Open Access

Background: Most individuals recover from the acute phase of infection with the SARS-CoV-2 virus, however, some encounter prolonged effects, referred to as the Post-COVID syndrome. Evidence exists that such persistent symptoms can significantly impact patients' ability to return to work. This paper gives a comprehensive overview of different care pathways and resources, both personal and external, that aim to support Post-COVID patients during their work-life reintegration process. By describing the current situation of Post-COVID patients pertaining their transition back to the workplace, this paper provides valuable insights into their needs. Methods: A quantitative research design was applied using an online questionnaire as an instrument. Participants were recruited via Post-COVID outpatients, rehab facilities, general practitioners, support groups, and other healthcare facilities. Results: The analyses of 184 data sets of Post-COVID affected produced three key findings: (1) The evaluation of different types of personal resources that may lead to a successful return to work found that particularly the individuals' ability to cope with their situation (measured with the FERUS questionnaire), produced significant differences between participants that had returned to work and those that had not been able to return so far (F = 4.913, p = 0.001). (2) In terms of organizational provisions to facilitate successful reintegration into work-life, predominantly structural changes (i.e., modification of the workplace, working hours, and task) were rated as helpful or very helpful on average (meanworkplace 2.55/SD = 0.83, meanworking hours 2.44/SD = 0.80; meantasks 2.55/SD = 0.83), while the remaining offerings (i.e., job coaching or health courses) were rated as less helpful or not helpful at all. (3) No significant correlation was found between different care pathways and a successful return to work. Conclusion: The results of the in-depth descriptive analysis allows to suggests that the level of ability to cope with the Post-COVID syndrome and its associated complaints, as well as the structural adaptation of the workplace to meet the needs and demands of patients better, might be important determinants of a successful return. While the latter might be addressed by employers directly, it might be helpful to integrate training on coping behavior early in care pathways and treatment plans for Post-COVID patients to strengthen their coping abilities aiming to support their successful return to work at an early stage.

Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.

Orofacial Myofunctional Disorder (OMD) is believed to affect approximately 30-50% of all children. The various causes of OMD often revolve around an incorrect resting position of the tongue and cause symptoms such as difficulty in speech and swallowing. While these symptoms can persist and lead to jaw deformities, such as overjet and open bite, manual therapy has been shown to be effective, especially in children. However, much of the therapy must be done as home exercises by children without the supervision of a therapist. Since these exercises are often not perceived as exciting by the children, half-hearted performance or complete omission of the exercises is common, rendering the therapy less effective or completely useless. To overcome this limitation, we implemented the LudusMyo platform, a serious game platform for OMD therapy. While children are the main target group, the acceptance (and usability) assessment by experts is the first milestone for the successful implementation of an mHealth application for therapy. For this reason, we conducted an expert survey among OMD therapists to gather their input on the LudusMyo prototype. The results of this expert survey are reported in this manuscript.

Esports athletes face significant mental health challenges, often due to inadequate support. This paper presents the MindAthlete mHealth application designed to monitor the mental health of esports athletes. A qualitative study with experts in (sport) psychology identified key features such as mood tracking, stress monitoring, and risk screening. Based on their input, a prototype was developed.