Publications by authors named "Rüdiger Pryss"

42 Publications

Using a visual analog scale (VAS) to measure tinnitus-related distress and loudness: Investigating correlations using the Mini-TQ results of participants from the TrackYourTinnitus platform.

Prog Brain Res 2021 20;263:171-190. Epub 2021 May 20.

Institute of Databases and Information Systems, Ulm University, Ulm, Germany.

Introduction: Tinnitus, a perception of ringing and buzzing sound in the ear, has not been completely understood yet. It is well known that tinnitus-related distress and loudness can change over time. However, proper comparability for the data collection approaches requires further focused studies. In this context, technology such as the use of mobile devices may be a promising approach. Repeated assessments of tinnitus-related distress and loudness in Ecological Momentary Assessment (EMA) studies require a short assessment, and a Visual Analogic Scale (VAS) is often used in this context. Yet, their comparability with psychometric questionnaires remains unclear and thus was the focus of this study. Research goals: The evaluation of the appropriateness of VAS in measuring tinnitus-related distress and loudness is pursued in this paper.

Methods: The Mini Tinnitus Questionnaire (Mini-TQ) measured tinnitus-related distress once. Tinnitus-related distress and tinnitus loudness were measured repeatedly using VAS on a daily basis during 7 days in the TrackYourTinnitus (TYT) smartphone app and were summarized per day using mean and median results. Then, correlations between summarized VAS tinnitus-related distress and summarized VAS tinnitus loudness, on the one side, and Mini-TQ, on the other side, were calculated.

Results: Correlations between Mini-TQ and VAS tinnitus-related distress ranged between r = 0.36 and r = 0.52, while correlations between Mini-TQ and VAS tinnitus loudness ranged between r = 0.25 and r = 0.36. The more time difference between the Mini-TQ and the VAS assessments is, the lower the correlations between them. Mean and median VAS values per day resulted in similar correlations.

Conclusions: Mobile-based VAS seems to be an appropriate approach to utilize daily measurements of tinnitus-related distress.
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http://dx.doi.org/10.1016/bs.pbr.2020.08.008DOI Listing
May 2021

"How Come You Don't Call Me?" Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic.

Int J Environ Res Public Health 2021 06 8;18(12). Epub 2021 Jun 8.

Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany.

Loneliness and lack of social well-being are associated with adverse health outcomes and have increased during the COVID-19 pandemic. Smartphone communication data have been suggested to help monitor loneliness, but this requires further evidence. We investigated the informative value of smartphone communication app data for predicting subjective loneliness and social well-being in a sample of 364 participants ranging from 18 to 78 years of age (52.2% female; mean age = 42.54, SD = 13.22) derived from the CORONA HEALTH APP study from July to December 2020 in Germany. The participants experienced relatively high levels of loneliness and low social well-being during the time period characterized by the COVID-19 pandemic. Apart from positive associations with phone call use times, smartphone communication app use was associated with social well-being and loneliness only when considering the age of participants. Younger participants with higher use times tended to report less social well-being and higher loneliness, while the opposite association was found for older adults. Thus, the informative value of smartphone communication use time was rather small and became evident only in consideration of age. The results highlight the need for further investigations and the need to address several limitations in order to draw conclusions at the population level.
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http://dx.doi.org/10.3390/ijerph18126212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227237PMC
June 2021

An Albanian translation of a questionnaire for self-reported tinnitus assessment.

Int J Audiol 2021 Jun 28:1-6. Epub 2021 Jun 28.

Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.

Objective: To our knowledge, there is no published study investigating the characteristics of people experiencing tinnitus in Albania. Such a study would be important, providing the basis for further research in this region and contributing to a wider understanding of tinnitus heterogeneity across different geographic locations. The main objective of this study was to develop an Albanian translation of a standardised questionnaire for tinnitus research, namely the European School for Interdisciplinary Tinnitus Research-Screening Questionnaire (ESIT-SQ). A secondary objective was to assess its applicability and usefulness by conducting an exploratory survey on a small sample of the Albanian tinnitus population.

Design And Study Sample: Three translators were recruited to create the Albanian ESIT-SQ translation following good practice guidelines. Using this questionnaire, data from 107 patients attending otolaryngology clinics in Albania were collected.

Results: Participants reporting various degrees of tinnitus symptom severity had distinct phenotypic characteristics. Application of a random forest approach on this preliminary dataset showed that self-reported hearing difficulty, and tinnitus duration, pitch and temporal manifestation were important variables for predicting tinnitus symptom severity.

Conclusions: Our study provided an Albanian translation of the ESIT-SQ and demonstrated that it is a useful tool for tinnitus profiling and subgrouping.
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http://dx.doi.org/10.1080/14992027.2021.1933221DOI Listing
June 2021

Editorial: Smart Mobile Data Collection in the Context of Neuroscience.

Front Neurosci 2021 25;15:698597. Epub 2021 May 25.

Institute of Databases and Information Systems, Ulm University, Ulm, Germany.

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http://dx.doi.org/10.3389/fnins.2021.698597DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185150PMC
May 2021

Quality of Physical Activity Apps: Systematic Search in App Stores and Content Analysis.

JMIR Mhealth Uhealth 2021 06 9;9(6):e22587. Epub 2021 Jun 9.

Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.

Background: Physical inactivity is a major contributor to the development and persistence of chronic diseases. Mobile health apps that foster physical activity have the potential to assist in behavior change. However, the quality of the mobile health apps available in app stores is hard to assess for making informed decisions by end users and health care providers.

Objective: This study aimed at systematically reviewing and analyzing the content and quality of physical activity apps available in the 2 major app stores (Google Play and App Store) by using the German version of the Mobile App Rating Scale (MARS-G). Moreover, the privacy and security measures were assessed.

Methods: A web crawler was used to systematically search for apps promoting physical activity in the Google Play store and App Store. Two independent raters used the MARS-G to assess app quality. Further, app characteristics, content and functions, and privacy and security measures were assessed. The correlation between user star ratings and MARS was calculated. Exploratory regression analysis was conducted to determine relevant predictors for the overall quality of physical activity apps.

Results: Of the 2231 identified apps, 312 met the inclusion criteria. The results indicated that the overall quality was moderate (mean 3.60 [SD 0.59], range 1-4.75). The scores of the subscales, that is, information (mean 3.24 [SD 0.56], range 1.17-4.4), engagement (mean 3.19 [SD 0.82], range 1.2-5), aesthetics (mean 3.65 [SD 0.79], range 1-5), and functionality (mean 4.35 [SD 0.58], range 1.88-5) were obtained. An efficacy study could not be identified for any of the included apps. The features of data security and privacy were mainly not applied. Average user ratings showed significant small correlations with the MARS ratings (r=0.22, 95% CI 0.08-0.35; P<.001). The amount of content and number of functions were predictive of the overall quality of these physical activity apps, whereas app store and price were not.

Conclusions: Apps for physical activity showed a broad range of quality ratings, with moderate overall quality ratings. Given the present privacy, security, and evidence concerns inherent to most rated apps, their medical use is questionable. There is a need for open-source databases of expert quality ratings to foster informed health care decisions by users and health care providers.
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http://dx.doi.org/10.2196/22587DOI Listing
June 2021

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.

Front Psychiatry 2021 14;12:660534. Epub 2021 May 14.

Department of Clinical Psychology and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.

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. 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). A three-armed multicenter cluster-randomized controlled non-inferiority trial will be conducted comparing two implementations of blended psychotherapy (PSYCHOnlineTHERAPY) 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 = 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. 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. The trial is registered in the German Clinical Trials Register (DRKS00023973; date of registration: December 28th 2020).
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http://dx.doi.org/10.3389/fpsyt.2021.660534DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160118PMC
May 2021

Motivating Developers to Use Interoperable Standards for Data in Pandemic Health Apps.

Stud Health Technol Inform 2021 May;281:1027-1028

Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine ("Netzwerk Universitätsmedizin (NUM)") tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.
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http://dx.doi.org/10.3233/SHTI210339DOI Listing
May 2021

Effective Adoption of Tablets for Psychodiagnostic Assessments in Rural Burundi: Evidence for the Usability and Validity of Mobile Technology in the Example of Differentiating Symptom Profiles in AMISOM Soldiers 1 Year After Deployment.

Front Public Health 2021 15;9:490604. Epub 2021 Apr 15.

Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.

Research on the use of mobile technology in health sciences has identified several advantages of so-called mHealth (mobile health) applications. Tablet-supported clinical assessments are becoming more and more prominent in clinical applications, even in low-income countries. The present study used tablet computers for assessments of clinical symptom profiles in a sample of Burundian AMISOM soldiers (i.e., African Union Mission to Somalia; a mission approved by the UN). The study aimed to demonstrate the feasibility of mHealth-supported assessments in field research in Burundi. The study was conducted in a resource-poor setting, in which tablet computers are predestined to gather data in an efficient and reliable manner. The overall goal was to prove the validity of the obtained data as well as the feasibility of the chosen study setting. Four hundred sixty-three soldiers of the AMISOM forces were investigated after return from a 1-year military mission in Somalia. Symptoms of posttraumatic stress disorder (PTSD) and depression were assessed. The used data-driven approach based on a latent profile analysis revealed the following four distinct groups, which are based on the soldiers' PTSD and depression symptom profiles: Class 1: moderate PTSD, Class 2: moderate depression, Class 3: low overall symptoms, and Class 4: high overall symptoms. Overall, the four identified classes of soldiers differed significantly in their PTSD and depression scores. The study clearly demonstrates that tablet-supported assessments can provide a useful application of mobile technology in large-scale studies, especially in resource-poor settings. Based on the data collected for the study at hand, it was possible to differentiate different sub-groups of soldiers with distinct symptom profiles, proving the statistical validity of the gathered data. Finally, advantages and challenges for the application of mobile technology in a resource-poor setting are outlined and discussed.
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http://dx.doi.org/10.3389/fpubh.2021.490604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8083058PMC
May 2021

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment.

Curr Top Behav Neurosci 2021 Apr 11. Epub 2021 Apr 11.

First Department of Otorhinolaryngology, Head and Neck Surgery, National and Kapodistrian University of Athens, Hippokration General Hospital, Athens, Greece.

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients' characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient.The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature.The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level.In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection.
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http://dx.doi.org/10.1007/7854_2021_229DOI Listing
April 2021

Reasons for Discontinuing Active Participation on the Internet Forum Tinnitus Talk: Mixed Methods Citizen Science Study.

JMIR Form Res 2021 Apr 8;5(4):e21444. Epub 2021 Apr 8.

Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems an der Donau, Austria.

Background: Tinnitus Talk is a nonprofit online self-help forum. Asking inactive users about their reasons for discontinued usage of health-related online platforms such as Tinnitus Talk is important for quality assurance.

Objective: The aim of this study was to explore reasons for discontinued use of Tinnitus Talk, and their associations to the perceptions of Tinnitus Talk and the age of users who ceased logging on to the platform.

Methods: Initially, 13,745 users that did not use Tinnitus Talk within the previous 2 months were contacted and the response rate was 20.47% (n=2814). After dataset filtering, a total of 2172 past members of Tinnitus Talk were included in the analyses. Nine predefined reasons for discontinued usage of Tinnitus Talk were included in the survey as well as one open question. Moreover, there were 14 predefined questions focusing on perception of Tinnitus Talk (usefulness, content, community, and quality of members' posts). Mixed methods analyses were performed. Frequencies and correlation coefficients were calculated for quantitative data, and grounded theory methodology was utilized for exploration of the qualitative data.

Results: Quantitative analysis revealed reasons for discontinued use of Tinnitus Talk as well as associations of these reasons with perceptions of Tinnitus Talk and age. Among the eight predefined reasons for discontinued use of Tinnitus Talk, the most frequently reported was not finding the information they were looking for (451/2695, 16.7%). Overall, the highest rated perception of Tinnitus Talk was content-related ease of understanding (mean 3.9, SD 0.64). A high number (nearly 40%) of participants provided additional free text explaining why they discontinued use. Qualitative analyses identified a total of 1654 specific reasons, more than 93% of which (n=1544) could be inductively coded. The coding system consisted of 33 thematically labeled codes clustered into 10 categories. The most frequent additional reason for discontinuing use was thinking that there is no cure or help for tinnitus symptoms (375/1544, 24.3%). Significant correlations (P<.001) were observed between reasons for discontinued usage and perception of Tinnitus Talk. Several reasons for discontinued usage were associated with the examined dimensions of perception of Tinnitus Talk (usefulness, content, community, as well as quality of members' posts). Moreover, significant correlations (P<.001) between age and reasons for discontinued use were found. Older age was associated with no longer using Tinnitus Talk because of not finding what they were looking for. In addition, older participants had a generally less positive perception of Tinnitus Talk than younger participants (P<.001).

Conclusions: This study contributes to understanding the reasons for discontinued usage of online self-help platforms, which are typically only reported according to the dropout rates. Furthermore, specific groups of users who did not benefit from Tinnitus Talk were identified, and several practical implications for improvement of the structure, content, and goals of Tinnitus Talk were suggested.
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http://dx.doi.org/10.2196/21444DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063098PMC
April 2021

Applying Eye Movement Modeling Examples to Guide Novices' Attention in the Comprehension of Process Models.

Brain Sci 2021 Jan 7;11(1). Epub 2021 Jan 7.

Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.

Process models are crucial artifacts in many domains, and hence, their proper comprehension is of importance. Process models mediate a plethora of aspects that are needed to be comprehended correctly. Novices especially face difficulties in the comprehension of process models, since the correct comprehension of such models requires process modeling expertise and visual observation capabilities to interpret these models correctly. Research from other domains demonstrated that the visual observation capabilities of experts can be conveyed to novices. In order to evaluate the latter in the context of process model comprehension, this paper presents the results from ongoing research, in which gaze data from experts are used as Eye Movement Modeling Examples (EMMEs) to convey visual observation capabilities to novices. Compared to prior results, the application of EMMEs improves process model comprehension significantly for novices. Novices achieved in some cases similar performances in process model comprehension to experts. The study's insights highlight the positive effect of EMMEs on fostering the comprehension of process models.
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http://dx.doi.org/10.3390/brainsci11010072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827780PMC
January 2021

Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring.

Sci Rep 2020 12 31;10(1):22459. Epub 2020 Dec 31.

Institute of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.

The recording of Ecological Momentary Assessments (EMA) can assist people with chronic diseases in monitoring their health state. However, many users quickly lose interest in their respective EMA platforms. Therefore, we studied the adherence of users of the mHealth app TRACKYOURTINNITUS (TYT). The app is used to record EMA in people with tinnitus. 1292 users, who interacted with the app between April 2014 and February 2017, were analyzed in this work. We defined "adherence" based on the dimensions of interaction duration and interaction continuity. We propose methods that are able to predict the (dis)continuation of interaction with the app and identify user segments that are characterized by similar patterns of adherence. For the prediction task we used the data of the questionnaires MiniTF and TSCHQ, which are filled in when the users enter TYT for the first time. Additionally, time series of the eight items of the daily EMA questionnaire were used. The distribution of user activity pertaining to the adherence dimension of interaction duration revealed a very skewed distribution, with most users giving up after only 1 day of interaction. However, many users returned after interrupting for some time. Some of the MiniTF items indicated that the worries of users might have lead to an increased likelihood of returning back to the app. The MiniTF score itself was not predictive, though. The answers to the TSCHQ items, in turn, pointed to user strata (more than 65 years of age at registration), which tended towards higher interaction continuity. As the registration questionnaires predicted adherence only to a limited extent, it is promising to study the activities of the users in the very first days of interaction more deeply. It turned out in this context that the effects of interaction stimulants like personalized and non-personalized tips, pointers to information sources, and mechanisms used in online treatments for tinnitus (e.g., in iCBT) should be further investigated.
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http://dx.doi.org/10.1038/s41598-020-79527-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775469PMC
December 2020

Contemporary Review of Smartphone Apps for Tinnitus Management and Treatment.

Brain Sci 2020 Nov 17;10(11). Epub 2020 Nov 17.

Institute of Distributed Systems, Ulm University, 89081 Ulm, Germany.

Tinnitus is a complex and heterogeneous psycho-physiological disorder responsible for causing a phantom ringing or buzzing sound albeit the absence of an external sound source. It has a direct influence on affecting the quality of life of its sufferers. Despite being around for a while, there has not been a cure for tinnitus, and the usual course of action for its treatment involves use of tinnitus retaining and sound therapy, or Cognitive Behavioral Therapy (CBT). One positive aspect about these therapies is that they can be administered face-to-face as well as delivered via internet or smartphone. Smartphones are especially helpful as they are highly personalized devices, and offer a well-established ecosystem of apps, accessible via respective marketplaces of differing mobile platforms. Note that current therapeutic treatments such as CBT have shown to be effective in suppressing the tinnitus symptoms when administered face-to-face, their effectiveness when being delivered using smartphones is not known so far. A quick search on the prominent market places of popular mobile platforms (Android and iOS) yielded roughly 250 smartphone apps offering tinnitus-related therapies and tinnitus management. As this number is expected to steadily increase due to high interest in smartphone app development, a contemporary review of such apps is crucial. In this paper, we aim to review scientific studies validating the smartphone apps, particularly to test their effectiveness in tinnitus management and treatment. We use the PRISMA guidelines for identification of studies on major scientific literature sources and delineate the outcomes of identified studies.
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http://dx.doi.org/10.3390/brainsci10110867DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698350PMC
November 2020

Experiences of Psychotherapists With Remote Psychotherapy During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study.

J Med Internet Res 2020 11 27;22(11):e20246. Epub 2020 Nov 27.

Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria.

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<.001). Psychodynamic (P=.001) and humanistic (P=.005) therapists reported a higher comparability of telephone-based psychotherapy to in-person psychotherapy than behavioral therapists. Experiences with remote therapy (both web- and telephone-based) were more positive than previously expected (P<.001). Psychodynamic therapists reported more positive experiences with telephone-based psychotherapy than expected compared to behavioral (P=.03) and systemic (P=.002) therapists. In general, web-based psychotherapy was rated more positively (regarding comparability to psychotherapy with personal contact and experiences vs expectations) than telephone-based psychotherapy (P<.001); however, psychodynamic therapists reported their previous expectations to be equal to their actual experiences for both telephone- and web-based psychotherapy.

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.
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http://dx.doi.org/10.2196/20246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704121PMC
November 2020

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios.

J Vis Exp 2020 10 6(164). Epub 2020 Oct 6.

Institute of Clinical Epidemiology and Biometry, University of Würzburg;

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to use. Therefore, new approaches like immersive analytics are welcome. Immersive analytics promise to experience high-dimensional data sets in a convenient manner for various user groups and data sets. Technically, virtual-reality devices are used to enable immersive analytics. In Industry 4.0, for example, scenarios like the identification of outliers or anomalies in high-dimensional data sets are pursued goals of immersive analytics. In this context, two important questions should be addressed for any developed technical solution on immersive analytics: First, is the technical solutions being helpful or not? Second, is the bodily experience of the technical solution positive or negative? The first question aims at the general feasibility of a technical solution, while the second one aims at the wearing comfort. Extant studies and protocols, which systematically address these questions are still rare. In this work, a study protocol is presented, which mainly investigates the usability for immersive analytics in Industry 4.0 scenarios. Specifically, the protocol is based on four pillars. First, it categorizes users based on previous experiences. Second, tasks are presented, which can be used to evaluate the feasibility of the technical solution. Third, measures are presented, which quantify the learning effect of a user. Fourth, a questionnaire evaluates the stress level when performing tasks. Based on these pillars, a technical setting was implemented that uses mixed reality smartglasses to apply the study protocol. The results of the conducted study show the applicability of the protocol on the one hand and the feasibility of immersive analytics in Industry 4.0 scenarios on the other. The presented protocol includes a discussion of discovered limitations.
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http://dx.doi.org/10.3791/61349DOI Listing
October 2020

Process-Driven and Flow-Based Processing of Industrial Sensor Data.

Sensors (Basel) 2020 Sep 14;20(18). Epub 2020 Sep 14.

Institute of Databases and Information Systems, University of Ulm, 89081 Ulm, Germany.

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
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http://dx.doi.org/10.3390/s20185245DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570670PMC
September 2020

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study.

Sensors (Basel) 2020 Aug 14;20(16). Epub 2020 Aug 14.

Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.

Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights into process model comprehension.
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http://dx.doi.org/10.3390/s20164561DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472239PMC
August 2020

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment.

JMIR Mhealth Uhealth 2020 08 18;8(8):e21767. Epub 2020 Aug 18.

Institute of Distributed Systems, Ulm University, Ulm, Germany.

Background: Modern smartphones contain sophisticated high-end hardware features, offering high computational capabilities at extremely manageable costs and have undoubtedly become an integral part in users' daily life. Additionally, smartphones offer a well-established ecosystem that is easily discoverable and accessible via the marketplaces of differing mobile platforms, thus encouraging the development of many smartphone apps. Such apps are not exclusively used for entertainment purposes but are also commonplace in health care and medical use. A variety of those health and medical apps exist within the context of tinnitus, a phantom sound perception in the absence of any physical external source.

Objective: In this paper, we shed light on existing smartphone apps addressing tinnitus by providing an up-to-date overview.

Methods: Based on PRISMA guidelines, we systematically searched and identified existing smartphone apps on the most prominent app markets, namely Google Play Store and Apple App Store. In addition, we applied the Mobile App Rating Scale (MARS) to evaluate and assess the apps in terms of their general quality and in-depth user experience.

Results: Our systematic search and screening of smartphone apps yielded a total of 34 apps (34 Android apps, 26 iOS apps). The mean MARS scores (out of 5) ranged between 2.65-4.60. The Tinnitus Peace smartphone app had the lowest score (mean 2.65, SD 0.20), and Sanvello-Stress and Anxiety Help had the highest MARS score (mean 4.60, SD 0.10). The interrater agreement was substantial (Fleiss κ=0.74), the internal consistency was excellent (Cronbach α=.95), and the interrater reliability was found to be both high and excellent-Guttman λ6=0.94 and intraclass correlation, ICC(2,k) 0.94 (95% CI 0.91-0.97), respectively.

Conclusions: This work demonstrated that there exists a plethora of smartphone apps for tinnitus. All of the apps received MARS scores higher than 2, suggesting that they all have some technical functional value. However, nearly all identified apps were lacking in terms of scientific evidence, suggesting the need for stringent clinical validation of smartphone apps in future. To the best of our knowledge, this work is the first to systematically identify and evaluate smartphone apps within the context of tinnitus.
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http://dx.doi.org/10.2196/21767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463412PMC
August 2020

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study.

J Med Internet Res 2020 06 30;22(6):e15547. Epub 2020 Jun 30.

Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria.

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.
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http://dx.doi.org/10.2196/15547DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367527PMC
June 2020

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

Sensors (Basel) 2020 Jun 18;20(12). Epub 2020 Jun 18.

Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany.

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.
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http://dx.doi.org/10.3390/s20123456DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349608PMC
June 2020

The cycle of violence as a function of PTSD and appetitive aggression: A longitudinal study with Burundian soldiers.

Aggress Behav 2020 09 3;46(5):391-399. Epub 2020 May 3.

Dr. Amelung Private Clinic for Psychiatry, Psychotherapy, and Psychosomatics, Königstein im Taunus, Germany.

During deployment, soldiers face situations in which they are not only exposed to violence but also have to perpetrate it themselves. This study investigates the role of soldiers' levels of posttraumatic stress disorder (PTSD) symptoms and appetitive aggression, that is, a lust for violence, for their engaging in violence during deployment. Furthermore, factors during deployment influencing the level of PTSD symptoms and appetitive aggression after deployment were examined for a better comprehension of the maintenance of violence. Semi-structured interviews were conducted with 468 Burundian soldiers before and after a 1-year deployment to Somalia. To predict violent acts during deployment (perideployment) as well as appetitive aggression and PTSD symptom severity after deployment (postdeployment), structural equation modeling was utilized. Results showed that the number of violent acts perideployment was predicted by the level of appetitive aggression and by the severity of PTSD hyperarousal symptoms predeployment. In addition to its association with the predeployment level, appetitive aggression postdeployment was predicted by violent acts and trauma exposure perideployment as well as positively associated with unit support. PTSD symptom severity postdeployment was predicted by the severity of PTSD avoidance symptoms predeployment and trauma exposure perideployment, and negatively associated with unit support. This prospective study reveals the importance of appetitive aggression and PTSD hyperarousal symptoms for the engagement in violent acts during deployment, while simultaneously demonstrating how these phenomena may develop in mutually reinforcing cycles in a war setting.
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http://dx.doi.org/10.1002/ab.21895DOI Listing
September 2020

The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study.

JMIR Mhealth Uhealth 2020 03 27;8(3):e14479. Epub 2020 Mar 27.

Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria.

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.
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http://dx.doi.org/10.2196/14479DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148545PMC
March 2020

Smartphone Apps in the Context of Tinnitus: Systematic Review.

Sensors (Basel) 2020 Mar 19;20(6). Epub 2020 Mar 19.

Institute of Distributed Systems, Ulm University, 89081 Ulm, Germany.

Smartphones containing sophisticated high-end hardware and offering high computational capabilities at extremely manageable costs have become mainstream and an integral part of users' lives. Widespread adoption of smartphone devices has encouraged the development of many smartphone applications, resulting in a well-established ecosystem, which is easily discoverable and accessible via respective marketplaces of differing mobile platforms. These smartphone applications are no longer exclusively limited to entertainment purposes but are increasingly established in the scientific and medical field. In the context of tinnitus, the ringing in the ear, these smartphone apps range from relief, management, self-help, all the way to interfacing external sensors to better understand the phenomenon. In this paper, we aim to bring forth the smartphone applications in and around tinnitus. Based on the PRISMA guidelines, we systematically analyze and investigate the current state of smartphone apps, that are directly applied in the context of tinnitus. In particular, we explore Google Scholar, CiteSeerX, Microsoft Academics, Semantic Scholar for the identification of scientific contributions. Additionally, we search and explore Google's Play and Apple's App Stores to identify relevant smartphone apps and their respective properties. This review work gives (1) an up-to-date overview of existing apps, and (2) lists and discusses scientific literature pertaining to the smartphone apps used within the context of tinnitus.
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http://dx.doi.org/10.3390/s20061725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146490PMC
March 2020

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain.

Front Neurosci 2020 28;14:164. Epub 2020 Feb 28.

Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.

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.
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http://dx.doi.org/10.3389/fnins.2020.00164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058696PMC
February 2020

Measuring Mental Effort for Creating Mobile Data Collection Applications.

Int J Environ Res Public Health 2020 03 3;17(5). Epub 2020 Mar 3.

Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany.

To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N = 80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.
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http://dx.doi.org/10.3390/ijerph17051649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084515PMC
March 2020

'Help for trauma from the app stores?' A systematic review and standardised rating of apps for Post-Traumatic Stress Disorder (PTSD).

Eur J Psychotraumatol 2020 9;11(1):1701788. Epub 2020 Jan 9.

Institute of Psychology and Education, Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany.

: Mobile health applications (apps) are considered to complement traditional psychological treatments for Post-Traumatic Stress Disorder (PTSD). However, the use for clinical practice and quality of available apps is unknown. : To assess the general characteristics, therapeutic background, content, and quality of apps for PTSD and to examine their concordance with established PTSD treatment and self-help methods. : A web crawler systematically searched for apps targeting PTSD in the British Google Play and Apple iTunes stores. Two independent researchers rated the apps using the Mobile App Rating Scale (MARS). The content of high-quality apps was checked for concordance with psychological treatment and self-help methods extracted from current literature on PTSD treatment. : Out of 555 identified apps, 69 met the inclusion criteria. The overall app quality based on the MARS was medium (M = 3.36, SD = 0.65). Most apps (50.7%) were based on cognitive behavioural therapy and offered a wide range of content, including established psychological PTSD treatment methods such as processing of trauma-related emotions and beliefs, relaxation exercises, and psychoeducation. Notably, data protection and privacy standards were poor in most apps and only one app (1.4%) was scientifically evaluated in a randomized controlled trial. : High-quality apps based on established psychological treatment techniques for PTSD are available in commercial app stores. However, users are confronted with great difficulties in identifying useful high-quality apps and most apps lack an evidence-base. Commercial distribution channels do not exploit the potential of apps to complement the psychological treatment of PTSD.
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http://dx.doi.org/10.1080/20008198.2019.1701788DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6968629PMC
January 2020

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:3951-3955

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.
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http://dx.doi.org/10.1109/EMBC.2019.8857854DOI Listing
July 2019

Learning to Read by Learning to Write: Evaluation of a Serious Game to Foster Business Process Model Comprehension.

JMIR Serious Games 2020 Jan 9;8(1):e15374. Epub 2020 Jan 9.

Institute of Databases and Information Systems, Ulm University, Ulm, Germany.

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 (P<.001). A Hedges g of 0.77 also indicated a medium to large effect size. In study 2, follow-up game group participants showed a better performance compared to participants from the follow-up control group (P=.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: P<.001; study 2: P<.001).

Conclusions: Participants who played the serious game showed better performance in the comprehension of process models when comparing both studies.
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http://dx.doi.org/10.2196/15374DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996776PMC
January 2020

Anomaly Detections for Manufacturing Systems Based on Sensor Data-Insights into Two Challenging Real-World Production Settings.

Sensors (Basel) 2019 Dec 5;19(24). Epub 2019 Dec 5.

Institute of Databases and Information System, University of Ulm, 89081 Ulm, Germany.

To build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.
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http://dx.doi.org/10.3390/s19245370DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960738PMC
December 2019

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.

JMIR Mhealth Uhealth 2019 10 30;7(10):e13978. Epub 2019 Oct 30.

Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria.

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.
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http://dx.doi.org/10.2196/13978DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913730PMC
October 2019