Persuasive technologies design for mental and behavioral health platforms: A scoping literature review.

This review investigates persuasive design frameworks within eHealth, concentrating on methodologies, their prevalence in mental and behavioral health applications, and identifying current research gaps. An extensive search was conducted across 8 databases, focusing on English publications with full text available. The search prioritized primary research articles, post-2011 applications, and eHealth platforms emphasizing treatment or support. The inclusion process was iterative, involving multiple authors, and relied on detailed criteria to ensure the relevance and contemporaneity of selected works. The final review set comprised 161 articles, providing an overview of persuasive design frameworks in eHealth. The review highlights the state of the art in the domain, emphasizing the utilization and effectiveness of these frameworks in eHealth platforms. This review details the restricted adoption of persuasive design frameworks within the field of eHealth, particularly in the mental and behavioral sectors. Predominant gaps include the scarcity of comparative evaluations, the underrepresentation of tailored interventions, and the unclear influence of persuasive components on user experience. There is a notable requirement for further scrutiny and refinement of persuasive design frameworks. Addressing these concerns promises a more substantial foundation for persuasive design in eHealth, potentially enhancing user commitment and platform efficiency.

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis.

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (µEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.

Pervasive under-dominance in gene expression underlying emergent growth trajectories in Arabidopsis thaliana hybrids.

BACKGROUND: 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 turn to hybrids, which provide a facile means for creating many novel allele combinations. RESULTS: We focus on the interaction between alleles of the same locus, i.e., dominance, and perform a transcriptomic study involving 141 random crosses between different accessions of the plant model species Arabidopsis 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 F1 rosette size. Non-additive, dominant gene expression in F1 hybrids is much more common, with the vast majority of genes (over 90%) being expressed below the parental average. Unlike in the additive genes, regulatory rare-allele burden in the dominant gene set is strongly correlated with F1 rosette size, even though it only mildly covaries with the expression level of these genes. CONCLUSIONS: Our study underscores under-dominance as the predominant gene action associated with emergence of rosette growth trajectories in the A. thaliana hybrid model. Our work lays the foundation for understanding molecular mechanisms and evolutionary forces that lead to dominance complementation of rare regulatory alleles.

Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s12652-023-04635-4.

Predicting the presence of tinnitus using ecological momentary assessments.

Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%.

Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth.

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%.

First clinical experience of high-power ablation of atrial fibrillation with a novel contact force-sensing gold-tip catheter.

BACKGROUND: Contact force (CF)-sensing catheters are commonly used in the field of radiofrequency (RF) ablation to treat atrial fibrillation (AF). Increasing ablation power (e.g., 50 W) has been suggested as a method to reduce procedure times whilst creating safe and lasting lesions. METHODS: We report the first clinical evidence of a 50 W point-by-point RF ablation in 25 consecutive patients with symptomatic AF using a novel CF-sensing catheter with a gold tip (AlCath Force, Biotronik). We collected and analyzed procedural and ablation parameters. The safety and efficacy of the catheter were evaluated. RESULTS: Altogether, 985 RF lesions in 25 patients were created with a mean number of 39.4 ± 16.3 lesions per patient. The total skin-to-skin procedure time was 116.1 ± 35.1 min, and the mean total area dose product was 10.9 ± 5.1 Gy*cm2. The mean RF time per procedure was 13.2 ± 6.6 min. The mean RF time per lesion was 20.2 ± 8.4 s. The mean CF was 15.7 ± 7.6 g. We observed a mean force time integral of 274.7 ± 11.1 gs (range: 53 to 496 gs). Acute procedural success, defined as entrance and exit block in all pulmonary veins, could be obtained in all cases. No procedure- or device-related serious adverse events were observed. No audible steam pops occurred. Optical inspection of the catheter after the procedure showed neither charring nor clotting. CONCLUSIONS: We provide the first evidence for the safety and efficacy of 50 W ablation using the AlCath Force gold-tip catheter. These data must be supported by a larger multi-center study.

Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit.

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.

eSano - An eHealth Platform for Internet- and Mobile-based Interventions.

The prevention and treatment of mental disorders and chronic somatic diseases is a core challenge for health care systems of the 21th century. Mental- and behavioral health interventions provide the means for lowering the public health burden. However, structural deficits, reluctance to use existing services, perceived stigma and further personal and environmental reasons restrict the uptake of these evidence-based approaches. Internet- and mobile-based interventions (IMIs) might overcome some of the limitations of on-site interventions by providing an anonymous, scalable, time- and location-independent, yet evidence-based approach. In order to implement digital mental and behavioral health concepts across the life-span into practice, a technical solution to support the design, creation, and execution of IMIs is needed. However, there are various conceptual, technical as well as legal challenges to implementing a corresponding software solution in the healthcare domain. Therefore, the work at hand (1) identifies these challenges and derives a number of respective requirements, (2) introduces the eHealth platform eSano, a software project developed by an interdisciplinary team of computer scientists, psychologists, therapists, and other domain experts, with the aim to serve as a flexible basis for mental and behavioral research and health care, and (3) provides technical insights into the developed platform and its approach to address the aforementioned requirements.

Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users.

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.

Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders.

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).

Myocardial deformation assessed among heart failure entities by cardiovascular magnetic resonance imaging.

AIMS: Although heart failure (HF) is a leading cause for hospitalization and mortality, normalized and comparable non-invasive assessment of haemodynamics and myocardial action remains limited. Moreover, myocardial deformation has not been compared between the guideline-defined HF entities. The distribution of affected and impaired segments within the contracting left ventricular (LV) myocardium have also not been compared. Therefore, we assessed myocardial function impairment by strain in patients with HF and control subjects by magnetic resonance imaging after clinically phenotyping these patients. METHODS AND RESULTS: This prospective study conducted at two centres in Germany between 2017 and 2018 enrolled stable outpatient subjects with HF [n = 56, including HF with reduced ejection fraction (HFrEF), HF with mid-range ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF)] and a control cohort (n = 12). Parameters assessed included measures for external myocardial function, for example, cardiac index and myocardial deformation measurements by cardiovascular magnetic resonance imaging, left ventricular global longitudinal strain (GLS), the global circumferential strain (GCS) and the regional distribution of segment deformation within the LV myocardium, as well as basic phenotypical characteristics. Comparison of the cardiac indices at rest showed no differences neither between the HF groups nor between the control group and HF patients (one-way ANOVA P = 0.70). The analysis of the strain data revealed differences between all groups in both LV GLS (One-way ANOVA: P < 0.01. Controls vs. HFpEF: -20.48 ± 1.62 vs. -19.27 ± 1.25. HFpEF vs. HFmrEF: -19.27 ± 1.25 vs. -15.72 ± 2.76. HFmrEF vs. HFrEF: -15.72 ± 2.76 vs. -11.51 ± 3.97.) and LV GCS (One-way ANOVA: P < 0.01. Controls vs. HFpEF: -19.74 ± 2.18 vs. -17.47 ± 2.10. HFpEF vs. HFmrEF: -17.47 ± 2.10 vs. -12.78 ± 3.47. HFrEF: -11.41 ± 3.27). Comparing the segment deformation distribution patterns highlighted the discriminating effect between the groups was much more prominent between the groups (one-way ANOVA P < 0.01) when compared by a score combining regional effects and a global view on the LV. Further analyses of the patterns among the segments affected showed that while the LVEF is preserved in HFpEF, the segments impaired in their contractility are located in the ventricular septum. The worse the LVEF is, the more segments are affected, but the septum remains an outstanding location with the most severe contractility impairment throughout the HF entities. CONCLUSIONS: While cardiac index at rest did not differ significantly between controls and stable HF patients suffering from HFrEF, HFmrEF, or HFpEF, the groups did differ significantly in LV GLS and LV GCS values. Regional strain analysis revealed that the LV septum is the location affected most, with reduced values already visible in HFpEF and further reductions in HFmrEF and HFrEF.

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices.

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.

The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications.

Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients' quality of life. Recent advances in mHealth have enabled patients to maintain a 'disease journal' of ecologically-valid momentary assessments, improving patients' 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' 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 & 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.

Multilayer myocardial strain improves the diagnosis of heart failure with preserved ejection fraction.

AIMS: The diagnostic and treatment of patients with heart failure with preserved ejection fraction (HFpEF) are both hampered by an incomplete understanding of the pathophysiology of the disease. Novel imaging tools to adequately identify these patients from individuals with a normal cardiac function and respectively patients with HF with reduced EF are warranted. Computing multilayer myocardial strain with feature tracking is a fast and accurate method to assess cardiac deformation. Our purpose was to assess the HFpEF diagnostic ability of multilayer strain parameters and compare their sensitivity and specificity with other established parameters. METHODS AND RESULTS: We included 20 patients with a diagnosis of HFpEF and, respectively, 20 matched controls. We assessed using feature-tracking cardiac magnetic resonance longitudinal and circumferential myocardial strain at three distinct layers of the myocardium: subendocardial (Endo-), mid-myocardial (Myo-), and subepicardial (Epi-). Comparatively, we additionally assessed various others clinical, imaging, and biochemical parameters with a putative role in HFpEF diagnostic: left ventricular end-diastolic volume (LVEDV), left ventricular mass (LVM), interventricular septum (IVS) wall thickness and free wall thickness, left atrial volume and strain, septal and lateral mitral annular early diastolic velocity (e`), E/e´ ratio, and plasma levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP). Global longitudinal strain (GLS) is significantly impaired at Endo (-20.8 ± 4.0 vs. -23.2 ± 3.4, P = 0.046), Myo- (-18.0 ± 3.0 vs. -21.0 ± 2.5, P = 0.002), and Epi- (-12.2 ± 2.0 vs. -16.2 ± 2.5, P < 0.001) levels. Compared with any other imaging parameter, an Epi-GLS lower than 13% shows the highest ability to detect patients with HFpEF [area under the curve (AUC) = 0.90 (0.81-1), P < 0.001] and in tandem with NT-proBNP can diagnose with maximal sensibility (93%) and specificity (100%), patients with HFpEF from normal, composed variable [AUC = 0.98 (0.95-1), P < 0.001]. In a logistic regression model, a composite predictive variable taking into account both GLS Epi and NT-proBNP values in each individual subject reached a sensitivity of 89% and a specificity of 100% with an AUC of 0.98 (0.95-1), P < 0.001, to detect HFpEF. CONCLUSIONS: Epi-GLS is a promising new imaging parameter to be considered in the clinical assessment of HFpEF patients. Given its excellent specificity, in tandem with a highly sensitive parameter such as NT-proBNP, Epi-GLS holds the potential to greatly improve the current diagnostic algorithms.

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

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.

Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study.

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.

CMR Tissue Characterization in Patients with HFmrEF.

The characteristics and optimal management of heart failure with a moderately reduced ejection fraction (HFmrEF, LV-EF 40-50%) 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 (-15.7 ± 2.1) and GCS (-19.9 ± 4.1) were moderately reduced in HFmrEF, resembling systolic dysfunction. Native T1 relaxation times were elevated in HFmrEF (1027 ± 40 ms) and HFrEF (1033 ± 54 ms) compared to healthy controls (972 ± 31 ms) and HFpEF (985 ± 32 ms). T2 relaxation times were elevated in HFmrEF (55.4 ± 3.4 ms) and HFrEF (56.0 ± 6.0 ms) compared to healthy controls (50.6 ± 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.

Early detection of cardiac alterations by left atrial strain in patients with risk for cardiac abnormalities with preserved left ventricular systolic and diastolic function.

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 < 0.01) and was the most sensitive parameter to detect early LA alterations in comparison with other LA functional parameters (rate of impaired LA strain rate, LA total emptying fraction, and LA expansion index 3.8%, 7.3%, and 3.8%, respectively). Moreover, in patients with risk for cardiac abnormalities LA strain was altered even in the absence of subtle LV systolic and diastolic alterations (rates 13.9% and 6.8%), albeit to a lesser extent than in patients with an abnormal LV longitudinal systolic strain or abnormal mitral annular e' velocities (rates 48.5% and 24.4%). Regarding the clinical relevance of these findings, an impaired LA strain (i.e. < 23%) was significantly linked to exertional dyspnea (OR 3.5 [1.7-7.0]) even adjusting the analyses by age, gender and subtle LV abnormalities. In conclusion, the findings from this study suggest that LA strain measurements could be useful to detect early cardiac alterations in patients with risk for cardiac abnormalities with preserved LV systolic and diastolic function and that these early LA strain alterations could be linked to exertional dyspnea.