Refine
Has Fulltext
- yes (26)
Is part of the Bibliography
- yes (26)
Document Type
- Journal article (26)
Language
- English (26)
Keywords
- tinnitus (6)
- ecological momentary assessment (5)
- machine learning (5)
- mHealth (4)
- crowdsensing (3)
- mobile health (3)
- COVID-19 (2)
- self-help (2)
- Apple Watch 7 (1)
- Business Process Models (1)
- Eye Movement Modeling Examples (1)
- Fitbit Sense (1)
- Garmin Fenix 6 Pro (1)
- HiGHmed (1)
- Location-based algorithms (1)
- MDR (1)
- Mobile application engineering (1)
- Mobile augmented reality (1)
- Mobile augmented reality game (1)
- PTSD (1)
- Process Model Comprehension (1)
- Serious game (1)
- Withings ScanWatch (1)
- acupressure (1)
- age differences (1)
- aggression (1)
- anomaly detection (1)
- app (1)
- architectural design (1)
- bibliometric analysis (1)
- bioelectronics (1)
- cbt (1)
- chronic disorders (1)
- clinical measurement in health technology (1)
- clinical routine data (1)
- cloud-native (1)
- cognition (1)
- community detection (1)
- computer science (1)
- condition prediction (1)
- coronavirus (1)
- crowdsourcing (1)
- cyber-physical systems (1)
- data collection (1)
- data stream processing (1)
- deployment (1)
- digital phenotyping (1)
- ecological momentary assessments (EMA) (1)
- ehealth (1)
- electrodermal activity (1)
- end-user programming (1)
- environmental sound (1)
- explainable machine learning (1)
- eye tracking (1)
- fitness trackers (1)
- geospatial data (1)
- gradient boosting machine (1)
- health sciences (1)
- health tracker (1)
- healthcare (1)
- heart failure (1)
- human-centered design (1)
- internet of things (1)
- intersession experiences (1)
- intersession processes (1)
- intervention (1)
- loneliness (1)
- longitudinal studies (1)
- medical analytics (1)
- medical data integration center (1)
- medical device regulation (1)
- medical device software (1)
- medical informatics initiative (1)
- mental effort (1)
- mental health monitoring (1)
- mobile app (1)
- mobile application (1)
- mobile apps (1)
- mobile crowdsensing (1)
- mobile crowdsensing (MCS) (1)
- mobile healthcare application (1)
- mobile operating system differences (1)
- mobile phone (1)
- multidisciplinary (1)
- multimodal data (1)
- noise measurement (1)
- openEHR (1)
- passive data (1)
- personalized medicine (1)
- photoplethysmography (1)
- process model (1)
- process model comprehension (1)
- processing pipeline (1)
- production machines (1)
- psychology (1)
- psychotherapy (1)
- public health (1)
- public mental health (1)
- reference architecture (1)
- risk prediction scores (1)
- scalability (1)
- self help (1)
- semantic interoperability (1)
- sensor (1)
- sensor data (1)
- sensor networks (1)
- sentiment analysis (1)
- signs and symptoms (1)
- smart mobile devices (1)
- smart-phone (1)
- smartphone app (1)
- smartphone communication (1)
- smartwatch (1)
- social media use (1)
- social well-being (1)
- soldiers (1)
- stream processing (1)
- stress (1)
- system architecture design (1)
- time series (1)
- tinnitus research (1)
- tinnitus therapy (1)
- topic modeling (1)
- usability study (1)
- violence (1)
- visual analytics (1)
- wearable (1)
Institute
- Institut für Klinische Epidemiologie und Biometrie (25)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (3)
- Institut für Informatik (2)
- Medizinische Klinik und Poliklinik I (2)
- Deutsches Zentrum für Herzinsuffizienz (DZHI) (1)
- Institut für Hygiene und Mikrobiologie (1)
- Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie (1)
- Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie (1)
Sonstige beteiligte Institutionen
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.
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.
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.
Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educational counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasibility study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone app unsupervised during their daily routine over a period of four months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of 0.408, while the improvements on the Tinnitus Handicap Inventory (THI) were much smaller with an effect size of 0.168. An analysis of user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively reported a stronger improvement in the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.
Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses
(2021)
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.