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Simple Summary: Large datasets concerning childhood cancers are rare. Therefore, it is important to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), datasets can be merged, without disclosing the patients' identities. Although PPRL is already implemented or described in various settings, use case descriptions are fragmented and incomplete. The present paper gives an overview of current and future use cases of PPRL in childhood cancer. We screened the literature, projects, and trial protocols, analysed a hypothetical patient journey, and discussed use cases with experts. All the identified use cases were structured along six key dimensions. We conclude that PPRL is a key concept in childhood cancer. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. [ABSTRACT FROM AUTHOR]

Background: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. Objectives: The primary objective was to correlate wearable device data with subjective sleep quality perceptions. Methods: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier. Results: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%. Discussion: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior. [ABSTRACT FROM AUTHOR]

Background: Approximately 40% of all recorded deaths in Austria are due to behavioral risks. These risks could be avoided with appropriate measures. Objectives: Extension of the concept of EHR and EMR to an electronic prevention record, focusing on primary and secondary prevention. Methods: The concept of a structured prevention pathway, based on the principles of P4 Medicine, was developed for a multidisciplinary prevention network. An IT infrastructure based on HL7 FHIR and the OHDSI OMOP common data model was designed. Results: An IT solution supporting a structured and modular prevention pathway was conceptualized. It contained a personalized management of prevention, risk assessment, diagnostic and preventive measures supported by a modular, interoperable IT infrastructure including a health app, prevention record webservice, decision support modules and a smart prevention registry, separating primary and secondary use of data. Conclusion: A concept was created on how an electronic health prevention record based on HL7 FHIR and the OMOP common data model can be implemented. [ABSTRACT FROM AUTHOR]

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms. [ABSTRACT FROM AUTHOR]

E-Bikes in telerehabilitation programs could be a new intervention for more sustainable rehabilitation results. The aim is to design and build a prototype of an E-Bike usable for rehabilitation - a HEALTHeBIKE. It should avoid over-exercising, work independently of the environment and it should enable cycling in a group despite different reference exercise intensities. To achieve these goals, requirements for this system architecture have been identified. A system architecture including an Arduino microcontroller, an Android smartphone and a telemonitoring platform was presented. A power output regulated proportional-integral controller to adjust the motor assistance has been implemented. A feasibility study with two subjects cycling in a group was performed. Seven test rides on varying terrain (flat, hilly, mountainous and uphill) with the same and different exercise intensities were completed. The mean power output was close to or below the target power output of the cyclist for all test rides with a maximal error of 6.7 % above and 27.6 % below the target. Although the exercise intensities of the two subjects were clearly different, cycling in a group was possible without over-exercising. [ABSTRACT FROM AUTHOR]

Functional evaluation of elderly patients is one key component in a comprehensive Geriatric Assessment. The increased workload and high costs associated to close and continuous monitoring in clinical settings may be counterbalanced by the application of ICT-supported remote follow-up. Although clinical parameters and questionnaires can be supported with smartphones and smart gadgets, physical performance trials pose a challenge for community-based approaches with difficulties arising from setting up trials, the lack of guidance and supervision. We developed an ultrasonic-based device to overcome all of these barriers and enable elderly people to perform the Timed Up-and-Go test in an autonomous and unsupervised setting. Moreover, we introduce an algorithm to verify the successful performance of the test in order to increase the reliability of the information provided. [ABSTRACT FROM AUTHOR]

Opsoclonus Myoclonus Syndrome (OMS) is a rare disease in children which is often associated with neuroblastoma and, therefore, requires treatment by pediatric neurologists and oncologists. The ongoing OMS trial investigates questions related to OMS and potentially underlying neuroblastomas. To support this trial with an adequate IT infrastructure, linkage of neuroblastoma research databases with the OMS electronic data capture (EDC) system was required. Therefore, an EDC system for the OMS trial was developed and integrated into the research infrastructure of the European Network for Cancer Research in Children and Adolescents (ENCCA) project. Application of ENNCA's pseudonymization concept enabled linkage of the OMS trial with neuroblastoma trials from two different scientific societies, while being compliant with current data protection regulations. Linkage of the neurological and the oncological domain could successfully be demonstrated and a promising concept for secondary use of the data of both domains has been developed, proofing the broad potential of the concepts for cross-domain research as promoted in the ENCCA project. [ABSTRACT FROM AUTHOR]

Background: Electronic Patient Reported Outcomes (ePRO) gathered using telemonitoring solutions might be a valuable source of information in rare cancer research. Objectives: The objective of this paper was to develop a concept and implement a prototype for introducing ePRO into the existing neuroblastoma research network by applying Near Field Communication and mobile technology. Methods: For physicians, an application was developed for registering patients within the research network and providing patients with an ID card and a PIN for authentication when transmitting telemonitoring data to the Electronic Data Capture system OpenClinica. For patients, a previously developed telemonitoring system was extended by a Simple Object Access Protocol (SOAP) interface for transmitting nine different health parameters and toxicities. Results: The concept was fully implemented on the front-end side. The developed application for physicians was prototypically implemented and the mobile application of the telemonitoring system was successfully connected to OpenClinica. Future work will focus on the implementation of the back-end features. [ABSTRACT FROM AUTHOR]

Data from two contexts, i.e. the European Unresectable Neuroblastoma (EUNB) clinical trial and results from comparative genomic hybridisation (CGH) analyses from corresponding tumour samples shall be provided to existing repositories for secondary use. Utilizing the European Unified Patient IDentity Management (EUPID) as developed in the course of the ENCCA project, the following processes were applied to the data: standardization (providing interoperability), pseudonymization (generating distinct but linkable pseudonyms for both contexts), and linking both data sources. The applied procedures resulted in a joined dataset that did not contain any identifiers that would allow to backtrack the records to either data sources. This provided a high degree of privacy to the involved patients as required by data protection regulations, without preventing proper analysis. [ABSTRACT FROM AUTHOR]

Today, progress in biomedical research often depends on large, interdisciplinary research projects and tailored information and communication technology (ICT) support. In the context of the European Network for Cancer Research in Children and Adolescents (ENCCA) project the exchange of data between data source (Source Domain) and data consumer (Consumer Domain) systems in a distributed computing environment needs to be facilitated. This work presents the requirements and the corresponding solution architecture of the Advanced Biomedical Collaboration Domain for Europe (ABCD-4-E). The proposed concept utilises public as well as private cloud systems, the Integrating the Healthcare Enterprise (IHE) framework and web-based applications to provide the core capabilities in accordance with privacy and security needs. The utility of crucial parts of the concept was evaluated by prototypic implementation. A discussion of the design indicates that the requirements of ENCCA are fully met. A whole system demonstration is currently being prepared to verify that ABCD-4-E has the potential to evolve into a domain-bridging collaboration platform in the future. [ABSTRACT FROM AUTHOR]

Data that has been collected in the course of clinical trials are potentially valuable for additional scientific research questions in so called secondary use scenarios. This is of particular importance in rare disease areas like paediatric oncology. If data from several research projects need to be connected, so called Core Datasets can be used to define which information needs to be extracted from every involved source system. In this work, the utility of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) as a format for Core Datasets was evaluated and a web tool was developed which received Source ODM XML files and - via Extensible Stylesheet Language Transformation (XSLT) - generated standardized Core Dataset ODM XML files. Using this tool, data from different source systems were extracted and pooled for joined analysis in a proof-of-concept study, facilitating both, basic syntactic and semantic interoperability. [ABSTRACT FROM AUTHOR]

Congestive Heart Failure (CHF) is a common chronic heart disease with high socioeconomic impact. Conventional treatment of CHF is often ineffective and inefficient, since self-management is complex and patients are insufficiently involved in therapy management. With telemedical concepts, continuous monitoring of the health status can be ensured, and consequently therapy management can be adapted to the individual requirements of every individual patient. Therefore, a mobile phone based patient terminal for the concurrent acquisition of biosignals (e.g. ECG) and bioparameters (e.g. blood pressure) for patients with CHF has been developed and prototypically implemented. Usability and interoperability aspects were especially considered by using Bluetooth and Near Field Communication (NFC) technology for data acquisition and standardized data formats for transmission of the data to a central monitoring centre. Results indicated that even complicated measurements like the acquisition of ECG signals could be accomplished autonomously by the patients in an intuitive and easy-to-use way. Through the usage of IHE conform HL7 messages, self-measured data could easily be integrated into a higher-ranking eHealth infrastructure. [ABSTRACT FROM AUTHOR]

State-of-the-art decubitus prevention focuses mainly on special decubitus mattresses, which are indicated for extremely high risk only, while other risk factors such as nutrition or physical activity are hardly considered. Therefore, a monitoring system for decubitus prevention for persons with medium risk has been developed. The system consisted of an unobtrusive sensor system and a tablet for manual input of decubitus-relevant data concerning nutrition, drinking behavior and physical activity. The system was tested in a feasibility study. Results indicate that the system is usable and can provide useful information for decubitus prevention. Future work will include a field study, evaluating the system in a long-term study. [ABSTRACT FROM AUTHOR]

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms.

Background: Telehealth services for chronic diseases are becoming more and more popular since they are expected to improve health outcomes and reduce costs. Especially for diabetes patients, life-long disease management is required. However, there are situations in a patient's life, when motivation to continue the participation in disease management programs is low and the dropout-risk is high., Objectives: We analysed if an adherence management module provided to healthcare professionals within a pre-existing diabetes telehealth service can improve the long-term adherence., Methods: The adherence to the agreed data submission protocol was determined prior and post implementation of the adherence management module., Results: Adherence to the agreed data submission protocol was higher after implementation of the adherence management module as compared to previous years., Conclusion: Adherence to the agreed data submission protocol can be improved by helping healthcare professionals to identify patients at risk of dropout. Further analyses are indicated to proof these results in a prospective study.

Congestive Heart Failure (CHF) is a common chronic heart disease with high socioeconomic impact. Conventional treatment of CHF is often ineffective and inefficient, since self-management is complex and patients are insufficiently involved in therapy management. With telemedical concepts, continuous monitoring of the health status can be ensured, and consequently therapy management can be adapted to the individual requirements of every individual patient. Therefore, a mobile phone based patient terminal for the concurrent acquisition of biosignals (e.g. ECG) and bioparameters (e.g. blood pressure) for patients with CHF has been developed and prototypically implemented. Usability and interoperability aspects were especially considered by using Bluetooth and Near Field Communication (NFC) technology for data acquisition and standardized data formats for transmission of the data to a central monitoring centre. Results indicated that even complicated measurements like the acquisition of ECG signals could be accomplished autonomously by the patients in an intuitive and easy-to-use way. Through the usage of IHE conform HL7 messages, self-measured data could easily be integrated into a higher-ranking eHealth infrastructure.