fCPR) is a system of color-coded ratings for tropical forest conservation performance that can be implemented for local areas, countries, regions, and the entire pan-tropics. The ratings reward tropical forest conservation in three dimensions: (1) Progress toward elimination of tropical forest clearing by 2050; (2) progress toward achieving more ambitious REDD goals; and (3) achieving an immediate reduction in forest clearing. We assign green ratings to areas that meet condition (2); yellow to areas that meet (1) only; dark red to areas that fail both conditions, with forest clearing still increasing; and light red to areas that fail both conditions, but with declining forest clearing. This paper introduces quarterly conservation performance ratings for 56 tropical forest countries, as well as 781 of their states and provinces that contain tropical forests. We also combine the fCPR country ratings to produce ratings for major regions and the entire pan-tropics. Overall, we find that conservation performance has deteriorated significantly since 2005 at the global and regional levels. Some gains were made at the height of the global economic crisis, but they have proven to be temporary. Since 2010, forest clearing has exhibited rapid growth in most of tropical Asia, Latin America and Africa

This note introduces and illustrates fCPR (Forest Conservation Performance Rating), a system of color-coded ratings for tropical forest conservation performance that can be implemented for local areas, countries, regions, and the entire pan-tropics. The ratings reward tropical forest conservation in three dimensions: (1) exceeding expectations, given an area’s forest clearing history and development status; (2) meeting or exceeding global REDD goals; and (3) achieving an immediate reduction in forest clearing. We assign green ratings to areas that meet condition (2); yellow to areas that meet (1) only; and red to countries that fail to meet either condition. We have developed fCPR at the Center for Global Development (CGD), using monthly forest clearing indicators from CGD’s FORMA (Forest Monitoring for Action). This first release rates the quarterly conservation performance of 27 countries currently tracked by FORMA, as well as 242 of their states and provinces that contain tropical forests. The 27 countries accounted for 94 percent of tropical forest clearing during the period 2000-2005. Future releases will include additional countries as FORMA begins tracking them.

This report summarizes recent trends in large-scale tropical forest clearing identified by FORMA (Forest Monitoring for Action). Our analysis includes 27 countries that accounted for 94 percent of clearing during the period 2000-2005. We highlight countries with relatively large changes since 2005, both declines and increases. FORMA produces indicators that track monthly changes in the number of 1-sq.-km. tropical forest parcels that have experienced clearing with high probability. This report and the accompanying spreadsheet databases provide monthly estimates for 27 countries, 280 primary administrative units, and 2,907 secondary administrative units. Countries’ divergent experiences since 2005 have significantly altered their shares of global clearing in some cases. Brazil’s global share fell by 11.2 percentage points from December 2005 to August 2011, while the combined share of Malaysia, Indonesia, and Myanmar increased by 10.8. The diverse patterns revealed by FORMA’s first global survey caution against facile generalizations about forest clearing in the pantropics. During the past five years, the relative scale and pace of clearing have changed across regions, within regions, and within countries. Although the overall trend seems hopeful, it remains to be seen whether the decline in forest clearing will persist as the global economy recovers.

In this paper, we develop and illustrate a prototype incentive system for promoting rapid reduction of forest clearing in tropical countries. Our proposed Tropical Forest Protection Fund (TFPF) is a cash-on-delivery system that rewards independently monitored performance without formal contracts. The system responds to forest tenure problems in many countries by dividing incentive payments between national governments, which command the greatest number of instruments that affect forest clearing, and indigenous communities, which often have tenure rights in forested lands. The TFPF incorporates both monetary and reputational incentives, which are calculated quarterly. The monetary incentives are unconditional cash transfers based on measured performance, while the reputational incentives are publicly disclosed, color-coded performance ratings for each country. The incentives include rewards for: (1) exceeding long-run expectations, given a country’s forest clearing history and development status; (2) meeting or exceeding global REDD goals; and (3) achieving an immediate reduction in forest clearing. Drawing on monthly forest clearing indicators from the new FORMA (Forest Monitoring for Action) database, we illustrate a prototype TFPF for eight East Asian countries: Cambodia, China, Indonesia, Lao PDR, Malaysia, Myanmar, Thailand, and Vietnam. A system with identical design principles could be implemented by single or multiple donors for individual or multiple forest proprietors within one or more countries, as well as national or local governments in individual countries, tropical regions, or the global pan-tropics. Our results demonstrate the importance of financial flexibility in the design of the proposed TFPF. Its incentives are calculated to induce a massive, rapid reduction of tropical forest clearing. If that occurs, a TFPF for East Asia will need standby authority for disbursements that may total $10–14 billion annually for the next two decades. This financial burden will not persist, however, because the TFPF is designed to self-liquidate once all recipient countries have achieved clearly specified benchmarks. We estimate that the TFPF can be closed by 2070, with its major financial responsibility discharged by 2040.

The geopolitical world of the 21st century is very different than that of the post–World War II era. In this new world order, what constitutes a system of global governance? We argue that it has to balance representation, which is made credible by the inclusion of key parts of the global community, and effectiveness, which means involving as small a number of actors as possible while having access to the resources - and clout - to turn decisions/intentions into action/results. In this paper, we propose simple, fundamental criteria - based on global shares of GDP and population - around which global governance might be organized. We analyze the role that these criteria would assign to different countries and compare them with some of the key components of the system of governance currently in place - the Bretton Woods institutions and the United Nations. We also examine the implications of our analysis for membership in the G-20 and the OECD. We find major disparities, which suggest the need for fundamental changes in sharp contrast to the incremental changes that are currently being considered. Overall, our analysis points to the need for a more comprehensive approach, and for much more than incremental solutions.

Rising concern about carbon emissions from deforestation has led donors to finance UN-REDD (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries), a program that offers direct compensation for forest conservation. Sustainable operation of UN-REDD and other direct-compensation programs will require a transparent, credible, frequently updated system for monitoring deforestation. In this paper, we introduce FORMA (Forest Monitoring for Action), a prototype system based on remotely sensed data. We test its accuracy against the best available information on deforestation in Brazil and Indonesia. Our results indicate that publicly available remotely sensed data can support accurate quarterly identification of new deforestation at 1 km spatial resolution. More rapid updates at higher spatial resolution may also be possible. At current resolution, with efficient coding in publicly available software, FORMA should produce global updates on one desktop computer in a few hours. Maps of probable deforestation at 1 km resolution will be accessible with Google Earth and Google Maps, with an open facility for ground-truthing each pixel via photographs and text comments.

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.