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The application of virtual reality (VR) as a supportive tool in psychotherapy has gained great popularity in recent years. Especially for addiction therapy, a combination of virtual exposure and learning or training coping skills by using biofeedback has a high potential to improve conventional therapy. To add value, the new therapy system has to meet the needs of patients and practitioners likewise. Added values consistently named by experts included, but were not limited to, new possibilities of creating individual exposition or coping scenarios, enhanced psychoeducation, a shorter duration of treatment, telemedical aspects, the possibility of measuring and predicting craving and finally an improvement in abstinence. Besides literature research, we evaluated existing technical solutions in the field of virtual addiction treatment, surveyed experts and evolved a concept that led to a first prototype. The prototype consists of a wireless VR headset and a wireless multi-sensor system for measuring the physiological reaction to stimuli or the effectiveness of coping strategies by means of biofeedback. For further studies we developed both, a virtual exposure and a coping scenario and tested the hardware and software in a pilot study in order to elaborate factors that could negatively affect the therapy adherence, the effectiveness of exposition (immersion) and possible hurdles in practical use. Cybersickness and the lack of haptic feedback turned out to be the main limiting factors. Concepts for the next iteration of the therapy system will reflect these points for upcoming clinical studies. In our proof of concept, we demonstrated that virtual therapy can be implemented with a reasonable effort of time and costs. The combination of software and hardware, that supplements the traditional therapeutic approach, lays the foundation for upcoming clinical use and trials to prove the better outcome of VR enhanced addiction therapy.

Bioimpedance analysis is a label-free and easy approach to obtain information on cellular barrier integrity and cell viability more broadly. In this work, we introduce a small, low-cost, portable in vitro impedance measurement system for studies where a shadow-free exposure of the cells is a requirement. It can be controlled by a user-friendly web interface and can perform measurements automated and autonomously at short intervals. The system can be integrated into an existing IoT network for remote monitoring and indepth analyses. A single-board computer (SBC) serves as the central unit, to control, analyze, store and forward the measurement data from the single-chip impedance analyzer. Various materials and manufacturing methods were used to produce a purpose-built lid on top of a modified 24-well microtiter plate in a “do it yourself” fashion. Furthermore, three different sensor designs were developed utilizing anodic aluminum oxide (AAO) membranes and gold-plated electrodes. Preliminary tests with potassium chloride (KCl) showed first promising results.

In the scientific field, data acquisition using commercial or self-developed sensors is a necessity for many research activities. Data security and data privacy are important requirements in all types of IoT applications, especially in the medical context. IoTree42 is a powerful, OpenSource platform that closes the gap between cost-efficiency, the ease of use and digital sovereignty. The flexible and user-friendly design makes the platform ideal for the originally conceived research context. Nevertheless, it can be used for smaller budget-oriented setups and industrial applications with thousands of sensors and actuators likewise. The platform was developed with security in mind by minimizing interfaces and the use of stable software components and transport protocols. IoTree42 utilizes lightweight OpenSource software cast into a flexible backend, allowing the deployment not only on full-fledged servers but also on single board computers like the Raspberry Pi. Data is transmitted with low overhead via MQTT, a robust protocol optimized for machine to machine communication. The transmission technology between all components is freely selectable. Besides data acquisition, IoTree42 enables control of actuators and automation through interfaces with visual programming tools. This allows for remote interventions without requiring prior programming experience. The incoming data can conveniently be visualized in dashboards or easily be exported for further analysis. The network consists of a central server and satellites (gateways) arranged in a star topology. Sensors and actuators connect to the gateway. The initial setup of the server and the gateways is automated, well documented, and can be done within minutes. Thanks to the increasing usage and the contribution of the community, the pool of code examples for sensors is growing and lowers the entry hurdle for users without programming background. IoTree42 is a fast-growing, multi-user, hardware and cloud agnostic, easy to deploy, privacy compliant and competitive solution compared to commercial IoT alternatives.

After the success of virtual reality (VR) applications in supporting exposure-based and psychoeducational cognitive behavioural therapy for various mental illnesses, the technology is also gaining traction in addiction medicine. This paper will describe the concept behind a custom-developed VR simulation for this application with a particular emphasis on alcohol-related addictive disorders. Using Unreal Engine 4, an exposure simulation and a scenario for learning and applying coping strategies were developed for the Oculus Quest. Because of the common association of supermarkets with the procurement of alcohol among addicts, a small shop was implemented as initial scenario. Since the feeling of presence is an important parameter in the quality assessment of a simulation, the degree of immersion was evaluated in initial practical tests after development. In surveys with ten healthy testers, the majority showed a feeling of immersion. Cybersickness and unrealistic collision physics were identified as disruptive factors to the extent of immersion. This work provides the basis for a novel concept for addiction therapy which focusses on a fusion of playful psychoeducation, adjustable exposure, and the internalization of coping strategies with the help of multisensory biofeedback as well as the documentation of the objective physiological measurements for assessment of therapy success. The aim is to best possible address the specific requirements for clinical and home use in terms of self-containment, immersion, and ease of use. To evaluate and exploit the scientific potential of this concept scientific studies and technical improvements to both simulation and hardware are required.

Recording of heart rate variability (HRV) is a noninvasive and continuous measurement method that allows investigating the autonomic nervous system (ANS) and its reaction to environmental influences. For a precise measurement of HRV data, a carefully chosen study design and environment is required to minimize secondary influences. One major influence to be avoided is movement. However, in the daily routine and for some scientific questions, movement can often not be avoided. If so, a manual or automated method to differentiate between artifacts caused by body movement and the actual psychophysiological effect is needed to ensure the data quality. In this approach, a chest-worn sensor was developed, that measures the heart rate using a single lead ECG and filters the measured change of the HRV caused by movement. Data from an integrated accelerometer is used to detect upper body movements that affect the resting heart rate. The movementcorresponding time stamps are then used to filter the Interbeat Intervals (IBI) accordingly. Functionality and effectiveness of the sensor system have been tested against state-of-the art sports- or clinical devices in varying scenarios. As our test series showed, motion filtering has a decisive effect when motion occurs, two-thirds of all cases showed a significant effect of motion filtering, with small to medium effect sizes for the parameters SD2, SD2/SD1, and SDNN. Thereby, automatic filtering of motion artifacts can help to significantly reduce the need for costly post-processing of distorted data sets. The results show a better data quality of HRV measurement, a method that is commonly used for the investigation of physiological processes in the field of chronic pain, psychology, psychiatry, or sports medicine.

Introduction: Patient education is an important part of the management of rheumatic and musculoskeletal diseases. Given that patients with diverse diseases do not have the same needs, it is crucial to assess the educational requirements of targeted groups to provide tailored educational interventions. The aim of our study was to assess educational needs of a large cohort of patients with different rheumatic and musculoskeletal diseases attending a health facility in Austria. Methods: We assessed educational needs, via an online survey of patients with fibromyalgia (FMS), rheumatoid arthritis (RA), and ankylosing spondylitis (AS) recruited from an Austrian health‐care facility, using the Austrian version of the Educational Needs Assessment Tool (OENAT). Results: For our sample of 603 patients, AS (62%), RA (15%), and FMS (24%), there were no educational need differences for the domains of movements, disease process, and self‐help measures. Patients with FMS had less need for pain management education and greater need for education about feelings, than other disease groups. Patients with RA had a greater need for education related to treatments than other groups, and patients with AS had a greater need for treatment education than patients with FMS. Patients with AS reported greater need for support system education than other patient groups. Conclusion: Educational needs vary by disease groups, suggesting that health‐care professionals should assess disease‐specific needs for education to provide optimal assistance in disease management for patients. [ABSTRACT FROM AUTHOR]

Objectives Design Methods Results Conclusions Patients with rheumatic and musculoskeletal diseases (RMDs) often experience poor well‐being. Common limitations of the studies on this topic involve using variable‐centred and deficit‐based approaches. In this study, we used the person‐centred approach to identify profiles of positive (life satisfaction and health status) and negative (depression, anxiety, fatigue, and stress) indicators of well‐being among patients with RMDs. Moreover, we tested self‐forgiveness, forgiveness of others, gratitude, and sociodemographics as contributors to latent profile membership.A cross‐sectional questionnaire survey.Using a latent profile analysis, we investigated well‐being profiles among 892 patients with RMDs (759 patients with arthritis and 133 with fibromyalgia [FM]) and examined the correlates of latent profile membership.We identified four profiles of well‐being: (1) ‘life dissatisfaction’ (9.2%), (2) ‘high well‐being’ (43.4%), (3) ‘suboptimal well‐being’ (35.2%), and (4) ‘very poor well‐being’ (12.2%). Members of Profile 2 had higher levels of self‐forgiveness and gratitude than members of the remaining profiles, had higher levels of forgiveness of others than Profile 3, and were older than members of Profile 4. Moreover, members of Profile 2 had a higher proportion of patients with arthritis relative to those with FM than all other profiles and men to women than Profile 4.Patients with RMDs are heterogeneous in terms of well‐being. Self‐forgiveness, gratitude, and forgiveness of others may serve as psychological capital that enhances patients' well‐being. Special attention should be paid to patients with FM, women, and younger patients since they can be especially susceptible to poor well‐being. [ABSTRACT FROM AUTHOR]