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[This corrects the article DOI: 10.1371/journal.pone.0238906.].

Vertigo symptoms can lead to more or less vertigo-related handicap. This longitudinal study investigated whether depression, anxiety, and/or somatization mediate the relationship between vertigo symptoms and vertigo-related handicap.N=111 patients with vertigo/dizziness provided complete data on the following measures: Vertigo symptoms at baseline, depression at 6-month follow-up, anxiety at 6-month follow-up, somatization at 6-month follow-up, and vertigo handicap at 12-month follow-up. Mediation analyses with bootstrapping were performed to investigate the mediating role of anxiety, depression, and somatization in the relationship between vertigo symptoms and vertigo-related handicap.When the mediating role of anxiety, depression, and somatization was evaluated separately from each other in single mediation models, the effect vertigo symptoms at baseline exerted on vertigo-related handicap at 12-month follow-up was significantly mediated by depression at 6-month follow-up (p0.05), by anxiety at 6-month follow-up (p0.05), as well as by somatization at 6-month follow-up (p0.05). When statistically controlling for the other mediators in a multiple mediator model, only depression at 6-month follow-up mediated the effect of vertigo symptoms at baseline on vertigo-related handicap at 12-month follow-up (p0.05).Psychological distress is an important mechanism in the process how vertigo symptoms lead to vertigo-related handicap.

The body is central in many mindfulness-based interventions. Body mindfulness has been defined as observing body experiences and as a consequence appreciating body experiences. Since only a few psychometrically sound questionnaires are available to measure the observing aspect of body mindfulness and no scale specifically addresses the appreciating aspect of body mindfulness, the Body Mindfulness Questionnaire (BMQ) was created. This study presents the development and the psychometric evaluation of the BMQ. Exploratory and confirmatory factor analyses suggested two scales: “Experiencing Body Awareness” and “Appreciating Body Awareness”. Evidence for the reliability (internal consistency and retest reliability), validity (factorial validity and construct validity), and sensitivity to change is reported. The findings suggest that the BMQ is a valid and reliable instrument that allows assessment of essential facets of body mindfulness.

The Swiss legal and judicial control of price (related) terms in standard form contracts is fairly complex. For historical reasons and partially by coincidence, the legislator relies on a hybrid approach that combines elements of the law of obligations and the law against unfair competition. As a result, the control of standard contract terms lacks legislative coherence and raises challenging questions of interpretation for legal scholars and courts.

Purpose: Pain and depression have been shown to have a bidirectional interaction. Although several outcome studies have been conducted, it is still unclear if and how depression influences pain outcome. The current study aims to further clarify this relationship by comparing the predicting value of an interview- and a questionnaire-based assessment of depression. Patients and methods: This retrospective study analyzed data of N=496 chronic pain patients who received a multimodal pain management program. Multilevel models were performed with depression as predictor, pain measures as dependent variables, and the respective pain score at baseline as covariate. Depression was measured at baseline with (1) a semi-structured psychiatric interview corresponding to the ICD 10 and (2) the Center for Epidemiologic Studies Depression Scale (CES-D). Pain outcomes were pain intensity assessed with the Numeric Rating Scale (NRS), pain disability measured with the Pain Disability Index (PDI), and affective as well as sensory pain perception assessed with the Pain Perception Scale (PPS-A / PPS-S). Results: At post-treatment, pain intensity (NRS) was higher in patients with depression. This result emerged for interview- (ICD-10) and questionnaire- (CES-D) based depression. These results were significant after correction for multiple testing as well. Moreover, affective pain perception (PSS-A) at post-treatment was higher in patients with depression. Again, this result emerged for interview- (ICD-10) and questionnaire- (CES-D) based depression but it was not significant anymore after correction for multiple testing. Furthermore, pain disability (PDI) was higher at post-treatment in patients with depression according to the CES-D than in those without CES-D depression and this difference in the PDI did not emerge for interview-based depression. Yet, this difference on the PDI between the CES-D depression group and the CES-D no depression group was not significant anymore after correction for multiple testing. Conclusion: The hypothesis that how depression is assessed – interview-based corresponding to the ICD 10 or with the CES-D – contributes to the association between depression and pain treatment outcome could not be confirmed. Future research should use more than one interview and questionnaire to assess depression, since our results are limited to the clinical ICD-10 interview and the CES-D.

Studies have shown that pain acceptance is associated with a better pain outcome. The current study explored whether changes in pain acceptance in the very early treatment phase of an interdisciplinary cognitive-behavioral therapy (CBT)-based treatment program for chronic pain predict pain outcomes. A total of 69 patients with chronic, non-malignant pain (at least 6 months) were treated in a day-clinic for four-weeks. Pain acceptance was measured with the Chronic Pain Acceptance Questionnaire (CPAQ), pain outcomes included pain intensity (Numeric Rating Scale, NRS) as well as affective and sensory pain perception (Pain Perception Scale, SES-A and SES-S). Regression analyses controlling for the pre-treatment values of the pain outcomes, age, and gender were performed. Early changes in pain acceptance predicted pain intensity at post-treatment measured with the NRS (B = &minus, 0.04 (SE = 0.02), T = &minus, 2.28, p = 0.026), affective pain perception at post-treatment assessed with the SES-A (B = &minus, 0.26 (SE = 0.10), 2.79, p = 0.007), and sensory pain perception at post-treatment measured with the SES-S (B = -0.19 (SE = 0.08), T = -2.44, p = 0.017) . Yet, a binary logistic regression analysis revealed that early changes in pain acceptance did not predict clinically relevant pre-post changes in pain intensity (at least 2 points on the NRS). Early changes in pain acceptance were associated with pain outcomes, however, the impact was beneath the threshold defined as clinically relevant.

Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine., publishedVersion

BACKGROUND Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. OBJECTIVE In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. METHODS TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. RESULTS Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. CONCLUSIONS In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

BACKGROUND The management and comprehension of business process models are of utmost importance for almost any enterprise. To foster the comprehension of such models, this paper has incorporated the idea of a serious game called Tales of Knightly Process. OBJECTIVE This study aimed to investigate whether the serious game has a positive, immediate, and follow-up impact on process model comprehension. METHODS A total of two studies with 81 and 64 participants each were conducted. Within the two studies, participants were assigned to a game group and a control group (ie, study 1), and a follow-up game group and a follow-up control group (ie, study 2). A total of four weeks separated study 1 and study 2. In both studies, participants had to answer ten comprehension questions on five different process models. Note that, in study 1, participants in the game group played the serious game before they answered the comprehension questions to evaluate the impact of the game on process model comprehension. RESULTS In study 1, inferential statistics (analysis of variance) revealed that participants in the game group showed a better immediate performance compared to control group participants (PP=.01); here, a Hedges g of 0.82 implied a large effect size. Finally, in both studies, analyses indicated that complex process models are more difficult to comprehend (study 1: PP CONCLUSIONS Participants who played the serious game showed better performance in the comprehension of process models when comparing both studies.

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.

Objective Patients' sudden deterioration in symptomatology, also called sudden losses, is a rarely explored phenomenon. Method Psychological distress of 1,763 patients treated by 140 therapists was monitored after every therapy session. Patient-reported outcome measures and patients' therapy satisfaction was assessed. Therapists rated their experience of difficulties for every patient repeatedly over the course of therapy. Results More than one-quarter of patients (26.5%) experienced at least one sudden loss during therapy. Patients with sudden losses did not differ significantly in psychotherapy outcome and therapy satisfaction from patients without sudden shifts. Therapists did not experience professional self-doubt more often when working with sudden loss patients. Conclusion Sudden losses were not necessarily harmful for the outcome of psychotherapy and patients' global therapy satisfaction. The results suggest that sudden losses can be compensated over the course of treatment.

In two-view geometry, camera models and motion types are used as key knowledge along with the image point correspondences in order to solve several key problems of 3D vision. Problems such as Structure-from-Motion (SfM) and camera self-calibration are tackled under the assumptions of a specific camera projection model and motion type. However, these key assumptions may not be always justified, i.e., we may often know neither the camera model nor the motion type beforehand. In that context, one can extract only the point correspondences between images. From such correspondences, recovering two-view relationship – expressed by the unknown camera model and motion type– remains to be an unsolved problem. In this paper, we tackle this problem in two steps. First, we propose a method that computes the correct two-view relationship in the presence of noise and outliers. Later, we study different possibilities to disambiguate the obtained relationships into camera model and motion type. By extensive experiments on both synthetic and real data, we verify our theory and assumptions in practical settings.

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 number of mobile health apps (MHAs), which are developed to promote healthy behaviors, prevent disease onset, manage and cure diseases, or assist with rehabilitation measures, has exploded. App store star ratings and descriptions usually provide insufficient or even false information about app quality, although they are popular among end users. A rigorous systematic approach to establish and evaluate the quality of MHAs is urgently needed. The Mobile App Rating Scale (MARS) is an assessment tool that facilitates the objective and systematic evaluation of the quality of MHAs. However, a German MARS is currently not available. OBJECTIVE The aim of this study was to translate and validate a German version of the MARS (MARS-G). METHODS The original 19-item MARS was forward and backward translated twice, and the MARS-G was created. App description items were extended, and 104 MHAs were rated twice by eight independent bilingual researchers, using the MARS-G and MARS. The internal consistency, validity, and reliability of both scales were assessed. Mokken scale analysis was used to investigate the scalability of the overall scores. RESULTS The retranslated scale showed excellent alignment with the original MARS. Additionally, the properties of the MARS-G were comparable to those of the original MARS. The internal consistency was good for all subscales (ie, omega ranged from 0.72 to 0.91). The correlation coefficients (r) between the dimensions of the MARS-G and MARS ranged from 0.93 to 0.98. The scalability of the MARS (H=0.50) and MARS-G (H=0.48) were good. CONCLUSIONS The MARS-G is a reliable and valid tool for experts and stakeholders to assess the quality of health apps in German-speaking populations. The overall score is a reliable quality indicator. However, further studies are needed to assess the factorial structure of the MARS and MARS-G.

Background The number of mobile health apps (MHAs), which are developed to promote healthy behaviors, prevent disease onset, manage and cure diseases, or assist with rehabilitation measures, has exploded. App store star ratings and descriptions usually provide insufficient or even false information about app quality, although they are popular among end users. A rigorous systematic approach to establish and evaluate the quality of MHAs is urgently needed. The Mobile App Rating Scale (MARS) is an assessment tool that facilitates the objective and systematic evaluation of the quality of MHAs. However, a German MARS is currently not available. Objective The aim of this study was to translate and validate a German version of the MARS (MARS-G). Methods The original 19-item MARS was forward and backward translated twice, and the MARS-G was created. App description items were extended, and 104 MHAs were rated twice by eight independent bilingual researchers, using the MARS-G and MARS. The internal consistency, validity, and reliability of both scales were assessed. Mokken scale analysis was used to investigate the scalability of the overall scores. Results The retranslated scale showed excellent alignment with the original MARS. Additionally, the properties of the MARS-G were comparable to those of the original MARS. The internal consistency was good for all subscales (ie, omega ranged from 0.72 to 0.91). The correlation coefficients (r) between the dimensions of the MARS-G and MARS ranged from 0.93 to 0.98. The scalability of the MARS (H=0.50) and MARS-G (H=0.48) were good. Conclusions The MARS-G is a reliable and valid tool for experts and stakeholders to assess the quality of health apps in German-speaking populations. The overall score is a reliable quality indicator. However, further studies are needed to assess the factorial structure of the MARS and MARS-G.

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.

Over the last decade, plenty business process modeling notations emerged for the documentation of business processes in enterprises. During the learning of a modeling notation, an individual is confronted with a cognitive load that has an impact on the comprehension of a notation with its underlying formalisms and concepts. To address the cognitive load, this paper presents the results from an exploratory study, in which a sample of 94 participants, divided into novices, intermediates, and experts, needed to assess process models expressed in terms of eight different process modeling notations, i.e., BPMN 2.0, Declarative Process Modeling, eGantt Charts, EPCs, Flow Charts, IDEF3, Petri Nets, and UML Activity Diagrams. The study focus was set on the subjective comprehensibility and accessibility of process models reflecting participant’s cognitive load (i.e., mental load and mental effort). Based on the cognitive load, a factor reflecting the mental difficulty for comprehending process models in different modeling notations was derived. The results indicate that established modeling notations from industry (e.g., BPMN) should be the first choice for enterprises when striving for process management. Moreover, study insights may be used to determine which modeling notations should be taught for an introduction in process modeling or which notation is useful to teach and train process modelers or analysts.

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.

Mobile health technologies have become more and more important in psychotherapy research and practice. The market is being flooded by several psychotherapeutic online services for different purposes. However, mobile health technologies are particularly suitable for data collection and monitoring, as data can be recorded economically in real time. Currently, there is no appropriate method to assess intersession experiences systematically in psychotherapeutic practice. The aim of our project was the development of a smartphone application framework for systematic recording and controlling of intersession experiences. Intersession-Online, an iOS- and Android-App, offers the possibility to collect data on intersession experiences easily, to provide the results to therapists in an evaluated form and, if necessary, to induce or interrupt intersession experiences with the primary aim to improve outcome of psychotherapy. In general, the smartphone application could be a helpful, evidence-based tool for research and practice. Overall speaking, further research to investigate the efficacy of Intersession-Online is necessary.

Due to the constant use of smartphones in daily life, mHealth apps might bear great potential for the use in health care support. In this chapter the potentials, limitations, current quality and future directions of mHealth apps will be discussed. First, we describe potential benefits like quicker facilitation of information, patient empowerment and inclusion of undersupplied population groups. Furthermore, the use of mHealth apps for diverse somatic and mental health conditions will be discussed. Beyond, the chapter provides the reader with a short overview on the efficacy of mHealth apps for different indications: Exemplary, we provide evidence for the efficacy of mHealth apps in the realm of asthmatic disease, depression and anxiety disorder. Despite the availability of mHealth solutions, the acceptance of among health care providers is still moderate to low. This represents a substantial problem, as health care providers are important gate keepers for intervention uptake. In this context we describe methods to foster acceptance. Furthermore, we address potential risks of mHealth app use including low responsiveness towards critical situations (e.g. self-harm) or the difficulty for users to assess the quality of the app’s content. Here we refer to standardized instruments to assess app quality. With respect to the massive amount of sensitive data already being collected through such mHealth apps, we also reflect on the latest current legal situation in Europe and the United States.

This study re-investigated data of a randomized controlled trial on Internet-based Acceptance and Commitment Therapy for chronic pain (ACTonPain). Baseline psychological inflexibility was examined as a moderator of the outcome pain interference. In the ACTonPain trial, participants with chronic pain were randomized to one of three conditions: guided Internet-based ACT (n = 100), unguided Internet-based ACT (n = 101), and waitlist (n = 101). Moderation analyses were performed with the SPSS macro PROCESS. Pain interference according to the Multidimensional Pain Inventory (MPI) was the primary outcome in this trial, and the potential moderator psychological inflexibility was measured with the Acceptance and Action Questionnaire (AAQ-II). Psychological inflexibility at baseline moderated the outcome between guided Internet-based ACT and waitlist 9-weeks as well as 6-months after randomization. (both p < 0.05). Between unguided Internet-based ACT and waitlist, psychological inflexibility moderated the outcome 6-months after randomization (p < 0.05). Internet-based ACT was superior to waitlist for participants with less psychological inflexibility at baseline, but Internet-based ACT became increasingly comparable to waitlist at higher AAQ-II baseline values. Future research should investigate whether the results can be replicated in more individualized and tailored face-to-face settings., publishedVersion

We tested the efficacy of an integrative psychotherapeutic group treatment (IPGT) in reducing vertigo/dizziness-related impairment along with depression, anxiety, and somatization by conducting a randomized controlled superiority trial comparing IPGT to self-help groups moderated by a clinical psychologist (SHG). Adult patients with functional vertigo and dizziness symptoms were randomly allocated to either the IPGT or SHG as active control group. Outcomes were assessed at baseline (t0), after treatment lasting 16 weeks (t1), and 12 months after treatment (t2). A total of 81 patients were assigned to IPGT and 78 patients were assigned to SHG. Vertigo-related impairment was reduced in both conditions (IPGT: t0–t1: d = 1.10, t0–t2: d = 1.06, SHG: t0–t1: d = 0.86, t0–t2: d = 1.29), showing the efficiency of both IPGT and SHG. Clinically relevant improvements were also obtained for depression in both groups. Linear mixed model analyses revealed no differences between groups for all outcomes (effect of group for the primary outcome: b = −1.15, SE = 2.13, t = −0.54, p = 0.59). Attrition rates were higher in SHG (52.6%) than in IPGT (28.4%). Both conditions improved primary and secondary outcomes while IPGT was better accepted by patients than SHG. Trial registration: ClinicalTrials.gov, Identifier: NCT02320851.

Since the beginning of the COVID-19 pandemic a decline in mental health has been reported. This online study investigated mental health and well-being in Austria during a strict lockdown. In total, N = 1505 participants were recruited between 23 December 2020 and 4 January 2021 and levels of depression (PHQ-9), anxiety (GAD-7), sleep quality (ISI), well-being (WHO-5), quality of life (WHO-QOL) and stress (PSS-10) were measured. 26% scored above the cut-off for moderate depressive symptoms (PHQ-9 ≥ 10, ♀ = 32%, ♂ = 21%), 23% above the cut-off for moderate anxiety (GAF-7 ≥ 10, ♀ = 29%, ♂ = 17%) and 18% above the cut-off for moderate insomnia (ISI ≥ 15, ♀ = 21%, ♂ = 16%). Mean-scores for quality of life (psychological WHO-QOL) were 68.89, for well-being (WHO-5) 14.34, and for stress (PSS-10) 16.42. The youngest age group (18–24) was most burdened and showed significantly more mental health symptoms compared with the oldest age group (65+) in depressive symptoms (50% vs. 12%), anxiety symptoms (35% vs. 10%), and insomnia (25% vs. 11%, all p-values &lt, 0.05). Mental health decreased compared to both the first lockdown earlier in 2020 and pre-pandemic data. Further analyses indicate these findings were especially apparent for the under 24-year-olds, women, single/separated people, low incomes and those who do not partake in any physical activity (all p-values &lt, 0.05). We highlight the need for ongoing mental health support, particularly to the most burdened groups.

Telephone emergency services play an important role in providing low-threshold, anonymous crisis intervention free of cost. The current study aims to examine the mental well-being and perceived stress level of counselors as well as the main topics of helpline callers during the COVID-19 pandemic in Austria. In the current study, 374 counselors were recruited within the Austrian nationwide organization TelefonSeelsorge during the second wave of COVID-19 infection in Austria. The mental well-being (WHO-5) and perceived stress-level (PSS-10) were assessed and counselors were asked about the frequency of different topics thematized by callers and changes compared to pre-pandemic times. Compared to a reference group of the Austrian general population, counselors experienced less stress (13.22 vs. 16.42) and higher mental well-being (66.26 vs. 57.36, p &lt, 0.001). The most frequent topics during the second wave of the COVID-19 pandemic in Austria were loneliness and mental health. More calls were registered in 2020 compared to 2019 and especially the topics loneliness, mental health, professional activities and relationships were reported to be thematized more often during the COVID-19 pandemic compared to the time before (p &lt, 0.001). The results contribute to an understanding of the impact of the COVID-19 pandemic on telephone crisis intervention.

The authors regret that there was a typo in the results section of the abstract of the original article and “119%” should actually be “19%”. The corrected abstract in full can be found below. Abstract Background: The impact of Coronavirus disease (COVID-19) and the governmental restrictions on mental health have been reported for different countries. This study evaluated mental health during COVID-19 lockdown in Austria and the effect of age, gender, income, work, and physical activity. Methods: An online survey was performed through Qualtrics® after four weeks of lockdown in Austria to recruit a representative sample regarding gender, age, education, and region. Indicators of mental health were quality of life (WHO-QOL BREF), well-being (WHO-5), depression (PHQ-9), anxiety (GAD-7), stress (PSS-10), and sleep quality (ISI). Results: In total, N = 1005 individuals were included (53% women). 21% scored above the cut off ≥10 points (PHQ-9) for moderate depressive symptoms, 19% scored above the cut-off ≥10 points (GAD-7) for moderate anxiety symptoms, and 16% above the cut-off ≥15 points (ISI) for clinical insomnia. ANOVAs, Bonferroni-corrected post-hoc tests, and t-tests showed highest mental health problems in adults under 35 years, women, people with no work, and low income (all p values

Background The current situation around the COVID-19 pandemic and the measures necessary to fight it are creating challenges for psychotherapists, who usually treat patients face-to-face with personal contact. The pandemic is accelerating the use of remote psychotherapy (ie, psychotherapy provided via telephone or the internet). However, some psychotherapists have expressed reservations regarding remote psychotherapy. As psychotherapists are the individuals who determine the frequency of use of remote psychotherapy, the potential of enabling mental health care during the COVID-19 pandemic in line with the protective measures to fight COVID-19 can be realized only if psychotherapists are willing to use remote psychotherapy. Objective This study aimed to investigate the experiences of psychotherapists with remote psychotherapy in the first weeks of the COVID-19 lockdown in Austria (between March 24 and April 1, 2020). Methods Austrian psychotherapists were invited to take part in a web-based survey. The therapeutic orientations of the psychotherapists (behavioral, humanistic, psychodynamic, or systemic), their rating of the comparability of remote psychotherapy (web- or telephone-based) with face-to-face psychotherapy involving personal contact, and potential discrepancies between their actual experiences and previous expectations with remote psychotherapy were assessed. Data from 1162 psychotherapists practicing before and during the COVID-19 lockdown were analyzed. Results Psychotherapy conducted via telephone or the internet was reported to not be totally comparable to psychotherapy with personal contact (P Conclusions Psychotherapists found their experiences with remote psychotherapy (ie, web- or telephone-based psychotherapy) to be better than expected but found that this mode was not totally comparable to face-to-face psychotherapy with personal contact. Especially, behavioral therapists were found to rate telephone-based psychotherapy less favorably than therapists with other theoretical backgrounds.

Objectives: COVID-19 has led to changes in the provision in mental health services. The current study investigated influencing factors on: (i) the comparability of psychotherapy via internet/telephone with psychotherapy in face-to-face contact as well as (ii) the actual experience with psychotherapy via internet/telephone compared to respective prior expectations in CBT therapists. Methods: A quantitative cross-sectional study was conducted in the form of an online survey. The research samples, registered cognitive-behavioral therapy (CBT) psychotherapists in Austria and Germany, were contacted by e-mail. Results: One hundred and ninety CBT therapists were analyzed in this study. The total number of patients treated via telephone/internet is a decisive factor for the subjective evaluation of the comparability of psychotherapy via telephone/internet and psychotherapy in personal contact. This factor also influences the extent (positive/negative) of the assessment of the actual experience with psychotherapy via internet/telephone compared to previous expectations. Neither age nor gender were associated with comparability of psychotherapy via internet/telephone with psychotherapy in face-to-face contact or the actual experience with psychotherapy via internet/telephone compared to respective prior expectations. Conclusions: Implications of the results are that attitudes towards remote psychotherapy might be increased in CBT therapists when they treat more patients remotely and experiences with remote psychotherapies should be included in psychotherapy training.

Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.

Seit Jahrzehnten zielt die Regionalpolitik darauf ab, die landlichen Regionen bei ihrer Entwicklung zu unterstutzen. Auch wenn es durchaus Fortschritte gibt, bleiben die Disparitaten zwischen urbanen und landlichen Raumen jedoch weitgehend unverandert. Im Rahmen der vorliegenden Studie wurde untersucht, warum einigen landlichen Regionen trotzdem eine langfristig positive Entwicklung gelingt. In einem 2-stufigen Untersuchungsansatz wurden zunachst die Sichtweisen von Regionalentwicklungsexperten aus der Wissenschaft, Verwaltung und Praxis und anschliesend von Vertretern der Fallstudienregionen Achental (Deutschland), Bregenzerwald (Osterreich) und Entlebuch (Schweiz) erhoben. Anhand der 3 Case Studies konnte eine Liste mit 25 Erfolgsfaktoren zusammengestellt werden. Auf dieser Grundlage wurde ein Modell entwickelt, das die Perspektive der ubergeordneten Experten integriert und alle identifizierten Erfolgsfaktoren in einen zyklischen Zusammenhang im Sinne einer nachhaltigen Regionalentwicklung bringt.

Presently, mHealth technology is often applied in the context of chronic diseases to gather data that may lead to new and valuable medical insights. As many aspects of chronic diseases are not completely understood, new data sources might be promising. mHealth technology may help in this context as it can be easily used in everyday life. Moreover, the bring your own device principle encourages many patients to use their smartphone to learn more about their disease. The less is known about a disorder (e.g., tinnitus), the more patients crave for new insights and opportunities. Despite the fact that existing mHealth technology like mobile crowdsensing has already gathered data that may help patients, in general, less is known whether and how data gathered with different mobile technologies may differ. In this context, one relevant aspect is the contribution of the mobile operating system itself. For example, are there differences between Android and iOS users that utilize the same mHealth technology for a disease. In the TrackYourTinnitus project, a mobile crowdsensing mHealth platform was developed to gather data for tinnitus patients in order to reveal new insights on this disorder with high economic and patient-related burdens. As many data sets were gathered during the last years that enable us to compare Android and iOS users, the work at hand compares characteristics of these users. Interesting insights like the one that Android users with tinnitus are significantly older than iOS users could be revealed by our study. However, more evaluations are necessary for TrackYourTinnitus in particular and mHealth technology in general to understand how smartphones affect the gathering of data on chronic diseases when using them in the large.

Many studies have revealed that therapeutic homework is beneficial for the efficacy of therapies. Interestingly, the latter have been less supported by IT systems so far and, hence, therapeutic opportunities have been neglected. For example, mobile devices can be used to notify patients about assigned homework and help them to accomplish it in a timely manner. In general, the use of mobile devices as well as their sensors seem to be promising for the support of remote therapeutic interventions. In the Albatros project, we have been developing a framework that enables domain experts to flexibly define the homework required in the context of a remote therapeutic intervention. More precisely, the various tasks of a homework can be specified as a mobile process, which is then run on the mobile device of the respective patient. To realize this vision, a configurator component using a model-driven approach was developed. In particular, the Albatros configurator shall relieve domain experts from complex technical issues when defining a homework. The study presented in this paper investigates whether domain experts are actually able to use the configurator component. In particular, the study revealed three insights. First, basic interventions can be easily defined with an acceptable number of errors. Second, for defining complex interventions (e.g., using a sensor when performing an exercise) several issues could be identified that will contribute to improve the Albatros configurator. Third, additional studies are needed to evaluate the overall mental effort of domain experts when using the configurator. Altogether, the Albatros framework may be a reasonable alley to empower domain experts in creating homework in the context of remote therapeutic interventions.

Treatment of patients with tinnitus is mainly pursued on the basis of screening data, encompassing answers to questionnaires or audiological examinations. Since tinnitus affects the quality of life of patients and is associated with comorbidities like depression, the screening step involves the acquisition of extensive amounts of information, which may contribute to the design of a personalized treatment. Often times, it can be observed that patients give up their treatment before completion (i.e., they constitute “dropout” cases). In this study, we investigate how multi-target classification on the screening data can contribute to characterize patients that will drop out of the study. For our analysis, we base or considerations on the target variable “tinnitus loudness”, i.e. the subjectively perceived loudness of the phantom signal. Following this, we attempt to identify variables that explain the tinnitus loudness together with the likelihood of interrupting the treatment. To be more precise, we report on results from gathered data of 1419 tinnitus patients from the University Hospital of Regensburg.

We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-the-art methods based on holistic 3D regression, our method works on dense pixel-wise estimation. This is achieved by careful design choices in pose parameterization, which leverages both 2D and 3D properties of depth map. Specifically, we decompose the pose parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat maps and unit 3D directional vector fields. The 2D/3D joint heat maps and 3D joint offsets are estimated via multi-task network cascades, which is trained end-to-end. The pixel-wise estimations can be directly translated into a vote casting scheme. A variant of mean shift is then used to aggregate local votes while enforcing consensus between the the estimated 3D pose and the pixel-wise 2D and 3D estimations by design. Our method is efficient and highly accurate. On MSRA and NYU hand dataset, our method outperforms all previous state-of-the-art approaches by a large margin. On the ICVL hand dataset, our method achieves similar accuracy compared to the currently proposed nearly saturated result and outperforms various other proposed methods. Code is available $\href{"https://github.com/melonwan/denseReg"}{\text{online}}$.

We present the Android app TYDR (Track Your Daily Routine) which tracks smartphone sensor and usage data and utilizes standardized psychometric personality questionnaires. With the app, we aim at collecting data for researching correlations between the tracked smartphone data and the user's personality in order to predict personality from smartphone data. In this paper, we highlight our approaches in addressing the challenges in developing such an app. We optimize the tracking of sensor data by assessing the trade-off of size of data and battery consumption and granularity of the stored information. Our user interface is designed to incentivize users to install the app and fill out questionnaires. TYDR processes and visualizes the tracked sensor and usage data as well as the results of the personality questionnaires. When developing an app that will be used in psychological studies, requirements posed by ethics commissions / institutional review boards and data protection officials have to be met. We detail our approaches concerning those requirements regarding the anonymized storing of user data, informing the users about the data collection, and enabling an opt-out option. We present our process for anonymized data storing while still being able to identify individual users who successfully completed a psychological study with the app., Comment: Accepted for publication at the 5th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft '18)

The exploitation of sensor features offered by present smart mobile devices is a trend that becomes increasingly important in various domains. In healthcare, for example, these sensors are used to cheaply gather valuable data for chronic disease management or health care. Regarding the latter, health insurers crave for effective methods that can be offered to their customers. Moreover, smart mobile devices provide many advantages compared to approaches hitherto applied in the aforementioned contexts as they can be easily used in everyday life. Thereby, when taking these advantages properly into account, new mobile application types become possible. Body sensor networks are such an application type that aim at monitoring users in vivo. Furthermore, data gathered with body sensor networks may be a valuable basis to provide user interventions. This paper presents an application that shall support users to walk mindfully. The motivation was to create a mobile tool that can make mindful walking more effective to reduce stress and to target noncommunicable diseases such as diabetes or depression. It is a mobile personalized tool that senses the walking speed and provides haptic feedback thereof. The mindful walking procedure, the technical prototype as well as preliminary study results are presented and discussed in this work. The reported user feedback and the study results indicate promising perspectives for a tool that supports a mindful walking behavior. Altogether, the use of smart mobile device sensors constitutes a promising instrument for realizing mobile applications in the context of health care and disease management.

Background Many research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios. Objective In order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants. Methods Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework. Results Statistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P

BACKGROUND Many research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios. OBJECTIVE In order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants. METHODS Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework. RESULTS Statistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P CONCLUSIONS In conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

The perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the recent progress, several challenges remain, particularly the computational complexity and the unknown camera focal length. In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length. In the template-based case, we provide a method to estimate four parameters of the camera intrinsics. For the template-less scenario of NRSfM, we propose a method to upgrade reconstructions obtained for one focal length to another based on local rigidity and the so-called Maximum Depth Heuristics (MDH). On its basis we propose a method to simultaneously recover the focal length and the non-rigid shapes. We further solve the problem of incorporating a large number of points and adding more views in MDH-based NRSfM and efficiently solve them with Second-Order Cone Programming (SOCP). This does not require any shape initialization and produces results orders of times faster than many methods. We provide evaluations on standard sequences with ground-truth and qualitative reconstructions on challenging YouTube videos. These evaluations show that our method performs better in both speed and accuracy than the state of the art., Lecture Notes in Computer Science, 11217, ISSN:0302-9743, ISSN:1611-3349, Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, ISBN:978-3-030-01260-1, ISBN:978-3-030-01261-8

Business process models have been adopted by enterprises for more than a decade. Especially for domain experts, the comprehension of process models constitutes a challenging task that needs to be mastered when creating or reading these models. This paper presents the results we obtained from an eye tracking experiment on process model comprehension. In detail, individuals with either no or advanced expertise in process modeling were confronted with models expressed in terms of Event-driven Process Chains (EPCs), reflecting different levels of difficulty. The first results of this experiment confirm recent findings from one of our previous experiments on the reading and comprehension of process models. On one hand, independent from their level of expertise, all individuals face similar patterns, when being confronted with process models exceeding a certain level of difficulty. On the other, it appears that process models expressed in terms of EPCs are perceived differently compared to process models specified in the Business Process Model and Notation (BPMN). In the end, their generalization needs to be confirmed by additional empirical experiments. The presented experiment continues a series of experiments that aim to unravel the factors fostering the comprehension of business process models by using methods and theories stemming from the field of cognitive neuroscience and psychology.

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly., Comment: CVPR 2019, for implementation see https://github.com/janinethoma

Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using ‘rules’ on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art., Lecture Notes in Computer Science, 11208, ISSN:0302-9743, ISSN:1611-3349, Computer Vision – ECCV 2018, ISBN:978-3-030-01224-3, ISBN:978-3-030-01225-0